Remote sensing of urban tree carbon stocks: A methodological review

作者：Hesong Dong, Lina Tang, Liu Jin-hui, Xiangyun Hu, Guofan Shao · 发表于：ISPRS Journal of Photogrammetry and Remote Sensing · 年份：2025 · DOI：10.1016/j.isprsjprs.2025.06.030 · 被引用次数：16 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Forest ecology and management

Detailed and accurate assessments of urban tree carbon stocks (UTCS), achieved through the combination of field surveys and remote sensing techniques, are crucial for understanding the ecosystem services provided by urban trees, informing the terrestrial carbon cycle, and supporting sustainable urban planning. Since the 2010 s, breakthroughs in Earth observation and artificial intelligence have revolutionized UTCS remote sensing, transforming it into a dynamic field characterized by diverse methodological approaches. However, the methodological diversity complicates the selection of optimal approaches, and a standardized solution remains elusive. Furthermore, existing UTCS remote sensing methodologies face limitations in accuracy and scalability, particularly for large-scale, multi-city assessments. In response to these challenges, this study reviews UTCS remote sensing from a methodological perspective. Building on advances in remote sensing of urban trees, we integrate recent UTCS remote sensing methodologies into a unified framework and classify them into three categories: (1) land use stratification methods, representing early-stage strategies that estimate carbon stocks using field-sampled carbon densities assigned to different urban strata; (2) area-based inversion methods, which predict UTCS at the area level (e.g., plot-based units) by statistically modeling the relationship between field-measured carbon stocks and remote sensing metrics; and (3) individual tree detec...