Click to edit Master title style Earth Observation of Burned Areas Emilio Chuvieco Department of Geography, University of Alcalรก (Spain) emilio.chuvieco@uah.es
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Main University Building: 1502
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Our city
OurClick “kangaroos” to edit Master title style
Activities UAH research Click toof edit Master titlegroup style • Fire risk estimation: – Generation of input variables: Fuel type classification, Lidar, FMC, Socio-economic variables. – Integration & validation methods.
• Fire effects assessment: – Mapping burned areas at global and regional scales. – Burn severity estimation. – Input of Burned Area into Global Vegetation Models.
Outline of edit the seminar Click to Master title style • Global mapping of burned areas: – Background – ESA fire_cci project
• Approaches to map burn severity from RS data.
ESA-CCI programme Click to edit Master title style GHG cci
Aerosol cci
Sea-level cci
Glaciers cci
Fire cci
Land Cover cci
Ice Sheets
Ocean Colour cci
Cloud cci
Sea Surface Temperature cci
Ozone cci
Soil moisture
Sea Ice cci
CMUG
Fire_cci science contexttitle style Click to edit Master • Fire affects: – GHG and aerosol emissions. – Carbon budgets and vegetation cycles. – Land cover change (defforestation)
• Fire is affected by: – Temperature-rainfall trends, particularly heat waves and “El Niño” episodes (climate prediction) – Socio-economic changes (land use policy).
Science questions Click to edit Master title style • What are the recent trends in fire activity? • What factors are behind fire occurrence? • What is the actual magnitude of fire impacts? – How much area is burned annually? – How much biomass is actually consumed? – What is the combustion efficiency (CO/CO2)? – What is the role of fire in carbon accounting? Is biomass burning “carbon neutral”?
¿How much area is burned every Click to edit Master title style year? • Inconsistencies between RS products and official forest fire statistics. • Inconsistencies between RS products. • Internal uncertainty of each RS product.
Click to edit Master title style FRA2010 FAO (FRA2010): 0.6 Mkm². Only 78 countries are covered.
GVED v3 Average 4 Mkm²
Different EO Click to edit Master title style BA estimations -L3JRC: 3.5 - 4.5 Mkm² (2000-07) -MCD45 c5: 3.3 - 3.6 Mkm² (2000–2006) -GFED v3: 3.39 - 4.31 Mkm² (1997-2009). % of BA from different satellite products Red: over estimation Blue: under estimation
(Giglio et al., 2010).
Inconsistency in derived products Click to edit Master title style Comparison between SEVIRI FRP and GFED estimates of combusted biomass SEVIRI
2004 From FREEVAL final report. Courtesy of Martin Schultz
GFEDv2
Uncertainty within a product Click to edit Master title style (GFED v3)
Burned area proportion
Uncertainty Giglio, L., J. T. Randerson, G. R. van der Werf, P. S. Kasibhatla, G. J. Collatz, D. C. Morton y R. S. DeFries (2010): Assessing variability and long-term trends in burned area by merging multiple satellite fire products. Biogeosciences Discuss., 7: 1171-1186, doi:10.5194/bg-7-1171-2010
This is very relevant for climate-carbon modelers!
Scientific of fire_CCI Click togoals edit Master title style 1. Refine definition of user requirements (GCOS are unrealistic). 2. Improve current estimations of global burned area (based on European sensors: VGT-ATSRMERIS). 3. Validate and intercompare existing BA global products. 4. Test improvements of climate-vegetationcarbon models with new BA data.
Consortium Click to edit composition Master title style Climate Modeling User Group (CMUG)
Science Coordinator
Project Manager
EO Science Team
Data preprocessing
Climate Modelers
Algorithm Development & Intercomparison
International Science Working Group: •UMD – MODIS team •FAO REDD •JRC EFFIS •NGO, CI
System Engineering
Validation
fire_cci targets Click production to edit Master title style • Temporal series of BA over 10 selected study sites (500x500 km) (1995-2009): – Assure spatial accuracy and stability. – Consistency across multiple satellites – Demonstrate full-time series available.
• Global coverage for five years (1999, 2000, 2003, 2005 and 2008): – Demonstrate the semi-operational processing. – Ensemble chain, bulk processing of data.
