Figure 10 and 11 outlines the overall method adopted for delineating ecologically sensitive villages in the Netravathi River basin. Land use dynamics is understood as per the standard protocol using remote sensing data as described in Figure 10, which involved i) data acquisition, ii) data preprocessing and iii) classification.

i) Data Acquisition: The process of data acquisition involves the collection of Primary and Secondary Data. Primary Data includes Remote Sensing Data and Field Data. Remote Sensing Data (of about 10 scenes) of Landsat 8 for the year 2016 was downloaded from USGS web portal ( https://earthexplorer.usgs.gov). GPS (pre calibrated Global Positioning System) and AGPS based field surveys were done in order to supplement land use analysis (with training data, geocontrol points, etc.). Secondary Data collection involves the collection of ancillary data such a French institute Puducherry vegetation maps (Pascal, 1986), Geographical Survey of India topographic land use maps (http://www.portal.gsi.gov.in), Biodiversity portal (http://indiabiodiversity.org/), and Virtual earth data such as  Google Earth (http://earth.google.com), Bhuvan (http://bhuvan.nrsc.gov.in). The Secondary data provide additional input to the field data for data pre-processing and classification.

ii) Data Preprocessing: This involves Geo-referencing and Radiometric correction of RS data. GPS based field data along with online spatial data (Google earth; http://earth.google.com, Bhuvan: http://bhuvan.nrsc.gov.in) were used to geo-reference the remote sensing data. Error up to 30 meter (1 pixel) was allowed during the process of Geo-referencing the data. RS data was checked for radiometric errors and enhancement was carried out for those datasets having errors. The correct image was cropped to the area of interest (Netravathi Basin).

iii) Classification: Remote sensing data classification is a process of producing a thematic map by assigning categories to each pixel based on the spectral signatures obtained from a stack of multi-band RS data (Lillesand et al., 2004; Gonzalez and Woods, 2007). The process of classification involves the creation of False Color Composite, selection of training sites, classification and accuracy assessment. Creation of FCC helps in identifying the heterogeneous feature. NIR, Red and Green bands are stacked to create an FCC. Secondary data and Field data are used in association with Remote Sensing data to delineate heterogeneous features covering at least 15% of the scene area. Features such as Forests (evergreen, deciduous, scrub, mixed forest), Grasslands, Built-up, Agriculture (Sown and Fallow), Plantation (Coconut, Rubber, Tea, Coffee, etc.), Water bodies, Others (Quarry, Sand, Open lands, Rocky outcrops) were identified. Maximum likelihood classifier is one of best and most commonly used classification tool (Bharath et al., 2012; Vinay et al., 2013; Bharath et al., 2014a, b; Ramachandra et al., 2016; Ramachandra et al., 2017). The supervised classification scheme of Gaussian maximum likelihood classifier (GMLC) scheme is adopted for land use analysis under 6 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 assessment to validate the classification. The test samples are then used to create error matrix (also referred as confusion matrix) Kappa (κ) statistics and overall (producer's and user's) accuracies to assess the classification accuracies (Lillesand et al., 2014).
The study area is divided into 5’× 5’ equal area grids (74) covering approximately 9 x 9 km2 to account the changes at micro scale. The data of various themes were also collected based on literature review, unpublished datasets, and ground-based surveys. A detailed database has been created for various themes covering all aspects of land to the estuarine ecosystem. A series of maps pertaining to various themes were developed based on the data. The weightage metric score has been computed to captures the priorities associated with various themes (Figure 11). Developing a weightage metric score analysis requires combining knowledge from a wide array of disciplines (Termorshuizen and Opdam, 2009), planning should acknowledge and actively integrate present and future needs for landscape (Ramachandra et al., 2017). The approach has chosen a framework  based on respective themes’ relative weights (Beinat, 1997) for developing eco-sensitive regions. This approach provides a transparent system for combining multiple data sets together to infer the significance of a particular region.
The weightage is defined in Equation 1:

 … (1)

Where n is the number of data sets, Vi is the value associated with criterion i, and Wi is the weight associated with that criterion. Each criterion is described by an indicator mapped to a value normalized between 10 to 1. The value 10 corresponds to very higher priority for conservation whereas 1 is converse to above. The value 7, 5 and 3 corresponds to high, moderate, low levels of conservation. In particular, the weightages, which is based on an individual proxy and draws extensively on GIS techniques, stands out as the most effective method. The final ESR map will result as a guide for the conservation of most sensitive regions and rest. The map can be used by decisionmakers as a basis for effective planning.

Figure 10: Method followed for land use analysis

Figure 11: Computation of ecologically sensitive regions.