The Effect of Land Use and Land Cover Changes on Soil Erosion in Semi-arid Areas Using Cloud-based Google Earth Engine Platform and GIS-based RUSLE Model

The Effect of Land Use and Land Cover Changes on Soil Erosion in Semi-arid Areas Using Cloud-based Google Earth Engine Platform and GIS-based RUSLE Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Effect of Land Use and Land Cover Changes on Soil Erosion in Semi-arid Areas Using Cloud-based Google Earth Engine Platform and GIS-based RUSLE Model Maryam Nourizadeh, Hamed Naghavi, Ebrahim Omidvar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3131140/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jan, 2024 Read the published version in Natural Hazards → Version 1 posted 4 You are reading this latest preprint version Abstract Soil erosion has recently attracted the attention of researchers and managers as an environmental crisis. One of the effective factors in soil erosion is land use/land cover change (LU/LCC). Use of satellite imagery is a method for generating LU/LCC maps. Recently, Google has launched the cloud-based Google Earth Engine (GEE) platform, which enabled the processing of satellite images online. Accordingly, the purpose of the present study is to investigate the effect of LU/LCC on soil erosion in a semi-arid region in the south-west of Iran. LU/LCC map was prepared over a period of 30 years (1989–2019) using a new approach and classification of the Normalized Difference Vegetation Index (NDVI) index time series on the GEE. For classifying the NDVI time series, a non-parametric Support Vector Machine (SVM) classification method was employed. The LU/LC maps were also used as an input factor in the soil erosion estimation model. The amount of soil erosion in the region was estimated using the Revised Universal Soil Loss Equation (RUSLE) empirical model in the Geographical Information System (GIS) environment. Validation of LU/LC maps generated in GEE indicated overall accuracy higher than 86% and the kappa coefficient higher than 0.82. The study of LU/LCC trends showed that the area of ​​forests, pastures, and rock outcrop in the region has diminished, but the area of ​​agricultural and man-made LUs has been expanded. Also, the highest rate of LU/LC conversion was related to the conversion of forests to agricultural lands. Estimating the amount of soil erosion in the region using the RUSLE model revealed that the average annual erosion in 1989 and 2019 was 15.48 and 20.41 tons per hectare, respectively, which indicates an increase of 4.93 tons in hectares, while the hot spots of erosion in the area have increased at the confidence levels of 90, 95, and 99%. Matching the LU/LCC map with the soil erosion map indicated that the degradation of forests and their conversion to agricultural lands had the greatest impact on increasing soil erosion. Based on the findings, we can conclude that GEE, as an online platform, has a high capability in preparing LU/LC maps and other effective factors in soil erosion estimation models. Satellite Images NDVI SVM Empirical Models Time series Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Soil is one of the non-renewable natural resources, which as an environment for the growth of plants and agricultural products, provides about 95% of human nutritional demands both directly and indirectly (FAO, 2015 ; Saha et al., 2022 ). For this reason, any factor that causes destruction of this vital layer of the earth's surface is considered a threat to the lives of humans and other organisms. On a global scale, there are various processes for land degradation, of which soil erosion is one of the most important issues (Prăvălie, 2021 ; Thomaz et al., 2022 ). According to the report of the Food and Agriculture Organization of the United Nations (FAO), the process of soil erosion is worsening in the continents of Asia, Africa, and Latin America (Pennock, 2019 ; Admas et al., 2022 ). Considering the increase in soil erosion and its negative impact on soil fertility, production of agricultural products, quality of water resources, the capacity of dams, quality of the environment, animal habitat, food security of living organisms, carbon sequestration cycle, and other ecosystem services, this phenomenon is considered as a complex and dynamic global environmental issue and attracted the attention of researchers, managers, and governments (Alkharabsheh et al., 2013 ; Aiello et al., 2015 ; Singh and Panda, 2017 ; Barman et al., 2020 ; Admas et al., 2022 ; Bag et al., 2022 ; Gong et al., 2022 ; Senanayake et al., 2022 ). Today, it has been proven that various factors such as climate change, LU/LCC, population increase, soil characteristics, physiographic and geomorphological characteristics of the watershed, land management systems, rainfall, human factors, and deforestation influence the amount of soil erosion in different areas (Aiello et al., 2015 ; El Jazouli et al., 2019 ; Becker et al., 2021 ; Gong et al., 2022 ; Senanayake et al., 2022 ). Managing and controlling soil erosion requires access to up-to-date data as well as estimating and predicting its amount in different areas. Nowadays, due to the costly and time-consuming soil erosion measurement using traditional and field methods, the use of soil erosion estimation models has become popular (Alkharabsheh et al., 2013 ; Barman et al., 2020 ). In general, the models for estimating soil erosion can be classified into three categories: empirical, physical, and conceptual models (Ganasri and Ramesh, 2016 ; Singh and Panda, 2017 ). One of the widely used empirical models is the Universal Soil Loss Equation (USLE) model and its revised version called RUSLE, which are used globally (Ganasri and Ramesh, 2016 ; Barman et al., 2020 ). The advantages of using the RUSLE model include no need for complex input data, simple and comprehensible structure, suitability for a regional scale, and implementability in forests, pasture, agricultural, and man-made areas (Zhang et al., 2006 ; Uddin et al., 2018 ; Barman et al., 2020 ). In addition, the RUSLE model, with the potential to be implemented in the GIS environment and the use of remote sensing data, allows for spatial and temporal estimation of the soil erosion rate in different areas (Barman et al., 2020 ; Gong et al., 2022 ). As mentioned, LU/LCC is an effective and important factor in soil erosion rate in different regions. For this reason, access to the map of LU/LCC is a key point in identifying the critical point of soil erosion and its sustainable management. Nowadays, the use of satellite imagery has become a common method for generating LU/LC maps, with advantages such as wide coverage, access to images at different times, and access to information on inaccessible areas (Naghavi et al ., 2013; Adam et al., 2016 ; Naseri et al., 2019 ; Prasai et al., 2021 ; Faruque et al., 2022 ; Kuma et al., 2022 ). Satellite images with different spatial resolutions can be used to prepare the LU/LC map. Use of high spatial resolution satellite imagery can boost the accuracy of LU/LC maps (Luo and Ji, 2022 ), but application of these images is usually costly, which is a challenge in projects with a limited budget in developing and underdeveloped countries. For this reason, the use of images from the Landsat satellites with medium spatial resolution has been developed in these areas, which allows free access to their image archives from previous decades (Cohen and Goward, 2004 ; Delfan et al., 2020 ; Ang et al., 2021 ; Ding et al., 2022 ). One of the challenges that researchers face in processing satellite images is the time-consuming processing of time series images as well as images related to large areas. In 2010, Google launched an advanced cloud-based platform called GEE for the online processing of satellite images (Ang et al., 2021 ; Peng and Dai, 2022 ; Nghia et al., 2022 ). This cloud space, while providing access to a huge archive of satellite images, eliminates the limitations of traditional methods and offers users the possibility of online processing of time series of satellite images with a large volume using millions of servers around the world (Gemitzi and Koutsias, 2022 ). Given the development of this platform in recent years and the mentioned advantages, its use is growing, and many researchers including Huang et al., ( 2017 ), Zurqani et al., ( 2018 ), Ghorbanian et al., ( 2020 ), Prasai et al., ( 2021 ), Ang et al., ( 2021 ), and Becker et al., ( 2021 ) have employed this cloud-based platform to prepare LU/LC maps. In order to estimate the soil erosion using satellite images and the RUSLE model, various studies have been conducted by researchers worldwide. Wang and Zhao ( 2020 ), estimated the amount of soil erosion in the Tahoe River area of ​​China in 2005, 2010, 2015, and 2018 as 1424, 1195, 1129, 1099, and 1124 ton. ha − 1 . year − 1 , respectively. Enashar et al ., (2021) estimated the average soil erosion in the middle, upper, and lower parts of the Blue Nile basin in Ethiopia as 39.73, 57.98, and 6.40 ton. ha − 1 . year − 1 , respectively. Petito et al., ( 2022 ) investigated the impact of conservation agriculture on soil erosion in the south of Italy and concluded that the area of soil erosion in the conservation agriculture system has diminished compared to traditional management. In all three mentioned studies, the RUSLE model and the GEE were used to estimate soil erosion. Also, researchers such as Alkharabsheh et al., ( 2013 ), Uddin et al., ( 2018 ), El Jazouli et al., ( 2019 ), and Gong et al., ( 2022 ) investigated the impact of LU/LCC on soil erosion in different areas using RUSLE model and satellite imagery. Considering the importance of soil erosion, the purpose of this research is to examine the effect of LU/LCC on soil erosion in a semi-arid watershed using the GEE platform and GIS-based RUSLE model. In this way, the LU/LCC map was generated in a 30-year period using a new approach, by combining the time series of the NDVI spectral index related to each year and classifying it via the non-parametric SVM classification method in the GEE. Then, its effect on the rate of soil erosion changes was investigated using the RUSLE model. 2. Materials and Methods 2.1. Study Area Khorramabad watershed with an area of 1608.22 km 2 is located in the geographical coordinate range of \(48^\circ {4}^{{\prime }}37”\) to \(48^\circ {46}^{{\prime }}37”\) east longitude and \(33^\circ {15}^{{\prime }}16"\) to \(33^\circ 43{\prime }52"\) north latitude in Lorestan and south-west of Iran (Fig. 1 ). This region has a semi-arid climate with an average annual temperature of 15 \(℃\) and an average annual rainfall of 405 mm. The minimum, maximum, and average altitudes of the area are 1174, 3000, and 1695.3 m above sea level, respectively. Also, the average slope of the watershed is 24.36% (Mohammadlou and Zeinivand, 2019 ). The main LU/LC in the region includes oak forests, agricultural lands, pastures, and man-made areas. 2.2. Methods 2.2.1. Preparing LU/LCC Maps Using GEE In order to prepare the LU/LCC maps, the time series of Operational Land Imager (OLI) sensor images of Landsat 8 satellite related to 2019 and the time series of Thematic Mapper (TM) sensor images of Landsat 5 satellite related to 1989 were utilized ( https://earthengine.google.com ). For this purpose, each year was divided into three periods of four months where the images of each period were called in GEE and clip using the region border vector layer. Then, all images were converted to NDVI vegetation index using Eq. 1 , and all NDVI values ​​of each period were converted into one band through the Maximum Value Composite (MVC) function (Eq. 2 ) (Huang et al., 2017 ). Finally, an image containing three bands for each year was generated, with the values ​​of each band of this image showing the maximum value of the NDVI in each four-month period (Fig. 2 ) (Ahrari, 2020 ). Next, it was necessary to introduce training samples to GEE for supervised classification. In this regard, the samples were randomly collected in different classes, including forest, pasture, man-made, agricultural, and rock outcrop, using field data, aerial photos, and GEE. Overall, 3000 samples were collected for each year, of which 1000, 1000, 600, 200, and 200 samples were related to forest, agriculture, pasture, man-made, and rock outcrop parts, respectively. Also, 70% of the samples were used for classification, and the remaining 30% were employed to evaluate the accuracy of the classification results. $$NDVI=\frac{{\rho }_{NIR}-{\rho }_{R}}{{\rho }_{NIR}+{\rho }_{R}}$$ 1 Where ρ NIR is the reflectance of near-infrared band and, ρ Red is the reflectance of red band. $$MVC=\text{max}{\left(NDVI\right)}_{i}^{j}$$ 2 Where i is the earliest scene and j is the last image acquired in a given four-months period. Today, different classification methods are used for classifying satellite images. The SVM method is a non-parametric regression and classification method used by researchers, which does not have the limitations of parametric statistical methods. This method was introduced by Vapnik ( 1999 ), and today it has many applications in remote sensing (Mountrakis et al., 2011 ; Pourghasemi et al., 2020 ). This method tries to find an optimal separating hyperplane that can separate classes (Kalantar et al., 2018 ). The SVMs are applicable based on linear, polynomial, radial basis function (RBF), as well as sigmoid kernels, where the selection of each of these kernels affects the accuracy of the obtained outputs (Bag et al., 2022 ). In this research, image classification was done using the SVM algorithm and RBF kernel on GEE. Finally, to evaluate the accuracy of classification results using test samples, the kappa coefficient, overall accuracy, user accuracy, and producer accuracy were calculated. Finally, the classified maps in this stage were used to investigate the spatiotemporal LU/LCC in the 30-year period, as well as to estimate the cover management factor (C-factor) in the RUSLE model. 2.2.2. Estimation of Soil Erosion Rate Using RUSLE Model In this study, the RUSLE model was used to calculate annual soil erosion. This model uses the following equation (Renard, 1997 ): $$A=R\times K\times LS\times C\times P$$ 3 Where A is the average annual soil loss (ton. ha − 1.y − 1 ), R denotes the rainfall erosivity factor (MJ.mm.ha − 1 .h − 1 .y − 1 ), K shows the soil erodibility factor (ton.h.MJ − 1 .mm − 1 ), LS reflects the slope length factor (unitless), C represents the cover management factor (unitless), and P is the support practice factor (unitless). The five factors of the RUSLE model were calculated as follows. 2.2.2.1. R-factor In this study, the R factor was calculated based on Fournier's index using the monthly and annual rainfall data of 17 rain gauge stations. The rain data were obtained from the statistics recorded by the Ministry of Energy and the Iranian Meteorological Organization during a 30-year period (1989–2019). In order to calculate the R-factor, Fournier's index was applied based on the following equations (Renard and Ferreira, 1993 ): $$F=\frac{\sum _{i=1}^{12}{p}_{i}^{2}}{\sum _{i=1}^{12}p}$$ 4 \(R=(95.77-6.081+0.4778{F}^{2})/17.2\) F ≥ 55 (5) \(R=(0.07397\times {F}^{1.847})/17.2\) F < 55 (6) Where R represents the erosivity of rain (MJ.mm. ha − 1.h − 1 . y − 1 ), p i denotes the average rainfall (mm) in month i, p is the average annual rainfall (mm), and F shows the Fournier index. The R-factor map of the Khorramabad watershed was prepared by interpolation of the R-factor values ​​in the stations using the inverse distance weighting (IDW) method. The IDW interpolation method is based on the assumption that the estimated value of a point is more influenced by known nearby points than distant points (Weber and Englund, 1992 ). The IDW method was chosen, since in this method the effect of the R-factor measured at the station points is considered very important and during the interpolation process weights are determined for the station points. Thus, as the distance from the point increases, the value of the R-factor decreases (Weber and Englund, 1994 ; Belasri and Lakhouili, 2016 ). 2.2.2.2. K-factor For calculating the K factor, the equation proposed by Renard ( 1997 ) was used for limited data. This equation suggested by Römkens et al., ( 1997 ) for calculation of K-factor is as follows: $$K=0.0034+0.0405\text{exp}\left[-0.5 {\left(\frac{\text{log}\left(Dg\right)1.659}{0.7101}\right)}^{2} \right]$$ 7 $$Dg\left(mm\right)=\text{e}\text{x}\text{p}\left(0.01\sum {f}_{i}ln{m}_{i}\right)$$ 8 Where, K is the soil erodibility factor (ton.h.MJ − 1 .mm − 1 ), m i denotes the average diameter of clay, silt and sand (mm), f i indicates the percentage of each component of silt, clay, and sand in the soil sample, and Dg represents the geometric mean diameter of the soil particles. In this study, the information related to the characteristics of soil granularity was used to provide the K factor map, which was prepared in previous studies by the General Department of Natural Resources, the Research and Agriculture and Natural Resources Center of Lorestan Province, and the Faculty of Agriculture and Natural Resources of Lorestan University. For this purpose, first the value of K factor was calculated in the sampled points using Eq. 7 . Then, the characteristics related to the variogram of the K factor values ​​in different points were calculated in the GS + 9 software and its information was imported to the ArcGIS 10.8 software, with the corresponding map prepared using the Kriging method (Kavian et al ., 2011; Fallah et al., 2016 ). 