Historical Mangrove Changes on Bangka Island Derived from Thirty Years of Landsat Data

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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Publication Details

Journal
Remote Sensing
Published
2026-03-20
DOI
https://doi.org/10.3390/rs18060947
Primary Topic
Coastal wetland ecosystem dynamics
Type
article
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article

Historical Mangrove Changes on Bangka Island Derived from Thirty Years of Landsat Data

Nico Koedam, Suci Puspita Sari, Frieke Van Coillie, Tom Van der Stocken
Remote Sensing
Coastal wetland ecosystem dynamics
article

Historical Mangrove Changes on Bangka Island Derived from Thirty Years of Landsat Data

Nico Koedam, Suci Puspita Sari, Frieke Van Coillie, Tom Van der Stocken
article en

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.

Remote SensingVol. 18(6)
Université Libre de Bruxelles (BE), Vrije Universiteit Brussel (BE), University of Bangka Belitung (ID), Ghent University (BE), International Union for Conservation of Nature (CH), Hasselt University (BE)
Life below water
Openalex Percentile: Top 15%
Coastal wetland ecosystem dynamics
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Historical Mangrove Changes on Bangka Island Derived from Thirty Years of Landsat Data — Nico Koedam, Suci Puspita Sari, et al. · Remote Sensing (2026) | TGRS Research Map | TGRS