Historical Mangrove Changes on Bangka Island Derived from Thirty Years of Landsat Data
Sari, S.P.; Koedam, N.; Van der Stocken, T.; Van Coillie, F. (2026). Historical Mangrove Changes on Bangka Island Derived from Thirty Years of Landsat Data. Remote Sens. 18(6): 947. https://dx.doi.org/10.3390/rs18060947
In: Remote Sensing. MDPI: Basel. ISSN 2072-4292; e-ISSN 2072-4292, more
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| Author keywords |
Bangka Island;Google Earth Engine;Landsat;LandTrendr;mangroves;mangrove index;spatiotemporal change;tin mining;difference water index;forest;climate;ndwi;dynamics;images;china;world |
| Authors | | Top |
- Sari, S.P., more
- Koedam, N.
- Van der Stocken, T., more
- Van Coillie, F.
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| Abstract |
Bangka's mangroves contribute to Indonesia's species-rich coastal ecosystems, yet they have experienced substantial degradation, largely driven by human activities such as tin mining. Establishing long-term records of mangrove extent is essential for understanding distribution dynamics, assessing impacts, and guiding conservation strategies. In this study, we applied change detection techniques, a random forest classifier, and the LandTrendr algorithm to analyze Landsat time-series data from 1994 to 2023 across Bangka Island. We quantified multi-decadal changes in mangrove extent, periods of disturbance and recovery, and discrepancies between local and global datasets. Mangrove dynamics were spatially heterogeneous, with both expansion and loss observed across regions in landward and seaward settings. Over the 30-year period, total gains reached 4956.39 ha (10.30% of the baseline), yet the net change indicated an overall loss of 1055.85 ha. LandTrendr analysis further revealed sustained mangrove expansion since 1989. Observed changes reflect the combined influence of natural processes, including accretion and erosion, and human pressures, particularly tin mining. Although net area loss aligns with national trends, the drivers in this mining-dominated region differ from those elsewhere, and some mangrove areas remain absent from global datasets. These findings emphasize the need to better capture local gain-loss dynamics to support effective management and conservation. |
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