Detection and prediction of lake degradation using landscape metrics and remote sensing dataset

Environmental Science and Pollution Research https://doi.org/10.1007/s11356-021-12522-8

RESEARCH ARTICLE

Detection and prediction of lake degradation using landscape metrics and remote sensing dataset Ali Azareh 1 & Elham Rafiei Sardooi 2 & Hamid Gholami 3 & Amirhosein Mosavi 4,5 & Ali Shahdadi 1 & Saeed Barkhori 2 Received: 4 December 2020 / Accepted: 13 January 2021 # The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021

Abstract Monitoring changes in natural ecosystems is considered essential to natural resource management. Despite the global importance of the lakes’ quality monitoring, there is currently a research gap in the simultaneous predictive modeling of lakes’ land-use changes and ecosystem measurements. In the present study for projecting the water bodies of lakes and their surrounding ecosystems, the land-use changes and the landscape analysis of different periods, i.e., 1987, 2002, 2018, and 2030, are studied using remote sensing data and various metrics. The trend of land-use and landscape changes is projected for 2030. The results indicate significant degradation of rangelands and forests due to the conversion to agriculture and construction and the declining trend of lakes’ water body and their transformation to salt lake and salt lands. The increase of agricultural lands and the overuse of groundwater wells upstream of the lakes could be one of the reasons for this decline. Decreasing the lakes’ water body and subsequently increasing salt lands are considered a severe threat to human health and the ecosystem services of the lakes. Besides, the dust generated by salt lands could also decrease crop yield in the study area.

Keywords Lake degradation . Land-use change . Landscape metrics . Remote sensing . Artificial intelligence . Machine learning

Introduction Land-use change is the most significant stimulus of environmental changes over time (Lambin et al. 2000; Nielsen and Zöbisch 2001; Jian et al. 2003; Wondie et al. 2011; Perring et al. 2016; Tolessa et al. 2017). In recent years, lakes are one of the most critical environmental factors that have been endangered and have been regarded as valuable water resources

affecting ecosystem components and biodiversity (Davies et al. 2007; Mendoza et al. 2011; Singh et al. 2017; Ren et al. 2019; Ansari and Golabi 2019). Climate change and continuous droughts, ignoring environmental water requirements of lakes because of dams, increasing agricultural lands, digging unauthorized wells, and the overuse of groundwater wells upstream of lakes have been considered as change principle drivers of lakes (Arsanjani et al. 2015; Sajedipour et al.

Responsible Editor: Philippe Garrigues * Elham Rafiei Sardooi ellrafiei@ujiroft.ac.ir

1

Department of Geography, University of Jiroft, Kerman, Iran

* Amirhosein Mosavi amirhosein.mosavi@tdtu.edu.vn

2

Department of Ecological Engineering, Faculty of Natural Resources, University of Jiroft, Kerman, Iran

3

Department of Range and Watershed Management, University of Hormozgan, Bandar Abbas, Hormozgan, Iran

4

Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

5

Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Ali Azareh aliazareh@ujiroft.ac.ir Hamid Gholami hgholami@hormozgan.ac.ir Ali Shahdadi alishahdadi@ujiroft.ac.ir Saeed Barkhori barkhori.s@ujiroft.ac.ir

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Environ Sci Pollut Res

2017; Ansari and Golabi 2019). Lake deterioration has ecological, environmental, and economic dimensions. Therefore, the monitoring, maintenance, restoration, and proper management of lakes’ ecosystems are essential. Remote sensing and geographic information system (GIS) are useful tools for monitoring and understanding change trends of lakes and surrounding ecosystems. Many studies have investigated change trend of land use and the ecosystem of lakes (e.g., Mushtaq and Pandey 2014; Scavia et al. 2014; Poikane et al. 2015; Wang et al. 2015; Dile et al. 2016; Mamat et al. 2016, Couture et al. 2018; Bucak et al. 2018; Hu et al. 2018; Chique et al. 2018; Hassen and Assen 2018; Jackson 2017).

Modeling and predicting tools have been developed to evaluate LULC changes in ecosystem functioning and help improve landscape management (Mas et al. 2014). There are many prediction models in use, such as Geographical Modeling (GEOMOD) (Pontius et al. 2001; Brown et al. 2007; Paudel and Yuan 2012; Novin and Khosravi 2017), Conversion of Land Use and its Effects (CLUE) (Zare et al. 2017; You et al.

2017; Mei et al. 2018; Mohammady et al. 2018), Cellular Automata Markov (CA-Markov) (Naboureh et al. 2017; Mosammam et al. 2017; Fu et al. 2018; Jahanishakib et al.

2018), and Land change Modeller (LCM) (Nagabhatla et al.

