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Accurate information of the fine-scale spatial patterns of trees and their interactions within a stand is critical for explaining the competition, health and vigor status, and future development of a stand. There are a number of indices which can show such patterns, but the stand spatial structure index is the most important. This index can be quantified based on the spatial information of trees (tree positions) within a stand and has paramount importance in identifying candidate trees to be thinned. This study develops a software tool (algorithm), which can conveniently and accurately estimate the stand spatial structure index. Our proposed algorithm considers the spatial relationships between a reference tree and its four adjacent trees, and computes the three most important stand structure indices (neighborhood comparison, uniform angle index and species mingling) using GIS (ArcEngine) and the C# programming language. The implementation of the developed algorithm (stand spatial structure index) shows that, for any reference tree assumed, its four adjacent trees identified by each of the three stand spatial structure indices (uniform angle index—UAI, mingling—M and neighborhood comparison—NC) are the same, indicating the consistency and accuracy between the three-stand spatial structural indices. For the same tree species in a forest stand, the computational results from each of the spatial structure indices used (UAI, M, NC) are also the same. Thus, the results of this algorithm developed in this study are consistent with that of the Winkelmass1.0 software (a type of software used to simulate stand spatial structure). As this article is based on the GIS technique, the computational results can be visually displayed and implemented on actual maps, making it more convenient and intuitive for forest management. The proposed approach will be useful for accurately identifying the trees to be thinned and helpful for maintaining the vigor stand structure. This study also demonstrates the implementation of the algorithm to the real-world data and proves that the computational process is simple and efficient. The application of this algorithm for the identification of trees to be thinned may help the stakeholders to focus their attention towards multi-functional forest management. The algorithm will also provide an important basis for optimizing thinning and maintaining well-structured forest stands. [ABSTRACT FROM AUTHOR]

Climate change is exacerbating the vulnerability of temperate forests to severe disturbances, potentially increasing tree mortality rates. Despite the significance of this issue, there has been a lack of comprehensive research on tree survival across extensive forest areas under the background of global climate change. To fill this gap, we conducted a detailed analysis of tree survival within a canopy nitrogen and water addition experimental platform in central China, utilizing data from two censuses and evaluating contributing factors. Our findings revealed 283 dead trees within the plots, predominantly of very small diameters (1–10 cm). The distribution of these dead trees varied among subplots, influenced by both biotic and abiotic factors. Notably, three dominant tree species were responsible for 64.8% of the deadwood biomass. The study determined that both the breast diameter and the quantity of dead trees, affected by surrounding trees and environmental conditions, played a critical role in deadwood biomass accumulation. This research offers an in-depth examination of deadwood biomass patterns in a temperate forest, highlighting the need to consider both experiment treatments and abiotic elements like topography in studies of forest ecosystem carbon. The insights gained from this study enhance our understanding of warm temperate forests' role in the global carbon cycle and offer valuable guidance for forest conservation and management strategies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study presents auxiliary support techniques for tree selection strategies based on the spatial structure indices and three competition indices in secondary forests, and discusses the importance of tree competition in forest management. The spatial structure parameter in the structured management is used as a quantitative index—the uniform angle index and three competition indices are used in the design of the algorithm for selective thinning for secondary forest. Based on the target tree-based management principles, simulation of selective thinning was carried out using GIS and C# programming languages. Data for this study were collected from experimental sample plots at Jilin Wangqing Forestry Bureau in China. The simulation results strongly support the use of auxiliary technology for scientifically selecting trees for thinning, avoiding the subjectivity of the traditional manual selection. Selection is largely based on the uniform angle index and competition index. Hegyi's competition index and its improved version used in the algorithm provided almost identical simulation results, i.e., thinning intensities suggested by these indices for the first sample plot are 21.8% and 21.5%, respectively, and for the second plot are 21.3% and 21.1%, respectively. Thus, one of these competition indices can be used to select trees for thinning. The comprehensive competition index (CCI, a combination of an improved version of Hegyi's competition index with tree species mingling) can avoid the selection of individual trees with high mingling and help maintain the tree species diversity. CCI suggests thinning intensities of 18.3% and 18.4% for the first and second sample plots, respectively. Presented methods and results may provide auxiliary supports for scientific thinning and help promote the application of information technology in forest management. [ABSTRACT FROM AUTHOR]

