Assessment of soil erosion based on satellite remote sensing data

Yeoh, Zi Xiang (2023) Assessment of soil erosion based on satellite remote sensing data. Final Year Project, UTAR.

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Abstract

Soil erosion is one of the significant issues in the river basins of Malaysia that will negatively result in sedimentation and decreased agricultural output. Klang River Basin is experiencing significant environmental changes due to extensive land use changes, economic growth, population growth, and uncontrolled urbanization. This research aims to assess the annual soil erosion in Klang River Basin using the Revised Universal Soil Loss Equation (RUSLE) model with the assistance of satellite remote sensing (RS) techniques and geographic information systems (GIS). Precipitation, wind velocity, temperature, and humidity are the meteorological and hydrological parameters that influence soil moisture and can contribute to soil erosion. The RUSLE model was implemented to estimate the annual soil erosion rates in Klang River Basin. Several factors were evaluated in the RUSLE model, which are rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and conservation practices (P). To increase the accuracy of soil erosion prediction, the RUSLE model was integrated with RS and GIS by incorporating spatially explicit datasets on rain gauge data, land use, soil type, and digital elevation model (DEM). The calculated values of the R, K, LS, C and P factors varied from 771.76 to 1165.43 MJ mm ha-1 h -1 yr-1 , 0.11 to 0.13 Mg h MJ−1 mm−1 , 0 to 40.8963, 0 to 1, and 0.1 to 1.0. The geographical distribution of annual soil erosion ranges from 0 to 300 tons ha-1 yr-1 . The values of potential soil erosion were divided into seven groups: very low, low, moderate, high, severe, extreme, and exceptional, with a numeric range of 50 tons ha-1 yr-1 . The research concluded that most of the study area in Klang River Basin had a very low risk of erosion, and every smaller location had a significant risk of erosion. Although the RUSLE model does not directly incorporate soil moisture as a factor, it can still influence soil erosion rates indirectly by affecting rainfall erosivity, soil erodibility, vegetation cover, etc. Therefore, it is crucial to consider all relevant factors, including soil moisture, to predict soil erosion rates accurately. The findings from this research can serve as essential information to aid in conservation management and land-use planning. Lastly, the methods employed in this research can facilitate the recognition of regions in the Klang River Basin that are prone to soil erosion.

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