Cloud Computing and Machine Learning Approach for Mangrove Mapping in Marine Protected Area (MPAs) of Banggai, Sulawesi Island Archipelago, Indonesia

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AUTHOR

Hidayat Arismunandar Katili
SSRS Banggai Advanced Research Facility, Banggai Regency, Indonesia

Abd. Malik A. Madinu
SSRS Banggai Advanced Research Facility, Banggai Regency, Indonesia

Khairani Putri Marfi
SSRS Indonesia Tropical Ecological Observatory, SSRS Institute Indonesia, Bogor, Indonesia

Verawati Ayu Lestari
SSRS Indonesia Biodiversity Hub, SSRS Institute Indonesia, Bogor, Indonesia

Aulia Ulfa
SSRS Institute Indonesia, Bogor, Indonesia

Usnil Khotimah
SSRS Indonesia Biodiversity Hub, SSRS Institute Indonesia, Bogor, Indonesia

Nurah Anggraeni
SSRS Indonesia Biodiversity Hub, SSRS Institute Indonesia, Bogor, Indonesia

Danik Septianingrum
SSRS Indonesia Biodiversity Hub, SSRS Institute Indonesia, Bogor, Indonesia

Nihawa Hajar Pudjawati
SSRS Indonesia Tropical Ecological Observatory, SSRS Institute Indonesia, Bogor, Indonesia

Ardya Hwardaya Gustawan
Pemulihan Bumi Indonesia (Pulih Indonesia), Bogor, Indonesia

Azelia Dwi Rahmawati
Forest Resources and Environmental Science, Faculty of Bioresources, Mie University, Japan

Rahmat Asy’Ari
SSRS Institute Indonesia, Bogor, Indonesia

Ongky Ongky
SSRS Banggai Advanced Research Facility, Banggai Regency, Indonesia

Moh Zulfajrin
Computational Soil Science Research Group of Indonesia, Bogor Regency, Indonesia

Diky Dwiyanto
Agrotechnology Study Program, PSDKU Tojo Una-Una, Faculty of Agriculture, Tadulako University, Palu City, Indonesia

Fahri Fahri
Biology Department, Faculty of Science and Mathematics, Tadulako University, Palu City, Indonesia

Mihwan Sataral
Faculty of Agriculture, University of Tompotika Luwuk, Banggai Regency, Indonesia

Yuni Rustiawati
Faculty of Agriculture, University of Tompotika Luwuk, Banggai Regency, Indonesia

Hasrudin Usman
Department of Aquatic Resources, Faculty of Fisheries, Alkhairaat University, Central Sulawesi, Indonesia

Rahmat Pramulya
Traceability and Sustainability System for Indonesian Agricultural Commodities (Calgris Indonesia), Bogor Regency, Indonesia
https://orcid.org/0000-0001-8569-7203

Yudi Setiawan
Center for Environmental Research, IPB University, Bogor Regencym Indonesia
https://orcid.org/0000-0001-8569-7203

Neviaty Putri Zamani
Center for Transdisciplinary and Sustainability Sciences (CTSS), IPB University, Bogor Regency 16680, Indonesia
https://orcid.org/0000-0001-7687-9443

Cecep Kusmana
Department of Silviculture, Faculty of Forestry and Environment, IPB University, Bogor Regency, Indonesia

Ferdy Salamat
Fishery Agency of Banggai Kepulauan Regency, Indonesia

ABSTRACT

Mangroves are coastal forest ecosystems that serve multiple functions for coastal communities. Their ecological role as essential habitats for coastal and estuarine fauna, combined with their economic function as areas for fish production, makes mangrove ecosystems crucial for coastal and marine management. Unfortunately, within the context of sustainable fisheries, mangrove areas in Sulawesi remain underexplored, especially using satellite-based approaches. Therefore, this study focuses on quantifying the extent of mangroves within the Marine Protected Areas (MPAs) of Banggai – Banggai Kepulauan – Baggai Laut, Sulawesi Seascape. Utilizing a combination of Landsat-8 and Landsat-9 satellites through the Google Earth Engine (GEE) cloud computing platform, the mangrove mapping process incorporated machine learning techniques. In the MPAs of Banggai-Bangkep-Balut, mangroves were mapped covering an area of 5,323 hectares, approximately 0.62% of the total designated protected area. Mangrove detection using the two Landsat satellites achieved high accuracy levels, with an overall accuracy of 0.91 and a kappa statistic of 0.83. The error matrix indicated 6 misclassifications out of 73 validation points. These findings confirm the reliability of mangrove data within the Banggai MPAs management framework, supporting its use as blue carbon data to strengthen national emission reduction policies. In addition, cloud computing has proven to be excellent in extracting mangrove data in MPA areas and is highly recommended in future monitoring to create sustainable MPA governance.

CITATION

Katili HA, Madinu AMA, Marfi KP, Lestari VA, Ulfa A, Khotimah U, Anggraeni N, Septianingrum D, Pudjawati NH, Gustawan AH, Rahmawati AD, Asy’Ari R, Zulfajrin M, Dwiyanto D, Fahri F, Sataral M, Rustiawati Y, Usman H, Pramulya R, Setiawan Y, Zamani NP, Kusmana C, Selamat F. 2025. Cloud Computing and Machine Learning Approach for Mangrove Mapping in Marine Protected Area (MPAs) of Banggai, Sulawesi Island Archipelago, Indonesia. SSRS Journal A: Agro-Environmental Research. 3: 01 – 15. https://publishing.ssrs.or.id/ojs/index.php/ssrs-a/article/view/40