文献类型：期刊文献

英文题名：Prediction of Individual Tree Diameter Using a Nonlinear Mixed-Effects Modeling Approach and Airborne LiDAR Data

作者：Fu, Liyong[1,2,3] Duan, Guangshuang[2,4] Ye, Qiaolin[5] Meng, Xiang[2,3] Luo, Peng[2] Sharma, Ram P.[6] Sun, Hua[1] Wang, Guangxing[7] Liu, Qingwang[2]

第一作者：符利勇;Fu, Liyong

通信作者：Liu, QW[1]

机构：[1]Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]Natl Forestry & Grassland Adm, Key Lab Forest Management & Growth Modeling, Beijing 100091, Peoples R China;[4]Xinyang Normal Univ, Coll Math & Stat, Xinyang 464000, Peoples R China;[5]Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Peoples R China;[6]Tribhuwan Univ, Inst Forestry, Kathmandu 44600, Nepal;[7]Southern Illinois Univ Carbondale, Dept Geog & Environm Resources, Carbondale, IL 62901 USA

年份：2020

卷号：12

期号：7

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20201908635584);Scopus(收录号：2-s2.0-85084262880);WOS:【SCI-EXPANDED(收录号:WOS:000537709600016)】；

基金：We thank the Thirteenth Five-year Plan Pioneering project of High Technology Plan of the National Department of Technology (No. 2017YFC0503906), the Central Public-interest Scientific Institution Basal Research Fund (Grant No. CAFYBB2019QD003) and the Chinese National Natural Science Foundations (Grant Nos. 31570627 and 31570628) for financial support, and the National Program on Key Basic Research Project (973 Program) (No. 2007CB714400) for data support.

语种：英文

外文关键词：Picea crassifolia Kom; random effects; calibration; leave-one sub-sample plot-out cross- validation; prediction accuracy

摘要：Rapidly advancing airborne laser scanning technology has become greatly useful to estimate tree- and stand-level variables at a large scale using high spatial resolution data. Compared with that of ground measurements, the accuracy of the inferred information of diameter at breast height (DBH) from a remotely sensed database and the models developed with traditional regression approaches (e.g., ordinary least square regression) may not be sufficient. Thus, this regression approach is no longer appropriate to develop accurate models and predict DBH from remotely sensed-related variables because DBH is subject to the random effects of forest stands. This study developed a generalized nonlinear mixed-effects DBH estimation model from remotely sensed imagery data. The light detection and ranging (LiDAR)-derived stand canopy density, crown projection area, and tree height were used as predictors in the DBH estimation model. These variables can be more readily measured over an extensive forest area with higher accuracy compared to the conventional field-based methods. The airborne LiDAR data for a total of 402 Picea crassifolia Kom trees on a sample plot that were divided into 16 sub-sample plots and located in the most important distribution region of western China were used. The leave-one sub-sample plot-out cross-validation method was applied to evaluate the model's prediction accuracy. The results indicated that the random effects of the sub-sample plot on the prediction of DBH were large and their inclusion into the DBH model significantly improved the prediction accuracy. The prediction accuracy of the proposed model at the mean (M) response was also substantially improved relative to the accuracy obtained from the base model. Among several tree selection alternatives evaluated, a sample size of the two largest trees per sub-sample plot used in estimating the random effects showed a significantly higher accuracy compared to other sampling alternatives. This sample size would balance both the measurement cost and potential prediction errors. The nonlinear mixed-effects DBH estimation model at the M response can also be applied if obtaining the estimates of individual tree DBH with a relatively lower cost, and a lower prediction accuracy was the purpose of the study.