Background: Fast and accurate forest aboveground biomass (AGB) estimation and mapping is the basic work of forest management and ecosystem dynamic investigation, which is of great significance to evaluate forest quality, resource assessment, and carbon cycle and management. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), as one of the latest launched spaceborne light detection and ranging (LiDAR) sensors, can penetrate the forest canopy and has the potential to obtain accurate forest vertical structure parameters on a large scale. However, the along-track segments of canopy height provided by ICESat-2 cannot be used to obtain comprehensive AGB spatial distribution. To make up for the deficiency of spaceborne LiDAR, the Sentinel-2 images provided by google earth engine (GEE) were used as the medium to integrate with ICESat-2 for continuous AGB mapping in our study. Ensemble learning can summarize the advantages of estimation models and achieve better estimation results. A stacking algorithm consisting of four non-parametric base models which are the backpropagation (BP) neural network, k-nearest neighbor (kNN), support vector machine (SVM), and random forest (RF) was proposed for AGB modeling and estimating in Saihanba forest farm, northern China. Results: The results show that stacking achieved the best AGB estimation accuracy among the models, with an R2 of 0.71 and a root mean square error (RMSE) of 45.67 Mg/ha. The stacking resulted in the lowest estimation error with the decreases of RMSE by 22.6%, 27.7%, 23.4%, and 19.0% compared with those from the BP, kNN, SVM, and RF, respectively. Conclusion: Compared with using Sentinel-2 alone, the estimation errors of all models have been significantly reduced after adding the LiDAR variables of ICESat-2 in AGB estimation. The research demonstrated that ICESat-2 has the potential to improve the accuracy of AGB estimation and provides a reference for dynamic forest resources management and monitoring. [ABSTRACT FROM AUTHOR]

Accurate estimate of tree biomass is essential for forest management. In recent years, several climate-sensitive allometric biomass models with diameter at breast height (D) as a predictor have been proposed for various tree species and climate zones to estimate tree aboveground biomass (AGB). But the allometric models only account for the potential effects of climate on tree biomass and do not simultaneously explain the influence of climate on D growth. In this study, based on the AGB data from 256 destructively sampled trees of three larch species randomly distributed across the five secondary climate zones in northeastern and northern China, we first developed a climate-sensitive AGB base model and a climate-sensitive D growth base model using a nonlinear least square regression separately. A compatible simultaneous model system was then developed with the climate-sensitive AGB and D growth models using a nonlinear seemingly unrelated regression. The potential effects of several temperature and precipitation variables on AGB and D growth were evaluated. The fitting results of climatic sensitive base models were compared against those of their compatible simultaneous model system. It was found that a decreased isothermality ([mean of monthly (maximum temperature-minimum temperature)]/(Maximum temperature of the warmest month-Minimum temperature of the coldest month)) and total growing season precipitation, and increased annual precipitation significantly increased the values of AGB; an increase of temperature seasonality (a standard deviation of the mean monthly temperature) and precipitation seasonality (a standard deviation of the mean monthly precipitation) could lead to the increase of D. The differences of the model fitting results between the compatible simultaneous system with the consideration of climate effects on both AGB and D growth and its corresponding climate-sensitive AGB and D growth base models were very small and insignificant (p > 0. 0 5). Compared to the base models, the inherent correlation of AGB with D was taken into account effectively by the proposed compatible model system developed with the climate-sensitive AGB and D growth models. In addition, the compatible properties of the estimated AGB and D were also addressed substantially in the proposed model system. [ABSTRACT FROM AUTHOR]