2012; Gibson et al. 2018; Oñate-Valdivieso and Sendra 2010; Shooshtarian et al. 2018; Johnson 2009; Romano et al. 2018; Sharma et al. 2018).

Using remotely sensed data and land-use change modeling is not sufficient for studying land-use changes. To this end, more quantitative analyses are essential to determine and evaluate the ecosystem changes using landscape metrics, a method broadly applied in landscape ecology (e.g., Peng et al. 2010; Asgarian et al. 2015; Peng et al. 2016; Cardille and Turner 2017; Inkoom et al. 2018; Kumar et al. 2018). Numerous spatial metrics are used for quantifying several spatial characteristics land-use scenarios, such as percentage of landscape (Fan and Myint 2014; Abrantes et al. 2016; Xu et al. 2019), shape complexity (Feng and Liu 2015; Mirghaed et al. 2018; Taubenböck et al. 2019), largest patch index (Rallings et al.

2019; Inkoom et al. 2018; Yang et al. 2019). Incorporating landscape metrics with remote sensing can be a useful tool to monitor land degradation and assess different structural dimensions of land use changes such as distribution, size, shape, heterogeneity, shape complexity, and fragmentation (Yu and Ng 2006; Ji et al. 2006; Fichera et al. 2012; Coelho et al. 2018; Liu and Yang 2015).

This study has attempted to highlight the utility of land use changes modeling that, when applied in combination with landscape metrics, is able to investigate the spatio-temporal change of lakes and their surrounding ecosystems in current and future periods. So far, no comprehensive research has been conducted on the simultaneous modeling of land-use changes and lakes’ land ecosystem measurements in future periods.

Given the global importance of lakes, there is a need to study their changes for future periods. Therefore, in the present study, due to the drying of lakes in Iran, especially in the Bakhtegan Maharloo area and its socio-economic and environmental consequences (Arsanjani et al. 2015; Esandari et al.

2016; Sajedipour et al. 2017), land-use changes and land landscape analysis of different periods, including 1987, 2002, 2018, and 2030, were studied using different landscape metrics with remote sensing dataset. Also, the trend of land use and land landscape changes was predicted in the future time horizon. In general, notice of the trend of land-use changes and the dynamics of the current and future ecosystem of lakes provide insights into prioritizing national and international decisions to rehabilitate lakes.

Material and methods Study area Bakhtegan-Maharloo basin located in southern Iran, in Fars Province within 29° 00′–31° 14′ N and 52° 10′–54° 40′ E, includes Bakhtegan, Tashk, and Maharloo Lakes, the most important lakes of southern Iran, and covers an area of 20,247.4 km2 (Fig. 1). Bakhtegan Lake is Iran’s secondlargest lake, and the Kor, and Sivand Rivers supply water to this lake, whereas Tashk Lake is fed by the marshes overflow in the west and a large permanent spring in its northwest.

Maharloo Lake is located in the west of these two lakes and is fed by Khoshk and Soltanabad rivers (Fig. 1). These lakes prepare essential wintering habitat for migratory birds.

In recent years, constructing several dams on the rivers Kor, Sivand, and Soltanabad had significantly decreased water flow into the Bakhtegan, Tashk, and Mahrloo lakes.

Besides, continuous droughts, land-use changes, and industrial agriculture have increased soil and water salinity.

Consequently, biodiversity had been significantly disrupted, which is apparent from the population’s drop of flamingos and other migratory birds. The rainfall regime of this basin is the Mediterranean. From 1987 to 2018, the average annual temperature and precipitation varied from −28 °C at mountains to 45 °C at plains and from 200 mm at the lowlands to 700 mm at elevations, respectively.

Land use change detection Landsat Multi-spectral Scanner (MSS), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images for 1987, 2002, and 2018 were used to assess land-use change trends. The supervised classification method of maximum likelihood (ML) was applied in ENVI 5.1 software to prepare the land use maps. Maximum likelihood classification is used in a variety of applications. In this method, a pixel is allocated to a

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Fig. 1 The location of the study area in Iran

class considering its likelihood of belonging to a specific class whose covariance and average are modeled as creating a normal distribution in multispectral feature space (Sisodia et al. 2014).

The training polygons were determined based on field visits and Google earth images. Then, all land uses in the study area were categorized into eight classes (rangeland, agricultural lands, forest lands, bare rock, waterbody, salt land, salt lake, and residential lands). Kappa coefficient was used for the accuracy assessment of classified maps. LCM under TerrSet software also adopted to monitor past land-use changes and predict future land-use change (2030). For predicting land use 2030, LCM was run under three scenarios, including scenario 1 (1987–2002), scenario 2 (2002–2018), and scenario 3(19872018) using multi-layer perceptron neural network (MLP) approach.

