文献类型：期刊文献

英文题名：Modeling and estimating aboveground biomass of Dacrydium pierrei in China using machine learning with climate change

作者：Wu, Chunyan[1,2] Chen, Yongfu[1] Peng, Changhui[2,3] Li, Zhaochen[4] Hong, Xiaojiang[5]

第一作者：Wu, Chunyan

通信作者：Chen, YF[1];Peng, CH[2]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Univ Quebec Montreal, Inst Environm Sci, Dept Biol Sci, Montreal, PQ, Canada;[3]Northwest A&F Univ, Coll Forestry, Ctr Ecol Forecasting & Global Change, Yangling, Shaanxi, Peoples R China;[4]Asia Pacific Network Sustainable Forest Managemen, Beijing, Peoples R China;[5]Hainan Bawangling Natl Nat Reserve, Chaneang 572722, Hainan, Peoples R China

年份：2019

卷号：234

起止页码：167-179

外文期刊名：JOURNAL OF ENVIRONMENTAL MANAGEMENT

收录：;EI(收录号：20233214521150);Scopus(收录号：2-s2.0-85060041082);WOS:【SCI-EXPANDED(收录号:WOS:000460195000019)】；

基金：The authors thank the Bawangling Natural Reserve of Hainan Island, which provided the test site and experimental materials. The work was financially supported by the Chinese Academy of Forestry Fund Project (170015). CW would also like to thank the China Scholarship Council (CSC) for offering a scholarship at the University of Quebec at Montreal (UQAM). CP acknowledges the funding provided by the National Science and Engineering Research Council of Canada (NSERC) Discover Grant.

语种：英文

外文关键词：Aboveground biomass; Climate scenarios; Female; Forest regeneration; Male; Rare and endangered tree species

摘要：Accurate estimations of the aboveground biomass (AGB) of rare and endangered species are particularly important for protecting forest ecosystems and endangered species and for providing useful information to analyze the influence of past and future climate change on forest AGB. We investigated the feasibility of using three developed and two widely used models, including a generalized regression neural network (GRNN), a group method of data handling (GMDH), an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a support vector machine (SVM), to estimate the AGB of Dacrydium pierrei (D. pierrei) in natural forests of China. The results showed that these models could explain the changes in the AGB of the D. pierrei using a limited amount of meteorological data. The GRNN and ANN models are superior to the other models for estimating the AGB of D. pierrei. The GMDH model consistently produced comparatively poor estimates of the AGB. Three climate scenarios, including the representative concentration pathway (RCP) 2.6, RCP 4.5, and RCP 8.5, were compared with the climate situation of 2013-2017. Under these scenarios, the AGB of D. pierrei females with the same diameter at breast height (DBH) would increase by 13.0 31.4% (mean standard deviation), 16.6 +/- 30.7%, and 18.5 +/- 30.9% during 2041-2060 and 15.6 +/- 32.1%, 21.2 +/- 33.2%, and 24.8 +/- 32.7% during 2061-2080; the AGB of males would increase by 16.3 +/- 32.3%, 21.7 +/- 32.5%, and 22.9 +/- 32.6% during 2041-2060 and 22.3 +/- 30.8%, 27.2 +/- 31.8%, and 30.1 +/- 34.4% during 2061-2080. The R-2 values of all models range from 0.82 to 0.95. In conclusion, this study suggests that these advanced models are recommended to estimate the AGB of forests, and the AGB of forests would increase in 2041-2080 under future climate scenarios.