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Open AccessArticle

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

1
Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
2
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
3
Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, China
4
College of Mathematics and Statistics, Xinyang Normal University, Xinyang 464000, China
5
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
6
Institute of Forestry, Tribhuwan University, Kritipur, Kathmandu 44600, Nepal
7
Department of Geography and Environmental Resources, Southern Illinois University at Carbondale, Carbondale, IL 62901, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2020, 12(7), 1066; https://doi.org/10.3390/rs12071066
Received: 24 February 2020 / Revised: 22 March 2020 / Accepted: 22 March 2020 / Published: 26 March 2020
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. View Full-Text
Keywords: Picea crassifolia Kom; random effects; calibration; leave-one sub-sample plot-out cross- validation; prediction accuracy Picea crassifolia Kom; random effects; calibration; leave-one sub-sample plot-out cross- validation; prediction accuracy
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MDPI and ACS Style

Fu, L.; Duan, G.; Ye, Q.; Meng, X.; Luo, P.; Sharma, R.P.; Sun, H.; Wang, G.; Liu, Q. Prediction of Individual Tree Diameter Using a Nonlinear Mixed-Effects Modeling Approach and Airborne LiDAR Data. Remote Sens. 2020, 12, 1066.

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