MLP neural network is a transition potential modeling tool that is provided in LCM and is trained based on the backpropagation algorithm by iteratively spreading the errors from the output layer to the input (Eastman and Toledano 2018; Kumar et al. 2015; Gibson et al. 2018; Mas et al. 2014).

A transition potential represents each pixel’s relative potential to transition from one state to another (Eastman and Toledano 2018). MLP models the transition potential of each land use (such as rangeland) to another land use (such as agriculture) based on explanatory spatial variables (such as slope angle, elevation), and finally, transition potential maps

are created. In this study, the transition potential modeling was performed based on the explanatory variables that were entered into each sub-model structure. The MLP neural network created pixels for each selected transition and then regulated the weights to increase accuracy as the RMSE (root mean square error) declines. Finally, for each scenario, six each transition sub-models were created as follows: rangeland to agriculture, forest to rangeland, forest to agriculture, salt lake to salt land, water bodies to salt lake, and water bodies to salt land.

In this study, for all scenarios, the six explanatory spatial variables comprising digital elevation model (DEM), slope aspect, slope angle, distance to the river, distance to the water bodies, and distance to the agricultural lands (see Fig. 2) were applied.

These variables have been widely used in land use modeling (Pérez-Vega et al. 2012; Megahed et al. 2015; Tajbakhsh et al. 2018). For example, proximity to water bodies and agricultural lands has been reported as essential variables in modeling land-use changes in the southern USA (Shrestha and Dwivedi 2017). Elevation, slope aspect, slope angle, and proximity to the rivers were also identified as essential factors to evaluate land-use changes in South Korea (Kim et al. 2011). Cramer’s V was applied to examine the level of relation between an explanatory spatial variable and each change recorded between two time periods (Gibson et al.

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Fig. 2 The explanatory variables used in LCM. a Digital elevation model (DEM). b Slope aspect. c Slope angle. d Distance to the river. e Distance to the water body. f Distance to the agricultural land

2018). Generally, the variables with a Cramer’s V of about 0.15 or higher are useful, while those with values of 0.4 or higher are good (Eastman 2016).

After the MLP was trained on all of the explanatory variables, a sensitivity analysis was performed to determine the most influential factors in modeling and test the relative power of explanatory variables and their effects on LCM output (Ferchichi et al. 2018). For this purpose, the model is firstly run with all variables. It then holds constant every variable, in turn, to characterize variables with the least and the most impact on skill measure and accuracy of the model (Eastman 2016). Skill measure is computed as the accuracy of transition prediction minus the accuracy expected by chance (Mas et al.

2014). If a variable is kept fixed and the skill and accuracy do not reduce much, it suggests that that variable has negligible effect and can be removed. Eliminating variables without power has the useful effect of decreasing the probability of overfitting (lack of generality). Therefore, it can sometimes happen that the model’s skill and accuracy slightly enhance as some of the variables are eliminated (Eastman 2016).

In the next step, Markov chain’s analysis was applied to determine the change quantity (change probability) for the

predicted period considered, which, together with potential transition maps, create land use map for the selected period (Eastman 2016). The transition probability matrix derived from the Markov chain analysis calculates a cross-tabulation of transitions between the land use maps of the two historical periods (e.g., 1987–2018). The land use map for 2018 was then simulated based on the change quantity obtained from a Markov chain. The mentioned steps were also performed for scenario 1 (1987–2002) and scenario 2 (2002–2018) and scenario 3 (1987–2018), separately.

Kappa statistics were applied to assess the effectiveness of the simulation and the agreement between ground truth and simulated land use maps of 2018 for all scenarios. The scenario with the highest kappa coefficients is selected for predicting land use 2030.

Kappa statistics range from −1 to 1, though it usually is between 0 and 1 where 1 shows a perfect agreement, and 0 shows that there is no agreement between a pair of maps.

Kappa statistic expresses the expected agreement between two categorical datasets when the given class is assigned accidentally over the map (van Vliet et al. 2011). Kappa indices used in this study are Kno, Klocation, Klocation Strata, and Kstandard,

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Environ Sci Pollut Res Table 1

The landscape metrics used in this study

Metrics

Range

CA (class area) NP (number of patches) LPI (largest patch index) PD (patch density) PLAND (percentage of landscape) LSI (landscape shape index) NLSI (normalized LSI)

CA≥0 NP≥1 0 < LPI≤100 PD≥0 0 <PLAND≤ 100 LSI≥1 0 < NLSI ≤100

Source: McGarigal et al. (2002)

where the Kno is kappa for no information (total agreement) and Klocation is the kappa for grid-cell level location (agreement due to location at the grid cell level), Klocation Strata is the kappa for the stratum-level site (agreement due to location at the stratified level), and kstandard is the kappa standard (agreement due to quantity).

