Estimating Soil Erosion Risk in District Diamer, Pakistan Using RUSLE Model: A Spatial Analysis Approach

Maria Anum; Arshad Ashraf; Kalim-Ullah; Durr-e-Adan; Muhammad Awais

Authors

Maria Anum Department of Meteorology, Comsats University Islamabad
Arshad Ashraf Principal Scientific Officer, National Agriculture Research Centre, NARC Park Road, Islamabad
Kalim-Ullah Department of Meteorology, Comsats University Islamabad
Durr-e-Adan Department of Meteorology, Comsats University Islamabad
Muhammad Awais GIS Analyst, DHA Gujranwala, Gujranwala

Keywords:

Remote sensing, GIS, Soil Erosion, Revised Universal Soil Loss Equation, Land-use & Land-cover

Abstract

Soil erosion is a critical issue in the hilly regions of Diamer, Pakistan, due to the region's varying topography and significant precipitation patterns. This study uses an effective combination of Geographic Information System (GIS) technologies and the Revised Universal Soil Loss Equation (RUSLE) model to calculate soil erosion rates within the region's complex topography. Different GIS layers, such as rainfall erosivity (R), slope length and steepness (LS) factor, soil erodibility (K), conservation practices (P), and cover management factor (C), were merged by utilizing satellite data and the Normalized Difference Vegetation Index (NDVI). The resulting map showed a maximum soil loss of 2279.3 t/ha/year over the region. Notably, the greatest soil loss was observed in the western regions of Diamer, where rainfall and rainfall erosivity are also recorded as high in these areas. Five separate categories of soil erosion were identified, with a mean soil loss rate of 27 t/ha/year. According to the GIS analysis, 95% of the overall area experienced less severe erosion than the severe erosion classes, accounting for 5%. Additionally, the study included the computation of composite NDVI estimates for 2023 using Google Earth Engine (GEE). This method improved both the scalability and usability of the study by enabling effective processing and storage of data in the cloud. GEE enables the computation of NDVI quickly and precisely. This pioneering study is an important step toward understanding and resolving soil erosion issues in Diamer, Pakistan. The study offers valuable insights for decision-making and management planning initiatives by utilizing cutting-edge GIS tools and RUSLE modeling.

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