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

Abstract

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.