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The growth and yield of mixed-species stands has become an important topic of research since there are certain advantages of this type of forest as regards functions and services. However, the concepts and methods used to characterize mixed stands need to be understood, as well as harmonized and standardized. In this review we have compiled a set of measures, indices, and methods at stand level to characterize the structure, dynamics, and productivity of mixed stands, and we discuss the pros and cons of their application in growth and yield studies. Parameters for the characterization of mixed stand structure such as stand density, species composition, horizontal (intermingling) and vertical tree distribution pattern, tree size distribution, and age composition are described, detailing the potential as well as the constraints of these parameters for understanding resource capture, use, and efficiency in mixed stands. Furthermore, a set of stand-level parameters was evaluated to characterize the dynamics of mixed stands, e.g. height growth and space partitioning, self- and alien-thinning, and growth partitioning among trees. The deviations and changes in the behaviour of the analysed parameters in comparison with pure stand conditions due to inter-specific interactions are of particular interest. As regards stand productivity, we reviewed site productivity indices, the growth–density relationship in mixed stands as well as methods to compare productivity in mixed versus monospecific stands. Finally, we discuss the main problems associated with the methodology such as up-scaling from tree to stand level as well as the relevance of standardized measures and methods for improving forest growth and yield research in mixed stands. The main challenges are also outlined, especially the need for qualitatively sound data.

In the context of the reduction of greenhouse gas emissions caused by deforestation and forest degradation (the REDD+ program), optical very high resolution (VHR) satellite images provide an opportunity to characterize forest canopy structure and to quantify aboveground biomass (AGB) at less expense than methods based on airborne remote sensing data. Among the methods for processing these VHR images, Fourier textural ordination (FOTO) presents a good method to detect forest canopy structural heterogeneity and therefore to predict AGB variations. Notably, the method does not saturate at intermediate AGB values as do pixelwise processing of available space borne optical and radar signals. However, a regional-scale application requires overcoming two difficulties: (1) instrumental effects due to variations in sun-scene-sensor geometry or sensor-specific responses that preclude the use of wide arrays of images acquired under heterogeneous conditions and (2) forest structural diversity including monodominant or open canopy forests, which are of particular importance in Central Africa. In this study, we demonstrate the feasibility of a rigorous regional study of canopy texture by harmonizing FOTO indices of images acquired from two different sensors (Geoeye-1 and QuickBird-2) and different sun-scene-sensor geometries and by calibrating a piecewise biomass inversion model using 26 inventory plots (1 ha) sampled across very heterogeneous forest types. A good agreement was found between observed and predicted AGB (residual standard error [RSE] = 15%; R 2 = 0.85; P < 0.001) across a wide range of AGB levels from 26 Mg/ha to 460 Mg/ha, and was confirmed by cross validation. A high-resolution biomass map (100-m pixels) was produced for a 400-km 2 area, and predictions obtained from both imagery sources were consistent with each other ( r = 0.86; slope = 1.03; intercept = 12.01 Mg/ha). These results highlight the horizontal structure of forest canopy as a powerful descriptor of the entire forest stand structure and heterogeneity. In particular, we show that quantitative metrics resulting from such textural analysis offer new opportunities to characterize the spatial and temporal variation of the structure of dense forests and may complement the toolbox used by tropical forest ecologists, managers or REDD+ national monitoring, reporting and verification bodies.

