Br. Ginting, Devica Natalia
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BATHYMETRY EXTRACTION FROM SPOT 7 SATELLITE IMAGERY USING RANDOM FOREST METHODS Setiawan, Kuncoro Teguh; Suwargana, Nana; Br. Ginting, Devica Natalia; Manessa, Masita Dwi Mandini; Anggraini, Nanin; Adawiah, Syifa Wismayati; Julzarika, Atriyon; Surahman, Surahman; Rosid, Syamsu; Supardjo, Agustinus Harsono
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (819.189 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3085

Abstract

The scope of this research is the application of the random forest method to SPOT 7 data to produce bathymetry information for shallow waters in Indonesia. The study aimed to analyze the effect of base objects in shallow marine habitats on estimating bathymetry from SPOT 7 satellite imagery. SPOT 7 satellite imagery of the shallow sea waters of Gili Matra, West Nusa Tenggara Province was used in this research. The estimation of bathymetry was carried out using two in-situ depth-data modifications, in the form of a random forest algorithm used both without and with benthic habitats (coral reefs, seagrass, macroalgae, and substrates). For bathymetry estimation from SPOT 7 data, the first modification (without benthic habitats) resulted in a 90.2% coefficient of determination (R2) and 1.57 RMSE, while the second modification (with benthic habitats) resulted in an 85.3% coefficient of determination (R2) and 2.48 RMSE. This research showed that the first modification achieved slightly better results than the second modification; thus, the benthic habitat did not significantly influence bathymetry estimation from SPOT 7 imagery.