Data: Survey of India (SOI) toposheets of 1:50000 and 1:250000 scales were used to generate base layers. Field data was collected with a handheld GPS. The time series remote sensing data acquired from Landsat Series Multispectral sensor (57.5m) and Thematic mapper (28.5m) sensors were downloaded from public domain (http://glcf.umiacs.umd.edu/data). Google Earth data (http://earth.google.com) served in pre and post classification process and validation of the results. Latest data for 2014 (IRS – Indian remote Sensing) was procured from the National remote Sensing Centre (http://www.nrsc.gov.in), Hyderabad. The methods adopted in the analysis involved:
Pre-processing of data: The remote sensing data were geo-referenced, rectified, and cropped pertaining to the study area. Geo-registration of remote sensing data (Landsat data) was done using ground control points collected from the field using pre calibrated GPS (Global Positioning System) and also from known points (such as road intersections, etc.) collected from geo-referenced topographic maps published by the Survey of India. Geo-referencing of acquired remote sensing data to latitude-longitude coordinate system with Evrst 56 datum: Landsat bands, IRS MSS bands, were geo-corrected with the known ground control points (GCP’s) and projected to Polyconic with Evrst 1956 as the datum, followed by masking and cropping of the study area.
Land use analysis: The analyses of land use were carried out using supervised pattern classifier - Gaussian maximum likelihood classifier (GMLC) for Landsat and IRS data. The method involves: i) generation of False Colour Composite (FCC) of remote sensing data (bands – green, red and NIR). This helped in locating heterogeneous patches in the landscape ii) selection of training polygons (these correspond to heterogeneous patches in FCC) covering 15% of the study area and uniformly distributed over the entire study area, iii) loading these training polygons co-ordinates into pre-calibrated GPS, vi) collection of the corresponding attribute data (land use types) for these polygons from the field. GPS helped in locating respective training polygons in the field, iv) supplementing this information with Google Earth (latest as well as archived data),  v) 60% of the training data has been used for  classification, while the balance is used for validation or accuracy assessment.