GEO-STATISTICAL BASED SUSCEPTIBILITY MAPPING OF SOIL EROSION AND OPTIMIZATION OF ITS CAUSATIVE FACTORS: A CONCEPTUAL FRAMEWORK
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Date
2017-11
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Publisher
School of Engineering, Taylor’s University
Abstract
Soil erosion hazard is the second biggest environmental challenges after
population growth causing land degradation, desertification and water
deterioration. Its impacts on watersheds include loss of soil nutrients, reduced
reservoir capacity through siltation which may lead to flood risk, landslide, high
water turbidity, etc. These problems become more pronounced in human altered
mountainous areas through intensive agricultural activities, deforestation and
increased urbanization among others. However, due to challenging nature of
soil erosion management, there is great interest in assessing its spatial
distribution and susceptibility levels. This study is thus intend to review the
recent literatures and develop a novel framework for soil erosion susceptibility
mapping using geo-statistical based support vector machine (SVM), remote
sensing and GIS techniques. The conceptual framework is to bridge the
identified knowledge gaps in the area of causative factors’ (CFs) selection. In
this research, RUSLE model, field studies and the existing soil erosion maps for
the study area will be integrated for the development of inventory map. Spatial
data such as Landsat 8, digital soil and geological maps, digital elevation model
and hydrological data shall be processed for the extraction of erosion CFs. GISbased SVM techniques will be adopted for the establishment of spatial
relationships between soil erosion and its CFs, and subsequently for the
development of erosion susceptibility maps. The results of this study include
evaluation of predictive capability of GIS-based SVM in soil erosion mapping
and identification of the most influential CFs for erosion susceptibility
assessment. This study will serve as a guide to watershed planners and to
alleviate soil erosion challenges and its related hazards.
Description
Keywords
Soil erosion, Causative factors, SVM, Susceptibility mapping, GIS