MINUS-C

Minus-C

A Scaleable Remote Sensing and GIS-based approach in Estimating Carbon Storage in City of Torino

Delaram Ebrahimi Nia - 925998 Supervisor: Prof. Mattia Previtali Assistant Supervisor: Marzia Gabriele Academic Year 2021/2022 POLITECNICO DI MILANO Scuola di Architettura Urbanistica Ingegneria delle Costruzioni Landscape Architecture - Land Landscape Heritage

POLITECNICO DI MILANO Scuola di Architettura Urbanistica Ingegneria delle Costruzioni Landscape Architecture - Land Landscape Heritage

Minus-C A Scaleable Remote Sensing and GIS-based approach in Estimating Carbon Storage in City of Torino

Delaram Ebrahimi Nia - 925998 Supervisor: Prof. Mattia Previtali Assistant Supervisor: Marzia Gabriele Academic Year 2021/2022

ABSTRACT Carbon sequestraion is a crucial ecosystem servise that provides us with a liveable planet. Vegetation above-ground biomass is a key indicator of the Carbon storage and sequestration capacity and plays a crucial role in ecological effect of an urban ecosystem. Accurate assessment of vegetation biomass provides an understanding of Carbon circulation and is important for sustainable management of urban environment. The best method to estimage vegetation biomass is based on locally collected data, however, limited field data and challenging accessibility to field data calles out for more feasebel techniques using Remote Sensing. Recently launcehd Sentinel imegry offers a new opportunity for Aboveground biomass monitoring. In this study, CCI biomass map as field observation and Sentinel-2A image data were used to construct a model for estimating vegetation above-ground biomass in the case study of Torino, northern Italy. Vegetation indices and biophysical variables based on 150 measured field plots were used to develope biomass prediction models through machine learning techniques such as Artificial Neural Network (ANN), Support Vectore machine for Regression (SVR) and Random Forest (RF). Results show that Random Forest technique has achieved the best performance (r= 0.72, MAE= 34 and RMSE= 46). A simple mathematic equation defines the values of Carbon sequestration from above-ground biomass values. The final results of Carbon sequestration estimation can be a basis landscape architects to have a more spatially focused and quantitative design.

ABSTRACT Il sequestro del carbonio è un servizio fondamentale dell’ecosistema che ci fornisce un pianeta vivibile. La biomassa della vegetazione fuori terra è un indicatore chiave della capacità di stoccaggio e sequestro del carbonio e svolge un ruolo cruciale nell’effetto ecologico di un ecosistema urbano. Una valutazione accurata della biomassa vegetale fornisce una comprensione della circolazione del carbonio ed è importante per la gestione sostenibile dell’ambiente urbano. Il metodo migliore per stimare la biomassa vegetale si basa su dati raccolti localmente, tuttavia, dati sul campo limitati e una difficile accessibilità ai dati sul campo richiedono più tecniche di fattibilità utilizzando il telerilevamento. Il recente lancio di Sentinel imegry offre una nuova opportunità per il monitoraggio della biomassa in superficie.

In questo studio, la mappa della biomassa CCI come osservazione sul campo e i dati dell’immagine Sentinel-2A sono stati utilizzati per costruire un modello per la stima della biomassa della vegetazione fuori terra nel caso di studio di Torino, nel nord Italia. Gli indici di vegetazione e le variabili biofisiche basate su 150 campi misurati sono stati utilizzati per sviluppare modelli di previsione della biomassa attraverso tecniche di apprendimento automatico come Artificial Neural Network (ANN), Support Vectore machine for Regression (SVR) e Random Forest (RF). I risultati mostrano che la tecnica Random Forest ha ottenuto le migliori prestazioni (r= 0,72, MAE= 34 e RMSE= 46).

Una semplice equazione matematica definisce i valori di sequestro del carbonio dai valori della biomassa in superficie. I risultati finali della stima del sequestro del carbonio possono essere una base per gli architetti del paesaggio per avere un design più incentrato sullo spazio e quantitativo.

CONTENT Figures ....................................................................................................................

v

Maps ..........................................................................................................................

vii

Tables .......................................................................................................................

ix

Acknowledgement ...................................................................................

xi

Abreviations ......................................................................................................

xiii

01

Introduction ......................................................................................................

1

02

Urbanization as a driver and a recipient of global environmental change ..........................................................................

2.1.

History of urbanization ....................................................................

9 13

2.2.

Emergence of human driven problems ...............................

21

2.3.

Carbon emission as a global challenge and the importance of storing it ...................................................................

27

Trends of urbanization and Carbon emission .................

33

Carbon sequestration to help the globe ............................

3.1.

Ecosystem services; a gift from nature .................................

3.1.1. Definition of ecosystem services .............................

3.1.2. Urban ecosystem services ...........................................

39

Carbon sequestration in urban areas ....................................

3.2.1. Carbon cycle .........................................................................

3.2.2. The process of carbon sequestration ..................

Quantitative role of vegetation to absorb Carbon ........

3.3.1. Methods of calculating above-ground vegetation biomass ......................................................................

51

2.4.

03

3.2.

3.3.

i

43 43 47 51 56 57 58

04

Above-ground biomass estimation using remote Sensing and geographic information system (GIS) ...........................................................................................................................

4.1.

4.1.1. Principles of remote sensing ......................................

4.1.2. Resolutions of remote sensing Satellites ...........

66 75 78

Land surface monitoring satellites: Copernicus program ...................................................................................................

Using sentinel-2A satellite for AGB estimation ...............

81 85

Work methodology in city of torino ........................................

89

5.1.

Objectives ...............................................................................................

93

5.2.

Project objectives according to sustainability programs ..........................................................................................................

4.2.

4.3.

05

Sky is not the limit; An introduction to history and principles of remote sensing ......................................................

61

5.3.

Above-ground biomass literature review ...........................

97 99

5.4.

A case study of Torino, northern Italy ....................................

103

5.4.1. Torino; from antiquity to new days .........................

5.4.2. Trend of greenhouse gas emission; from national level to municipal level ...............................

106

ii

111

Emission inventories at national level ..................

Emission inventories at local level ..........................

Torino air quality monitoring network ..................

Governmental legislation and strategies to reduce pollution .................................................................

Landscape characteristics of Torino ...................

113 115 119 120

Estimating carbon sequestration in city of Torino ..

131

6.1.

Selection of Sentinel-2A data .....................................................

135

6.2.

Selection of predictors ....................................................................

145

6.2.1. Normalized Difference Vegetation Index (NDVI) ........................................................................................

149

6.2.2 . Normalized Difference Index Using Band 4 and 5 (NDI45) .......................................................................

6.2.3 . Transformed Normalized Difference Vegetation Index (TNDVI) .............................................................

6.2.4 . Soil-Adjusted Vegetation Index (SAVI) .................

151

6.2.5 . Inverted Red-Edge Chlorophyll Index (IRECI)

157

6.2.6 . Leaf Area Index (LAI) .......................................................

159

6.2.7 . Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) .............................................

161

6.2.8 . Fraction of Green Vegetation Cover (FCover)

163

6.3.

Field observation ................................................................................

165

6.4.

Analysing the relationship between field AGB and predictors .................................................................................................

169

6.5.

Modeling the relationship between field AGB and predictors .................................................................................................

171

6.6.

Mapping above-ground biomass ...........................................

175

6.7.

Estimating Carbon stored in urban vegetation ...............

189

5.4.3.

5.4.4.

5.4.5.

5.4.6.

5.4.7

06

iii

123

153 155

07

Future scenario and possible improvements ..............

193

7.1.

Carbon sequestration as a potential land-use management tool for mitigation of GHG emissions ..............

199

7.1.1. Land use, land use change and forestry (LULUCF) as an inventory to cover emissions of GHGs ....................................................................................

201

7.1.2. Torino small-scale scenario; landscape units under evaluation ................................................................

204

Future application of geo-locating Carbon sequestration ..........................................................................................................

Improvement on the above-ground biomass estimation model .................................................................................. ...........

215

Conclusion ..............................................................................................

220

Bibliography .....................................................................................................

225

Sitography ..........................................................................................................

231

7.2.

7.3.

7.4.

iv

217

FIGURES Fig. 01 - Lascaux Cave painting, circa 15,000 BC, Lascaux, France Fig. 02 - Nuremberg Chronicles 1493 Fig. 03 - Painting Manchester from Kersal Moor in 1851 Fig. 04 - Howard Ebenezer, Garden City Fig. 05 - Percentage of urbanization throughout time (1800-2016) Fig. 07 - The Nested Scales of Urban Impacts on the Biosphere Fig. 08 - Water Pollution Fig. 09 - Soil Pollution Fig. 10 - Air Pollution Fig. 11 - Climate change initiatives Fig. 12 - Establishment of IPCC in 1988 by World Meteorological Organization (WMO) and UN Environment Program (UNEP) Fig. 13 - Global greehouse gas emissions and warming scenarios Fig. 14 - Effects of Global Warming on the Planet Fig. 15 - Percentage of population residing in urban areas for all countries of the world, by geographic region and population size, 1950, 2018 and 2050 Fig. 16 - Annual CO2 Emission from Fossil Fuels Fig. 17 - Commutative CO2 emission by region Fig. 18 - Cascade Model Fig. 19 - Caron Cycle Fig. 20 - Timeline of the history of Remote Sensing Fig. 21 - Aerial photos taken by pigeons with with Julius Neubronner’s cameras Fig. 22 - Use of cameras in aircrafts in war Fig. 23 - V-2 German rocket Fig. 24 - Russian Sputnik- 1 Fig. 25 - NASA footage of first Landsat satellite Fig. 26 - IKONOS satellite for earth observation Fig. 27 - Sentinel-1 operation Fig. 28 - Principles of Remote Sensing

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14 16 16 18 20 23 25 26 26 27 29 30 32

35 37 37 46 54 67 71 72 72 73 74 74 74 75

Fig. 29 - The electromagnecti spectrum and reflectance spectra of some of earth’s materials Fig. 30 - Digital number (DN) principles Fig. 31 - Comparison of Multispectral and Hyperspectral images Fig. 32 - Power of bit and gray-scale values Fig. 33 - Copernicus satellites Fig. 34 - Sentinel family deployement scheme Fig. 35 - Sentinel-2A into making Fig. 36 - Sentinel-2 resoloution in different bands Fig. 37 - Work methodology for estimating Carbon sequestration Fig. 38 - Literatire review of above-ground biomass estimation Introduction to case study, city of Torino Fig. 39 - Torino through time Fig. 40 - National greenhouse gas emissions from 1990 to 2018 by gases and by sector Fig. 41 - Emission in Torino in the year of 2015 divided into sectors Fig. 42 - Piemonte landscape components; Naturalistic components Fig. 43 - Piemonte landscape components; Historical components Fig. 44 - Piemonte landscape components; Perceptual components Fig. 45 - Piemonte landscape components; Morphological components Fig. 46 - CCI forest biomass, Italy 2017 FIg. 47 - selected poitnts

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76 78 79 80 82 84 85 88 96 102 106 110 112 118 127 127 128 128 165 168

MAPS Map. 01 - Introduction to case study, city of Torino Map. 02 - Landscape assets of Torino Map. 03 - Landscape components of Torino Map. 04 - Sentinel-2 image representing Spring Map. 05 - Sentinel-2 image representing Summer Map. 06 - Sentinel-2 image representing Autumn Map. 07 - Sentinel-2 image representing Winter Map. 08 - Top of Atmosphere Map. 09 - Bottom of Atmosphere Map. 10 - Finalized pre-processed image representing Spring Map. 11 - Finalized pre-processed image representing Summer Map. 12 - Finalized pre-processed image representing Autumn Map. 13 - Finalized pre-processed image representing Winter Map. 14 - NDVI maps of study area in May/ July/ Sep/ Nov Map. 15 - NDI45 maps of study area in May/ July/ Sep/ Nov Map. 16 - TNDVI maps of study area in May/ July/ Sep/ Nov Map. 17 - SAVI maps of study area in May/ July/ Sep/ Nov Map. 18 - IRECI maps of study area in May/ July/ Sep/ Nov Map. 19 - LAI maps of study area in May/ July/ Sep/ Nov Map. 20 - FAPAR maps of study area in May/ July/ Sep/ Nov Map. 21 - FCover maps of study area in May/ July/ Sep/ Nov Map. 22 - Map of selected field plots in the study are Map. 23 - Map of predicted above-ground biomass in the study area Map. 24 - Map of very low above-ground biomass Map. 25 - Map of low above-ground biomass Map. 26 - Map of medium above-ground biomass Map. 27 - Map of high above-ground biomass Map. 28 - Map of very high above-ground biomass Map. 29 - Map of estimated Carbon stored in the vegetation of the study area Map. 30 - Map of habitat map in the study area

vii

106 124 130 137 138 139 140 141 141 143 143 144 144 150 152 154 156 158 160 162 164 167 178 179 181 183 185 187 192 206

Map. 31 - Map of Carbon sequestration in grassland in the study area Map. 32 - Map of Carbon sequestration in forest and woodland in the study area Map. 33 - Map of Carbon sequestration in agriculture and horticultural land in the study area Map. 34 - Map of Carbon sequestration in artificial land in the study area

viii

211 212 213 214

TA B L E S Fig. 06 Map. 1 Introduction to case study, city of Torino Map. 02 Landscape assets of Torino Map. 03 Landscape components of Torino Map. 04 Sentinel-2 image representing Spring Map. 05 Sentinel-2 image representing Summer Map. 06 Sentinel-2 image representing Autumn Map. 07 Sentinel-2 image representing Winter Map. 08 Top of Atmosphere Map. 09 Bottom of Atmosphere Map. 10 Finalized pre-processed image representing Spring Map. 11 Finalized pre-processed image representing Summer Map.12 Finalized pre-processed image representing Autumn Map.13 Finalized pre-processed image representing Winter Map. 14 I. NDVI map of study area in May II. NDVI map of study area in July III. NDVI map of study area in September IV. NDVI map of study area in November Map. 15 I. NDI45 map of study area in May II. NDI45 map of study area in July

ix

Contents 22 106 106 124 124 130 130 137 137 138 138 139 139 140 140 141 141 141 141 143 143 143 143 144 144 144 144 150 150 150 150 150 152 152 152

x

ACKNOWLEDGMENT

xi

xii

A B B R EV IAT ION S AGB

Above-Ground Biomass

ANN

Artificial Neural Network

BOA

Bottom Of Atmosphere

CCC

Canopy Chlorophyll Content

CCI

Climate Change Initiative

CICES

Common International Classification of Ecosystem Services

DBH

Diameter at Breast Height

DIAS

Data and Information Access Services

DN

Digital Number

EA

Earth Observation

ECV

Essential Climate Variable

EEA

European Environmental Agency

EM

Electro Magnetic

ESA

European Space Agency

ES

Ecosystem Service

FAPAR

Fraction of Absorbed Photosynthetically Active Radiation

FCover

Fraction of green vegetation Cover

GCC

Global Carbon Cycle

GCOS

Global Climate Observing System

GHG

GreenHouse Gas

GPP

Gross Primary Production

HSI

Hyper Spectral Instrument

IFOV

Instantaneous Field of View

IPCC

Intergovernmental Panel on Climate Change

xiii

IRECI

Inverted Red-Edge Chlorophyll Index

LAI

Leaf Area Index

LCC

Leaf Chlorophyll Content

LSAT

Land Surface Air Temperature

MA

Millennium ecosystem Assessment

MSI

Multi Spectral Instrument

NBS

Nature Based Solution

NDI45

Normalized Difference Index Using Band 4 and 5

NDVI

Normalized Difference Vegetation Index

NIR

Near InfraRed

OM

Organic Matter

RF

Random Forest

RS

Remote Sensing

SAR

Synthetic Aperture Radar

SAVI

Soil-Adjusted Vegetation Index

SDG

Sustainable Development Goal

SIC

Soil Inorganic Carbon

SNAP

Sentinel Application Platform

SOC

Soil Organic Carbon

SVR

Support Vector machine for Regression

SWIR

Short Wave Near InfraRed

TNDVI

Transformed Normalized Difference Vegetation Index

TOA

Top Of Atmosphere

TSS

Total Suspended Solids

UNEP

United Nation Environment Program

UNFCC

UN Framework Convention on Climate Change

USEPA

United States Environmental Protection Agency

USGA

United States Geological Survey

VBV

Vegetation Biophysical Variables

VI WMO

Vegetation Index World Meteorological Organization

xiv

-01-

INTRODUCTION

Urbanization, one of the most important landuses of the twenty-first century, is a rapid transformation of the ecosystem introduced by human. Nowadays, more than 50% of the world’s population already lives in urban centers (Unit-

Above ground biomass (AGB) is the most visible, dominant, dynamic and important pool of the terrestrial ecosystem, constituting around 30% of the total terrestrial ecosystem Carbon pool

ed Nations, 2018).

Carbon sequestration, the process of capturing and storing Carbon from the atmosphere, is one of the most important services provided by the ecosystem. This long-term removal of Carbon from the atmosphere mitigates possible pollution and can help governments move towards more healthy and resilient cities. There are various ways that can restore the existing Carbon and one of which is vegetation.

Vegetation can be counted as a natural solution to absorb Carbon from the air as they use it for photosynthesis. Calculating the amount of Carbon that is being restored in vegetation and above ground biomass will provide a quantitative analysis which can be a foundation for future urban strategies.

There are various methods to estimate Carbon stored in urban vegetation biomass. The traditional destructive methods of calculating by hand which sometimes require cutting down trees and removing vegetation from their habitat to laboratories. This method was sometimes spatially limited as access could not be possible in all the areas especially in dense forests in

These growing urban centers consume numerous resources such as energy, minerals, fuel, water, and food and generate large waste. This drastic ecological transformation and consumption of natural resources is a major anthropogenic cause of global change, including global warming.

Urbanized areas consist of two opposing areas. Built environment such as buildings, roads, etc., and green spaces such as parks, forests, agricultural or horticultural gardens. While efficient use of energy, water, and minerals in the built environment is extremely important, sustainable management of green spaces is critical to restoring the Carbon budget in the urban ecosystem. It can be said that (Lal & Augustin, 2012) urbanized areas influence global Carbon cycle through alterations in source and sink of Carbon.

There are numerous potential Carbon sinks in urbanized areas and in urban ecosystems that include all green spaces. Improved management of these green spaces can sequester Carbon in the above and below-ground biomass.

3

(Lalit & Onisimo, 2017).

higher altitudes. This constraint is not limited to forest landscapes, but it can also be found in other landscapes. The feasibility of the traditional method in urban areas is also limited. This limit can be a result of large scale study areas (sometimes a whole city or region) or limited accessibility because the green areas are privately owned such as private gardens. However, with the emergence of remote sensing (RS) and satellite imagery, these methods have transformed into less destructive, more frequently observed and indirect methods.

Various types of remote sensing data are being used to estimate above ground biomass, ranging from optical sensor data, radio detection and ranging (radar) data, and light detection and ranging (LiDAR) data, with each one having certain advantages over the others.

When vegetation above-ground biomass is estimated, a simple equation computes the amount of Carbon stored in it (50% of AGB is counted to be Carbon). By determining the amount of Carbon that is sequestrated in vegetation, the final goal of this study is achieved.

To have a more in depth understanding of the framework of this study, each chapter will be introduced briefly in the

4

following paragraphs.

Chapter 2 presents urbanization and its brief history throughout the time. The focus here is to tie the history of urbanization and the environment together and study their impact on each other.

This is then followed by an introduction to human driven issues based on concentration of population in one area.

Among the issues, Carbon emission has been studied more (as one of the main challenges humans are facing today) and finally the trends of urbanization and Carbon emission have been compared and illustrated.

Chapter 3 study Carbon sequestration as an ecosystem service (ES) which has an important role in urban ecosystem and conservation of air quality in urban areas. To understand this ecosystem service, Carbon cycles in general and specifically in urban areas have been studied.

Among all the elements that perform sequestration, vegetation is the most natural tool and they help absorb Carbon in their biomass. In order to estimate the amount of Carbon that vegetation biomass absorbs, several methods have been studied and the most effective one, which is by using remote sensing, has been chosen.

To fully comprehend how remote sensing as a new technology is used in estimating above-ground biomass, chapter 4 focuses on analysing the fundamentals of remote sensing, its history and its principals. Since this project uses Sentinel-2 satellite to carry out the work, the focus of the chapter is on this satellite, its main goal and program.

Chapter 5 presents the work methodology and literature review of the references that have laid the basis for each step of this study. The chapter first introduces the methodology that will be carried out in the following chapters and then later introduces a number of scientific papers which talk about the estimation of vegetation above-ground biomass in various landscapes. Even though there were limited number of papers focusing on AGB estimation in urbanized areas, the workflow of this study has been extracted using these papers.

Next, an introduction to the case study, City of Torino, has been executed. A brief introduction to the general characteristics of the city such as its geographical, social and environmental aspects has been carried out. For a better understanding of the case study area, the trends of pollution has also been studied in local levels and the highlights have been pointed out.

5

The final section of this chapter is dedicated to the landscape characteristics of Torino, in which important landscape assets and components have been pointed out and mapped.

Chapter 6 illustrates the ability of Sentinel-2 imagery for predicting urban vegetation biomass and this was evaluated in the test area of the metropolitan city of Torino (Northern Italy). To do so, a series of steps were taken to determine the accuracy of the evaluation of the above-ground biomass:.

I. First, a field observation is required to record the information of existing above-ground biomass. For this reason, 150 plots were chosen in the field and their above ground biomass was estimated to be later used to determine the relationship between the field-measurements and Sentinel based predictors. These predictors were chosen from vegetation indices (VIs) (NDVI, NDI45, TNDVI, SAVI, IRECI) and biophysical variables (VBV) (LAI, FAPAR, FCover). Unlike the current literature, the innovation of this study is that above-ground biomass in this step has not been estimated with traditional methods. The estimation has been

done using the CCI BIOMASS map provided by the European Space Agency (ESA) in 2018.

II. In the second step, a model is made which shows the relationship between field plot values and Sentinel based predictors.

Regardless, no method is best for estimating AGB in all areas.

Therefore, in this work, three machine learning methods including Random Forest (RF), Support Vector Regression (SVR) and Artificial Neural Network (ANN) were used for building AGB models. The efficiency of these models are studied using three error measurements, namely, the Correlation Coefficient (r), the Root Mean Square Error (RMSE), and the Mean Absolute Error (MAE).

III. Finally when the models are approved, an estimation of AGB in other areas can be made. This estimation can later be exported to QGIS and a final map can be produced. The final map of AGB is divided into five categories showing different levels of biomass in various landscapes.

Having the estimation of above ground biomass helps estimate the amount

6

of Carbon that can be restored in the vegetation, which according to the current literature, is a kind of investigation that has rarely been conducted.

Biomass and Carbon calculations are usually calculated together because Carbon is stored in biomass and has a tight mathematical relationship. The amount of biomass is multiplied by 0.5 to get estimated Carbon because 50% of biomass is assumed as stored Carbon (Uykun, 2018).

Finally, chapter 7 marks how the results of this study can be adapted in future scenarios. First, the results of Carbon sequestration can be compared to its landuse. If this is done in a period of time, the results can determine whether a particular landuse is a Carbon sink or a Carbon source. Understanding this behaviour can help governments to prepare a better action plan regarding their Carbon emission and absorption plan. Another scenario in which the final results can be employed is when the values of Carbon sequestration are extracted from the values of Carbon emission in a given region, and the final value shows how much Carbon is still in the air. This information can assist landscape architects in creating more conscientious designs.

Urbanized areas are the major sources of greenhouse gases (CO2 being one

of the main greenhouse gases that affects cities and their residents), having a wide range of emission sources in urban ecosystems including use of electricity and natural gas for residential, commercial and industrial activities. Therefore, management of urban landscape is very important in moderation of greenhouse gases (GHGs).

Nowadays, each city has its own method of gathering data regarding the amount of greenhouse gas, especially the amount of Carbon, it is producing.

They usually gather this information based on sectors (transportation, industry, housing, …).

As mentioned above, having an estimation of the amount of Carbon that can be restored in urban vegetation provides an insight for urban designers and landscape architects to provide a more specific and quantitative based design for the city based on the

7

amount of pollution each city is struggling with.

In the new world, the importance of vegetation as a tool for creating resilient, climate adaptive and nature-inclusive cities is clear. However, to have a design that properly mitigates the effect of climate change and Carbon emissions, landscape architects and urban designers need to have a clear understanding of actually how much is the mentioned effect.

The result of this study can be used by various majors. City planners can use this to base their regional strategies.

On the other hand, landscape architects can base their design projects on the quantitative results of this study and have a new approach that allows them to choose plant species and their quantity in a project more wisely.

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-02+

+

URBANIZATION AS A DRIVER AND A RECIPIENT OF GLOBAL ENVIRONMENTAL CHANGE

11

I N T R O D U C T I O N The aim of this chapter is to provide an overview of the urbanization phenomenon and its history over time, as well as to emphasize the link between urbanization, natural environments, human activities, and strong mutual interactions between human and natural elements in the shaping of the surrounding landscape. The goal is to demonstrate how the expansion of urbanized areas and the concentration of people in one place has resulted in new issues arising from human activity. The release of greenhouse gases, particularly CO2, is one of them, and arguably the most important.

It is beyond the scope of this chapter to provide a comprehensive history of urbanization because it is influenced by a variety of factors and has distinct characteristics in different parts of the world (Europe, Asia, Central and South America), and a comprehensive study of urbanization phenomena would necessarily require a separate chapter. For a more in-depth analysis of the urbanization phenomena the reader is addressed to What Happened In History (Childe, 1942),The Natural History of Urbanization (Mumford, 1956), Iraq: A Country Study (Metz, 1988), Global Change And The Ecology Of Cities (Grimm et al., 2008), A Global Outlook on Urbanization (Seto et al., 2013) and Urbanization: World History Encyclopedia (Mark, 2014).

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2.1.

HISTORY OF URBANIZATION

“Urbanization involves a complex set of economic, demographic, social, cultural, technological, and environmental processes that result in an increase in the proportion of the population of a territory that lives in towns and cities. An increased concentration of population in the larger settlements of the territory, and an increasing density of population within urban settlements

”

- Knox, 2009

Urbanization began in ancient Mesopotamia in Uruk in the period of 43003100 BC (Mark, 2014). Helen Chapin Metz, middle-east historian and analyst proposes that the growth and development of the cities in Mesopotamia was

13

the result of the inhabitants struggling to adapt to the environment. She writes (Metz, 1988): “...The civilized life that emerged at Sumer was shaped by two conflicting factors: I. the unpredictability of the Tigris and Euphrates rivers, which at any time could unleash devastating floods that wiped out entire peoples II. the extreme fertility of the river valleys, caused by centuries-old deposits of soil.

The earliest city which appeared in the region of Mesopotamia is considered to be “Uruk” which dated around 4500 BCE, and then later, the city of

“Ur” around 3800 BCE, both of which were situated close to the banks of the Euphrates River. On the other hand, to the Sumerians, the first city was “Eridu”, which was founded in 5400 BCE (Mark, 2014). The important breakthrough shifts in urbanization history are briefly summarized in the following paragraphs, with the focus, as mentioned in introduction, on the link between human and natural environment.

Neolithic Ages; when the Neolithic Revolution or as it was called the first agriculture revolution took place, is said to be the first wide transition of lifestyle in history. This revolution transformed the lifestyle of hunting and gathering into a lifestyle of agriculture and settlements. As for this revolution, denser human populations were settled in specific areas.

The Neolithic Revolution had other consequences besides increase in population and that was the increase in need for food every year (Childe, 1950). While some inhabitants continued farming and agriculture, some others began to develop specialized occupations.

The start of this process in various locales has been estimated to be between 10,000 and 8,000 BCE throughout the Middle East (Fertile Crescent) (Barker, 2009).

14

Early cities, such the ones that are found in Mesopotamia and Egypt, maintain the symbiotic relation with agriculture that we find in the village nowadays (Mumford, 1956).

The city (Childe,1942), as a form of shelter that man has found on his own, relatively secures from the immediate pressure of raw, external nature.

Ancient cities were known for their diversity in shape and function, and also for geographical diversity. Some cities were lightly populated political capitals, while others were commercial

Fig. 01 Lascaux Cave painting, circa 15,000 BC Lascaux, France

hubs and trade centers, while still others were largely religious. Increase in the population of ancient civilizations, the establishment of ancient empires that concentrated political authority, and the growth of commerce and manufacturing all resulted in increasingly larger capital cities and centers of commerce and industry.

Urban revolution (Childe, 1936), the transformation of agrarian village communities into socially, economically, and politically complex urban societies was introduced again by the archaeologist Gordon Childe in 1930s. He identified the underlying drivers of the urban revolution to be the cumulative growth of technology and the rising availability of food surpluses as capital (Britannica, 2016).