Landscape metrics Land-use changes can be quantified using landscape metrics.

Landscape metrics describe and quantify the shape, geometrical properties, and distribution of landscape components and

are suitable tools to apply ecological principles in land use planning (Peng et al. 2010). These metrics can also be appropriate criteria to assess land degradation (Asgarian et al. 2015; Cardille and Turner 2017). Calculating landscape metrics is based on land use maps. To analyze and understand landscape metrics at the class level, calculating only one metric is not sufficient, and it must be paid to the analysis of several parameters (McGarigal 2017). Class level metrics are helpful in understanding landscape development (Yu et al. 2017).

Six class-level landscape metrics were chosen: total class area (CA), number of patches (NP), percentage of landscape (PLAND), patch density (PD), normalized landscape shape index (NLSI), and largest patch index (LPI). These six metrics were commonly adopted as useful indices of landscape change that have been widely applied in related studies (Asgarian et al.

2015; Cardille and Turner 2017; Yu et al. 2017). Based on the output land use maps from ArcGIS 10.2.2, these metrics were calculated using the most commonly applied landscape pattern analysis software, FRAGSTATS 3.3 (McGarigal et al. 2002).

CA indicates the sum of the areas (m2) of all patches of the considered class type. This metric shows how much of the landscape is consisted of a specific patch type. The class area is applied in the calculations for many of the class and landscape metrics (Mc Garigal and Marks 1995). NP (number of patches) is a simple measurement of the fragmentation extent of

Fig. 3 Flowchart of the study implementation procedure

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Fig. 4 Land use maps a 1987, b 2002, and c 2018

the patch type. NP = 1 when the landscape contains only 1 patch of the considered patch type, which is when the class includes a single patch (Matsushita et al. 2006; Weng 2007).

PLAND indicates the percentage of the landscape consisted of a specific patch type, in other words, the relative allotment of each class in the landscape (Xu et al. 2019; Sertel et al. 2018).

PD is similar to the number of patch index, except that it states the number of patches on a per unit area basis that prepares comparisons among landscapes with different sizes (McGarigal et al. 2002). LPI shows the ratio of the largest patch area to the investigated area (Chakraborti et al. 2018; Smiraglia et al. 2019). This metric quantifies the percentage of the total landscape area composed of the largest patch. NLSI is a normalized landscape shape index and prepares a simple measurement of class aggregation or clumpiness. NLSI = 0 when the landscape contains a single square and increases when the patch type is increasingly disaggregated (Aithal and Ramachandra 2016; TaubenbĂśck et al. 2019).

The used landscape metrics have been summarized in Table 1 (McGarigal et al. 2002; McGarigal et al. 2012;

McGarigal, 2015). Figure 3 shows the procedure of the study implementation.

Results and discussion Spatio-temporal changes of lakes and their surrounding ecosystems After pre-processing satellite images and applying the maximum likelihood classification method, the study areaâ&#x20AC;&#x2122;s land use map was prepared in three periods 1987, 2002, and 2018 Table 2 Accuracy of prepared land use maps

Overall accuracy

Kappa value

Year

86.7% 82.9% 92.4%

0.85 0.76 0.89

1987 2002 2018

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Environ Sci Pollut Res Table 3 Evaluation of MLP for transition potential modeling

Sub-model

Accuracy rate

Training RMSE

Testing RMSE

Rangeland to agriculture Forest to rangeland Forest to agriculture Salt lake to salt land Water bodies to salt lake

86.9% 83.4% 78.5% 62.71% 81.2%

0. 18 0.19 0.35 0.54 0.29

0.13 0.15 0.26 0.38 0.17

Water bodies to salt land

64.2%

0.62

0.41

(Fig. 4). Finally, land use maps were classified into eight classes: forest, rangeland, salt land, salt lake, waterbody, agricultural lands, residential lands, and bare rock.

Table 2 shows the accuracy of land use maps produced using the maximum likelihood classification method. Kappa values indicate the high accuracy of the land use maps produced.

Land use prediction using LCM First, transition potential modeling was performed using the MLP neural network. The three factors, accuracy rate, training RMSE, and testing RMSE, were applied to evaluate the transition potential modeling (Table 3). The results showed a high accuracy rate of MLP in all transition sub-models (62.71– 86.9%).

Table 4 shows the association between explanatory spatial variable and land use classes; the historic change was recorded between 1987 and 2018 based on Cramer’s V coefficient. The elevation is a variable with the most Cramer’s V and has the most relation with land-use changes between 1987 and 2018.

Generally, all of the variables have a Cramer’s V higher than 0.15 and are useful.

