文献类型：期刊文献

英文题名：Understanding the contribution of structural diversity to stand biomass for carbon management of mixed forests using machine learning algorithms

作者：He, Xiao[1] Lei, Xiangdong[1] Liu, Di[1] Lei, Yuancai[1] Gao, Wenqiang[1] Lan, Jie[2]

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;[2]Hubei Minzu Univ, Coll Forestry & Hort, Enshi 445000, Peoples R China

年份：2024

外文期刊名：EUROPEAN JOURNAL OF FOREST RESEARCH

收录：;Scopus(收录号：2-s2.0-85210403641);WOS:【SCI-EXPANDED(收录号:WOS:001363446800001)】；

基金：This research was supported by the National Natural Science Foundation of China (Grant No.32301588) and the National Key R&D Program of China (Grant No. 2022YFD2200501).

语种：英文

外文关键词：Stand biomass; Stand structural diversity; Machine learning; Carbon management

摘要：The structural properties of mixed stands and its potential impact on forest carbon sink function have attracted the attention of forest managers. Comprehensively understanding how stand factors and structural diversity influence forest biomass is critical for enhancing carbon management. However, data and information on biomass variability and its relationships to stand structural features are still insufficient. The purpose of this study was to develop models that delineated the relationships between stand biomass (above and below ground) and stand factors, with a particular emphasis on structural diversity in natural mixed forests. Four machine learning (ML) algorithms named support vector machine (SVM), artificial neural network (ANN), random forest (RF) and boosted regression trees (BRT) were trained. The results indicated that SVM and ANN provided more accurate stand biomass estimations than RF and BRT algorithms. The ANN, incorporating tree size diversity, achieved the highest accuracy (R2 = 0.9255 +/- 0.0421), followed by SVM (R2 = 0.9252 +/- 0.0385). Structural diversity proved to be a reliable predictor of biomass estimation in mixed stand surpassing stand average height traditionally used. The positive correlation between stand biomass and structural diversity with the relative importance value ranged from 10.21 to 15.32% suggested that complex stand structural diversity facilitated larger biomass accumulation. Thus, our study offered a ML protocol for predicting stand biomass of natural coniferous-broadleaved mixed forests, and suggested that using comprehensive management measures such as properly promoting tree differentiation can help forest managers enhance ecosystem carbon. center dot Artificial neural network and support vector machine outperformed random forest and boosted regression trees for stand biomass prediction.center dot Stand structural diversity positively affected stand biomass.center dot The contribution of stand structural diversity to stand biomass ranged from 10.21 to 15.32% for natural coniferous-broadleaved mixed forests.