Vegetation maps, mapping needs and scope of digital processing of Landsat Thematic Mapper data in tropical region of south-west India

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Abstract
This study was aimed at assessing the scope of Landsat Thematic Mapper (TM) data for vegetation classification and mapping needs in a tropical region of south-west India. Outputs generated through common digital enhancement/classification techniques were compared with the vegetation map prepared from visual interpretation of black and white panchromatic aerial photographs with a scale of 1:15000 (approximately), in terms of extractable thematic information and cost/time incurred. It has been shown that digital processing of TM data is capable of satisfying the classification and mapping needs in the country with a reasonable degree of precision (85 per cent), in much less cost and time when compared with the aerial photographs. Supervised classification using raw data was found to be more effective in discriminating vegetation types than enhancements like band ratioing and principal component analysis. It was possible to classify forest vegetation with respect to variability in bioclimatic and structural attributes by classification of digital data. In view of varied vegetation classification and mapping needs in India, it is suggested that as detailed a land cover classification as possible should be attempted initially. Subsequently, the detailed classification output, which is often difficult to read or understand, may be converted to user-specific simplified outputs by appropriate aggregation of classes.