After training the MLP, a sensitivity analysis was done to characterize the most influential land change modeling factors. Figure 5 and Table 5 show the results of the sensitivity analysis of parameters. According to the results, the variable of distance to the water body (Var. 5) had the most effect on the model’s skill and accuracy. In other words, when the variable of distance to the water body was held constant, the skill and accuracy decreased much. Therefore, this variable was the most influential, followed by DEM (Var. 1), slope (Var.2), distance to the river (Var. 4), distance to agriculture (Var. 3).

Table 4 Cramer ’s V coefficient between explanatory variables and land use changes

Also, according to the results, aspect (Var.6) was the least influential (Table 5). So that, withholding this parameter constant, the skill and accuracy did not decrease much (Fig. 5).

In the next step, Markov chain analysis was used to assign the change quantity. The transition probability matrices determine the possibility of transition between land operates over time. The diameter data of transition probability demonstrate the probability of a land-use remaining the same, whereas the off-diagonal data indicate the transition probability from one land use to another (Guan et al. 2011). Table 6 shows transition possibilities derived from the Markov chain analysis between 1987 and 2018. According to the results, the probability of rangeland persisting is 55.6%, with the highest likelihood of pasture being lost is to agriculture (32.3%) and urban (10.1%). The rangelands tended to convert into urban areas and agriculture because urbanization is easier in rangelands than agricultural lands and forests due to the flat topography of rangelands (Sari 2020). The persistence probability of forest lands is 53.3%, with a transition probability to rangelands (35.1%) and agriculture lands (11.53%). The persistence probability of the water body is very low (4.47%) with a transition probability to the salt lake (60.68%) and salt land (34.85%). Water bodies were the most unstable class and experienced the highest decrease in comparison with other classes during 1987–2018. After 32 years, there is a 4.47% probability that the current water pixel will still remain water. Therefore, the water bodies tended to convert into salt lakes and salt land that could be due to climate change and human activities upstream of water bodies. The persisting possibility of bare rock and urban classes is 100%, and these lands showed unchanged and were the most stable classes. The urban lands in this area cannot be converted into other land-use

Variable number

Variable types

Overall Cramer ’s V coefficient

Var. 1 Var. 2 Var. 3 Var. 4 Var. 5 Var. 6

Digital elevation model (DEM) Slope Distance to the agriculture lands Distance to the rivers Distance to the water body Aspect

0.61 0.55 0.35 0.52 0.43 0.49

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Environ Sci Pollut Res Table 5 Sensitivity of model to forcing a single independent variable to be constant Model

Accuracy (%)

Skill measure

Influence order

With all variables Var. 1 constant Var. 2 constant Var. 3 constant

78.20 59.65 60.38 67.87

0.6160 0.4620 0.4717 0.5717

N/A 2 3 5

Var. 4 constant Var. 5 constant Var. 6 constant

66.89 34.90 78.28

0.5585 0.1320 0.6170

4 1 (most influential) 6 (least influential)

types and will remain unchanged. Also, the bare rock class (mountain) will be constant. This sounds rational because turning mountainous regions to other lands is not a simple and prevalent process (Nadoushan et al.

2015).

Then, the land use map for 2018 was simulated based on potential transition maps and change quantity derived from the Markov chain. The whole steps were also done for two other scenarios (1987–2002 and 2002–2018). The kappa statistics were used to determine the agreement between the simulated and the reference map in 2018 for all scenarios. These indices estimate the best fitting between two datasets.

According to Table 7, scenario 3 (1987–2018) has the highest kappa coefficients among two other scenarios. Also, relatively high values of kappa coefficients indicate the LCM model’s appropriate performance to simulate land-use in 2018 that is compatible with the findings of Oñate-Valdivieso and Sendra (2010), Johnson. (2009), Anand et al. (2018), Ansari and Golabi (2019), Jahanifar et al. (2018), Sardari et al. (2019), Shooshtarian et al. (2018), and Mehrabi et al. (2019).

Therefore, scenario 3 (1987–2018) was used for predicting land use 2030. Figure 6 shows land use maps calibrated for 2018 and predicted for 2030 using LCM.

Change trend study using LCM The results show that land uses of the study area have relatively significant changes. Figure 7 shows net changes of

different land use classes during the historical period of 1987–2018. As depicted in Fig. 7, the highest increase is related to agricultural lands 1669.1 km2, and the highest decrease is related to forest lands 2058.66 km2 and water bodies 904.46 km2. Also, the area of rangelands has been decreased by 241.82. The area of salt lands and salt lake and urban lands has been increased by 1261, 249.33, and 25.51 km2, respectively, and bare rocks have remained unchanged.

Table 8 and Fig. 8 show each land use area in 1987, 2002, 2018, and 2030. The results indicate that the area of agricultural lands, urban lands, salt lands, and the salt lake will be increased by 3992.4, 25.92, 1336.57, and 276.52 km2, respectively, the area of rangelands, forest lands, and water bodies will be decreased by 1742.31, 2958.99 and 930.11 km2, respectively, and bare rocks will be unchanged during 1987– 2030 (Table 8).