2.2.2.3. LS-factor In this study, in order to prepare the LS factor map in Khorramabad watershed, the digital elevation model (DEM) map of Aster sensor with 30-meter pixel was employed. The LS-factor was prepared based on the method provided by Desmet and Govers, ( 1996 ) in the SAGA GIS 6 software. 2.2.2.4. C-factor In the RUSLE model, the C-factor is usually determined based on empirical equations (Ochoa-cueva et al., 2015 ). According to past research, the vegetation map, and the conditions of the study area, the values ​​of the C-factor, according to Table 1 , are assigned to each LU/LC (Dabral et al., 2008 ; Ochoa-cueva et al., 2015 ; Panagos et al., 2015 ; Rawat et al., 2016 ). Thus, two C-factor maps were produced based on the LU/LC maps prepared for the years 1989 and 2019. Table 1 C-factor value based on LU/LC. LU/LC C-factor Man-made 0.002 Forest 0.013 Agriculture 0.35 Rock 1 Pasture 0.2 2.2.2.5. P-factor Wischmeier and Smith ( 1978 ) have presented Table 2 in order to estimate the P-factor through different slopes. As such, first, using the DEM map, the slope map of the study area was prepared based on which the P-factor map was provided (Teng et al., 2016 ). Table 2 P-factor in different slopes. Slope (%) P-factor 3> 0.6 3–6 0.5 6–9 0.5 9–12 0.6 12–15 0.7 15–20 0.8 20–25 0.9 > 25 1 2.2.2.6. Combination of Layers and Preparation of Soil Erosion Map After generating all layers related to the RUSLE model factors with the same pixel size (30×30 meters), according to Eq. 3 and using the Raster calculator tool in the Arc GIS 10.8 software, the layers were multiplied together and the soil erosion map (A) was calculated. Finally, assuming that other factors of the RUSLE model are constant and the C-factor changes due to the effect of LU/LCC, the annual average soil loss map was prepared for the years 1989 and 2019. The final soil loss maps were also classified into five classes of very low, low, medium, high, and very high erosion based on the Natural break method. Note that the quantitative values ​​of the range of erosion classes in each of the erosion intensity classes in both maps related to the years 1989 and 2019 were considered the same. 2.2.2.7. Hot and Cold Spots Analysis of Soil Erosion in the Study Area According to the method presented by Dissanayake et al., ( 2019 ), hot spots analysis was used for the spatial clustering pattern of soil loss for the years 1989 and 2019. This analysis was performed based on a 100×100 m grid in ArcGIS 10.8 using the tool box, optimized hotspot analysis (Getis-Ord Gi*). The average extent of soil erosion computed by the RUSLE model was calculated for each grid cell. The optimized hotspot analysis toolbox calculates the Gi* statistic, which represents the Z-score. Higher positive Z values ​​indicate hot spots and lower negative Z values ​​reveal cold spots. The z value determines the significance of clustering for a certain range based on the confidence level (ESRI, 2016 a, b). The implementation process of this research is displayed in Fig. 3 . 3. Results 3.1. Investigation of LU/LCC Using GEE Figure 4 depicts the LU/LC maps of 1989 and 2019, which were prepared through the classification of NDVI time series using the SVM method in GEE. The validation of the maps using test samples revealed the overall accuracy and kappa coefficient of 87.83% and 0.83 for 1989 and 86.51% and 0.82% for 2019 (Table 3 ). The investigation of the LU/LCC trend from 1989 to 2019 indicated that the area of ​​man-made and agricultural areas increased by 2602.66 and 13303.35 hectares, while the area of ​​forests, rocks, and pastures decreased by 12584.3593, 47.06, and 3274.02 hectares. In other words, man-made and agricultural areas have grown by 77.34% and 28.39%, respectively, while forest, rock outcrop, and pasture have decreased by 17.40%, 0.85%, and 10.01%, respectively. According to the obtained results, most changes are related to agricultural lands and forests while the least changes are associated with rock outcrops (Table 4 ). Also, the investigation of the conversion rate of LU/LC showed that the highest conversion rate was related to the change of forests and pastures to agricultural lands (Table 5 and Fig. 5 ). Table 3 Validation of satellite image classification results of 1989 and 2019. 1989 Man-made Forest Agriculture Rock Pasture Producer accuracy (%) 83.33 93.53 95.52 75 72.65 User accuracy (%) 98.21 81.03 85.05 87.80 85.86 Overall accuracy (%) 87.83 Kappa coefficient 0.83 2019 Producer accuracy (%) 85.05 97.37 84.50 73.33 83 (%)User accuracy 98.97 84.47 82.40 89.80 88.30 Overall accuracy (%) 86.51 Kappa coefficient 0.82 Table 4 Area (hectares) and percentage of LU/LCC. LU/LC Area (ha) Changes (ha) Changes (%) 1989 2019 Total Annual Total Annual Man-made 3365.28 5967.94 2602.66 86.76 77.34 2.58 Forest 72346.97 59762.04 -12584.93 -419.50 -17.40 -0.58 Agriculture 46854.36 60157.71 13303.35 443.44 28.39 0.945 Rock 5558.28 5511.22 -47.06 -1.57 -0.85 -0.03 Pasture 32697.15 29423.13 -3274.02 -109.13 -10.01 -0.33 Table 5 LU/LC conversion rates. To 2019 From 1989 Man-made (ha) Forest (ha) Agriculture (ha) Rock (ha) Pasture (ha) Man-made (ha) Forest (ha) Agriculture (ha) Rock (ha) Pasture (ha) - 941.04 1721.79 275.58 722.34 121.23 - 7579.71 421.54 5300.46 582.03 15643.35 - 744.57 9081 13.05 779.85 319.32 - 735.39 445.86 7781.76 3488.58 512.82 - No change (ha) 103610.80 3.2. Estimation of RUSLE Model Factors The estimation of the rain erosivity factor in 17 meteorological stations based on the Fournier index and the IDW interpolation method indicated that the value of the R-factor for the study area was between 98.50 and 292.89 MJ.mm. ha − 1.h − 1 . y − 1 . The examination of this index in the watershed revealed that the highest value of this index was estimated in the northern, north-eastern, and southern regions of the watershed due to high altitudes and high rainfall. Also, the R-factor values have been lower in the central and western plains and lowlands where the amount of precipitation is less (Fig. 6 a). The examination of the K-factor map, which was prepared using soil samples, indicated that the erodibility of the soil in the study area has been between 0.07 and 0.24 ton.h.MJ − 1 .mm − 1 (Fig. 6 b). The LS-factor map shown in Fig. 6 indicates that the value of this factor in the study area has been between 0.03 and 38.14. The lower value of P-factor indicates that this factor plays a greater role in reducing water erosion (Fig. 6 c). Based on the literature review, the value of the C- factor varies between 0.002 and 0.35, and when these values ​​are closer to zero, it shows the desirable condition of vegetation management. Also, the value of P-factor in the studied study area was between 0.5 and 1(Fig. 6 d, e). 3.3. Estimation of Annual Soil Erosion Using RUSLE Model After providing the map of RUSLE factors, the average annual soil erosion map was estimated for the years 1989 and 2019. The results of the statistical analysis of the erosion in these years are presented in Table 6 . The results revealed that the estimated average extent of annual soil erosion in the watershed during 1989 and 2019 has been 15.48 and 20.41 (ton. ha − 1 . y − 1 ), respectively (Table 6 ). The results of the study of soil erosion changes during this 30-year period indicated that the average annual erosion rate has increased by 4.93 (ton. ha − 1 . y − 1 ). Table 6 Values ​​of average, maximum, and minimum soil erosion statistics in the study area. Year (t.ha − 1 .y − 1 ) Average (t.ha − 1 .y − 1 ) Maximum (t.ha − 1 .y − 1 ) Minimum (t.ha − 1 .y − 1 ) Standard deviation 1989 2019 15.48 20.41 999.84 1049.39 0 0 43.22 54.16 The results of the area and percentage of erosion classes indicated that in 1989, the erosion class of very low, low, medium, high, and very high covered 64.78%, 14.61%, 6.32%, 4.38%, and 9.91% of the Khorramabad watershed, respectively. Also, in 2019, very low, low, medium, high, and very high classes covered 60.91%, 14.54%, 6.84%, 4.88%, and 12.83% of the entire watershed, respectively (Table 7 and Fig. 7 ). According to the results, most changes are related to very low and very high erosion classes, while the least changes are associated with low erosion class (Table 7 ). Also, the investigation of the conversion rate soil erosion classes showed that the highest conversion rate was related to the change of very low erosion class to very high erosion class (Table 8 and Fig. 8 ). Table 7 Area of ​​annual soil erosion classes in the Khorramabad watershed. Area (ha) Changes Classes t/ha/year 1989 2019 ha % Very low erosion 0–5 104173.92 97952.91 -6221.01 -5.97 Low erosion 5–15 23496.24 23387.71 -108.53 -0.46 Moderate erosion 15–30 10172.20 11000.71 828.51 8.14 High erosion 30–50 7038.21 7852.41 814.20 11.57 Very high erosion > 50 15941.47 20628.30 4686.83 29.40 Table 8 Soil erosion classes conversion rates. To 2019 From 1989 Very low erosion (ha) Low erosion (ha) Moderate erosion (ha) High erosion (ha) Very high erosion (ha) Very low erosion (ha) Low erosion (ha) Moderate erosion (ha) High erosion (ha) Very high erosion (ha) - 1521.45 1133.19 886.59 1198.08 2427.93 - 229.95 123.21 1609.11 2024.19 399.78 - 126.45 332.82 1565.91 156.15 551.70 - 148.50 3318.57 2774.25 419.31 743.31 - No change (ha) 139131.59 3.4. Hot spots and cold spots analysis The results of the hot spots analysis of soil erosion are presented in Fig. 9 . The spatial clustering pattern showed that in both maps of 1989 and 2019, hot spots (99% confidence level) were distributed in the north-west to the south-east of the watershed. Also, the hot spots areas in 1989 and 2019 covered 19.2% and 21.7% of the Khorramabad watershed, respectively. These areas have the most vulnerability to soil erosion. On the other hand, cold spots are more widespread in the southwestern areas. Compared to other areas, these areas are the least vulnerable to soil erosion. Also, not-significant areas were scattered across the entire watershed between hot spots and cold spot areas. In not-significant areas, there is a possibility that they will soon become a hot spot area. The changes in the spots of soil erosion during 1990–2019 period showed that in 2019, compared to 1989, the area of ​​hot spots increased by 90, 95 and 99% in all three levels. Although an increase can be seen in cold spots with a confidence level of 99% (6.2%) in 2019 compared to 1989, instead the area of ​​cold spots has diminished at 95 and 90% levels (Table 9 ). Table 9 The area of hot and cold spots of erosion. Area (ha) Changes 1989 2019 ha % Cold spots − 99% confidence 56931.01 47120.86 9810.15 20.82 Cold spots − 95% confidence 15278.09 21871.80 -6593.71 -30.15 Cold spots − 90% confidence 5950.42 8523.57 -2573.15 -30.19 Not Significant 40527.15 45834.28 -5307.13 -11.58 Hot spots − 90% confidence 2573.15 2412.33 160.82 6.67 Hot spots − 95% confidence 4663.84 4181.37 482.47 11.54 Hot spots − 99% confidence 34898.38 30877.83 4020.55 13.02 4. Discussion Soil erosion is a complex environmental issue in different parts of the world, which has caused irreversible economic and environmental losses. One of the important factors affecting soil erosion rate is LU/LCC. A procedure to prepare LU/LCC map is to use satellite images. Researchers have used Landsat satellite images widely, especially in underdeveloped and developing countries, due to the free access to their images. In recent years, by launching the GEE online platform, Google has made it possible to access and process satellite images online using different servers worldwide. In this regard, the current research was conducted to examine the effect of LU/LCC on soil erosion in a semi-arid region using the GEE platform and the RUSLE model. 4.1. Investigation of LU/LCC using GEE The LU/LC map was prepared using the time series classification of the NDVI index for each year in GEE. Validation of the prepared maps revealed an overall accuracy value of more than 86% and a kappa coefficient of more than 0.82, which indicates the high accuracy of this method. In this regard, Huang et al., ( 2017 ) also reported the overall accuracy and kappa coefficient of the LU/LCC map prepared using NDVI and GEE as 86.61% and 0.82, respectively. Prasai et al., ( 2021 ) also prepared the LU/LC map of Florida using GEE. They reported the overall accuracy and Kappa coefficient of 86% and 0.79, respectively. Also, Zurgani et al ., (2018) reported an overall accuracy rate of 76–79% and a Kappa coefficient of 0.72 to 0.77 for LU/LC classification in a 16-year period. Note that in studies such as Ang et al., ( 2021 ) and Becker et al., ( 2021 ), the overall accuracy and Kappa coefficient have been higher than the results of this study. It seems that factors such as the spatial and spectral resolution of satellite images, the type and number of LU/LC, the number of training samples, physiographic conditions, and classification algorithm affect the accuracy of output maps. Analysis of the LU/LCC trend indicated that compared to the initial area, man-made areas and agricultural lands have increased, while forest, rock outcrop, and pasture areas have decreased. Also, the results revealed that most changes were related to agricultural lands and forests while the least changes were linked to rock outcrop areas. The highest rate of conversion was related to the change of forests and pastures to agricultural lands. Degradation of forests and pastures in the region are mainly because of climatic changes, unauthorized cutting of trees for charcoal, fires, and pests and diseases. Yet, the results of this study demonstrated that, one of the most important reasons for reduced area of ​​forests and pastures is the cutting of trees and shrubs as well as the conversion of forests into agricultural lands. It seems that life issues and high unemployment rate among the people living in forests of the region have caused people to resort to illegal harvesting of these forests. In this regard, Naseri et al., ( 2019 ), Delpasand et al., ( 2022 ), Parma et al., ( 2017 ), and Vafaei et al., ( 2013 ) noted the reduction of forests and pastures as well as the increase of agricultural lands. In other regions of the world, researchers such as Khoi and Murayama ( 2011 ), Zurqani et al., ( 2018 ), Jazouli et al., ( 2019 ), Kumar et al ., (2020), and Banyongha et al ., (2020) have pointed out the reduction of forests in their studies. On the other hand, the results of Wang et al., ( 2021 ) and Gong et al., ( 2022 ) reported an increase in the area of forests, which seems to be due to enhanced level of implementation of appropriate protection and management measures as well as afforestation. 4.2. The Effect of LU/LCC on Soil Erosion The average extent of annual soil erosion in the watershed for the years 1989 and 2019 was estimated as 15.48 and 20.41 ton.ha − 1 .y − 1 , respectively, which indicates an increase of 4.93 ton.ha − 1 .y − 1 . Investigating the spatial distribution of different erosion classes related to the years 1989 and 2019 revealed that classes with very low and low erosion risk are scattered throughout the entire region. The average erosion class is also more scattered in the central and north-western regions. Further, the classes with high and very high erosion risk are observed in the north, south-west, and central parts, which are affected by the large changes of LS, K and P-factors, showing the highest amount of soil erosion in these areas. In this regard, researchers such as Gupta and Kumar ( 2017 ), Uddin et al., ( 2018 ), Tilahum et al ., (2018), and El Jazouli et al., ( 2019 ) reported the rising trend of soil erosion in their studies; in contrast, the research results of Alkharabsheh et al., ( 2013 ) showed descending trend of soil erosion, which seems to be the result of proper management of the watershed, conservation of forests and pastures, as well as afforestation in these areas. The results of hot spots analysis showed the increasing trend of hot spots in 2019 compared to 1989. This finding shows the increase of vulnerable areas to soil erosion and land destruction during this 30-year period. However, cold spots have increased slightly in some areas, which indicates suitable LU/LC in these areas (Bagwan and Gavali, 2020). The investigation of the spatial distribution of the spots in 1989 and 2019 indicated that the hot spots were mostly located in steep areas and pasture lands, or in areas where the change from forests to pastures and agricultures has occurred. In contrast to cold spots, plain areas with a low slope and no change in LU/LC are more widespread. A significant part of the studied area is covered with non-significant areas. In non-significant areas, there is a possibility that they will soon become a hot spot area (Ranagalage et al., 2018 ; Dissanayake et al., 2019 ). Thus, in order to prevent deterioration of the erosion situation in these areas, it is necessary to pay special attention to soil protection operations. Matching of LU/LCC maps to the map of soil erosion changes revealed that the change of forests to other uses, especially agricultural lands, had the greatest impact on the increase in erosion; the average annual erosion in the area related to forests in 1989 was 3.15 ton. ha − 1 .y − 1 , but this value within the same area in 2019 was 18.77 ton.ha − 1 .y − 1 . In other words, during a 30-year period, with a 17.40% reduction in the ​​forests, soil erosion has grown by 15.62 ton.ha − 1 .y − 1 in the same area. When the forest is converted into other LU/LCs such as agriculture, the canopy cover of the trees is damaged, which acts as a protective layer for the soil against rain. Further, with the degradation of forests, the amount of soil organic matter and soil organisms also decreases, so that the soil permeability declines and as a result, the soil erosion increases. Ganasri and Ramesh ( 2016 ), El Jazouli et al., ( 2019 ), and Gong et al., ( 2022 ) pointed out that the degradation of forests and pastures as well as their conversion to other LU/LCs causes increased soil erosion. 