International Journal of Remote Sensing and Earth Sciences (IJReSES)
Vol 14, No 2 (2017)

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

Aulia Ilham (Institut Teknologi Bandung)
Marza Ihsan Marzuki (Unknown)



Article Info

Publish Date
08 Jan 2018

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%.

Copyrights © 2017






Journal Info

Abbrev

ijreses

Publisher

Subject

Earth & Planetary Sciences

Description

International Journal of Remote Sensing and Earth Sciences (IJReSES) is expected to enrich the serial publications on earth sciences, in general, and remote sensing in particular, not only in Indonesia and Asian countries, but also worldwide. This journal is intended, among others, to complement ...