Method

Quantification of Spatial Patterns of Landscape Dynamics
Figure 3 outlines the protocol adopted for the analysis. Multi-resolution RS data used for spatial analyses are Landsat multispectral sen-sor (MSS-1973; http://landsat.ugs.gov), Opera-tional Land Imager (OLI-2016) and online Goo-gle Earth data (http://earth.google.com). The ancillary data is used to classify the remote sens-ing data and the interpretation of different land use types. Topographic maps (http://surveyof india. gov.in) provided ground control points to rectify remotely sensed data and digitized paper maps (topographic maps). The Survey of India (SOI) toposheets (1:50000 and 1:250000 scales) and vegetation map of South India (http//www. ifpindia.org/ifpsitedata/presentation) developed by French Institute (1986) of scale 1:250000 was digitized to identify various forest cover types and temporal analyses to find out the changes in vegetation. Field survey was carried out with the pre-calibrated GPS (Global Positioning System - Garmin GPS unit). Ground control points are used to geometrically correct remote sensing data and also to validate the classified land use information. The supervised classification scheme of Gaussian maximum likelihood classifier (GMLC) scheme is adopted for land use analysis under 10 different land use categories using GRASS GIS (Geographical Analysis Support System). GRASS is a free and open source geospatial software with the robust functionalities for processing vector and raster data available at (http:// wgbis.ces.iisc.ernet.in/grass/ ). The training data (60%) collected has been used for classification, while the balance is used for accuracy assess-ment to validate the classification. The test sam-ples are then used to create error matrix (also re-ferred as confusion matrix) kappa (κ) statistics and overall (producer’s and user’s) accuracies to assess the classification accuracies (Lillesand et al. 2014).