Study Clicksites to edit Master title style
Target Clickproducts to edit Master title style • Burned pixels (mixing all three sensors whenever possible): – Monthly files with date of detection. – Minimum Mapping Unit (MMU) is under discussion. – GeoTiff format
• Grid product: – 0.5 x 0.5 degree (CGM) / improvements to 0.25 or 0.1 degrees are foreseen. – NetCDF format.
Tiles forto the pixel product Click edit Master title style
In addition to standard tiles, the user will have a web tool to interactively select his/her target site and apply for personal downloads
Project Click status to edit Master title style Raw Data: ATSR, VGT, MERIS
User requirements
Product specifications External BA algorithms
BA reference data Done In process
Calibrated reflectances
Development of BA algorithms
Geometric correction Water-snowcloud masking
Round robin
Topographic shadow correction
Merging algorithm
Atmospheric correction
Global BA production
Validation BA reference data
DEM
Testing of models
Major Clickdeliverables to edit Master title style • • • •
User Requirement Document (URD). Product Specification Document (PSD). Product Validation Plan (PVP). Comprehensive Error Characterisation Report (CERC). • ATBDs (Pre-processing, BA algorithms, Merging). • System Requirement Document (SRD) • System Specification Document (SSD).
Pre-processing Click to edit Master title style • • • •
Geometric correction. Masking (cloud, haze, snow, water). Atmospheric correction (ATCOR). 10 sites x 3 sensors x (12-9-5) years: more than 70,000 corrected reflectance images + masks have been processed. • Global processor is being implemented.
BRDF: time series ClickTOA/BOA to edit Master title style • Time series RGB (Meris bands 7, 5, 3):
Meris-FRS, Australia, Pixel 844,555 before atmospheric correction (TOA)
Meris-FRS, Australia, Pixel 844,555 after atmospheric correction (BOA)
Meris-FRS: Blue 1-4, Green 5, Red 6-6, Red-Edge 8, NIR 9-15, O2-Absorption 11, Water vapour 15
BAClick VGT to Algorithm (ISA,title Portugal) edit Master style Pereira and Mota, 2012
BAClick Algorithm Results to editVGT Master title style AUSTRALIA
VGT detection dates
VGT vs MODIS
MERIS (UAH,title Spain) Clickalgorithm to edit Master style
Auxiliary Click tolayers edit Master title style
6 layers for a monthly product: example from June 2005 Australian study site: BA, CL, days between burn date and last valid bservation before that date, valid obs, all obs, cloud obs
MERIS Clickresults: to edit Australian Master titlesite style
2005
2006
MERIS sitestyle Clickresults: to edit Canadian Master title
2005
2006
MERIS Clickresults: to edit Kazakhstan Master titlesite style
2005
2006
Sensor Combinations (PL1)
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ATSR Algo1 (1km)
500km2 Area stats ATSR
VGT Algo1 (1km)
Sensor combo (1km)
– 3691km2 – core burns – overlap 89.9%
VEGETATION – 4211km2 – core burns – overlap 86.7%
MERIS MERIS Algo2 (300m)
Uncertainty reduction: Multiple observation of same burn
– 6977km2 – cores, detail, other events – overlap 56.9%
Date of detection PL2
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ATSR Algo1 (JD)
Julian Day (August)
VGT Algo1 (JD)
Earliest detection (JD)
• Dates brought back • Patch progression
MERIS Algo2 (JD)
Uncertainty reduction: Reduced time lapse of observation
GridMaster outputtitle (1) style Click to edit Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Burned pixels (1km)
Sum burned m2(0.5 degree)
Unsuitable obs %. (0.5 deg)
GridMaster output (2) Click to edit title style Jan
Feb
Mar
Apr
May
Jun
Apr
May
Jun
Jul
Aug
Sep
Jul
Aug
Sep
Oct
Nov
Dec
Burned pixels (1km)
Confidence (0.5 degree)
Conf sd (0.5 degree)
GridMaster output (3) Click to edit title style Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Burned pixels (1km)
Homog. ind (0.5 degree)
Land cover (0.5 degree)
Validation (UAHMaster – GAF)title style Click to edit • Standard CEOS Validation protocol. • 250 Landsat-TM/ETM+ multitemporal pairs are being processed: – Temporal validation: study sites. – Spatial validation: stratified random sampling.