5. Conclusion This research examined the effect of LU/LCC on the extent of soil erosion in a semi-arid region. LU/LC maps were prepared in a thirty-year period using a new approach and time series classification of NDVI spectral index of every year in GEE. Validation of the produced maps revealed that the utilized method is suitable for providing LU/LC maps with high accuracy. The study of the LU/LCC indicated an increase in the area of ​​man-made areas and agricultural lands, while the area of ​​forest, pasture, and rock outcrop areas decreased, with the highest rate of LU/LC conversion being linked to the conversion of forest to agricultural land. Estimation of erosion rate using RUSLE model in GIS environment showed that the average annual erosion in the region has increased by 4.93 ton.ha − 1 .y − 1 . The findings of hot spots analysis also indicated increased vulnerable areas in the watershed. Matching the LU/LCC map to the soil erosion map revealed that the degradation of forests and pastures as well as their conversion to agricultural lands has had the greatest impact on the increase in soil erosion. Finally, it can be stated that GEE, as a platform that allows users to access the archive of satellite images and online image processing, has a high capability in preparing LU/LCC maps as well as other effective factors in soil erosion estimation models. Declarations Author contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Maryam Nourizadeh, Hamed Naghavi and Ebrahim Omidvar. The first draft of the manuscript was written by Hamed Naghavi and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval Ethical approval is not required. Consent to participate Not applicable. Consent for publication All authors agree with the content of this manuscript, and all give explicit consent to submit in NH. Competing interests The authors declare no competing interests. References Adam HE, Csaplovics E, Elhaja ME (2016), June A comparison of pixel-based and object-based approaches for land use land cover classification in semi-arid areas, Sudan. In IOP Conference Series: Earth and Environmental Science (Vol. 37, No. 1, p. 012061). IOP Publishing. doi: 10.1088/1755-1315/37/1/012061 Admas BF, Gashaw T, Adem AA, Worqlul AW, Dile YT, Molla E (2022) Identification of soil erosion hot-spot areas for prioritization of conservation measures using the SWAT model in Ribb watershed, Ethiopia. Resour Environ Sustain 8:100059. https://doi.org/10.1016/j.resenv.2022.100059 Ahrari AH (2020) Google Earth Engine tutorial. 2nd edition. Tehran, Iran. 290 p. https://girs.ir/gee-cookbook Aiello A, Adamo M, Canora F (2015) Remote sensing and GIS to assess soil erosion with RUSLE3D and USPED at river basin scale in southern Italy. CATENA 131:174–185. https://doi.org/10.1016/j.catena.2015.04.003 Alkharabsheh MM, Alexandridis TK, Bilas G, Misopolinos N, Silleos N (2013) Impact of land cover change on soil erosion hazard in northern Jordan using remote sensing and GIS. Procedia Environ Sci 19:912–921. https://doi.org/10.1016/j.proenv.2013.06.101 Ang MLE, Arts D, Crawford D, Labatos Jr BV, Ngo KD, Owen JR, …, Lechner AM (2021) Socio-environmental land cover time-series analysis of mining landscapes using Google Earth Engine and web-based mapping. Remote Sens Applications: Soc Environ 21:100458. https://doi.org/10.1016/j.rsase.2020.100458 Bag R, Mondal I, Dehbozorgi M, Bank SP, Das DN, Bandyopadhyay J, …, Nguyen XC (2022) Modelling and mapping of soil erosion susceptibility using machine learning in a tropical hot sub-humid environment. J Clean Prod 364:132428. https://doi.org/10.1016/j.jclepro.2022.132428 Bagwan WA, Gavali RS (2021) Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed, Maharashtra, India. Model Earth Syst Environ 7(3):2113–2126. https://doi.org/10.1007/s40808-020-00965-w Barman BK, Rao KS, Sonowal K, Prasad NSR, Sahoo UK (2020) Soil erosion assessment using revised universal soil loss equation model and geo-spatial technology: A case study of upper Tuirial river basin, Mizoram, India. AIMS Geosci 6(4):525–545. https://doi.org/10.3934/geosci.2020030 Becker WR, Ló TB, Johann JA, Mercante E (2021) Statistical features for land use and land cover classification in Google Earth Engine. Remote Sens Applications: Soc Environ 21:100459. https://doi.org/10.1016/j.rsase.2020.100459 Becker WR, Ló TB, Johann JA, Mercante E (2021) Statistical features for land use and land cover classification in Google Earth Engine. Remote Sens Applications: Soc Environ 21:100459. https://doi.org/10.1016/j.rsase.2020.100459 Belasri A, Lakhouili A (2016) Estimation of soil erosion risk using the universal soil loss equation (USLE) and geo-information technology in Oued El Makhazine Watershed, Morocco. J Geographic Inform Syst 8(01):98. 10.4236/jgis.2016.81010 Bunyangha J, Majaliwa MJ, Muthumbi AW, Gichuki NN, Egeru A (2021) Past and future land use/land cover changes from multi-temporal Landsat imagery in Mpologoma catchment, eastern Uganda. Egypt J Remote Sens Space Sci 24(3):675–685. https://doi.org/10.1016/j.ejrs.2021.02.003 Cohen WB, Goward SN (2004) Landsat's role in ecological applications of remote sensing. Bioscience 54(6):535–545. https://doi.org/10.1641/0006-3568(2004)054 [0535:LRIEAO]2.0.CO;2 Dabral PP, Baithuri N, Pandey A (2008) Soil erosion assessment in a hilly catchment of North Eastern India using USLE, GIS and remote sensing. Water Resour Manage 22(12):1783–1798. https://doi.org/10.1007/s11269-008-9253-9 Delfan E, Naghavi H, Maleknia R, Nouredini A (2020) Comparing the Capability of Sentinel 2 and Landsat 8 Satellite imagery in land use and land cover mapping using pixel-based and object-based classification methods. Desert Ecosyst Eng J 8(25):1–12. 10.22052/deej.2018.7.25.25 Delpasand S, Maleknia R, Naghavi H (2022) Modelling of forest cover change to identify suitable areas for REDD + projects‎(case‎ study: Lordegan county)‎. For Res Dev 7(4):577–594. 10.30466/JFRD.2021.53301.1528 Desmet PJJ, Govers G (1996) A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. J Soil Water Conserv 51(5):427–433 Ding Q, Shao Z, Huang X, Altan O, Hu B (2022) Area, China. Int J Appl Earth Obs Geoinf 113:103001. https://doi.org/10.1016/j.jag.2022.103001 . Time-series land cover mapping and urban expansion analysis using OpenStreetMap data and remote sensing big data: A case study of Guangdong-Hong Kong-Macao Greater Bay Dissanayake DMSLB, Morimoto T, Ranagalage M (2019) Accessing the soil erosion rate based on RUSLE model for sustainable land use management: A case study of the Kotmale watershed, Sri Lanka. Model Earth Syst Environ 5(1):291–306. https://doi.org/10.1007/s40808-018-0534-x El Jazouli A, Barakat A, Khellouk R, Rais J, Baghdadi E, M (2019) Remote sensing and GIS techniques for prediction of land use land cover change effects on soil erosion in the high basin of the Oum Er Rbia River (Morocco). Remote Sens Applications: Soc Environ 13:361–374. https://doi.org/10.1016/j.rsase.2018.12.004 Elnashar A, Zeng H, Wu B, Fenta AA, Nabil M, Duerler R (2021) Soil erosion assessment in the Blue Nile Basin driven by a novel RUSLE-GEE framework. Sci Total Environ 793:148466. https://doi.org/10.1016/j.scitotenv.2021.148466 ESRI (2016a) How hot spot analysis (Getis-Ord Gi*) works. Available online: https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm ESRI (2016b) What is a z-score? What is a p-Value? Available online: https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/what-is-a-z-score-what-is-a-p-value.htm Fallah M, Kavian A, Omidvar E (2016) Watershed prioritization in order to implement soil and water conservation practices. Environ Earth Sci 75(18):1–17. https://doi.org/10.1007/s12665-016-6035-1 FAO (2015) Healthy soils are the basis for healthy food production. Fao 4. http://www.fao.org/3/a-i4405e.pdf Faruque MJ, Vekerdy Z, Hasan MY, Islam KZ, Young B, Ahmed MT, …, Kundu P (2022) Monitoring of land use and land cover changes by using remote sensing and GIS techniques at human-induced mangrove forests areas in Bangladesh. Remote Sens Applications: Soc Environ 25:100699. https://doi.org/10.1016/j.rsase.2022.100699 Ganasri BP, Ramesh H (2016) Assessment of soil erosion by RUSLE model using remote sensing and GIS-A case study of Nethravathi Basin. Geosci Front 7(6):953–961. https://doi.org/10.1016/j.gsf.2015.10.007 Ganasri BP, Ramesh H (2016) Assessment of soil erosion by RUSLE model using remote sensing and GIS-A case study of Nethravathi Basin. Geosci Front 7(6):953–961. https://doi.org/10.1016/j.gsf.2015.10.007 Gemitzi A, Koutsias N (2022) A Google Earth Engine code to estimate properties of vegetation phenology in fire affected areas–A case study in North Evia wildfire event on August 2021. Remote Sens Applications: Soc Environ 26:100720. https://doi.org/10.1016/j.rsase.2022.100720 Ghorbanian A, Kakooei M, Amani M, Mahdavi S, Mohammadzadeh A, Hasanlou M (2020) Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS J Photogrammetry Remote Sens 167:276–288. https://doi.org/10.1016/j.isprsjprs.2020.07.013 Gong W, Liu T, Duan X, Sun Y, Zhang Y, Tong X, Qiu Z (2022) Estimating the Soil Erosion Response to Land-Use Land-Cover Change Using GIS-Based RUSLE and Remote Sensing: A Case Study of Miyun Reservoir, North China. Water 14(5):742. https://doi.org/10.3390/w14050742 Gupta S, Kumar S (2017) Simulating climate change impact on soil erosion using RUSLE model – A case study in a watershed of mid-Himalayan landscape. J Earth Syst Sci 126(3):1–20. https://doi.org/10.1007/s12040-017-0823-1 Huang H, Chen Y, Clinton N, Wang J, Wang X, Liu C, …, Zhu Z (2017) Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sens Environ 202:166–176. https://doi.org/10.1016/j.rse.2017.02.021 Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S (2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics Nat Hazards Risk 9(1):49–69. https://doi.org/10.1080/19475705.2017.1407368 Kavian A, Hoseinpoor Sabet S, Solaimani K, Jafari B (2017) Simulating the effects of land use changes on soil erosion using RUSLE model. Geocarto Int 32(1):97–111. 10.1080/10106049.2015.1130083 Khoi DD, Murayama Y (2011) Modeling deforestation using a neural network-Markov model. Spatial Analysis and Modeling in Geographical Transformation Process. Springer, Dordrecht, pp 169–190. DOI: 10.1007/978-94-007-0671-2_11 Kuma HG, Feyessa FF, Demissie TA (2022) Land-use/land-cover changes and implications in Southern Ethiopia: evidence from remote sensing and informants. Heliyon 8(3):e09071. https://doi.org/10.1016/j.heliyon.2022.e09071 Kumar S, Jain K (2020) A multi-temporal Landsat data analysis for land-use/land-cover change in Haridwar Region using remote sensing techniques. Procedia Comput Sci 171:1184–1193. https://doi.org/10.1016/j.procs.2020.04.127 Luo M, Ji S (2022) Cross-spatiotemporal land-cover classification from VHR remote sensing images with deep learning based domain adaptation. ISPRS J Photogrammetry Remote Sens 191:105–128. https://doi.org/10.1016/j.isprsjprs.2022.07.011 Mohammadlou M, Zeinivand H (2019) Comparison of different base flow separation methods in a semiarid watershed (case study: Khorramabad watershed, Iran). Sustainable Water Resources Management 5(3):1155–1163. https://doi.org/10.1007/s40899-018-0292-y Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: A review. ISPRS J Photogrammetry Remote Sens 66(3):247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001 Naghavi H, Fallah A, Shataee S, Latifi H, Soosani J, Ramezani H, Conrad C (2014) Canopy cover estimation across semi-Mediterranean woodlands: application of high-resolution earth observation data. J Appl Remote Sens 8(1):083524. https://doi.org/10.1117/1.JRS.8.083524 Naseri S, Naghavi H, Soosani J, Nouredini AR (2019) Modeling the spatial changes of Zagros forests using satellite imagery and LCM model (Case study: Bastam, Selseleh). Geogr Dev Iran J 17(54):107–120. 10.22111/GDIJ.2019.4350 Nghia BPQ, Pal I, Chollacoop N, Mukhopadhyay A (2022) Applying Google earth engine for flood mapping and monitoring in the downstream provinces of Mekong river. Progress in Disaster Science 100235. https://doi.org/10.1016/j.pdisas.2022.100235 Ochoa-Cueva P, Fries A, Montesinos P, Rodríguez‐Díaz JA, Boll J (2015) Spatial estimation of soil erosion risk by land‐cover change in the Andes of southern Ecuador. Land Degrad Dev 26(6):565–573. https://doi.org/10.1002/ldr.2219 Panagos P, Ballabio C, Borrelli P, Meusburger K, Klik A, Rousseva S, …, Alewell C (2015) Rainfall erosivity in Europe. Sci Total Environ 511:801–814. https://doi.org/10.1016/j.scitotenv.2015.01.008 Parma R, Maleknia R, Shataee S, Naghavi H (2017) Land cover change modeling based on artificial neural networks and transmission potential method in LCM (case study: forests Gilan-e Gharb, Kermanshah Province). Town and Country Planning 9(1):129–151. 10.22059/JTCP.2017.61410 Peng X, Dai Q (2022) Drivers of soil erosion and subsurface loss by soil leakage during karst rocky desertification in SW China. Int Soil Water Conserv Res 10(2):217–227. https://doi.org/10.1016/j.iswcr.2021.10.001 Pennock D (2019) Soil erosion: The greatest challenge for sustainable soil management. 100 pp. ISBN 978-92-5-131426-5. https://www.fao.org/3/ca4395en/ca4395en.pdf Petito M, Cantalamessa S, Pagnani G, Degiorgio F, Parisse B, Pisante M (2022) Impact of Conservation Agriculture on Soil Erosion in the Annual Cropland of the Apulia Region (Southern Italy) Based on the RUSLE-GIS-GEE Framework. Agronomy 12(2):281. https://doi.org/10.3390/agronomy12020281 Pourghasemi HR, Sadhasivam N, Kariminejad N, Collins AL (2020) Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process. Geosci Front 11(6):2207–2219. https://doi.org/10.1016/j.gsf.2020.03.005 Prasai R, Schwertner TW, Mainali K, Mathewson H, Kafley H, Thapa S, …, Drake J (2021) Application of Google earth engine python API and NAIP imagery for land use and land cover classification: A case study in Florida, USA. Ecol Inf 66:101474. https://doi.org/10.1016/j.ecoinf.2021.101474 Prăvălie R (2021) Exploring the multiple land degradation pathways across the planet. Earth Sci Rev 220:103689. https://doi.org/10.1016/j.earscirev.2021.103689 Ranagalage M, Estoque RC, Zhang X, Murayama Y (2018) Spatial changes of urban heat island formation in the Colombo District, Sri Lanka: Implications for sustainability planning. Sustainability 10(5):1367. https://doi.org/10.1007/s40808-018-0534-x Rawat KS, Mishra AK, Bhattacharyya R (2016) Soil erosion risk assessment and spatial mapping using LANDSAT-7 ETM+, RUSLE, and GIS—a case study. Arab J Geosci 9(4):1–22. https://doi.org/10.1007/s12517-015-2157-0 Renard KG (1997) Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). United States Government Printing Renard KG, Ferreira VA (1993) RUSLE model description and database sensitivity. J Environ Qual 22(3):458–466. https://doi.org/10.2134/jeq1993.00472425002200030009x Römkens MJM, Young RA, Poesen JWA, McCool DK, El-Swaify SA, Bradford JM (1997) Soil erodibility factor (K). Compilers) In: Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC, editors. Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Washington, DC, USA: Agric. HB , (703), 65–99 Saha M, Sauda SS, Real HRK, Mahmud M (2022) Estimation of annual rate and spatial distribution of soil erosion in the Jamuna basin using RUSLE model: A geospatial approach. Environ Challenges 8:100524. https://doi.org/10.1016/j.envc.2022.100524 Senanayake S, Pradhan B, Alamri A, Park HJ (2022) A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction. Sci Total Environ 845:157220. https://doi.org/10.1016/j.scitotenv.2022.157220 Singh G, Panda RK (2017) Grid-cell based assessment of soil erosion potential for identification of critical erosion prone areas using USLE, GIS and remote sensing: A case study in the Kapgari watershed, India. Int Soil Water Conserv Res 5(3):202–211. https://doi.org/10.1016/j.iswcr.2017.05.006 Teng H, Rossel RAV, Shi Z, Behrens T, Chappell A, Bui E (2016) Assimilating satellite imagery and visible–near infrared spectroscopy to model and map soil loss by water erosion in Australia. Environ Model Softw 77:156–167. https://doi.org/10.1016/j.envsoft.2015.11.024 Thomaz EL, Marcatto FS, Antoneli V (2022) Soil erosion on the Brazilian sugarcane cropping system: an overview. Geogr Sustain. https://doi.org/10.1016/j.geosus.2022.05.001 Tilahun AK, Haregeweyn N, Pingale SM (2018) Landscape changes and its consequences on soil erosion in Baro river basin, Ethiopia. Model Earth Syst Environ 4(2):793–803. https://doi.org/10.1007/s40808-018-0466-5 Uddin K, Abdul Matin M, Maharjan S (2018) Assessment of land cover change and its impact on changes in soil erosion risk in Nepal. Sustainability 10(12):4715. https://doi.org/10.3390/su10124715 Vafaei S, Darvishsefat AA, Pir Bavaghar M (2013) Monitoring and predicting land use changes using LCM module (Case study: Marivan region). Iran J For 5(3):323–336. http://www.ijf-isaforestry.ir/article_4740.html Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Network 10(5):988–999. https://doi.org/10.1109/72.788640 Wang H, Zhao H (2020) Dynamic changes of soil erosion in the taohe river basin using the RUSLE model and google earth engine. Water 12(5):1293. https://doi.org/10.3390/w12051293 Wang H, Liu X, Zhao C, Chang Y, Liu Y, Zang F (2021) Spatial-temporal pattern analysis of landscape ecological risk assessment based on land use/land cover change in Baishuijiang National nature reserve in Gansu Province, China. Ecol Ind 124:107454. https://doi.org/10.1016/j.ecolind.2021.107454 Weber DD, Englund EJ (1994) Evaluation and comparison of spatial interpolators II. Math Geol 26(5):589–603. http://dx.doi.org/10.1007/BF02089243 Weber D, Englund E (1992) Evaluation and comparison of spatial interpolators. Math Geol 24(4):381–391. http://dx.doi.org/10.1007/BF00891270 Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses: a guide to conservation planning, vol 537. Department of Agriculture, Science and Education Administration Zhang H, Wang Q, Dai L, Shao G, Tang L, Wang S, Gu H (2006) Quantifying soil erosion with GIS-based RUSLE under different forest management options in Jianchang Forest Farm. Sci China Ser E: Technol Sci 49(1):160–166. https://doi.org/10.1007/s11434-006-8117-z Zurqani HA, Post CJ, Mikhailova EA, Schlautman MA, Sharp JL (2018) Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. Int J Appl Earth Obs Geoinf 69:175–185. https://doi.org/10.1016/j.jag.2017.12.006 Cite Share Download PDF Status: Published Journal Publication published 04 Jan, 2024 Read the published version in Natural Hazards → Version 1 posted Reviewers agreed at journal 14 Jul, 2023 Reviewers invited by journal 14 Jul, 2023 Editor assigned by journal 06 Jul, 2023 First submitted to journal 01 Jul, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Naghavi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYHACNiCWYDBgYD54AMxnJl4LWwJJWhiAWngMDhDlKnP249ce/GyzADLOfDjMw2Anz8DO+wCvFsuenHLD3jYJICN3A1BLsmEDM7sBXi0GB3LSJHjOAP1yAKyFOYGBmQ2/wwzOv0mT/APScv7NA6CWeiK03Eg/Js1TAdRyI4cBqOUwMVresEnLVEjwGNx4ZnBwjsFxwzbCDkt/JvnGoE7O4HzywwdvKqrl+fmP4dfCAIwOMAk1ARZNeAH7A8JqRsEoGAWjYGQDAOVAPM20qzx7AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-2734-2831","institution":"Lorestan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hamed","middleName":"","lastName":"Naghavi","suffix":""},{"id":218540808,"identity":"549327fc-eb76-414d-ad03-b48e695f839a","order_by":2,"name":"Ebrahim Omidvar","email":"","orcid":"","institution":"University of Kashan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ebrahim","middleName":"","lastName":"Omidvar","suffix":""}],"badges":[],"createdAt":"2023-07-01 14:14:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3131140/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3131140/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11069-023-06375-2","type":"published","date":"2024-01-04T15:01:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":40256353,"identity":"6ae9c33e-b1ea-4731-9271-8780f564ae15","added_by":"auto","created_at":"2023-07-19 14:18:01","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":265676,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the test site: Landsat 8 true color composite image (a), location of study area in Lorestan province (b) and in Iran (c).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3131140/v1/4f38b6631f6d1d9e4f4cb9bb.jpeg"},{"id":40256355,"identity":"0ddbc5c6-a464-4f84-a5c5-6ac17b53fdf0","added_by":"auto","created_at":"2023-07-19 14:18:01","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":329863,"visible":true,"origin":"","legend":"\u003cp\u003eComposition of synthetic bands generated using MVC of NDVI for 1989 (a) and 2019 (b) in GEE.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3131140/v1/c1f04dee25837d6799f44785.jpeg"},{"id":40256358,"identity":"2b202a1a-aaf9-462f-adbc-4287fb258164","added_by":"auto","created_at":"2023-07-19 14:18:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":187725,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3131140/v1/5cd69d4abe745fbbf591158d.png"},{"id":40256360,"identity":"cec5efdf-a883-4b27-8c32-49bea0a0aeba","added_by":"auto","created_at":"2023-07-19 14:18:01","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":376012,"visible":true,"origin":"","legend":"\u003cp\u003eLU/LC classified maps of 1989 (a) and 2019 (b).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3131140/v1/7bf504148d79aef17ad7c23b.jpeg"},{"id":40257367,"identity":"b144fe4d-b617-4a6f-a6d1-e5d25e240e0b","added_by":"auto","created_at":"2023-07-19 14:26:01","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":701799,"visible":true,"origin":"","legend":"\u003cp\u003eThe LU/LC conversion map.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3131140/v1/2b752c6f3756d33cbed9064f.jpeg"},{"id":40256359,"identity":"30596c73-6f56-4672-b6f8-ce4bd2a88570","added_by":"auto","created_at":"2023-07-19 14:18:01","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":421790,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of RUSLE model factors: R-factor (a), K-factor (b), LS-factor (c), C-factor-1989 (d), C-factor-2019 (e), and P-factor (f).\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3131140/v1/8232fa9caaaa710be45b269f.jpeg"},{"id":40256354,"identity":"7003c443-39c9-48fa-aa5e-b40cb1b305a9","added_by":"auto","created_at":"2023-07-19 14:18:01","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":370351,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of soil erosion classes in the study area in 1989 (a), and 2019 (b).\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3131140/v1/da97babfdc3780d8e4ba5257.jpeg"},{"id":40257368,"identity":"2aaefb99-28a7-4e0b-938f-2c830adb0283","added_by":"auto","created_at":"2023-07-19 14:26:01","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":570981,"visible":true,"origin":"","legend":"\u003cp\u003eThe soil erosion classes conversion map.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3131140/v1/24a2ff959cd576c0ef633059.jpeg"},{"id":40256356,"identity":"bc3dc764-8d53-4f0a-8119-084b38ae58ac","added_by":"auto","created_at":"2023-07-19 14:18:01","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":348705,"visible":true,"origin":"","legend":"\u003cp\u003eHot and cold spots of soil erosion for the years 1989 (a), and 2019 (b).\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3131140/v1/89a438e1da0f7a6ac31b4d3f.jpeg"},{"id":49315716,"identity":"f3844c10-e892-4e0e-be34-16907f4538b4","added_by":"auto","created_at":"2024-01-08 15:09:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2117294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3131140/v1/0c25cddf-53a5-433e-97e0-eec931f03f21.pdf"}],"financialInterests":"","formattedTitle":"The Effect of Land Use and Land Cover Changes on Soil Erosion in Semi-arid Areas Using Cloud-based Google Earth Engine Platform and GIS-based RUSLE Model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSoil is one of the non-renewable natural resources, which as an environment for the growth of plants and agricultural products, provides about 95% of human nutritional demands both directly and indirectly (FAO, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Saha et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For this reason, any factor that causes destruction of this vital layer of the earth's surface is considered a threat to the lives of humans and other organisms. On a global scale, there are various processes for land degradation, of which soil erosion is one of the most important issues (Prăvălie, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Thomaz et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to the report of the Food and Agriculture Organization of the United Nations (FAO), the process of soil erosion is worsening in the continents of Asia, Africa, and Latin America (Pennock, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Admas et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Considering the increase in soil erosion and its negative impact on soil fertility, production of agricultural products, quality of water resources, the capacity of dams, quality of the environment, animal habitat, food security of living organisms, carbon sequestration cycle, and other ecosystem services, this phenomenon is considered as a complex and dynamic global environmental issue and attracted the attention of researchers, managers, and governments (Alkharabsheh et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Aiello et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Singh and Panda, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Barman et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Admas et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bag et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gong et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Senanayake et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Today, it has been proven that various factors such as climate change, LU/LCC, population increase, soil characteristics, physiographic and geomorphological characteristics of the watershed, land management systems, rainfall, human factors, and deforestation influence the amount of soil erosion in different areas (Aiello et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; El Jazouli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Becker et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gong et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Senanayake et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eManaging and controlling soil erosion requires access to up-to-date data as well as estimating and predicting its amount in different areas. Nowadays, due to the costly and time-consuming soil erosion measurement using traditional and field methods, the use of soil erosion estimation models has become popular (Alkharabsheh et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Barman et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In general, the models for estimating soil erosion can be classified into three categories: empirical, physical, and conceptual models (Ganasri and Ramesh, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Singh and Panda, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). One of the widely used empirical models is the Universal Soil Loss Equation (USLE) model and its revised version called RUSLE, which are used globally (Ganasri and Ramesh, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Barman et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The advantages of using the RUSLE model include no need for complex input data, simple and comprehensible structure, suitability for a regional scale, and implementability in forests, pasture, agricultural, and man-made areas (Zhang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Uddin et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Barman et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, the RUSLE model, with the potential to be implemented in the GIS environment and the use of remote sensing data, allows for spatial and temporal estimation of the soil erosion rate in different areas (Barman et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gong et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As mentioned, LU/LCC is an effective and important factor in soil erosion rate in different regions. For this reason, access to the map of LU/LCC is a key point in identifying the critical point of soil erosion and its sustainable management.\u003c/p\u003e \u003cp\u003eNowadays, the use of satellite imagery has become a common method for generating LU/LC maps, with advantages such as wide coverage, access to images at different times, and access to information on inaccessible areas (Naghavi \u003cem\u003eet al\u003c/em\u003e., 2013; Adam et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Naseri et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Prasai et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Faruque et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kuma et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Satellite images with different spatial resolutions can be used to prepare the LU/LC map. Use of high spatial resolution satellite imagery can boost the accuracy of LU/LC maps (Luo and Ji, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but application of these images is usually costly, which is a challenge in projects with a limited budget in developing and underdeveloped countries. For this reason, the use of images from the Landsat satellites with medium spatial resolution has been developed in these areas, which allows free access to their image archives from previous decades (Cohen and Goward, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Delfan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ang et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ding et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the challenges that researchers face in processing satellite images is the time-consuming processing of time series images as well as images related to large areas. In 2010, Google launched an advanced cloud-based platform called GEE for the online processing of satellite images (Ang et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Peng and Dai, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nghia et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This cloud space, while providing access to a huge archive of satellite images, eliminates the limitations of traditional methods and offers users the possibility of online processing of time series of satellite images with a large volume using millions of servers around the world (Gemitzi and Koutsias, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given the development of this platform in recent years and the mentioned advantages, its use is growing, and many researchers including Huang et al., (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Zurqani et al., (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Ghorbanian et al., (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Prasai et al., (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Ang et al., (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Becker et al., (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have employed this cloud-based platform to prepare LU/LC maps.\u003c/p\u003e \u003cp\u003eIn order to estimate the soil erosion using satellite images and the RUSLE model, various studies have been conducted by researchers worldwide. Wang and Zhao (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), estimated the amount of soil erosion in the Tahoe River area of ​​China in 2005, 2010, 2015, and 2018 as 1424, 1195, 1129, 1099, and 1124 ton. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Enashar \u003cem\u003eet al\u003c/em\u003e., (2021) estimated the average soil erosion in the middle, upper, and lower parts of the Blue Nile basin in Ethiopia as 39.73, 57.98, and 6.40 ton. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Petito et al., (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated the impact of conservation agriculture on soil erosion in the south of Italy and concluded that the area of soil erosion in the conservation agriculture system has diminished compared to traditional management. In all three mentioned studies, the RUSLE model and the GEE were used to estimate soil erosion. Also, researchers such as Alkharabsheh et al., (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Uddin et al., (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), El Jazouli et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Gong et al., (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated the impact of LU/LCC on soil erosion in different areas using RUSLE model and satellite imagery.\u003c/p\u003e \u003cp\u003eConsidering the importance of soil erosion, the purpose of this research is to examine the effect of LU/LCC on soil erosion in a semi-arid watershed using the GEE platform and GIS-based RUSLE model. In this way, the LU/LCC map was generated in a 30-year period using a new approach, by combining the time series of the NDVI spectral index related to each year and classifying it via the non-parametric SVM classification method in the GEE. Then, its effect on the rate of soil erosion changes was investigated using the RUSLE model.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eKhorramabad watershed with an area of 1608.22 km\u003csup\u003e2\u003c/sup\u003e is located in the geographical coordinate range of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(48^\\circ {4}^{{\\prime }}37\u0026rdquo;\\)\u003c/span\u003e\u003c/span\u003e to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(48^\\circ {46}^{{\\prime }}37\u0026rdquo;\\)\u003c/span\u003e\u003c/span\u003e east longitude and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(33^\\circ {15}^{{\\prime }}16\u0026quot;\\)\u003c/span\u003e\u003c/span\u003e to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(33^\\circ 43{\\prime }52\u0026quot;\\)\u003c/span\u003e\u003c/span\u003e north latitude in Lorestan and south-west of Iran (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This region has a semi-arid climate with an average annual temperature of 15\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(℃\\)\u003c/span\u003e\u003c/span\u003e and an average annual rainfall of 405 mm. The minimum, maximum, and average altitudes of the area are 1174, 3000, and 1695.3 m above sea level, respectively. Also, the average slope of the watershed is 24.36% (Mohammadlou and Zeinivand, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The main LU/LC in the region includes oak forests, agricultural lands, pastures, and man-made areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Preparing LU/LCC Maps Using GEE\u003c/h2\u003e \u003cp\u003eIn order to prepare the LU/LCC maps, the time series of Operational Land Imager (OLI) sensor images of Landsat 8 satellite related to 2019 and the time series of Thematic Mapper (TM) sensor images of Landsat 5 satellite related to 1989 were utilized (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthengine.google.com\u003c/span\u003e\u003cspan address=\"https://earthengine.google.