Rock Units Erosion Susceptibility Detection and Classification Using Nonlinear Correlation Analysis and Landsat ETM+ Data
محورهای موضوعی : فصلنامه علمی پژوهشی سنجش از دور راداری و نوری و سیستم اطلاعات جغرافیایی
Ahmad Mokhtari
1
,
Kourosh Shirani
2
,
Farzad Heidari
3
1 - a Assistant Professor, Soil Conservation and Watershed Management department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran
2 - Assistant Professor, Soil Conservation and Watershed Management department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran
3 - Scientific board member, Soil Conservation and Watershed Management department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran
کلید واژه: Logistic regression, satellite data, Landsat ETM +, lithological mapping, soil erosion susceptibility,
چکیده مقاله :
the lithological maps is inevitable in the preparation of rock unit’s erosion susceptibility maps. In this study, rock unit outcrops in the Soh Basin (50 km Northern Isfahan) were extracted using nonlinear correlation analysis of satellite data. Moreover, rock unit’s erosion susceptibility such as marl, shale, and quaternary deposits and resistant rock units such as sandstone and limestone were extracted based on soil erosion intensity factors. The lithology of the basin was studied usingthe virtual variables method. Initially, rock units, as a virtual independent variable, and the PC1 (the first principal component) of ETM+ multispectral bands were by amultiple linear regression model. Afterward, rock units were in logistic regression analysis as virtual dependent variables. The results revealed that logistic regression analysis is a suitable model for rock unit’s extraction.
the lithological maps is inevitable in the preparation of rock unit’s erosion susceptibility maps. In this study, rock unit outcrops in the Soh Basin (50 km Northern Isfahan) were extracted using nonlinear correlation analysis of satellite data. Moreover, rock unit’s erosion susceptibility such as marl, shale, and quaternary deposits and resistant rock units such as sandstone and limestone were extracted based on soil erosion intensity factors. The lithology of the basin was studied usingthe virtual variables method. Initially, rock units, as a virtual independent variable, and the PC1 (the first principal component) of ETM+ multispectral bands were by amultiple linear regression model. Afterward, rock units were in logistic regression analysis as virtual dependent variables. The results revealed that logistic regression analysis is a suitable model for rock unit’s extraction.
