Principal polar spectral indices for mapping mangroves forest in South East Asia: study case Indonesia

Abstract

Identification and monitoring of species composition and richness is needed to formulate effective mangrove management and conservation priorities. Prior studies have used commercial satellite images which are cost prohibitive for national and global applications. Here, we used freely available Landsat satellite data and new indices to discriminate mangrove species in Maros Regency, South Sulawesi, Indonesia and Segara Anakan, West Java, Indonesia. We use sensitive algorithm of the principal polar spectral (PPS) indices to discriminate mangroves species. PPS Indices were produced from a set of 3-dimensional Landsat 8 Operational Land Imager (OLI) spectral indices (PPS Brightness, PPS Greenness, and PPS Wetness) determined by a polar change of the principal component axes of a spectral image of reference scene. We qualitatively compare this set of PPS indices with the set of conventional RGB multi-bands image composition and conventional Normalized Difference Vegetation Indices (NDVI) for mangroves species discrimination. The comparisons indicate that the set of PPS indices have the potential for regional and possibly global applications in mangroves species mapping and monitoring.

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