As urbanization, as a dynamic and complex process (Grimm et al., 2008), continued, the over-exploitation of the land to obtain raw material from natural resources resulted in the cities finally being abandoned (Mark, 2014). However, the same reason has led to the expansion of the physical boundaries of cities meaning the population needs more and more land to build (Seto et al., 2013).

Almost all ancient urban settlements led to destruction of the natural environment such as deforestation which

15

often resulted in disastrous consequences for soils and the water table.

Temperate forests were destroyed for making fire and construction material.

In tropical forest, trees were cut down for agricultural production (Mark, 2014).

What distinguishes a city from village is the loose bonds that connect the inhabitants of the city with nature. Moreover, residents of the city transform, eliminate, or replace its earth-bound aspects, covering the natural environment with an artificial environment that empowers the dominance of man and encourages an illusion of complete independence from nature.

Introducing paved paths as a form of the dominance of man which makes it largely independent of the weather and the seasons; the irrigation ditch, a man-made river system that insures the farmer from irregularities of seasonal precipitation; the pyramid, an artificial mountain that serves as symbolic reminder of man’s desire for permanence and continuity. All these inventions record the displacements of natural conditions with a collective artifact of urban origin (Mumford, 1956).

Pre-industrial cities grew into well-defined political units, whilst ancient cities arose naturally as commercial centers. A town was as much a political entity as a collection of buildings

Fig. 02 Nuremberg Chronicles 1493 Source: Schedel’sche Weltchronik

Fig. 03 Painting Manchester from Kersal Moor in 1851 Source: William Wyld

16

during the European Middle Ages. The proportion of urbanites in relation to the peasant population is low, at 10% in some communities.

The feudal system produced medieval cities as a byproduct. They ranged from sparsely populated to highly populated, and were built along riverbanks or near monasteries and castles.

A typical medieval city was primarily a commercial hub, with agriculture serving as a secondary source of income.

Each city had a fort in which the Count resided, and these cities served as the administrative heart of the region .

The majority of roadways are merely passageways for humans and animals in need of transportation. Buildings are low and densely packed. Congestion, along with a lack of scientific knowledge, has resulted in major sanitary issues (Sjoberg, 1955).

Pre-industrial urban settlements were typically built on land that had previously been utilized for other, primarily productive, purposes (Wilson, 2016), and their growth necessitated complex and dynamic connections with natural resource management, particularly food and water.

Urbanization and the development of varied, regional-scale production systems such as intensive agriculture, pastoralism, and the exploitation of marine resources are the two social processes

17

having the greatest potential impact on changing human-environment relations in pre-industrial societies (Dillehay et al., 2004).

The Industrial Revolution, which saw the transition from an agrarian and handicraft economy to one dominated by industry and machine production in the 18th century, drastically altered people’s perceptions of cities (Britannica, 2021). The Industrial Revolution laid the groundwork for altering cities and urban life. Cities grew significantly during the industrial era, becoming population and production centers, initially in Europe and subsequently in other countries, as new opportunities drew large numbers of migrants from rural populations into urban areas.

With all of the developments in machinery and the construction of enormous factories, two types of pollution (air and water pollution) emerged. The main source of energy for these industries was coal, and the coal smoke was emitted into the atmosphere. This resulted in smog forming across the cities, as well as harmful substances polluting the air. Because the industries were so close to the rivers, pollution was able to runoff into numerous major water sources. Water pollution occurred as a result of this contamination.

However, it is undeniable that indus-

trialization has led to urbanization as it created economic growth.

Low quality of life in industrial cities (health issues, contaminated water and air, etc.) had been a major factor in the development of a new type of urban tissue, the open pattern of the suburb, which was the consequence of a desire for an environment free of noise, dirt, and city overcrowding.

This suburban movement began in Elizabethan times as a reaction to the overbuilding and overpopulation that

Fig. 04 Howard Ebenezer, Garden City Source: To-morrow: A Peaceful Path to Real Reform, London: Swan Sonnenschein & Co., Ltd., 1898

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had happened in the city center following the industrialization (Mumford, 1956).

As modern man have thrown off natural restrictions, limitations and controls, he must now replace them with a man-made pattern that is at least as effective. One of these suggestions was the idea of Garden City which was introduced by Ebenezer Howard in 1898 in his book called “To-morrow: A Peaceful Path to Real Reform”. A proposal to oppose the great metropolis’s centralization by reviving colonization as a means of ensuring its continued growth. On land bordered by a swath of open country dedicated to agriculture, recreation, and rural occupation, Howard wanted to establish relatively self-contained, balanced towns, supported by local industry, with a permanent population of limited number and density.

The Urban and Rural Magnet theory was developed in response to a number of issues arising from the inflow of rural residents into cities, including a shortage of peaceful work in the city and a scarcity of minor labor in rural areas. Every city, according to Howard, is a magnet, and every person is a magnetic needle.

Nature’s objectives cannot be adequately reflected by city magnets or

country magnets. Both human culture and natural beauty should be valued equally. One magnet must be contained by two magnets. So that they have equal or even more social opportunities than dense cities, and can provide residents with a sense of security in the beauty of nature.

With more awareness regarding ecology in mid-20s, Environmental Movement was created which was also called Conservation Movement.

The movement focused on wise and efficient use of natural resources. Three key principles underpinned the emerging conservation ethic: I. Human activity harmed the en-

This idea was popularized by James Ranald Martin, who published a number of medico-topographical papers demonstrating the extent of the damage caused by large-scale deforestation and desiccation.

With these examples in front of us, we can get a sense of what the future task of urbanization will be:

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vironment II. There was a civic obligation to preserve the environment for future generations III. Scientific, empirically based means should be used to ensure this duty was fulfilled.

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The restoration of the ecological balance that existed between city and country in the early stages of urbanization, in a more complex unity, with full use of the resources of modern science and techniques.

The climax stage of urbanization is neither the obliteration of the landscape nor the elimination of the city. Rather, it is the foresight and provident balancing of city populations and regional resources in order to keep all of the aspects (social, economic, and agricultural) essential to their common life at a high stage of development.

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Fig. 05 Percentage of urbanization throughout time (18002016) Source: OurWorldInData, 2018

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2.2.

EMERGENCE OF HUMAN DRIVEN PROBLEMS

The world population increased from 2.53 billion in 1950 to 7.7 billion in 2019 (United Nations, 2019), a triple increase in number. From 7.7 billion people worldwide in 2019, the medium-variant projection indicates that the global population could grow to around 8.5 billion in 2030, 9.7 billion in 2050, and 10.9 billion in 2100 (World Population Prospect,

(United Nations, 2018).

Additionally, world’s urban population

It can be said that the rate of urbanization (Seto et al., 2013) is one of the main characteristics of the twenty-first century. The urban population reached one billion when it turned to 1960. However, by 1985 the population doubled.

Rapid urban growth provides a significant opportunity, but it also presents challenges to implementation of an ambitious urban development plan

has grown rapidly from 751 million in 1950 to 4.2 billion in 2018. In 2018, an estimated 55.3% of the world’s population lived in urban settlements. By 2030, urban areas are expected to house 60% of people globally and one in every three people will live in cities with at least half a million inhabitants

that seeks to make cities and human settlements inclusive, safe, resilient and sustainable. However, it is worth bearing in mind that human settlements are both drivers and recipients of global environmental change.

Many of the global environmental challenges that we are facing and talking

2019).

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Fig. 06 Share of the population living in urban areas, 1950 and 2020 Source: OurWorldInData, 2018

about today become tangible in cities.

At the urban scale, cities will certainly require to implement and enforce international and national standards (Sassen, 2012).

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The damage caused in urban areas and the pollution created are at micro-level such as cars, buildings or factories but the impact is visible and measurable all around the globe even in the poles.

The presence or introduction of dangerous or damaging substances into cities and urban areas is referred to as “urban pollution” (Martinez-Bravo, 2019). Natural sources of pollution may contribute to urban pollution, but human-caused emissions are the most harmful. Due to the general local con-

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The figure depicts the world’s interconnectedness from the greatest to the smallest scales, with watersheds visible throughout regions. Energy and materials flood into the metropolis, releasing CO2, SO2, acid rain, hormone disruptors, heavy metals, PCBs, and other poisons that are frequently trafficked from developed to poor countries. Each urban combination of features, as well as how it fits into local and regional ecosystems, is distinct.

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II. Soil pollution is caused by the untreated discharge of industrial wastes into the soil; these wastes include high levels of harmful

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I. Water pollution is defined as the presence of chemical, physical, or biological components or factors that cause a water body’s capacity to perform a vital service beneficial use (Schwitzer, 2018).

Water pollution has become a major issue around the world, resulting in a lack of usable water. As a consequence, there has been a pressing need to develop environmentally friendly wastewater treatment technologies.

Source: Cities and the Biosphere, 2012

GL O

I. Water II. Soil III. Air

Fig. 07 The Nested Scales of Urban Impacts on the Biosphere

CO NT I

centration of humans and human activities, anthropogenic sources of pollution, such as factories, industries, transportation, and so on, are often aggravated in cities. Pollution in cities, for example, is influenced by locally generated environmental issues such as waste management, recycling, and light and noise generation.

The most common method of classifying urban pollution is by the affected environmental sector:

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pollutants, resulting in soil pollution. Industrial wastes contain varying amounts of harmful substances and dangerous compounds, which, when deposited in soil, alter the soil layer strength in the top soil, lowering soil fertility and biological activity (Senthilkumar, 2020). Furthermore, a harmful consequence causes ecological imbalances, causing crop production problems. Apart from that, when soil and crops are contaminated with poisonous chemicals and dangerous compounds, it may provide a serious health risk to those who consume such foods.

III. WHO defines air pollution as contamination of the indoor or outdoor environment by any chemical, physical or biological agent that modifies the natural characteristics of the atmosphere (WHO, 2019). Chemical composition, reactions, emissions, persistence in the environment, ability to be transported over long or short distances, and eventual repercussions on human health and/or the environment are all characteristics of air pollutants (Fino, 2019).

Urban areas modify their local

24

and regional climate through emission of greenhouse gases and creating urban heat island effect and by altering precipitation patterns, which together will have significant impacts on net primary production, functions of ecosystems, and biodiversity (Seto et al., 2013). These changes may have serious impacts on cities and their inhabitant.

It can be said that some of the greenhouse gases are emitted exclusively from human activities (e.g., synthetic halocarbons).

While others might occur naturally but again are found due to human inputs (e.g., Carbon dioxide). Anthropogenic sources for greenhouse gases are from energy-related activities (e.g., combustion of fossil fuels in the electric utility and transportation sectors), agriculture, land-use change, waste management and treatment activities, and various industrial processes (US EPA, 2017). CO2 produced by human activities is the largest contributor to global warming. By 2020, its concentration in the atmosphere had risen to 48% above its pre-industrial level (before 1750) (European Comission, 2016).

In this perspective, green spaces and in general urban vegetation have great influence in moderating the impact of the negative consequences of climate change. Based on the current trend in the near future, when climate is likely to change in urban areas regardless of their geographic location, maintaining the balance of the city’s natural urban environment will be of utmost importance. Greening our cities would need participation from all households, neighborhoods, and businesses, making everyone of them an active contributor to environmental sustainability.

Cities are complex systems, and this decentralization of greening activities can be woven into the urban fabric. (Sassen, 2012). This newfound appreciation for the importance of environment, culture, and landscapes in climate adaptation and mitigation is supporting their preservation (Van Leeuwen, 2012).

Fig. 08 Water Pollution Source: NRDC, 2018/ PHYS, 2016

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Fig. 09 Soil Pollution

Fig. 10 Air Pollution

Source: FAO, 2018/ Reveal, 2014

Source: IQAir, 2022/ ARVIND YADAV, 2021

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2.3.

CARBON EMISSION AS A GLOBAL CHALLENGE AND THE IMPORTANCE OF STORING IT

Throughout history, human have played an important role in the emission of Carbon and other greenhouse gases which are now the primary driver of climate change and one of the world’s most critical challenges. Nowadays, the link between greenhouse gases and increase in temperature is

Fig. 11 Climate change initiatives

undeniable. From the preindustrial period (1850–1901) to the current day (1999–2018), worldwide averaged land surface air temperature (LSAT) has risen faster than global mean surface temperature (i.e., combined LSAT and sea surface temperature).

According to the single longest and

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Kyoto Protocol

2030 Agenda

UNFCC adapted with the aim of stabilizing GHGs in the atmosphere

the first document with legally binding obligation

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17 SDGs to be achieved with the aim of reaching sustainable development

most comprehensive dataset, mean land surface air temperature climbed by 1.53°C (very likely range 1.38°C to 1.68°C) from 1850 to 1900 to 2006– 2015, while global mean surface temperature increased by 0.87°C (very likely range 0.75°C to 0.99°C). The LSAT increase was 1.41°C (1.31–1.51°C) between 1880 and 2018, when four independently created datasets were available, where the range shows the spread in the datasets’ median estimates (IPCC, 2018). In general, Global temperature has risen sharply over the last few decades. In overall, the average temperature has risen up by 1.1 degree in almost 170 years (1850-2019).

Climate zones have shifted as a result of anthropogenic warming, with an increase in dry climates and a decrease in polar climates. Climate change is expected to bring new, hot climates to tropical regions, as well as move climate zones poleward in the mid- to

high-latitudes and upward in higher-elevation regions. As a result of global warming, the frequency and intensity of some extreme weather and climate events has increased, and this trend will continue under medium and high emission scenarios.

Despite the considerable uncertainties in evaluating climate change, the first IPCC report (Intergovernmental Panel on Climate Change) in 1990 emphasised the risk of global warming due to an unbalance in the climate system caused by increased anthropogenic greenhouse gas emissions generated by industrial development and the use of fossil fuels. In 2014, IPCC clearly stated in their assessment report (AR5) that (IPCC, 2014) : “... Anthropogenic greenhouse gas emissions have increased since the pre-industrial era, driven largely by economic and

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Specifically aims to limit global warming below 2C

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population growth, and are now higher than ever. This has led to atmospheric concentrations of Carbon dioxide, methane and nitrous oxide that are unprecedented in at least the last 800,000 years. Their effects, together with those of other anthropogenic drivers, have been detected throughout the climate system and are extremely likely to have been the dominant cause of the observed warming since the mid-20th century.”

Fig. 12 Establishment of IPCC in 1988 by World Meteorological Organization (WMO) and UN Environment Program (UNEP) Source: UNFCCC, 2014

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Climate change is gaining speed, and its consequences are becoming more severe. Human activities are thought to be causing climate change 170 times faster than environmental factors. Extreme weather, ice loss, sea level rise, and ocean heat and acidification are all becoming more prevalent. Global temperatures are anticipated to reach almost 2.9 °C by 2100 if current policies are followed. Human activities have caused approximately 1.2 °C of global warming above pre-industrial levels (Pereira, 2022).

The European Union took the first step in this direction at the end of 1990, when it asked member states to plan and implement environmental and energy efficiency measures. The contents of the EU statement served as the foundation for the UN Framework Convention on Climate Change (UNFCC) negotiations, which were adopted in New York on May 9, 1992, and signed during the Earth Summit in Rio de Janeiro in June 1992.

The Kyoto Protocol, which establishes emission reduction goals for the period 2008-2012, was accepted by Parties to the Convention in 1997. In specifically, the European Union as a whole agreed to a reduction of 8% within the time frame.

In December 2015, a newer worldwide agreement was established in Paris for

the period following 2020. The agreement intends to enhance the global response to climate change by keeping global temperature increases well below 2 degrees Celsius above pre-industrial levels and pursuing efforts to keep temperature increases to 1.5 degrees Celsius above pre-industrial levels.

With all the global programs that exist on climate change, even the Kyoto Protocol and the United Nations Framework Convention on Climate Change, none mention specific references to local government or city-level initiatives to satisfy Protocol commitments.

One of the only mentions might be the Kyoto Protocol’s Article 10 which recognizes that regional programs may be useful in improving the quality of local emission factors (UN, 1998): “All Parties, taking into account their common but differentiated responsibilities and their specific national and regional development priorities, objectives and circumstances, without introducing any new commitments for Parties not included in Annex I, ...”

To follow this suggestion as a strategy,

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Fig. 13 Global greehouse gas emissions and warming scenarios Source: OurWorldInData

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the application of science and technology in methods that could increase the number of connections between cities and the biosphere stands out. This is just one step on a path that should try to fully exploit the complexity of cities—their multi-scalar and ecological characteristics (Sassen, 2012) .

The Carbon cycle of urban areas plays a significant part in the feedback between urban development and global environmental change.

The process of photosynthesis allows Carbon to enter urban habitats.

Light, atmospheric CO2 concentration, air temperature, water availability, nitrogen (N) supply, and tropospheric ozone (O3) concentration are the key influences on photosynthetic rate.

Total amount of Carbon stored in urban ecosystems is determined by the density of the city’s built environment, dominant vegetation types, rates of Carbon uptake and release by vegetation, Carbon release by soil, as well as management of vegetation and soil.

In this regard, Carbon storage is an essential attribute of a stable urban ecosystem and a key link in the global Carbon cycle. The potential of urban settlements to store Carbon is underestimated. A recent study (Churkina, 2012) showed that human settlements of the US currently store 10% of the total Carbon stored in all US ecosystems.

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I.

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Fig. 14 Effects of Global Warming on the Planet II. Increase in the speed of icebergs melting III. Sever droughts in urbanized areas IV. More frequent fires in forests and woodlands Source: Unsplash

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2.4.

TRENDS OF URBANIZATION AND CARBON EMISSION

Given the previous section’s study of the link between urbanization and Carbon emissions, it’s noteworthy to compare and contrast the developments in both subjects to see how they relate and interact.

As previously said, during the most of human history, most people lived in small villages all across the world. This has changed drastically over the ages, and especially in recent decades since

more than 50% higher than the global population as a whole. Thus, between 1950 and 2018, the world’s population dramatically urbanized, with the proportion of people living in cities rising from 30% in 1950 to 55% in 2018

a huge migration of people from rural to urban areas has occurred.

Between 1950 and 2018, the world’s urban population increased by more than fourfold, from 0.8 billion to 4.2 billion people. During this time, the average yearly rate of change of the urban population, estimated at 2.54%, was

lion people each. In 2016, the world’s urban population grew to 4 billion people, while the world’s rural population grew very slightly to 3.4 billion.

World Urbanization Prospect 2018 has studied the change in urbanization rate and its growth in the world. In 1970 the highest percentage of urbanization

33

(Worlds Urbanization Prospect, 2018).

In 1960 rural areas had twice as many inhabitants (2 billion people) as urban areas (1 billion). In 2007, the urban and rural population totaled 3.33 bil-

was seen in Oceania and after that Europe and northern America with 60-80 percent urban areas. The lowest rate is seen in Africa and East Asia with 0-20 percent. However, with the growth of urbanization, the trend accelerated in 1990 in Africa and East Asia while it stays the same in other areas. Finally, the situation in 2018 represents a high rate of urbanization in America in both the northern and southern parts.

Moreover, a more in depth study of the growth rate of urban agglomeration from 1970 to 2030 was conducted.

What is interesting from a comparison of these two sets of studies is that however, in 1970 the percentage of urbanization was higher in developed countries such as the USA in America and Italy, France, Germany, Austria and Scandinavian countries in Europe, on the other hand, the growth rate of urban agglomeration was the lowest in these countries (less than 1 percent). The same rate has been higher in under developed or developing countries in Africa and East Asia ( more than 3 percent and in some cases even more than 5 percent). So it can be said that there are significant disparities in urbanization patterns between the more developed and less developed regions. While rural areas are home to slightly under half of the people in the less developed regions, urban areas are home to the vast

34

majority of the population in the more developed regions. The urban population of less developed regions, on the other hand, has been expanding at a far quicker rate than that of more developed regions.

The same trend continues for growth rate in 1990, 2018 and even the forecast for 2030, however, the growth rate decreases to 1-3 percent.

While the world’s population is predicted to continue to urbanize, the rate of urbanization is expected to decline in the future, with both the absolute size of the urban population and the proportion of the population living in cities expected to expand at a slower rate.

Thus, between 2018 and 2030, the global urban population is expected to grow at an average annual rate of 1.7 percent, substantially slower than the 3.0 percent growth seen between 1950 and 1970, 2.6 percent growth between 1970 and 1990, and 1.7 percent growth between 1990 and 2018. (2.2 per cent) (Worlds Urbanization Prospect, 2018).

To have a closer look at trends in Europe it is worth mentioning that From 1961 to 2018, the proportion of people living in rural areas as a percentage of the overall population decreased, but the proportion of people living in towns and cities increased steadily.

Built-up areas are predicted to devel-

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op across the EU by 2030. The highest absolute growth will be in Italy (+144 thousand ha), followed by Germany (+127 thousand ha) and Poland (+121 thousand ha). Romania and Belgium are predicted to have the highest relative growth, at over 6%. Bulgaria and Croatia, on the other hand, are likely to see a reduction in built-up land (Developments and Forecasts on Continuing Urbanisation, 2015).

Cities are particularly vulnerable to the effects of climate change since they are responsible for a high level of energy use and, as a result, produce around 70% of global GHG emissions.

In 2016, 91 percent of the world’s urban population breathed air with a level of particle matter higher than the World Health Organization’s recommended standard (PM 2.5). More over half of these people were exposed to levels of air pollution at least 2.5 times greater than WHO safety recommendations. Furthermore, in 2016, high levels of ambient air pollution killed an estimated 4.2 million individuals (Developments and Forecasts on Continuing Urbanisation, 2015).

The growth of global emissions from the mid-eighteenth century to today may be observed in this graph.

Emissions were quite minimal prior to the Industrial Revolution. Emissions

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growth was rather moderate until the mid-twentieth century. The globe emitted 6 billion tonnes of CO2 in 1950. By 1990, it had nearly doubled to more than 22 billion tonnes. Emissions have continued to rise fast, with annual emissions already exceeding 34 billion tonnes.

The global CO2 emissions are broken down by region in this interactive graph. Europe and the United States controlled worldwide emissions until far into the twentieth century. More than 90% of emissions were produced in Europe or the United States in 1900, and by 1950, they accounted for more than 85% of annual emissions. However, this has altered dramatically in recent decades. The remainder of the globe, mainly Asia, and most notably China, sees a major increase in emissions in the second half of the twentieth century. The United States and Europe currently account for slightly over a third of global emissions.

Based on energy statistics, historical fossil fuel CO2 emissions might well be traced back to 1751. The production volumes of several types of fossil fuels are detailed in these reconstructions (coal, brown coal, peat and crude oil).

The graph below shows annual CO2 emissions in different continents from 1800 to 2020. In 2017, Asia is the world’s leading emitter, accounting for 53%

of total emissions. However, because Asia is home to 60% of the world’s population, per capita emissions in Asia are slightly lower than the global average.

North America, which is dominated by the United States, is the second greatest emitter in the world, accounting for 18% of worldwide emissions. It is closely followed by Europe, which has 17 percent. Africa and South America are both small emitters, each accounting for 3–4% of world emissions. Both emit about the same amount of pollution as international aviation and shipping.

National and regional emissions do not include aviation or shipping (Ritchie & Roser, 2020).

Achieving an overall balance between greenhouse gas emissions generated and greenhouse gas emissions removed from the environment is referred to as “net zero emissions” (Climate Council, 2021). The Paris Agreement emphasizes the importance of net zero emissions, urging governments to “achieve a balance between anthropogenic emissions by sources and reductions of greenhouse gases by sinks in the second half of this century.”

Net zero is the internationally agreed-upon objective for limiting

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global warming in the second half of the century, and the IPCC stated that net zero CO2 emissions by 2050 are required to keep global warming below 1.5 degrees Celsius.

Fig. 16 Annual CO2 Emission from Fossil Fuels Fig. 17 Commutative CO2 emission by region Source: OurWorldInData , 2021

Emissions are based on territorial emissions (production-based) and do not account for emissions embedded in trade. This me sures CO₂ emissions from fossil fuels and cement production only – land use change is not included.

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CARBON SEQUESTRATION; NATURE’S POTENTIAL TO ALLOW LIFE ON PLANET EARTH

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I N T R O D U C T I O N This chapter focuses on Carbon sequestration as one of the most important ecosystem services that has been overlooked in the past and now it has come to the spotlight with the rise of global warming. To fully understand how this specific ecosystem service works, the basics of Carbon cycle in the globe and in urban areas have been defined.

Carbon sequestration can happen using many different natural or artificial tools. In this study the focus is on one particular tool; vegetation. Vegetation as the main tool that nature offers has been one of the main sources of Carbon sequestration since the beginning of time but its value has gradually been forgotten. Vegetation absorb Carbon in their biomass through the process of photosynthesis and help purify the air. By estimating the amount of aboveground biomass, the amount of Carbon absorbed can be calculated as well.

Different methods of estimating above-ground biomass has been introduced in this chapter and the most efficient one, being remote sensing, has been chosen to carry out the work in this study.

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3.1.

ECOSYSTEM SERVICES; A GIFT FROM NATURE 3.1.1.

DEFINITION OF ECOSYSTEM SERVICES

“An ecosystem is a dynamic complex of plant, animal, and microorganism communities and the nonliving environment interacting as a functional unit.

Humans are an integral part of ecosystems

”

- Millennium Ecosystem Assessment Board, 2015

The Millennium Ecosystem Assessment (MA), a major UN-sponsored effort to analyze the impact of human actions on ecosystems and human well-being, defined Ecosystem Services as the benefits people derive from ecosystems (Millennium Ecosystem Assessment Board, 2015). More specifically, services are produced by living or

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nonliving components of ecosystems, through the conditions and processes in ecosystems (Quijas & Balvanera, 2013).

Ecology, which addresses the underlying principles and interactions of species and the environment, is necessary for understanding ecosystem services.

As the ranges at which these entities interact can range from microorganisms to landscapes, milliseconds to millions of years, describing energy and material movement between them remains one of the most difficult tasks.

MA identified four major categories of ecosystem services: provisioning, regulating, cultural and supporting services.

I. Provisioning services are

the tangible resources or goods derived from ecosystems by humans. These are limited, renewable, and can be eaten, appropriated, and traded directly (Quijas & Balvanera, 2013). These services can be food, fuel, fiber, fresh water,and a wide range of abiotic and biotic resources derived from terrestrial and aquatic ecosystems.

II. Regulating services are the benefits people obtain from the regulation of ecosystem processes (Millennium Ecosystem Assessment Board, 2015) and are the product of various ecosystem processes contributing to ecosystem functioning, including air quality maintenance, climate regulation, erosion control, regulation of human diseases, and water purification.

III. Cultural services are the non-material benefits people obtain from ecosystems. Individual species or entire ecosystems can provide spiritual and aesthetic experiences, cultural heritage, recreation, scientific research, and discovery chances that make up cultural services (Malanson & Alftine, 2016).

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IV. Supporting services are those that are necessary for the production of all other ecosystem services, such as primary production, production of oxygen, and soil formation (Millennium Ecosystem Assessment Board, 2015). Providing living spaces

for plants or animals and maintaining a diversity of plants and animals, are supporting services and the basis of all ecosystems and their services (FAO, 2021).

In 2013 an International Classification of Ecosystem Services (CICES) was released by the European Environmental Agency (EEA). The concept of a single international classification is significant since it was recognized that in order to build ecological accounting systems and make comparisons, some standardization in the way we characterize ecosystem services was required. Where the link to economic accounting must be made, standardization was considered as particularly vital (CICES, 2013). CICES uses a five-level hierarchical structure to explain what are considered possible or presumptive ‘final ecosystem services.’

Each level becomes more precise and specific as it progresses. The Cascade Model, as described by Potschin and Haines-Young, can be used to explain

the conceptual backdrop for CICES. The cascade model was created to describe how the concept of ecosystem services may be utilized to better comprehend human-nature relationships.

According to the model, in order to comprehend these connections, we must first define the functional qualities of ecosystems that give rise to services, as well as the benefits and values that they support. The Cascade model connects the five areas of ecosystem service flow: structures (and processes), functions, services, benefits, and values. The entire chain begins with biophysical structures, which, in conjunction with fundamental natural processes, establish the potential for ecosystem functioning. Ecosystem services are created when ecosystems function properly. Benefits are defined as the portion of the whole pool of ecosystem service potential that is used. For the benefits, the values are determined.

The phrase ‘ecosystem services’ was picked from the middle to serve as an umbrella term, and indicators for the other four steps (structure, function, benefit, and value) of the Cascade models were created to enhance awareness of the full ecosystem service flow.

The cascade’s five elements are intended to encourage users to question the distinction between’services’ and

45

‘benefits,’ as well as to investigate the specific ‘functional’ characteristics of ecosystems that arise services, as opposed to the more general ecological procedures and systems that support them.

