Modelling and Mapping of Soil Erosion Susceptibility of Murree, Sub-Himalayas Using GIS and RS-Based Models

Abstract

Soil erosion is one of Pakistan’s most serious environmental threats. This study used geospatial modelling to identify the distinct zones susceptible to soil erosion in Murree, Pakistan. Using a machine learning technique in the Google Earth engine (GEE) and Google Earth, we identified 1250 soil erosion events. The inventory (dependent variable) was separated into two datasets, one for training (70%) and one for testing (30%). Elevation, slope, aspect, curvature, stream, precipitation, LULC, lithology, soil, NDVI, and distance to road were prepared in ArcGIS and considered as independent variables in the current research. GIS and RS-based models such as WOE, FR, and IV were used to assess the relationship between both variables and produce soil erosion susceptibility maps. Finally, the Area Under Curve (AUC) approach was used to confirm the research results. According to the validation data, the SRC for WOE, FR, and IV were 88%, 91%, and 87%, respectively. The present study’s validation results show that the PRC for WOE, FR, and IV are 92%, 94%, and 90%, respectively. Based on the AUC validation approach, we determined that the FR model had the highest accuracy when compared to the other two techniques, the WOE and IV models. The current analysis and final susceptibility maps of soil erosion could be useful for decision-makers in the future to prevent soil erosion and its negative repercussions.