Open AccessDEM and land slope based method of RUSLE LS factor calculation for soil erosion assessment
Author(s) -
A. Yudhistira,
Chandra Setyawan,
Ngadisih,
R. Tirtalistyani
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/686/1/012033
Subject(s) - universal soil loss equation , environmental science , erosion , digital elevation model , hydrology (agriculture) , watershed , r value (soils) , soil science , remote sensing , soil loss , geology , soil water , geotechnical engineering , geomorphology , computer science , machine learning
Soil erosion is one of the essential factors causing land degradation in tropical regions. The application of a model for soil erosion calculation enables a rapid assessment with a reliable result. The Revised Universal Soil Loss Equation (RUSLE) is the most used soil erosion model which can be applied under various climate conditions. This study was conducted to evaluate the performance of the Digital Elevation Model (DEM) and land slope-based method to estimate the RUSLE LS factor value for soil erosion calculation in the upstream of Progo Watershed Indonesia. Parameters of the RUSLE model including LS factor estimation in a DEM based were calculated by using a Geographic Information System (GIS) tool and presented in 30 meters grid size. RUSLE model validation was performed by using reference value from two different studies in Java Island, Indonesia. The result revealed that average soil erosion in the study area was 71.12 tons/ha/year based on the LS factor calculated by using DEM. Whereas, average soil erosion based on LS factor calculated by using land slope-based method was 438.68 tons/ha/year. According to the RUSLE model validation value, the RUSLE LS factor calculation by using DEM was more accurate than the LS factor calculated by using the land slope factor. This study provides a useful reference for soil erosion study particularly to develop methods for RUSLE model parameter assessment under different climate conditions.