The objective of this research is to develop remote sensing and GIS techniques with hydrological approach for critical land identification and mapping. This research was done by weighting method using three variables, i.e., slope, runoff curve number and percent land cover derived from digital and visual analysis techniques. Critical land identification was known by integrating Runoff Curve Number resulted from combination of land use and Hidrological Soil Group, Slope and Percent Land Cover. Land use and Percent Land Cover information were derived from Landsat data, while Hidrological Soil Group results were from 1:50000 soil type map, and slope was from 1;25000 scale of topographic map. Critical land modelling was done using three parameters, slope, runoff Curve Number and percent land cover. This research examined thirteen a, b and c combinations. Mapping accuracy of the model was evaluated using erosion hazard based on USLE formula: A=RKLSCP. The study shows that remote sensing data can be used to produce land use/land cover for curve number prediction and land cover percent as critical land identicication and mapping variables, having accuracy of 62, 59 percent.  

The objective of this research is to develop remote sensing and GIS techniques with hydrological approach for critical land identification and mapping. This research was done by weighting method using three variables, i.e., slope, runoff curve number and percent land cover derived from digital and visual analysis techniques. Critical land identification was known by integrating Runoff Curve Number resulted from combination of land use and Hidrological Soil Group, Slope and Percent Land Cover. Land use and Percent Land Cover information were derived from Landsat data, while Hidrological Soil Group results were from 1:50000 soil type map, and slope was from 1;25000 scale of topographic map. Critical land modelling was done using three parameters, slope, runoff Curve Number and percent land cover. This research examined thirteen a, b and c combinations. Mapping accuracy of the model was evaluated using erosion hazard based on USLE formula: A=RKLSCP. The study shows that remote sensing data can be used to produce land use/land cover for curve number prediction and land cover percent as critical land identicication and mapping variables, having accuracy of 62, 59 percent.