文献类型：期刊文献

英文题名：Harmonizing remote sensing and ground data for forest aboveground biomass estimation

作者：Su, Ying[1,2] Wu, Zhifeng[1,2] Zheng, Xiaoman[3] Qiu, Yue[1,2] Ma, Zhuo[4] Ren, Yin[1,2] Bai, Yanfeng[5]

第一作者：Su, Ying

通信作者：Bai, YF[1]

机构：[1]Chinese Acad Sci, Inst Urban Environm, Key Lab Urban Environm & Hlth,Inst Urban Environm, Fujian Key Lab Watershed Ecol,Key Lab Urban Metab, Xiamen 361021, Peoples R China;[2]Univ Chinese Acad Sci, Beijing 100049, Peoples R China;[3]Sanming Univ, Sch Informat Engn, Sanming 365004, Peoples R China;[4]Fujian Agr & Forestry Univ, Coll JunCao Sci & Ecol, Coll Carbon Neutral, Fuzhou, Fujian, Peoples R China;[5]Chinese Acad Forestry, Res Inst Forestry, Key Lab Tree Breeding & Cultivat State Forestry &, Beijing 100091, Peoples R China

年份：2025

卷号：86

外文期刊名：ECOLOGICAL INFORMATICS

收录：;Scopus(收录号：2-s2.0-85214584539);WOS:【SCI-EXPANDED(收录号:WOS:001417317300001)】；

基金：This work was supported by National Key Research Program of China (2022YFF1303001), National Natural Science Foundation of China (42001210, 31972951, 31670645, 42171100, 41801182, 41807502), National Social Science Fund (17ZDA058), Fujian Provincial Department of S&T Project (2023T3021, 2022T3047, 2021I0041, 2021T3058, 2019J01136), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA23020502), Xiamen S&T Project (3502Z20226016), Educational Research Project for Young and Middle-aged Teachers of Fujian Provincial Department of Education (JAT220354). We are grateful to Dr. Hamid Reza Matinfar for his constructive suggestions.

语种：英文

外文关键词：Forests; Aboveground biomass; Remote sensing data; Machine learning

摘要：Accurate aboveground biomass (AGB) estimation is crucial for evaluating management and conservation policy of forests. However, the complexity of forest ecosystems and the diversity of geography bring great challenges to traditional biomass estimation methods. This study aims to develop an optimized AGB estimation framework that integrates heterogeneous data sources (i.e., ground survey data, National Forest Continuous Inventory (NFCI) data, and both active and passive remote sensing data) to enhance estimation accuracy and address the needs of future satellite missions and forest monitoring efforts. Using Longyan City, Fujian Province, China, as a case study, we construct a machine learning-based AGB estimation framework and generate high-resolution AGB spatial distribution maps through stepwise variable selection, hyperparameter optimization, and incremental integration of data sources. The effectiveness of this approach was demonstrated by a 0.67 increase in the correlation coefficient R2, a 43.57 % reduction in the root mean square error (RMSE), and a 68.00 % reduction in the mean square error (MSE) achieved through the optimal combination of data sources. The optimization framework not only significantly improves AGB estimation accuracy but also facilitates the identification of key areas for afforestation through the generated spatial distribution map, offering a scientific foundation for targeted forest management and ecological restoration. This study highlights the potential of combining heterogeneous data sources with machine learning techniques, providing a scalable solution for future forest monitoring tasks.