Abstract
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.
Highlights
Indonesia is one of the countries having the largest tropical peatlands area in the world
This study evaluated two different image transformations: i.e., Principle Component Analysis (PCA), Minimum Noise Fraction (MNF) and two different scatterplot, i.e. global and local, to detect the best endmember which is vital to obtain high quality burned peat fraction image
Analysis of image dimensionality was reduced by using PCA
Summary
Indonesia is one of the countries having the largest tropical peatlands area in the world. Land-cover classification using medium spatial resolution data such as Landsat is often difficult because of limitations in spatial resolution of the data and the heterogeneity of features on the ground especially for successional stages in a degraded tropical forest region (Souza, 2000; Stone, 1998; Eraldo, 2010). This problem often produces poor classification accuracy when conventional algorithms such as the maximum likelihood classifier are used. Several methods have been developed to solve the mixed pixels problem, one of them being the Spectral Mixture Analysis (SMA)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
