Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds & nbsp;
文献类型：期刊论文
| 作者 | Lin, Jiayuan2,3; Chen, Decao2,3; Wu, Wenjian2,3; Liao, Xiaohan1 |
| 刊名 | URBAN FORESTRY & URBAN GREENING
 |
| 出版日期 | 2022-03-01 |
| 卷号 | 69页码:13 |
| 关键词 | AGB Allometric equation Canopy radius Point cloud Tree height Urban forest |
| ISSN号 | 1618-8667 |
| DOI | 10.1016/j.ufug.2022.127521 |
| 通讯作者 | Lin, Jiayuan(joeylin@swu.edu.cn) |
| 英文摘要 | Urban forest is a crucial part of urban ecological environment. The accurate estimation of its tree aboveground biomass (AGB) is of significant value to evaluate urban ecological functions and estimate urban forest carbon storage. It has a high accuracy to estimate the forest AGB with field measured canopy structure parameters, but unsuitable for large-scale operations. Limited by low spatial resolution or spectral saturation, the estimated forest AGBs based on various satellite remotely sensed data have relatively low accuracies. In contrast, Unmanned Aerial Vehicle (UAV) remote sensing provides a promising way to accurately estimate the tree AGB of fragmented urban forest. In this study, taking an artificial urban forest in Ma'anxi Wetland Park in Chongqing City, China as an example, we used UAVs equipped with a digital camera and a LiDAR to acquire two point cloud data. One was produced from overlapping images using Structure from Motion (SfM) photogrammetry, and the other was resolved from laser scanned raw data. The dual point clouds were combined to extract individual tree height (H) and canopy radius (R-c), which were then input to the newly established allometric equation with tree H and R-c as predictor variables to obtain the AGBs of all dawn redwood trees in study area. In accuracy assessment, the coefficient of determination (R-2 ) and Root Mean Square Error (RMSE) of extracted H were 0.9341 and 0.59 m; the R-2 and RMSE of extracted R-c were 0.9006 and 0.28 m; the R-2 and RMSE of estimated AGB were 0.9452 and 17.59 kg. These results proved the feasibility and effectiveness of applying dual-source UAV point cloud data and the new allometric equation on H and R-c to accurate AGB estimation of urban forest trees.& nbsp; |
| WOS关键词 | FROM-MOTION PHOTOGRAMMETRY ; MODEL ; LIDAR |
| 资助项目 | National Natural Science Foundation of China[32071678] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19050501] |
| WOS研究方向 | Plant Sciences ; Environmental Sciences & Ecology ; Forestry ; Urban Studies |
| 语种 | 英语 |
| WOS记录号 | WOS:000789606300004 |
| 出版者 | ELSEVIER GMBH |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/175743]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Lin, Jiayuan |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Southwest Univ, Sch Geog Sci, Chongqing Jinfo Mt Karst Ecosyst Natl Observat & R, Chongqing 400715, Peoples R China 3.Southwest Univ, Chongqing Engn Res Ctr Remote Sensing Big Data App, Sch Geog Sci, Chongqing 400715, Peoples R China |
| 推荐引用方式 GB/T 7714 |
Lin, Jiayuan,Chen, Decao,Wu, Wenjian,et al. Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds & nbsp; [J]. URBAN FORESTRY & URBAN GREENING,2022,69:13. |
| APA |
Lin, Jiayuan,Chen, Decao,Wu, Wenjian,&Liao, Xiaohan.(2022). Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds & nbsp; .URBAN FORESTRY & URBAN GREENING,69,13. |
| MLA |
Lin, Jiayuan,et al." Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds & nbsp; ".URBAN FORESTRY & URBAN GREENING 69(2022):13. |
入库方式： OAI收割
来源：地理科学与资源研究所
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