Prediction of soil erosion in Pansoon sub-basin, Malaysia using RUSLE integrated in Geographical Information System

• Main Authors: Noor Fadzilah Yusof,, Tukimat Lihan,, Wan Mohd Razi Idris,, Zulfahmi Ali Rahman,, Muzzneena Ahmad Mustapha,, Mohd. Abdul Wahab Yusof,
• Format: Article
• Language: English
• Published: Penerbit Universiti Kebangsaan Malaysia 2019
• Online Access: http://journalarticle.ukm.my/14438/1/26%20Noor%20Fadzilah%20Yusof.pdf; http://journalarticle.ukm.my/14438/; http://www.ukm.my/jsm/malay_journals/jilid48bil11_2019/KandunganJilid48Bil11_2019.html

Water-borne erosion problem is one of the environmental problems faced globally particularly in developing countries. The objective of this study was to estimate the erosion rate at the Pansoon sub-basin using combination of conventional approach and remote sensing technology. Pansoon sub-basin is the upper stream of Langat watershed, Malaysia located in the mountainous area dominated by steep slopes and various type of soils which are the important factors contributed to soil erosion. The Revised Universal Soil Loss Equation (RUSLE) integrated in a Geographical Information System used to predict the soil erosion rate and spatially maps its distribution using rainfall, soil series and topography data to generate rainfall erosivity factor, soil erodibility factor and topography factor. Land use map was used to produce coverage and management practice factor. The result shows that 66% (7433 ha) of the Pansoon sub-basin is classified at very low risk, 22% of low risk (2433 ha), 5% of moderate (582 ha), 2% of the area with high risk (251 ha) and 5% of very high risk of erosion (549 ha). Pansoon sub-basin is prone to soil erosion problem on the southwest region may due to soil erodibility factor, slope length and slope steepness. Accuracy assessment was obtained between prediction model and field observation data (p=0.97) which means the RUSLE approach integrated in GIS is suitable to be used to predict and assessing the soil erosion rate. In conclusion, the prediction of soil erosion using RUSLE in GIS can be accurately assessed with the combination of field observation data.