Forest structure regulates the spatial heterogeneity of absorbed photosynthetically active radiation (APAR), thereby shaping ecosystem functioning and productivity. However, how neighborhood spatial configuration and competition dynamics control APAR distribution remains poorly understood, particularly in structurally heterogeneous and topographically complex natural forests. To address this, we proposed FASAR (Forest Architecture–Solar Absorption Response), a structure–radiation coupling framework for complex-terrain natural forests. FASAR establishes an integrated workflow for structure–radiation coupling analysis by integrating multi-source LiDAR-based structural parameterization, Adaptive Leaf Synthesis Algorithm (ALSA)-assisted 3D forest reconstruction, 3D radiative transfer simulation, and structure-stratified ensemble modeling. It converts heterogeneous forest architecture into plot-, neighborhood-, and tree-level descriptors and links these descriptors with spatially explicit APAR patterns. Applied to 144 plots (5.76 ha, 3,417 trees) in a natural Qinghai spruce forest in western China, FASAR revealed that large trees absorb more light with vertically stable patterns, while canopy stratification, competition, and size disparity intensify vertical displacement of light and suppress central-tree absorption. Structural dominance and near-natural spatial configurations enhanced APAR interception (ρ = 0.08–0.49, p < 0.001), while moderate aggregation and openness improved light scattering (ρ = 0.06–0.19, p < 0.001). Slope (ρ = –0.37, p < 0.001) and gap density (ρ = –0.18, p < 0.05) showed dual facilitation–inhibition effects on mean APAR. Structure-specific Bagging models achieved moderate performance (R2 = 0.457–0.546). Variable importance analysis identified crown area (CA) as the dominant driver across all structural types (contribution: 68.95–81.78 %), followed by tree height, spatial density, and other structural heterogeneity indices. This framework provides an operational pathway for quantifying how neighborhood-scale forest architecture regulates individual-tree light absorption under complex-terrain conditions. The structure–radiation mechanisms revealed in this study may also provide physically based constraints for APAR/fAPAR remote sensing retrieval and related research. FASAR has the potential to be extended across forest types and successional stages, with its performance further evaluated in future studies.

Current agroforestry management systems still lack a computable and reusable framework for explicit structure-radiation evaluation, rapid optimization, and spatially explicit analysis. Existing tools provide limited support for light-environment characterization, and the integration of simulation, optimization, and usable-space mapping remains insufficient. To address this, we propose a novel light-informed computational framework based on structure-radiation coupling, including four core modules: (1) Constructing a virtual stand light-field digital twin to reconstruct three-dimensional stand scenes and designing a novel GPU-accelerated, parallel, component-wise differentiable forward radiative tracing simulator (GPU-PCDFRT) to unify structure reconstruction, quantitative light-transport solution, and radiation-response evaluation; (2) Constructing a structure-radiation surrogate model for rapid representation and repeated invocation of attribute–structure–radiation relationships; (3) Designing a light-integrated stand structure optimization agent (LiSSO-Agent) based on an Elite-enhanced Soft Actor-Critic (E-SAC) to generate selective-harvesting decisions under dual constraints of structural quality and light-environment quality; (4) Identifying, delineating, and mapping potentially allocable understorey space after harvesting to support management-oriented space identification based on post-harvest light-environment reconstruction. The framework enabled detailed three-dimensional reconstruction for 50 pure Chinese fir (Cunninghamia lanceolata) plots and effectively captured radiation distribution patterns within complex stands. The generalized additive model (GAM) trained on 1868 individual-tree samples achieved an R2 of 0.840 in ten-fold cross-validation. The E-SAC-based LiSSO-Agent improved three core optimization metrics NL, LightQ, and SR by 83.45 %, 11.33 %, and 22.58 %, respectively, with optimized NL consistently surpassing the initial state and overall outperforming heuristic baselines, and showing high stability across the 50 plots (mean CV = 0.149 %). The framework also reconstructed post-harvest understorey PAR patterns and identified potentially allocable space for the spatial arrangement of light-demanding understorey crops in agroforestry systems. Overall, this study presents a light ecological process-based optimization paradigm that explicitly integrates light into management decisions, enabling coordinated stand structure-radiation regulation and precise understorey resource allocation in agroforestry systems.

