In the present study, efforts have been made to identify and map areas affected by various land degradation in Kheragarah tehsil of Agra, Uttar Pradesh, India. IRS-P6 LISS III satellite data of three dates viz., February, May and October, 2009 have been used in the study. Three remote sensing derived indices have been used such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Soil Brightness Index (SBI) for identifying vegetation, waterlogged area and salt affected land respectively. Decision Tree Classifier (DTC) has incorporated these derived indices for delineating and mapping different types of degradation. Results revealed that about 41.24% of area is non agricultural land in which four categories of degradation could be identified i.e. degraded hill (4.05%), degraded forest (3.46%), wetland (6.26%) and ravinous land (3.26%). The remaining (58.76%) is agricultural land out of which 75.08% is normal land and (24.92%) suffers from two types of degradation viz., chemical (salinity) and physical deterioration (waterlogged). An attempt was done to ensure the efficiency of DTC by comparing it with supervised classification approach .The values of the Kappa statistics were used to compare the performance of the classifiers and it was found to be higher (0.95) for the DTC than supervised classification (0.75). The Z statistics was computed for comparing Kappa coefficients obtained from the error matrices of two above mentioned classifications. Z value was found to be 21.08 which implied that there was a significant difference between Kappa coefficients in both approaches