• Validation metrics: – Accuracy (agreement global-reference data). – Error balance (over-under estimation). – Temporal consistency.
Temporal Click tovalidation edit Master title style
Temporal Click tovalidation edit Master title style Canada Colombia Brazil Portugal Angola 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Light green, SLC-OFF
South Africa Kazachstan Russia Australia Borneo
Spatial Clickvalidation to edit Master title style
Generation of reference Click to edit Master perimeters title style • ABAMS: (Bastarika et al. 2011). Based on a twophase algorithm: – Seed detection. – Region-growing algorithm. – Includes multitemporal images.
• Results are visually reviewed and cross-check with another interpreter. • Standard documentation protocol (CEOS).
Examples fireMaster reference Click toof edit titledata style •
Canada
Examples Click to edit Master title style •
Brasil
Examples Click to edit Master title style pre
post
Angola
Fuzzy error matrix Click to edit Master title style Error matrix Reference data
Global product
commission
omission
true burned
true unburned
Burned Unburned
Global total
Burned
p11
p12
p1+
Unburned
p21
p22
p2+
Reference Total
p+1
p+2
p=1
Round ClickRobin to editresults Master title style • BA algorithms/products tend to underestimate (red areas), with exceptions (green areas)
Modeling with BAstyle data Click to exercises edit Master title • Monthly C emissions from biomass burning for the period of the ESA fire product • Carbon budgets and Vegetation dynamics (Orchidee). • Update Mouillot & Field 2005 historical database. • Estimating errors in existing historical C emissions reconstructions • Comparing regional and global estimations.
International Click to editscope Master title style • Critical phases are monitored by Key Science bodies. • Close connection with GOFC-GOLD Fire IT and CEOS Cal-Val. • Openness: Round Robin exercise. • Regional validation workshops: – Tropical: Brazil. – Boreal: ¿Russia?
Stresa R-R workshop (17-18 Click to edit Master title style October, 2011)
Main challenges of fire_CCI Click to edit Master title style • GCOS requirements very demanding. • Input data for BA mapping: – None of the input sensors (ATSR, VGT, MERIS) was designed for BA mapping. – Little experience with ESA sensors. • None for MERIS • Limited for VGT and ATSR (Globcarbon and L3JRC)
– Existing MODIS BA products (2000-2011).
• Time constrains, particularly for BA algorithms.
Fortitudes of fire_CCI Click to edit Master title style • Output product will combine three input sensors. • Validation, temporal and spatial datasets. • Uncertainty and error characterization. • Strong connections with climate and international science community (GOFC-GOLD Fire IT).
http://www.esa-fire-cci.org/ Click to edit Master title style
Burn severity Click to edit Master title style • Degree of post-fire disturbance. • Factors: – Previous biomass loads. – Fire behaviour (intensity and duration).
• Importance: – BS is critical for post-fire regeneration and soil degradation. – BS is an key factor in estimating gas emissions.
Temporal Click toscales edit Master title style ECOLOGICAL CONSECUENCES ON BIOPHYSICAL PRE-FIRE COMPONENTS IMMEDIATELY AFTER THE FIRE
DESCRIBE THE RECOVERY OF THE ECOSYSTEM FROM FIRE IMPACTS
From Key (2006)
FireClick behaviour variations to edit Master title style
Field methods Click to edit Master title style • Quantitative measurements: – – – – –
Depth of charcoal layer. Ash/charcoal proportion. Amount of dead species. Depth of soil organic layer affected. Thickness of the minimum branch left.
• Qualitative observations: – Visual estimations: ordinal ranks: low, medium, high. – Quantitative ranges, visually estimated: CBI (Key and Benson, 2005).
Remote Sensing methods Click to edit Master title style • Empirical models: – Collection of field samples. – Extraction of satellite information (after calibration). – Generation of statistical fittings.
• Simulation models: – Find a good model. – Provide sound input parameters (realistic scenarios). – Find a good inversion method.
