A forest ecological three-dimensional visualization system

Abstract

This invention relates to the field of 3D visualization technology, specifically a 3D visualization system for forest ecology. The system includes a spatiotemporal raster modeling module, a forest spatial distribution analysis module, a dynamic forest boundary prediction module, and a forest spatial structure visualization module. Through detailed data collection and processing of different environmental factors, this invention can dynamically reflect forest boundaries and their spatial changes. During spatial difference analysis, it extracts changes in environmental conditions over different time periods, providing accurate data for predicting forest boundary and ecological space changes. By combining spatiotemporal raster data of the forest with dynamic boundary change prediction, it can present the evolution of forest space from multiple perspectives. It not only displays the current state of the forest but also clearly reflects the trend of forest ecosystem changes over time through a dynamic 3D view. Furthermore, by showcasing a more detailed spatial structure, it improves the accuracy and operability of forest resource management.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional visualization technology, and in particular to a three-dimensional visualization system for forest ecology. Background Technology

[0002] The field of 3D visualization technology is based on computer graphics, image processing, spatial data processing and other technologies. It generates and displays 3D data models through computers, enabling users to view, analyze and interact from multiple perspectives. It is widely used in fields such as Geographic Information Systems (GIS), Virtual Reality (VR), Augmented Reality (AR), medical imaging, engineering design, and Building Information Modeling (BIM). It can intuitively display complex spatial relationships, dynamic changes and potential interactive behaviors, providing users with more realistic and accurate information expression, and enabling more effective data analysis, decision support and virtual simulation.

[0003] The forest ecosystem 3D visualization system aims to showcase key information about forest ecosystems, such as spatial distribution, species composition, and ecological processes, using 3D visualization technology. By integrating Geographic Information System (GIS) data, remote sensing technology, and ecological models, the system can present multi-dimensional dynamic information about plants, animals, and soil in forests. Its main applications include ecological environment monitoring, forest resource management, species conservation, and forest health assessment, helping researchers, policymakers, and environmental managers better understand the current status and changing trends of forest ecosystems and formulate more scientific and precise management measures.

[0004] Current 3D visualization technologies for forest ecosystems primarily focus on static displays, lacking real-time dynamic reflection of changes in forest ecosystems. They mainly rely on static datasets, such as remote sensing images or historical data at a single point in time, failing to fully consider long-term changes in forest space under the influence of various environmental factors. They cannot accurately represent the dynamic changes in forest boundaries influenced by multiple factors such as climate change, soil moisture, and vegetation distribution. Furthermore, existing technologies often fail to effectively capture the complex nonlinear relationships within forest ecosystems, resulting in simplistic prediction models and visualizations of forest spatial distribution, and an inability to provide sufficient early warning information for potential future ecological changes. Due to the lack of in-depth analysis of multi-dimensional changes, current technologies struggle to meet the demands for real-time, accurate data in ecological management, limiting the effectiveness and precision of forest health assessments, species conservation, and resource management. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a three-dimensional visualization system for forest ecology.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a three-dimensional visualization system for forest ecology, comprising: The spatiotemporal raster modeling module collects and integrates multi-time period raster data of forests, records forest topography, climate, soil and vegetation information, aligns the data and unifies geographic coordinates to obtain spatiotemporal raster data of forests. Based on the aforementioned forest spatiotemporal raster data, the forest spatial distribution analysis module extracts the spatial distribution of forest vegetation under different environmental conditions, analyzes spatial differences over time, reveals the spatial changes of forest over different time periods based on the spatial difference analysis results, identifies forest ecological distribution patterns, and outputs forest spatial distribution characteristics. The dynamic forest boundary prediction module assesses the changing trend of forest boundaries at different time points based on the spatial distribution characteristics of the forest, analyzes the dynamic positional changes of forest boundaries under different environmental conditions in the future based on the assessment results, and constructs a dynamic change prediction map of forest boundaries. The forest spatial structure visualization module, based on the predicted map of dynamic changes in forest boundaries, displays the spatial distribution characteristics of the forest and the prediction results of the boundaries in three dimensions. Based on the three-dimensional visualization results, it highlights the spatial distribution of forest trees and the information on the forest hierarchical structure, displays the spatial layout and dynamic changes of the forest from multiple perspectives, and outputs a three-dimensional visualization model of the forest spatial structure.

