Methods

The methods adopted in the analysis included image fusion, LULC analyses, change detection using temporal data and temporal forest fragmentation analysis.

1. Data Preprocessing: Base layers like district boundary, drainage network, water bodies, etc. were mapped from the Survey of India (SOI) toposheets of scale 1:50000. Landsat bands, IRS LISS III MS bands were geocorrected with the known ground control points (GCP’s) and projected to geographic latitude-longitude with WGS-84 as the datum, followed by masking and cropping of the study region (region of interest – ROI). Resampling of the data using nearest neighbourhood technique were carried out for (i) bands 1- 4 of Landsat (1972) data to 79 m, (ii) bands 1-6 of Landsat TM (1989) to 30 m, (iii) bands 1-5 and 7 of Landsat ETM (2000) to 30 m and band 8 to 15 m, (iv) IRS LISS-III MS (2007) bands 1-3 to 23.5 m and IRS PAN band to 5.8 m. IRS PAN band of 5.8 m spatial resolution was merged with the LISS-III MS bands of 2007 using Multi-resolution analysis based on the wavelet transformation (Nunez et al., 1999). Landsat ETM+ PAN band (band 8) of 15 m spatial resolution was fused with bands 1, 2, 3, 4, 5 and 7 of the same satellite. Subsequently, all bands were resampled to 5.8 m for consistency and easier comparison of LU class statistics across the data sets.

2. LULC analysis: NDVI was computed to segregate regions under vegetation, soil and water. Signature separation corresponding to the LU classes was done using Transformed divergence (TD) matrix and Bhattacharrya (or Jeffries-Mastusuta) distance. Both the TD and Jeffries-Mastusuta measures are real values between 0 and 2, where ‘0’ indicates complete overlap between the signatures of two classes and ‘2’ indicates a complete separation between the two classes. Both measures are monotonically related to classification accuracies. The larger the separability values, the better the final classification results (Richards, 1986). Supervised classification using MLC with the training sets uniformly distributed representing / covering the study area was performed on the four temporal datasets. Accuracy assessment was done using error matrix by computing producer's accuracy, user's accuracy, overall accuracy and Kappa statistics (Campbell, 2002; Lillesand and Kiefer, 2002) by overlaying the test data not used in classification. Receiver operating characteristic (ROC) curves were plotted to assess the accuracy of the classified data (Fawcett, 2006). In the absence of historical data, the classified images of 1972, 1989 and 2000 were validated by visual interpretation using the tone, texture and other interpretation keys from the false colour composite images.

3. Spatial change analysis: Pixel to pixel change was mapped for each category from 1972 to 2007 using PCA - Principal Component Analysis (Zhang and Zhang, 2007), CA - Correspondence Analysis (Cakir et al., 2006) and NDVI image differencing (Lyon et al., 1998). The absolute and relative changes on the original bands of the two time periods were also computed. If there is a change between the two dates, the pixel had either negative or positive values. However, subtle change in brightness values between two dates also occur due to atmospheric conditions at different dates, sensor differences, etc., even after radiometric normalisation. Brightness values of no-change areas were distributed around the mean value of each difference image.

4. Forest fragmentation analysis: Pf and Pff in a kernel of 3 x 3 were computed (Riitters et al., 2000) to identify forest fragmentation categories. Based on these forest fragmentation indices (Hurd et al., 2002) Total forest proportion (TFP: ratio of area under forests to the total geographical extent excluding water bodies), weighted forest area (WFA) and Forest continuity (FC) were computed. TFP provides an extent of forest cover in a region ranging from 0 to 1. Weighted values for the weighted forest area (WFA) are derived from the median Pf value for each fragmentation class as given by equation 1 and FC is computed by equation 2.

                                  (1)

                                  (2)

Six patch level metrics – largest patch index, number of patches, patch density, total edge, edge density and landscape shape index were calculated using Fragstats (McGarigal et al., 2002).