文献类型：期刊文献

英文题名：Prediction of Diameter Distributions with Multimodal Models Using LiDAR Data in Subtropical Planted Forests

作者：Zhang, Zhengnan[1] Cao, Lin[1] Mulverhill, Christopher[2] Liu, Hao[1] Pang, Yong[3] Li, Zengyuan[3]

第一作者：Zhang, Zhengnan

通信作者：Cao, L[1]

机构：[1]Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, Nanjing 210037, Jiangsu, Peoples R China;[2]Univ British Columbia, Fac Forestry, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada;[3]Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2019

卷号：10

期号：2

外文期刊名：FORESTS

收录：;EI(收录号：20190706495595);Scopus(收录号：2-s2.0-85061261599);WOS:【SCI-EXPANDED(收录号:WOS:000460744000049)】；

基金：This research was funded by the National Key Research and Development Program (grant number: 2017YFD0600904) and National Natural Science Foundation of China (grant number: 31770590). This research was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

语种：英文

外文关键词：diameter distribution; LiDAR; Weibull; finite mixture model; KNN; Lorenz curves; planted forests

摘要：Tree diameter distributions are essential for the calculation of stem volume and biomass, as well as simulation of growth and yield and to understand timber assortments. Accurate and reliable prediction of tree diameter distributions is critical for optimizing forest structure compositions, scheduling silvicultural operations and promoting sustainable management. In this study, we investigated the potential of airborne Light Detection and Ranging (LiDAR) data for predicting tree diameter distributions using a bimodal finite mixture model (FMM) and a multimodal k-nearest neighbor (KNN) model (compared to the unimodal Weibull model (UWM)) over a subtropical planted forest in southern China. To do so, we first evaluated the capability of various LiDAR predictions (i.e., the bimodality coefficient (BC) and Lorenz-based indicators) to stratify forest structural types into unimodal and multimodal stands. Once the best LiDAR prediction for the differentiation was determined, the parameters of UWM (in non-specific and species-specific models) and FMM (in structure-specific models) were estimated by LiDAR-derived metrics and the tree diameter distributions of stands were generated by the estimated LiDAR parameters. When KNN was applied for constructing diameter distributions, optimal KNN strategies, including number of neighbors k, response configurations and imputation methods (i.e., Most Similar Neighbor (MSN) and Random Forest (RF)) for different species were heuristically determined. Finally, the predictive performance of estimated LiDAR the parameters of UWM, FMM and KNN for predicting diameter distributions were assessed. The results showed that LiDAR-predicted Lorenz-based indicators performed best for differentiation. Parameters of UWM and FMM were predicted well and the species-specific models had higher accuracies than the non-specific models. Overall, RF imputation from KNN with an optimal response set (i.e., DBH) were was stable than MSN imputation when k = 5 neighbors. In addition, the inclusion of bimodal FMM for differentiated all plots generally produced a more accurate result (Mean e(R) = 40.85, Mean e(P) = 0.20) than multimodal KNN (Mean e(R) = 52.19, Mean e(P) = 0.26), whereas the UWM produced the lowest performance (Mean e(R) = 52.31, Mean e(P) = 0.26). This study demonstrated the benefits of multimodal models with LiDAR for estimating diameter distributions for supporting forest inventory and sustainable forest management in subtropical planted forests.