[0007] As a further aspect of the present invention, the steps for acquiring the forest spatiotemporal raster data are as follows: Based on multi-time satellite imagery and ground observation data, information on forest topography, climate, soil, and vegetation is extracted to generate a preliminary forest feature dataset. The preliminary forest feature dataset is cleaned and normalized to correct data biases and fill in missing values, using the following formula: ; Calculate the data quality index for each grid point. This yields a quality-controlled forest feature dataset, in which... Representing the Data for a specific time period This represents the average value of data across all time periods. This represents the reliability score of the data. The number of points in time; Using the quality-controlled forest feature dataset, spatial alignment is performed, and the data is adjusted to match unified geographic coordinates to generate spatiotemporal raster data of the forest.

[0008] As a further aspect of the present invention, the analysis steps for the spatial differences over time are as follows: Based on the aforementioned forest spatiotemporal raster data, topographic and climate change data are integrated to generate an initial record of forest vegetation spatial distribution. Based on the initial forest vegetation spatial distribution records, spatial analysis was performed to compare the vegetation spatial distribution at different time points, using the following formula: ; Calculate the variation in vegetation distribution The spatial differences over time were obtained, among which, and The vegetation distribution data represent two different time points. Based on the spatial differences over the aforementioned time period, and considering relevant environmental factors, the following formula is used: ; Calculate the total change for each time period The results of the comprehensive spatial difference analysis over a period of time were obtained, among which, Indicates the number of time periods for evaluation. Indicates the first Differences in vegetation change over time periods.

[0009] As a further aspect of the present invention, the step of obtaining the spatial distribution characteristics of the forest is as follows: Based on the comprehensive time-space difference analysis results, the correlation between the difference factors and vegetation changes is compared, and the correlation analysis between the change amount and the influencing factors is carried out to generate the correlation analysis results between the environment and vegetation. Based on the results of the environment and vegetation correlation analysis, the ecological distribution patterns of different forest areas are classified and analyzed to determine the key ecological distribution patterns and obtain the results of forest ecological distribution patterns. Based on the results of the forest ecological distribution pattern, the spatial distribution and diffusion trends of the differential ecological patterns are analyzed, and the spatial distribution characteristics of the forest are output.

[0010] As a further aspect of the present invention, the evaluation steps for the forest boundary change trend at the different time points are as follows: Using the results of the forest spatial feature analysis, forest tree density, vegetation type and land use are recorded, and the data are summarized and integrated to generate a comprehensive forest spatial feature description record; By comparing and analyzing the comprehensive forest spatial feature description records with satellite image data at different time points, the actual location changes of forest boundaries at all time points are identified and recorded to obtain time series forest boundary change data; Based on the aforementioned time-series forest boundary change data, the following formula is used: ; Calculation time point and forest boundary change rate The analysis results of forest boundary change trends at different time points were generated, among which, and Representing time points and The location of the forest boundary, and This indicates the corresponding point in time.

[0011] As a further aspect of the present invention, the steps for constructing the forest boundary dynamic change prediction map are as follows: Based on the analysis results of the forest boundary change trend at the aforementioned time points, combined with precipitation and temperature data, the potential impact of future environmental conditions on the forest boundary location is analyzed, and the future environmental impact analysis results are generated. Based on the analysis results of the future environmental impact, and combined with the simulation of vegetation growth dynamics and land use change, the location of the forest boundary at the future prediction time point is analyzed to obtain dynamic prediction data of the forest boundary. By using the aforementioned dynamic forest boundary prediction data and combining it with visualization tools, the future movement trend and range of change of forest boundaries are revealed, and a dynamic change prediction map of forest boundaries is generated.

[0012] As a further aspect of the present invention, the three-dimensional visualization display steps are as follows: The forest boundary dynamic change prediction map is called, and combined with the forest spatial distribution data, the data is merged to generate comprehensive forest spatial data; Based on the comprehensive spatial data of the forest, a three-dimensional model was created, and the model accuracy and scale were adjusted to highlight the spatial distribution and boundary changes of the forest, thus obtaining preliminary modeling results. The preliminary modeling results are then subjected to 3D visualization processing. The viewpoint and lighting are set, and a 3D scene is rendered to obtain preliminary 3D visualization results.