Human wellbeing is commonly viewed as a critical component of the ecosystem service model. Interpretation

ENVIRONMENT SUPPORTING SERVICES Biophysical structure or process woodland habitat or net primary production

Function Slow passage of water or biomass

Limit pressure by policies and actions

Pressure

on how shifts in the output of ecosystem services affect well-being is tightly linked to the challenge of sustainable ecosystem management, which includes issues such as what is being sustained and why, and therefore how human well-being and sustainability can be achieved through ecosystem service management. It is now well recognized that investing in natural capital

can help a city or territory both socially and economically.

The EU Framework Programme for Research and Innovation ‘Horizon 2020’ has integrated sustainability and competitiveness across key societal problems in order to promote raw material security, improve well-being, and strengthen resilience to future social and economic shocks.

SOCIAL AND ECONOMIC SYSTEM FINAL SERVICES

GOODS AND BENEFITS

Services flood protection or harvastable products

Benefits contribution to aspects of well-being such as health or safety

Value willingness to pay for woodland protection or for more woodland

46

Fig. 18 Cascade Model Source: Potschin, M. and R. Haines-Young (2011)

3.1.2.

URBAN ECOSYSTEM SERVICES

Cities and urbanised areas are viewed as complex human-dominated ecosystems in urban ecology. These systems are dynamically intertwined with the city’s social, ecological, economic, and technological/built infrastructure.

In this view, patterns and processes in urban systems emerge from interactions and feedbacks among city components and systems, emphasising the need to consider multiple sources of social-ecological patterns and processes in order to comprehend reciprocal interactions between climate change and urban ecosystems (‌McPhearso et al., 2018).

Cities have far-reaching environmental consequences on a local, regional, and global scale that extend far beyond their borders. Despite the fact that urbanized regions make just a small percentage of the planet’s surface, they account for the majority of anthropogenic impacts on the biosphere. Urban ecosystems are the primary source of world emissions, accounting for 78% of total Carbon dioxide emissions. In ad-

47

dition, 60% of residential water usage and 76% of industrial wood consumption occur in urban areas (Lal, 2012).

In general Urban Ecosystem refers to any ecological system found within a city or other highly populated area, or, in a broader sense, the entire metropolitan area’s ecological system.

The urban ecosystem, like any other ecosystem, is constituted of biological elements like plants, animals, and other forms of life, as well as human populations, demographic characteristics, institutional structures, and the social and economic tools they use, as well as physical elements like soil, water, air, climate, and topography). Buildings, transit networks, modified surfaces (e.g., parking lots, roofing, landscaping), and environmental changes resulting from human decision-making contribute to the physical complexity. Energy utilization and the import, transformation, and export of resources are all factors to consider. Not only do these energy and material transformations result in

good items (such as transportation and housing), but they also result in pollution, waste, and excess heat. Introduced plant and animal species with a long history of interaction with humans and adaptations to urban settings dominate urban environments.

The majority of ecosystem services consumed in cities are generated by ecosystems that are located outside of cities, frequently half a world away. As a result, the scope of urban ecosystem research extends beyond the city limits; it includes not only the ecological infrastructure within cities, but also the hinterlands that are directly impacted by energy and material fluxes from the city core and suburban lands.

The involvement of water and vegetation in or near the built environment in supplying ecosystem services at various spatial scales is referred to as ecological infrastructure (building, street, neighborhood, and region). Parks, cemeteries, gardens and yards, urban allotments, urban forests, single trees, green roofs, wetlands, streams, rivers, lakes, and ponds are all examples of ‘green and blue spaces’ found in urban and peri-urban environments (EEA 2011).

General classifications must be tailored to specific types of ecosystems since different habitats supply different sorts

48

of ecosystem services. Urban ecosystems play a critical role in delivering services that have a direct impact on human health and safety.

Erik Gómez-Baggethun (Gómez-Baggethun et al., 2013), grouped ecosystem services in four major categories: provisioning, regulating, habitat, and cultural and amenity services based on Millennium Ecosystem Assessment (2005) and the Economics of Ecosystems and Biodiversity initiative (2012).

I. Provisioning services such as food supply and water supply II. Regulating services such as Urban Temperature Regulation, Noise Reduction, Air Purification, Carbon Sequestration, Moderation of Climate Extremes, Runoff Mitigation, Waste Treatment, Pollination, Pest Regulation and Seed Dispersal, Global Climate Regulation III. Cultural services such as Recreation, Aesthetic Benefits, Cognitive Development, Place Values and Social Cohesion IV. Habitat services such as Habitat for Biodiversity Nature in cities serves as an ecological

foundation for human–nature interactions and the creation of Urban Ecosystem Service. In cities, biodiversity and ecosystems are becoming more closely linked to human health and well-being, livability, and the quality of life. Urban trees, for example, can help clean the air, provide shade during hot weather, absorb and store Carbon, and provide spaces for contemplation, aesthetic and spiritual satisfaction, and social cohesiveness (‌McPhearso et al., 2018).

Although the term “Urban Ecosystem Services” is new, the notion is not. Municipalities are used to working with stormwater management and green spaces, for example, but working with the relationships between these programmes is new and often difficult.

Green and blue structures can both help address difficulties and contribute to developing healthy environments by collaborating early in the planning process amongst different disciplines and stakeholders.

A literature review of the evolution of urban ecosystem services done by Kang (kang et al., 2020) between the years of 2010 and 2019 illustrated that a few keywords have characterized the development and evolution of urban ecosystem services literature in the last decade. Accordingly, it is import-

49

ant to clarify the significance and implications of those keywords, in terms of their definitions and roles in forming urban ecosystem services knowledge systems. Throughout the evolution of urban ecosystem services research, the term “urbanization” has consistently been used. Not surprisingly, many parts of the world are still being urbanized, a factor that impacts ecosystem services at all scales in the 21st century. As such, the concept of ecosystem services is, as it should be, in line with the concept of sustainable development. Sustainable development aims to preserve the ecosystem and improve human quality of life in the process. “Sustainability”, “resilience”, “social-ecological system”, and “nature-based solution” are other keywords that can be used to interpret key concepts and visions of human efforts in association with urban ecosystem services.

The quality of the air, the quality and availability of water, waste processing and recycling systems, and many other aspects of the ambient environment that contribute to human well-being can all be jeopardized by urban expansion. The metropolitan area and its surrounding region provide a more appropriate scale for understanding the interactions between urban growth and local ecosystems. People in cities have

historically relied largely on neighboring systems for food, clean water, trash disposal, and a variety of other services.

Urban and peri-urban ecosystems are highly influenced by human-caused environmental change, but they also reflect plants, animals, and microorganisms’ abilities to endure and exploit these changes.

Most pre-existing ecosystems are likely to be substantially altered, if not completely transformed, by the time a specific area gets urbanized. Even ecosystems linked with relatively undeveloped regions are likely to be impacted by the habitat fragmentation and pollution that commonly accompany urbanization. Ecosystems can provide a particularly diverse range of functions in metropolitan centers and their outskirts. The most well-known services connected with green spaces are air filtering, micro-climate regulation, noise reduction, surface water drainage, nutrient retention, genetic library, pollination, seed distribution, and insect pest control.

The focus of this study will be on Carbon Sequestration as a crucial ecosystem service that aids cities and urban areas in the fight against climate change and is a fundamental idea to include in future planning for cities.

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3.2.

CARBON SEQUESTRATION IN URBAN AREAS 3.2.1.

CARBON CYCLE

The Carbon cycle illustrates the continuous movement of Carbon atoms from the atmosphere to the biosphere, pedosphere, geosphere, hydrosphere and back to the atmosphere. The amount of Carbon in this system does not fluctuate because our planet and its atmosphere are a closed system.

The location of Carbon, whether in the atmosphere or on Earth, is continually changing (US Department of Commerce,

they flow across ecosystems, energy and organic Carbon are inextricably intertwined. Some of the recently fixed organic Carbon in ecosystems is not quickly returned to the atmosphere through respiration, but instead persists in stabilized forms like biomass, soil, and deep ocean sediments. Carbon is then transferred from plants to animals via the food chain. Consumer species, ranging from protozoans

National Oceanic and Atmospheric Ad-

to humans, devour the stored products and change them into different forms. When plants and animals die, the Carbon in their bodies, wood, and leaves decomposes, releasing it into the ground. When species die, volcanoes erupt, flames burn, fossil fuels are burned, and a variety of other activities

ministration, 2021).

When atmospheric Carbon dioxide (CO2) is converted to generate organic Carbon compounds during photosynthesis, a process fueled by solar energy, energy enters ecosystems. As

51

release Carbon into the atmosphere.

In the case of the ocean, Carbon is constantly transferred between the ocean’s surface waters and the atmosphere, or it is stored in the ocean depths for lengthy periods of time.

All living things contribute to the Carbon cycle (humans, animals, vegetation). The atmosphere, oceans, geological sources, biota, and the terrestrial biosphere are all interrelated reservoirs for Carbon. With 38,000 Petagrams (Pg) ,with each Pg equals to 1015 grams, the oceanic pool has the most Carbon, followed by the geologic pool (fossil fuels) with 5,000 Pg. The geologic pool comprises about 4000 Pg of Carbon in the form of coal, 500 Pg of Carbon in the form of oil, and 500 Pg of Carbon in the form of natural gases.

With 2300 Pg of Carbon, the terrestrial pool is the third largest, with 1,550 Pg of Soil Organic Carbon (SOC) and 750 Pg of Soil Inorganic Carbon (SIC). The atmospheric pool, which contains 760 Pg, is the next largest, followed by the biotic pool, which contains 600 Pg of live mass and detritus material (US EPA, 2011).

In general, Carbon undergoes two different cycles; slow cycle and fast cycle.

The Slow Carbon Cycle takes between 100 and 200 million years for

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Carbon to flow between rocks, soil, ocean, and atmosphere due to a sequence of chemical reactions and tectonic activity. Rain, which generates a weak acid that dissolves rock when it hits the ground (a process known as chemical weathering), starts the flow of Carbon from the atmosphere to the lithosphere (rocks). The freed material is subsequently delivered to the ocean by rivers. Layers of shells and sediment become cemented together and convert to rock over time, trapping Carbon in limestone and its derivatives.

Through volcanoes, the slow cycle returns Carbon to the atmosphere (Riebeek, 2011).

The Fast Carbon Cycle is primarily concerned with the transfer of Carbon through Earth’s life forms, or biosphere.

The rapid Carbon cycle is driven by plants and phytoplankton. Plants absorb Carbon dioxide and sunlight during photosynthesis to form plant structures. The fast (biological) Carbon cycle is built around this process. The fast Carbon cycle is so intertwined with plant life that the way Carbon dioxide swings in the atmosphere might reveal the growth season. When few terrestrial plants are growing and many are decomposing in the Northern Hemisphere winter, atmospheric Carbon dioxide concentrations rise. Concentrations diminish in the spring as plants

begin to develop again. It is as if the Earth is taking a breath (Riebeek, 2011).

Human activity has significantly disrupted the global Carbon cycle since the industrial revolution by transferring vast amounts of Carbon from the geosphere. Changes in vegetation and other land use have also continued to affect the natural component functions of the terrestrial biosphere.

In the feedback between urban development and global environmental change, the Carbon cycle of metropolitan regions plays a critical role. On the one hand, it is a driver of global environmental change, as urban areas account for more than 70% of worldwide CO2 emissions. Global and regional climatic changes such as heat waves, water scarcity, and air pollution, on the other hand, have an impact on the urban Carbon cycle.

Carbon cycling in urban contexts is intrinsically linked between natural (e.g., urban vegetation and soils) and human components (e.g., buildings, furniture, landfills, etc.).

The Carbon cycle in urban environments includes both organic and inorganic Carbon cycling. Carbon is cycled and stored in organic form in live biomass such as trees and grasses, as well as in artifacts generated from bio-

53

mass such as wooden furniture, building structures, paper, natural clothing, and shoes. Meanwhile, inorganic or fossil Carbon is predominantly cycled and stored in human-made items such as concrete, plastic, asphalt, and bricks.

The way organic and inorganic forms of Carbon return to a gaseous state differs significantly. Organic Carbon can be returned to the atmosphere through decomposition of organic stuff, whereas inorganic Carbon requires energy input, such as burning.

Carbon cycling in cities is determined by its source of origin, which can be both natural and anthropogenic. Carbon enters urban ecosystems through photosynthesis when the Carbon cycle has a natural source, as it does in other terrestrial or aquatic ecosystems. Carbon uptake occurs in urban ecosystems with increased CO2 concentrations, higher temperatures, and under the effect of a variety of contaminants in general. Organic matter (OM) breakdown, as well as plant development and maintenance respiration, all contribute to Carbon release in urban ecosystems.

Plant respiration provides energy for a plant’s acquisition of nutrients, as well as the production and maintenance of biomass. Finally, urban vegetation and

soils store Carbon. The total amount of Carbon stored in urban ecosystems is determined by the city’s population density, dominant vegetation types, rates of Carbon uptake and release by vegetation, Carbon release by soil, and management of vegetation and soils.

Co2

Atmosphere H+

Co2

H2O

H

H2O

H+

+

Hydrosphere

Co3 -2

HCO3

H2O H2O

H+

O2

H2O or other metal ions

Co2

Biological activity O2

Chemical precipitation

Co3

Biomass and dead organic material

-2

Metamorphic rocks Co3 -2

Co2

Fermentation

Sedimentation and burial Sedimentary rocks Co3 -2

Source: Encyclopedia Britannica

H2O

Biogenic carbonates

Volcanism and outgassing

Fig. 19 Caron Cycle

O2 CH4

Organic material in sedimentary rocks

Deep burial

Oxidation by reaction with components of rocks

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Organic material in metamorphic rocks

Use of fossil fuels, natural processes of uplift and erosion

Uplift and erosion

O2

3.2.2.

THE PROCESS OF CARBON SEQUESTRATION

“ The method of collecting and storing atmospheric Carbon dioxide is known as Carbon sequestration. It is one way of lowering Carbon dioxide levels in the atmosphere with the purpose of minimizing global climate change

”

USGS, 2019

Reservoirs that store Carbon and prevent it from entering the atmosphere are called “Carbon Sinks”. A Carbon sink is any natural or man-made reservoir that accumulates and stores some Carbon-containing chemical component for an indeterminate amount of time, and they are critical for keeping Carbon dioxide levels in the atmosphere under control. Undisturbed forests and soils, oceans, untapped fossil fuel wells, and terrestrial plant photosynthesis are all common Carbon sinks. The world’s greatest Carbon sinks are the ocean, atmosphere, soil, and forests. Protecting these key ecosystems is critical for combating cli-

55

mate change and maintaining a stable climate.

As Carbon is held in the reservoir and separated from other components of the ecosystem, any increase in the Carbon content of a reservoir in an ecosystem may be referred to as sequestration. Carbon sinks are indeed an effective component in the fight against climate change, but they are unable to mitigate the negative impacts on their own without significant changes in human behavior.

There are two types of Carbon sequestration; geological and biological.

The injection and subsequent longterm storage of Carbon dioxide in an underground location and subsurface geologic formations, such as rocks, is known as Geological Carbon Sequestration. Carbon dioxide (a greenhouse gas) levels in the atmosphere are reduced as a result (US EPA, 2021). CO2 is normally compressed until it becomes a liquid, then injected into porous rock formations in geologic basins (USGS, 2019). The capture of

CO2, which is possibly one of the most difficult components of geological sequestration due to the high cost of separation, is the first stage. The most effective storage sites are those where CO2 is immobile because it is trapped permanently under a thick, low-permeability seal or is chemically converted to solid minerals or is adsorbed on the surfaces of coal or mineral micropores or through a combination of physical and chemical trapping mechanisms (Hadi Mosleh et al., 2019).

The storage of atmospheric Carbon in vegetation, soils, woody products, and aquatic ecosystems is referred to as Biological Carbon Sequestration (USGS, 2019). During this process plants and microorganisms remove CO2 from the atmosphere and store it in vegetative biomass and soils. At the ecosystem level, terrestrial plants absorb CO2 as organic molecules during photosynthesis, a Carbon flux known as gross primary production (GPP). The biggest Carbon flux between Earth and the atmosphere is terrestrial GPP, which is the annual photosynthetic Carbon uptake of all leaves across a land region (Lorenz, 2013).

Similarly, the most important Carbon sinks in urban areas are the green areas covering the surface of cities. Mul-

56

tiple valuable ecosystem services are provided by urban green areas, including managing storm water infiltration, improving air quality, reducing urban heat island effects, providing food and habitat for many species, and offering enjoyment and nature education to city people. Among these ecosystem services Carbon sequestration and storage is the one to focus on for this study.

The direct removal, or sequestration, of Carbon dioxide, the primary greenhouse gas, from the atmosphere by urban plants can affect climate change.

By absorbing Carbon during photosynthesis and storing it as biomass, vegetation acts as a Carbon sink. Cities, on the other hand, appear to be deficient in vocabulary and planning to connect diverse mitigation and adaptation methods, particularly those aimed at sequestering and storing CO2 in urban trees. In order to maintain biodiversity and ecosystem services in both rural and urban contexts, the EU has developed a Green Infrastructure Strategy and is currently advocating for Nature-Based Solutions and City re-naturing.

3.3.

QUANTITATIVE ROLE OF VEGETATION TO ABSORB CARBON

“Above ground biomass (AGB) is defined as the aboveground standing dry mass of live or dead matter from tree or shrub (woody) life forms, expressed as a mass per unit area, typically Mg ha–1

”

Wilkes et al., 2018

Above-ground biomass, below-ground biomass, litter, woody debris, and soil organic matter are the five terrestrial ecosystem Carbon pools involving biomass identified by the Intergovernmental Panel on Climate Change (IPCC).

Above-ground biomass, which accounts for roughly 30% of the total ter-

57

restrial ecosystem Carbon pool, is the most visible, dominant, dynamic, and significant pool of the terrestrial ecosystem (Lalit & Mutanga, 2017). Because of increased awareness of climate change and the role forest biomass plays in Carbon sequestration and greenhouse gas release due to deforestation, above-ground biomass estimation has attracted a lot of attention in recent decades.

Above-ground biomass is the most observable of all the Carbon pools, and changes in it are a strong indicator of change or the impact of an intervention on both Carbon reduction and other benefits. Most land-based initiatives rely on above-ground biomass as a source of energy.

3.3.1.

METHODS OF CALCULATING ABOVEGROUND VEGETATION BIOMASS

Traditional above-ground biomass estimation is a field inventory-based estimation. In this method usually the trees in the study site have to be removed. In this sense, this method can be destruc-

as an alternative non-destructive way to avoid excessively expensive and impractical sampling. Individual species’ biomass can be directly measured or translated from standing tree volume

tive and only a limited number of trees can be removed for the purpose of the study. This is a time-consuming and labor-intensive procedure. Field measurements are inherently subject to uncertainty and bias, especially when dealing with large trees and tropical concerns (Chen at al., 2018). This method does not allow for timely monitoring and, more significantly, may be harmful to the phenomenon being studied to some level (Li et al., 2020). Moreover, collection of data and repeated measurement over a period of time, which are necessary for monitoring the evolution of vegetation and their above-ground biomass, is particularly difficult.

Allometric regression equations, which connect the volume or weight of a tree to readily visible parameters such as trunk diameter at breast height (DBH) or height, have been widely employed

using wood density or specific gravity data (Wu, 2019).

58

The United Nations Framework Convention on Climate Change (UNFCCC) establishes a “Tier” system to distinguish between the various methods of Carbon accounting that rely on varying degrees of sophistication in estimating above-ground biomass density (AGBD), as well as the geographic extent and emissions from land use and land use change activities (Hill et al., 2013). Tier 1 is based on regional or global estimates of AGBD (i.e. the IPCC default values) and requires the most basic, and least disaggregated activity data. Also it has the assumption that changes in stocks are equal to emissions. Higher tiers often use more complex methodologies and emission factors that are source-specific, tech-

nology-specific, region-specific, and/or country-specific, and are often based on measurements. They also typically require more extensively disaggregated activity data. Tier 2 expands on Tier 1 by including country-specific AGBD data for dominant land uses and emission estimates from other activities; and Tier 3 provides high-resolution data at sub-national scales and may include modeling of emissions based on observed changes in stocks (Hill et al., 2013; UNFCCC, n.d).

Above-ground biomass can be estimated also in a non-destructive method. These methods have been in use since the emergence of new technologies and remote sensing imageries.

Remote sensing allows access to inaccessible regions and can offer data over broad areas at a fraction of the cost of intensive sampling. Remote sensing satellite data is available at a variety of scales, from local to global, and on a variety of platforms. Moreover, by using remote sensing, data can be provided at any time and repetitively, so changes in above-ground biomass can be more easily tracked. There are also numerous sets of information, such as optical, radar, and LiDAR, each with its own set of advantages.

Since AGB cannot be measured directly from the air or space, remote sensing

59

approaches for estimating AGB are developed by integrating data acquired from distantly sensed data to AGB values collected on the ground (Zhu & Liu, 2014).

In general, there are two types of approaches for linking remote sensing data and AGB measurements. Empirical models are the first type, in which various predictor variables collected from remotely sensed images are empirically connected to AGB field plot observations. Simple linear regression to advanced machine learning algorithms are among the empirical methodologies (Chen et al., 2018; Laurin et al., 2018; Vafaei et al., 2018 ).

Physical models are the second group. Physical approaches, unlike empirical methods, use inversion of radiometric transfer models to obtain forest structure characteristics that have a strong association with AGB, and then use the retrieved structural parameters to estimate AGB (Zhu & Liu, 2014).

As mentioned before, there are various data to retrieve above-ground biomass from.

Optical remote sensing, because of its global coverage, repeatability, and cost-effectiveness, is arguably the best option to above-ground biomass estimation using field sampling. Optical

remote sensing, which contains visible, near-infrared, and shortwave-infrared wavelengths, has been widely utilized to estimate aboveground biomass of various green area types in a variety of environments. However, depending on seasonality and local weather conditions, the availability of free-cloud satellite data in overcast locations can be limited or non-existent. The tropical Andes area of Ecuador encompasses a geographical range from the Andean highlands to the coast, with annual cloud cover averaging over 90% (Ochoa et al., 2019). Data from optical remote sensing platforms such as IKONOS, Quickbird, Worldview, SPOT, Sentinel, Landsat, and MODIS is available. Spatial resolutions range from a few centimeters to hundreds of meters (Lalit & Mutanga, 2017; Chen et al., 2018; Li et al., 2020).

Radar remote sensing has gained popularity for above-ground biomass estimation in recent years because of its cloud penetration capability and rich vegetation structure information, while airborne Synthetic Aperture Radar (SAR) systems have been in use for a long time, space-based systems such as Terra-SAR, ALOS, and PALSAR have only been accessible since 2000.

Repetition and cost-effectiveness have been enabled as a result of this (Lalit & Mutanga, 2017).

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LiDAR is a relatively recent technology that has gained popularity in the assessment of biomass. It can sample the vertical distribution of canopy and ground surfaces, giving precise structural data on vegetation. As a result, estimates of basal area, crown size, tree height, and stem volume are more accurate. A lot of studies have shown substantial links between LiDAR parameters and above-ground biomass (Lalit & Mutanga, 2017).

-04-

ABOVE-GROUND BIOMASS ESTIMATION USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM (GIS)

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I N T R O D U C T I O N Remote sensing is introduced in this chapter as a non-destructive method for estimating above-ground biomass. Data from Remote Sensing satellites are available at a variety of scales, from local to global, and from a variety of platforms. There are also various data sources, such as optical, radar, and LiDAR, with each having distinct advantages over the others.

This chapter focuses solely on Optical Remote Sensing, discussing its history from 18th century until today. The principles of Remote sensing in general have been discussed as well to give the readers a basic understanding of how the final images are produced and used in further chapters .

Since this study makes use of Sentinel-2 satellite imagery, a section is dedicated to studying the Copernicus program as a land surface monitoring program by the European Space Agency, with a focus on Sentinel-2 mission.

Finally, Sentinel-2 satellite is discussed briefly, introducing its different multispectral bands and their resolution. The Sentinel-2 mission provide a unique set of observations for the Copernicus program, starting with the all-weather, day and night and high resolution images.

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4.1.

SKY IS NOT THE LIMIT; AN INTRODUCTION TO HISTORY AND PRINCIPLES OF REMOTE SENSING

“Remote sensing is the practice of detecting and monitoring the physical features of an area from a distance by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft). Remotely sensed images are collected by special cameras, allowing researchers to “sense” characteristics of the Earth

.”

- USGS, 2020

Remote sensors, which provide a global outlook and a richness of data about Earth systems, allow for data-informed decision making based on our planet’s current and future state. (EarthData |NASA, 2019).

65

The data is frequently taken in the form of picture data from a distance. By examining photos collected from different points in time, we can identify the composition and nature of the Earth’s surface and atmosphere at local, regional, and global sizes, as well as analyze changes. Remote sensing is beneficial in this regard since it can provide spatial information that would otherwise be difficult or impossible to get.

It was 1859 when French photographer, Gaspard-Felix Tournachon, took an angled photograph of a small village near Paris from a balloon and that is how Remote Sensing began in the 1840s as professionals took pictures of the ground by flying in balloons us-

ing the newly invented photo-camera.

After that by 1903 a new version of remote sensing was invented and that was pigeons wearing cameras. During World War I aerial photography became a valuable reconnaissance tool using aircrafts. For Earth observation, aircraft proved to be more efficient and stable than balloons. Between World Wars I and II, the civil usage of aerial photographs got off to a good start. At the time, geology, forestry, agriculture, and cartography were among the applications sectors for aerial pictures.

As a result of these advancements, cameras, films, and interpreting technology have vastly improved. During World War II, the most significant advancements in aerial photography and image interpretation occurred. Other imaging techniques, such as near-infrared photography, thermal sensing, and radar, were developed during this time period.

The addition of automated photo-camera systems on the seized German V-2 rockets launched from White Sands, NM, marked the beginning of remote sensor entry into space. The idea of installing film cameras on orbiting spacecraft was achieved with the launch of Sputnik in 1957. As they circumnavigated the world, the first cosmonauts and astronauts carried cameras to capture designated places and targets of op-

66

portunity. Sensors designed to collect black-and-white television-like views of the Earth were installed aboard meteorological satellites that first flew in the 1960s. The United States began putting remote sensors in orbit for earth studies in the early 1960s.

The first meteorological satellite was TIROS (Television Infrared Observation Satellite). This one was followed by a slew of other weather satellites.

Instruments launched on Skylab (and subsequently, the Space Shuttle) and Landsat, the first satellite devoted especially to monitoring land and ocean surfaces to record natural and cultural resources, helped to advance remote sensing in the 1970s. The satellite’s sensors were able to acquire high-resolution photos with reasonable detail.

Remote sensing gained worldwide prominence as a beneficial technique as a result of these photographs.

The principal benefits noted at the time were: • Immediate picture availability for the majority of the globe • Lack of political, security, and copyright constraints • Low cost • Repeating multi-spectral coverage • Minimum image distortion Landsat had been privatized by the

1980s, and commercial remote sensing had become common in the United States, France, Russia, Japan, and other countries.

Other successful earth observation missions carried out by other countries followed the Landsat program.

The French government decided to create their own earth monitoring program in 1978. The first SPOT satellite was launched in 1986 as a result of this initiative. The Indian Remote Sensing Programme (IRS), which began in 1988, the Russian Resurs series, which began in 1985, and the Japanese ADEOS (Advanced Earth

Observation Satellite), which launched in 1996, are all examples of earth observation missions. In 1991, the European Space Agency (ESA) launched ERS-1, the first remote sensing satellite.

(Planetek, 2012; GIMA, n.d).

Below there is a timeline of important events throughout the history of Remote Sensing, starting from 18th century with the first balloon image continuing until today with advanced and high technology satellites. The launch of important satellites have also been marked in the timeline as main milestones in the history.

Fig. 20 Timeline of the history of Remote Sensing

1903

1914

A newer version of Remote Sensing was invented with pigeons wearing cameras and flying around the city

Aerial photography became an important tool in WW I

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1910

Gaspard-Felix Tournachon took the first photo from a balloon and Remote Sensing was born

1900

1859

1957

Advanecemnts in Remote Sensing witht the start if WW II

Entry of Remote Sensing into space with the launch of German V-2 rocket, carrying automated photo cameras

Launch of Sputnik rocket carrying film cameras on orbiting spacecraft

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1950

1942

1940

1930

1939

1982 Launch of JPL’s Shuttle Imaging Radar (SIR-A), the first non-military radar system

The first astronauts carried cameras to document selected regions Sensors on meteorological satellites were tuned to obtaining black and white TV-like images of the Earth

1970

1985

Skylab Remote Sensing Observations from Space

Russia launched Resurs Series

1986

1972

Launch of SPOT satellite, French Commercial Earth Observation Satellite

Introduction of Landsat as the first satellite dedicated to land surface monitoring

Sensors could make measurements of atmospheric properties over a range of heights.

