文献类型：期刊文献

英文题名：Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest

作者：He, Qisheng[1] Chen, Erxue[2] An, Ru[1] Li, Yong[1]

第一作者：He, Qisheng

通信作者：He, QS[1]

机构：[1]Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2013

卷号：4

期号：4

起止页码：984-1002

外文期刊名：FORESTS

收录：;EI(收录号：20134917059089);Scopus(收录号：2-s2.0-84888774017);WOS:【SCI-EXPANDED(收录号:WOS:000330520100014)】；

基金：This paper has been supported jointly by the Natural Science Foundation of China (Grant No. 41101308, 41101374 and 41271361), the Fundamental Research Funds for the Central Universities (Grant No. 2011B06714), and the National Science and Technology Support Plan During the 12th Five-year Plan Period of China (2012BAC19B03; 2013BAC10B01). The authors also wish to thank all people who participated in the field experiment. The authors also wish to thank all the people that have given helped prepare this paper. The authors are sincerely grateful to the four anonymous reviewers for the constructive and insightful comments which improved this study.

语种：英文

外文关键词：above-ground biomass; biomass components; LiDAR; coniferous forest; Qilian Mountain

摘要：This study aims to estimate forest above-ground biomass and biomass components in a stand of Picea crassifolia (a coniferous tree) located on Qilian Mountain, western China via low density small-footprint airborne LiDAR data. LiDAR points were first classified into ground points and vegetation points. After, vegetation statistics, including height quantiles, mean height, and fractional cover were calculated. Stepwise multiple regression models were used to develop equations that relate the vegetation statistics from field inventory data with field-based estimates of biomass for each sample plot. The results showed that stem, branch, and above-ground biomass may be estimated with relatively higher accuracies; estimates have adjusted R-2 values of 0.748, 0.749, and 0.727, respectively, root mean squared error (RMSE) values of 9.876, 1.520, and 15.237 Mg.ha(-1), respectively, and relative RMSE values of 12.783%, 12.423%, and 14.163%, respectively. Moreover, fruit and crown biomass may be estimated with relatively high accuracies; estimates have adjusted R-2 values of 0.578 and 0.648, respectively, RMSE values of 1.022 and 5.963 Mg.ha(-1), respectively, and relative RMSE values of 23.273% and 19.665%, respectively. In contrast, foliage biomass estimates have relatively low accuracies; they had an adjusted R-2 value of 0.356, an RMSE of 3.691 Mg.ha(-1), and a relative RMSE of 26.953%. Finally, above-ground biomass and biomass component spatial maps were established using stepwise multiple regression equations. These maps are very useful for updating and modifying forest base maps and registries.