The Impact of the Karo-Langkat Bypass Road Development on Land Cover Dynamics and Suitability Evaluation
Aramico, Erwin Nyak Akub, Nurdin Sulistyono
South Asian Journal of Social Studies and Economics · pp. 50–63 · Published 15 Feb 2025
10.9734/sajsse/2025/v22i3969Abstract
The construction of the 44.85 km Karo-Langkat bypass road, which traverses critical conservation areas such as Mount Leuser National Park and the Bukit Barisan Forest Park, has significantly improved accessibility and economic development in Karo and Langkat Regencies. However, this infrastructure development has also triggered substantial land cover changes, raising concerns over long-term environmental sustainability. This study analyzes land cover dynamics from 2012 to 2024, predicts future trends for 2037, and evaluates their alignment with the North Sumatra Provincial Spatial Plan (RTRW) 2017–2037. Using Landsat 7, 8, and 9 imagery, validated with 60 ground truth points, land cover changes were examined through the Land Change Modeler (LCM), with future projections generated via the Markov Chain model. Results indicate a 470-hectare loss of primary forest over 12 years, while shrubland and open land increased by 200 hectares and 170 hectares, respectively. If current trends persist, primary forest may shrink to 1,700 hectares by 2037, raising concerns over biodiversity loss and carbon emissions. The evaluation against the RTRW 2017–2037 reveals that a majority of land cover changes are inconsistent with designated conservation areas, emphasizing the urgent need for regulatory intervention. This study highlights the policy implications of road-induced deforestation, advocating for stricter spatial planning enforcement, agroforestry initiatives, and forest rehabilitation programs to mitigate environmental degradation. The findings provide data-driven insights for policymakers, balancing regional development with ecosystem protection. Additionally, this research demonstrates the value of Markov Chain modeling in land-use planning, offering a predictive framework for sustainable decision-making.
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