After studying land-use changes in different periods, landscape metrics were used to quantify the changes in lakes and their ecosystems. Table 9 shows the class-level landscape metrics in the study area during 1987–2030. Also, the changes in landscape metrics (NLSI, NP, PLAND, and LPI) in different land uses have been shown in Fig. 9. According to results, rising NP, PD, and NLSI along with falling CA, PLAND, and LPI values for rangelands and forests indicate the dissociation and destruction of these lands due to the conversion to agriculture and residential lands during 1987–2030 which is similar to the results of Bateni et al. (2012) and Jaafari et al.

(2016). The increase of CA, PLAND, and LPI values and the decrease of NLSI for agricultural and residential lands are due to the increasing demand for food production and construction during 1987–2018. Also, NP and PD values of these lands have increased in this period. This trend will also be continued by 2030. Increasing NP and PD of agricultural and residential lands despite growing class area (CA) indicates patchy development of these land uses.

Decreasing NP, PD, and NLSI and increasing CA, PLAND, and LPI for salt lands and salt lake show the growing trend of these lands. Falling CA, PLAND, and LPI values of water bodies indicate that polygon area of habitat is decreasing. Large water bodies have been converted to salt lands and salt lake land between 1987 and 2018. If this trend continues,

Fig. 5 Variation in model skill forcing a single independent variable to be constant

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Environ Sci Pollut Res Table 6 Matrix of transition probability from 1987 to 2018

Table 7 The kappa statistics of agreement between the simulated and the reference map of 2018 based on different scenarios

Land use

Rangeland

Forest

Agriculture

Salt land

Salt lake

Water body

Bare rock

Urban

Rangeland Forest Agriculture Salt land Salt lake Water body Bare rock Urban

0.5563 0.351 0 0 0 0 0 0

0 0.5329 0 0 0 0 0 0

0.3232 0.1153 0.9074 0 0 0 0 0

0.0188 0 0.0923 0.9972 0.6849 0.3485 0 0

0 0 0 0.0028 0.3151 0.6068 0 0

0 0 0 0 0 0.0447 0 0

0 0 0 0 0 0 1 0

0.1017 0.0008 0.0003 0 0 0 0 1

Statistics

Scenario 1 (1987–2002)

Scenario 2 (2002–2018)

Scenario 3 (1987–2018)

Kno Klocation Klocation Strata Kstandard

0.92 0.91 0.90 0.85

0.94 0.96 0.95 0.87

0.97 0.98 0.98 0.90

water bodies will be disappeared in 2030. All metrics for bare rocks are constant, indicating unchanged in this class type during 1987–2030.

Results revealed the high performance of CA, NP, PD, NLS, LPI, and PLAND metrics which is in accordance with findings of Fan and Myint (2014), Xu et al. (2019), Sertel et al.

(2018), Abrantes et al. (2016), Abdullah and Nakagoshi (2006), Yu et al. (2017), Matsushita et al. (2006), Weng

(2007), and Lausch and Herzog (2002). Also, regarding to the results, the appropriate performance of LPI and NLSI metrics are in line with findings of Herzog et al. (2001), De Barros Ferraz et al. (2005), Matsushita et al. (2006), Peng et al.

(2010), Ramachandra et al. (2012), Fan and Myint (2014), Feng and Liu (2015), Aithal and Ramachandra (2016), Jaafari et al. (2016), Chakraborti et al. (2018), Elif et al.

(2018), Chakraborti et al. (2018), Zhang et al. (2019), Yang

Fig. 6 Land use/land cover map calibrated for 2018 and predicted for 2030 using LCM

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Environ Sci Pollut Res Fig. 7 Net changes in land use between 1987 and 2018

Table 8

The area of land uses in 1987, 2002, 2018, and 2030

Land use

Agriculture Forest Rangeland Bare rock Salt lake Salt land Urban Waterbody Total area

1987

2002

2018

2030

km2

%

km2

%

km2

%

km2

%

4146.7 4421.3 9905.7 111.9 295.9 311.6 119.1 935.6 20247.7

20.5 21.8 48.9 0.6 1.5 1.5 0.6 4.6 100

5208.8 3200.9 9732.2 111.9 482.9 1106.0 136.6 268.5 20247.7

25.7 15.8 48.1 0.6 2.4 5.5 0.7 1.3 100

5815.8 2362.7 9663.8 111.9 545.3 1572.6 144.6 31.1 20247.7

28.7 11.7 47.7 0.6 2.7 7.8 0.7 0.2 100

8139.1 1462.3 8163.3 111.9 572.5 1648.2 145.0 5.5 20247.7

40.2 7.2 40.3 0.6 2.8 8.1 0.7 0.03 100

2002

2018

2030

Fig. 8 The area of each land use in different years

1987 12000

Area (km2)

