The remote sensing data are processed to quantify the land use of respective basins broadly into 6 classes – forest and vegetation; agriculture and cultivated area; open scrub and barren; water bodies; built-up; and others (includes categories like rocky outcrop, etc.). The multi-spectral data of Indian Remote Sensing (IRS) LISS-III with a spatial resolution of 23.5m were analyzed using IDRISI Andes (Eastman, 2006; http://www.clarklabs.org). The image analyses included image registration, rectification and enhancement, false colour composite (FCC) generation. The image analyses were undertaken for each of the scenes cropping with respect to the extent of basins falling within each scene. The classification of the multi-spectral remote sensing data was carried through a multi-stage classification process: unsupervised and supervised. In the unsupervised classification the number of clusters for classification is identified through the number of distinct peaks obtained from the histogram. For the supervised classification the signatures were derived from the training data obtained in the field using Global Positioning System (GPS) for distinctive land cover and some of the land cover features obtained from unsupervised classification. FCC aided in the identification of heterogeneous locations. Attribute data (type of heterogeneous patches/vegetation, number of individuals per unit area, etc.) corresponding to these heterogeneous locations (training polygons) using hand held pre-calibrated GPS (Global Positioning System), These data (spatial location of training polygons with attribute data) were used to classify the remote sensing data corresponding to the respective river basins. The signatures generated for each of the land use were also verified with the false color composite image and Google Earth (http://www.googleearth.com). Based on these signatures, corresponding to various land features, supervised image classification was carried out using Gaussian Maximum Likelihood Classifier (GMLC) to the final six categories.