Tree architecture analysis is fundamental to forestry, but complex trees challenge the accuracy and efficiency of point-cloud-based reconstruction. Here, we present SmartQSM, a novel quantitative structure model designed for reconstructing individual trees and extracting their parameters using ground-based laser scanning data. The method achieves point cloud contraction and forms the thin structures required for skeletonization by iteratively applying a sparse-convolution-based residual U-shaped network (ResUNet) to predict point movement towards the medial axis. This process is integrated with techniques from previous studies to form a complete reconstruction pipeline. Following the organization and QSM-based quantification of 47 individual-scale, 26 organ-scale, and 8 plot-scale parameters, the proposed method provides comprehensive support for extracting these metrics using the input point cloud and its outputs, including the skeleton and mesh. The performance was verified using the two-period leaf-off LiDAR data of a natural coniferous and broad-leaved mixed forest plot (in Qingyuan county, Liaoning province, China), and 2 open forest datasets. The existing major QSMs was used for comparison. The inference network adopted a three-stage hierarchical spatial compression architecture, initiating with 8 input channels and predicting with multi-layer perceptron. The reconstruction was insensitive to remaining leaves and the model did not have apparent distortion. The processing speed is efficient, about 12,000 points per second. In terms of major architectural parameters, the R2 scores for trunk length, trunk volume, and bole height on the tested two period data of different tree species in the plot reached 0.97, 0.957, and 0.949, respectively, which were 0.043, 0.114, 0.029 higher than existing methods. the R2 scores for branch length, branching angle, and tip deflection angle remained around 0.95. The overestimation of stem volume or aboveground biomass has been alleviated. The high reconstruction quality, efficiency, rich parameters, and unique visual interaction capabilities of the proposed method offer a novel and practical solution for forestry research and broader domains. The implementation code is currently available at: https://github.com/project-lightlin/SmartQSM.

Accurate individual tree species classification is essential for forest inventory, management, and conservation. However, existing methods relying primarily on single-source remote sensing data (e.g., spectral, LiDAR, or RGB) often suffer from insufficient feature representation and noise interference, particularly in subtropical forests with high species diversity, leading to increased classification errors. To address these challenges, we proposed the Multi-source Tree Species Classification Fusion Network (MTSCFNet), a novel deep learning framework that integrates RGB imagery, LiDAR-derived feature maps, and GF-2 satellite data through a modified UNet backbone, which incorporates a three-branch encoder and a Triple Branch Feature Fusion (TBFF) module within a middle fusion strategy. We evaluated the MTSCFNet in Chinese-fir mixed forests located in the Shanxia Forest Farm, Jiangxi Province, China. The results showed that: (1) MTSCFNet outperformed four baseline models, achieving Macro F1 (0.78 ± 0.01), Micro F1 (0.93 ± 0.01), Weighted F1 (0.93 ± 0.01), a Matthews correlation coefficient (MCC) (0.89 ± 0.01), Cohen’s ĸ (0.89 ± 0.01), and mIoU (0.69 ± 0.01), with respective improvements of 4.05% in Macro F1, 1.89% in Micro F1, 0.09% in Weighted F1, 1.67% in MCC, 1.64% in Cohen’s ĸ, and 5.92% in Mean IoU over the second best model, SwinUNet; (2) Compared to the best two-source combinations (R + S, R + L), MTSCFNet achieved up to 1.50%, 3.28%, 3.42%, 6.72%, 6.76%, and 3.51% higher Macro F1, Micro F1, Weighted F1, MCC, Cohen’s ĸ, and mIoU, and up to 8.11%, 2.63%, 2.88%, 5.01%, 4.99%, and 11.48% improvements over single-source inputs, while also exhibiting the lowest variability, indicating strong robustness; (3) Under different fusion strategies, MTSCFNet with middle fusion surpassed early and late fusion by up to 15.31%, 3.74%, 3.99%, 7.66%, 7.76%, 22.33% and 24.13%, 5.76%, 6.20%, 11.48%, 11.57%, 32.96% in Macro F1, Micro F1, Weighted F1, MCC, Cohen’s ĸ, and mIoU, respectively, validating the effectiveness of feature-level multi-modal integration; (4) In cross-region transfer experiments, MTSCFNet demonstrated strong spatial generalizability, achieving average scores of 0.78 (Macro F1),0.87 (Micro F1), 0.86 (Weighted F1), 0.59 (MCC), 0.59 (Cohen’s ĸ), and 0.68 (mIoU), and outperformed SwinUNet by up to 38.80%, 9.40%, 18.58%, 22.48%, 26.17%, and 33.00% in Macro F1, Micro F1, Weighted F1, MCC, Cohen’s ĸ, and mIoU across varying forest densities. Overall, MTSCFNet offers a robust, accurate, and transferable solution for tree species classification in complex subtropical forest environments. Graphical abstract