Development Hyperspectral Sensors

1978 France started its first monitoring system

1988

1980

1970

1960

India launched its first Remote Sensing program

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2001 Launch of QuickBird, very high spatial resolution sensor system

1991

2015 2002

Launch of first RADAR satellite ERS-1 by ESA

Launch of Envisat-1 with optical and radar instruments by ESA

1999 Launch of IKONOS, very high spatial resolution sensor system

2016 Launch of Sentinel-2 by ESA

2009

2010

Launch of WorldView-2 by DigitalGlobe

2000

1990

Launch of Sentinel-1

by ESA

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Fig. 21 Aerial photos taken by pigeons with with Julius Neubronner’s cameras Source: Humboldt State University, 2016

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Fig. 22 Use of cameras in aircrafts in war Source: Smithsonian National Air and Space Museum, 2022

22.

23.

Fig. 23 V-2 German rocket Source: The Gaurdian, 2016

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24.

Fig. 24 Russian Sputnik- 1 Source: SCMP, 2017

25.

Fig. 25 NASA footage of first Landsat satellite Source: The Verge, 2012

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26.

Fig. 26 IKONOS satellite for earth observation Source DigitalGlobe, 2010

27.

Fig. 27 Sentinel-1 operation Source: ESA, n.d

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4.1.1.

PRINCIPLES OF REMOTE SENSING

The basic principles of Remote Sensing uses electromagnetic (EM) radiation coming from an object (in case of earth observation: the Earth’s surface).

These radiations will then be measured and translated into information about the object or information on processes related to the object. In the measurement phase the following components are relevant:

sensor

energy source

intervening atmosphere

Fig. 28 Principles of Remote Sensing

interacting with the target

transmission, reception and processing

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I. The source of the EM radiation II. The path through the atmosphere III. The interaction with the object IV. The recording of the radiation by a sensor There are two main classification types of remote sensing based on the source of signal they use.

Active sensors which illuminate the items they detect with their own source of energy. An active sensor emits radiation in the direction of the studied target. The sensor then detects and measures reflected or backscattered radiation from the target.

Passive sensors which on the other hand detect natural energy (radiation) that is released or reflected by the object or scene being observed. Reflected sunlight is the most common source of radiation detected by passive sensors. (NASA EarthData, 2014).

Optical remote sensing is a passive earth observation technology that uses solar illumination. Optical remote

10 -14

ultraviolet rays

X-rays

gamma rays

10 -10

10 -12

400

10 -8

infrared rays

10 -6

10 -4

500

visible

radars

FM

10 -2

TV

10

10 2

600

700

SWIR

NIR

50 45 40 reflecance (%)

35 30 25 20 15 10 5

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

wavelenght (micrometers) water

clear water

healthy vegetation

soil

altered rock

Fig. 29 The electromagnecti spectrum and reflectance spectra of some of earth’s materials

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shortwave AM

10 4 wavelenght (meters)

wavelenght (nanometers)

sensing detects solar energy reflected from targets on the ground using visible, near infrared, and short-wave infrared sensors to create images of the earth’s surface. At different wavelengths, different materials reflect and absorb differently.

The objects can thus be distinguished in remotely sensed photographs based on their spectral reflectance fingerprints. Solar radiation can be transmitted, absorbed, or reflected when it reaches a target surface. At different wavelengths, different materials reflect and absorb differently. The reflectance spectrum of a material is a plot of the fraction of radiation reflected as a function of incident wavelength that acts as the material’s distinctive characteristic (CRISP, 2022). In theory, a material’s spectral reflectance signature can be used to identify it if the sensing device has enough spectral resolution to distinguish its spectrum from that of other materials. This is the foundation for multispectral remote sensing. Image data is captured via multispectral imaging in specified wavelength ranges across the electromagnetic spectrum.

Instruments sensitive to specific wavelengths can be used to detect and separate the wavelengths. Sentinel-2 as a multispectral instrument (MSI) produces optical images in the visible and shortwave infrared ranges with a reso-

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lution of 10 meters and a 290 kilometer image breadth in 13 spectral channels which requires a large field of view of 20.6°.

The main goal of MSI is to make regular monitoring of global landuse/landcover change possible at local, regional, national, and global stages. The MSI, on the other hand, is projected to provide chances for mapping near-surface water constituents, such as total suspended solids (TSS) and chlorophyll (Chl) concentrations, as well as possible absorption by coloured dissolved organic matter (CDOM), in nearshore coastal and inland waterways (Pahlevan, 2017).

The following figure shows the signature spectra of several material.

In an optical/near-infrared image, vegetation has a distinct spectral signature that allows it to be recognised from other types of land cover. Because chlorophyll absorbs light for photosynthesis, reflectance is low in both the blue and red parts of the spectrum. It reaches a pinnacle in the green zone, when vegetation takes on a green hue. Because of the cellular structure in the leaves, the reflectance in the near infrared (NIR) area is substantially higher than in the visible band. As a result, vegetation can be distinguished by its high NIR but low visible reflectances (CRISP, 2022).

4.1.2.

RESOLUTIONS OF REMOTE SENSING SATELLITES

The resolution of a sensor affects how it may be utilized. The resolution of a satellite depends on its orbit and sensor design. All Remote Sensing systems have four main resolutions. (NASA EarthData, 2019): I. Spatial Resolution; The area on Earth’s surface depicted by each pixel in a digital image is defined by the size of each pixel within that image. The more detail visible in the image, which is the ground area imaged for the sensor’s instantaneous field of view (IFOV), or the linear dimension on the ground represented by each pixel, the finer the resolution (the lower the number) (Liang et al., 2020).

In remote sensing, spatial resolution refers to how much detail is apparent to the unaided eye in a photograph.

One way of characterising what we term spatial resolution is the ability to resolve minute details, which refers to the size of the smallest item that can be resolved on the ground. The lowest resolvable object must be larger than

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the pixel size. The instantaneous field of view of the sensor, which is a measure of the ground area observed by a single detector element in a particular instant in time, determines the inherent resolution of an imaging system (Gabri-

1 2 2 44

3

4

5

6

7

8

9 band 1

3 4 5

band 2

23

6 7

80

8 9

Fig. 30 Digital number (DN) principles Pictorial representation of DN values which is a combination of rows and columns corresponding to each pixel.

band n

ele, 2019).

The radiance of the light reflected from the little ground area observed by the corresponding detector is represented by the DN (Digital Number) of a pixel. The area on the ground that corresponds to one pixel will be smaller the smaller the detector. The pixel size on the ground refers to the size of the ground resolution cell.

II. Spectral Resolution; which is a sensor’s ability to distinguish finer wavelengths, i.e. having more and smaller bands. Many sensors are multispectral, meaning they have three to ten bands. Some sensors, referred to as hyperspectral, have hundreds or even thousands of bands (NASA, 2019).

Images are captured in over a hundred contiguous spectral bands using hyperspectral imaging systems. Hy-

Fig. 31 Comparison of Multispectral and Hyperspectral images Pictorial representation the difference between multispectral and hyperspectral images. As it can be understood, MSI has several specific bands. However, HSI has a continuous spectral.

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perspectral imagery enables for the identification and characterization of materials, whereas multispectral imagery is beneficial for discriminating land surface features and landscape patterns. Individual pixel evaluation is typically beneficial for detecting unique objects in the image, in addition to mapping the distribution of materials.

In terms of identifying and quantifying molecule absorption, hyperspectral sensors have an advantage over multispectral sensors. A hyperspectral imager’s high spectral resolution allows for surface material detection, identification, and quantification, as well as inferring biological and chemical activities.

The narrower the range of wavelengths for a given band, the finer the spectral resolution. Radiance is measured for a spectrum range rather than

a single wavelength. A spectral band, also known as a wavelength band, is a portion of the electromagnetic spectrum where the average radiance is calculated. A panchromatic camera, a radar sensor, or a laser scanner can only measure in one spectral band, whereas a multispectral scanner or a digital camera can measure in multiple spectral bands at once.

Finer the radiometric resolution of a sensor, it is more sensitive in detecting small differences in reflected or emitted energy. Bits are used to measure radiometric resolution. The more bits in an image, the more grey-scale values can be saved, allowing for greater changes in reflection on land surfaces to be detected.

Every pixel in 8-bit data has a Digital Number that ranges from 0 to 255. This resolu-

III. Temporal resolution is the time it takes a satellite to complete an orbit and return to the same observation region is referred to as temporal resolution. The orbit, sensor parameters, and swath width all influence the resolution.

(NASA, 2019).

The intelligence community was the first to use this resolution when repeated coverage indicated changes in infrastructure, unit deployment, or equipment introduction/modification. When there is cloud cover over a specified object or area, it is reasonable to repeat the collection of that location. In addition, the temporal resolution is used to calculate the time elapsed between the sensor’s consecutive photos of the same ground location.

IV. Radiometric resolution; Sensitivity of the sensor to the magnitude of the received electromagnetic energy determines the radiometric resolution.

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tion is also used to describe the frequency with which the sensor covers a certain region, which is related to pixel size, pointing capabilities, and sensor field of view. Radiometric resolution, on the other hand, is becoming increasingly popular in change detection, global change research, and Earth monitoring investigations.

1 Bit: 21 = 2

4 Bit: 24 = 16

8 Bit: 28 = 256

Fig. 32 Power of bit and grayscale values As it can be understood from the figure, the higher is the bit value, the more greyscale values can be differentiated by a sensor.

4.2.

LAND SURFACE MONITORING SATELLITES: COPERNICUS PROGRAM

Copernicus is a European Union program that aims to produce European information services based on satellite Earth Observation and in situ (nonspace) data. The European Commission (EC) is implementing Copernicus with the help of the European Space Agency (ESA) for the space component and the European Environment Agency (EEA) for the in situ component. Copernicus mission is to monitor

are fully open and free to its users, most of whom are public authorities. Dedicated satellites (the Copernicus Sentinel family) and a series of extra Contributing Missions support the Copernicus Program (Copernicus, 2021).

Copernicus is a major provider of Earth observation data, that is used by service providers, governments, and other international institutions to better the lives of European inhabitants. By

and forecast the status of the environment on land, at sea, and in the atmosphere in order to assist climate change mitigation and adaptation strategies, effective emergency management, and increased citizen security.

Copernicus is a user-driven initiative, and the information services it provides

contributing to marine surveillance, the obtained EO data aid emergency management, global food security, border control, and homeland security. Policymakers and public authorities are the primary users of Copernicus services, as they use the data to build environmental legislation and policies, as well

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I.

II.

Fig. 33 Copernicus satellites I. Copernicus Sentinel-1 II. Copernicus Sentinel-2 III. Copernicus Sentinel-3 IV. Copernicus Sentinel-4 V. Copernicus Sentinel-5 VI. Copernicus Sentinel-5P VII. Copernicus Sentinel-6 Source: Copernicus, 2022

as to make vital decisions in the event of an emergency, such as a natural disaster or a humanitarian crisis. Many value-added services can be customised to specific public or commercial needs using Copernicus services and data acquired by Sentinels and contributing missions, resulting in new economic prospects.

The European Commission has financed the development of five cloud-based platforms that provide centralised access to Copernicus data and information, as well as processing tools, to facilitate and standardise data access. The DIAS, or Data and Information Access Services, is the name given to these platforms.

The aim of Sentinel program is to re-

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III.

IV.

V.

VI.

VII.

place former Earth observation missions that have either retired or are reaching the end of their operational life cycle, such as the ERS and Envisat missions. This will ensure that data is not lost and that existing research are not disrupted. Each mission focuses on a particular area of Earth observation, such as atmospheric, oceanic, or land monitoring, and the data may be used for a variety of purposes.

The Sentinel-1 mission is formed of a constellation of two polar-orbiting satellites that collect imagery day and night, regardless of the weather. It’s a Synthetic aperture radar (SAR) sensor with a spatial resolution of 9-40 m and a revisit time of 6 days at the equator. The mission of Sentinel-1 is to monitor both land and sea. In April of 2014, the first Sentinel-1 satellite was launched.

The Copernicus Sentinel-2 mission consists of a constellation of two polar-orbiting satellites oriented at 180 degrees to each other in the same sun-synchronous orbit. Its vast swath width of 290 km, high revisit duration of 10 days at the equator with one satellite and 5 days with two satellites in cloud-free circumstances (resulting in 2-3 days at mid-latitudes), and wide sweep width of 290 km will facilitate monitoring of Earth’s surface changes.

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In June 2015, the first Sentinel-2 satellite was launched.

Sentinel-3 consists of three satellites.

The principal sensor for the Sentinel-3 mission is a radar altimeter, although the polar-orbiting satellites will carry a variety of instruments, including optical imagers. The major goal of the Sentinel-3 mission is to provide high-accuracy and reliable measurements of sea surface topography, sea and land surface temperature, and ocean and land surface color to support ocean forecasting systems, environmental monitoring, and climate monitoring.

The missions Sentinel-4, Sentinel-5, and Sentinel-5 precursor (S4, S5, and S5P, respectively) are dedicated to air quality monitoring and are intended to be complementary pieces of a constellation servicing the unique demands of the CAMS. The hourly data on tropospheric elements will be provided by the geostationary S4 mission over Europe, primarily for air quality purposes.

The main air quality indices NO2, O3, SO2, HCHO, CHOCHO, and aerosols are among the Sentinel-4 mission target species.

Sentinel-6 The next radar altimetry reference mission, Michael Freilich, will extend the legacy of sea-surface height

measurements until at least 2030. It is an Earth Observation satellite mission that began in 1992 with the TOPEX/ Poseidon mission to offer greater continuity to the highly steady time series of mean sea level measurements and ocean sea condition (Sentinel Online, 2014)

Fig. 34 Sentinel family deployement scheme Source: Copernicus Overview, n,d

2014

2015

2016

2017

2018

2020

2019

2021

2022

Sentinel- 1A

2023

2024

2025

2027

2026

2029

2028

2030

potentially extended lifetime

Sentinel- 1B Sentinel- 1C Sentinel- 1D Sentinel- 2A

potentially extended lifetime

Sentinel- 2B

B

Sentinel- 2C Sentinel- 2D

Sentinel- 3A

potentially extended lifetime

Sentinel- 3B Sentinel- 3C Sentinel- 3D Sentinel- 4A

potentially extended lifetime

Sentinel- 4B Sentinel- 5 Precursor

potentially extended lifetime

Sentinel- 5A Sentinel- 5B Sentinel- 6A

potentially extended lifetime

Sentinel- 6B

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4.3.

USING SENTINEL-2 SATELLITE FOR AGB ESTIMATION

Sentinel-2 is one of the earth observation missions carried out by the Copernicus Program and has been developed and operated by the European Space Agency.

This mission contains two twin satellites Sentinel-2A which was launched on 23 June 2015 and Sentinel-2B which was launched on 7 March 2017. Both satellites obtain optical imagery at high spatial resolution ranging between 10m to 60m. Each Sentinel-2 satellite is about 1.2 tonnes in weight. The European launcher VEGA was used to launch both Sentinel-2A and Sentinel-2B. The satellite has a 7.25-year life cycle, which includes a three-month in-orbit commissioning phase. Batteries and propellants have been provided to

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support 12 years of operations, including de-orbiting manoeuvres at the end of the mission. Two identical Sentinel-2 satellites function in a sun-synchroFig. 35 Sentinel-2A into making Source: ESA, n,d

nous orbit at a mean height of 786 km, phased at 180° to each other. A dual-frequency Global Navigation Satellite System (GNSS) receiver measures the position of each Sentinel-2 satellite in its orbit. A specialised propulsion system ensures orbital precision. An industry team led by Astrium GmbH created the Sentinel-2 satellite system (Germany). The MultiSpectral Instrument is the responsibility of Astrium SAS (France).

As the European Space Agency describes in its website, the objective of the mission of Sentinel-2 is set as below (Sentinle Online, 2021): “ I. Systematic global acquisitions of high-resolution, multispectral images allied to a high revisit frequency II. Continuity of multi-spectral imagery provided by the SPOT series of satellites and the USGS LANDSAT Thematic Mapper instrument III. Observation data for the next generation of operational products, such as land-cover maps, land-change detection maps and geophysical variables” Sentinel-2 mission is characterized by having multi-spectral instrument (MSI) with 13 bands in the visible, near infrared and short-wave infrared. From

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these 13 bands, four bands are at 10 m, six bands at 20 m and three bands at 60 m spatial resolution. Revisiting time in this mission is every 10 days under the same viewing angles. At high latitudes, Sentinel-2 swaths overlap and some regions will be observed twice or more every 10 days, but with different viewing angles. Sentinel-2 mission images will cover land surfaces of all continents (between latitudes 56° South and 82.8° North), all coastal waters with a maximum of 20 km from the shore and all islands with a minimum surface of 100km2 .

Images from Sentinel-2 mission can have different levels of processing and each provide a specific kind of data (Sentinle Online, 2021).

Level-0 is compressed raw data and has all the information that is needed to produce upper-level data.

Level-1A is uncompressed raw data with spectral bands coarsely coregistered and ancillary data appended.

Level-1B data is radiance data that has been radiometrically adjusted. The physical geometric model is added to the product, but not applied.

Level-1C product provides orthorectified Top-Of-Atmosphere (TOA) reflectance, with sub-pixel multispectral registration. The product comes with cloud and land/water masks.

All data acquired by the MSI instrument are systematically processed to Level-1C by the Payload Data Ground Segment (PDGS) and only the Level-1C and Level-2A products are released to Users.

The Copernicus Sentinel-2 mission aims at monitoring variability in land surface conditions and coastal water and will support monitoring of Earth’s surface changes. It provides high res-

Table. 01 Sentinel-2 band description

olution optical image data to support Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas.

The mission provides information for agriculture such as crop monitoring and forestry which help manage food security. These satellite images can be used to determine several vegetation indices. Besides monitoring plant growth and health, Sentinel-2 is used to map changes in land cover which can be useful for environmental monitoring.

Moreover, it provides data on lake pollution and coastal waters which helps marine environmental monitoring and zone mapping.

coastal aerosol blue green red vegetation red edge vegetation red edge vegetation red edge NIR vegetation red edge water vapour SWIR

Wavelength (nm) 0.443 0.490 0.560 0.665 0.705 0.740 0.783 0.842 0.856 0.945 1.375

Resolution (m) 60 10 10 10 20 20 20 10 20 60 60

B11

SWIR

1.610

20

B12

SWIR

2.190

20

Band

Band description

B1 B2 B3 B4 B5 B6 B7 B8 B8a B9 B10

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Fig. 36 Sentinel-2 resoloution in different bands Source: Sentinel Online, 2021

SWIR

visible

Band 8 (842 nm)

600 nm

Band 4 (665 nm)

400 nm

Band 3 (560 nm)

Band 2 (490 nm)

10 m spatial resolution

NIR

800 nm

1000 nm

1200 nm

1400 nm

1000 nm

1200 nm

1400 nm

1000 nm

1200 nm

1600 nm

1800 nm

2000 nm

1600 nm

1800 nm

2000 nm

1600 nm

1800 nm

2000 nm

2200 nm

2400 nm

2200 nm

2400 nm

2200 nm

2400 nm

visible

Band 12 (2190 nm)

Band 11 (1610 nm)

800 nm

Band 8a (865 nm)

600 nm

400 nm

Band 7 (783 nm) Band 6 (740 nm) Band 5 (705 nm)

20 m spatial resolution

SWIR NIR

SWIR

visible

800 nm

Band 10 (1375 nm)

600 nm

Band 9 (940nm)

400 nm

Band 1 (443 nm)

60 m spatial resolution

NIR

1400 nm

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-05-

WORK METHODOLOGY IN CITY OF TORINO

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I N T R O D U C T I O N The objectives of the study have been highlighted in this chapter, and the main framework for achieving the objectives has been developed, demonstrating every step that must be performed to achieve the final goal, which is estimating the quantity of carbon sequestration in the study area of Torino. To gain a better understanding of how the project objectives fit into wider frameworks, a study of current sustainability programs was conducted, which revealed that the project objectives align with global sustainability programs and missions.

Literature review as an importent step in any study has also been discussed in chapter 5 in detail.. A number of scientific articles that contributed to the study’s framework have been referenced and discussed..

Finally, the rest of the chapter introduces the study area of Torino in southern Italy. A brief overview of Torino’s urbanization history highlights why this city was chosen as the study location. Furthermore, pollution patterns on a local scale have been investigated based on the source of the pollution. Finally, a look at the landscape characteristics of the city of Torino broadens our viewpoint on how the emission and sequestration process can appear.

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5.1.

OBJECTIVES

The number of people living in cities, especially metropolitans, are increasing and on the other hand, cities have to deal with the effects of climate change. The increase in global temperature is causing an increase in the number of extreme events and catastrophic disasters such as floods and droughts especially in these urbanized areas. With that, the governments are looking for new ways to provide more resilient cities and living environments.

They are trying to have more conscious planning for the existing and upcoming cities. Planning and designing a new city, using recent technologies, seems to be less complicated than adapting the already existing cities to the new changes which needs in-

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depth planning and analysis.

Given the importance of this topic, recent movements pushing to address sustainable development from an urban perspective have taken place throughout the world. results from this movement can be seen in Sustainable Development Goal (SDG) 11: “make cities and human settlements inclusive, safe, resilient and sustainable” Which has an impact on a number of other Sustainable Development Goals such as SDG 7 (affordable and clean energy) and SDG 15 (life on land).

Green vegetation is the most natural solution that always comes to mind

when talking about solutions to fight against climate change. Every year many landscape projects are carried out in cities with the idea of sequestering Carbon and to provide an urban ecosystem service. However, an important question has never been asked and it is set to be the project question: • •

How much Carbon is being absorbed by urban vegetation?

And comparing with the amount of Carbon that is being produced in each city, how much more of this Carbon should be absorbed and sequestered?

In order to contribute to studying the effect of urban vegetation in absorbing Carbon, a new methodology is offered to actually estimate how much the existing urban green vegetation in our cities is helping the inhabitants by absorbing Carbon which is one of the main greenhouse gases and an important factor in climate change in a broader aspect. Therefore, an innovative method is proposed in this study that carries out the estimation using remote sensing and satellite imagery, in this study sentinel-2A, which is a technique that is less destructive and more accurate compared to old techniques that use man-force.

The results will lay a basis and frame-

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work for future landscape projects which aim to fight climate change. This quantitative analysis will help governments and city planners to be able to set more specific policies and more importantly will help landscape architects have a new vision when carrying out new design in which they have a more in-depth understanding of their area of design. It is clear to all the landscape architects that their design is a tool for creating more resilient cities in which the habitants are safe and healthy.

The goal of this study is to provide the quantity of Carbon sequestration specifically in urbanized areas and in the presence of settlements.

Sentinel-2A Multispectral Imagery One image representing Spring with observation date of 25.05.2018 One image representing Summer with observation date of 31.07.2018 One image representing Autumn with observation date of 29.09.20 One image representing Winter with observation date of 28.11.2018

Pre-processing

Atmospheric correction

Extraction of predictors

Application of “Sen2Cor” to reduce fog or cloud of level-1C image and creating a level -2A image with “bottom-of-atmosphere” reflectance

Vegetation indices

Resampling Adjusting the size of pixels and set on a desire value to match the process of field plot observation data So the pixels are resampled to to 100m resolution

- Normalized Difference Vegetation Index (NDVI) - Normalized Difference Index with bands 4 and 5 (NDI45) - Inverted Red-Edge Chlorophyll Index (IRECI) - Transformed Normalized Difference Vegetation Index (TNDVI) - Soil-Adjusted Vegetation Index (SAVI)

Vegetation biophysical variables

Choosing the location of study site

- Leaf Area Index (LAI) - Fraction of Absorbed Photosynthetically Active Radiation (FPAR) - Fraction of Green Vegetation Cover (FCover)

SNAP

Subsetting

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Field plots are chosen using data from CCI Biomass map (ESA) and their value is extracted

QGIS

Field Plot Observation

Pearson correlation coefficient is used to evaluate the correlation between eight predictors and above-ground biomass estimated from CCI biomass map.

EXCEL

Statistic Analysis

AGB Modeling A model will be made based on the selected predictors and existing above-ground biomass. These models are made by using machine learning techniques.

Random Forest (RF)

Artificial Neural Network (ANN)

WEKA

Support Vector Machine for Regression (SVR)

Accuracy Assessment

FINAL AGB MAP

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QGIS

Correlation Coefficient (r) Root Mean Square Error (RMSE) Mean Absolute Error (MAE)

EXCEL

The accuracy of the models are evaluated using three parameters as below:

Fig. 37 Work methodology for estimating Carbon sequestration

5.2.

PROJECT OBJECTIVES ACCORDING TO SUSTAINABILITY PROGRAMS

I. The 2030 Agenda for Sustainable Development The Sustainable Development Goals are a global call to action to end poverty, safeguard the environment, and improve the lives and future of all people everywhere. All UN Member States adopted the 17 Goals in 2015 as part of the 2030 Agenda for Sustainable Development, which outlined a 15year timeline to accomplish the Goals.

on environment which are goal 13: Climate Action, goal 14: Life Below Water and goal 15: Life on Land.

To achieve sustainable development, three key factors must be balanced: economic growth, social inclusion, and environmental conservation. These factors are linked, and they are all necessary for the well-being of individuals and civilizations (UN SDGs, 2021).

Among these 17 goals, some focus

ambitious and cost-effective path to attaining climate neutrality by 2050; Encourage the creation of green jobs while maintaining the EU track record of reducing greenhouse gas emissions and increasing the economy. Encourage international partners to step up their efforts to limit global tempera-

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II. 2030 Climate Target Plan The Commission proposes, with the 2030 Climate Target Plan, raising the EU’s aim to reduce greenhouse gas emissions by at least 55 percent below 1990 levels by 2030. The plan’s goals are as follows: Establish a more

ture rise to 1.5°C and avert the most catastrophic consequences of climate change (EEA, 2021). It is also consistent with the Paris Agreement goal of keeping global temperature rise to far below 2°C and pursuing efforts to keep it to 1.5°C (2030 Climate Target Plan, 2021).

III. The European Green Deal The European Green Deal will turn the EU into a modern, resource-efficient, and competitive economy by ensuring no net greenhouse gas emissions by 2050, decoupled economic growth from resource use, and no person or place left behind.

The European Green Deal will improve citizens’ and future generations’ well-being and health by providing: clean air, clean water, healthy soil and biodiversity, renovated, energy-efficient buildings, healthy and affordable food, more public transportation, cleaner energy and cutting-edge clean technological innovation, longer-lasting products that can be repaired, recycled, and re-used, future-proof jobs and skills training for the transition, globally competitive and resiliency (A European Green Deal, 2021).

IV. New European Bauhaus The New European Bauhaus adds a

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cultural and artistic component to the European Green Deal, demonstrating how sustainable innovation may provide real, pleasant experiences in our daily lives. This involves a variety of policy initiatives as well as financial options. The Commission will establish an Innovative European Bauhaus Lab, which will function as a “think and do tank” to co-create, develop, and test new tools, solutions, and policy proposals. The Lab will carry on the movement’s collaborative ethos, which draws individuals from all walks of life together and reaches out to society, industry, and politics to link people and find new ways to create together (New European Bauhaus, 2021).

V. URBAN GreenUP The goal of Urban GreenUP is to design a specialized methodology to (1) help the co-development of Re-natuinog Urban Plans centered on climate change mitigation and adaptation, as well as efficient water management, and (2) to aid in the effective implementation of NBS. As part of the project activities, NBS classification and parametrization will be addressed, and some tools to aid decision making will be produced (CORDIS | European Commission, 2021).

5.3.

ABOVE-GROUND BIOMASS LITERATURE REVIEW

Given the importance of above-ground biomass in understanding and monitoring ecosystem dynamics, as well as its contribution to the global Carbon cycle, scientists have been working hard to create innovative approaches and techniques for estimating aboveground biomass. Many recent studies have focused on estimating forest above-ground biomass using remote sensing techniques and the applied

data such as Optimization Of Second-Order Grey-Level Texture In High-Resolution Imagery For Statistical Estimation Of Above-Ground Biomass carried out in a mixed vegetation area of central Kenya by Ouma and Tateishi (Ouma & Tateishi, 2006) showed that combining spectral band entropy with second-moment texture measurements in the near infrared (NIR) produced substantially superior

techniques can also be used in urbanized areas with some modifications.

