| Issue |
BIO Web Conf.
Volume 185, 2025
The International Symposium on Marine and Fisheries (SYMARFISH 2025)
|
|
|---|---|---|
| Article Number | 05001 | |
| Number of page(s) | 13 | |
| Section | Marine and Fisheries Geographic Information System | |
| DOI | https://doi.org/10.1051/bioconf/202518505001 | |
| Published online | 14 August 2025 | |
Spatio-temporal dynamics of mangrove ecosystems using remote sensing and machine learning in Google Earth Engine; A case study of Tongke-Tongke Indonesia
1 Departemen of Marine Science, Hasanuddin University, Indonesia
2 Marine Spatial Planning Research Group, Hasanuddin University, Indonesia
3 Student of Department of Marine Science, Hasanuddin University, Indonesia
4 Departemen of Urban and Regional Planning, Hasanuddin University, Indonesia
5 Departemen of Fisheries Hasanuddin University, Indonesia
* Corresponding author: ahmad.faizal@unhas.ac.id
This study aimed to map changes in mangrove ecosystem conditions in Tongke-tongke Village, Sinjai Regency, South Sulawesi, using the Google Earth Engine (GEE) platform. Mangroves are vital coastal ecosystems that are vulnerable to degradation owing to anthropogenic activities. Therefore, efficient and large-scale monitoring of mangrove conditions is essential for sustainable mangrove management. This study analysed changes in mangrove density and health between 2013 and 2023 through NDVI (Normalized Difference Vegetation Index (NDVI) and Mangrove Health Index (MHI) transformations. Accuracy testing was conducted using confusion matrix analysis and ground truthing using hemispherical photography. The NDVI classification results indicated an increase of 6.77 hectares in dense mangrove areas, while MHI analysis revealed 25.07 hectares increase in healthy mangrove areas. The overall classification accuracy reached 88.89%, with a kappa coefficient of 0.83, which was categorized as very good. These findings demonstrate that GEE is an effective tool for long-term and large-scale mangrove monitoring and provides valuable spatial information to support decision-making in coastal zone management.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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