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For this purpose, each year was divided into three periods of four months where the images of each period were called in GEE and clip using the region border vector layer. Then, all images were converted to NDVI vegetation index using Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and all NDVI values ​​of each period were converted into one band through the Maximum Value Composite (MVC) function (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Huang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Finally, an image containing three bands for each year was generated, with the values ​​of each band of this image showing the maximum value of the NDVI in each four-month period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Ahrari, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Next, it was necessary to introduce training samples to GEE for supervised classification. In this regard, the samples were randomly collected in different classes, including forest, pasture, man-made, agricultural, and rock outcrop, using field data, aerial photos, and GEE. Overall, 3000 samples were collected for each year, of which 1000, 1000, 600, 200, and 200 samples were related to forest, agriculture, pasture, man-made, and rock outcrop parts, respectively. Also, 70% of the samples were used for classification, and the remaining 30% were employed to evaluate the accuracy of the classification results.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$NDVI=\\frac{{\\rho }_{NIR}-{\\rho }_{R}}{{\\rho }_{NIR}+{\\rho }_{R}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere ρ\u003csub\u003eNIR\u003c/sub\u003e is the reflectance of near-infrared band and, ρ\u003csub\u003eRed\u003c/sub\u003e is the reflectance of red band.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$MVC=\\text{max}{\\left(NDVI\\right)}_{i}^{j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere i is the earliest scene and j is the last image acquired in a given four-months period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eToday, different classification methods are used for classifying satellite images. The SVM method is a non-parametric regression and classification method used by researchers, which does not have the limitations of parametric statistical methods. This method was introduced by Vapnik (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), and today it has many applications in remote sensing (Mountrakis et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pourghasemi et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This method tries to find an optimal separating hyperplane that can separate classes (Kalantar et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The SVMs are applicable based on linear, polynomial, radial basis function (RBF), as well as sigmoid kernels, where the selection of each of these kernels affects the accuracy of the obtained outputs (Bag et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this research, image classification was done using the SVM algorithm and RBF kernel on GEE. Finally, to evaluate the accuracy of classification results using test samples, the kappa coefficient, overall accuracy, user accuracy, and producer accuracy were calculated. Finally, the classified maps in this stage were used to investigate the spatiotemporal LU/LCC in the 30-year period, as well as to estimate the cover management factor (C-factor) in the RUSLE model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Estimation of Soil Erosion Rate Using RUSLE Model\u003c/h2\u003e \u003cp\u003eIn this study, the RUSLE model was used to calculate annual soil erosion. This model uses the following equation (Renard, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1997\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$A=R\\times K\\times LS\\times C\\times P$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere A is the average annual soil loss (ton. ha\u0026thinsp;\u0026minus;\u0026thinsp;1.y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), R denotes the rainfall erosivity factor (MJ.mm.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), K shows the soil erodibility factor (ton.h.MJ\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.mm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), LS reflects the slope length factor (unitless), C represents the cover management factor (unitless), and P is the support practice factor (unitless). The five factors of the RUSLE model were calculated as follows.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.1. R-factor\u003c/h2\u003e \u003cp\u003eIn this study, the R factor was calculated based on Fournier's index using the monthly and annual rainfall data of 17 rain gauge stations. The rain data were obtained from the statistics recorded by the Ministry of Energy and the Iranian Meteorological Organization during a 30-year period (1989\u0026ndash;2019).\u003c/p\u003e \u003cp\u003eIn order to calculate the R-factor, Fournier's index was applied based on the following equations (Renard and Ferreira, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1993\u003c/span\u003e):\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$F=\\frac{\\sum _{i=1}^{12}{p}_{i}^{2}}{\\sum _{i=1}^{12}p}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(R=(95.77-6.081+0.4778{F}^{2})/17.2\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003eF\u0026thinsp;\u0026ge;\u0026thinsp;55\u003c/em\u003e (5)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(R=(0.07397\\times {F}^{1.847})/17.2\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003eF\u0026thinsp;\u0026lt;\u0026thinsp;55\u003c/em\u003e (6)\u003c/p\u003e \u003cp\u003eWhere R represents the erosivity of rain (MJ.mm. ha\u0026thinsp;\u0026minus;\u0026thinsp;1.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), p\u003csub\u003ei\u003c/sub\u003e denotes the average rainfall (mm) in month i, p is the average annual rainfall (mm), and F shows the Fournier index.\u003c/p\u003e \u003cp\u003eThe R-factor map of the Khorramabad watershed was prepared by interpolation of the R-factor values ​​in the stations using the inverse distance weighting (IDW) method. The IDW interpolation method is based on the assumption that the estimated value of a point is more influenced by known nearby points than distant points (Weber and Englund, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The IDW method was chosen, since in this method the effect of the R-factor measured at the station points is considered very important and during the interpolation process weights are determined for the station points. Thus, as the distance from the point increases, the value of the R-factor decreases (Weber and Englund, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Belasri and Lakhouili, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.2. K-factor\u003c/h2\u003e \u003cp\u003eFor calculating the K factor, the equation proposed by Renard (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) was used for limited data. This equation suggested by R\u0026ouml;mkens et al., (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) for calculation of K-factor is as follows:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$K=0.0034+0.0405\\text{exp}\\left[-0.5 {\\left(\\frac{\\text{log}\\left(Dg\\right)1.659}{0.7101}\\right)}^{2} \\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$Dg\\left(mm\\right)=\\text{e}\\text{x}\\text{p}\\left(0.01\\sum {f}_{i}ln{m}_{i}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, K is the soil erodibility factor (ton.h.MJ\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.mm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), m\u003csub\u003ei\u003c/sub\u003e denotes the average diameter of clay, silt and sand (mm), f\u003csub\u003ei\u003c/sub\u003e indicates the percentage of each component of silt, clay, and sand in the soil sample, and Dg represents the geometric mean diameter of the soil particles.\u003c/p\u003e \u003cp\u003eIn this study, the information related to the characteristics of soil granularity was used to provide the K factor map, which was prepared in previous studies by the General Department of Natural Resources, the Research and Agriculture and Natural Resources Center of Lorestan Province, and the Faculty of Agriculture and Natural Resources of Lorestan University. For this purpose, first the value of K factor was calculated in the sampled points using Eq.\u0026nbsp;\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Then, the characteristics related to the variogram of the K factor values ​​in different points were calculated in the GS\u0026thinsp;+\u0026thinsp;9 software and its information was imported to the ArcGIS 10.8 software, with the corresponding map prepared using the Kriging method (Kavian \u003cem\u003eet al\u003c/em\u003e., 2011; Fallah et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.3. LS-factor\u003c/h2\u003e \u003cp\u003eIn this study, in order to prepare the LS factor map in Khorramabad watershed, the digital elevation model (DEM) map of Aster sensor with 30-meter pixel was employed. The LS-factor was prepared based on the method provided by Desmet and Govers, (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) in the SAGA GIS 6 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.4. C-factor\u003c/h2\u003e \u003cp\u003eIn the RUSLE model, the C-factor is usually determined based on empirical equations (Ochoa-cueva et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). According to past research, the vegetation map, and the conditions of the study area, the values ​​of the C-factor, according to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, are assigned to each LU/LC (Dabral et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ochoa-cueva et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Panagos et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rawat et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Thus, two C-factor maps were produced based on the LU/LC maps prepared for the years 1989 and 2019.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eC-factor value based on LU/LC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLU/LC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-factor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMan-made\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.5. P-factor\u003c/h2\u003e \u003cp\u003eWischmeier and Smith (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) have presented Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e in order to estimate the P-factor through different slopes. As such, first, using the DEM map, the slope map of the study area was prepared based on which the P-factor map was provided (Teng et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eP-factor in different slopes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-factor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.6. Combination of Layers and Preparation of Soil Erosion Map\u003c/h2\u003e \u003cp\u003eAfter generating all layers related to the RUSLE model factors with the same pixel size (30\u0026times;30 meters), according to Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and using the Raster calculator tool in the Arc GIS 10.8 software, the layers were multiplied together and the soil erosion map (A) was calculated.\u003c/p\u003e \u003cp\u003eFinally, assuming that other factors of the RUSLE model are constant and the C-factor changes due to the effect of LU/LCC, the annual average soil loss map was prepared for the years 1989 and 2019. The final soil loss maps were also classified into five classes of very low, low, medium, high, and very high erosion based on the Natural break method. Note that the quantitative values ​​of the range of erosion classes in each of the erosion intensity classes in both maps related to the years 1989 and 2019 were considered the same.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.7. Hot and Cold Spots Analysis of Soil Erosion in the Study Area\u003c/h2\u003e \u003cp\u003eAccording to the method presented by Dissanayake et al., (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), hot spots analysis was used for the spatial clustering pattern of soil loss for the years 1989 and 2019. This analysis was performed based on a 100\u0026times;100 m grid in ArcGIS 10.8 using the tool box, optimized hotspot analysis (Getis-Ord Gi*). The average extent of soil erosion computed by the RUSLE model was calculated for each grid cell. The optimized hotspot analysis toolbox calculates the Gi* statistic, which represents the Z-score. Higher positive Z values ​​indicate hot spots and lower negative Z values ​​reveal cold spots. The z value determines the significance of clustering for a certain range based on the confidence level (ESRI, 2016 a, b).\u003c/p\u003e \u003cp\u003eThe implementation process of this research is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Investigation of LU/LCC Using GEE\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the LU/LC maps of 1989 and 2019, which were prepared through the classification of NDVI time series using the SVM method in GEE. The validation of the maps using test samples revealed the overall accuracy and kappa coefficient of 87.83% and 0.83 for 1989 and 86.51% and 0.82% for 2019 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The investigation of the LU/LCC trend from 1989 to 2019 indicated that the area of ​​man-made and agricultural areas increased by 2602.66 and 13303.35 hectares, while the area of ​​forests, rocks, and pastures decreased by 12584.3593, 47.06, and 3274.02 hectares. In other words, man-made and agricultural areas have grown by 77.34% and 28.39%, respectively, while forest, rock outcrop, and pasture have decreased by 17.40%, 0.85%, and 10.01%, respectively. According to the obtained results, most changes are related to agricultural lands and forests while the least changes are associated with rock outcrops (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Also, the investigation of the conversion rate of LU/LC showed that the highest conversion rate was related to the change of forests and pastures to agricultural lands (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidation of satellite image classification results of 1989 and 2019.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e1989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMan-made\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducer accuracy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser accuracy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall accuracy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e87.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducer accuracy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(%)User accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall accuracy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e86.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea (hectares) and percentage of LU/LCC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLU/LC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eChanges (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eChanges (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1989\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMan-made\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3365.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5967.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2602.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72346.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59762.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12584.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-419.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-17.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46854.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60157.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13303.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e443.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5558.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5511.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-47.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32697.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29423.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3274.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-109.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLU/LC conversion rates.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTo 2019\u003c/p\u003e \u003cp\u003eFrom 1989\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan-made (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForest (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgriculture (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRock (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePasture (ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMan-made (ha)\u003c/p\u003e \u003cp\u003eForest (ha)\u003c/p\u003e \u003cp\u003eAgriculture (ha)\u003c/p\u003e \u003cp\u003eRock (ha)\u003c/p\u003e \u003cp\u003ePasture (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e941.04\u003c/p\u003e \u003cp\u003e1721.79\u003c/p\u003e \u003cp\u003e275.58\u003c/p\u003e \u003cp\u003e722.