Several studies suggest that texture-based modeling of above-ground biomass estimation provides more possibilities in various contexts as it shows the high variance in tree crowns when using high-resolution satellite

results.

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The idea of estimating above-ground biomass using Vegetation Indices and Biophysical Variables have been explored in many studies and discussed by a great number of authors in literature.

Evaluating The Capabilities Of Sentinel-2 For Quantitative Estimation Of Biophysical Variables In Vegetation (Frampton et al., 2013) explores the possibility of using Red Edge Band which has a higher quality in Sentinel-2 satellite and estimates canopy chlorophyll content in Sicily, Italy using different vegetation indices.

Estimating Urban Vegetation Biomass From Sentinel-2a Image Data (Li et al., 2020) measures above-ground biomass in urbanized areas in east Chinese city of Xuzhou, classifying the vegetation type into low vegetation, broadleaved forest and coniferous forest. 116 variables were introduced in this study which contained different features such as Spectral reflectance, Vegetation abundance, Topographical features, Vegetation indices and Textural features. This study concluded that Stepwise Regression modeling has outperformed those models with Multiple Linear Regression.

One method employed by Chen et al.

(Chen et al., 2018) in Estimation Of Forest Above-Ground Biomass By Geographically Weighted Regression And Machine Learning With Sentinel Imagery is Machine Learning techniques such as Random Forest (RF), Artificial Neural Network (ANN) and

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Support Vector Machine for Regression (SVM). This study that has been carried out in Southeast, Northeast and Center of China concludes that SVM has the best performance when predicting and mapping above-ground biomass.

Introduction of different satellites has also empowered scientists to combine the techniques. Such as the study Estimation And Mapping Of AboveGround Biomass Of Mangrove Forests And Their Replacement Land Uses In The Philippines Using Sentinel Imagery (Castillo et al., 2017) combines Sentinel-1 and Sentinel-2 using raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables.

The same results have been suggested by a study done by Vafaei et al.

(Vafaei et al., 2018) named Improving Accuracy Estimation Of Forest Aboveground Biomass Based On Incorporation Of Alos-2 Palsar-2 And Sentinel-2a Imagery And Machine Learning: A Case Study Of The Hyrcanian Forest Area (Iran). The study takes advantage of data driven from Sentinel-2A and ALOS-2 PALSAR-2 and build estimation models using Machine Learning techniques such as Random Forest (RF), Support Vec-

tor Machine for Regression (SVM), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The results indicate that SVM has shown the highest prediction accuracy.

The literature available on urban vegetation is not as much as the other landscapes such as forests or agricultural landscapes. This seems to have only recently come to the spotlight and more scientists are taking this lack of literature into mind. For this reason,

YEAR 2004 2011

the choice of vegetation indices and estimation techniques uses the results of papers on other landscapes rather than only urbanized landscapes.

While there has been much research on estimating above-ground biomass, few researchers have taken the next step to estimate the amount of Carbon absorbed by this biomass. The innovation of this study is the fact that following the estimation of above-ground biomass, it estimates the amount of Carbon sequestration.

JOURNAL IEEE journal of applied earth observations and remote sensing ISPRS journal of photogrammetry and remote sensing

2012 2013 2014

Forest biomass

International journal of applied earth o bservation and geoinformation Journal of applied remote sensing

2015 Journal of ecological application 2016

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Fig. 38 Literatire review of aboveground biomass estimation

MODEL Discrete anisotropic radiative transfer (dart) Gaussian processes Generalized linear mixed model Geographically weighted regression

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Africa Australia

AHS Artificial neural network

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ASD sensor CHRIS mode 1

China

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Costa rica France

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5.4.

A CASE STUDY OF TORINO, NORTHERN ITALY

Torino is located in the extreme north-western part of the Italian peninsula and extends over an equal land area of 130.2 km2. Torino is the capital city of Piedmont and of the Metropolitan City of Torino. The city is primarily located on the western bank of the Po River, below the Susa Valley, and is bordered on three sides by the Alps: on the western and northern side, and on the eastern side, by a high hill that is a

research of pollution events, which are heavily influenced by the atmosphere’s dispersive capacity and therefore by the sort of meteorological occurrences that occur on a local scale.

The Po valley, to which the territory of the province of Torino belongs, is characterized by a continental climate. In the area under examination, moreover, the barrier effects of the Alpine arc are highlighted, which mainly give rise to

natural continuation of the Monferrato hills. The Po and three of its tributaries, the Dora Riparia, the Stura di Lanzo, and the Sangone, all pass through the city.

The study of a site’s orographic features is important not only for meteorological characterization, but also for the

phenomena of thermal origin compared to dynamic ones. Added to this is the presence of the Torino hills, which alters, on a local scale, the uniformity of the climate typical of the flat area, especially with regard to low-altitude winds.

The influence of the Alps means that most of the perturbations affecting the

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province of Torino originate from the passage of a cold front coming from the north or north-west (Arpa, 1998).

The city preserves the ancient Roman road network, which was developed and updated by the Savoy dynasty, who began to give their city a systematic and strict urban design in the 16th century, which was perfected and completed in the following centuries.

The emergence of the first working-class districts coincided with the beginning of Torino’s industrial boom and the construction of an urban proletariat at the end of the nineteenth century. The Torino metropolitan area was founded in the second half of the twentieth century, defined by regional order in 1972 and consisting of 52 surrounding towns. Torino has implemented a true reinvention of its urban structure with the regulation plan authorized in 1995, addressing first and foremost the problem of the restructuring of huge open urban spaces, largely consisting of abandoned industrial sectors (this is an area of almost 3,000.000 m 2).

The establishment of new significant highways and tree-lined avenues, as well as the construction of new buildings and green areas, was remarkable in the nineteenth and twentieth centuries, with the creation of new buildings and green areas, among which the ex-

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quisite Valentino park stands out.

Along with the railway link project, a system of integrated railway connections that impacts the metropolitan area, the most major modifications were concentrated along the so-called Spina Centrale, a north-south axis along which there are extensive unused industrial sites.

Density of urban greenery in metropolitan city of Torino in the years of 2015 has been 15% on the municipal area (19,6 km2) whereas in 2019 has been 15.28% (19,8 km2). Among which historical green has the highest share in urban green areas covering 42% of the green surfaces (8,35 km2). following that is the share of big urban parks with 12.82% of coverage (2,54 km2). On the end of the scale sits vegetable gardens with the lowest share of green coverage with 0.40% (77,918 m2).

Torino, which has grown into the seat of a significant mechanical and textile sector, is the driving force behind a big metropolitan agglomeration, the second largest in Italy after Milan.

Mechanics and metallurgy, particularly in the automobile sector, are Torino’s main industries. Textiles are another important economic sector, as are the iron and steel industries (cast iron and steel), metallurgy (aluminum), chemical, tanning, food and confec-

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Map. 1 Introduction to case study, city of Torino this map illustrates the principal elements of Torino with a focus on sources of emission. A special focus has been on industrial area and their concentration in different landscapes, their proximity to urbanised areas and forest.

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tionery industries, footwear, distilleries, and wine-making businesses, all of which produce liqueurs. The graphic and publishing industries, as well as world-famous aperitifs.

All the major industries in Torino are huge sources of pollution and emission of greenhouse gases. The presence of these industries brings pollution to the city and affects the air quality.

Due to Torino’s geographical location (being in a valley), pollution is trapped.

Furthermore, high levels of traffic, high concentrations of industrial plants, and emissions from home sources all contribute to increased greenhouse gas and pollutant emissions. As a result, Torino is one of Italy’s most polluted cities.

In 2019, the number of days when the air concentration of ozone and Particulate Matter (PM10) exceeded the established EU threshold in the city of Torino was 147 days, meaning 40% of the year, air pollution has existed in the city.

Although the relationship between climate change and air quality is very complex and physico-chemical mechanisms that describe the mutual influence between climate change and air quality are not fully known, this still has led to the IPCC , in its V report, published at the end of 2013, to address this issue for the first time.

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Climate models predict that as the world warms, northern Italy will become hotter, drier, and more vulnerable to floods caused by more intense precipitation and catastrophic weather events. The Po River, which runs through Torino, is one of Italy’s most vulnerable rivers to flooding. Climate change would undoubtedly bring new extremes, and the city and its citizens needed to be prepared.

5.4.1.

Torino; From Antiquity To New Days

In Roman times, Torino was known as “Augusta Taurinorum” and was located in the Taurini province. It was founded as a military colony, possibly on the site of the old city Taurasia, and quickly grew in importance due to its strategic location in the subalpine region. The two conventional orthogonal axes intersected the square city, each measuring around 700 meters on each side.

The city system was in a state of distress during the barbarian invasions. Even Turin’s inhabited center had shrunk significantly: there were many undeveloped regions within the “square” city, with big vacant spaces, lawns, vegetable gardens, and so on. Cities, on the other hand, are beginning to flourish again as the entire European area recovers economically and culturally.

The Roman military colony was transformed into a largely artisanal and commercial town. With the growth of the population and the addition of shops, maximizing the space available within the historic walls becomes a challenge.

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The Duke of Savoy decided to move the duchy’s capital from Chambery (in France) to Turin about the middle of the 16th century (February 7, 1563).

If it was formerly a modest artisan and peasant community with a healthy level of commercial activity and a strategic location at the junction of multiple canals and major communication routes, it is now completely defined by its new function as capital city. The population, which had remained between 5,000 and 6,000 during the communal and noble eras, grew to almost 30,000 in 1570, and new buildings were constructed, giving the city its distinctive Baroque mark. By the turn of the century, Torino had developed into a bustling metropolis with a diverse range of commercial and cultural activity. At 18°, the population had risen to about 90,000 people. In 19th century during the time of Carlo Alberto, the city experienced a period of great economic and urban development, reaching up to 130,000 inhabitants in 1849.

The nobility were to be housed in the new capital during the first expansion.

The size of the blocks and the presence of vast gardens were credited with giving these villages an aristocratic aura. The structures that resulted were far too large to house a single family. The owners therefore restricted themselves to only using the first floor (dubbed the “noble floor”), designating the servants’ mezzanines to the cadet branches of the family and renting the remaining floors to other households.

The cotton industry sparked the city of Torino industrial growth, but by 1889, the engineering industry employed 40% of the workforce. The automobile sector quickly became the major specialism, with 37 car manufacturers in 1907.

Torino thus became, along with Genoa and Milan, one of the country’s most developed industrial and technological poles in a matter of years, and, like the other two cities in the ‘industrial triangle,’ its population grew exponentially between 1881 and 1921, nearly doubling from 250,655 to 499,823 inhabitants.

The recall of industrial enterprises in the 1950s and 1960s, following WWII, resulted in a fresh significant flow of immigration, primarily from the Veneto and the South, causing a slew of serious organizational and cultural issues.

Torino was, in fact, the city with the largest population growth during this peri-

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od, growing from 719,300 to almost one million people, a 42.6 percent rise; 46.6 percent of them worked in factories, a far larger percentage than the other two more industrialized cities.

Today the metropolitan area of Torino is home to more than 2,230,946 people and still continues to be one of the most leading economical and industrial faces of the country.

Fig. 39 Torino through time I. The Po and the Valentino Castle II. Via Roma before the arcades III. The Royal Gardens IV. Piazza Vittorio and the Church of the Gran Madre Source: guidatorino, 2021

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5.4.2.

Trend Of Greenhouse Gas Emission; From National Level to Municipal Level

Italy, being one of Europe’s most powerful economies, is also one of the continent’s most polluting countries. In 2020, Italy was the European Union’s second-largest Carbon dioxide emitter, after only Germany. Despite this, Italy has one of the lowest per capita greenhouse gas emissions in the European Union. In 2020 (STATISTICA, 2021), Italy released approximately 287 million metric tons of Carbon dioxide into the atmosphere. Carbon dioxide is a greenhouse gas that is the primary cause of global warming. Despite no change in annual CO2 emissions in Italy during the last five years, they have decreased by approximately 30% since 2010.

Between 1990 and 2018, total greenhouse gas emissions in CO2 equivalent, excluding emissions and removals from LULUCF, declined by 17.2%, ranging from 516 to 428 CO2 equivalent million tonnes (Mt). CO2, the most major greenhouse gas, has decreased by 20.5 percent between 1990 and 2018, accounting for 81.4 percent of global CO2 equivalent emissions.

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CO2 emissions in the energy industry, in particular, are 18.5 percent lower in 2018 than they were in 1990 (ISPRA, 2020). These decreases correlate with the country’s rising use of renewable energy.

The energy industry (27.4%), as well as transportation, produce the most significant emissions (29.6 percent ).

Nonindustrial combustion accounting for 22.6 percent, manufacTorinog and construction industries for 15.2 percent, and industrial processes (4.4 percent) and other sectors account for the remaining emissions (0.8 percent ).

The cities in Italy with the highest levels of pollutant emissions are often situated in the north, notably in the Po Valley region. Because the area is surrounded by mountains, pollution from sources such as automobiles is amplified. When there is no wind, pollution is confined in the valley, making it one of Europe’s most polluted areas. The metropolitan area of T ​​ orino is affected by a phenomenon of serious atmospheric pollution with frequent exceeding of

the air quality limits imposed by current legislation and with measurable effects on human health. Torino has been among the most polluted cities in Italy and Europe for the past decade or so.

Fig. 40 National greenhouse gas emissions from 1990 to 2018 by gases and by sector Source: ISPRA, 2020

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5.4.3.

Emission Inventories At National Level

At a national level, the National Inventory of Atmospheric Emissions (l’Inventario Nazionale delle Emissioni in atmosfera), created by ISPRA (Istituto superiore per la protezione e la ricerca ambientale), is the main instrument for verifying compliance with the commitments that Italy has assumed at international level on the protection of the atmospheric environment. This inventory was established by the Legislative Decree 51 of March 7th 2008 and confirmed by the Legislative Decree 30 of March 13th 2013. According to Article 5.1 of the Kyoto Protocol, Annex I Parties must have a National System in place by the end of 2006 for estimating anthropogenic greenhouse gas emissions by sources and removals by sinks, as well as reporting and archiving inventory data according to UNFCCC norms (ISPRA, 2020).

ISPRA is in charge of the inventory’s overall administration, as well as all parts of its preparation, reporting, and quality control. The collection and processing of data from various data sources,

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the selection of appropriate emission factors and estimation methods in accordance with the IPCC Guidelines, the compilation of the inventory, the assessment of uncertainty, the preparation of the National Inventory Report and reporting through the Common Reporting Format, the response to the review process, and the updating and data storage are among the activities.w In general, Italy is committed to the United Nations Framework Convention on Climate Change (UNFCCC), United Nations Economic Commission Convention on Transboundary Air Pollution (UNECE-CLRTAP) and EU Emissions Limitation Directive. This support is essential for the planning and implementation of effective environmental policies.

ISPRA collaborates with ad hoc expert panels, as well as national and regional authorities and commercial entities, to cross-check parameters and estimations and increase the inventory’s completeness and transparency.

The inventory includes all significant

sources and sinks, as well as direct and indirect gases, as defined by the IPCC and its data consist of five main sectors (ISPRA, 2020); I. Energy sector consisting of energy industries, manufacTorinog industries and construction, transport, residential-public-commercial sector and fugitive emissions from fuel.

Ii. Industrial processes and product use consisting of mineral industry, chemical ndustry, metal industry, non-energy products from fuels and solvent use, electronic industry, product uses as ods substitutes and other product manufacture III. Agriculture consisting of enteric fermentation, manure management, rice cultivation, agricultural soils, prescribed burning of savannas, field burning of agricultural residues, liming, urea application and other Carbon-containing fertilizers IV. Land use, land use change

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and and forestry consisting of forest land, cropland, grassland, wetland, settlements, other lands and harvested wood product V. Wastet consisting of solid waste disposal, biological treatment of solid waste, incineration and open burning of waste and waste water treatment and discharge

5.4.4.

Emission Inventories At Local Level

Consistent with this regulatory framework, over the last few years the air quality assessment activities in the Piedmont Region have been carried out with a view to gradually integrating the three main information tools available: I. the Regional Air Quality Survey System (SRRQA); II. the Regional Inventory of Atmospheric Emissions (IREA); III. the Regional Modeling System SRRQA (La rete di Rilevamento della Qualità dell’Aria) is an Air Quality Survey Network in Piedmont which currently consists of 58 fixed stations, of which 4 are privately owned, located near industrial plants and in particular an incinerator, a methane-fired thermoelectric plant and a chemical company.

At a more local scale, The IREA (L’Inventario Regionale delle Emissioni) which is the Regional Emissions Inventory created by the Environment Directorate of the Piedmont Region, can therefore be considered, for the pur-

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poses of regional spatial planning interventions, the main tool in a path that, starting from the emission factors and appropriate indicators for the definition of the emission contributions of the various sources, then passes through various processes of validation and processing of the emission data present in the Inventory, to arrive at the simulation techniques of dispersion modeling of pollutants in the atmosphere, the results of which are finally validated on the basis of the comparison with the data collected by the air quality monitoring network.

The Inventory of the Piedmont Region, created according to the CORINAIR methodology (Coordination of information on the environment), developed by the European Environment Agency (EEA) to establish an inventory of emissions of air pollutants in Europe, provides an estimate of the total annual emissions, and spatially distributed on a municipal scale, of the following pollutants: Sulfur dioxide (SO2)

Carbon monoxide (CO) Nitrogen oxides (NOx) Suspended particles (PM10) Non-methane volatile organic compounds (NMVOC), Ammonia (NH3) Carbon dioxide or Carbon dioxide (CO2) Methane (CH4) Nitrous oxide (N2O) All anthropogenic and natural activities that can give rise to emissions into the atmosphere are divided into eleven macro-sectors (Arpa Piemonte, 2021) : I. Energy production and fuel transformation II. Non-industrial combustion III. Combustion in industry IV. Production processes V. Extraction and distribution of fossil fuels, geothermal VI. Use of solvents and other products VII. Road transport VIII. Other moving sources and IX. machinery X. Waste treatment and disposal Agriculture XI. Other sources of emission and absorption The data on air quality are collected by the Regional Agency for the Environment of Piedmont (ARPA Piemonte), through a network of fixed detection

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stations and mobile vehicles. The detection units contain the measuring instruments that detect the concentration of a given pollutant and the data are transmitted to an ARPA operational center which will validate and process them to transform them into environmental information. The detection stations, which according to the nearest sources of pollution are defined as Bottom - Traffic - Industrial, are located in areas of territory that can be described as Urban - Suburban or Rural.

The stations of the Suburban Fund type are located in urban areas in areas with residential or commercial characteristics. The instrumental equipment includes nitrogen oxide (NOx) analyzers. The stations of the Urban Traffic type are located in an urban area characterized by a strong presence of traffic. The instrumentation includes Carbon monoxide (CO) and nitrogen oxide (NOx) meters.

In this study, the main datasets that have been used for next chapter has been driven from regional service of Piemonte, Inventario Regionale delle Emissioni in Atmosfera (IREA), which is The Regional Inventory of Atmospheric Emissions in Piemonte region. Unfortunately the latest update of the data is for 2015 and because of the lack of data provided for the years after, the 2015 data has been used.

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0101 - Production of electricity 0102 - District heating 0201 - Commercial and institutional installations 0202 - Residential installations 0203 - Plants in agriculture, forestry and aquaculture 0301 - Combustion in boilers, turbines and internal combustion engines 0303 - Combustion processes with contact 0402 - Processes in iron and steel industries 0403 - Processes in non-ferrous metal industries 0404 - Processes in inorganic chemical industries 0406 - Processes in wood industry pulp for paper, food, beverages and more 0505 - Distribution of gasoline 0506 - Gas distribution network 0601 - Painting 0602 - Degreasing dry cleaning and electronic components 0603 - Production or processing of chemicals 0604 - Other use of solvents 0701 - Cars 0702 - Light vehicles < 3.5t 0703 - Heavy vehicles > 3.5t and buses 0704 - Mopeds (<50 cm3) 0705 - Motorcycles (>50 cm3) 0706 - Petrol vehicles, evaporative emissions 0709 - Re-suspension (for road type) 0802 - Railways 0805 - Air traffic 0806 - Agriculture 0807 - Forestry 0809 - Gardening and other household activities 0902 - Waste incineration 0904 - Burial of solid waste 0910 - Other waste treatments 1001 - Crops with fertilizers 1002 - Crops without fertilizers 1003 - Stubble combustion 1004 - Enteric fermentation 1005 - Waste management referred to organic compounds 1009 - Waste management referred to nitrogen compounds 1010 - Particulate emissions from farms 1103 - Forest fires and other vegetation 1106 - Waters 1111 - Managed deciduous forests 1112 - Managed coniferous forests 1125 - Others 1131 - Forests (absorption)

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Fig. 41 Emission in Torino in the year of 2015 divided into sectors Source: IREA, 2015

5.4.5.

Torino Air Quality Monitoring Network

1998 was the first year that city of Torino evaluated the pollution in the city using a new systme of Air Quality Monitoring Network consisting of 35 measurement stations distributed reasonably on the territory to find timely information and elements of connection between the various areas concerned.

Today (2019) the network is run by ARPA Piemonte and is composed of 18 publicly owned fixed stations, 3 privately owned fixed stations and a mobile vehicle for the construction of air quality survey campaigns. All of the workstations are connected to the data acquisition center through telephone lines, which transmits the results of the hourly measurements, allowing for constant control of the primary parameters that affect air quality.

A significant issu is the location of measurement stations on the territory. In certain cases, the chosen sites must reflect a vast area of territory, while in others, stations must represent specific pollution situations such as traffic hotspots or single source emissions.

The placement of measuring points in

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strategic locations provides highly representative data on air quality.

The information obained from these networks allowed administrators to foresee immediate interventions in the face of critical levels of pollution, to identify the areas most at risk and to intervene, also through the territorial planning tools of the Province, with a longer-term planning.

The average yearly values of CO in 2018 were between 0.4 and 0.9 mg/m3.

Legislative Decree 155/2010 confirms compliance with the human health protection level of 10 mg/m3 computed as an eight-hour moving average. The greatest value of the 8-hour average is 2.9 mg / m3, which is substantially below the regulatory amount. CO observations reveal a dramatic reduction in average concentrations in the To-Rebaudengo station, which is a traffic hotspot, from the early 1980s to 2008.

This decrease is attributable to the advancement of positive ignition engine technology for automobiles, as well as the introduction of catalytic converters to treat exhaust emissions (Arpa, 2018).

5.4.6.

Governmental Legislation And Strategies To Reduce Pollution

The first Italian systematic law on environmental pollution, Law N. 615 of July 13, 1966, “Measures Against Environmental Pollution,” establishes fresh air as a public good that must be preserved by limits. The latter was replaced by President of the Republic (DPR) Decree N. 203 of May 24, 1988, which met four European recommendations on air quality and pollution.

The notion of environmental preservation and human health protection is introduced in this D.P.R., which was absent in prior legislation. Furthermore, it defines environmental pollution as “every change in the structure or composition of the atmosphere caused by the presence of one or more substances in a quantity capable of affecting normal environmental conditions and air quality; which can pose a direct or indirect risk to human health; which can jeopardize recreational and other legitimate activities within this given environment; which negatively affect biological assets. Several other laws are as below: The European Directive 96/62/CE in

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1996 to establish a uniform strategy for evaluating and managing air quality and defining air quality criteria in order to prevent or mitigate the detrimental effects of pollutants on human health and the environment Legislative Decree N. 351 passed in 1999 on the Implementation of the 96/62/CE Directive on Ambient Air Quality and Environment Monitoring and Management Ministerial Decree N. 60 of April 2, 2002 of the Ministry for the Environment and Protection of the Land, implementing the first daughter directive which sets limit values for NOx, SO2, Pb and PM10; and implementing the second daughter directive which relates to benzene and Carbon monoxide; Ministerial Decree N. 183 of May 21, 2004 implementing the third daughter directive on ozone levels; Ministerial Decree N. 152 of August 3, 2007 implementing the fourth daughter directive which targets a list of pollutants posing a risk to human health (as cadmium, mercury, nickel,

and polycyclic aromatic hydroCarbons) And finally The Italian law on air pollution, Legislative Decree 155 of 2010 which established that air quality management and monitoring falls in the competency of the country’s Regions. In fact, 16 Regions out of 20 had air quality legislation by 2010.

The Region is the body in charge of the protection and rehabilitation of air quality, through a series of acts such as the preparation of the regional plan for the rehabilitation and protection of air quality, the preparation and management of the system for measuring the quality of the air. air, the zoning of the territory and the issuing of directives for the prevention and reduction of atmospheric pollution.

The Municipality is responsible for protecting the health of citizens, and for applying the directives issued by the State and the Region. The air quality limits that must be respected in Italy are shown in the table. ???.

The first agreement between the regions of the Po basin for the prevention and reduction of atmospheric pollution was signed on 7 February 2007 between the Emilia Romagna, Friuli Venezia Giulia, Lombardy, Piedmont, Aosta Valley, the Autonomous Provinces of Trento and Bolzano and the Republic and Canton of Ticino. The purpose of

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the agreement was to share elements and actions of a technical-organizational nature on which to coordinate a synergistic action of the territories involved. On 19 December 2013, an Agreement was therefore signed between the Ministry of the Environment and the Protection of the Territory and the Sea (Ministero dell’Ambiente e della Tutela del Territorio e del Mare), the Ministry of Economic Development (Ministero dello Sviluppo Economico), the Ministry of Infrastructure and Transport (Ministero delle Infrastrutture e dei Trasporti), the Ministry of Agricultural, Food and Forestry Policies (Ministero delle Politiche Agricole), Ministry of Health (Ministero della Salute), Regions and Autonomous Provinces of the Po Valley (Regioni e Province Autonome del bacino padano), for the coordinated and joint adoption of air quality remediation measures.

The Agreement led to the establishment of 9 working groups between the Ministry and the Regions for the preparation of proposals for ministerial decrees regarding: I. certification of combustion plants II. biomass fueled combustion plants III. the cogeneration of new industrial plants fueled by biomass

IV. support for the energy requalification of buildings V. updating of guidelines for urban mobility plans VI. review of motorway speed and further traffic reduction measures VII. vehicle traffic regulations VIII. the diffusion of electric vehicles XI. the preparation of guidelines for the reduction of emissions from agricultural and livestock

activities The planning has focused on transport, energy, agriculture and industry sectors. which have also been identified as areas of intervention in the PRQA (Piano Regionale di Qualita dell’Aria) and whose implementation, due to the policies and resources provided, could have repercussions on the same plan .

As far as Piedmont is concerned, four projects have been carried out which in total cost approximately 56 million Euros.

Table. 02 The air quality limits in Italy Source: ARPAT, 2013

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hourly limit value for the protection of human health

maximum daily average calculated over 8 hours

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information threshold

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alarm threshold

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240

goal value

maximum daily average calculated over 8 hours

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target value for the protection of vegetation

aot40, calculated on the basis of 1 hour values f​​ rom may to july

18000 as a 5-year average

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5.4.7.

Landscape Characteristics Of Torino

The Piemonte region is classified by 12 Macro-areas that are further subdivided into 76 Landscape areas based on geographic, settlement, and identity factors. Torino is located in the 11th category “Urbanized Landscape of the Plain and Hills of Torino”. Landscape areas are subdivided into 535 landscape units, which are further subdivided into 9 regulatory typologies based on the relevance, integrity, and transformational dynamics of the dominant landscape characteristics. map. ??? presents the landscape areas, landscape units and landscape assets of the study area in detail, providing an appropriate representation through specific cards with descriptive and regulatory content, bounded according to criteria defined in agreement with the Ministry.

The elements designated as landscape assets were chosen based on a survey of protected areas conducted between 2009 and 2014 in accordance with Articles 136 and 142 of the Code and shared with the Technical Committee, which verified the bound-

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aries of all landscape assets present in the Piedmont area. The total area of landscape assets in Piedmont corresponds to 61% of the regional territory.

These assets are classified into two parts: • The first part describes in detail the properties and areas referred to in Code articles 136 and 157; for these areas, the PPR defines precise use criteria.

The cultural heritage of landscape significance falling inside each single property was recognized with the assistance of the Superintendencies. Following the execution of some Observations, a scheme with application indications for the prescriptions was added; additionally, a specific in-depth section for free places of high landscape and panoramic value was included.

• The second section is devoted to legally protected areas (ex Galasso), as defined by Article 142, paragraph 1 of the Code, whose discipline is regulated by legislation for components as described in the

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Map. 02 Landscape assets of Torino

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Landscape Plan Implementation Rules. The reconnaissance and computerization of assets aims to create a regional information and cartographic database of all areas subject to landscape protection, such as territories adjacent to lakes, rivers, streams, watercourses, mountains, glaciers, and glacial cirques, national or regional parks, territories covered by forests and woods, areas assigned to agricultural universities, and archaeologically significant areas.

