Keywords
weighted sum analysis
soil erosion susceptibility
SRTM
remote sensing
GIS
Imo River Basin
How to Cite
Abstract
Gullies and other forms of erosion have been the greatest environmental problem and catastrophe in most high- and low-income countries. The challenge posed by soil erosion has compromised agricultural productivity, en-vironmental biodiversity and food safety for the world’s population. It is important to identify vulnerable areas to soil erosion in each region to initiate remedial measures. This study demonstrates the use of watershed morphometry coupled with weighted sum analysis (WSA) to estimate the soil erosion susceptibility of the Imo River Basin sub-wa-tersheds (SWs) in South-Eastern Nigeria using satellite remote-sensing data and geographic information system (GIS) analysis. To this end, Shuttle Radar Topography Mission (SRTM), a Digital Elevation Model (DEM) with 30 m spatial resolution was used to extract and analyse 18 morphometric parameters including basic, linear, shape and relief. The method of receiver operating characteristics (ROC) curves was used to validate the model’s prediction accuracy. This morphometry-based analysis resulted in the SWs being classified into zones of low, medium, high and very high erosion susceptibility. With regard to erosion susceptibility, 41.51% of the basin (2494.68 km2) is in the very high pri-ority zone; while 10.50%, 44.33% and 3.66% of the basin are in the high, medium and low priority zones respectively. Validation of the final erosion susceptibility map showed a prediction accuracy of 81%. The use of satellite imagery and morphometric analysis in this study was cost- and time-effective for identifying areas susceptible to soil erosion.
Funding
The authors would like to thank the United States Geological Survey (USGS) for unrestricted access to the SRTM data through the Earth Explorer web portal. The authors also thank the editor and anonymous reviewers of Quaestiones Geographicae for their comments which helped to enhance the quality of the paper.
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