A precise distribution map of wetlands can provide basic data of wetland conservation and management for Ramsar parties in each region. In this study, based on the Google Earth Engine (GEE) platform and Sentinel-2 images, the integrated inundation dynamic, phenological, and geographical features for a multi-class tropical wetland mapping method (IPG-MTWM) was used to generate the Southeast Asia wetland cover map (SEAWeC) in 2020, which has a 10 m spatial resolution with 11 wetland types. The overall accuracy (OA) of SEAWeC was 82.52%, which, in comparison with other mappings the SEAWeC, performs well. The results of SEAWeC show that (1) in 2020, the total wetland area in Southeast Asia was 123,268.61 km², (2) for the category I, the coastal wetlands has the largest area, reaching 58,534.78 km², accounting for 47.49%, (3) for the category II, the coastal swamp has the largest area, reaching 48,002.66 km², accounting for 38.94% of the total wetland area in Southeast Asia, and (4) significant difference in wetland rate (WR) between countries in Southeast Asia, in which Singapore has a WR of 6.96%, ranking first in Southeast Asia. The SEAWeC can provide the detailed spatial and type distribution data as basic data for the Southeast Asia to support the Ramsar strategic plan 2016–24.

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.

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 (H_CB), 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, H_CB, and CW, respectively; 6.5%, 7.6%, and 8.9% for BHN’s H, H_CB, and CW, respectively; and 13.3%, 15.7%, and 13.4% for MH’s H, H_CB, and CW, respectively. (2) The spatial-structural parameters of crowding ($C_i$), mingling ($M_i$), and dominance ($U_i$) 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 $C_i,M_i$ for SM, 25 scenes based on the value class of $C_i,U_i$ for BHN, and 125 scenes based on the value class of $C_i,M_i,U_i$ 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.

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.