The Piemonte landscape is read by its components, beginning with “Natural Environment” and extending to “Historical and Cultural Components” This is subsequently followed by a visual perception of the “Identity” of the area.

The final component reflects the “Anthropic Configuration” of the territory.

All these components together define the landscape of Piemonte. Each component corresponds to a distinct discipline. The implementation rules for each component include the aims of protection and enhancement, as well as the specific discipline, in the form of recommendations, directions, and prescriptions.

I. Naturalistic-environmental components; including mountain areas, hydrographic system,

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lakes and neighboring territories, territories covered by forests and woods, areas and elements of specific geomorphological and naturalistic interest, protected natural areas and other biodiversity conservation areas.

II. Historical-cultural components; including historical traffic and railway heritage, areas of archaeological interest, historic centers and nuclei, historical rural heritage, villas, gardens and parks, areas and facilities for leisure and tourism, areas and plants of industrial and energy production of historical interest, poles of religion and fortification systems.

III. Perceptual-identity components; including belvedere, panoramic beauties, sites of scenic and aesthetic value, visual relationships between settlement and context, rural areas of specific landscape interest and places and elements of identity.

IV. Morphological-settlement components; including consolidated urban areas, suburban discontinuous fabrics, specialized organized settlements,

areas of settlement dispersion, specialized “islands” and infrastructural complexes and rural settlements.

These elements and components that define the landscape of the study area are also linked by a network that combines elements of the ecological network (consisting of an integrated system of interconnected natural resources), the historical-cultural network (consisting of a set of systems for enhancing cultural and natural heritage), and the fruitive network (made up of a set of historical, cultural and natural destinations representative of the regional landscape linked together by themed itineraries and structured by territorial areas).

The map.??? depicted all of these landscape elements, which collectively comprise the landscape characteristics of the study site. Understanding the landscape characteristics of the area under study provides a more in-depth understanding of the reasons behind present trends in Carbon sequestration, as well as an understanding of how strategies might be proposed in the next chapters.

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Fig. 42 Piemonte landscape components; Naturalistic components

Fig. 43 Piemonte landscape components; Historical components

I. The Alpes Source: Arno Senoner, 2020

I. Bardonecchia Source: Piemonte, 2018

II. Pian Ciamarella Source: piemonteparchi, 2018

II. Isola Bella Source: Isola Bella, 2022

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Fig. 44 Piemonte landscape components; Perceptual components

Fig. 45 Piemonte landscape components; Morphological components

I. Langhe Source: Piemonte, 2018

I. Torino, Piazza della repubblica Source: AMondointasca, 2020

II. Piemonte hilly landscape Source: Piemonte, 2018

II. Torino Porta Susa Source: Italudiscove, 2015

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ESTIMATING CARBON SEQUESTRATION IN CITY OF TORINO

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I N T R O D U C T I O N This chapter contains the main study of the current thesis. After outlining the framework in the previous chapter, each of the steps indicated in the framework will be carried out here.

The chapter starts with an introduction to the main images obtained from Sentinel-2A satellite. After they have been pre-processed, eight main predictors containing vegetation indices and biophysical variables will be extracted from them. The predictors are a tool to help estimate above-ground biomass as it is not possible to obtain that value directly from satellite imagery. Later, the relation between these predictors and field above-ground biomass is studied and based on that a model has been made using machine learning techniques.

The best performing model which is Random Forest technique is chosen (r= 0.72, MAE= 34 and RMSE= 46) and finally the above-ground biomass of the study area is predicted using this model.

The main innovation and final goal of the study which is calculation the value of Carbon sequestration is based on the map of above-ground biomass as there is a simple mathematic relation between these two values indication 50% of above-ground biomass equal to Carbon value.

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6.1.

SELECTION OF SENTINEL-2A DATA

In this study, cloud-free Sentinel-2 multispectral imagery was freely obtained from CREODIAS website. CREODIAS is a seamless environment and a cloud infrastructure that was signed in 2017 from the European Space Agency, on behalf of the European Commission, that processes Earth Observation data

Table. 03 Remote sensing image data acquired for the study

Mission

Product

Sentinel-2A

Multispectral image Level-2A

Sentinel-2A

Multispectral image Level-2A

Sentinel-2A

Multispectral image Level-2A

Sentinel-2A

Multispectral image Level-1C

(EODATA - EO DATA Free Archive) (CREODIAS, 2021).

To be able to study the changes of the study area in one complete year of 2018, four images were selected where each of them represent one season of the year. The selected images are from • •

25 May 2018, 31 July 2018,

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• •

29 September 2018, 28 November 2018

Also this selection was based on available and cloud-free data. It has been tried to obtain images with the lowest cloudiness which was successful for the three first images. However, the final image (of November) was chosen with almost 65% of cloudiness, however, this will not be an issue as the images are processed in the following steps of the work.

The images also have 13 spectral bands in the visible, near infrared, and short-wave infrared regions, and had 10 m (band 2–4, 8), 20 m (band 5–7, 8a, 11–12), and 60 m (band1, 9–10) spatial resolutions. The table below

shows more information about the selected images.

Pre-processing operations, also known as image restoration and rectification, are used to correct data distortions caused by sensor and platform-specific radiometric and geometric errors.

The pre-processing is carried out in SNAP application (Sentinel Application Platform, version 8.0). SNAP is a common architecture for all Sentinel Toolboxes. The software is developed by Brockmann Consult, Skywatch, Sensar and C-S. SNAP is suited for processing and analyzing Earth observation (EO) data. The Earth Observation Community can utilize the SNAP user tool for free thanks to the European Space Agency Center of Earth Observation

Observation Date

Cloudiness

Uniform Resource Identifier (URI)

25.05.2018

11%

S2A_MSIL2A_20180525T103021_N0208_R108_ T32TLQ_20180525T133900

31.07.2018

6.9%

S2A_MSIL2A_20180731T102021_N0208_R065_ T32TLQ_20180731T133841

29.09.2018

1.5%

S2A_MSIL2A_20180929T102021_N0208_R065_ T32TLQ_20180929T130452

28.11.2018

64.8%

S2A_MSIL1C_20181128T102351_N0207_R065_ T32TLQ_20181128T122538

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Map. 04 Sentinel-2 image representing Spring observation date: 25.05.2018

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Map. 05 Sentinel-2 image representing Summer observation date: 31.07.2018

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Map. 06 Sentinel-2 image representing Autumn observation date: 29.09.2018

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Map. 07 Sentinel-2 image representing Winter observation date: 28.11.2018

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(ESA-ESRIN) (ESA, 2022). The various toolboxes implemented for SNAP allow the user to not only open, visualize, and export satellite data, but also to apply various levels of processing, ranging from simple data pre-processing steps to fairly advanced processing chains that can be set-up (and batch run) through the Graph Builder tool (CSEOL, 2022).

Map. 08 Top of Atmosphere This image shows the raw satellite image which is processed to level 1C and shows the reflectant at the top of atmosphere.

Map. 09 Bottom of Atmosphere The final image after the raw image has been atmospherically corrected and processed to level 2A, which shows reflectant at the bottom of atmosphere

The preprocessing of the images consists of three main steps.

I. Atmospheric correction The atmospheric correction is the process of removing the effects of the atmosphere that contribute to the signal measured by a satellite sensor (Hoepffne, 2009) and the reflectance values of images taken by satellites or airborne sensors (Pacifini et al., 2014). The effects of atmosphere in optical remote sensing imagery are significant and complex, and they alter the spectral nature of the radiation twice (Schowengerdt, 2007). Once the radiation from the sun to the object and again from the object to the sensor. These distortions are corrected using different approaches and techniques. In Sentinel-2 images, processing level of 1C product is defined as top-of-atmosphere (TOA) data and they have to be processed to level 2A product which is defined as bot-

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In this study three of four images (May, July and September) were already level 2A and did not need to be atmospherically corrected. However, the image from November was processed to reach the desired level by using Sen2Cor atmospheric correction processor. The process was done using Sen2Cor 280 Processor plugin (version 2.8).

II. Subsetting Process of subsetting for satellite imagery is to choose a section or a portion of a larger image.

Satellite data imagery usually covers a vast area and to be able to focus on the desired study area, the image needs to be cut to a specific location.

This process was done for all of the four images using SNAP software. For the purpose of this study, the images have been subsetted in a way to focus more on the city of Torino and its surrounding. The image is bordered in the south by Parco Naturale di Stupinigi, in the east by a cluster of villages in Valle Sauglio, Trofarello, and Pacetto Torinese, and moves parallel to the river Po until Riserva Naturale del Meisino e dell’Isolone di Bertolla, in the north by Torino and the main road of Stura di Lanzo, and finally in the west by city of Rivoli.

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III. Resampling Resampling is the process of changing the resolution of an image by either adding or removing extra pixels.

The process of resampling was also done in SNAP. SNAP offers three methods to resample an image; I. One is based on referenced band from source product which means in this case 10m, 20m and 60m resolution.

II. Another method is to define the width and height of the pixels III. The final method which was the method used for this study is to define a specific resolution in mind.

For the purpose of this study, the images have been resampled to pixel size of 100m. There reason for choosing this value is that in the following process of the study, the images can be used in a comparison with a second database by the European Space Agency. This comparison is later carried out in field observation section.

The images below present the final pre-processed images that are the basis for the whole study.

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Map. 10 Finalized pre-processed image representing Spring Map. 11 Finalized pre-processed image representing Summer

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Map.12 Finalized pre-processed image representing Autumn Map.13 Finalized pre-processed image representing Winter

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6.2.

SELECTION OF PREDICTORS

As previously stated, it is not possible to estimate above-ground biomass (AGB) directly from remote sensing data and Sentinel-2 mission imagery.

Instead, the estimation can be calculated using a series of so-called “predictors”. Later a formula needs to be made using these predictors to finalize the estimation of above-ground biomass.

These predictors are among vegetation indices and biophysical variables.

etation indices and LAI, FAPAR and FCover as biophysical variables. The predictors are defined more in the following sections.

According to previous researches

trends is common. It is used to emphasize the presence of green, vegetation components in the image, making it easier to differentiate them from other things in the scene. Various characteristics of the plant cover in the image can be evaluated depending on the transformation method and spectral

(Frampton et al., 2016; Li et al., 2020; Chen et al, 2018; Adamu et al., 2021; Vafaei et al, 2018; Cao et al, 2018) eight predic-

tors were selected and extracted from Sentinel-2A images using SNAP application. The predictors include NDVI, NDI45, TNDVI, SAVI and IRECI as veg-

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A Vegetation Index is a specific quantity obtained by converting observations from several spectral bands into a single value. The use of vegetation indices as a criterion for analysing land cover changes and tracking

bands employed, such as the percentage of vegetation cover, quantity of chlorophyll content, leaf area index, and so on.

In general, all of the ratio indicators are unaffected by the lighting conditions at the time of acquisition or slope effects.

Vegetation index is often calculated using linear or nonlinear combination operations on remotely sensed red and near-infrared (NIR) reflectance data (Liang et al., 2020), and it is a simple and effective measure for describing vegetation cover and growth state.

Many Vegetation Indices exist, and many of them are functionally similar.

The inverse relationship between Red and Near-infrared reflectance associated with healthy green vegetation is used in several of the indices.

Scientists have been using satellite remote sensing to track changes in vegetation at the Earth’s surface since the 1960s. Leaf area index, percent green cover, chlorophyll content, green biomass, and absorbed photosynthetically active radiation are some of the vegetative properties that are measured.

Vegetation Indices derived from remote sensing-based canopies are simple and effective algorithms for assessing vegetation cover, vigor, and growth dynamics, among other things. Chlorophyll absorbs a considerable percentage of the red and blue spectrum for

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photosynthesis and strongly reflects as green in healthy plants. Furthermore, leaf structure and condition cause significant near-infrared (NIR) reflection.

For each pixel in a remote sensing image, a vegetation index calculates the biomass and vigor of the vegetation.

These indices have been widely used in Remote Sensing applications on a variety of platforms, including airborne and satellite systems.

Due to the complexity of diverse light spectrum combinations, instruments, platforms, and resolutions employed, there is no unifying mathematical equation that defines all VIs. As a result, customized algorithms based on specific mathematical expressions that combine visible light radiation from vegetation, primarily in the green spectra region, and non-visible spectra to obtain proxy quantifications of the vegetation surface have been developed and tested against a variety of applications.

Any vegetative attribute that can be quantified, such as pigments, chemical constituents, structural variables, but also variables related to plant photosynthesis, productivity, or illnesses, is characterized as a Vegetation Biophysical Variable. Leaf Area Index (LAI), leaf chlorophyll content (Cab), fraction of Absorbed Photosynthet-

ically Active Radiation (fAPAR), and fractional vegetation cover (FCover) are the four basic biophysical metrics.

Optical sensors can recover these four critical variables since they all affect the spectral signature of the viewed surface in the visible and infrared spectrum.

Given the complexity of vegetation canopies and processes, quantitative estimate of vegetation biophysical variables is a fundamental element in the successful application of remote sensing in vegetation monitoring, a significant goal in terrestrial ecology and a long-term research objective.

The eight predictors employed in this study to estimate vegetation aboveground biomass are listed in the table below.

As can be seen, the predictors that are among the vegetative indices are also defined by a formula that determines which of the Sentine-2 multispectral bands is being employed. The common band that is observed in all the forumals are bands 4 and 8 which are respectively Red band and NiR band.

However, the same type of formula is not offered for predictors classified as biophysical variables. This is because statistical correlations between field-measured data and vegetation indexes are routinely used to estimate

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them (which is probably the oldest and simplest method of estimating these variables). It is also feasible to estimate their values using LAI, which is the most fundamental value among all vegetative biophysical variables.

Empirical models in the context of vegetation biophysical variable retrieval from remote sensing data rely mostly on the learning of the relationship between the sought variable and the observed spectral signature (Richter et al., 2012).

Source image

Predictors

Vegetation Indices

Sentinel-2A

Biophysical Variables

Table. 04 Sentinel-based imagery data predictors of above-ground biomass

Description

Bands

NDVI

Normalized Difference Vegetation Index

(B8 - B4)/(B8 + B4)

NDI45

Normalized Difference Index with bands 4 and 5

(B5 - B4)/(B5 + B4)

TNDVI

Transformed Normalized Difference Vegetation Index

[(B8 - B4)/(B8 + B4) + 0.5]1/2

SAVI

Soil-Adjusted Vegetation Index

[(B8 - B4)/ [(B8 + B4)]. (1 + L)

IRECI

Inverted Red-Edge Chlorophyll Index

[(B8 - B4)/ [(B6 / B5)]

LAI

Leaf Area Index

-

FAPAR

Fraction of Absorbed Photosynthetically Active Radiation

-

FCover

Fraction of Green Vegetation Cover

-

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6.2.1.

Normalized Difference Vegetation Index (NDVI)

Normalized Difference Vegetation Index (NDVI), introduced by Tucker (1979), is a simple graphical indicator that assesses whether or not the target that is being observed contains live green vegetation (Abao, 2021). This index can be used to analyse remote sensing measurements, often from a space platform.

This index takes advantage of the contrast of characteristics between two bands from a multispectral raster dataset. The chlorophyll pigment absorption in the red band and the high reflectivity of plant material in the near-infrared (NiR) band (Huete, 1998).

NDVI = (NiR - Red) / (NiR + Red) NDVI = (B8 - B4)/(B8 + B4) NDVI has a value between -1 to +1.

Negative values of NDVI correspond to water elements, values that are closer to 0 show barren areas of rocks, sand, or even snow and positive values correspond to the presence of vegetation.

If there is a low positive value, it rep-

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resents grasslands and shrubs, while higher positive values indicate more dense vegetation such as forests. Researchers can develop images and other products that give an approximate measure of vegetation type, abundance, and condition on land surfaces around the world by converting raw satellite data into NDVI values.

The NDVI is a common and widely used remote sensing index (Viana et al., 2019). The average of NDVI values throughout time can be used to determine “typical” growing conditions in a specific region at a certain time of year. Further analysis can then be used to characterise the state of the vegetation in that location in comparison to the norm. When evaluated over time, NDVI can identify where vegetation is flourishing and where it is suffering, as well as changes in vegetation caused by human activities like deforestation, natural disturbances like wildfires, or changes in plant phenological stage (SUGS, 2018). NDVI is closely linked to drought conditions (Drisya et al., 2018)

Map. 14 I. NDVI map of study area in May II. NDVI map of study area in July III. NDVI map of study area in September IV. NDVI map of study area in November the four maps below illustrate the NDVI of the year 2018 in different seasons. As it can be seen the lowest values in urbanized areas are present in the map of May 2018. On the other hand the highest values can be spotted on the map of September 2018, rspecially in the surrounding areas of the city and in both agricultural and forest areas located at western part of the area of interest.

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6.2.2.

Normalized Difference Index Using Band 4 and 5 (NDI45)

Sentinel-2 has a lot of appeal for vegetation monitoring because it will allow repeated operational reflectance measurements on and around the Red Edge bands at a considerably higher spatial resolution. With synthesised Sentinel-2 data, some algorithms have previously examined the best bands to employ in the NDVI calculation. The ideal combination of bands 4 and 5 was discovered through research by Delegido, referred to as the NDI45. As it can be observed in the formula below, the similarity is high between NDVI and NDI45 and the only difference is the use of Band 4 and 5.

NDI45 = (NIR R)/(NIR + R) NDI45 = (B5 B4)/(B5 + B4) studies suggest (Frampton et al., 2013) that When comparing the NDVI and the NDI45 the change from using reflectance measurements in the NIR (band 7) to RE1 (band 5) has increased spread at lower canopy chlorophyll content values but made NDI45 more

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linear with less saturation at higher values than the NDVI. As a result NDI45 is almost the best performing vegetation inex with respect to leaf area index and has a high correlation coefficient with respect to canopy chlorophyll content.

Map. 15 I. NDI45 map of study area in May II. NDI45 map of study area in July III. NDI45 map of study area in September IV. NDI45 map of study area in November the four maps below illustrate the NDI45 values of the year 2018 in different seasons. As it can be seen the lowest values in urbanized areas are present in the map of May 2018. On the other hand the highest values can be spotted on the map of September 2018, rspecially in the surrounding areas of the city and in both agricultural and forest areas located at western part of the area of interest.

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6.2.3.

Transformed Normalized Difference Vegetation Index (TNDVI)

Transformed Normalized Difference Vegetation Index (TNDVI) is a ratio-based index that has no negative values. TNDVI shows the same sensitivity as the soil adjusted vegetation index (SAVI) to the optical proprieties of bare soil subjacent to the cover. It does not saturate like NDVI and SAVI and it shows excellent linearity as a function of the rate of vegetation cover (Bannari et al., 2002).

In this sense, its interest lies in the detection of changes in land-use and the monitoring of the seasonal dynamics of vegetation on regional and/or global scales, production the rate of the biomass, etc. TNDVI is a metric that measures the amount of green biomass present in a pixel.

Similar to NDVI, TNDVI also uses near-infrared and red band to calculate the presence of vegetation.

TNDVI= [(NiR - Red / NiR + Red) + 0.5]1/2 TNDVI= [(B8 - B4)/(B8 + B4) + 0.5]1/2

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This index’s potential is assessed by comparing it to the Soil Adjusted Vegetation Index (SAVI), which reduces artefacts caused by bare soil’s optical qualities, and the Normalised Difference Vegetation Index (NDVI), which addresses linearity and saturation issues. The TDVI outperforms the NDVI and SAVI when all radiometric issues (sensor drift, atmospheric effects, etc.) are adjusted for remote sensing imaging or in situ observations.

TNDVI does not saturate like NDVI or SAVI, has excellent linearity as a function of vegetation cover rate, and has the same sensitivity to the optical properties of bare soil adjacent to vegetation cover as SAVI (Bannari, 2002).

Map. 16 I. TNDVI map of study area in May II. TNDVI map of study area in July III. TNDVI map of study area in September IV. TNDVI map of study area in November The four maps below show TNDVI of the year 2018 in different seasons. TNDVI maps follow almost the same trend as NDVI maps. The lowest values can be seen in map of May in urbanized areas, whereas, thi highest values are in month of September.

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Soil-Adjusted Vegetation Index (SAVI)

Soil-Adjusted Vegetation Index (SAVI) is a vegetation index that minimizes soil brightness influences. Originally data that was driven from NDVI showed signs of instability when in presence of soil moisture and high density vegetation. In locations with low vegetative cover (i.e., 40%) and exposed soil surfaces, light reflection in the red and near-infrared spectrum can alter vegetation index values. Therefore, a new index was introduced by Hueten (1998) to solve these problems using a soil-brightness correction factor. SAVI is comparable to NDVI in that spectral indices can be adjusted so that soil fluctuations are normalised and do not affect vegetation canopy observations. Because SAVI accounts for changes in soils, these NDVI enhancements are beneficial SAVI = [(NiR - Red) / (NiR + Red + L)] x (1 + L) SAVI = [(B8 - B4)/ [(B8 + B4)] x (1 + L) Outputs values of SAVI range between

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-1 and +1. The L value is a factor that varies depending on the amount of green vegetative cover.

Generally, in areas with no green vegetation cover, L equals 1; in areas of moderate green vegetative cover, L equals 0.5; and in areas with very high vegetation cover, L equals 0 (which is equivalent to the NDVI) (Huete, 1998).

SAVI can be specially used in areas with low cover vegetation or when comparisons are being made among different soil types that may reflect different amounts of light in the red and near-infrared wavelengths. However, Adjusting for soil effect reduces the sensitivity of the vegetation index. SAVI is less responsive to changes in vegetation (quantity and cover of green vegetation) and more sensitive to atmospheric variables than NDVI.

Map. 17 I. SAVI map of study area in May II. SAVI map of study area in July III. SAVI map of study area in September IV. SAVI map of study area in November The four maps below present SAVI values of the year 2018 in different seasons. SAVI maps follow a reverse trend comparing to NDVI since it is showing soil values.

So it can be seen that the highest values are mapped in months of July and May and the lowest values are seen in September.

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6.2.5.

Inverted Red-Edge Chlorophyll Index (IRECI)

Inverted Red-Edge Chlorophyll Index (IRECI) was developed for its application in Sentinel-2 imagery. It uses Red and the three Red-Edge bands of Sentinel (near-infrared band, band 5 and band 6), capturing the slope in the Red-Edge section.

Red-Edge bands have proven to be a useful spectral band since it is the prominent spectral feature of vegetation located between the red absorption maximum and high reflectance in the NIR. Moreover, knowledge of canopy chlorophyll content and leaf chlorophyll concentration (LCC) can indicate plant health (Frampton et al., 2013).

This index was first proposed by Frampton (2013) as a method to estimate biophysical variables and has a strong linear relationship with canopy chlorophyll content without saturation at higher values. The index takes advantage of Bands 4,5,6 and 8 which in respect presents red band, vegetation red edge band and NIR band.

IRECI= (NIR - Red) / (Red-Edge1 /

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Red-Edge2) IRECI= [(B8 - B4)/ [(B6 / B5)] IRECI does not place a great emphasis on red, which helps to minimize saturation, while still utilizing the strong contrast of the SR, which is sensitive to leaf area index and has a high performance in terms of leaf area index.

Map. 18 I. IRECI map of study area in May II. IRECI map of study area in July III. IRECI map of study area in September IV. IRECI map of study area in November The four maps below present IRECI values of the year 2018 in different seasons. IRECI maps have the lowest values in May and the highest values in September in the area of the case study.

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Leaf Area Index (LAI)

Leaf Area Index (LAI) is one the most essential biophysical variables and represents the amount of foliage in the plant canopy and it is one of the basic ecological characteristics of plants.

LAI is calculated by one half the total green leaf area per unit horizontal ground surface (Fang, 2014). This ratio has been linked to gas-vegetation exchange processes such photosynthesis, evaporation and transpiration, rainfall interception, and carbon flow (Zheng & Moskel, 2009).

LAI= [Leaf area (m2)/Ground cover (m2)] Because leaf surfaces are the primary border of energy and mass exchange (Fang, 2014), LAI is one of the main driving forces of net primary production, water and nutrient use, and Carbon balance (Breda, 2008).

The data and value that is driven from satellite imagery corresponds to the total LAI of all the canopy layers, including the understory which may

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represent a very significant contribution, particularly for forests. So it can be said that the LAI represents the thickness of the vegetation cover. Therefore, LAI is recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS) (Copernicus Global Land Service, 2021).

LAI derived from optical remotely sensed data is a critical statistic for estimating aboveground biomass in forest stands and, increasingly, in any other landscape. In recent years, a hypothesis based on the spectral invariant feature of leaves has been used to derive LAI and physical canopy height from optical sensors using single and multiple-angle measurements.

Map. 19 I. LAI map of study area in May II. LAI map of study area in July III. LAI map of study area in September IV. LAI map of study area in November The four maps below present LAI values of the year 2018 in different seasons. LAI has the highest values in the months of May and July where foliage coverage is at its maximum. On the contrary, the lowest values are observed in November.

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6.2.7.

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is among basic biophysical variables and represents the fraction of the solar radiation that is absorbed by live leaves for the photosynthesis activity.

PAR (Photosynthetically Active Radiation) is the amount of light available for photosynthesis, which is light with a wavelength range of 400 to 700 nanometers. PAR varies periodically and according to latitude and time of day.

During the summer, levels are highest about midday. Therefore, it refers only to the green and alive parts of the plants. Viña and Gitelson (2005) calculate FAPAR as below which is only by the photosynthetic component of the vegetation: FAPAR = FAPAR × (LAIgreen/LAItotal) Values of FAPAR range between “0” (in deserts) to “1” (in dense and homogeneous layers), however, maximum values has never been recorded in practice (NOAA, 2021). Moreover, the value

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depends on the canopy structure, vegetation element optical properties, atmospheric conditions, and angular configuration. FAPAR has been recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS) (Copernicus Global Land Service, 2021). FAPAR is crucial in determining the primary productivity of canopies, the corresponding fixation of atmospheric CO2, and the surface energy balance (GCOS, 2019).

Map. 20 I. FAPAR map of study area in May II. FAPAR map of study area in July III. FAPAR map of study area in September IV. FAPAR map of study area in November The four maps below present FAPAR values of the year 2018 in different seasons. Althought the maps seem to be having similar values, the lowest values are in month of May and the highst values have been spotted in the month of September.

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6.2.8.

Fraction of Green Vegetation Cover (FCover)

Fraction of Green Vegetation Cover (FCover) is the last of biophysical variables and indicates the percentage of land covered by green vegetation In practice, it estimates the vegetation’s spatial area. FCover is an excellent option for replacing traditional vegetation indices for ecosystem monitoring because it is independent of illumination direction and sensitive to vegetation amount (Copernicus Global Land Service, 2021). Monitoring FCover helps have a better understanding of plant biomass. An increase in the value of FCover through time signifies development of leaf area or above-ground biomass (EO4SD, 2015).

FCover not only indicates the amount of the plant photosynthetic area and the density of vegetation growth, but it also, to some part, depicts the growth tendency of vegetation. FCover is widely utilized in relevant research on climate change, soil, and hydrology as a crucial parameter for the balance and evolution of terrestrial ecosystems. The advancement of remote

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sensing technology has resulted in a potential instrument for estimating FCover.

The website of Sentinel states that although there is still a research for the source of this biophysical variable, it is being calculated by a NDVI method bases and and a SAVI scaled method.

Map. 21 I. FCover map of study area in May II. FCover map of study area in July III. FCover map of study area in September IV. FCover map of study area in November The four maps below present FCover of the year 2018 in different seasons.As it can be seen, the highest values are the same in all the seasons however their distrinution is at its maximum in month of July. On the contrary, the lowest values are visible in the month of November.

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6.3.

FIELD OBSERVATION

Field observation is an important step towards estimating the above-ground biomass. In this part a number of plots are chosen in the study area and the value of existing above-ground biomass is calculated in these plots. This information will later be used for accuracy assessment and studying the relation between the selected predictors and above-ground biomass.

Normally, calculating the aboveground biomass in selected plots will be carried out manually and in traditional methods using allometric equations. Typically, plot biomass is the sum of the dry weights of all the plants in the plot. On the field, tree species and other parameters, such as tree height, diameter at breast height (DBH), crown width,

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and vegetation coverage is recorded for further research on urban biomass.

Despite its excellent accuracy, this procedure necessitates the removal of the Fig. 46 CCI forest biomass, Italy 2017 Source:ESA, 2017

plant under investigation. As such, it is appropriate for primeval woodland or experimental plots but not for urban green space.