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.23\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e7579.71\u003c/p\u003e \u003cp\u003e421.54\u003c/p\u003e \u003cp\u003e5300.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e582.03\u003c/p\u003e \u003cp\u003e15643.35\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e744.57\u003c/p\u003e \u003cp\u003e9081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.05\u003c/p\u003e \u003cp\u003e779.85\u003c/p\u003e \u003cp\u003e319.32\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e735.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e445.86\u003c/p\u003e \u003cp\u003e7781.76\u003c/p\u003e \u003cp\u003e3488.58\u003c/p\u003e \u003cp\u003e512.82\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo change (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e103610.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Estimation of RUSLE Model Factors\u003c/h2\u003e \u003cp\u003eThe estimation of the rain erosivity factor in 17 meteorological stations based on the Fournier index and the IDW interpolation method indicated that the value of the R-factor for the study area was between 98.50 and 292.89 MJ.mm. ha\u0026thinsp;\u0026minus;\u0026thinsp;1.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The examination of this index in the watershed revealed that the highest value of this index was estimated in the northern, north-eastern, and southern regions of the watershed due to high altitudes and high rainfall. Also, the R-factor values have been lower in the central and western plains and lowlands where the amount of precipitation is less (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The examination of the K-factor map, which was prepared using soil samples, indicated that the erodibility of the soil in the study area has been between 0.07 and 0.24 ton.h.MJ\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.mm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The LS-factor map shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicates that the value of this factor in the study area has been between 0.03 and 38.14. The lower value of P-factor indicates that this factor plays a greater role in reducing water erosion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Based on the literature review, the value of the C- factor varies between 0.002 and 0.35, and when these values ​​are closer to zero, it shows the desirable condition of vegetation management. Also, the value of P-factor in the studied study area was between 0.5 and 1(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed, e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Estimation of Annual Soil Erosion Using RUSLE Model\u003c/h2\u003e \u003cp\u003eAfter providing the map of RUSLE factors, the average annual soil erosion map was estimated for the years 1989 and 2019. The results of the statistical analysis of the erosion in these years are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The results revealed that the estimated average extent of annual soil erosion in the watershed during 1989 and 2019 has been 15.48 and 20.41 (ton. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The results of the study of soil erosion changes during this 30-year period indicated that the average annual erosion rate has increased by 4.93 (ton. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValues ​​of average, maximum, and minimum soil erosion statistics in the study area.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) Average\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) Maximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) Minimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) Standard deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1989\u003c/p\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.48\u003c/p\u003e \u003cp\u003e20.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e999.84\u003c/p\u003e \u003cp\u003e1049.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.22\u003c/p\u003e \u003cp\u003e54.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the area and percentage of erosion classes indicated that in 1989, the erosion class of very low, low, medium, high, and very high covered 64.78%, 14.61%, 6.32%, 4.38%, and 9.91% of the Khorramabad watershed, respectively. Also, in 2019, very low, low, medium, high, and very high classes covered 60.91%, 14.54%, 6.84%, 4.88%, and 12.83% of the entire watershed, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). According to the results, most changes are related to very low and very high erosion classes, while the least changes are associated with low erosion class (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Also, the investigation of the conversion rate soil erosion classes showed that the highest conversion rate was related to the change of very low erosion class to very high erosion class (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea of ​​annual soil erosion classes in the Khorramabad watershed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eChanges\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClasses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et/ha/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery low erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104173.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97952.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6221.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23496.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23387.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-108.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10172.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11000.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e828.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7038.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7852.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e814.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery high erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15941.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20628.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4686.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSoil erosion classes conversion rates.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTo 2019\u003c/p\u003e \u003cp\u003eFrom 1989\u003c/p\u003e\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery low erosion (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow erosion (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate erosion (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh erosion (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVery high erosion (ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery low erosion (ha)\u003c/p\u003e \u003cp\u003eLow erosion (ha)\u003c/p\u003e \u003cp\u003eModerate erosion (ha)\u003c/p\u003e \u003cp\u003eHigh erosion (ha)\u003c/p\u003e \u003cp\u003eVery high erosion (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e1521.45\u003c/p\u003e \u003cp\u003e1133.19\u003c/p\u003e \u003cp\u003e886.59\u003c/p\u003e \u003cp\u003e1198.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2427.93\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e229.95\u003c/p\u003e \u003cp\u003e123.21\u003c/p\u003e \u003cp\u003e1609.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2024.19\u003c/p\u003e \u003cp\u003e399.78\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e126.45\u003c/p\u003e \u003cp\u003e332.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1565.91\u003c/p\u003e \u003cp\u003e156.15\u003c/p\u003e \u003cp\u003e551.70\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e148.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3318.57\u003c/p\u003e \u003cp\u003e2774.25\u003c/p\u003e \u003cp\u003e419.31\u003c/p\u003e \u003cp\u003e743.31\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo change (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e139131.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Hot spots and cold spots analysis\u003c/h2\u003e \u003cp\u003eThe results of the hot spots analysis of soil erosion are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The spatial clustering pattern showed that in both maps of 1989 and 2019, hot spots (99% confidence level) were distributed in the north-west to the south-east of the watershed. Also, the hot spots areas in 1989 and 2019 covered 19.2% and 21.7% of the Khorramabad watershed, respectively. These areas have the most vulnerability to soil erosion. On the other hand, cold spots are more widespread in the southwestern areas. Compared to other areas, these areas are the least vulnerable to soil erosion. Also, not-significant areas were scattered across the entire watershed between hot spots and cold spot areas. In not-significant areas, there is a possibility that they will soon become a hot spot area. The changes in the spots of soil erosion during 1990\u0026ndash;2019 period showed that in 2019, compared to 1989, the area of ​​hot spots increased by 90, 95 and 99% in all three levels. Although an increase can be seen in cold spots with a confidence level of 99% (6.2%) in 2019 compared to 1989, instead the area of ​​cold spots has diminished at 95 and 90% levels (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe area of hot and cold spots of erosion.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eChanges\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1989\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold spots \u0026minus;\u0026thinsp;99% confidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56931.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47120.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9810.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold spots \u0026minus;\u0026thinsp;95% confidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15278.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21871.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6593.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold spots \u0026minus;\u0026thinsp;90% confidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5950.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8523.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2573.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40527.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45834.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5307.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot spots \u0026minus;\u0026thinsp;90% confidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2573.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2412.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot spots \u0026minus;\u0026thinsp;95% confidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4663.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4181.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e482.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot spots \u0026minus;\u0026thinsp;99% confidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34898.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30877.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4020.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSoil erosion is a complex environmental issue in different parts of the world, which has caused irreversible economic and environmental losses. One of the important factors affecting soil erosion rate is LU/LCC. A procedure to prepare LU/LCC map is to use satellite images. Researchers have used Landsat satellite images widely, especially in underdeveloped and developing countries, due to the free access to their images. In recent years, by launching the GEE online platform, Google has made it possible to access and process satellite images online using different servers worldwide. In this regard, the current research was conducted to examine the effect of LU/LCC on soil erosion in a semi-arid region using the GEE platform and the RUSLE model.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Investigation of LU/LCC using GEE\u003c/h2\u003e \u003cp\u003eThe LU/LC map was prepared using the time series classification of the NDVI index for each year in GEE. Validation of the prepared maps revealed an overall accuracy value of more than 86% and a kappa coefficient of more than 0.82, which indicates the high accuracy of this method. In this regard, Huang et al., (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) also reported the overall accuracy and kappa coefficient of the LU/LCC map prepared using NDVI and GEE as 86.61% and 0.82, respectively. Prasai et al., (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also prepared the LU/LC map of Florida using GEE. They reported the overall accuracy and Kappa coefficient of 86% and 0.79, respectively. Also, Zurgani \u003cem\u003eet al\u003c/em\u003e., (2018) reported an overall accuracy rate of 76\u0026ndash;79% and a Kappa coefficient of 0.72 to 0.77 for LU/LC classification in a 16-year period. Note that in studies such as Ang et al., (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Becker et al., (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the overall accuracy and Kappa coefficient have been higher than the results of this study. It seems that factors such as the spatial and spectral resolution of satellite images, the type and number of LU/LC, the number of training samples, physiographic conditions, and classification algorithm affect the accuracy of output maps.\u003c/p\u003e \u003cp\u003eAnalysis of the LU/LCC trend indicated that compared to the initial area, man-made areas and agricultural lands have increased, while forest, rock outcrop, and pasture areas have decreased. Also, the results revealed that most changes were related to agricultural lands and forests while the least changes were linked to rock outcrop areas. The highest rate of conversion was related to the change of forests and pastures to agricultural lands. Degradation of forests and pastures in the region are mainly because of climatic changes, unauthorized cutting of trees for charcoal, fires, and pests and diseases. Yet, the results of this study demonstrated that, one of the most important reasons for reduced area of ​​forests and pastures is the cutting of trees and shrubs as well as the conversion of forests into agricultural lands. It seems that life issues and high unemployment rate among the people living in forests of the region have caused people to resort to illegal harvesting of these forests. In this regard, Naseri et al., (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Delpasand et al., (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Parma et al., (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Vafaei et al., (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) noted the reduction of forests and pastures as well as the increase of agricultural lands. In other regions of the world, researchers such as Khoi and Murayama (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), Zurqani et al., (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Jazouli et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Kumar \u003cem\u003eet al\u003c/em\u003e., (2020), and Banyongha \u003cem\u003eet al\u003c/em\u003e., (2020) have pointed out the reduction of forests in their studies. On the other hand, the results of Wang et al., (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Gong et al., (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported an increase in the area of forests, which seems to be due to enhanced level of implementation of appropriate protection and management measures as well as afforestation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2. The Effect of LU/LCC on Soil Erosion\u003c/h2\u003e \u003cp\u003eThe average extent of annual soil erosion in the watershed for the years 1989 and 2019 was estimated as 15.48 and 20.41 ton.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, which indicates an increase of 4.93 ton.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Investigating the spatial distribution of different erosion classes related to the years 1989 and 2019 revealed that classes with very low and low erosion risk are scattered throughout the entire region. The average erosion class is also more scattered in the central and north-western regions. Further, the classes with high and very high erosion risk are observed in the north, south-west, and central parts, which are affected by the large changes of LS, K and P-factors, showing the highest amount of soil erosion in these areas. In this regard, researchers such as Gupta and Kumar (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Uddin et al., (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Tilahum \u003cem\u003eet al\u003c/em\u003e., (2018), and El Jazouli et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported the rising trend of soil erosion in their studies; in contrast, the research results of Alkharabsheh et al., (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) showed descending trend of soil erosion, which seems to be the result of proper management of the watershed, conservation of forests and pastures, as well as afforestation in these areas.