文献类型：期刊文献

英文题名：Developing machine learning models with multiple environmental data to predict stand biomass in natural coniferous-broad leaved mixed forests in Jilin Province of China

作者：He, Xiao[1] Lei, Xiangdong[1] Liu, Di[1] Lei, Yuancai[1]

第一作者：He, Xiao

通信作者：Lei, XD[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, State Key Lab Efficient Prod Forest Resources, Key Lab Forest Management & Growth Modelling,Natl, Beijing 100091, Peoples R China

年份：2023

卷号：212

外文期刊名：COMPUTERS AND ELECTRONICS IN AGRICULTURE

收录：;EI(收录号：20234014817607);Scopus(收录号：2-s2.0-85172444471);WOS:【SCI-EXPANDED(收录号:WOS:001068165600001)】；

语种：英文

外文关键词：Stand biomass; Machine learning; Climate and soil variables; Variable importance

摘要：Forest biomass is influenced by multiple environmental factors. Multi-source data on site-specific soil, climate, and stand factors are essential for stand biomass prediction. While previous studies concerning planted forest biomass at stand level highlighted the importance of environmental factors, few have been conducted on the effects of climate and soil on stand biomass estimation, especially for natural mixed forest with complicated stand structure. Machine learning (ML) algorithms provide new tools for examining the effects, but the applications are still limited. Therefore, the objectives of this paper were to develop ML models for stand biomass estimation with stand and multiple environmental predictors, and quantify the relative importance of environmental variables. Tree above- and below-ground biomass at stand level was set as output variable with the random forest (RF) and boosted regression tree (BRT) regression methods. The data were from 286 sample plots from the National Forest Inventory (NFI) in Jilin Province, northeast China. The results showed that models with the inclusion of stand, soil, and climate variables explained > 89% of forest biomass variations. BA (stand basal area) and H (stand average height) were the most important stand variables, DD18 (degree-days above 18 C) was the most important climate variable, and bdod (bulk density) and pH were the most important soil variables. Relative importance of climate and soil factors was 0.5% to 5.1% and 1.2% to 4.1%, respectively, which were weak but important predictors of stand biomass. In the RF model, climate variables were more important than soil variables, but the opposite in the BRT models. We concluded that above forest stand, climate and soil factors were significant input variables for regional forest biomass mapping using ML.