Problem
Research questions and friction points this paper is trying to address.
land cover change
vegetation loss
carbon stock decline
urban development
Forest City
From Forest to Future Capital: Tracking Land Cover Change in Ibu Kota Nusantara (IKN) from 2021 to 2026 with PlanetScope Imagery
🤖 AI Summary
This study addresses the ecological impacts of rapid urbanization in the Core Government Area (KIPP) of Nusantara, Indonesia’s new capital, focusing on forest loss, vegetation degradation, and declining carbon stocks. Leveraging high spatiotemporal resolution PlanetScope SuperDove imagery from 2021 to 2026, combined with spectral indices—including NDVI, NDRE, and NDWI—and a support vector machine (SVM) supervised classification approach, the research provides the first systematic quantification of land-use and vegetation dynamics in the region. Findings reveal distinct phases of development intensity and ecological response under the “forest city” vision: an 18.1% reduction in vegetated area, a 17.1% decline in mean NDVI, a 672% expansion of developed land, and a 0.28% decrease in carbon storage. Notably, a brief period of ecological recovery was observed between 2024 and 2025, offering empirical insights for sustainable planning in emerging tropical cities.
📝 Abstract
Indonesia's relocation of its political and administrative capital from Jakarta to Ibu Kota Nusantara (IKN) has been framed around a ``Forest City'' vision, yet rapid construction within the Core Government Area (KIPP) raises concerns over land conversion, vegetation loss, and carbon stock decline. This study applies remote sensing techniques to systematically assess land use and vegetation cover change in KIPP from 2021 to 2026 using PlanetScope SuperDove satellite imagery. Cloud-free mosaics were prepared and analysed through spectral indices, including the Normalised Difference Vegetation Index (NDVI), Normalised Difference Red Edge (NDRE), and Normalised Difference Water Index (NDWI), alongside supervised land use and land cover (LULC) classification using a Support Vector Machine algorithm. Results show substantial environmental transformation, with mean NDVI declined by 17.1\%, total carbon stock decreased by 0.28\%, developed land expanded by 672\%, and total vegetation declined by 18.1\%. Vegetation loss was most extensive between 2023 and 2024, although a temporary recovery in NDVI and carbon stock occurred from 2024 to 2025 as active clearing slowed and development shifted towards already-cleared land. Overall, the findings demonstrate that remote sensing provides an effective approach for monitoring the environmental impacts of large-scale urban development, while highlighting the need for higher-resolution, hyperspectral, and SAR-based methods to improve detection of construction stages and plantation-related land cover changes.
Innovation
Methods, ideas, or system contributions that make the work stand out.
PlanetScope imagery
spectral indices
Support Vector Machine
land cover change
carbon stock monitoring
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