\u003c/p\u003e \u003cp\u003eThe results of hot spots analysis showed the increasing trend of hot spots in 2019 compared to 1989. This finding shows the increase of vulnerable areas to soil erosion and land destruction during this 30-year period. However, cold spots have increased slightly in some areas, which indicates suitable LU/LC in these areas (Bagwan and Gavali, 2020). The investigation of the spatial distribution of the spots in 1989 and 2019 indicated that the hot spots were mostly located in steep areas and pasture lands, or in areas where the change from forests to pastures and agricultures has occurred. In contrast to cold spots, plain areas with a low slope and no change in LU/LC are more widespread. A significant part of the studied area is covered with non-significant areas. In non-significant areas, there is a possibility that they will soon become a hot spot area (Ranagalage et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dissanayake et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, in order to prevent deterioration of the erosion situation in these areas, it is necessary to pay special attention to soil protection operations.\u003c/p\u003e \u003cp\u003eMatching of LU/LCC maps to the map of soil erosion changes revealed that the change of forests to other uses, especially agricultural lands, had the greatest impact on the increase in erosion; the average annual erosion in the area related to forests in 1989 was 3.15 ton. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, but this value within the same area in 2019 was 18.77 ton.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. In other words, during a 30-year period, with a 17.40% reduction in the ​​forests, soil erosion has grown by 15.62 ton.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the same area. When the forest is converted into other LU/LCs such as agriculture, the canopy cover of the trees is damaged, which acts as a protective layer for the soil against rain. Further, with the degradation of forests, the amount of soil organic matter and soil organisms also decreases, so that the soil permeability declines and as a result, the soil erosion increases. Ganasri and Ramesh (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), El Jazouli et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Gong et al., (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) pointed out that the degradation of forests and pastures as well as their conversion to other LU/LCs causes increased soil erosion.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis research examined the effect of LU/LCC on the extent of soil erosion in a semi-arid region. LU/LC maps were prepared in a thirty-year period using a new approach and time series classification of NDVI spectral index of every year in GEE. Validation of the produced maps revealed that the utilized method is suitable for providing LU/LC maps with high accuracy. The study of the LU/LCC indicated an increase in the area of ​​man-made areas and agricultural lands, while the area of ​​forest, pasture, and rock outcrop areas decreased, with the highest rate of LU/LC conversion being linked to the conversion of forest to agricultural land. Estimation of erosion rate using RUSLE model in GIS environment showed that the average annual erosion in the region has increased by 4.93 ton.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The findings of hot spots analysis also indicated increased vulnerable areas in the watershed. Matching the LU/LCC map to the soil erosion map revealed that the degradation of forests and pastures as well as their conversion to agricultural lands has had the greatest impact on the increase in soil erosion. Finally, it can be stated that GEE, as a platform that allows users to access the archive of satellite images and online image processing, has a high capability in preparing LU/LCC maps as well as other effective factors in soil erosion estimation models.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution \u003c/strong\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Maryam Nourizadeh, Hamed Naghavi and Ebrahim Omidvar. The first draft of the manuscript was written by Hamed Naghavi and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e Ethical approval is not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e All authors agree with the content of this manuscript, and all give explicit consent to submit in NH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdam HE, Csaplovics E, Elhaja ME (2016), June A comparison of pixel-based and object-based approaches for land use land cover classification in semi-arid areas, Sudan. In \u003cem\u003eIOP Conference Series: Earth and Environmental Science\u003c/em\u003e (Vol.\u0026nbsp;37, No. 1, p.\u0026nbsp;012061). IOP Publishing. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1088/1755-1315/37/1/012061\u003c/span\u003e\u003cspan address=\"10.1088/1755-1315/37/1/012061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdmas BF, Gashaw T, Adem AA, Worqlul AW, Dile YT, Molla E (2022) Identification of soil erosion hot-spot areas for prioritization of conservation measures using the SWAT model in Ribb watershed, Ethiopia. Resour Environ Sustain 8:100059. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.resenv.2022.100059\u003c/span\u003e\u003cspan address=\"10.1016/j.resenv.2022.100059\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhrari AH (2020) Google Earth Engine tutorial. 2nd edition. Tehran, Iran. 290 p. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://girs.ir/gee-cookbook\u003c/span\u003e\u003cspan address=\"https://girs.ir/gee-cookbook\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAiello A, Adamo M, Canora F (2015) Remote sensing and GIS to assess soil erosion with RUSLE3D and USPED at river basin scale in southern Italy. CATENA 131:174\u0026ndash;185. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.catena.2015.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.catena.2015.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlkharabsheh MM, Alexandridis TK, Bilas G, Misopolinos N, Silleos N (2013) Impact of land cover change on soil erosion hazard in northern Jordan using remote sensing and GIS. Procedia Environ Sci 19:912\u0026ndash;921. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.proenv.2013.06.101\u003c/span\u003e\u003cspan address=\"10.1016/j.proenv.2013.06.101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAng MLE, Arts D, Crawford D, Labatos Jr BV, Ngo KD, Owen JR, \u0026hellip;, Lechner AM (2021) Socio-environmental land cover time-series analysis of mining landscapes using Google Earth Engine and web-based mapping. Remote Sens Applications: Soc Environ 21:100458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsase.2020.100458\u003c/span\u003e\u003cspan address=\"10.1016/j.rsase.2020.100458\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBag R, Mondal I, Dehbozorgi M, Bank SP, Das DN, Bandyopadhyay J, \u0026hellip;, Nguyen XC (2022) Modelling and mapping of soil erosion susceptibility using machine learning in a tropical hot sub-humid environment. J Clean Prod 364:132428. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2022.132428\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2022.132428\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagwan WA, Gavali RS (2021) Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed, Maharashtra, India. Model Earth Syst Environ 7(3):2113\u0026ndash;2126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40808-020-00965-w\u003c/span\u003e\u003cspan address=\"10.1007/s40808-020-00965-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarman BK, Rao KS, Sonowal K, Prasad NSR, Sahoo UK (2020) Soil erosion assessment using revised universal soil loss equation model and geo-spatial technology: A case study of upper Tuirial river basin, Mizoram, India. AIMS Geosci 6(4):525\u0026ndash;545. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3934/geosci.2020030\u003c/span\u003e\u003cspan address=\"10.3934/geosci.2020030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecker WR, L\u0026oacute; TB, Johann JA, Mercante E (2021) Statistical features for land use and land cover classification in Google Earth Engine. Remote Sens Applications: Soc Environ 21:100459. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsase.2020.100459\u003c/span\u003e\u003cspan address=\"10.1016/j.rsase.2020.100459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecker WR, L\u0026oacute; TB, Johann JA, Mercante E (2021) Statistical features for land use and land cover classification in Google Earth Engine. Remote Sens Applications: Soc Environ 21:100459. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsase.2020.100459\u003c/span\u003e\u003cspan address=\"10.1016/j.rsase.2020.100459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelasri A, Lakhouili A (2016) Estimation of soil erosion risk using the universal soil loss equation (USLE) and geo-information technology in Oued El Makhazine Watershed, Morocco. J Geographic Inform Syst 8(01):98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4236/jgis.2016.81010\u003c/span\u003e\u003cspan address=\"10.4236/jgis.2016.81010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBunyangha J, Majaliwa MJ, Muthumbi AW, Gichuki NN, Egeru A (2021) Past and future land use/land cover changes from multi-temporal Landsat imagery in Mpologoma catchment, eastern Uganda. Egypt J Remote Sens Space Sci 24(3):675\u0026ndash;685. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejrs.2021.02.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrs.2021.02.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen WB, Goward SN (2004) Landsat's role in ecological applications of remote sensing. Bioscience 54(6):535\u0026ndash;545. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1641/0006-3568(2004)054\u003c/span\u003e\u003cspan address=\"10.1641/0006-3568(2004)054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[0535:LRIEAO]2.0.CO;2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDabral PP, Baithuri N, Pandey A (2008) Soil erosion assessment in a hilly catchment of North Eastern India using USLE, GIS and remote sensing. Water Resour Manage 22(12):1783\u0026ndash;1798. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11269-008-9253-9\u003c/span\u003e\u003cspan address=\"10.1007/s11269-008-9253-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelfan E, Naghavi H, Maleknia R, Nouredini A (2020) Comparing the Capability of Sentinel 2 and Landsat 8 Satellite imagery in land use and land cover mapping using pixel-based and object-based classification methods. Desert Ecosyst Eng J 8(25):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.22052/deej.2018.7.25.25\u003c/span\u003e\u003cspan address=\"10.22052/deej.2018.7.25.25\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelpasand S, Maleknia R, Naghavi H (2022) Modelling of forest cover change to identify suitable areas for REDD + projects\u0026lrm;(case\u0026lrm; study: Lordegan county)\u0026lrm;. For Res Dev 7(4):577\u0026ndash;594. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.30466/JFRD.2021.53301.1528\u003c/span\u003e\u003cspan address=\"10.30466/JFRD.2021.53301.1528\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesmet PJJ, Govers G (1996) A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. J Soil Water Conserv 51(5):427\u0026ndash;433\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing Q, Shao Z, Huang X, Altan O, Hu B (2022) Area, China. Int J Appl Earth Obs Geoinf 113:103001. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jag.2022.103001\u003c/span\u003e\u003cspan address=\"10.1016/j.jag.2022.103001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Time-series land cover mapping and urban expansion analysis using OpenStreetMap data and remote sensing big data: A case study of Guangdong-Hong Kong-Macao Greater Bay\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDissanayake DMSLB, Morimoto T, Ranagalage M (2019) Accessing the soil erosion rate based on RUSLE model for sustainable land use management: A case study of the Kotmale watershed, Sri Lanka. Model Earth Syst Environ 5(1):291\u0026ndash;306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40808-018-0534-x\u003c/span\u003e\u003cspan address=\"10.1007/s40808-018-0534-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Jazouli A, Barakat A, Khellouk R, Rais J, Baghdadi E, M (2019) Remote sensing and GIS techniques for prediction of land use land cover change effects on soil erosion in the high basin of the Oum Er Rbia River (Morocco). Remote Sens Applications: Soc Environ 13:361\u0026ndash;374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsase.2018.12.004\u003c/span\u003e\u003cspan address=\"10.1016/j.rsase.2018.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElnashar A, Zeng H, Wu B, Fenta AA, Nabil M, Duerler R (2021) Soil erosion assessment in the Blue Nile Basin driven by a novel RUSLE-GEE framework. Sci Total Environ 793:148466. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.148466\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2021.148466\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eESRI (2016a) How hot spot analysis (Getis-Ord Gi*) works. Available online: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm\u003c/span\u003e\u003cspan address=\"https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eESRI (2016b) What is a z-score? What is a p-Value? Available online:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/what-is-a-z-score-what-is-a-p-value.htm\u003c/span\u003e\u003cspan address=\"https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/what-is-a-z-score-what-is-a-p-value.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFallah M, Kavian A, Omidvar E (2016) Watershed prioritization in order to implement soil and water conservation practices. Environ Earth Sci 75(18):1\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-016-6035-1\u003c/span\u003e\u003cspan address=\"10.1007/s12665-016-6035-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO (2015) Healthy soils are the basis for healthy food production. Fao 4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org/3/a-i4405e.pdf\u003c/span\u003e\u003cspan address=\"http://www.fao.org/3/a-i4405e.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaruque MJ, Vekerdy Z, Hasan MY, Islam KZ, Young B, Ahmed MT, \u0026hellip;, Kundu P (2022) Monitoring of land use and land cover changes by using remote sensing and GIS techniques at human-induced mangrove forests areas in Bangladesh. Remote Sens Applications: Soc Environ 25:100699. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsase.2022.100699\u003c/span\u003e\u003cspan address=\"10.1016/j.rsase.2022.100699\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanasri BP, Ramesh H (2016) Assessment of soil erosion by RUSLE model using remote sensing and GIS-A case study of Nethravathi Basin. Geosci Front 7(6):953\u0026ndash;961. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gsf.2015.10.007\u003c/span\u003e\u003cspan address=\"10.1016/j.gsf.2015.10.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanasri BP, Ramesh H (2016) Assessment of soil erosion by RUSLE model using remote sensing and GIS-A case study of Nethravathi Basin. Geosci Front 7(6):953\u0026ndash;961. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gsf.2015.10.007\u003c/span\u003e\u003cspan address=\"10.1016/j.gsf.2015.10.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGemitzi A, Koutsias N (2022) A Google Earth Engine code to estimate properties of vegetation phenology in fire affected areas\u0026ndash;A case study in North Evia wildfire event on August 2021. Remote Sens Applications: Soc Environ 26:100720. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsase.2022.100720\u003c/span\u003e\u003cspan address=\"10.1016/j.rsase.2022.100720\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhorbanian A, Kakooei M, Amani M, Mahdavi S, Mohammadzadeh A, Hasanlou M (2020) Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS J Photogrammetry Remote Sens 167:276\u0026ndash;288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.isprsjprs.2020.07.013\u003c/span\u003e\u003cspan address=\"10.1016/j.isprsjprs.2020.07.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong W, Liu T, Duan X, Sun Y, Zhang Y, Tong X, Qiu Z (2022) Estimating the Soil Erosion Response to Land-Use Land-Cover Change Using GIS-Based RUSLE and Remote Sensing: A Case Study of Miyun Reservoir, North China. Water 14(5):742. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w14050742\u003c/span\u003e\u003cspan address=\"10.3390/w14050742\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta S, Kumar S (2017) Simulating climate change impact on soil erosion using RUSLE model \u0026ndash; A case study in a watershed of mid-Himalayan landscape. J Earth Syst Sci 126(3):1\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12040-017-0823-1\u003c/span\u003e\u003cspan address=\"10.1007/s12040-017-0823-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang H, Chen Y, Clinton N, Wang J, Wang X, Liu C, \u0026hellip;, Zhu Z (2017) Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sens Environ 202:166\u0026ndash;176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2017.02.021\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2017.02.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S (2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics Nat Hazards Risk 9(1):49\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/19475705.2017.1407368\u003c/span\u003e\u003cspan address=\"10.1080/19475705.2017.1407368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKavian A, Hoseinpoor Sabet S, Solaimani K, Jafari B (2017) Simulating the effects of land use changes on soil erosion using RUSLE model. Geocarto Int 32(1):97\u0026ndash;111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10106049.2015.1130083\u003c/span\u003e\u003cspan address=\"10.1080/10106049.2015.1130083\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhoi DD, Murayama Y (2011) Modeling deforestation using a neural network-Markov model. Spatial Analysis and Modeling in Geographical Transformation Process. Springer, Dordrecht, pp 169\u0026ndash;190. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-94-007-0671-2_11\u003c/span\u003e\u003cspan address=\"10.1007/978-94-007-0671-2_11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuma HG, Feyessa FF, Demissie TA (2022) Land-use/land-cover changes and implications in Southern Ethiopia: evidence from remote sensing and informants. Heliyon 8(3):e09071. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2022.e09071\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2022.e09071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar S, Jain K (2020) A multi-temporal Landsat data analysis for land-use/land-cover change in Haridwar Region using remote sensing techniques. Procedia Comput Sci 171:1184\u0026ndash;1193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.procs.2020.04.127\u003c/span\u003e\u003cspan address=\"10.1016/j.procs.2020.04.127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo M, Ji S (2022) Cross-spatiotemporal land-cover classification from VHR remote sensing images with deep learning based domain adaptation. ISPRS J Photogrammetry Remote Sens 191:105\u0026ndash;128. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.isprsjprs.2022.07.011\u003c/span\u003e\u003cspan address=\"10.1016/j.isprsjprs.2022.07.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammadlou M, Zeinivand H (2019) Comparison of different base flow separation methods in a semiarid watershed (case study: Khorramabad watershed, Iran). Sustainable Water Resources Management 5(3):1155\u0026ndash;1163. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40899-018-0292-y\u003c/span\u003e\u003cspan address=\"10.1007/s40899-018-0292-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: A review. ISPRS J Photogrammetry Remote Sens 66(3):247\u0026ndash;259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.isprsjprs.2010.11.001\u003c/span\u003e\u003cspan address=\"10.1016/j.isprsjprs.2010.11.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaghavi H, Fallah A, Shataee S, Latifi H, Soosani J, Ramezani H, Conrad C (2014) Canopy cover estimation across semi-Mediterranean woodlands: application of high-resolution earth observation data. J Appl Remote Sens 8(1):083524. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1117/1.JRS.8.083524\u003c/span\u003e\u003cspan address=\"10.1117/1.JRS.8.083524\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaseri S, Naghavi H, Soosani J, Nouredini AR (2019) Modeling the spatial changes of Zagros forests using satellite imagery and LCM model (Case study: Bastam, Selseleh). Geogr Dev Iran J 17(54):107\u0026ndash;120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.22111/GDIJ.2019.4350\u003c/span\u003e\u003cspan address=\"10.22111/GDIJ.2019.4350\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNghia BPQ, Pal I, Chollacoop N, Mukhopadhyay A (2022) Applying Google earth engine for flood mapping and monitoring in the downstream provinces of Mekong river. Progress in Disaster Science 100235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pdisas.2022.100235\u003c/span\u003e\u003cspan address=\"10.1016/j.pdisas.2022.100235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOchoa-Cueva P, Fries A, Montesinos P, Rodr\u0026iacute;guez‐D\u0026iacute;az JA, Boll J (2015) Spatial estimation of soil erosion risk by land‐cover change in the Andes of southern Ecuador. Land Degrad Dev 26(6):565\u0026ndash;573. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ldr.2219\u003c/span\u003e\u003cspan address=\"10.1002/ldr.2219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanagos P, Ballabio C, Borrelli P, Meusburger K, Klik A, Rousseva S, \u0026hellip;, Alewell C (2015) Rainfall erosivity in Europe. Sci Total Environ 511:801\u0026ndash;814. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2015.01.008\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2015.01.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParma R, Maleknia R, Shataee S, Naghavi H (2017) Land cover change modeling based on artificial neural networks and transmission potential method in LCM (case study: forests Gilan-e Gharb, Kermanshah Province). Town and Country Planning 9(1):129\u0026ndash;151. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.22059/JTCP.2017.61410\u003c/span\u003e\u003cspan address=\"10.22059/JTCP.2017.61410\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng X, Dai Q (2022) Drivers of soil erosion and subsurface loss by soil leakage during karst rocky desertification in SW China. Int Soil Water Conserv Res 10(2):217\u0026ndash;227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.iswcr.2021.10.001\u003c/span\u003e\u003cspan address=\"10.1016/j.iswcr.2021.10.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePennock D (2019) Soil erosion: The greatest challenge for sustainable soil management. 100 pp. ISBN 978-92-5-131426-5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/3/ca4395en/ca4395en.pdf\u003c/span\u003e\u003cspan address=\"https://www.fao.org/3/ca4395en/ca4395en.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetito M, Cantalamessa S, Pagnani G, Degiorgio F, Parisse B, Pisante M (2022) Impact of Conservation Agriculture on Soil Erosion in the Annual Cropland of the Apulia Region (Southern Italy) Based on the RUSLE-GIS-GEE Framework. Agronomy 12(2):281. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy12020281\u003c/span\u003e\u003cspan address=\"10.3390/agronomy12020281\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePourghasemi HR, Sadhasivam N, Kariminejad N, Collins AL (2020) Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process. Geosci Front 11(6):2207\u0026ndash;2219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gsf.2020.03.005\u003c/span\u003e\u003cspan address=\"10.1016/j.gsf.2020.03.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrasai R, Schwertner TW, Mainali K, Mathewson H, Kafley H, Thapa S, \u0026hellip;, Drake J (2021) Application of Google earth engine python API and NAIP imagery for land use and land cover classification: A case study in Florida, USA. Ecol Inf 66:101474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoinf.2021.101474\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoinf.2021.101474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrăvălie R (2021) Exploring the multiple land degradation pathways across the planet. Earth Sci Rev 220:103689. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.earscirev.2021.103689\u003c/span\u003e\u003cspan address=\"10.1016/j.earscirev.2021.103689\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRanagalage M, Estoque RC, Zhang X, Murayama Y (2018) Spatial changes of urban heat island formation in the Colombo District, Sri Lanka: Implications for sustainability planning. Sustainability 10(5):1367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40808-018-0534-x\u003c/span\u003e\u003cspan address=\"10.1007/s40808-018-0534-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRawat KS, Mishra AK, Bhattacharyya R (2016) Soil erosion risk assessment and spatial mapping using LANDSAT-7 ETM+, RUSLE, and GIS\u0026mdash;a case study. Arab J Geosci 9(4):1\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12517-015-2157-0\u003c/span\u003e\u003cspan address=\"10.1007/s12517-015-2157-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRenard KG (1997) Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). United States Government Printing\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRenard KG, Ferreira VA (1993) RUSLE model description and database sensitivity. J Environ Qual 22(3):458\u0026ndash;466. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/jeq1993.00472425002200030009x\u003c/span\u003e\u003cspan address=\"10.2134/jeq1993.00472425002200030009x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026ouml;mkens MJM, Young RA, Poesen JWA, McCool DK, El-Swaify SA, Bradford JM (1997) Soil erodibility factor (K). \u003cem\u003eCompilers) In: Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC, editors. Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Washington, DC, USA: Agric. HB\u003c/em\u003e, (703), 65\u0026ndash;99\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaha M, Sauda SS, Real HRK, Mahmud M (2022) Estimation of annual rate and spatial distribution of soil erosion in the Jamuna basin using RUSLE model: A geospatial approach. Environ Challenges 8:100524. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envc.2022.100524\u003c/span\u003e\u003cspan address=\"10.1016/j.envc.2022.100524\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSenanayake S, Pradhan B, Alamri A, Park HJ (2022) A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction. Sci Total Environ 845:157220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2022.157220\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2022.157220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh G, Panda RK (2017) Grid-cell based assessment of soil erosion potential for identification of critical erosion prone areas using USLE, GIS and remote sensing: A case study in the Kapgari watershed, India. Int Soil Water Conserv Res 5(3):202\u0026ndash;211. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.iswcr.2017.05.006\u003c/span\u003e\u003cspan address=\"10.1016/j.iswcr.2017.05.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeng H, Rossel RAV, Shi Z, Behrens T, Chappell A, Bui E (2016) Assimilating satellite imagery and visible\u0026ndash;near infrared spectroscopy to model and map soil loss by water erosion in Australia. Environ Model Softw 77:156\u0026ndash;167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envsoft.2015.11.024\u003c/span\u003e\u003cspan address=\"10.1016/j.envsoft.2015.11.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomaz EL, Marcatto FS, Antoneli V (2022) Soil erosion on the Brazilian sugarcane cropping system: an overview. Geogr Sustain. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geosus.2022.05.001\u003c/span\u003e\u003cspan address=\"10.1016/j.geosus.2022.05.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTilahun AK, Haregeweyn N, Pingale SM (2018) Landscape changes and its consequences on soil erosion in Baro river basin, Ethiopia. Model Earth Syst Environ 4(2):793\u0026ndash;803. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40808-018-0466-5\u003c/span\u003e\u003cspan address=\"10.1007/s40808-018-0466-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUddin K, Abdul Matin M, Maharjan S (2018) Assessment of land cover change and its impact on changes in soil erosion risk in Nepal. Sustainability 10(12):4715. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su10124715\u003c/span\u003e\u003cspan address=\"10.3390/su10124715\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVafaei S, Darvishsefat AA, Pir Bavaghar M (2013) Monitoring and predicting land use changes using LCM module (Case study: Marivan region). Iran J For 5(3):323\u0026ndash;336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ijf-isaforestry.ir/article_4740.html\u003c/span\u003e\u003cspan address=\"http://www.ijf-isaforestry.ir/article_4740.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Network 10(5):988\u0026ndash;999. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/72.788640\u003c/span\u003e\u003cspan address=\"10.1109/72.788640\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Zhao H (2020) Dynamic changes of soil erosion in the taohe river basin using the RUSLE model and google earth engine. Water 12(5):1293. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w12051293\u003c/span\u003e\u003cspan address=\"10.3390/w12051293\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Liu X, Zhao C, Chang Y, Liu Y, Zang F (2021) Spatial-temporal pattern analysis of landscape ecological risk assessment based on land use/land cover change in Baishuijiang National nature reserve in Gansu Province, China. Ecol Ind 124:107454. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2021.107454\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2021.107454\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeber DD, Englund EJ (1994) Evaluation and comparison of spatial interpolators II. Math Geol 26(5):589\u0026ndash;603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/BF02089243\u003c/span\u003e\u003cspan address=\"10.1007/BF02089243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeber D, Englund E (1992) Evaluation and comparison of spatial interpolators. Math Geol 24(4):381\u0026ndash;391. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/BF00891270\u003c/span\u003e\u003cspan address=\"10.1007/BF00891270\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWischmeier WH, Smith DD (1978) Predicting rainfall erosion losses: a guide to conservation planning, vol 537. Department of Agriculture, Science and Education Administration\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Wang Q, Dai L, Shao G, Tang L, Wang S, Gu H (2006) Quantifying soil erosion with GIS-based RUSLE under different forest management options in Jianchang Forest Farm. Sci China Ser E: Technol Sci 49(1):160\u0026ndash;166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11434-006-8117-z\u003c/span\u003e\u003cspan address=\"10.1007/s11434-006-8117-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZurqani HA, Post CJ, Mikhailova EA, Schlautman MA, Sharp JL (2018) Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. Int J Appl Earth Obs Geoinf 69:175\u0026ndash;185. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jag.2017.12.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jag.2017.12.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Satellite Images, NDVI, SVM, Empirical Models, Time series","lastPublishedDoi":"10.21203/rs.3.rs-3131140/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3131140/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil erosion has recently attracted the attention of researchers and managers as an environmental crisis. One of the effective factors in soil erosion is land use/land cover change (LU/LCC). Use of satellite imagery is a method for generating LU/LCC maps. Recently, Google has launched the cloud-based Google Earth Engine (GEE) platform, which enabled the processing of satellite images online. Accordingly, the purpose of the present study is to investigate the effect of LU/LCC on soil erosion in a semi-arid region in the south-west of Iran. LU/LCC map was prepared over a period of 30 years (1989\u0026ndash;2019) using a new approach and classification of the Normalized Difference Vegetation Index (NDVI) index time series on the GEE. For classifying the NDVI time series, a non-parametric Support Vector Machine (SVM) classification method was employed. The LU/LC maps were also used as an input factor in the soil erosion estimation model. The amount of soil erosion in the region was estimated using the Revised Universal Soil Loss Equation (RUSLE) empirical model in the Geographical Information System (GIS) environment. Validation of LU/LC maps generated in GEE indicated overall accuracy higher than 86% and the kappa coefficient higher than 0.82. The study of LU/LCC trends showed that the area of ​​forests, pastures, and rock outcrop in the region has diminished, but the area of ​​agricultural and man-made LUs has been expanded. Also, the highest rate of LU/LC conversion was related to the conversion of forests to agricultural lands. Estimating the amount of soil erosion in the region using the RUSLE model revealed that the average annual erosion in 1989 and 2019 was 15.48 and 20.41 tons per hectare, respectively, which indicates an increase of 4.93 tons in hectares, while the hot spots of erosion in the area have increased at the confidence levels of 90, 95, and 99%. Matching the LU/LCC map with the soil erosion map indicated that the degradation of forests and their conversion to agricultural lands had the greatest impact on increasing soil erosion. Based on the findings, we can conclude that GEE, as an online platform, has a high capability in preparing LU/LC maps and other effective factors in soil erosion estimation models.\u003c/p\u003e","manuscriptTitle":"The Effect of Land Use and Land Cover Changes on Soil Erosion in Semi-arid Areas Using Cloud-based Google Earth Engine Platform and GIS-based RUSLE Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-19 14:17:56","doi":"10.21203/rs.3.rs-3131140/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2023-07-14T09:08:57+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-07-14T08:59:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-06T15:01:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2023-07-01T10:14:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8ff835a2-fafe-43cc-8d7d-0610cb8b0e10","owner":[],"postedDate":"July 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-01-08T15:06:13+00:00","versionOfRecord":{"articleIdentity":"rs-3131140","link":"https://doi.org/10.1007/s11069-023-06375-2","journal":{"identity":"natural-hazards","isVorOnly":false,"title":"Natural Hazards"},"publishedOn":"2024-01-04 15:01:17","publishedOnDateReadable":"January 4th, 2024"},"versionCreatedAt":"2023-07-19 14:17:56","video":"","vorDoi":"10.1007/s11069-023-06375-2","vorDoiUrl":"https://doi.org/10.1007/s11069-023-06375-2","workflowStages":[]},"version":"v1","identity":"rs-3131140","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3131140","identity":"rs-3131140","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}