Forest aboveground biomass (AGB) serves as a crucial indicator of productivity and carbon storage capacity. While the impact of stand structure on AGB is well-documented for pure moso bamboo stands, the specific structural factors influencing AGB and the mechanisms driving these effects in mixed moso bamboo forests, characterized by species diversity and structural complexity, require further elucidation. This study analyzed 9453 bamboos and arbor trees within the TianBao MetaPlot, which were tessellated into 108 standard plots in Tianbaoyan National Nature Reserve, Fujian, China. Using a multi-method voting approach, we identified the key structural factors influencing stand AGB and employed Partial Least Squares Path Modeling (PLS-PM) to assess their direct and indirect effects. We found that the stand density, moso bamboo mixing ratio, Shannon’s index, Simpson’s index, mean tree height, openness, and tree size variation coefficient were the key structural factors influencing the stand AGB. The PLS-PM analysis showed that stand density had a negative effect on stand AGB, which can be explicitly decomposed through a direct negative effect and an indirect negative effect. Tree diversity showed a strong positive effect, supporting the niche complementarity theory. The stand mean tree height and stand tree size variation had positive effects on stand AGB, while stand openness had a negative effect. The direct effects of tree diversity, stand mean tree height, and stand openness were stronger than the indirect effects on stand AGB, while the indirect effect of stand density was greater than the aforementioned effects. These results highlight the complex interactions between stand structure and stand AGB in mixed moso bamboo forests. The negative effect of stand density on stand AGB is in contrast with previous findings on arbor forests, wherein a higher stand density often promotes AGB, highlighting the unique structural characteristics of mixed moso bamboo forests. To promote biomass accumulation and enhance carbon sequestration in mixed moso bamboo stands, it is recommended to increase the tree size variability, enhance the tree species diversity, and apply rational thinning of moso bamboo, based on site-specific conditions.

Cunninghamia lanceolata is an important species in plantations and is widely planted in sub-tropical regions of China because of its fast-growing and productive characteristics. However, the monoculture planting is carried out in the pursuit of economic value. This planting mode has led to problems such as the exhaustion of soil fertility, decrease in vegetation diversity, and decrease in woodland productivity. In order to restore soil fertility and increase timber production, the introduction of broad-leaved tree species to plantations is an effective transformation model. Understanding how forest age changes and stand structure differences drive the composition and diversity of soil microbial communities is helpful in understanding the trend of soil–microbial changes in plantations and evaluating the effects of the introduction of broad-leaved tree species in soil–plant–microbial ecosystems in plantations. Therefore, the purpose of our study is to investigate the effects of forest age and pure forest conversion on C. lanceolata–P. bournei-mixed forest soil microbial community structure and diversity by detecting soil nutrients, enzyme activities, and soil microbial 16S and ITS rRNA gene sequencing. According to the findings, the diversity and abundance of bacterial communities in C. lanceolata plantations of different ages increased first and then decreased with the increase in forest age, and the max value was in the near-mature forest stage. The fungal abundance decreased gradually with stand age, with the lowest fungal diversity at the near-mature stand stage. During the whole growth process, the bacterial community was more limited by soil pH, nitrogen, and phosphorus. After introducing P. bournei into a Chinese fir plantation, the abundance and diversity of the bacterial community did not improve, and the abundance of the fungal community did not increase. However, soil nutrients, pH, and fungal community diversity were significantly improved. The results of these studies indicate that the introduction of broad-leaved tree species not only increased soil nutrient content, but also had a significant effect on the increase in the diversity of soil fungal communities, making the microbial communities of mixed forests more diverse.

The stand structure of ancient tea tree (Camellia taliensis) communities is critical for maintaining their structural and functional stability. Therefore, this study employed backpack laser scanning (BLS) technology to extract individual tree parameters (diameter at breast height, tree height, relative coordinates, etc.) in seven sample plots (25 m × 25 m each) to analyze their spatial and non-spatial structure characteristics. Firstly, the accuracy of diameter at breast height (DBH) and tree height (TH) estimations using BLS resulted in a root mean square error (RMSE) of 4.247 cm and 2.736 m and a coefficient of determination (R2) of 0.948 and 0.614, respectively. Secondly, in this community, trees exhibited an aggregated spatial distribution (average uniform angle > 0.59), with small differences in DBH among adjacent trees (average dominance > 0.48) and a high proportion of adjacent trees belonging to different species (average mingling > 0.64). Ancient tea trees in the 5–15 cm diameter class face considerable competitive pressure, with values ranging from 14.28 to 179.03. Thirdly, this community exhibits rich species composition (more than 7 families, 8 genera, and 10 species, respectively), strong regeneration capacity (with an inverse J-shaped diameter distribution), uniform species distribution (Pielou evenness index > 0.71), and high species diversity (with a Shannon–Wiener diversity index ranging from 1.65 to 2.47 and a Simpson diversity index ranging from 0.71 to 0.91), and the ancient tea trees maintain a prominent dominant status and important value ranging from 19.36% to 49%. The results indicate that, under the current conditions, the structure and function of this community collectively exhibit relatively stable characteristics. BLS provides a powerful tool for the research and conservation of rare and endangered species.