[0013] As a further aspect of the present invention, the steps for obtaining the three-dimensional visualization model of the forest spatial structure are as follows: Analyze the preliminary 3D visualization results, identify key forest areas and tree species, calibrate the tree data of each layer, and generate calibrated forest tree spatial distribution data; Based on the calibrated spatial distribution data of forest trees, the height, angle and zoom level of the viewing angle are adjusted to reveal the spatial layout and dynamic changes of the forest from multiple angles, and the actual viewing effect data is obtained. Based on the actual perspective display data, 3D rendering is applied to optimize the visual effects, reveal the dynamic changes in the forest ecosystem, and generate a 3D visualization model of the forest spatial structure.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by collecting and processing detailed data on different environmental factors, the forest boundary and its spatial changes can be dynamically reflected. When conducting spatial difference analysis, the changes in environmental conditions at different times are extracted, providing an accurate basis for predicting changes in forest boundaries and ecological space. By combining the spatiotemporal raster data of the forest with dynamic boundary change prediction, the evolution process of forest space can be presented from multiple perspectives. It not only shows the current state of the forest, but also clearly reflects the trend of forest ecosystem changes over time through dynamic three-dimensional views. Furthermore, through a more detailed spatial structure display, the accuracy and operability of forest resource management are improved. Attached Figure Description

[0015] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart illustrating the acquisition process of forest spatiotemporal raster data in this invention. Figure 3 This is a flowchart illustrating the analysis of spatial differences over time in this invention. Figure 4 This is a flowchart illustrating the process of obtaining the spatial distribution characteristics of forests according to the present invention. Figure 5 This is a flowchart illustrating the evaluation process for the changing trends of forest boundaries at different time points in this invention. Figure 6 This is a flowchart illustrating the construction process of the forest boundary dynamic change prediction map of the present invention. Figure 7 This is a flowchart illustrating the three-dimensional visualization of the present invention; Figure 8 This is a flowchart illustrating the process of obtaining a three-dimensional visualization model of the forest spatial structure according to the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0018] Please see Figure 1 A three-dimensional visualization system for forest ecology includes: The spatiotemporal raster modeling module collects and integrates multi-time period raster data of forests, records forest topography, climate, soil and vegetation information, aligns the data and unifies geographic coordinates to obtain spatiotemporal raster data of forests. The forest spatial distribution analysis module is based on forest spatiotemporal raster data. It extracts the spatial distribution of forest vegetation under different environmental conditions, analyzes spatial differences over time, reveals the spatial changes of forest over different time periods based on the spatial difference analysis results, identifies forest ecological distribution patterns, and outputs forest spatial distribution characteristics. The dynamic forest boundary prediction module assesses the trend of forest boundary changes at different time points based on the spatial distribution characteristics of forests, analyzes the dynamic positional changes of forest boundaries under different environmental conditions in the future based on the assessment results, and constructs a dynamic change prediction map of forest boundaries. The forest spatial structure visualization module is based on the prediction map of dynamic changes in forest boundaries. It displays the spatial distribution characteristics of forests and the prediction results of boundaries in three dimensions. Based on the three-dimensional visualization results, it highlights the spatial distribution of forest trees and the information of forest hierarchical structure, displays the spatial layout and dynamic changes of forests from multiple perspectives, and outputs a three-dimensional visualization model of forest spatial structure.

[0019] Forest spatiotemporal raster data includes forest topographic elevation data, climate temperature and humidity data, soil type data, and vegetation cover data; forest spatial distribution characteristics include records of forest cover changes over different time periods, records of forest vegetation distribution under different environmental conditions, and forest distribution patterns; forest boundary dynamic change prediction map includes data on the location of forest boundaries and boundary change trends at future time points; forest spatial structure three-dimensional visualization model includes forest hierarchical structure data and a three-dimensional view of forest spatial layout map.

[0020] Please see Figure 2 The steps for acquiring forest spatiotemporal raster data are as follows: Based on multi-time satellite imagery and ground observation data, information on forest topography, climate, soil, and vegetation is extracted to generate a preliminary forest feature dataset. The process of extracting forest topography, climate, soil, and vegetation information from multi-time satellite imagery and ground observation data involves several steps. First, imagery data from different satellite platforms is collected, covering multiple seasons to capture seasonal changes in climate and vegetation. Next, the data is preprocessed, including correcting geometric and radiometric distortions in the satellite images. Then, machine learning algorithms, such as support vector machines and random forests, are used to classify ground types, distinguishing forest topography, different types of vegetation, and soil conditions. Multiple spectral bands and texture information are integrated to generate a preliminary forest feature dataset, which will directly support subsequent forest management and research.

[0021] The preliminary forest feature dataset was cleaned and normalized to correct data bias and fill in missing values ​​using the following formula: ; Calculate the data quality index for each grid point. This yields a quality-controlled forest feature dataset, in which... Representing the Data for a specific time period This represents the average value of data across all time periods. This represents the reliability score of the data. The number of points in time; set up Time point data Reliability rating ,average value They are respectively: ; ; ; ; ; Summation Divide by ,have to The results show that, after considering the reliability of the data points, the average quality index of this dataset is [value missing]. This means that the data has high reliability after quality control and can be used for further forest monitoring and analysis.

