Munajat Nursaputra, Syamsu Rijal, A. Chairil, Siti Halimah Larekeng, Nasri Nasri, A. Siady Hamzah, Yunus Aris Wibowo, Hendy Fatchrohman, Dian Adhetya Arief, Lesan Permonojati, S. Suriadi, Dwi Kurniawan, Rikki Afrizal, Sri Rahayu Ayuba
Mangroves, a unique coastal forest ecosystem with many benefits, are currently threatened with degradation due to anthropogenic activities and climate change combined. Based on periodic data from the Ministry of Environment and Forestry, the distribution of mangroves in South Sulawesi has decreased from 1990-2020 by 80%. However, because the annual decline in mangrove areas is unknown, information about when this degradation happens in periodic data cannot be determined precisely. This ecosystem mapping is essential for the conservation and sustainable development of blue carbon in low-carbon planning policies. Methodology and technology are needed to continuously obtain the latest information on the area and density of mangrove vegetation. This study conducted research on the potential of machine learning-based geospatial technology to classify mangroves. This research utilizes remote sensing data that can be accessed for free such as multispectral remote sensing data from Sentinel-2, which is processed on Google Earth Engine (GEE), a cloud computing platform. In the GEE platform, Sentinel 2 is classified to map mangrove conditions using several methods such as Random Forest, Cart, and Super Vector Machine (SVM). The development of this technology will further expand the range for the availability of potential mangrove data on a multi-temporal basis, which is not only one piece of information per year but also several pieces of information within one year in accordance with the availability of remote sensing image data. © 2025 American Institute of Physics Inc.. All rights reserved.
Forestry Study Program, Universitas Hasanuddin, Makassar, Indonesia; Forest Engineering Study Program, Universitas Hasanuddin, Makassar, Indonesia; Forest Conservation Study Program, Universitas Hasanuddin, Makassar, Indonesia; Geography Education Department, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia; Department of Earth Technology Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia; Department of Geography, Universitas Negeri Padang, Padang, Indonesia; Master Program in Planning and Management of Coastal Area and Watershed, Yogyakarta, Indonesia; Urban and Regional Planning Department, Kuantan Singingi Islamic University, Riau, Indonesia; Geography Study Program, Universitas Muhammadiyah Gorontalo, Gorontalo, Indonesia; Biodiversity Research Group, Universitas Hasanuddin, Makassar, Indonesia