Understanding forest photosynthetic capacity is essential for monitoring carbon dynamics under global change. UAV-based imaging spectroscopy is a powerful tool for assessing canopy leaf traits, but the extension of spectraltrait relationships to the canopy scale remains unclear. This study uses UAV-based hyperspectral imaging data to evaluate the photosynthetic characteristics of larch forests across different climate zones in China. We investigate UAV-derived imaging spectroscopy for mapping canopy-level leaf physiological traits, including chlorophyll content, leaf nitrogen, and photosynthetic capacity (Vc, max and Jmax) across three distinct climate zones. Highresolution UAV imaging spectral data and ground-based leaf trait measurements, including biochemical (chlorophyll, leaf nitrogen), morphological (leaf mass per area, LMA), and physiological traits (Vc, max and Jmax), were collected from 150 tree crowns at all sites. We developed and validated models for estimating physiological traits from canopy spectra using Partial Least Squares Regression (PLSR), focusing on the transferability of leaflevel models to the canopy scale. The results show that UAV-based canopy spectra can effectively estimate canopy-level Vc, max25 (R2 = 0.56, RMSE = 9.57 mu mol CO2 m- 2 s-1, nRMSE = 17.7 %) and Jmax25 (R2 = 0.38, RMSE = 34.8, nRMSE = 18.6 %). Additionally, other leaf traits across all climate zones were accurately predicted, including leaf mass per area (LMA), leaf water content (LWC), chlorophyll content (Chl), nitrogen content (Narea), and phosphorus content (Parea), with R2 values ranging from 0.30 to 0.44 and nRMSE between 18.8 % and 24.4 %. Significant differences in canopy trait variability were observed, with Vc, max25 and Jmax25 values driven by climate variability. The range of Vc, max25 (40.5-70.6 mu mol CO2 m- 2 s-1) and Jmax25 (80.6-120.4 mu mol CO2 m- 2 s- 1) was wider at the ES site compared to the FS and TS sites, indicating that species differences have a greater impact on photosynthetic capacity. These models demonstrated good transferability, showing robust performance across forests in different climate zones with only slight differences in predictive accuracy. However, canopy structure significantly influenced spectral-trait relationships, particularly for Vc, max and Jmax. While canopy structure had a moderate impact on accuracy, canopy-scale models performed slightly lower than leaf-level models in some cases. This study offers new insights into UAV-based imaging spectroscopy for mapping canopy leaf physiological traits and emphasizes the need to understand different physiological mechanisms at the canopy scale when expanding spectral-trait relationships.

Forest phenotypic responses are significantly influenced by extreme climate conditions, particularly canopy structure and photosynthetic traits. However, the underlying mechanisms driving these responses, especially in conifer species, remain poorly understood. This study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis, quantifying changes in key physiological traits that light interception, gas exchange parameters stomatal conductance, and chlorophyll content. Developing 3D reconstruction algorithms tailored to conifer canopies is essential for simulating forest ecosystem responses under varying canopy densities. We investigate the following questions: (1) How does thinning affect canopy light penetration and photosynthetic efficiency? Thinning significantly increased light penetration from 15 % (CK) to 22 %, enhancing photosynthetic efficiency, resulting in an 18 % increase in carbon absorption under drought conditions. (2) How does reduced-rainfall affect photosynthetically active radiation (PAR) and stomatal conductance? Reduced-rainfall caused a 12 % decrease in PAR, a 20 % reduction in stomatal conductance, and an 8 % decrease in chlorophyll content. (3) What are the synergistic effects of thinning and reduced-rainfall in carbon absorption? Thinning under reduced-rainfall increased carbon absorption by 25 %. This study reveals a significant correlation between chlorophyll content, leaf nitrogen content, and canopy structural dynamics under drought and elevated temperature conditions, offering new insights into the adaptive mechanisms plants employ to adjust their photosynthetic processes. In conclusion, the development of 3D reconstruction algorithms tailored for conifer canopies, in regulating photosynthetic traits, is crucial for improving forest adaptation, contributing to functional trait-based forest management and ecosystem modeling.

