MACHINE LEARNING-BASED MANGROVE LAND CLASSIFICATION ON WORLDVIEW-2 SATELLITE IMAGE IN NUSA LEMBONGAN ISLAND

Authors

  • Aulia Ilham Program Studi Oseanografi, Fakultas Ilmu dan Teknologi Kebumian, Institut Teknologi Bandung, Jl. Ganesha No. 10, Bandung 40132
  • Marza Ihsan Marzuki Badan Riset dan Sumber Daya Manusia, Kementerian Kelautan dan Perikanan, Jl. Pasir Putih 1, AncolTimur, Jakarta 14430

DOI:

https://doi.org/10.30536/j.ijreses.2017.v14.a2820

Keywords:

clustering, machine learning, remote sensing data

Abstract

Machine learning is an empirical approach for regressions, clustering and/or classifying (supervised or unsupervised) on a non-linear system. This method is mainly used to analyze a complex system for  wide data observation. In remote sensing, machine learning method could be  used for image data classification with software tools independence. This research aims to classify the distribution, type, and area of mangroves using Akaike Information Criterion approach for case study in Nusa Lembongan Island. This study is important because mangrove forests have an important role ecologically, economically, and socially. For example is as a green belt for protection of coastline from storm and tsunami wave. Using satellite images Worldview-2 with data resolution of 0.46 meters, this method could identify automatically land class, sea class/water, and mangroves class. Three types of mangrove have been identified namely: Rhizophora apiculata, Sonnetaria alba, and other mangrove species. The result showed that the accuracy of classification was about 68.32%.

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Published

2025-11-26

Issue

Vol. 14 No. 2 (2017)

Section

Articles

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Copyright (c) 2017 Author (S)

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