• Empirical Clickmodels to edit
• Simulation Master titlemodels style N Cab Cw Cm
DIRECT 0.35
INVERSE
0.35 0.3
0.3 0.25 0.2
0.25 0.15 0.2 0.1 0.15 0.05 0.1 0 0.05
B3
0 B3
B4
B1
B2
B5
CBI 1.679 2.83dNDVI 9.574S
B6
B7
B4
B1
B2
B5
B6
B7
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Study case: Fire started on July 16, 2005 and it was caused by careslessness
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CBI method: strata
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Understory Plot Canopy
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D+E 5-30 m
B+C 1-5 m A Substrate
Models selected Click to edit Master title style • Leaf level: PROSPECT. • Canopy level: Kuusk. – Includes two vegetation layer + background. – Vegetation is assumed to be distributed homogeneously. • Reference: Kuusk, Journal of Quantitative Spectroscopy & Radiative Transfer 71 (2001): 1 –9
Model Clickoverview to edit Master title style Target CBI
PFA
N Cab Cw Cm
LAI s() PCC
LAI s()
l sl a
l sl a
s v v
s v v
SB
PROSPECT
() ()
Kuusk 2 Layer RTM
Fixed parameters Variables Model outputs
Direct mode Inverse mode
R(,s,v,v)
Variables theMaster simulation Click toinedit title style Strata
0.45
Variables
0.4 0.35
Substrate
Lineal Mixture of soil and char + ash spectra (SB)
Reflectividad
0.3
0 20
0.25
40 0.2
65 80
0.15 0.1 0.05 0 400
600
800
1000
1200
1400
1600
longitud de onda (nm )
Canopy
Understory
Percentage of Foliage Altered (PFA). Linear change from green to brown leaves
Percentage in Cover Change (PCC). Linear change of LAI
1800
2000
2200
2400
Model LUTstyle Clickimplementation: to edit Master title Canopy
Understory
Look up table
Final Substrate
Spectra
CBI B+C > CBI D+E: No canopy fires are more severe than
Simulated Spectra
understory fires.
CBI Filters
If CBI A = 3 then CBI B+C>2 regardless CBI D+E (Severe fires in the soil imply medium to heavy understory fires)
If CBI A < 3 then CBI B+C>CBI A (most commonly severity is higher in the understory than substrate)
LUTClick to Spectral Library title style to edit Master LUT
201 spectral bands
Spectral signatures for different CBI values
Convolution to fit target sensor (Landsat-TM)
Inversion SAM Click tocriterion: edit Master title style â&#x20AC;˘ Selects the LUT spectrum with the minimum angle to the target and assigns it the correspondent CBI value. â&#x20AC;˘ It is less sensitive to albedo variations than minimum distance. Reference spectrum
BAND 2
Spectral angle
BAND 1
Comparison with empirical results (De Click to edit Master model title style Santis and Chuvieco, 2007) R2= 0.66 Variables included: dNDVI+Sat Tendency to smooth CBI values Dr. Viegas
R2= 0.63 Supervised simulation
Empirical Simulation Click to-edit Master model title style 3.0 2.5 2.0 1.5 1.0 0.5 0.0 P-N7 P59
P46
P12
P86
P5
P11
CBI plot
P73
P45
P16
Supervised
Empirical fitting tends to smooth CBI range
P32
P71
Empirical
P49
P17
P96
P68
Problems CBI Click towith edit Master title style % OF DEAD LEAVES LITTER
CBI DOES NOT TAKE INTO ACCOUNT FCOV OF EACH VEGETATION STRATUM The results of RTM inversion suggest that both variables and their mixing effects are key factors of burn severity estimation from remotely sensed data.
GeoCBI Click to edit Master title style FIELD EXPERIENCE
SIMULATION ANALYSIS RESULTS
2 NEW VARIABLES PER STRATUM :
from 0 to 3 (for strata C, D and E ), as the original variables
% OF CHANGES IN THE LAI
0 to 1 (for strata B, C, D and E )
FCOV OF VEGETATION STRATA
WEIGHTING FACTOR
The new version of CBI proposed, called GeoCBI (which is short for Geometrically structured Composite Burn Index), was computed as follows: mn
GeoCBI
(CBI
m
* FCOVm )
m1 mn
FCOV
m
m1
where m is the identification of each stratum and n is the number of strata.
