The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 495–501, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-495-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 495–501, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-495-2015

  29 Apr 2015

29 Apr 2015

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

A. D. Sakti1,2 and S. Tsuyuki2 A. D. Sakti and S. Tsuyuki
  • 1Center for Remote Sensing, Bandung Institute of Technology (ITB), Bandung, Indonesia
  • 2Graduate School of Agricultural and Life Sciences, Global Forest Environmental Studies, The University of Tokyo, Tokyo, Japan

Keywords: Tropical Peatland Degradation, Spectral Mixture Analysis, Endmember Analysis, Burned Peat Fraction, Pelalawan District

Abstract. Indonesian peatland, one of the world’s largest tropical peatlands, is facing immense anthropogenic pressures such as illegal logging, degradation and also peat fires, especially in fertile peatlands. However, there still is a lack of appropriate tools to assess peatland land cover change. By taking Pelalawan district located in Sumatra Island, this study determines number of land cover endmembers that can be detected and mapped using new generation of Landsat 8 OLI in order to develop highquality burned peat fraction images. Two different image transformations, i.e. Principle Component Analysis (PCA), Minimum Noise Fraction (MNF) and two different scatterplot analyses, i.e. global and local, were tested and their accuracy results were compared. Analysis of image dimensionality was reduced by using PCA. Pixel Purity Index (PPI), formed by using MNF, was used 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 were obtained from the Pelalawan Fraction model. The results showed that local scatterplot analysis without PPI masking can detect high accuracy burned peat endmember and reduces RMSE value of fraction image to improve classification accuracy.