However, two main limits of the traditional method in this study was overcome by introduction of a new method. One of the limits is the large scale of the study area which makes the traditional method not feasible or even if possible, it requires a long time and high amount of labor. The second reason is the not similar to other studies on above-ground biomass of urbanized areas which only focus on public green spaces and tree rows in the streets, this study focus on all of the green spaces and elements present in the city including even the private gardens. So many parts of the study area are privately owned and accessing them would have been not possible and allowed.

Because of difficulty in accessing the whole study area, a new method was used to gather information on existing above-ground biomass. For this matter, CCI BIOMASS map, which is a global forest above-ground biomass map that has been provided by the European Space Agency (ESA) in 2018, was used as the fundamental and base information.

These maps were provided by a research team in ESA’s Climate Change initiative and provide a global view of

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above-ground biomass distribution and spatial density over three separate years; 2010, 2017 and 2018 (also the data for 2020 is being mapped).

These series of maps of aboveground biomass developed using satellite observations is intended to aid understanding of global carbon cycle and to support policy goals such as forest management, emissions reduction, and sustainable development.

The data for the maps has been driven from Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2). The spatial resolution of ESA’s global forest aboveground biomass is 100*100m and the temporal resolution is one year (annual maps) (ESA Climate Office, 2017).

Selection of field plots for study is based on the pixels of the CCI map and in general 150 plots have been selected randomly in the study area in the areas defined as forests and woodlands.

However, according to the product user guide (CCI_BIOMASS_PUG_V2) due to limitations in remote sensing data, it is strongly discouraged to rely on one pixel above-ground biomass value. For this matter data for plots were calculated by a buffer of 500m for each selected plot.

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150 selected plots are scattered all over the forest, woodlands and agricultural areas and cover a vasrious value of above-ground biomass.

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FIg. 47 selected poitnts The selected images show the variety of locations in which the plots have been chosen. These locations consist of forests in the North and South, heatlands, agrarian spots close to the rivers and agricultural and poplar woodlands close to urbanized areas.

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6.4.

ANALYSING THE RELATIONSHIP BETWEEN FIELD AGB AND PREDICTORS

In this part, an analysis was done to determine the correlation between field above-ground biomass and the selected predictors. The Pairwise Pearson’s product-moment correlation analysis was conducted to determine this process. The Pearson coefficient is the most commonly-used correlation coefficient that measures the strength and direction of the linear relationship between variables. The values range

In this study the correlation between 40 predictors (all eight predictors in four different months of May, July, September and November and an annual average) has been determined with the above-ground biomass that was derived from the CCI BIOMASS map.

As it can be understood from the table below, the predictors are significantly correlated with the plot values of above-ground biomass in the month

from -1 to +1, where ±1 represents the most complete agreement and 0 represents the most complete disagreement.

The Pearson coefficient was carried out in Microsoft Excel, where all of the tables from the previous section were gathered and analyzed.

of September with the highest being 0.55 for both FAPAR and FCover. In the same month the second highest is TNDVI and SAVI with correlation of 0.52.

On the other side of the scale, would be the month of July where the predictors have the lowest correlation

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Table. 05 Correlation of predictors with plot above-ground biomass

Predictors

Spring

Summer

Autumn

Winter

Annual

NDVI

0.3

0.05

0.51

0.19

0.35

NDI45

0.04

0.18

0.47

0.02

0.32

TNDVI

0.13

0.06

0.52

0.23

0.37

IRECI

0.23

0.0002

0.48

-0.41

-0.007

SAVI

0.17

0.01

0.52

-0.26

0.24

LAI

0.13

-0.27

0.51

-0.10

0.17

FAPAR

0.11

-0.11

0.55

-0.12

0.25

FCover

0.21

-0.05

0.55

-0.43

0.26

with plot above-ground biomass. The lowest correlation is for IRECI which equals to 0.0002 meaning almost zero correlation.

In total, 9 of the predictors had negative correlation with above-ground biomass with the highest if -0.41 for IRECI in the month of November.

Comparing the predictors in the category of Vegetation Indices, TNDVI and SAVI have the highest correlation in hte month of September followed by NDVI and LAI. In month of May, the highest correlation is seen for NDVI. Whereas, in the month of July the highest correlation is observed for NDI45. In the month of November, IRECI has the highest correlation but it has a negative correlation as well. The lowest correla-

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tion in months of May, July, September and November respectively are for NDI45, IRECI, NDI45 and again NDI45 meaning among all the predictors NDI45 have repeatedly had the lowest correlation in different months.

As for the Vegetation Biophysical Variables, the highest correlations are again observed in September with the highest correlation between FAPAR and FCover. The highest correlation in other months of May, July and November are for FCover, LAI and FCover, indicating that FCover have the highest correlation in all the months with the plot above-ground biomass. Whereas, the lowest correlations are seen for FAPAR, FCover and LAI, showing different behaviors in different months.

6.5.

MODELING THE RELATIONSHIP BETWEEN FIELD AGB AND PREDICTORS

This section continues using the results of the previous section. After determining the correlation between predictors and field above-ground biomass, their relationship has to be modeled. For creating this model, Machine Learning techniques have been used.

These techniques include Support Vector Machine for Regression (SVR), Random Forest (RF) and Artificial Neural Network (ANN). WEKA software was

square error (RMSE). It is noted that the above-ground biomass from the sample plots were used as the dependent variable, whereas the independent variables were derived from the Sentinel-2A predictors.

used to simulate the machine learning methods used in this study (version 3.8.5, The University of Waikato, Hamilton, New Zealand).

The models identified the optimal parameters for above-ground biomass prediction with the highest correlation coefficient (r) and lowest root mean

al risk minimization, Support Vector Machine for Regression (SVR) is a regression version of support vector machines that projects the training dataset from a lower dimensional space into a higher dimensional feature space using kernel functions to separate groups of input data in a linearized manner

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I. Support Vector Machine For Regression (SVR) Based on the Vapnik-Chervonenkis (VC) dimension theory and structur-

(Chen et al., 2018). The supervised learn-

ing technique, Support Vector Machine for Regression, is used to predict discrete values. SVMs and Support Vector Regression are both based on the same premise. SVR’s main premise is to locate the best-fitting line. The best fit line in SVR is the hyperplane with the greatest number of points. The SVR, unlike other regression models, aims to fit the best line inside a threshold value, rather than minimizing the error between the real and projected value.

The distance between the hyperplane and the boundary line is the threshold value (Raj, 2020).

In real-value function estimation, SVR has been shown to be a useful technique. SVR uses a symmetrical loss function to train as a supervised-learning strategy, penalizing both high and low mis-estimates equally. SVR’s computational complexity is independent of the input space’s dimensions, which is one of its key benefits. It also has a high prediction accuracy and great generalization capabilities (Awad & Khanna, 2015)

II. Random Forest (RF) Breiman presented Random Forest as a Classification and Regression Tree (CART) approach that combines bagging with random variable choices at each node to repeatedly produce a

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large number of CARTs. The classification output is a majority vote (classification) or an average (regression) from the whole ensemble, resulting in a more robust model than a single classification tree generated by a single model run. At each internal node, a number of decision trees in Random Forest pick their optimal splitting attributes from a random group of predictors without pruning. Random Forest ensures the minimal possible bias while also ensuring very low data variance via the bootstrap sampling process (Chen et al., 2018).

III. Artificial Neural Network (ANN) Artificial neural networks (ANNs) are computer networks that are inspired by biology. These networks are modeled after biological neural networks, although they only utilise a subset of the principles found in biological neural networks. ANN models, in particular, mimic the electrical activity of the brain and nervous system (Walczak, 2003).

The ANN models, like the real brain, are made up of a complex and nonlinear network of neurons. Weighted connections connect the neurons to one another. Learning and training methods are used to calculate all of the processes in ANN models, including data collection and analysis, network structure design, number of hidden layers,

network simulation, and weights/bias trade-off (Malekian & Chitsaz, 2021).

The table below represents the accuracies of three models for estimating above-ground biomass of 150 plots of which the above-ground biomass ranged from 4.12 to 280.29 Mg/ha.

The models have been validated using three approaches; 10-fold Cross Validation, Percentage Split and Training Set. Cross-validation is a technique for evaluating predictive models that divides the original sample into two parts: a training set for training the model and a test set for evaluating it. The original sample is randomly partitioned into k equal size subsamples in k-fold cross-validation. A single subsample from the k subsamples is kept as validation data for testing the model, while the remaining k-1 subsamples are used as training data. The cross-validation process is then repeated k times (the folds), with each of the k subsamples serving as validation data exactly once. This method has the advantage of using all observations for both training and validation, and each observation is utilized for validation only once (Vanschoren, 2022).

Each time you analyze a model, Percentage Split (Fixed or Holdout) ran-

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domly splits your dataset into training and testing partitions and removes a random N percent of the original data.

In this study, the data was divided into 70-30 percent shares, with 70 percent of the data used to create a model and the remaining data used to assess the model’s correctness.

The Training Set is the final evaluation model, which is not always the best model but was utilized in this study to compare. Which employs the same training data set as well as the test data set. So, in essence, it will provide high accuracy, and as a result, even if it produces the best results, it is preferable to utilize other models.

The difference in performance of each model has been studied using three error measurements; the Correlation Coefficient (r), the Root Mean Square Error (RMSE), and the Mean Absolute Error (MAE). These statistical criteria are widely employed in modeling above-ground biomass to assess the difference between the observed data and the predicted above-ground biomass data.

The root mean square error (RMSE) is a commonly used statistic for calculating regression model errors. Large numbers and outliers, on the other hand, have a considerable impact on the equation. As a result, MAE is com-

bined with RMSE to assess the fluctuation of model errors. The improved regression model has a lower RMSE and MAE value. Furthermore, a smaller gap between RMSE and MAE indicates a lower error variance (Vafaei et al., 2018).

The results show that all three models had satisfactory performances using all the different validation methods.

The highest fitness is observed when using training dataset and in the Random Forest model with r= 0.97, MAE= 12 and RMSE= 16 and then it is followed by Artificial Neural Network with r= 0.80, MAE= 33 and RMSE= 41. Finally the model that comes last is Support Vector Machine for Regression with r= 0.56, MAE= 44 and RMSE= 56. However as mentioned before, it is best to

evaluate other models as well and not base the study only on Training set model. In this sense, comparing the other two models, the best performing with a very slight difference is 10-fold cross validation with r= 0.72, MAE= 34 and RMSE= 46 for Random Forest technique.

On the other hand, the lowest performing model is SVR model with r=0.49 as the lowest value in 10-fold Cross Validation model. ANN model has not shown a significant values.

The high performance of Random Forest is clear in all the evaluations which is why it is the chosen model for continuing the study. Moroevr, the best accuracy assessment model is chosen to be 10-fold Cross Validation.

Table. 06 Accuracy assessment of SVR, RF and ANN models for estimating above-ground biomass

Model 10-fold cross validation

Percentage Split

Training Set

Machine Learning Technique

r

MAE

RMSE

SVR

0.49

47

59

RF

0.72

34

46

ANN

0.57

47

57

SVR

0.60

42

52

RF

0.70

30

43

ANN

0.55

39

50

SVR

0.56

44

56

RF

0.97

12

16

ANN

0.80

33

41

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6.6.

MAPPING ABOVE-GROUND BIOMASS

In the previous section, different models were trained and the best model that has the highest performance for estimating above-ground biomass was selected, which was Random Forest.

The finalized trained model is used in this section to estimate the aboveground biomass of urbanized areas of the city of Torino. To do this, the values of predictors extracted from the Senti-

is visualized there.

Since the study is done in urbanized areas and green areas have smaller surfaces and are more scattered, the resolution of the final maps is 10m which is the finest resolution offered by images from Sentinel-2.

The estimated values of above-ground biomass from Random Forest have the minimum of 21 Mg/ha and maximum of 180 Mg/ha. For a better comparison,

nel-2 images of the study area are used as base data in the Random Forest model and the values of above-ground biomass is estimated. This process is done in WEKA software (introduced in the last chapter). The final results which are exported as csv. Files are moved to QGIS and the final above-ground map

the values were categorized in five levels and different spatial distribution of above-ground biomass has been spotted.

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•

The category with the lowest value is defined as “very low” aboveground biomass with values of

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21 Mg/ha to 92 Mg/ha, which are mostly located inside the settlements. with the lowest values being located in industrial sites with almost no presence of vegetation.

This category is followed by “low” above-ground biomass with values of 92 Mg/ha to 103 Mg/ha and are still loctated inside the settlements and artificial land but also contain some parts of the agricultural areas with low crops.

The third category called “medium” above-ground biomass has values ranged between 103 Mg/ha and 120 Mg/ha which is spread all over the study area ans contain a very wide variety of landscapes and landuses.

The forth category is “high” aboveground biomass and has values of 120 Mg/ha to 138 Mg/ha. This category is less located in artificial land and mostly covers grasslands, forests and woodlands which are not characterized by high density.

There are however still some areas inside the city that are in this category. These areas are mostly small scale urban forests or natural parks.

On the other side of the scale and the last category is “very high” above-ground biomass which is the category with the highest values of above-ground biomass

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equal to 138 Mg/ha to 180 Mg/ ha which are spotted in the eastern side of the study area in areas characterized as high density vegetation such as forests and woodlands and more natural areas.

This map is the main guide to Carbon sequestration and is used in the following sections to estimate this phenomenon.

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Map. 23 Map of predicted above-ground biomass in the study area This map is derived from Random Forest technique in Machine Learning and has been trained using 8 predictors which contain 5 Vegetation Indices and 3 Biophysical Variables.

Above Ground Biomass Values 21 - 92 92 - 103 103 - 120 120 - 138 138 - 180 main road urbanization protected area (Natura2000) surface water 10

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Map. 24 Map of very low above-ground biomass This map shows the geographical location of pixels that are categorized as “very low above-ground biomass”. They contain a vast area of the study area and cover mostly the urbanized areas and some part of agricultural lands.

The lowest category which contain values of 21-92 Mg/ha, involve almost all of the urbanized areas, especially areas that are more built and less vegetated. As it can bee sen the river Po acts as a divider in the category since on the right side of the river lays forests and grasslands and they have higher values of above-ground biomass.

These values of above-ground biomass contain small green elements such as balcony plants or mowed grass in the front yard of the buildings in both residential and commercial buildings.

Other areas that this category of low value has been spotted in is the agricultural lands which are specifically in dry phases and are not under cultivation. Also agricultural lands that have recently been under cultivation and the vegetation is still not as dense and fully grown in their final phase, are also among the areas that contain low values of above-ground biomass.

Another interesting spot that lies under this category is the parking lot area. Parking lots are one of the areas in cities that are completely covered with paving and the permeability is low. These areas are well-known for creating hot environments and lack of vegetation in these areas are always under question. What seems to be interesting in this study is that presence of trees in these areas, as little as they may be, help increase the presence of above-ground biomass and the impact that comes with them; absorbing pollution being one of the most important areas.

Moreover, the median strips of grass or low vegetation on the streets or highways fall under low value of above-ground biomass category. However, it is important to keep in mind that even small pieces of green areas and spots in areas that have more presence of cars as pollution sources help in absorbing these mentioned pollutants.

The areas that have the lowest values are industrial sites which contain the lowest amount of vegetation and plants but on the other hand, they are areas that emit some high values of pollution and greenhouse gases. So this map can serve as a wake-up call for government of Torino and the city planners to have these areas as the important areas that need interventions in the furure planning and design framework of the city.

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Map. 25 Map of low above-ground biomass This map shows the geographical location of pixels that are categorized as “low aboveground biomass”. They contain a vast area of the study area and cover mostly the urbanized areas and some part of agricultural lands.

The second category which contain values of 92- 103 Mg/ha covers more area than the first category since it includes more areas than just built environment and artificial land and follows closely the area next to the first category.

This category involves areas that have slightly more vegetation inside the city and the artificial lands such as shrubs and denser grassland also includes small urban parks which does not have dense trees and other vegetation.

This category of low vegetation biomass involve rows of trees that are present in the streets are along highways and motorways.

When the rows of trees have bigger crowns and are more densely beside each other, they are shown in this category. Backyards and common gardens in buildings that have more than just grass but still are not so densely vegetated also fall under the category of low above-ground biomass.

Similar to the very low above-ground biomass, this category also involves some of the agricultural and horticultural lands that have low cultivation or are in resting phase.

Small pieces of lands on the eastern part of the study area which are also included inside this category, present small villages and towns next to the main road and are still artificial land with presence of low vegetation next to them. However, these areas on the eastern side of the river cover less surface comparing to the western side of the river.

It is also worth mentioning that comparing to the first category, there is no area that has high presence of “low” above-ground biomass and it seems as if this category is evenly spatially distributed which means most of the man-made landscape in the study area fall under this categiry.

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Map. 26 Map of medium above-ground biomass This map shows the geographical location of pixels that are categorized as “medium aboveground biomass”. They contain a vast area of the study area and cover mostly the dense vegetation in streets and agricultural lands under cultivation. Also some suburban areas with denser green spaces fall under this category.

The third category with values of 103-120 Mg/ ha covers areas with denser vegetation such as groups of trees and bushes. This category presents dense rows of trees in main streets and some of the squares that have vegetation cover and are designed with plants.

When zoomed-in in areas that contain this category of above-ground biomass, what catches the attention is the groups of vegetation in different layers and with different height next to each other. It can be said that the difference between this category and the ones before is in the area that they cover. The first two categories (very low and low above-ground biomass) covered mostly the buildings and sometimes the plain grass areas in between. However, this current category covers mostly the vegetation on the streets and more recreational areas that have designed green spaces such as urban parks.

It can be said that this medium above-ground category is a threshold between two different areas of cities that they cover bearing in mind that the Po river acts as a divider and a physical sign of this threshold.

As it can be seen, more areas in the eastern side of the Po river are included in this category which are defined as patches of settlements and built environment such as villages inside the forest.

These areas can be also defined as suburban areas very close to the mountains and the first layers of forests. However, they are not forests or any dense vegetation in that sense.

As for agricultural lands that are inside the category of medium above-ground biomass, the plots that are currently under cultivation and have grown crops or they have crops that are in the growing phase and are young.

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Map. 27 Map of high above-ground biomass This map shows the geographical location of pixels that are categorized as “high aboveground biomass”. They contain mostly suburban areas with dense and big patches of vegetation.

The fourth category containing values of 120-138 Mg/ha is categorized as high aboveground biomass. Compared to the previous categories, this category is less present in urbanized areas and inside the city. In this category we can see a clear shift from urbanized areas into more natural areas such as forests and woodlands. So this category contains areas located on the eastern side of the Po river.

However, there are still small patches inside high category that are located inside the urbanized areas. These areas are mostly parks and green recreational spaces. Also lines of trees which have higher density are included in this category. Some of the private gardens inside the residential areas are also marked in this category. However, there are not as many inside the city as there are outside and in suburban areas in the mountains.

Mountains on the east side of the study area are mostly covered with this specific category and they involve woodlands and dense forests with dense canopies which create huge and continuous patches of greenery. Mostly higher altitude forests have created big patches of values inside high category of above-ground biomass because they are less disrupted by human activities and creation of villages and agricultural areas.

There are also some parts of the agricultural areas in the western part of the study area that are also shown in this category. These land are lands under cultivation with grown crops which have currently presence of green vegetation. This trend can be also observed in the northern part of the study area as it is also characterized by presence of agricultural and horticultural land.

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Map. 28 Map of very high above-ground biomass This map shows the geographical location of pixels that are categorized as “very high above-ground biomass”. They contain mostly the landuses characterized as forests and woodlands and have the lowest presence in the urbanized areas.

The final category marked as very high values of above-ground biomass range from 138- 180 Mg/ha.

Similar to the previous category, the spots in the urban areas that fall in this category are the natural parks and green natural patches of greenery that exist inside the borders of the city and close to the urbanized areas. opposite to the first categories that include mostly urbanized areas, this category of very high above-ground biomass covers the outskirts of the city such as agricultural lands and semi-natural forests that grow into forests and woodlands.

So the pixels are mostly located in the eastern side of the Po valley and some are located in the western parts of the study area.

Very similar to the previous level of category, this one also includes agricultural lands that are under cultivation and have young to mature crops that have a visible and noticeable amount of biomass to be detected by the satellite imagery. It can be said that based on the amount of biomass that defines this category, mostly horticultural lands are visible as they have bigger masses of vegetation, plants and trees and have higher foliage cover to be seen in the satelite imagery.

Moreover, highly dense surfaces of vegetation which include different types of vegetation such as trees, shrubs, and low cover vegetation, fall under very high values of above-ground biomass and are the forest and woodland patches on the eastern part of the study area.

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6.7.

ESTIMATING CARBON STORED IN URBAN VEGETATION

Recently, biomass has been extensively exploited to contribute to the quantification of greenhouse gas pools and fluxes (GHG). The Kyoto Protocol emphasizes the importance of terrestrial vegetation and soil as key sinks of atmospheric CO2 and its derivatives.

The total above-ground biomass of the vegetation is a significant and active component of the Carbon cycle, and understanding the dynamics of

sult, research aimed at getting precise estimations of the Carbon stock in various landscapes, in this case, urbanised areas of Torino, are necessary.

With growing awareness about rising CO2 concentrations, the function of every landuse as a Carbon reservoir is becoming clear. Landuses with high and dense vegetation have traditionally been the focus of research because they can absorb large amounts of Car-

CO2 in different landscapes requires analysing and quantifying the total above-ground biomass of the vegetation. Through the photosynthetic process, vegetation stores Carbon in its biomass, thereby sequestering Carbon dioxide that would otherwise be released into the atmosphere. As a re-

bon. However, despite having a significant concentration of Carbon, metropolitan regions have never received much attention. As a result, the focus of this study has been on the city of Torino’s urbanized region.

Accurate assessment and mapping of vegetation biomass distribution is re-

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quired to answer questions about their involvement in carbon sequestration.

As Carbon is stored in vegetation biomass and has a close mathematical relationship, biomass and Carbon calculations are frequently done jointly.

Because 50% of biomass is believed to be stored Carbon, the amount of biomass is multiplied by 0.5 to yield an estimate of Carbon.

Map. ???? shows the estimated amount of Carbon that can be stored in urban vegetation in the study area with the lowest value of Carbon sequestration of 11 mg and the highest of 90 mg.

This map, similar to the map of aboveground biomass, is also divided into five categories which represent different levels of Carbon sequestration.

As expected, the urbanized areas of the study area have the lowest values of Carbon storage ranging from 11 to 46 mg because they have the least amount of vegetation biomass present. and because the presence of the vegetation is so scarce. However, there are spots in the urbanized areas that are characterized by higher density of vegetation and they are parks and small urban forests which absorb more amount of Carbon between 52 to 60 mg.

On the other hand, the most values of Carbon storage are spotted in forest and woodland areas, basically areas

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that are east of the Po river and have been less disturbed by human activities, with values of 69 to 90 mg. There are also areas in the western side of the study area which have high areas of agriculture and horticulture lands. In this category, the lands that are under cultivation which show presence of vegetation are also included in the category with the highest level of Carbon sequestration The annual production of aboveground biomass is critical in determining Carbon sequestration. This map is the study’s final goal, in which Carbon sequestration levels are presented in a specific location, demonstrating that the workflow of this study was successful. As a result, the similar procedure can be repeated in any chosen location.

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Map. 29 Map of estimated Carbon stored in the vegetation of the study area This map is derived from Random Forest technique in Machine Learning and has been trained using 8 predictors which contain 5 Vegetation Indices and 3 Biophysical Variables.

Carbon Sequestration Values 11 - 46 46 - 52 52 - 60 60 - 69 69 - 90 main road urbanization protected area (Natura2000) surface water 10

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-07-

FUTURE SCENARIOS AND POSSIBLE IMPROVEMENTS

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I N T R O D U C T I O N The final chapter of the study focuses on future scenarios and how the results of this research can be applied in actual projects and designs assisting various actors in city planning and design..

This chapter focuses on three main aspects. One, the relation between estimated Carbon sequestration and its corresponding landuse. This perspective indicates which landuse has the least and most values of Carbon sequestration and can help policy makers and urban designers to focus more on areas with lower sequestration values. The second view point emphasises on the role of having the data on Carbon sequestration and how it can help landscape architects to create a more conscious designs. In this step, the knowledge on Carbon values, both absorbed and not absorbed, can be combined with nature -based solutions to create healthier living environments for cities and their inhabitants. The final aspect talks about the methods that can enhance the estimation model itself by improving different steps of the process of model making from the choice of satellite image to different predictors and finally implementing various machine learning techniques.

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The final chapter focuses on the various future scenarios that could be derived from the results of the study. This is a crucial element of the study since it demonstrates how the findings may be applied in real-life circumstances and how they can benefit people.

The work done in the previous chapter resulted in two main maps depicting the values of above-ground biomass and Carbon sequestration in the study area, and this chapter will focus on how these values, as well as their distribution and geographic location, can assist governments cities, urban policy designers and landscape architects, and most importantly, citizens, in overcoming the challenges posed by greenhouse gas emissions.

For this purpose, It is necessary to examine many aspects of the final conclusion in order to present some conceivable future scenarios, and only in this case can varied viewpoints emerge. Several factors have been considered for this purpose and they have resulted in two main scenarios in which the results can be used and implemented not just for the case study area but also for any other city, and one probable scenario in which future studies can augment and improve the results. All three aspects will be covered in further depth below and more in the following sections

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I. What is the relationship between the estimated Carbon sequestration and its location based on different landuses ?

This scenario focuses mainly on the distribution of Carbon sequestration in various landuses and evaluates the capacity of each landscape unit in sequestrating Carbon.

A similar study is done every year by ISPRA which studies the emission of different greenhouse gases by various sources and also studies how each different landuse behaves in terms of being a sink or a source of Carbon. If the specific landuse absorbs more Carbon than it releases then it can be called a “Sink” of Carbon. On the other hand, if it releases more Carbon than it absorbs then it can be called a “Source” of Carbon. However, this is only valid if the study is covered in a period of time so the be havior can be analysed in a timeline.

In this current study, since it has a cover year of only one year, then it is only possible to conclude which landuses are the least and the most effective in absorbing and sequestrating Carbon.

The final results of this section are turned into maps to provide a better understanding of the quantity and location of Carbon sequestration in the area of the study. Moreover, the maps

are divided into categories of landuses to provide a better understanding of the situation.

II. What role may the findings of this study have in the future planning and design of our cities?

This aspect focuses on how the results of this study can help governments prepare a better-suited strategy for absorbing Carbon in their cities.

Air quality monitoring facilities have provided city planners with data on the amount and location of carbon that exists in each city. The amount of carbon absorbed, on the other hand, is a value that is not always known and is frequently calculated based on theories and studies.

This research provides a model for calculating the exact amount of carbon absorbed by current flora in cities and their surrounding landscape. When this information is accessible, it will be possible to estimate the quantity of carbon that will be present in the air, which will serve as a foundation for future landscape designs in the city. This value will aid landscape designers in making better informed decisions about plant species and quantity.

III. What can be done to improve the model employed in this study

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to have even more accurate results ?

The final section focuses on the study’s technical aspects and suggests some techniques for improving the model for calculating above-ground biomass.

There are three techniques to improve the estimation model, which will be thoroughly detailed in the following sections. These are the three methods: • The first step is to experiment with different satellite imaging options. Every day, better quality images are generated thanks to advancements in remote sensing technologies.

• This study carries out its modeling process with eight predictors.

However, there are other vegetation indices which can be included in the process and help strengthen the accuracy of the estimation.

• Finally, the machine learning technique which plays an important role in creating the final model. Only three different techniques have been used in this study. An enhancement solution could include more options in machine learning techniques.

7.1.

CARBON SEQUESTRATION AS A POTENTIAL LAND-USE MANAGEMENT TOOL FOR MITIGATION OF GHG EMISSIONS

Carbon sequestration on land has the potential to help reduce the recent increase in atmospheric Carbon dioxide (CO2), which is contributing to global warming in part. It is critical to understand how Carbon is cycled and distributed across the landscape if local governments and regional planning agencies are to make changes in landuse management that could reduce the effects of growing greenhouse gas

ecosystem and the atmosphere are influenced by human activities. Mitigation can be accomplished through operations in the Land Use, Land-Use Change, and Forestry (LULUCF) sectors that boost greenhouse gas removals from the atmosphere or reduce emissions by slowing Carbon stock loss.

The potential of ecosystems to operate as a CO2 sink and minimize socio-eco-

emissions. Only then can a cost-benefit analysis of Carbon sequestration as a potential land-use management technique for reducing greenhouse gas emissions be performed.

Changes in Carbon stocks between the terrestrial ecosystem’s Carbon reservoirs and between the terrestrial

nomic vulnerability to climate change is influenced both directly and indirectly by their exposure, sensitivity, and adaptive capacity. Natural ecosystems are typically well adapted to natural disturbance regimes including drought, flooding, storms, and wildfires. After catastrophic droughts and fires, some eco-

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systems, such as grasslands, are able to return to normal ecosystem function.

