Improvement of tropical vegetation mapping using a remote sensing technique: a case of Khao Yai National Park, Thailand
The forest ecosystems of Thailand are characterized by a diverse and complex vegetation structure. Classification of vegetation types of such forest ecosystems has been experienced as a difficult task, even with large-scale aerial photography. Satellite remote sensing, the digital technique in particular, has not been widely used for vegetation mapping in Thailand until now. The objective of this study was to explore the potential of digital image processing over the existing technique of visual interpretation of Landsat Thematic Mapper (TM) false colour composite (BGR-2, 3, 4) to produce forest cover maps in Thailand. Supervised and unsupervised classification methods were employed with different band combinations to discriminate vegetation types in the Khao Yai National Park using Landsat TM data. The results indicated that thematic classes derived from supervised classification produced higher overall accuracy than unsupervised classification. In addition, the combination of ratio bands R4/3, R5/2, R5/4 and R5/7 ranked the highest in terms of accuracy (65% for unsupervised and 79% for supervised) and the combination of bands 2, 3 and 4 gave the lowest (56% for both methods). Finally, it was concluded that, even within the limit of spectral information available in the image, the digital classification can improve the result of visual interpretation.
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