Current visual methods of forest dynamic growth mostly focus on the plot or stand level, which cannot express the morphological and structural characteristics of individual trees, as well as their statistical linkages, and causes each tree in the stand to grow at the same rate. In addition, these visual growth models still have some space for improvement in terms of prediction accuracy and multirelational data mining. In this article, uneven-aged Chinese fir (Cunninghamia lanceolata) plantations were chosen as our study subject and proposed a novel method of forest dynamic growth visualization modeling by incorporating spatial structure parameters and using convolutional neural network technique (FDGVM-CNN-SSP) to explore the effect of spatial structure on the morphological growth and to develop a prediction growth model of Chinese fir plantations by introducing a convolutional neural network (CNN) model. The results show that: first, spatial structural parameters C and U have a certain contribution to the forest growth, and C and U can explain 21.5%, 15.2%, and 9.3% of the variance in DBH, H, and CW growth models, respectively; second, CNN model outperformed machine learning algorithms SVR, MARS, Cubist, RF, and XGBoost in terms of prediction performance; third, based on FDGVM-CNN-SSP, we simulated Chinese fir plantations at individual tree level and stand level from 2018 to 2022 and found that DBH and H's fitting performance in measured and predicted data was highly consistent with R-2 and root-mean-square error (RMSE) of 86.8%, 2.06 cm in DBH and 79.2%, 1.11 m in H, but CW's R-2 and RMSE of 72.2%, 0.65 m caused crowding (C) inconsistency.

Modeling large-scale scenarios of diversity in real forests is a hot topic in forestry research. At present, there is a common problem of simple and poor model scalability in large-scale forest scenes. Forest growth is often carried out using a holistic scaling approach, which does not reflect the diversity of trees in nature. To solve this problem, we propose a method for constructing large-scale forest scenes based on forest hierarchical models, which can improve the dynamic visual effect of large-scale forest landscape polymorphism. In this study, we constructed tree hierarchical models of corresponding sizes using the detail attribute data of 29 subplots in the Shanxia Experimental Forest Farm in Jiangxi Province. The growth values of trees of different ages were calculated according to the hierarchical growth model of trees, and the growth dynamic simulation of large-scale forest scenes constructed by the integrated model and hierarchical model was carried out using three-dimensional visualization technology. The results indicated that the runtime frame rate of the scene constructed by the hierarchical model was 30.63 fps and the frame rate after growth was 29.68 fps, which met the operational requirements. Compared with the traditional integrated model, the fluctuation value of the frame rate of the hierarchical model was 0.036 less than that of the integrated model, and the scene ran stably. The positive feedback rate of personnel evaluation reached 95%. In this study, the main conclusion is that our proposed method achieves polymorphism in large-scale forest scene construction and ensures the stability of large-scale scene operation.

Crown simulation based on basis spline (b-spline) interpolation is a compatible method to simulate tree polymorphism at present. However, there are two problems when it simulates the crown: the first problem is that the derivative value at the top point needs to be given manually, and the second is that the type of value point needs to be collected equidistantly. To solve the above problems and realize convenient and accurate tree polymorphism simulation, this study took Chinese fir as the study object, set the crown morphological feature as the model value point, and constructed a coupling model of generalized B-spline curve and crown (CMGBCC) as the constraint condition of the crown shape to simulate the polymorphism in the process of a tree three-dimensional (3D) model. The position and size of the distribution on the 3D model of the branches were constrained by the curve, and the 3D modeling of a Chinese fir polymorphism was constructed. According to the collection of Chinese fir-type value points in the sample plot, the study realized the detailed types of value points’ precise simulation for three polymorphisms of the Chinese fir crown, including natural pruning, crown displacement, and crown shape difference. At the same time, the different withered existence states of the branches were considered preliminarily. Compared to the 3D model with the field survey data, indicating that constructed models could simulate the difference in tree crown morphology precisely, the branch models were separated by convenience to simulate the process of Chinese fir growth. In the process of construction, CMGBCC did not need to add the derivative value in a manual way and could collect the type of value points according to the characteristics of the crown morphological changes completely. Compared to the results of the crown curve constructed, which were based on generalized B-spline (GB-spline) interpolation and b-spline interpolation, it showed that the number of crown value points collected by the GB-spline interpolation method decreased by 18% on average. The precision of the crown shape constraint was improved by 7.63% compared to b-spline interpolation. The 3D modeling of tree polymorphism was combined with the relationship between tree morphology and environment. At the same time, it was convenient to simulate the behavior of forest management measures, such as pruning.

