Stem moisture content prediction model for Larix olgensis based on beta regression.

To investigate the longitudinal variation patterns of sapwood, heartwood, bark and stem moisture content along the trunk of artificial Larix olgensis, we constructed mixed effect models of moisture content based on beta regression by combining the effects of sampling plot and sample trees. We used two sampling schemes to calibrate the model, without limiting the relative height (Scheme Ⅰ) and with a limiting height of less than 2 m (Scheme II). The results showed that sapwood and stem moisture content increased gradually along the trunk, heartwood moisture content decreased slightly and then increased along the trunk, and bark moisture content increased along the trunk and then levelled off before increasing. Relative height, height to crown base, stand area at breast height per hectare, age, and stand dominant height were main factors driving moisture content of L. olgensis. Scheme Ⅰ showed the stable prediction accuracy when randomly sampling moisture content measurements from 2-3 discs to calibrate the model, with the mean absolute percentage error (MAPE) of up to 7.2% for stem moisture content (randomly selected 2 discs), and the MAPE of up to 7.4%, 10.5% and 10.5% for sapwood, heartwood and bark moisture content (randomly selected 3 discs), respectively. Scheme Ⅱ was appropriate when sampling moisture content measurements from discs of 1.3 and 2 m height and the MAPE of sapwood, heartwood, bark and stem moisture content reached 7.8%, 11.0%, 10.4% and 7.1%, respectively. The prediction accuracies of all mixed effect beta regression models were better than the base model. The two-level mixed effect beta regression models, considering both plot effect and tree effect, would be suitable for predicting moisture content of each part of L. olgensis well.

Height-diameter model of broad-leaved mixed forest based on species classification in Maoershan, Northeast China.

The complex stand structure and high species diversity of natural forests pose great challenges for analyzing stand growth and formulating reasonable plans for forest management. The height-diameter relationship is of great significance for predicting stand growth and formulating forest management measures. Based on survey data of 48 broad-leaved mixed forest plots in Maoershan, we classified 23 tree species into four groups based on species structure, growth characteristics and bionomics. We established a generalized model including stand, tree competition, species mixing and species diversity variables by reparameterization method, and a two-level mixed effect model of plot and tree species group. We tested the prediction ability of the model by leave-one-out cross-validation method. The results showed that the Ratkowsky (1990) model was the optimal basic model. The introduction of dominant height, basal area of trees larger than the object tree, basal area proportion of each species, and Shannon index could better explain the height-diameter relationship of broad-leaved mixed forest in Maoershan. The introduction of the mixed effect model of plot and tree species group could significantly improve the prediction accuracy of the model, with a Ra2 of 0.83. Under the same gradient of environmental factors, intolerant tree species exhibited higher tree heights than shade-tolerant tree species. In this study, we used the constructed tree height-diameter model to analyze the effects of species mixing and tree functional traits on tree height, which provided a theoretical basis for accurately predicting height of different tree species and analyzing the growth relationships in broadleaved mixed forests.

Model construction for height to crown base of Larix olgensis based on mixed-effects model and quantile regression.

Height to crown base is an important index reflecting the characteristics of tree crown. It is of great significance to accurately quantify height to crown base for forest management and increasing stand production. We used nonlinear regression to construct the height to crown base generalized basic model, and further extended that to the mixed-effects model and quantile regression model. The prediction ability of the models was evaluated and compared by the 'leave-one-out' cross-validate. Four sampling designs and different sampling sizes were used to calibrate the height to crown base model, and the best model calibration scheme was selected. The results showed that based on the height to crown base generalized model including tree height, diameter at breast height, basal area of the stand and average dominant height, the prediction accuracy of the expanded mixed-effects model and the combined three-quartile regression model were obviously improved. The mixed-effects model was slightly better than the combined three-quartile regression model, and the optimal sampling calibration scheme was to select five average trees. The mixed-effects model with five average trees was recommended to predict the height to crown base in practice.

[Construction and precision analysis of individual tree biomass model of Larix olgensis considering random effects]. / 考虑随机效应的长白落叶松立木生物量模型构建及精度分析.

Accurate estimation of forest biomass in China is crucial for the study of carbon cycle and mechanisms underlying carbon storage in global terrestrial ecosystems. Based on the biomass data of 376 individuals of Larix olgensis in Heilongjiang Province, we used seemingly unrelated regression (SUR) method to build a univariate biomass SUR model with diameter at breast height as the independent variable and considering the random effect at the sampling site level. Then, a seemingly unrelated mixed effect (SURM) model was constructed. As the calculation of random effects of SURM model did not require the empirically measured values of all dependent variables, we analyzed the deviations from the following four types in detail: 1) SURM1, the random effect was calculated according to the measured biomass of stem, branch and foliage; 2) SURM2, the random effect was calculated according to the measured value of tree height (H); 3) SURM3, the random effect was calculated according to the measured crown length (CL); 4) SURM4, the random effect was calculated according to the measured values of H and CL. The results showed that the fitting effect of branch and foliage biomass models was improved significantly after considering the horizontal random effect of the sampling plot, with R2 being increased by more than 20%. The fitting effect of stem and root biomass models were improved slightly, with R2 being increased by 4.8% and 1.7%, respectively. When using five randomly selected trees to calculate the horizontal random effect of the sampling plot, the prediction performance of SURM model was better than that of SUR model and SURM model considering only fixed effects, especially SURM1 model (MAPE% of stem, branch, foliage and root was 10.4%, 29.7%, 32.1% and 19.5%, respectively). Except for SURM1 model, the deviation of SURM4 in predicting stem, branch, foliage and root biomass was smaller than that of SURM2 and SURM3 models. In actual prediction, although the prediction accuracy of SURM1 model was the highest, it needed to measure aboveground biomass of several trees, and the use cost was relatively high. Therefore, the SURM4 modelled on measured H and CL was recommended to predict the standing tree biomass of L. olgensis.

The Feasibility of Modelling the Crown Profile of Larix olgensis Using Unmanned Aerial Vehicle Laser Scanning Data.

Unmanned aerial vehicle (UAV) laser scanning, as an emerging form of near-ground light detection and ranging (LiDAR) remote sensing technology, is widely used for crown structure extraction due to its flexibility, convenience, and high point density. Herein, we evaluated the feasibility of using a low-cost UAV-LiDAR system to extract the fine-scale crown profile of Larix olgensis. Specifically, individual trees were isolated from LiDAR point clouds and then stratified from the point clouds of segmented individual tree crowns at 0.5 m intervals to obtain the width percentiles of each layer as profile points. Four equations (the parabola, Mitscherlich, power, and modified beta equations) were then applied to model the profiles of the entire and upper crown. The results showed that a region-based hierarchical cross-section analysis algorithm can successfully delineate 77.4% of the field-measured trees in high-density (>2400 trees/ha) forest stands. The crown profile generated with the 95th width percentile was adequate when compared with the predicted value of the existing field-based crown profile model (the Pearson correlation coefficient (ρ) was 0.864, root mean square error (RMSE) = 0.3354 m). The modified beta equation yielded slightly better results than the other equations for crown profile fitting and explained 85.9% of the variability in the crown radius for the entire crown and 87.8% of this variability for the upper crown. Compared with the cone and 3D convex hull volumes, the crown volumes predicted by our profile models had significantly smaller errors. The results revealed that the crown profile can be well described by using UAV-LiDAR, providing a novel way to obtain crown profile information without destructive sampling and showing the potential of the use of UAV-LiDAR in future forestry investigations and monitoring.