用卫星影像追踪印尼新首都建设对植被和碳储量的影响。
From Forest to Future Capital: Tracking Land Cover Change in Ibu Kota Nusantara (IKN) from 2021 to 2026 with PlanetScope Imagery
- 用行星相机影像结合光谱指数分析土地利用变化。
- 2021-2026年植被减少18.1%，碳储量下降0.28%。
- 适合关注城市化环境影响与遥感监测的研究者。
印度尼西亚将政治和行政首都从雅加达迁至努桑塔拉首都（IKN），其愿景为“森林城市”，但核心政府区（KIPP）的快速建设引发对土地转化、植被损失和碳储量下降的担忧。本研究利用PlanetScope SuperDove卫星影像，系统评估2021至2026年期间KIPP地区土地利用与植被覆盖变化。通过构建无云影像拼接，采用归一化差异植被指数（NDVI）、归一化差异红边指数（NDRE）和归一化差异水体指数（NDWI）进行分析，并使用支持向量机（SVM）算法开展监督分类。结果表明，环境变化显著：平均NDVI下降17.1%，碳储量减少0.28%，建成区面积扩大672%，总植被覆盖减少18.1%。植被损失最严重发生在2023至2024年；2024至2025年因清表活动放缓、开发转向已清理区域，出现短暂的NDVI与碳储量回升。研究证实遥感技术可有效监测大规模城市开发的环境影响，同时指出需采用更高分辨率、高光谱及合成孔径雷达（SAR）方法以提升对施工阶段及林地变化的检测能力。
原文摘要 · Abstract (English)
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.
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