The dominant method for vegetation change detection using remotely sensed data is through vegetation indexes (Deering & Haas, 1980). Vegetation indexes are algorithms aimed at simplifying data from multiple reflectance bands to a single value correlating to physical vegetation parameters (such as biomass, productivity, leaf area index, or percent vegetation ground cover) (Tucker, 1979). These vegetation indexes are based on the well-documented unique spectral characteristics of healthy green vegetation over the visible to infrared wavelengths. Healthy green vegetation generally reflects very little solar energy in the visible wavelengths (0.4-0.7 um), with a sharp increase in reflectance in the near-infrared wavelength region (0.7-1.1 um). This “red edge” is unique to vegetation as a surface material. Dead or senescent vegetation and soil generally reflect relatively greater amounts of energy in the visible wavelengths and less in the near- infrared. This unique spectral property of green vegetation is used in various indexes ranging in complexity from applying correlation coefficients to brightness values of a near-infrared band, to multi-band ratioing combined with complex algorithms (Jensen, 1996). Arguably the most successful and commonly used of these techniques is the Normalized Difference Vegetation Index (NDVI). NDVI is the traditional vegetation index used by researchers for extracting vegetation abundance from remotely sensed data (Tucker, 1979). It divides the difference between reflectance values in the visible red and near-infrared wavelengths by the overall reflectance in those wavelengths to give an estimate of green vegetation abundance (Tucker, 1979). In essence, the algorithm isolates the dramatic increase in reflectance over the visible red to near infrared wavelengths, and normalizes it by dividing by the overall brightness of each pixel in those wavelengths. Specifically NDVI is:  where the values in either band have been converted from raw DN values to reflectance of solar electromagnetic radiation. The result of this algorithm is a single band data set, ranging from -1 to 1, with values corresponding to photosynthetic vegetation abundance. NDVI has been used extensively to measure vegetation cover characteristics on a broad-scale worldwide, and has been incorporated into many large-scale forest and crop assessment studies (Peterson et al., 1987; Asrar et al., 1984; Bausch, 1993; Benedetti & Rossini, 1993; Hatfield et al., 1985; Wanjura & Hatfield, 1987). It is used to provide weekly vegetation maps, monitor crops over large regions, monitor vegetation change in much of the tropics, and estimate biomass. The limitations of vegetation indexes emanate from the fact that relationships between vegetation abundance and electromagnetic reflectance values in complex forest structures (and areas with high vegetation abundance) are many times nonlinear, whereas vegetation indexes are simple linear algorithms. Therefore, because of increased mutual shadowing in mature stands, aging forests may show a decrease in NDVI while actual biomass increases. Consequently, once vegetation indexes reach a threshold level they no longer accurately correlate to actual vegetation abundance (Begue, 1993; Chance, 1981; Waller et al., 1981; Wanjura & Hatfield, 1987; Wiegand et al., 1991). 1.2.8.1. NDVI Extraction The NDVI is extracted from the LISS III, LANDSAT ETM+, ASTER and the MODIS images at different dates. Further the tea garden NDVI has been masked out. MODIS NDVI along with the Landuse/Landcover map has been used for 1 x 1 and 3 x 3 pixel extraction. Further analysis was then carried out. |