[0022] Using a quality-controlled forest feature dataset, spatial alignment is performed to adjust the data to match unified geographic coordinates, generating spatiotemporal raster data of the forest. Spatial alignment using quality-controlled forest feature datasets involves processes such as managing and manipulating the dataset using a Geographic Information System (GIS), estimating data values ​​for unknown locations based on known data points using interpolation algorithms such as Kriging or bilinear interpolation, thereby ensuring that the dataset matches a unified geographic coordinate system. The process requires precise adjustment of the spatial location of data points to ensure spatial consistency of all data. Through this fine-grained spatial processing, the generated spatiotemporal raster data of forests can provide an accurate spatial analysis basis for subsequent environmental assessments and resource management.

[0023] Please see Figure 3 The steps for analyzing spatial differences over time are as follows: Based on forest spatiotemporal raster data, integrating topographic and climate change data, an initial record of forest vegetation spatial distribution is generated; Information on vegetation type, tree density, and soil moisture was extracted from forest spatiotemporal raster data. Considering that the data covers the basic conditions for plant growth, topographic and climatic variables such as temperature and precipitation were then combined. The data came from the National Meteorological Administration and the Geographic Information System. The information was integrated through geospatial data processing technology to construct an initial spatial distribution model of forest vegetation. This model, based on the principles of plant ecology, considers the responses of different vegetation to environmental variables, thereby obtaining the initial spatial distribution of forest vegetation. This model helps to understand the possibilities of vegetation distribution under specific climatic and topographical conditions.

[0024] Based on the initial spatial distribution records of forest vegetation, spatial analysis was performed to compare the spatial distribution of vegetation at different time points, using the following formula: ; Calculate the variation in vegetation distribution The spatial differences over time were obtained, among which, and The vegetation distribution data represent two different time points. At the point of time The vegetation coverage rate is 40% ( =0.4), while the vegetation cover at time point t2 was 50% ( =0.5), then the difference in change is calculated as follows: This indicates that vegetation cover increased by 10% between the two time points. Such an increase may be due to environmental factors such as increased precipitation or temperature changes. This result reflects the positive impact of environmental conditions on forest vegetation.

[0025] Based on the spatial differences over time and in conjunction with relevant environmental factors, the following formula is used: ; Calculate the total change for each time period The results of the comprehensive spatial difference analysis over a period of time were obtained, among which, Indicates the number of time periods for evaluation. Indicates the first Differences in vegetation change over time periods; The vegetation change was assessed over three time periods, with differences of 0.05, 0.07, and 0.03 for periods 1, 2, and 3, respectively. The cumulative impact was calculated as follows: ; A result of 0.15 represents the cumulative difference in vegetation change over the three time periods examined, revealing the overall impact of environmental change on vegetation distribution. The significance of this figure lies in providing intuitive quantitative data to assess the response of vegetation to environmental change over a specific time period.

[0026] Please see Figure 4 The steps for obtaining the spatial distribution characteristics of forests are as follows: Based on the results of the comprehensive temporal spatial difference analysis, the correlation between the difference factors and vegetation changes is compared, the correlation analysis between the change amount and the influencing factors is carried out, and the results of the correlation analysis between the environment and vegetation are generated. Key data were extracted from the spatial difference analysis results over time, including specific values ​​of vegetation changes and detailed information on changes in related environmental factors at each time period. Preliminary data processing was performed through statistical analysis, and correlation analysis was used to compare the correlation between each factor and vegetation changes. By analyzing and establishing a correlation model between the amount of change and the influencing factors, it is possible to help understand how different environmental factors affect the ecological changes of forests, laying the foundation for further in-depth analysis. This generates the results of the correlation analysis between the environment and vegetation, providing a scientific basis for subsequent ecological pattern identification.

[0027] Based on the results of the environment and vegetation correlation analysis, the ecological distribution patterns of different forest areas are classified and analyzed to determine the key ecological distribution patterns and obtain the results of forest ecological distribution patterns. Statistical models are used to identify environmental factors that significantly influence forest vegetation changes. In this process, it is necessary not only to examine the P-values ​​and impact coefficients of the statistical output, but also to interpret the data more deeply by combining ecological theories. Community analysis methods are used to determine the main ecological distribution patterns. This analysis helps to distinguish which plant species are more prosperous under specific environmental conditions. The forest ecological distribution patterns obtained after integrating the analysis results can intuitively show the distribution of different ecological types under different environmental conditions, providing guidance for management and protection, thus generating forest ecological distribution pattern results.

