文献类型：期刊文献

英文题名：Forest digital twin: A new tool for forest management practices based on Spatio-Temporal Data, 3D simulation Engine, and intelligent interactive environment

作者：Qiu, Hanqing[1,2,4] Zhang, Huaiqing[1,2,4] Lei, Kexin[1,2,4] Zhang, Huacong[1,2,3] Hu, Xingtao[1,2,4]

第一作者：Qiu, Hanqing

通信作者：Zhang, HQ[1];Zhang, HQ[2];Zhang, HQ[3]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[3]Chinese Acad Forestry, Expt Ctr Subtrop Forestry, Fenyi 336600, Peoples R China;[4]Natl Long Term Sci Res Base Huangfengqiao Forest M, Youxian 412306, Peoples R China

年份：2023

卷号：215

外文期刊名：COMPUTERS AND ELECTRONICS IN AGRICULTURE

收录：;EI(收录号：20235115232092);Scopus(收录号：2-s2.0-85179629868);WOS:【SCI-EXPANDED(收录号:WOS:001127374300001)】；

基金：This work was supported by the National Natural Science Foundation of China (No. 32271877) , National Key Research and Development Program of China (No. 2022YFE0128100) , the National Natural Science Foundation of China (No. 32071681) and the Foundation Research Funds of IFRIT (CAFYBB2021ZE005, CAFYBB2019SZ004) .

语种：英文

外文关键词：Forest digital twin; Stand spatial structure; forest 3D visualization; Unreal engine; Forest management practices

摘要：Existing forest digitization studies focus on one-way forest management practice visualization simulation, lacking decision-making feedback and virtual-real interaction synchronization. This paper presents the vision of the forest digital twin paradigm. We construct a forest digital twin to explore a new digital carrier of forest resources using remote sensing data, forest inventory data, the Cesium Digital Earth Engine, forest planning theory and parametric 3D modeling technology. The two-way interaction and thinning experiments showed that the forest digital twin could provide a novel pattern for in-depth analysis of forest spatial structure, individual tree dynamic growth and human-digital twin interaction effects. The successful recognition rate in matching the forest structure seen on real forest structure images with the forest digital scene was 91.3%, indicating that the forest digital twin can characterize the real forest structure significantly. The prediction accuracy of the multigrade growth model integrating the Bayesian method for DBH, H was more than 90.4%. In addition, ASS-FDT interaction is superior to the assessors (ASS) and forest digital twin (FDT) for stand spatial structure overall optimization. The multi-dimensional stand spatial structure index (F-index) increased by 22.82%. The constructed forest digital twin model shows superior performance in optimizing the stand growth model and enhancing the overall stand spatial structure under the decision-making feedback and real-time interaction strategies. The automatic operation pattern provides a user-friendly forest management practice solution.