A tree crown is a mass of foliage and significantly correlated to biomass and growth of other parts of a tree. Crown width (CW) is defined as an average of two crown diameters perpendicular to each other, obtained from measurements of four crown radii (crown components). The CW is often used as a covariate predictor in various forest models and therefore, its accurate estimate can be useful for forest management. Although various CW models have been developed so far, all of them lack a detailed investigation of the additivity properties of crown components and inherent correlations among the components and total CW. This study compared three alternative procedures of developing the systems of CW models: nonlinear simultaneous equations (NSE), disaggregated model structure with both one-step proportional weighting system (DMS&OSPWS), and two-step proportional weighting system, and aggregated model structure with one constraint on the total CW. Other two commonly used additivity methods: adjustment in proportion and ordinary least square with separating regression were also evaluated. The comparisons were based on the analyses of data obtained from 2207 trees of Mongolian oak ( Quercus mongolica Fisch.) on 116 permanent sample plots located in northern China. Results showed that each method ensured a summation of crown components equal to twice as much as the total CW. The NSE better accounted for the inherent correlations among the crown components and total CW. Out of the model structures evaluated, DMS&OSPWS provided the best performance. This article emphasized more on the methodology and it was expected that the method could be applied by other researchers to develop similar systems of the CW models for other species as well. [ABSTRACT FROM AUTHOR]

How trees allocate photosynthetic products to primary height growth and secondary radial growth reflects their capacity to best use environmental resources. Despite substantial efforts to explore tree height–diameter relationship empirically and through theoretical modeling, our understanding of the biological mechanisms that govern this phenomenon is still limited. By thinking of stem woody biomass production as an ecological system of apical and lateral growth components, we implement game theory to model and discern how these two components cooperate symbiotically with each other or compete for resources to determine the size of a tree stem. This resulting allometry game theory is further embedded within a genetic mapping and association paradigm, allowing the genetic loci mediating the carbon allocation of stemwood growth to be characterized and mapped throughout the genome. Allometry game theory was validated by analyzing a mapping data of stem height and diameter growth over perennial seasons in a poplar tree. Several key quantitative trait loci were found to interpret the process and pattern of stemwood growth through regulating the ecological interactions of stem apical and lateral growth. The application of allometry game theory enables the prediction of the situations in which the cooperation, competition or altruism is an optimal decision of a tree to fully use the environmental resources it owns. [ABSTRACT FROM AUTHOR]

Crown width is a tree variable that is commonly used as an important predictor in forest growth and yield models that serve as decision-support tools in forestry. Here, we developed a generalized interregional nonlinear mixed-effects individual tree crown width model using data from 3369 Prince Rupprecht larch ( Larix principis - rupprechtii Mayr.) trees on 116 sample plots that were distributed across the two most important regions in northern China. Because measurements from the same sample plot were highly correlated with each other, random effects at the levels of both sample plots and stands with different site conditions (blocks) were used to develop a two-level nonlinear mixed-effects crown width model. To describe the interregional variability of crown width between the regions, a dummy variable, which accounts for region-specific differences, was introduced into the model. The results showed that the two-level interregional nonlinear mixed-effects crown width model accurately described the regional variability of crown width for Prince Rupprecht larch in northern China. Modeling the random effects at the block level alone led to significantly high correlations among the residuals. However, these correlations decreased significantly when the random effects were modeled at both the block and sample plot levels. Measuring the crown width of four randomly selected Prince Rupprecht larch trees per sample plot is recommended for localizing the mixed-effects crown width model and precisely predicting the crown widths of the remaining trees on each plot. [ABSTRACT FROM AUTHOR]

Tree height to crown base (HCB) is an important variable commonly included as one of the predictors in growth and yield models that are the decision-support tools in forest management. In this study, we developed a generalized nonlinear mixed-effects individual tree HCB model using data from a total of 3133 Mongolian oak ( Quercus mongolica ) trees on 112 sample plots allocated in Wangqing Forest Bureau of northeast China. Because observations taken from same sample plots were highly correlated with each other, the random effects at the levels of both sample plots and stands with different site conditions (blocks) were taken into consideration to develop a two-level nonlinear mixed-effects HCB model. The results showed that the significant predictors included total tree height, diameter at breast height (DBH), dominant height, and total basal area of all trees with DBH larger than a target tree per sample plot. Modelling the random effects at block level alone led to highly significant correlation among the residuals. The correlation significantly decreased when the random effects were modeled at both block and sample plot levels. Four alternatives of HCB sampling designs (selecting the largest, medium-size and smallest trees, and the randomly selected trees) and eight sample sizes (one to eight trees) for calibrating the mixed effects HCB model using an empirical best linear unbiased prediction approach were examined. It was found that the prediction accuracy of HCB model increased with increasing the number of sample trees for each alternative, but the largest increase occurred when four randomly selected sample trees were used to estimate the random effects. Thus, HCB measurements from four randomly selected trees per sample plot should be used to estimate the random effects of the model. [ABSTRACT FROM AUTHOR]