Problems arise as a result of the growing frequency and intensity of these disturbances as a result of climate change, as well as other stressors such as landuse change and pollution, leading disturbances to reoccur before the system can recover. This can cause the ecosystem’s adaptive capacity to plummet, resulting in a shift to a new community of species or a completely new ecosystem (Seddon et al., 2020).

Carbon sequestration is a potential of each landuse and nowadays it is being considered as a nature based sollution.

Due to the sheer considerable emissions from housing and transportation, urban areas account for a significant portion of total global greenhouse gas emissions. Carbon sequestration in many Agriculture, Forestry, and Other Land Uses activities has been recognized by the IPCC as a cost-effective mitigation option with possible synergistic effects to restrict these emissions and create a balance, even though Annually, due to future land-use development, Carbon storage capacity will be lost.

Landuse trade-offs exist in rapidly increasing metropolitan regions, where growth must be prioritized over other objectives, such as climate change mitigation. Although urban sprawl ar-

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eas have a higher energy consumption and generate more greenhouse gases per inhabitant than higher density urban areas, they have substantial Carbon sequestration potential. As a result, if green infrastructures like as nature based solutions are maintained, densification of existing urban areas will likely result in greater net environmental performance. As a result, future urban development plans will need to include policies to limit development in high-emission potential locations and more investment in maintaining green infrastructures (Jonsson et al., 2019).

Therefore, calculating the amount of Carbon sequestration in different landuses and quantifying this phenomenon is an important step in creating a framework for future planing to absorb Carbon in urban areas and inside cities as well as in any other landscape as a tool fo landuse management.

7.1.1.

Land Use, Land Use Change And Forestry (LULUCF) As An Inventory To Cover Emissions Of GHGs

United Nation Climate Change defines LULUC in its glossary of climate change as below (UNFCCC, 2020): “A greenhouse gas inventory sector that covers emissions and removals of greenhouse gases resulting from direct human-induced land use, land-use change and forestry activities.”

The rate of CO2 build-up in the atmosphere can be slowed by utilizing the fact that atmospheric CO2 can accumulate as Carbon in terrestrial ecosystems flora and soils. Any process, action, or mechanism that removes a greenhouse gas from the atmosphere is referred to as a “sink” under the United Nations Framework Convention on Climate Change (UNFCCC, 2020).

Land use, land-use change, and forestry (LULUCF) activities have an impact on terrestrial sinks, and as a result, the exchange of CO2 (Carbon cycle) between the terrestrial biosphere system and the atmosphere is altered.

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The importance of LULUCF operations in climate change mitigation has long been acknowledged. Changes in Carbon stocks between the terrestrial ecosystem’s Carbon reservoirs and between the terrestrial ecosystem and the atmosphere are influenced by human activities. Mitigation can be accomplished by LULUCF actions that improve the removal of greenhouse gases (GHGs) from the atmosphere or reduce emissions by preventing the loss of Carbon stocks.

The LULUCF sector uses a net Carbon sequestration pool to compute the forestry-related parts of the UNFCCC obligations. Changes in land use and management activities, as well as forestry operations and disturbances, all contribute to CO2 emissions and removal.

This sector removed 36.3 million tonnes of CO2 equivalent from the atmosphere in Italy in 2018. The table shows the trend in CO2 net emissions/ removals (kt GHG) from the LULUCF industry from 1990 to 2018 in Italy.

Land Use, Land-Use Change, and For-

estry sector is divided into six main sections; I. Forest II. Cropland III. Grassland VI. Wetland V. Settlements VI. Other lands The forest landscape has the largest

CO2 reductions, as seen in the table and as expected. Grasslands are second in terms of removing this greenhouse gas. The lack of data on CO2 removal in settlements is a noteworthy aspect in this table. Settlements are the largest sources of emission of greenhouse gases, and they are counted as the main source of these gases, as stated in earlier chapters, therefore the

Table. 07 Trend in CO2 net emissions/removals (kt GHG) from the LULUCF sector in the period 1990-2018 in Italy Source: ISPRA, 2020

CO2 LULUCF category

1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Forest

Cropland

Grassland

Wetland

Settlements

-17,852 -31,122 -26,004 -34,662 -34,319 -18,991 -30,836 -33,479 -36,658 -32,732 -28,659 -37,537 -38,717 -40,113 -37,073 -22,500 -33,472

2,293 1,057 -201 -1,679 -2,376 -2,466 -1,773 -2,102 -885 784 2,030 1,779 1,343 595 -795 -540 -96

3,645 -2,363 -2,162 -6,494 -6,799 -664 -6,525 -7,304 -9,553 -7,633 -4,831 -9,619 -9,191 -9,449 -8,413 -5,218 -8,396

N.O 5 8 8 8 8 8 130 130 130 130 130 130 130 53 53 53

N.O N.O N.O N.O N.O N.O N.O N.O N.O N.O N.O N.O N.O N.O N.O N.O N.O

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concentration of pollution is primarily situated within the limits of settlements and urbanized areas.

The absence of data in this landscape makes it difficult for policymakers and governments to develop a plan that focuses primarily on urbanized areas in order to create healthier and more resilient cities.

Many studies have been conducted to estimate the amount of Carbon stored in forests, grasslands, and croplands.

However, there are few studies on estimating Carbon storage in urban areas and other areas with concentration of settlements.

One of the main reasons for conducting this current study was this lack of information. As the model presented in the previous chapter suggests, we can estimate the amount of Carbon removed within cities and inside settlements using today’s technology.

One important point that needs to be taken into consideration is that the reports of LULUCF provides data in a continuous period of time, thus makes it possible to evaluate if a specific landscape is behaving as a source or a sink of Carbon. This current study has only studied Carbon sequestration in one year (2018) in a selected portion of Torino and its surroundings. So comparison between different years is not possible and only the ability of each

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landscape as a sink is studied with the model presented in the previous chapter.

7.1.2.

Torino Small-Scale Scenario; Landscape Units Under Evaluation

As a suggestion, the results of the previous chapter could be applied in a future scenario to estimate the amount of Carbon sequestered in various landscapes and to assess each landscape’s ability to remove Carbon from the atmosphere.

To Continue, landscape units have been chosen using Carta Habitat which has divided landscape into eight main categories of: I. Surface water II. Humid area III. Grassland IV. Heatland V. Woodlands and forests VI. Sparsly vegetated VII. Agriculture and horticultural land VIII. Artificial land However, to keep the similarities between the categories of Carta Habitat and LULUCF sectors for further analysis and comparison, not all of the categories of Carta Habitat have been

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involved in the evaluation. The categories used are grassland, woodlands and forests, agriculture and horticultural land and artificial land.

The map. ??? shows the distribution of different landscape units in the study area of Torino and its surrounding.

As it can be observed the dominent landscape unit is “Artificial land” since the focus of the study has been on urbanized areas with an area of more than 96 km2 covering 42% of the study area. Following is “Agriculture and horticultural land” as the secon largest landscape unit which is seen spreading around the borders of the city acting as a transition point between hard surfaces of urbanized areas and seetlemnets and soft areas of forests and woodlands. This landscape unit covers more than 80 km2 of the study area which equals to 35% of the surface.

The landscape unit with the smallest coverage is “Heatland” which is mostly located in the eastern part of the study area inside forests and woodlands and

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Location of different Landscape Units in the study area Map. 30 Map of habitat map in the study area

Landscape Unit C- surface water E- grassland F- heatland G- woodland and forests I- agriculture and horticultural land J- artificial land main road urbanization protected area (Natura2000) 8

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covers only 0.225 km2 of the surface (less than one percent of the area).

To be ble to estimate the quantity of Carbon sequestered in each landscape unit, two maps of Estimated Carbon and Carta Habitat were overlayed.

The table. ??? shows the final result.

As it can be seen results of Carbon sequestration have been categorised for each landscape unit and its subcategories.

The quantity of Carbon that can be sequestered based on the amount of above-ground biomass has been overlayed with each landscape unit to determine how much potential each landscape unit has.

Grassland (E) covers 6% of the whole study area and sequestrate around 9 kg of Carbon. Among its sub-categories, low and medium altitude mowing lawns cover the most surface (95% of the erea of grassland) and consequently absorbs the most Carbon.

Heatland (F) covers less than one percent of the whole area under evaluation (0.09%) and as a result absorbs the least amount of Carbon; it has the potential to only absorb 0.149 kg of Carbon.

Woodlands and forests (G) are the third

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largest landscape unit present in the area of case study and almost 14% of the whole area is covered with various types of woodlands which sequestrate an amount equal to 28 kg Carbon.

Western downy Oak forest and related communities largly cover this unit (54% of woodlands rest in this sub-category). therefore, the largest amount of Carbon is absorbed in these areas (an amount equal to 11 kg Carbon. The second largest area in this category belongs to Robinia sp. Although it is a native of North America, it has been widely planted and naturalized throughout temperate North America, Europe, and Southern Africa. These species are generally referred to as black locusts, and when they invade a region, they transform the grassland ecology into a wooded habitat, displacing the grasses. Robinia sp. covers 31% of the forest and woodland area in the study area and absorbs more than 6 kg Carbon.

On the end of the scale lays Quercus pubescens woods of northern Italy which cover less than one percent of the surface (0.05%).

Agriculture and horticultural land (I) cover 35% of the study area and they are the second largest landscape unit in the area. This landscape unit absorbs 47 kg of Carbon. In this unit, the largest sub-category covering is

cultivated parks and gardens which covers 54% of the area dedicated to agriculture and horticultural land. Consequently they absorb the most Carbon with an amounf equal to 25 kg Carbon. The smallest covering area is gardens which covers only 2% of the surface and absorbs 1 kg Carbon.

Finally, an important category, artificial land (J) which covers most of the study area (42%) of the area and has the capacity to absorb almost 50 kg of Carbon. This area is mostly covered by densly built-up areas, covering 77% of the surface dedicated to artifical land.

Moreoever, the results have been maped to provide a better understanding. Maps. ????????? respectively shoe Carbon sequestration in grasslands, forest and woodlands, agriculture and horticultural land and finally artificial land.

Finally, it is worth mentioning that this results can be explored even further formore than one year. This study is only for the year of 2018, so comparisons between years was not possible. As a suggestion for futre research, the same model of estimatiing Carbon storage can be used for a span year of 10 or 20 years and the final results can be used to study the transition between land-

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scape units and moreover, the results can determine if each landscape unit is behaving as a sink of Carbon (absorbing more Carbon than it relases) or as a Source of Carbon (releasing more Carbon than it absorbs)

Table. 08 Quantity of Carbon sequestration in various landscape units in study area of Torino

Landscape Unit

Landscape Unit Category C 1 - Still waters (including quarry lakes)

C - surface water

E - grassland

C 2.3 - Permanent watercourses with a potamale character (slow-flowing rivers) C 3.6 - Non-vegetated river banks with fine or mobile riparian sediments E 2.1 - Permanent mesophilic pastures and meadows grazed by livestock E 2.2 - Low and medium altitude mowing lawns E 2.6 - Artificially sown and fertilized lawns

F - heatland

G - woodlands and forests

I - agriculture and horticultural land

J - built environment

F 3.1 - Shrubs and temperate bushes F 9.1 - River and lake bushes of Salix sp.

FB.3 - Plantations of dwarf trees cultivated for ornamentation or for fruit FB.4 - Vineyards (plantations of Vitis sp.)

G 1.11 - Azonal riparian shrubs of Salix sp.

G 1.224 - Quercus sp. Fluvial forests G 1.31 - Mediterranean riparian forests with dominant Populus alba and Populus nigra G 1.71 - Western downy oak forests and related communities G 1.731 - Quercus pubescens woods of northern Italy G 1.A7 - Mixed deciduous forests of the Black and Caspian Seas G 1.C - Reforestation and highly artificial plantations of deciduous hardwoods G1.C1 - Plantations of Populus sp.

G1.C3 - Robinia sp.

G1.C4 - Plantations of other deciduous broad-leaved trees G1.D4 - Horticultural crops of fruit plants G 3.F - Reforestation and highly artificial plantations of conifers I 1.1 - Intensive monocultures (technologically advanced and high productivity) I 1.2 - Gardens I 1.3 - Extensive monocultures I 1.5 - Uncultivated and recently abandoned agricultural areas I 2 - Cultivated parks and gardens J 1 - Densely built-up urban areas J 2 - Sparsely built up areas J 4.2 - Road network J 4.3 - Railway network

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Surface (km2)

Surface Total (km2)

0.574 3,68 1,12 0.613 14,52

Carbon Storage Value (kg) 0.25

5,39

15,14

1,60 0.706 0.4 9,36

0.01

0.005

0.03 0.02 0.02 0.142 1,41 0.140

0.02 0.014 0.016 0.093 0.956 0.095

0.225

0.329

0. 213

17 0.01 0.02

11,54 0.01 0.017

31,32

0.246

7,61

0.09 9,72 1,18 0.755 0.387

0.056 6,61 0.781 0.49 0.263

19,61

11,29

1,81 5,28 10,12 43,74 75,39 0.03 20,63 0.902

Carbon Storage Total (kg)

80,58

96,96

1,07 3,43 6,50 25,58 38,09 0.018 11,03 0. 451

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9,77

0.149

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47,88

49,59

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Map. 31 Map of Carbon sequestration in grassland in the study area

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The map shows the distribution of grasslands in the study area As it can be seen, the grassland is sparsly located in the south-east of the study area where is more forests and woodlands. However, there are also huge areas of grassland on the north-west which have more artificial patterns and seem to be more manmade and made to be used for specific reasons.

E 2.1 E 2.2 E 2.6 main road urbanization protected area (Natura2000)

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Map. 32 Map of Carbon sequestration in forest and woodland in the study area

G 1.11 G 1.224

G 1.731 G 1.A7

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The map shows the distribution of forests and woodlands in the study area. As it can be clearly seen, the forests are located in the south-east part of the study area.

This landuse is divided into smaller and more separated patched. However, they merge together in the higher parts of the mountains and created bigger patches.

G 1.C3 G 1.C4

G 1.D4 G 1.C G 1.31 G 3.F G 1.C1 G 1.71 main road urbanization protected area (Natura2000)

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Map. 33 Map of Carbon sequestration in agriculture and horticultural land in the study area

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The map shows the distribution of agricultural and horticultural land in the study area Its distribution have a more regular pattern in the western part of the study area with clear borders. On the other hand, on the eastern part of the study area where the agricultural fields are more spreas in the forest land, they have more irregular patterns and shapes.

I 1.1 I 1.2 I 1.3 I 1.5 main road urbanization protected area (Natura2000)

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Map. 34 Map of Carbon sequestration in artificial land in the study area

12

14

The map shows the distribution of artificial land in the study area which is the settlements and the road network. This landuse shows the city of Torino.

J1 J2 J 4.2 J 4.3 main road urbanization protected area (Natura2000)

10

La loggia

C- surface water 214

16 km

7.2.

FUTURE APPLICATION OF GEO-LOCATING CARBON SEQUESTRATION

As Charles Waldheim (Waldheim, 2016) states in his book “Landscape As Urbanism”, landscape is a medium of urbanism that mitigates social, environmental and economic crisis that are emerging in modern cities. Acting as a medium, landscape will allow all ecological transition to pass, since its uniquely capable of responding to temporal changes.

Julia Czerniak (2001) once said: “Ecology provides a useful analogy for the complexity and diversity of urban processes.”

Considering landscape not just as a physical realm, but also as a sort of interweaving strategy between ecology and infrastructure, the results of the

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previous chapter can be used to create a framework to enhance the quality of environment in urbanized areas.

The results of the previous chapter, specifically the two main maps of above-ground biomass and Carbon sequestration, will be the basis of an analysis in the study area, providing information on the amount of aboveground biomass and its corresponding amount of Carbon stored.

Moreover, having data on amount of CO2 produced in the study area, a rough calculation will provide the amount of CO2 that is floating in the air and not absorbed.

Following this information, a series of nature based solutions (NBS) can be introduced to increase Carbon se-

questration as a service provided by urban ecosystem. IUCN defines nature based solutions as (IUCN, 2016): “actions to protect, sustainably manage, and restore natural or modified ecosystems, that address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits” Nature-based solutions are widely acknowledged to play an important role in addressing urban resilience issues.

Many international agreements and initiatives, like the Sendai Framework for Disaster Risk Reduction, the Sustainable Development Goals (SDGs), and the Paris Climate Agreement, support the need to enhance nature-based approaches.

These nature-based solutions will be more spatially focused since data on the areas with higher concentration of CO2 and less presence of vegetation is available. These areas are mainly found in densely populated locations where the majority of the land is covered with built structures and just a small portion of the land surface is allocated to greenery. In this instance.

Elevated greening is used as green areas not on the ground level but on higher elevations such as balconies

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and, more commonly, roofs. These areas are frequently under-utilized but have a lot of potential for hosting green spaces. By introducing vegetation to these areas, we not only help cities absorb more Carbon, but we also improve the social and, in some cases, economic aspects of the cities. These designed spaces can range from naturally created areas with the only intention of absorbing carbon and creating a small-scale ecosystem to areas with the sole purpose of enhancing biodiversity. The residents are unable to use these areas. On the other end of the scale, are constructed green areas with the intention of hosting people, resulting in a small-scale park that retains its original goal of absorbing carbon.

Moreover, the proposed nature-based solutions and strategies will contribute to a more quantitative design, possibly proposing more practical plant species and design forms. Because the goal of developing these urban green areas is to absorb the Carbon that is now floating in the air of the study area, it is evident how much Carbon must be sequestered. In this situation, plant selection can be more strategic and conscious because many researches reveal how much carbon is captured by each plant.

This study focuses on the urban area

of Torino, and the values of Carbon sequestration have been calculated for this area. Even though detailed data on the amount and location of Carbon emission in the study area is not available, we certainly hope with the improvement of remote sensing technology, satellites will be able to provide us with this data soon. Until then, the Carbon sequestration data can be roughly compared with Carbon emission data from the Air quality monitoring network which provides data for each municipality.

Even with this rough estimation, the solutions for increasing green spaces in dense urban areas, industrial zones, and areas with high concentration of Carbon pollution can be considered before. Only the quantity of the spaces in terms of vegetation can be considered later.

The primary focus of NBSs in this study should be “building solutions,” with “urban forest solution” and “open green space solution” being examined in rare cases when there is an available surface. When it comes to building solutions, two key zones comes to mind: the roof and the facade. Roofs can be extensive, consisting of drought-resistance plants with medium growth and a thinner soil level, with lower carbon sequestration capabilities, or intensive,

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with thicker soil that supports a wider range of flora and can also be used for recreational activities by the public.

These roofs have higher carbon sequestration values because they can sustain a variety of plants, and they also promote small-scale biodiversity on a neighborhood scale, which brings other ecosystem services as well.

Facades can also be transformed into green walls by using climbing plants that are rooted in the ground. Again, these green solutions will not only help urban dwellers have improved air quality, but they will also provide many ecosystem benefits.

Finally, the ultimate goal is to develop a more resilient city with healthy citizens.

To do so, we must concentrate on reducing greenhouse gases that are not absorbed in general, with a particular emphasis on CO2 gas, while also utilizing what nature has to offer and taking advantage of natural processes.

7.3.

IMPROVEMENT ON THE ABOVEGROUND BIOMASS ESTIMATION MODEL

For creating a model for estimating vegetation above-ground biomass, imagery from Sentinel-2 satellite were chosen. Furthermore, eight predictors including five vegetation indices and three biophysical variables were chosen as predictors and finally by analysing differnet Machine Learning techniques, Random Forest was chosen to create the final model of estimation.

Improvement of the final model in-

fectiveness. Data from Optical Remote Sensing is available from various platforms, such as IKONOS, Quickbird, Worldview, SPOT, Sentinel, Landsat and MODIS with each having different ranges of resolution from less than one meter to hunders of meters.

On the other hand, Radar remote sensing has gained popularity for aboveground biomass assessment in recent years because of its capacity to pene-

volves improvement in any or all of the phases leading to creating the model.

trate clouds and provide comprehensive vegetation structure information.

While airborne Synthetic Aperture Radar (SAR) systems have been in use for a long time, space-based systems like Terra-SAR, ALOS, and PALSAR have only been accessible since 2000.

Another new technique that has

I. The studies suggest that Optical remote sensing is arguably the greatest option to biomass assessment through field sampling Because of its global coverage, repeatability, and cost-ef-

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gained popularity in recent years is LiDAR, which is used to estimate biomass. LiDAR allows for the sampling of the vertical distribution of canopy and ground surfaces, resulting in rich structural data about vegetation. As a result, estimates of basal area, crown size, tree height, and stem volume are more accurate.

One of the possible enhancements can be made on the choice of satellite or even combination of different satellites and techniques such as the study carried out by Chen (Chen et al., 2018).

Different Remote Sensing techniques and satellite present different images and combining them will create a stronger base for the estimation process.

II. This current study included eight different predictors which are NDVI, NDI45, TNDV, SAVI, IRECI, LAI, FAPAR and FCover. These predictors were chosen based on their repetiotion in references and various researches.

There are, however, a number of other predictors that have been demonstrated to be beneficial in various research.

Choosing more predictors will result in a more thorough model because the photos will be analyzed in a variety of ways, reducing the likelihood of inaccuracy. Below is a list of other predictors that can be used in future for creating a more enhanced model of

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predicting above-ground biomass.

•

•

•

•

•

•

Various studies (Chen et al., 2018) have suggested using multispectral bands themselves, including bands 2, 3, 4, 5, 6, 7, 8, 8a, 11 and 12.

Green Normalized Difference Vegetation Index (GNDVI) which shows the photosynthetic activity of vegetation has been used

by Ghosh (Ghosh et al., 2018) because it is more sensitive for stressed and old vegetation.

Enhanced Vegetation Index (EVI) is another predictor which is used for above-ground biomass estimation. This predictor is an optimizev vegetation indice for high biomass regions but in nature is similar to NDVI. Modified Soil-Adjusted Vegetation Index (MSAVI) can also be used for studies more concentrated in agricultural areas as it minimizes the effect of bare soil.

Optimized Soil-Adjusted Vegetation Index (OSAVI) is a newly designed vegetation indice that is more suited for agricultural areas and can accept larger variability due to high soil background values.

Difference Vegetation Index (DVI) is a basic predictor that dis-

•

tinguishes between soil and vegetation, but it does not account for atmospheric factors or shadows that generate a difference in reflectance and brightness.

Standard Vegetation Index (SVI) is another predictor that can be used as a contributory element which takes into account meteorological conditions in the atmosphere closest to the ground as a factor that impacts vegetation conditions.

Considering that other satellites are used in the research, there are different types of predictors that will be used. For radar satelliets (such as Sentinel-1) the possibility of predictors can be as below: • VV (vertical transmit-vertical channel) • VH (vertical transmit-Horizontal channel) • V/H (VV/VH) • VH_CON, VV_CON (Contrast) • VH_DIS, VV_DIS (Dissimilarity) • VH_HOM, VV_HOM (Homogeneity) • VH_ASM, VV_ASM (Angular Second Moment) • VH_ENE, VV_ENE (Energy) • VH_MAX, VV_MAX (Maximum Probability) • VH_ENT, VV_ENT (Entropy)

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• • •

VH_MEA, VV_MEA GLCM (Mean) VH_VAR, VV_VAR GLCM (Variance) VH_COR, VV_COR GLCM (Correlation)

III. In this project, three different machine learning techniques have been used to find the best model for predicting above-ground biomass. These models were among the most used techniques and they were Rnadom Forest, Support Vector Machine for Regression and Artifical Neural Network.

However, there are many other techniques which can be used for creating the model.

It’s also worth noting that device limitations are something to keep in mind.

Adding more predictors, analyzing other sorts of images, or simply selecting larger areas to investigate all necessitate a device with complex configurations, and it is not always possible to build a very complete model using daily PCs.

7.4.

CONCLUSION

The current study was carried out to estimate the amount of Carbon sequestration in vegetation above-ground biomass, especially in urbanized areas.

The estimation process was performed in the city of Torino, Northern Italy. The goal of this study was to define a methodology that is scalable to any environment and landscape, and governments can use that all around the world to have a better understanding of the condition their cities are in so that they can prepare more specific policies and designs to have a more resilient city.

Global warming and its impact on our habitat and life are clear to everyone. in the past decade, there has been no city that has not been negatively affected by this phenomenon. Natural disasters

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such as catastrophic floods, droughts, earthquakes, and other natural disasters are becoming more common in cities. Studies suggest that human activities have been one of the major drivers of global warming.

Among all of these damaging actions, those that result in the production of greenhouse gases are the ones that have permanently altered our lives forever. These activities are now divided into a variety of categories, including agriculture, transportation, industry, energy production, and many others.

Today, mitigating the effects of greenhouse gas emissions has been the primary concern of governments. More and more cities are searching for a way to become more adaptable to these

effects. The state of air quality is one of the issues that supranational organizations are assessing; in particular, the United Nations (UN) launched the 2030 Agenda in 2016, a bold action plan that includes 17 Sustainable Development Goals (SDGs) that link various aspects of sustainability with economic, ecological, and social development, in order to promote both life sustainability and environmental conservation. The highest concentrations of pollutants are measured in the main urban areas, where population density and related activities reach high levels, and that is done in order to identify situations of health danger, such as to identify their removal as Objective No. 11 of the 2030 Agenda for Sustainable Development: “Making cities and human settlements inclusive, safe, long-lasting, and sustainable,” and in particular Target 11.6 By 2030: “reduce negative environmental impacts.”

There are many studies suggesting various methods in absorbing greenhouse gases, some of which go as far as building mega factories to absorb the pollutants, however, there have never been a more natural method like green vegetation.

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Vegetation and their biomass have long been a natural Carbon sink, and they have proven to be an efficient strategy and tool for reducing pollution levels in cities. The primary question that arises is how much Carbon is absorbed by these elements, which is the focus of the current study.

There are a variety of methods for estimating above-ground biomass, including destructive ones that include removing vegetation from their natural environment. The most current methods, on the other hand, make use of remote sensing. This study also employs remote sensing imaging as a foundation for estimating above-ground biomass in Torino’s urbanised areas and its surrounding landscape. This estimation is based on the use of eight predictors, including five vegetation indices (NDVI, NDI45, TNDVI, SAVI, and IRECI) and three biophysical variables (LAI, FAPAR and FCover).

After analysing the relationship between these predictors and aboveground biomass, a model was created using Machine Learning techniques.

The best model, Random Forest, was selected, and the estimation was completed. As a result, a map of aboveground biomass in the city of Torino was created at a resolution of 10 me-

ters. The above-ground biomass was used to export the final phase, which was the assessment of Carbon stored in vegetation biomass.

The final results of Carbon sequestration has been categorized in 5 sectors showing the “very low” sequestration areas which are mostly located in urbanized areas and where there is built environment and lowere presence of vegetation and greenery. Industrial sites are among the areas included in this sector as well. The areas of “low” and “medium” sequestration are still located inside the urbanized areas and where there are settlements. However, other areas are also included in these categories and they are agricultural and horticultural lands and also grasslands, which are all mostly located in the western part of the study area. The areas with “high” and “very high” sequestration shift towards the eastern part of the study area where there is the presence of forests and woodlands.

Also the riparian landscape which is located next to the rivers are also includ-

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ed in this categories.

The results of the above-ground biomass lead to the map of Carbon sequestration which is divided in the same categories (in 5 levels).

Finally, having the information on the geographical location and distribution of Carbon sequestration in the area of the case study, three possible scenarios have been proposed and discussed in detail. These scenarios talk about the possible ways in which the result of this study can be used and implemented in the real life.

Carbon sequestration data, for example, can be compared to existing landuse and used as a tool for landuse management. If this study is carried out in a period of years then even more information on the behaviou of each landuse and whether they are “sink” or “source” of Carbon can be determined.

Moreover, this data can be used to create a framework for landscape designers to focus more on the areas that are counted as hotspots of Carbon (areas with high presence of Carbon but low

amount of Carbon sequestration). The designers will have a more clear idea on the quantity of green spaces they have to create to be able to absorb the existing Carbon in th air in specifi locations and can have a better guidance on the choice of plant species to help them have a more practical design and take into consideration the function of green spaces.

Carbon emissions trends in general are data that is now available in various cities. On the other hand, thanks to recent technological advancements, it is now possible to quantify how much of this Carbon is absorbed by natural and man-made elements. With all of this big data, we can have a more conscious design in the realm of landscape architecture, as a tool to reduce the presence of pollutants in the atmosphere, and to participate in the fight against the impact of global warming, which is affecting everyone’s lives on the only planet we can live on.

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