MATERIALS AND METHODS

Shimoga district of Karnataka state is situated in the heart of the Western Ghats region, which is one of the ‘hot-spots of biodiversity’ in India. Shimoga district is situated between 13°27' and 14°39' N latitude and between 74°37' and 75°52' E longitude in about the mid-south western part of the Karnataka State (Fig. 1). The district receives an average annual rainfall of 2869 mm (Annual rainfall report, Govt. of Karnataka). The important rivers that flow through the Shimoga district are the Tunga, Bhadra, Tungabhadra, Sharavati, Kumudvati and Varada. The forest change dynamics was analyzed using temporal remote sensing data of the period 1973 to 2012. Remote sensing data analysis involved following steps: (Ramachandra et.al, 2012)

1. Pre-processing: The remote sensing data obtained were geo-referenced, rectified and cropped pertaining to the study area. Geo-registration of remote sensing data (Landsat data) has been 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. The Landsat satellite 1973 images have a spatial resolution of 57.5 m x 57.5 m (nominal resolution) were resampled to 28.5m comparable to the 1989 - 2010 data which are 28.5 m x 28.5 m (nominal resolution). Landsat ETM+ bands of 2010 were corrected for the SLC-off by using image enhancement techniques, followed by nearest-neighbour interpolation.

2. Vegetation Cover Analysis: Normalised Difference Vegetation index (NDVI) was computed to understand the changes in the vegetation cover during the study period. NDVI is the most common measurement used for measuring vegetation cover. It ranges from values -1 to +1. Very low values of NDVI (-0.1 and below) correspond to soil or barren areas of rock, sand, or urban builtup. Zero indicates the water cover. Moderate values represent low density vegetation (0.1 to 0.3), while high values indicate thick canopy vegetation (0.6 to 0.8).

3. Land Use Analysis: The method involves i) generation of False Colour Composite (FCC) of remote sensing data (bands – green, red and NIR).  ii) Selection of training polygons iii) loading these training polygons co-ordinates into pre-calibrated GPSiv) collection of the corresponding attribute data (land use types) for these polygons from the field  and supplementing this information with Google Earth v) 60% of the training data has been used for  classification, while the balance is used for validation or accuracy assessment.

Land use analysis was carried out using supervised pattern classifier - Gaussian

Figure 1: Study Area: Shimoga