Machine Learning-Based Mangrove Land Classification on Worldview-2 Satellite Image in Nusa Lembongan Island

Aulia Ilham and Marza Ihsan Marzuki (2017) Machine Learning-Based Mangrove Land Classification on Worldview-2 Satellite Image in Nusa Lembongan Island. International Journal of Remote Sensing and Earth Sciences, 14 (2). pp. 159-166. ISSN 0216-6739

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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%.

Item Type: Article
Uncontrolled Keywords: clustering, machine learning, remote sensing data
Subjects: Teknologi Penginderaan Jauh > Penelitian, Pengkajian, dan Pengembangan > Pemanfaatan Penginderaan Jauh > Pengolahan Data > Klasifikasi
Depositing User: Dinar Indrasasi
Date Deposited: 26 Aug 2021 01:20
Last Modified: 13 Oct 2021 08:00
URI: http://repositori.lapan.go.id/id/eprint/1185

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