文献类型：期刊文献

英文题名：Mapping species-specific aboveground forest biomass for Guangxi using wall-to-wall airborne laser scanning data

作者：Zhang, Xiaofang[1,2] Tang, Shouzheng[1,2] Fu, Liyong[1,2] Zhang, Huiru[1,2] Sharma, Ram P.[3] Lei, Yuancai[1,2] Wang, Guangyu[4] Zhao, Xiaodi[4,5] Li, Xiaoyao[1] Ye, Qiaolin[6] Xu, Zhongqi[7] Chen, Qiao[1]

第一作者：Zhang, Xiaofang

通信作者：Fu, LY[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forest Management & Growth Modeling, Beijing 100091, Peoples R China;[3]Tribhuvan Univ, Inst Forestry, Kathmandu 44600, Nepal;[4]Univ British Columbia, Fac Forestry, Vancouver, BC, Canada;[5]Chinese Acad Forestry, Res Inst Forestry Policy & Informat, Beijing 100091, Peoples R China;[6]Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210094, Jiangsu, Peoples R China;[7]Hebei Agr Univ, Coll Forestry, Baoding 071000, Peoples R China

年份：2025

卷号：178

外文期刊名：ECOLOGICAL INDICATORS

收录：;EI(收录号：20253619124873);Scopus(收录号：2-s2.0-105015090916);WOS:【SCI-EXPANDED(收录号:WOS:001569210400005)】；

基金：(CAFYBB2022ZB002) , and the Construction of UAV patrol monitoring system for comprehensive forest fire prevention system in Chongli District, Zhangjiakou City (DA2020001) .

语种：英文

外文关键词：Wall-to-wall ALS data; Species-specific AGB map; High resolution

摘要：Estimates of aboveground forest biomass (AGB) and carbon fluxes, especially on a species-specific basis, over an extensive area are crucial in decision-making in forestry. This study generated the species-specific AGB map with a resolution of 20 m using three data sources (field measurement, wall-to-wall airborne laser scanning (ALS) data, and aerial digital orthophoto map (DOM)). Field measurement and corresponding ALS data (point cloud density >= 2.0/m2) were collected from 1086 sample plots located across four arbor forest types (Chinese fir (Cunninghamia lanceolata), Eucalyptus (Eucalyptus grandis x Eucalyptus urophylla), Masson pine (Pinus massoniana), and other mixed broadleaved forests) in Guangxi province, China, between 2017 and 2019. Firstly, we developed the species-specific AGB models based on field measurement and ALS data using four modeling algorithms (linear regression (LR), support vector regression (SVR), random forest (RF), generalized additive modeling (GAM)). GAM models performed the best, with a relative RMSE ranged from 13.6 % to 35.3 %. Secondly, forest type assigned to each grid cell in the rasterized wall-to-wall ALS data was derived from DOMderived forest-type distribution map. Finally, a species-specific AGB map with a resolution of 20 m was generated by inputting forest type and ALS metrics for each grid cell in the rasterized wall-to-wall ALS data into the species-specific GAM models. Our AGB map could be useful to formulate the effective strategies of forest management. In the future, one can monitor the changes of AGB or carbon storage in Guangxi using our AGB map.