The comprehensive indexes approach based on stand structure parameters is mainly used to select trees for harvest. However, these indexes do not consider the comprehensive impact of horizontal and vertical structures, leading to an incomplete analysis of the forest structure and an inaccurate selection of trees for harvest. To solve this problem, we constructed a comprehensive decision index of logging (CDIL), integrating horizontal and vertical structure parameters which can identify harvest trees more scientifically. In this study, we took the Shanxia Forest Farm in the Jiangxi Province of China as the experimental area and used mixed broadleaf/conifer forests at different ages as our experimental sample. We selected eight horizontal and vertical spatial structure parameters to establish an efficient, objective, and accurate comprehensive decision index of logging. We combined 3D visualization technology to realize the dynamic visualization simulation of the index at different intensities of tending and felling management. The results indicated that the proposed CDIL-index could effectively optimize the forest spatial structure. From the perspective of stand structure adjustment, the optimal thinning intensity was 20%. The average CDIL in each plot decreased by more than 80% after logging, while the change range of each plot was between 30% and 70% after the F index was applied to implement tending and logging. The CDIL was 11.4% more accurate in selecting trees for harvesting than the F index. In this study, the main conclusion is that the CDIL would enable forest managers to more accurately choose trees for harvesting, leading to forest adjustment that would reduce the competition pressure among trees and improve the distribution and health of trees, possibly making the forest structure more stable.

The Populus euphratica desert riparian forest, predominantly distributed along the Tarim River in northwestern China, has experienced significant degradation due to climate change and anthropogenic activities. Despite its ecological importance, systematic assessments of P. euphratica stand structure across the entire Tarim River remain scarce. This study employed terrestrial laser scanning (TLS) to capture high-resolution 3D structural data from 2741 individual trees across 30 plots within six transects, covering the 1300 km mainstream of the Tarim River. ANOVA, PCA, and RDA were applied to examine tree structure variation and environmental influences. Results revealed a progressive decline in key structural parameters from the upper to lower reaches of the river, with the lower reaches showing pronounced degradation. Stand density decreased from 440 to 257 trees per hectare, mean stand height declined from 9.3 m to 5.6 m, mean crown diameter reduced from 4.1 m to 3.8 m, canopy cover dropped from 62% to 42%, and the leaf area index fell from 0.51 to 0.29. Age class distributions varied along the river, highlighting population structures indicative of growth in the upper reaches, stability in the middle reaches, and decline in the lower reaches. Abiotic factors, including groundwater depth, soil salinity, soil moisture, and precipitation, exhibited strong correlations with stand structural parameters (p < 0.05, R2 ≥ 0.69). The findings highlight significant spatial variations in tree structure, with healthier growth in the upper reaches and degradation in the lower reaches, enhance our understanding of forest development processes, and emphasize the urgent need for targeted conservation strategies. This comprehensive quantification of P. euphratica stand structure and its environmental drivers offer valuable insights into the dynamics of desert riparian forest ecosystems. The findings contribute to understanding forest development processes and provide a scientific basis for formulating effective conservation strategies to sustain these vital desert ecosystems, as well as for the monitoring of regional environmental changes.

