Estimation of above-ground carbon storage in urban green space trees incorporating crown volume structural parameters

作者：Jianpeng Zhang, Yuncheng Deng, Jinliang Wang, Rafael Antonio Chaparro Torres, Jie Li, Jiya Pan, Feng Cheng, Cheng Wang · 发表于：Trees Forests and People · 年份：2025 · DOI：10.1016/j.tfp.2025.101117 · 被引用次数：2 · 研究领域：Remote Sensing and LiDAR Applications、Urban Green Space and Health、Remote Sensing in Agriculture

Urban green spaces play a vital role in carbon sequestration and climate regulation. This study estimates the above-ground carbon (AGC) storage of urban trees in Chenggong District, Kunming City, using airborne laser scanning (ALS) point cloud data and GF-2 satellite imagery. Two primary objectives were addressed: (1) An above-ground biomass (AGB) estimation model for urban trees was developed by incorporating crown volume. The model, constructed using the XGBoost algorithm with tree height, diameter at breast height (DBH), and crown volume as predictors, incorporating crown volume significantly improved accuracy, with the R² of the training set increasing from 0.793 to 0.932 and the test set from 0.807 to 0.856. (2) The model was then applied to estimate spatially explicit AGC storage across the built-up area. Results show a total AGB of approximately 4.51×10 4 t, AGC storage of 2.26×10 4 t, and a mean carbon density of 6.69 kg/m 2 . Wujaying and Yuhua subdistricts were identified as AGC storage hotspots. Furthermore, the study found that traditional vegetation indices and texture features had limited effectiveness in indicating individual tree AGB under high-resolution imagery. A rapid AGC estimation workflow integrating LiDAR and optical remote sensing was established, providing robust technical support for urban carbon sink assessment and ecological planning.