• Four commonly used mortality models were considered for Prince Rupprecht larch. • Developed a generalized mixed-effects mortality model at diameter classes level. • Logistic mixed-effects mortality model better described effects of climate variables. • Mortality positive with number of trees and mean annual precipitation. • Recommended two smallest sample diameter classes to estimate random effects. Forest mortality is an important variable commonly included as one of the predictors in the growth and yield models that are the fundamental decision-making tools in forest management. The existing forest mortality models for Larch (Larix gmelinii var. principis-rupprechtii) forest, which plays key roles in maintaining forest ecosystem functions and reducing atmospheric carbon concentration through the sequestration, are based on the traditional modeling methods, and these models do not account for the effects of climate on the forest mortality. Developing climate sensitive mortality models are useful for formulating effective forest management strategies in the context of climate change. We developed the diameter class mortality models using the data of 102 temporary sample plots distributed across the Guandi Mountain National Forest Park and Wutai Mountain Boqiang State-owned Forest Farm in the Shanxi Province of northern China. Four commonly used mortality functions were used to fit the data. Among many climatic and dendrometric variables evaluated, number of trees per diameter class (NC), mean annual precipitation (MAP); median diameter class (MDC), and mean temperature difference (DT) contributed significantly highly to the mortality variations. Then, these variables were selected as predictors to develop the two-level nonlinear mixed-effects mortality models applicable to the diameter class levels. The random effects at the levels of both the sample plots and stands with different site quality (blocks) were taken into account to build the models. Compared to other three models derived from the Poisson; Zero-inflated Poisson; Hurdle Poisson functions, the two-level Logistic mixed-effects mortality model better explained the effects of climate variables on the forest mortality of Larch. Tree mortality increased with increasing NC and MAP, however, MDC and DT had the opposite effects with diameter class increasing. Modeling the random effects at the block-level alone led to significantly high correlations among the residuals, and correlations were significantly reduced when the random effects were modeled at both the block- and sample plot-levels. Excessive rain during the growing season decreased the rate of tree mortality, especially for diameter classes with high number of trees. Findings from this study can be combined with the knowledge of the adaptive management to reduce the risks and uncertainties associated with forest management decision. [ABSTRACT FROM AUTHOR]

Forest degradation has been considered as one of the main causes of climate change in recent years. The knowledge of estimating degraded forest areas without the application of remote sensing tools can be useful in finding solutions to resolve degradation problems through appropriate restoration methods. Using the existing knowledge through literature review and field-based primary information, we generated new knowledge by combining the information obtained from multi-criteria decision analyses with an analytic hierarchy process, and this was then used to estimate degraded forest area. Estimation involves determining forest degradation index (FDI) and degradation threshold. Continuous inventory data of permanent sample plots collected from degraded forests, consisting of various forest types divided by dominant tree species in the Guangdong province and Tibet autonomous region of China, were used for the purposes. We identified four different forest degradation levels through the determination and comprehensive evaluation of FDI. The degraded forest area with broad-leaved species as dominant tree species in the Guangdong province was estimated to be 83.3% of a total forest area of 24,037 km2. In the same province, the degraded forest area with eucalyptus as a dominant tree species was 59.5% of a total forest area of 18,665 km2. In the Tibet autonomous region, the degraded forest area with spruce as a dominant tree species was 99.1% of a total forest area of 17,614 km2, and with fir as a dominant tree species, the degraded area was 98.4% of a forest area of 12,103 km2. A sampling accuracy of forest areas with national forest inventory was about 95% in both provinces. Our study concludes that the FDI method used has a certain scientific rationality in estimating degraded forest area. The forest provides a variety of tangible and intangible goods and services for humans. Therefore, forest management should focus on the improvement of its overall productivity, which is only possible with improving forest site quality. One of the important steps to improve the quality of a forest site is to resolve its degradation issues. The presented method in this article will be useful in finding the solutions to forest degradation problems. This method, which does not need any remote sensing tool, is simple and can be easily applied for estimating any degraded forest area and developing effective forest restoration plans. [ABSTRACT FROM AUTHOR]