Aiming to enhance the efficiency and precision of multi-objective optimization in southwestern secondary growth of Pinus yunnanensis forests, this study integrated spatial and non-spatial structural indicators to establish objective functions and constraints for assessing forest structure. Felling decisions were made using the random selection method (RSM), Q-value method (QVM), and V-map method (VMM). Actions taken to optimize the forest stand structure (FSS) through tree selection were approached as decisions by a reinforcement learning (RL) agent. Leveraging RL’s trial-and-error strategy, we continually refined the agent’s decision-making process, applying it to multi-objective optimization. Simulated felling experiments conducted across circular sample plots (P1–P4) compared RL, Monte Carlo (MC), and particle swarm optimization (PSO) in FSS optimization. Notable enhancements in the values of the objective function (VOFs) were observed across all plots. RL-based strategies exhibited improvements, achieving VOF increases of 17.24%, 44.92%, 34.66%, and 17.10% for P1–P4, respectively, outperforming MC-based (10.73%, 41.54%, 30.39%, and 15.07%, respectively) and PSO-based (14.08%, 37.78%, 26.17%, and 16.23%, respectively) approaches. The hybrid M7 scheme, integrating RL with the RSM, consistently outperformed other schemes across all plots, yielding an average 26.81% increase in VOF compared to the average enhancement of all schemes (17.42%). This study significantly advances the efficacy and precision of multi-objective optimization strategies for Pinus yunnanensis secondary forests, emphasizing RL’s superior optimization performance, particularly when combined with the RSM, highlighting its potential for optimizing sustainable forest management strategies.

This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective function and constraints based on both spatial and non-spatial structural indices of the forest stand structure (FSS). The value of the objective function (VOF) served as an indicator for assessing FSS. Leveraging the random selection method (RSM) to select harvested trees, we propose the replanting foreground index (RFI) to enhance replanting optimization. The decision-making processes involved in selection harvest optimization and replanting were modeled as actions within MARL. Through iterative trial-and-error and collaborative strategies, MARL optimized agent actions and collaboration to address the collaborative optimization problem of FSS. We conducted optimization experiments for selection felling and replanting across four circular sample plots, comparing MARL with traditional combinatorial optimization (TCO) and single-agent reinforcement learning (SARL). The findings illustrate the superior practical efficacy of MARL in collaborative optimization of FSS. Specifically, replanting optimization based on RFI outperformed the classical maximum Delaunay generator area method (MDGAM). Across different plots (P1, P2, P3, and P4), MARL consistently improved the maximum VOFs by 54.87%, 88.86%, 41.34%, and 22.55%, respectively, surpassing those of the TCO (38.81%, 70.04%, 41.23%, and 18.73%) and SARL (54.38%, 70.04%, 41.23%, and 18.73%) schemes. The RFI demonstrated superior performance in replanting optimization experiments, emphasizing the importance of considering neighboring trees’ influence on growth space and replanting potential. Following selective logging and replanting adjustments, the FSS of each sample site exhibited varying degrees of improvement. MARL consistently achieved maximum VOFs across different sites, underscoring its superior performance in collaborative optimization of logging and replanting within FSS. This study presents a novel approach to optimizing FSS, contributing to the sustainable management of Pinus yunnanensis secondary forests in southwestern China.

Stand structural configuration dictates ecosystem functional performance. Mangrove ecosystems, located in ecologically sensitive coastal ecotones, require efficient acquisition of stand structure parameters and health assessments based on these parameters for practical applications. Effective assessment of mangrove ecosystem health, crucial for their functional performance in ecologically sensitive coastal ecotones, relies on efficient acquisition of stand structure parameters. This study developed a UAV (Unmanned Aerial Vehicle)-based framework for mangrove health evaluation integrating stand structure parameters, utilizing UAV visible-light imagery, field plot surveys, and computer vision techniques, and applied it to the assessment of a national nature reserve. We obtained the following results: (1) A deep neural network, combining UAV visible-light data with tree height constraints, achieved 88.29% overall accuracy in simultaneously identifying six dominant mangrove species; (2) Stand structure parameters were derived based on individual tree extraction results in seedling zones along forest edges (with canopy individual tree segmentation accuracy ≥ 78.57%), and a stand health evaluation model was constructed; (3) Health assessment revealed that the core zone exhibited significantly superior stand health compared to non-core zones. This method demonstrates high efficiency, significantly reducing the time and effort for monitoring, and offers robust support for future mangrove forest health assessments and adaptive conservation strategies.