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All Journal Jurnal Simetris Perfecting a Video Game with Game Metrics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknoinfo Jurnal Filsafat Indonesia Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Internet of Things and Artificial Intelligence Journal ROUTERS: Jurnal Sistem dan Teknologi Informasi
Agus Ambarwari
Politeknik Negeri Lampung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

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Studying How Machine Learning Maps Mangroves in Moderate-Resolution Satellite Images Agus Ambarwari; Emir Mauludi Husni
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.25263

Abstract

Intertidal mangrove forests are ecosystems that are extremely productive offering diverse socio-economic advantages. Preserving and appropriately using these ecosystems is crucial. However, safeguarding and restoring mangroves present challenges due to their extensive and hard-to-reach areas. Leveraging remote sensing technology and diverse image classification methods has shown promise in accurately mapping and monitoring mangroves. This study reviews the use of machine learning methods in mapping and monitoring mangroves, particularly using moderate-resolution multispectral satellite images. The literature study was conducted by systematically searching and analyzing articles published in Scopus-indexed journals from 2018 and 2023. The primary goals are to uncover methodologies for mapping mangroves with moderate-resolution imagery, identify advancements in machine learning algorithms, and assist researchers in staying updated in this field. The findings reveal that various machine-learning algorithms can be employed to map mangroves. Mangrove mapping with machine learning typically involves stages such as inputting multispectral images, image preprocessing, image classification, and assessing accuracy. Among the techniques, in the case of remote sensing data, ensemble tree-based approaches such as random forest outperform single classifiers. Potential and emerging issues for future research encompass automating the generation of training datasets for specific land cover classification, developing methods to transfer the classification model to different study areas, and making use of cloud-based technologies for processing remote sensing data.
Co-Authors Anung Wahyudi Dewi Kania Widyawati Dimitri Mahayana Emir Mauludi Husni Imam Ahmad Iqbal Yasin Irman Hermadi Leo Pradana Leo Muhammad Adam Putra Darma Muharman Lubis Neneng Neneng Qadhli Jafar Adrian Rohmat Indra Borman Septafansyah Dwi Putra Septafiansyah Dwi Putra Yusmaida Yusmaida Yusra Fernando