摘要
The Main Ethiopian Rift(MER)is an area of extreme topography underlain by post-Miocene volcanic rocks,Jurassic limestone and a Precambrian basement.A prime concern is the rapid expansion of wide gullies that are impinging on agricultural land.We investigate the potential contribution of Advanced Space-borne Thermal Emission and Reflection Radiometer(ASTER)data and geomorphologic parameters to discern patterns and features of gully erosion in the MER.Maximum Likelihood Classifica-tion(MLC),Support Vector Machine(SVM),and Minimum Distance(MD)classifiers are used to extract different gully shapes and patterns.Several spatial textures based on Grey Level Co-occurrence Matrices(GLCMs)are then generated.Afterwards,the same classifiers are applied to the ASTER data combined with the spatial texture information.We used geomorphologic parameters ex-tracted from SRTM and ASTER DEMs to describe the geomorphologic setting and the gullies' shapes.The classifications show accuracies varying between 67% and 89%.Maps derived from this quantitative analysis allow the monitoring and mapping of land degradation as a direct result of gully-widening.This study reveals the utility of combining ASTER data and spatial textural infor-mation in discerning areas affected by gully erosion.
The Main Ethiopian Rift （MER） is an area of extreme topography underlain by post-Miocene volcanic rocks, Jurassic limestone and a Precambrian basement. A prime concern is the rapid expansion of wide gullies that are impinging on agricultural land. We investigate the potential contribution of Advanced Space-borne Thermal Emission and Reflection Radiometer （ASTER） data and geomorphologic parameters to discern patterns and features of gully erosion in the MER. Maximum Likelihood Classifica- tion （MLC）, Support Vector Machine （SVM）, and Minimum Distance （MD） classifiers are used to extract different gully shapes and patterns. Several spatial textures based on Grey Level Co-occurrence Matrices （GLCMs） are then generated. Afterwards, the same classifiers are applied to the ASTER data combined with the spatial texture information. We used geomorphologic parameters extracted from SRTM and ASTER DEMs to describe the geomorphologic setting and the gullies＇ shapes. The classifications show accuracies varying between 67% and 89%. Maps derived from this quantitative analysis allow the monitoring and mapping of land degradation as a direct result of gully-widening. This study reveals the utility of combining ASTER data and spatial textural information in discerning areas affected by gully erosion.
基金
Supported by the German Academic Exchange Service