10000 8000 6000 4000 2000 0

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Environ Sci Pollut Res Table 9

Class level landscape metrics in the study area

Land use

Landscape metrics CA

PLAND LPI%

NP PD

NLSI

Urban

1990 2003 2016 2030 Salt lake 1990 2003 2016 2030 Agriculture 1990 2003 2016 2030 Salt land 1990 2003

11570.32 13549.46 14120.36 14125.33 29839.25 48450.72 54654.55 57052.34 412763.6 516325.6 568201.8 823928.7 55834.41 101925.4

0.31 0.36 0.38 0.42 0.8 1.3 1.47 1.54 11.15 13.94 15.35 22.22 1.5 2.75

0.29 0.32 0.36 0.39 0.52 0.53 0.61 1.09 7.54 10.13 10.69 16.93 0.44 1.68

6 9 10 10 17 11 6 4 130 152 174 450 30 29

0.0002 0.0002 0.0003 0.0003 0.0005 0.0003 0.0002 0.0001 0.0122 0.0035 0.0041 0.0047 0.00097 0.00081

0.197 0.184 0.176 0.143 0.13 0.12 0.08 0.07 0.157 0.148 0.142 0.136 0.21 0.17

2016 2030 Bare rock 1990 2003 2016 2030 Rangeland 1990 2003 2016 2030 Forest 1990 2003 2016 2030 Water body 1990 2003 2016 2030

133172.9 164724.8 10847.18 10847.18 10847.18 10847.18 1004563 977996.7 969699.5 817001.7 442108.1 321228.7 235935.6 146303.7 93095.42 27060.85 3235.12 494.78

3.59 4.45 0.29 0.29 0.29 0.29 27.14 26.42 26.19 22.07 11.94 8.67 6.37 3.95 2.51 0.73 0.08 0.01

3.42 4.09 0.1069 0.1069 0.1069 0.1069 20.3 19.25 10.41 7.76 2.6 1.72 1.39 1.09 1.04 0.43 0.05 0.01

21 12 10 10 10 10 348 379 466 496 81 106 107 168 3 2 2 1

0.00063 0.00035 0.0003 0.0003 0.0003 0.0003 0.0094 0.0102 0.0126 0.088 0.0022 0.0027 0.0029 0.0045 0.0001 0.0001 0.0001 0

0.14 0.12 0.29 0.29 0.29 0.29 0.13 0.14 0.15 0.16 0.17 0.18 0.22 0.24 0.06 0.06 0.06 0.06

et al. (2019), Smiraglia et al. (2019), Smiraglia et al. (2019), Sertel, et al. (2018), Inkoom et al. (2018), Taubenböck et al.

(2019), and Rallings et al. (2019).

The results showed that the combination of land-use modeling and landscape metrics, especially in the future, is an efficient tool to study the trend of land degradation and provide useful information for policymakers.

Conclusion Predicting the changes of water bodies of lakes and their surroundings regions, quantifying these changes,

and revealing the spatial pattern through landscape metrics indicate different processes of lake degradation in different places and periods within southern Iran. In recent years, land-use changes in the upstream of lakes and ignoring environmental water rights have led to a decrease in lakes’ water bodies. Relying only on realtime monitoring and quantifying land-use changes are not sufficient for risk management. The projection for one or several decades is essential for policymaking.

Thus, predictive modeling of the ecosystems’ future through the proposed methodology in this study will help policymakers making informed decisions to prevent further land degradation in the future. Investigating land-use changes during 1987–2018 revealed increasing agricultural lands, residential lands, salt lands, and salt lakes and decreasing water bodies, forests and rangelands, and bare rocks’ stability. Also, land-use2030 predicted by LCM reflects the same trend.

Evaluating landscape metrics in the study area during 1987–2030 showed decreasing CA, PLAND, and LPI and increasing NP and PD and NLSI for rangelands and forests. This shows that these lands have been degraded and fragmented into smaller patches due to the conversion of rangelands to agriculture and residential lands and the conversion of forests to rangelands and agriculture lands. This trend continues throughout 2030.

The results of landscape metrics for agricultural and residential lands indicated patchy development of these land uses. Agriculture is a semi-natural land use and its increasing trend indicates the increase of human intervention in the study area. Agriculture and construction are the main drivers of change, showing that the economy is the most significant land-use change factor.

