Abstract

 In response to growing concerns over the burning of peat swamp forests, researchers have begun developing methods of mapping forest fire. Forest fire is one of the major causes of deforestation of tropical peat swamps in Malaysia. A way of identifying which peat swamp forest is vulnerable to forest fire is to develop a fuel type map to classify forest fire into different risk levels. In this study, remote sensing and geographical information system (GIS) techniques were integrated. Landsat Thematic Mapper (TM) image dated April 3^rd, 1999, which corresponded to fire incident in this study area was used. The objective of this paper is to map fuel types in peat swamp forests. Results show that greenness and wetness components of Tasselled cap, used in classification, accurately captured greener and wetter area by combining supervised image and Tasselled cap image. The overall kappa statistics was 0.94 for combined supervised and Tasselled Cap classification. High values of kappa statistics for certain vegetation classes were due to the availability of representative pixels in the classes.

Key words: Forest fire, Peat swamp, forest fuel classification.