Click to edit Master title style
FIELD PLOTS
CBI
P95
GeoCBI
SPECTRAL SIGNATURES(TM)
2.7 0.25
P95 P86
REFLECTANCE
0.2
2.58 P86
2.85
P23 0.15 0.1 0.05 0 400
900
1400
WAVELENGTH (nm)
P23
2.8
1900
2400
New RTM Click to simulation edit Mastertools title style Model at leaf level: PROSPECT
Previous model at canopy level: Kuusk OVERSTORY UNDERSTORY SUBSTRATUM New model at canopy level: GeoSail
EASY TO COMPUTE (few inputs) SUCCESSFULLY APPLIED TO CONIFERS (Cheng et al., 2006; Kötz et al, 2003 y 2004; Zarco-Tejada et al., 2004)
EASY TO COMPUTE(few inputs) IT CAN SIMULATE SEVERAL VEG. LAYERS FITS WELL INTO THE STRUCTURE OF GeoCBI
Advantages of simulation models Click to edit Master title style • Interpretation is based on physical roots. • They are applicable everywhere (properly parametrized). • They can simulate a wide range of conditions (difficult to find in a single fire).
Other Clicksites to edit Master title style
De Santis and Chuvieco, 2009, RSE
From Burn Severity to Burning Click to edit Master title style efficiency Seiler and Crutzen [1980] model Mk,i = (BLi * BEi * BSi * AEk)*10-15 Mk,I = Emissions of gas k (Tg) BLi= Biomass loads (gr/m2) BEi = Burning efficiency (combustion completness) (0/1) BSi= Burned surface(m2) AEk= Emission factors (gr gas / Kg biomass)
BE is estimated from: • Standard coeffcients. • Fuel moisture content. • Remote sensing.
BE Click standard values to edit Master title style ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Land Cover Category Evergreen Needleleaf Woods Evergreen Broadleaf Forest Deciduous Needleleaf Wood Deciduous Broadleaf Woods Mixed Forest Woody Savanna (30-60% >2m) Savanna Trees (10-30%) Closed Shrubland (scrub) Open Shrubland (semidesert) Grassland Cropland Herbaceous & villages Barren deserts volcanos Urban/ suburban built-up Water +/- coastal Permanent wetlands Cropland/ grass-woods(Field-woods) Snow& Ice Tundra/ Paramo Woodlands trees (40-60%> 5)m Forest-Field Mix (40-60% woods) Mediterranean scrub
BE 0.250 0.250 0.250 0.250 0.250 0.350 0.400 0.500 0.950 0.950 0.800 0.200 0.100 0.100 0.500 0.400 0.000 0.300 0.400 0.300 0.700
Click to edit Master title style Emission estimate formula’s • Emission= Aburned x C x Eeff x Fload – – – –
Aburned is the area burned (retrieved) C is the combustion completeness (guessed) Eeff Emission Efficiency (guessed) Fload is the fuel load (computed with Biomass model)
• Emission = Efactor x
(Fire _ Radiative _ Energy).dt
Click to edit Master title style Martin J. Woosterâ&#x20AC;&#x2122;s results (in press)
BE Click fromto RSedit methods Master title style • Post-fire reflectance analysis: – BE from Burn Severity. – BE from simulation models.
• Energy released by the fire (FRP): – Instantaneous to total FRP. – Relation of FRP to biomass consumption.
BE Click fromto BSedit Master title style Local estimation of burned severity (RTM â&#x20AC;&#x201C; CBI)
BA map
Vegetation cover
Validation with Landsat TM
Regional estimation
Burning Severity
Max/Min values of BE BE Low
Med
Severe
Grass
0.85
0.9
0.98
Shrub
0.7
0.85
0.95
Conifer
0.25
0.42
0.57
Deciduous
0.25
0.4
0.56
Burning efficiency
Oliva and Chuvieco, 2012
Results Click to edit Master title style
Oliva and Chuvieco, 2012
Thank Clickyou!! to edit Master title style