Refined big data on carbon sequestration for urban trees: 3D information and spatial carbon stock
文献类型：期刊论文
| 作者 | Cui, Kailong3,4,5,6; Cui, Yaoping4,5,6; Deng, Xiangzheng2; Zhang, Chaosheng8; Jia, Yufei4; Zhao, Tianwei4,5,6; Li, Nan7; Shi, Zhifang4,5,6; Zhao, Xiang4,5,6; Qin, Hua1,9 |
| 刊名 | SUSTAINABLE CITIES AND SOCIETY
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| 出版日期 | 2025-11-15 |
| 卷号 | 134页码:106901 |
| 关键词 | RGB satellite images Urban tree Morphological operation 3D information Stacked random forest |
| ISSN号 | 2210-6707 |
| DOI | 10.1016/j.scs.2025.106901 |
| 产权排序 | 5 |
| 文献子类 | Article |
| 英文摘要 | Urban trees play a crucial role in regulating the urban environment. Their carbon stock capacity and the importance of 3D information are increasingly recognized by urban managers. However, accurately characterizing urban trees and estimating their carbon stock is hindered by the complexity of urban landscapes and the structural and spatial tree diversity. Here we used RGB satellite imagery and locally sampled data to extract 3D information on urban trees in Dublin, Ireland, and to calculate their carbon stock. Our method consisted of: 1) extracting complete urban tree patches (UTP) using a newly defined urban tree canopy index and morphological operations; 2) reconstructing 3D UTP parameters with a quantitative structure model and a stacked random forest regression (S-RFR) algorithm; 3) calculating UTP carbon stocks based on tree volume and basic wood density. Validation of the extracted UTP information (tree count, volume, height, and diameter at breast height) yielded accuracy and F1-score values of 0.90 and 0.89, respectively, with highly significant (p < 0.005) R-2 values consistently exceeding 0.86. Overall, we identified 190,000 UTP in Dublin containing 401,866 trees. Corresponding canopy coverage was 16.43 % for a total tree volume of 381,105 m(3) and a total carbon stock of 12.11 Mt. Moreover, our method was highly versatile and applicable to diverse tree species, maintaining high accuracy (R-2 = 0.93, p < 0.005) with fewer tree-species parameters than in the complete set of this study. In summary, the implemented method will facilitate the extraction of accurate 3D parameters and enables scientists to calculate the spatial distribution of carbon stocks in urban trees with minimal influence from geographic location or species, overcoming the constraints of species data and supports extensive and refined assessments of urban tree resources. By providing reliable carbon stock data, this method enables urban managers to optimize green space planning and enhance carbon stock capacity, thereby contributing significantly to urban sustainability and climate resilience. |
| URL标识 | 查看原文 |
| WOS关键词 | RANDOM FOREST ; BIOMASS ; DUBLIN ; VOLUME ; SEGMENTATION ; MODEL ; LIDAR |
| WOS研究方向 | Construction & Building Technology ; Science & Technology - Other Topics ; Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:001603977100002 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217778]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Cui, Yaoping; Deng, Xiangzheng |
| 作者单位 | 1.Chinese Univ Hong Kong, Sch Publ Policy, Shenzhen 518172, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; 3.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan, Peoples R China; 4.Henan Univ, Fac Geog Sci & Engn, Int Sch Technol, Zhengzhou 450046, Peoples R China; 5.Henan Univ, Key Lab Geospatial Technol Middle & Lower Yellow R, Minist Educ, Kaifeng 475004, Peoples R China; 6.Henan Univ, State Key Lab Spatial Datum, Zhengzhou 450046, Peoples R China; 7.Zhengzhou Normal Univ, Sch Geog & Tourism, Zhengzhou 450044, Peoples R China 8.Univ Galway, Sch Geog Archaeol & Irish Studies, Int Network Environm & Hlth, Galway, Ireland; 9.Univ Missouri, Div Appl Social Sci, Columbia, MO 65211 USA; |
| 推荐引用方式 GB/T 7714 | Cui, Kailong,Cui, Yaoping,Deng, Xiangzheng,et al. Refined big data on carbon sequestration for urban trees: 3D information and spatial carbon stock[J]. SUSTAINABLE CITIES AND SOCIETY,2025,134:106901. |
| APA | Cui, Kailong.,Cui, Yaoping.,Deng, Xiangzheng.,Zhang, Chaosheng.,Jia, Yufei.,...&Qin, Hua.(2025).Refined big data on carbon sequestration for urban trees: 3D information and spatial carbon stock.SUSTAINABLE CITIES AND SOCIETY,134,106901. |
| MLA | Cui, Kailong,et al."Refined big data on carbon sequestration for urban trees: 3D information and spatial carbon stock".SUSTAINABLE CITIES AND SOCIETY 134(2025):106901. |
入库方式： OAI收割
来源：地理科学与资源研究所
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