Spatial Targeting of Soil Loss Using RUSLE in GIS: the case of Asokore Mampong Municipality, Ghana

• Main Authors: Gift Dumedah, Evans Kyeremanteng, Ema Dari
• Format: Article
• Language: English
• Published: Politeknik Negeri Batam 2019-01-01
• Series: Journal of Applied Geospatial Information
• Subjects: Soil erosion, Revised Universal Soil Loss Equation (RUSLE), GIS, QGIS, Graphical Modeler, Spatial targeting
• Online Access: https://jurnal.polibatam.ac.id/index.php/JAGI/article/view/1029

Soil erosion is a serious environmental problem that is associated with societal impacts including flooding, poor water quality, and loss of plant nutrient leading to low agricultural productivity. Soil erosion wears away the top soil and is controlled by the interaction between several factors including rainfall, steepness of slope, length of slope, vegetation cover, and land management practices. This study developed Geographic Information System (GIS) graphical model based on the Revised Universal Soil Loss Equation (RUSLE), to calculate soil loss in the Asokore Mampong Municipality of the Ashanti region, Ghana. The estimated soil loss was examined the spatial patterns of soil loss and intensity per areas, as an important method for proper planning of management measures. The graphical model was developed using the popular open source GIS software, QGIS, ensuring the availability of the model, automation for any specific area, and its execution to the general public. Data sources used include Digital Elevation Model (DEM) derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), soil properties data obtained from the Global Soil Grids, land cover data from the Global Land Cover by National Mapping Organization (GLCNMO), NDVI (normalized difference vegetation index) data from MODIS (MOD13Q1, 16 Day), and rainfall data from GPCC version 7 (Global Precipitation Climatology Centre). Our results show high levels of soil loss (in tons per hectare per year) in the Municipality, with the capability to spatially target mitigation measures leading to cost effective environmental management.