Methodology Adopted

Analysis can be delineated into five major steps (1) data acquisition from public repositories such as USGS, land use classification and accuracy assessment using open source software GRASS. (2) Land use analysis and baseline vali-dation. (3) Developing data for three scenarios based on urban growth. (4) Calibrating the model to find out the best weights based on the influence on the neighbourhood pix-els. (5) Development of model and validation of the model with predicting of future scenario based on validated data and calculation of spatial metric for analysis of landscape configuration in various scenarios.
1. Image pre-processing Remote sensing data were acquired for specific years from USGS earth explorer (http://www.usgs.gov/). Remotely sensing data     obtained were geo-referenced, rectified and cropped pertaining to the study area.
2. Land use analysis Land use analysis was performed using supervised pattern classifier—Gaussian maximum likeli- hood classifier (GMLC). GMLC has     been already proved as one of the superior classification techniques due to use of cost function and probability determination techniques employed     (Duda et al. 2000; Bharath et al. 2018). Land use classification was performed and the classified data is categorised into four major classes built-up     area, vegeta- tion, open area, and water body as described in Table 2. Essentially land use analysis is performed in four broad steps as follows.
    a. Stacking as image composite-false colour composite to identify various classes of patches (bands–near infrared, green and red green).
    b. Collection of training data as training polygons using pre-calibrated GPS AND and using google earth (validated and corrected with a shift in posi             tion). Training data was collected in order to clas- sify and also to validate the results of the classifica tion. 70% of the training data collected were          used to derive the user-classified map. 30% of exact ground truth was used to validate it.
    c. Land use classification using GMLC using GRASS GIS (Geographical Analysis Support System) an open source software has been used for the                analysis, which has the robust support for processing both vector and raster files.
    d. Validation of land use by performing accuracy assessment and kappa statistics: accuracy assess- ments helps the data generators to determine the        quality of the information. The test samples from user classified map and validation ground truth maps generated is use to generate well-known          methods in validation of land use using error matrix and calcu- late kappa (κ) statistics.