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.

Accurate estimates of forest site productivity are essential for environmental planning and forest management. In this study, we developed a new productivity index, hereafter termed basal area potential productivity index (BAPP), to estimate site productivity for irregular and complex forests characterized by multi-aged, multi-species, and multi-layer stands. We presented the biological relevance of BAPP with its computational details. We also compared BAPP against basal area realized productivity (BARP) in order to verify the practicability and reliability of BAPP. Time-series data of the national forest inventory on 1912 permanent sample plots that were located in two main forest types and consisted of oak-dominated mixed forests and other broadleaf forests in northeast China were used to demonstrate the application of BAPP. The results showed that the value of BAPP for each sample plot was larger than or equal to the corresponding BARP value for each forest type. For appropriately managed stands with relatively better site conditions, the values of both BARP and BAPP were almost identical. The values of the difference between BAPP and BARP could therefore be used to effectively assess forest site productivity. Meanwhile, BAPP also provides much reliable and valuable information that can aid decision-making in forest management.

Reliable estimates of forest site productivity are a central element of forest management. The model of height-diameter relationship of dominant trees using algebraic difference approach (ADA) is a commonly used method to measure site productivity of natural uneven-aged stands. However, the existing models of this method do not recognize site type or sample plot specific variability in height curves; thus, it cannot be effectively used to estimate site type or sample plot-related site productivity for natural uneven-aged stands. Two primary subject-specific approaches, ADA with dummy variable (DV) (ADA + DV) and ADA with combination of dummy variable and nonlinear mixed-effects modelling (CM) (ADA + CM), were proposed for height–diameter modelling. Height–diameter models developed with ADA, ADA + DV and ADA + CM were compared using data from 4161 observations on 349 permanent sample plots of four major natural uneven-aged pure stands (Spruce, Korean Larch, Mongolian Oak, and White Birch) in northeastern China. It was found that models developed with ADA + CM provided the best performance, followed by the models with ADA + DV, and the models developed with ADA performed the worst. Random effects at the plot level were substantial, and their inclusion greatly improved the model’s accuracy. More importantly, the models developed with ADA + CM provide an effective method for quantifying site type- and sample plot-specific forest site productivity for uneven-aged pure stands.

As the dominant height of the stand at the baseline age, the site index is an important index to evaluate site quality. However, due to the variability of environmental factors, the growth process of the dominant height of the same tree species was variable in different regions which influenced the estimation results of the site index. In this study, a methodology that established site index modeling of larch plantations with site types as a random effect in northern China was proposed. Based on 394 sample plots, nine common base models were developed, and the best model (M8) was selected (R² = 0.5773) as the base model. Moreover, elevation, aspect, and slope position were the main site factors influencing stand dominant height through the random forest method. Then, the three site factors and their combinations (site types) were selected as random effects and simulated by the nonlinear mixed-effects model based on the model M8. The R² values had raised from 0.5773 to 0.8678, and the model with combinations (94 kinds) of three site factors had the best performance (R² = 0.8678). Considering the model accuracy and practical application, the 94 combinations were divided into three groups of site types (3, 5, and 8) by hierarchical clustering. Furthermore, a mixed-effects model considering the random effects of these three groups was established. All the three groups of site types got a better fitting effect (groups 3 R² = 0.8333, groups 5 R² = 0.8616, groups 8 R² = 0.8683), and a better predictive performance (groups 3 R² = 0.8157, groups 5 R² = 0.8464, groups 8 R² = 0.8479 for 20 percent of plots randomly selected per group in the calibration procedure) using the leave-one-out cross-validation approach. Therefore, groups 5 of site types had better applicability and estimation of forest productivity at the regional level and management plan design.

Accurate prediction of individual tree mortality is essential for informed decision making in forestry. In this study, we proposed machine learning models to forecast individual tree mortality within the temperate Larix gmelinii var. principis-rupprechtii forests in Northern China. Eight distinct machine learning techniques including random forest, logistic regression, artificial neural network, generalized additive model, support vector machine, gradient boosting machine, k-nearest neighbors, and naive Bayes models were employed, to construct an ensemble learning model based on comprehensive dataset from this specific ecosystem. The random forest model emerged as the most accurate, demonstrating 92.9% accuracy and 92.8% sensitivity, making it the best model among those tested. We identified key variables impacting tree mortality, and the results showed that a basal area larger than the target trees (BAL), a diameter at 130 cm (DBH), a basal area (BA), an elevation, a slope, NH4-N, soil moisture, crown density, and the soil’s available phosphorus are important variables in the Larix Principis-rupprechtii individual mortality model. The variable importance calculation results showed that BAL is the most important variable with an importance value of 1.0 in a random forest individual tree mortality model. By analyzing the complex relationships of individual tree factors, stand factors, environmental, and soil factors, our model aids in decision making for temperate Larix gmelinii var. principis-rupprechtii forest conservation.

Biomass productivity is of great significance for the evaluation of forest quality, which is important for the improvement of forest management. We propose the computational methods of biomass potential productivity (BPP) and biomass realistic productivity (BRP), both of which provide reliable practical guides for predicting forest growth under multi-aged, multi-species, and multi-layered canopy conditions. We used 2222 national forest inventory plots that were measured in four consecutive periods in the Jilin Province for this purpose. We analyzed and verified the computational methods of BPP based on the BRP and evaluated its practical significance. The results showed that growth models of the stand height, stand basal area, and stand biomass of four forest types (pure larch forest, larch broadleaf mixed forest, Mongolian oak pure forest, and Mongolian oak broadleaf mixed forest) fit adequately, BPP was greater than BRP, and this difference decreased with an increasing stand age, suggesting that the potential productivity of the middle-aged and young forest was higher than that of the mature forest, although the difference is minimal. In addition, the realistic productivity of stands with better site quality was close to the potential productivity, which is consistent with the biological significance of the potential productivity of the biomass. The degree of difference between the potential productivity of the biomass and the realistic productivity of biomass also decreases with the decline in site quality, and it can be termed as the potentially improved stand biomass. The BPP model was able to perform well in both the pure and mixed forests. The BRP not only verifies the rationality of the BPP but can be also used to quantify the forest site quality, which is helpful for evaluating forest growth and informed decision making in forestry.