Tools and Techniques

This includes fusion of multi-resolution data (of different spectral and spatial resolutions), classification of data to derive land use parameters, fragmentation analysis to understand the process of fragmentation at the landscape level and computation of spatial metrics to capture landscape dynamics.

1. Image Fusion
Earth observation satellites provide data at different spatial, spectral and temporal resolutions. Satellites, such as IRS (LISS III) have a high spatial resolution panchromatic (PAN) band (5.8 m) and low resolution multispectral (MS) bands (G, R, NIR of 23.5 m) in order to support both spectral and best spatial resolution while minimising on-board data handling needs (Cakir and Khorram, 2008). For many applications, the fusion of these data from multiple sensors aids in delineating objects with comprehensive information due to the integration of spatial information present in the PAN image and spectral information present in the low resolution MS data. Here we have used the À Trous algorithm based wavelet transform (ATW) for image fusion (Nunez et al., 1999).

2. Image Classification
The extraction of LU information from remote sensing data is often difficult since it is closely associated with the human intervention for which the data need to be obtained from other sources (Kandrika and Roy, 2008). Keeping all the requirements and constraints in view, Gaussian Maximum Likelihood classifier (GMLC) is a parametric classifier used for classifying the satellite data.

3. Change detection
LU change detection is performed by change/no-change recognition followed by boundary delineation on images of two different time periods (Zhang and Zhang, 2007; Lu et al., 2004). Change/no-change recognition extracts changes from an unchanged background. The pixel patches marked as changed are then checked and the boundaries are delineated to extract the changed areas. A variety of change detection algorithms such as Principal Component Analysis (Zhang and Zhang, 2007), Correspondence Analysis (Cakir et al., 2006) and image differencing (Lyon et al., 1998) have been tested to recognise LU changes from bi-temporal images.

4. Forest fragmentation
Forest fragmentation is the process whereby a large, contiguous area of forest is both reduced in area and divided into two or more fragments (Meyer and Turner, 1994). The primary concern is direct loss of forest area, and all disturbed forests are subject to edge effects of one kind or another. Forest fragmentation metrics with the total extent of forest and its occurrence as adjacent pixels is computed through fixed-area windows surrounding each forest pixel. The result is stored at the location of the centre pixel. It is computed through Pf (the ratio of pixels that are forested to the total non-water pixels in the window) and Pff (the proportion of all adjacent (cardinal directions only) pixel pairs that include at least one forest pixel, for which both pixels are forested). Based on the knowledge of Pf and Pff, six fragmentation categories were mapped (Riitters et al., 2000): (i) interior, for which Pf = 1.0; (ii), patch, Pf < 0.4;  (iii) transitional, 0.4 < Pf < 0.6; (iv) edge, Pf > 0.6 and Pf – Pff > 0; (v) perforated, Pf > 0.6 and Pf – Pff < 0, and (vi) undetermined, Pf > 0.6 and Pf = Pff.