The impact of land cover change on carbon stocks in a biocultural teak production landscape
DOI:
https://doi.org/10.61511/bioculture.v4i1.2026.3213Keywords:
carbon stock, land cover, land cover change, random forestAbstract
Background: Land cover change is one of the factors contributing to the increasing concentration of CO₂ in the atmosphere, with direct implications for the ecological and cultural sustainability of production landscapes shaped by long-term human–forest interaction. Monitoring land cover change and estimating carbon stocks are essential for supporting emission mitigation efforts at various spatial scales. This study analyzes the impact of land cover change on carbon stocks in Randublatung District, Central Java, a landscape historically managed as a teak production forest reflecting the interconnection between local livelihoods and ecosystem management, by integrating geographic information systems (GIS) and remote sensing data to assess land cover change and carbon stocks in Randublatung District during the 2013–2025 period using the Google Earth Engine (GEE) platform. Methods: This study employed the random forest (RF) algorithm for land cover classification, and carbon stock estimation was conducted using a Tier 2 approach based on carbon constants. Findings: Randublatung District is dominated by teak plantation forests. Forest cover expanded by 2,152.78 ha during 2013–2025, while settlement land, dryland agriculture, and paddy fields decreased sequentially by 1,042.30 ha, 595.57 ha, and 514.92 ha. These land cover changes are directly proportional to changes in carbon stocks, which increased by 200,636.08 tons, driven by a 211,790.81 tons increase from plantation forests, whereas other non-forest land-use classes experienced a decline. Conclusion: The increase in carbon stock indicates vegetation recovery or reforestation supporting climate change mitigation and illustrates how managed production landscapes can sustain both ecological function and community-based land use. Novelty/Originality of this article: The novelty of this study lies in the application of the Random Forest algorithm for carbon stock mapping at the sub-district level, as well as in its focus on teak production forest areas as a biocultural landscape, which have received relatively limited attention in carbon-related research.
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