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Indonesian peatland, one of the world’s largest tropical peatlands, is facing immense anthropogenic pressures such as illegallogging, degradation and also peat fires, especially in fertile peatlands. However, there still is a lack of appropriate tools toassess peatland land cover change. By taking Pelalawan district located in Sumatra Island, this study determines number ofland cover endmembers that can be detected and mapped using new generation of Landsat 8 OLI in order to develop highqualityburned peat fraction images. Two different image transformations, i.e. Principle Component Analysis (PCA), MinimumNoise Fraction (MNF) and two different scatterplot analyses, i.e. global and local, were tested and their accuracy results werecompared. Analysis of image dimensionality was reduced by using PCA. Pixel Purity Index (PPI), formed by using MNF, wasused to identify pure pixel. Four endmembers consisting of two types of soil (peat soil and dry soil) and two types of vegetation(peat vegetation and dry vegetation) were identified according to the scatterplot and their associated interpretations wereobtained from the Pelalawan Fraction model. The results showed that local scatterplot analysis without PPI masking can detecthigh accuracy burned peat endmember and reduces RMSE value of fraction image to improve classification accuracy

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Spectral Mixture Analysis (SMA) of Landsat Imagery for Land Cover Change Study of Highly Degraded Peatland in Indonesia