Environmental factors substantially influence the growth of trees. The current studies on tree growth simulation have mainly focused on the effect of environmental factors on diameter at breast height and tree height. However, the influence of environmental factors, especially light, on canopy morphology has not been considered, hindering the accurate understanding of the range of characteristics of tree morphology that occur due to environmental changes. To solve this problem, this study investigated the influence of light on the changes in canopy morphology and constructed a coupled canopy–light model (CCLM) to visually simulate the polymorphism of fir morphology. Using the Huangfengqiao Forestry Farm in You County, Hunan Province, China, as the study area, we selected a typical sample plot. Field surveys of the fir trees in the sample plot were conducted for three consecutive years to obtain longitudinal data of fir tree canopy shape. We constructed the canopy curves using a cubic uniform B-spline to construct 3D models of the fir trees in different years. The topographic and spatial location distribution data of the fir trees were used to construct a 3D scene of the sample plot in the UE4 3D engine, and the light distribution for each part of the canopy was calculated in a 3D scene by using the annual average photosynthetically active radiation (PAR) as the light parameter, which we combined with the ray-tracing algorithm. This study constructed the CCLM from the fir diameter using the breast-height growth model (BDGM) and the height–diameter curve model (HDCM), the fir trees’ canopy shape description from two years, and the light distribution data. We compared the canopy data obtained from canopy simulations using the CCLM with those obtained using a growth model based on spatial structure (GMBOSS) and those obtained from field surveys to identify any difference in the effectiveness of the canopy simulations using the CCLM and GMBOSS. Based on the BDGM and HDCM, we constructed the CCLM of firs with a determination coefficient (R2) of 0.829, combining data on canopy shape descriptions obtained from two years of field surveys and the light distribution data of each part of the canopy obtained through the ray-tracing algorithm. The Euclidean distance between the canopy description data obtained using the CCLM and the canopy description data obtained from the field survey was 15.561; that between the GMBOSS and the field survey was 23.944. A virtual forest stand environment was constructed from the survey data, combining ray-tracing algorithms to construct the CCLM model of fir in a virtual forest stand environment for growth visualization and simulation. Compared with the canopy description data obtained using the GMBOSS, the canopy description data obtained using the CCLM better fit the canopy description data obtained from the field survey, and the Euclidean distance decreased from 23.944 to 15.561.

Since tree morphological structure is strongly influenced by internal genetic and external environmental factors, accurate simulation of individual morphological–structural changes in trees is the premise of forest management and 3D simulation. However, existing studies have few descriptions, and the research on the impact of growth environments and stand spatial structures on tree morphological structure and growth is still limited. In our study, we constructed a comprehensive grade model of spatial structure (CGMSS) to comprehensively evaluate individual tree growth states of the stands and grade them from 0 to 10 correspondingly. In addition, we developed a Chinese fir morphological structure growth model based on CGMSS, and dynamically simulate the growth variations of Chinese fir stands. The results showed that the overall stand prediction accuracy of CGMSS-based Chinese fir diameter at breast height, tree height, crown width and under-living branch height growth models was more than 94%. According to the analysis of the comprehensive grade of spatial structure (CGSS) of trees in the stand, except for the prediction accuracy and systematic error of the under-living branch height growth model at the CGSS = 3–5 levels, the systematic error of the Chinese fir growth model at each level was lower than 21.2%, and the prediction accuracy was greater than 73%. Compared with the spatial structural unit (SSU)-based Chinese fir growth model proposed by Ma et al., all growth models fit better at all levels, except for the CGMSS-based Chinese fir tree height and under-living branch height growth models that fit significantly lower than the SSU-based Chinese fir growth model at CGSS = 3–5 levels. In this study, the main conclusion is that the simulation results of CGMSS’s Chinese fir morphological structure growth model are closer to the real growth state of trees, achieving accurate simulation of differential growth of trees in different growth dominance degrees and spatial structure states in forest stands, making visualized forest management more effective and realistic.