Landscape metrics values of salt lands and salt lake showed the increase of these lands area. Investigating landscape metrics of water bodies showed decreasing water bodies area. Large areas of water bodies have been converted to salt lands and salt lake land between 1987 and 2018. Providing continuing trends, water bodies of lakes will be completely dried up and disappeared in 2030. According to results, the number of patches (NP) and patch density (PD) for water bodies has declined since water bodies are disappearing. As a result, NP and PD indices for assessing land degradation should not be examined alone and should be investigated along with other indices such as CA, PLAND, and LPI. The factors such as constructing dams without considering environmental issues, continuous droughts, increasing agricultural lands, digging unauthorized wells, and the overuse of groundwater wells in upstream of Tashk, Maharloo, and Bakhtegan lakes have affected the water body of lakes in the study area.

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Environ Sci Pollut Res

Waterbody 1.2

PLAND

1 0.8

LPI%

2.5

PLAND

2

0.6

1.5

0.4

1

0.2

0.5

0

0 1987

2002

2018

2030

Rangeland

Rangeland 30

600

20

500

0.18

NP

15 15

0.12

400

0.1 300 0.08

10 200

NP

100

NLSI

5

LPI%

0.04 0.02

0

0

1987

2002

2018

0

0

2030

1987

2002

2018

2030

Agriculture

Agriculture 25

500

20

20

10

300

0.145

200

0.14

NP

10

0.155 0.15

LPI%

15

0.16

400

15

PLAND

5

5

0.135 NP

100

LPI%

0

NLSI

0

1987

2002

2018

0

2030

2002

3

10

LPI%

2.5

8

2

6

1.5

4

1

2

0.5

0

0

2018

2030

NP

PLAND

LPI%

12

2002

2018

2030

Forest 3.5

1987

0.13 0.125

1987

Forest 14

PLAND

0.06

NLSI

5

PLAND

180 160 140 120 100 80 60 40 20 0

0.3

0.25 0.2 0.15

NLSI

10

NLSI

0.14

20 LPI%

PLAND

25

0.16

25

PLAND

LPI%

3

0.1

NP NLSI

0.05

0

1987

2002

2018

2030

Fig. 9 The changes of landscape metrics (NLSI, NP, PLAND, and LPI) quantitatively in different land uses

Decreasing the water body area of Bakhtegan, Tashk, and Maharloo lakes is considered a serious threat to human health and other local creatures, especially migratory birds, because of

destroying lake ecosystem services, converting lakes to salt lands and salt lake, and subsequently producing salt landderived dust. Besides, the dust generated by salt lands could

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Environ Sci Pollut Res

0.4

0.3

0.3

0.2

0.2 PLAND

0.25

10

0.2 0.15

6

0.1

4

NP

2

NLSI

LPI%

2002

2018

0

1987

2030

Salt lake 2

1

0.6 PLAND LPI%

0

10 8

0.06

0.4

6

0.2

4

0.04 0.02

2 0

2030

0

1987

3 2 PLAND

LPI%

0

2018

4.5 4 3.5 3 2.5 2 1.5 1 0.5 0

2030

NP

PLAND

4

2002

2018

2030

35

LPI%

5

1987

2002

Salt land

Salt land

1

0.1

0.08

0

2018

0.12

NLSI

12

NP

0.8

NP

14

LPI%

PLAND

1 1.5

0.14

16

1.2

2002

2030

18

1.4

1987

2018

Salt lake

2.5

0.5

2002

NLSI

1987

0.05

0

0

0

0.1

0.25

30

NP

25

NLSI

0.2

20

0.15

15

0.1

NLSI

0.1

12

8 NP

0.4

NLSI

Urban

LPI%

PLAND

Urban

10 0.05

5

0

0

1987

2002

2018

2030

Fig. 9 (continued)

also be effective in reducing crop yield in the study area.

Consequently, the maintenance and restoration of the lake ecosystem are essential. The results indicated high accuracy of satellite images, land-use change modeling, and landscape metrics to analyze land-use changes. Decreasing rangelands, water bodies, and forests and increasing agricultural lands, residential lands, salt lands, and salt lakes indicated extensive land degradation in the study area. This study may prepare useful information to develop sustainable and appropriate conservation plans for the lakes and their surrounding ecosystems.

Acknowledgements This study was supported by the University of Jiroft under grant No 7824-97-2.

Authors’ contributions Conceptualization, AA and ERS; data preparation, ERS; formal analysis, AA, HG, and AM; investigation, AA, HG, and AS; methodology, AA and ERS; project administration, AA and ERS; supervision, AA and AM; validation, SB; visualization, AS and SB; writing–original draft, AA, ERS, and HG; writing–review and editing, AM. Data availability Not applicable.

Compliance with ethical standards Ethics approval Not applicable.

Consent to participate Not applicable.

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Environ Sci Pollut Res Consent to publish Not applicable.

Conflict of interest The authors declare that they have no conflict of interest.

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