发表机构
National University of Singapore; The Chinese University of Hong Kong（新加坡国立大学; 香港中文大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用PlanetScope影像结合遥感技术与支持向量机算法，追踪印尼新首都IKN核心区2021-2026年的土地覆盖变化，发现植被与碳储量下降、建设用地扩张，证实遥感可有效监测大型城市发展的环境影响。
AI 中文摘要
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），该搬迁以“森林城市”愿景为框架，但核心政府区（KIPP）内的快速建设引发了土地转换、植被流失和碳储量下降的担忧。本研究运用遥感技术，利用PlanetScope SuperDove卫星影像，系统评估2021至2026年KIPP的土地利用与植被覆盖变化。研究制备无云镶嵌图并通过光谱指数分析，包括归一化植被指数（NDVI）、归一化红边指数（NDRE）和归一化水指数（NDWI），同时采用支持向量机算法进行监督式土地利用与土地覆盖（LULC）分类。结果显示环境发生显著转变：平均NDVI下降17.1%，总碳储量减少0.28%，建设用地扩张672%，总植被量下降18.1%。植被流失在2023至2024年最为严重，但2024至2025年NDVI和碳储量出现暂时恢复，原因是主动清理放缓，开发转向已清理土地。总体而言，研究表明遥感为监测大规模城市发展的环境影响提供了有效方法，同时强调需更高分辨率、高光谱及合成孔径雷达（SAR）方法以改进对建设阶段和种植园相关土地覆盖变化的检测。
英文摘要
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.