Accurate, efficient, impersonal harvesting models play a very important role in optimizing stand spatial structural and guiding forest harvest practices. However, existing studies mainly focus on the single-objective optimization and evaluation of forest at the stand- or landscape-level, lacking considerations of tree-level neighborhood interactions. Therefore, the study explored the combination of the PSO algorithm and neighborhood indices to construct a tree-level multi-objective forest harvest model (MO-PSO) covering multi-dimensional spatial characteristics of stands. Taking five natural secondary forest plots and thirty simulated plots as the study area, the MO-PSO was used to simulate and evaluate the process of thinning operations. The results showed that the MO-PSO model was superior to the basic PSO model (PSO) and random thinning model Monte Carlo-based (RD-TH), DBH dominance (DOMI), uniform angle (ANGL), and species mingling (MING) were better than those before thinning. The multi-dimensional stand spatial structure index (L-index) increased by 1.0%~11.3%, indicating that the forest planning model (MO-PSO) could significantly improve the spatial distribution pattern, increase the tree species mixing, and reduce the degree of stand competition. In addition, under the four thinning intensities of 0% (T1), 15% (T2), 30% (T3), and 45% (T4), L-index increased and T2 was the optimal thinning intensity from the perspective of stand spatial structure overall optimization. The study explored the effect of thinning on forest spatial structure by constructing a multi-objective harvesting model, which can help to make reasonable and scientific forest management decisions under the concept of multi-objective forest management.

In order to build a green and beautiful Guangdong ecological construction digital twin demonstration area,this articleused point cloud generated tree 3D modeling technology to conduct 1∶1 simulation of the core location trees within the demonstration area.Based on high-resolution stereo image data obtained from oblique photography,a high-precision and standard geographic 3D forest scene was constructed by combining 3D models of trees,buildings,and terrain.At the same time,the Internet of Things data from four intelligent comprehensive monitoring points was connected,and expressed in the virtual 3D forest scene,achieving deep integration between the virtual 3D forest scene and the real forest scene.Combined with the forest growth model,the future state of the forest was simulated,and an immersive interactive digital twin system integrating data collection,model establishment,simulation analysis,and future prediction was constructed.A relatively mature technical systemwas formed through research,providing technical demonstrations and solutions for the construction of a digital twin forestry map in Guangdong Province.

[目的]针对森林碰撞检测研究中存在碰撞检测对象冗余、碰撞响应模式单一性等问题,研究突破碰撞检测算法时间复杂度高、响应模式缺乏与环境因子交互的技术瓶颈,实现虚拟森林场景快速碰撞检测与真实响应.[方法]以亚热带林业实验中心山下林场的典型林分杉木(Cunninghamia lanceolata)人工纯林为研究对象,选择碰撞检测总消耗的时间t1、包围盒交叉测试时间t2、包围盒构建时间t3、包围盒更新时间t4作为评价指标,比较单一包围盒树(axis-aligned bounding box,AABB)、混合包围盒树体(mixed bounding volume tree,MBVT)算法及引入最近4株树搜索法的MBVT优化算法的碰撞检测效率,分析不同规模大小(20、50、100、200、400、600、800、1 000)及林分株行距(1 m×1 m、2 m×2 m、3 m×3 m、4 m×4 m)双因子对碰撞检测效率的影响机制,最后模拟验证考虑光照因子碰撞响应策略的可行性.[结果]①相对MBVT算法,基于最近4株树搜索法的混合包围盒层次树MBVT优化算法耗时缩短了 13.75 ms,约为原MBVT算法耗时的29％,能有效减少层次包围盒(bounding volume hierarchies,BVHs)交叉测试时间消耗t2与BVHs构建消耗时间t3,而更新时间t4无明显差异.相对单一包围盒层次树AABB,MBVT算法与基于最近4株树搜索法的MBVT优化算法,分别缩短了 124.93、138.68 ms,约为单一包围盒层次树法AABB总耗时的73％、81％.②种群规模大小与BVHs碰撞检测总消耗时间t1、BVHs交叉测试时间t2及构建时间t3 均表现为正相关性;株行距大小与碰撞检测总耗时间t1与相交测试时间t2呈负相关性,与BVHs构建时间t3无明显关联性;随种群规模增大,株行距减小,总碰撞时间消耗t1、BVHs交叉测试时间t2呈增加趋势,而BVHs构建时间t3 几乎没有变化.③相比传统的碰撞响应模式,提出的顾及光照因子碰撞响应模式,考虑了植物的趋光性生长特征,模拟的杉木林虚拟场景更为真实.测试虚拟场景的帧率为8.6帧/s,准确度为100％,能很好地实现相邻树木之间碰撞检测及其响应.[结论]引入最近4株树搜索法的MBVT优化碰撞算法,优化混合包围盒层次树法MBVT的碰撞检测对象数量,减少了 BVHs交叉测试和构建消耗时间,从而有效提高杉木林虚拟场景的碰撞检测效率;顾及树木生长趋光性的相邻树木碰撞响应算法,通过兰伯特光照模型Lambert Model计算碰撞点周围的光照强度,结合碰撞响应函数完成树木碰撞后可能发生的趋光生长情景模拟,有效解决了森林碰撞响应缺乏与环境因子交互的问题,提高了杉木林虚拟场景的真实感.

Currently, 3D tree modeling in a highly heterogeneous forest environment remains a significant challenge for the modeler. Previous research has only focused on morphological characteristics and parameters, overlooking the impact of micro-environmental factors (e.g., spatial-structural diversification and habitat heterogeneity) and providing less structural information about the individual tree and decreasing the applicability and authenticity of 3D tree models in a virtual forest. In this paper, we chose a mixed-forest conversion of Chinese fir (Cunninghamia lanceolata) plantations in a subtropical region of China as our study subject and proposed a novel 3D tree-modeling method based on a structural unit (TMSU). Our approach modified traditional rule-based tree modeling (RTM) by introducing a nonlinear mixed-effect model (NLME) to study the coupling response between the spatial structures and morphological characteristics (e.g., tree height (H), height-to-crown base (HCB), and crown width (CW)) of three dominant trees (e.g., Cunninghamia lanceolata (SM), Machilus pauhoi (BHN), and Schima superba (MH)) and develop a prediction model of the morphological characteristic by incorporating forest-based structural parameters. The results showed that: (1) The NLME model in TMSU was found to better fit the data and predict the morphological characteristics than the OLS model in RTM. As compared to the RTM morphological model, the prediction accuracy of the TMSU model of morphological features was improved by 10.4%, 3.02%, and 17.8%, for SM’s H, HCB, and CW, respectively; 6.5%, 7.6%, and 8.9% for BHN’s H, HCB, and CW, respectively; and 13.3%, 15.7%, and 13.4% for MH’s H, HCB, and CW, respectively. (2) The spatial-structural parameters of crowding (Ci), mingling (Mi), and dominance (Ui) had a significant impact on the morphological characteristics of SM, BHN, and MH in TMSU. The degree of crowding, for example, had a positive relationship with tree height, height-to-crown base, and crown width in SM, BHN, and MH; under the same crowding conditions, mingling was positively correlated with tree crown width in SM, and dominance was positively correlated with tree height but negatively correlated with height-to-crown base in BHN; under the same crowding and mingling, dominance was positively correlated with height-to-crown base in MH. (3) Using 25 scenes based on the value class of Ci,Mi for SM, 25 scenes based on the value class of Ci,Ui for BHN, and 125 scenes based on the value class of Ci,Mi,Ui for MH, we generated the model libraries for the three dominating species based on TMSU. As a result, our TSMU method outperformed the traditional 3D tree-modeling method RTM in a complex and highly heterogeneous spatial structure of a forest stand, and it provided more information concerning the spatial structure based on the neighborhood relationships than the simple morphological characteristics; a higher morphological prediction accuracy with fewer parameters; and the relationship between the spatial-structural parameters and the morphological characteristics of a reference tree.

Tropical wetland resources have high ecological value to flood storage, greenhouse gas emissions, and biodiversity, with diverse types and widespread distribution. However, cloud pollution, inundation fluctuations, and spectral similarity pose unique challenges in the monitoring of tropical wetland using remote sensing. In this study, we proposed a comprehensive method for mapping the various types of tropical wetland using time series Sentinel-2 images based on the Google Earth Engine (GEE) platform integrated inundation dynamic, phenological, and geography features for multi-class topical wetland mapping (IPG-MTWM). Obtained a precise Vietnam wetland cover map (VWeC), which covers ten types of wetlands within a spatial resolution of 10 m. The overall accuracy (OA) of the VWeC was 82.07 %. compared the VWeC results with other wetland products showing the advantages of the IPG-MTWM in mapping complete wetland cover types with high spatial resolution and accuracy at large scales. The VWeC is the first thematic mapping on wetland cover types of Vietnam, it indicates that Vietnam has approximately 1,367,502.16 ha wetlands, of which coastal wetland accounts for approximately 44.27 % (605,318.3 ha). Vietnam’s wetlands are mainly distributed in South Vietnam (SV). The integrated inundation dynamic, phenological, and geography features method, highlights the potential of precise and fast multi-class topical wetland mapping at a large-scale using hydro-periods Sentinel-2 images and GEE platform. This study provides an important technique and dataset for tropical wetland monitoring, protection, and management.

[目的]定量研究林分空间结构对杉木枝下高的影响,构建基于空间结构的枝下高模型,结合杉木生长模型,应用三维可视化技术,实现杉木枝下高可视化模拟.[方法]利用湖南省黄丰桥国有林场6块杉木人工林临时样地的调查数据,选择5个常用枝下高基础模型,分析水平空间结构参数(PH)、垂直空间结构参数(Pv)和空间结构单元平均距离(dDIS)及其组合对枝下高的影响,构建综合指标较好且变量少的枝下高模型.基于林分三维模型实时生成方法,建立一种枝干可控的杉木三维模型;结合单木胸径连年生长量模型、树高-曲线模型和冠幅面积估计模型,模拟林木的生长状态.[结果]Logistic模型综合指标较好且模型参数可解释,可选为基础模型;3个空间结构参数中垂直空间结构影响较为显著,将Pv加入到Logistic模型中,改善了枝下高模型的拟合效果,决定系数(R2)从0.717提升到0.741,估计值的标准差从1.407 m减小到1.321 m,并使各项模型检验误差指标有所减小;构建的杉木三维模型可以动态调节枝干,实现了杉木枝下高模拟.[结论]构建的枝下高模型可以应用于林木年龄和部分林分信息未知的杉木林中,体现了林木间的相互竞争影响;结合枝干可控的杉木三维模型,模拟杉木生长过程,形象直观地表现了杉木枝下高的变化,为进一步研究林分生长动态可视化模拟和森林经营可视化模拟提供支持.

[目的]通过分析实测枝下高分布方向与空间竞争强度的关系,解决基于传统林学研究调查数据所构建的林木三维模型对不同方向枝下高分布差异难以直观表达,林木三维模型多态性表现不足的问题.[方法]以江西省新余市分宜县亚热带林业实验中心山下林场8块杉木临时样地为数据源,以已有枝下高模型为理论基础,将空间分析方法缓冲区构建与林分空间结构单元构建结合,构建对林木造成直接影响的水平空间结构参数与垂直空间结构参数,分析空间结构参数与枝下高相关性,并以此计算各方向空间竞争强度,建立空间竞争强度与实测枝下高的分布关系,再按照枝下高模型求解剩余方向枝下高,最终按照实测数据与分析计算结果加载分枝、主干模型,构建林木三维模型.[结果]所选模型变量包括林木属性与空间结构参数,原始模型决定系数为0.720,消除树高影响的调整后实测枝下高与水平空间结构参数相关系数为0.410、与垂直空间结构参数相关系数为0.782,且均呈正相关;将各自相关系数为权重计算对应方向空间竞争强度,将最小竞争强度方向空间结构参数代入模型,拟合结果决定系数为0.790,相比原始模型拟合精度有所提高;将实测枝下高分配到竞争强度最小的方向,根据模型可对其他方向枝下高进行估算.[结论]以杉木为例,通过空间竞争强度判别枝下高分布,在提高已有数据利用率、减小外业工作强度的基础上,可直观表现林木不同方向枝下高分布的差异性,增强了林木三维模型的多态性表达.