[0028] Based on the results of forest ecological distribution patterns, we analyze the spatial distribution and diffusion trends of differential ecological patterns and output the spatial distribution characteristics of forests. Mapping forest ecological distribution patterns to specific geographic locations using Geographic Information Systems (GIS) involves extensive data input and geocoding to ensure that the ecological data for each region accurately represents its spatial location. The resulting spatial distribution maps not only show the spatial distribution of different ecological patterns but also reveal the diffusion trends of these patterns over time. Charts are an important means of transforming complex ecological data into intuitive and visual information, providing a powerful decision support tool for forest management. By outputting the spatial distribution characteristics of forests, they help scientists and managers understand and predict forest responses to environmental changes.

[0029] Please see Figure 5 The steps for assessing the changing trends of forest boundaries at different time points are as follows: Using the results of forest spatial feature analysis, we record forest tree density, vegetation type and land use, summarize and integrate the data, and generate a comprehensive forest spatial feature description record. By utilizing the results of forest spatial characteristic analysis, key variables such as tree density, vegetation type, and land use are selected, and these variables are summarized and integrated to form a comprehensive description of forest spatial characteristics. This description reflects the overall ecological state and land use of the forest, thus providing basic data for more accurate analysis of forest boundary changes. The formation of this comprehensive forest spatial characteristic description relies on multi-source data fusion technology, including but not limited to satellite remote sensing data, ground-based measured data, and historical ecological records. Through cross-validation and pattern recognition of the data, an accurate description of forest spatial characteristics can be achieved. This description includes not only the physical boundaries of the forest but also information on biodiversity, forest health status, and vegetation growth trends.

[0030] By comparing and analyzing the comprehensive forest spatial feature description records with satellite image data at different time points, the actual location changes of forest boundaries at all time points are identified and recorded, resulting in time series forest boundary change data. By comparing and analyzing the comprehensive description of forest spatial features with satellite image data at different time points, and using advanced image processing techniques such as edge detection and image segmentation, the specific location changes of forest boundaries at each time point are identified. The process involves complex data processing, including image preprocessing, feature extraction, and change detection. Through image analysis, the dynamic changes of forest boundaries can be accurately tracked. The change data is obtained through time series analysis. The detailed time series forest boundary change data reflects the specific changes of forest boundaries over time, providing data support for subsequent trend analysis.

[0031] Based on time-series forest boundary change data, the following formula is used: ; Calculation time point and forest boundary change rate The analysis results of forest boundary change trends at different time points were generated, among which, and Representing time points and The location of the forest boundary, and Indicates the corresponding point in time; In time The forest boundary is located at 1000 meters, and the time... The forest boundary is located at 950 meters, and the time interval is 1 year. Substituting these values ​​into the formula, we get: This result indicates that from the time point arrive The forest boundary expanded outward by 50 meters, providing a method to quantify changes in forest boundary, which can effectively monitor and predict the expansion or contraction trend of forest boundary.

[0032] Please see Figure 6 The steps for constructing a prediction map of dynamic changes in forest boundaries are as follows: Based on the analysis results of forest boundary change trends at different time points, combined with precipitation and temperature data, the potential impact of future environmental conditions on the location of forest boundaries is analyzed, and the results of future environmental impact analysis are generated. Detailed data on forest boundary changes at different time points are retrieved, including detailed records of historical climate change and images of forest boundary changes over the past few decades. Statistical analysis is performed, integrating the data with environmental variables predicted by climate models. The analysis examines how multiple environmental factors, including precipitation, temperature, and CO2 concentration, influence the migration and expansion of forest boundaries. Linear regression and multivariate analysis methods are used to predict the specific contribution rate of each environmental factor to forest boundary changes. This involves not only data integration and processing but also model calibration and validation to ensure that the generated future environmental impact analysis results are both accurate and consistent with the dynamic changes of actual forest ecosystems.

[0033] Based on the results of the future environmental impact analysis, and combined with the dynamics of vegetation growth and land use change, the location of the forest boundary at the future prediction time point is analyzed to obtain dynamic prediction data of the forest boundary. The results of future environmental impact analysis are input into a system dynamics model, which integrates the combined effects of vegetation growth dynamics, land use change, and climate factors to simulate the possible movement of future forest boundaries. This model uses field measurement data obtained from Geographic Information Systems (GIS) and remote sensing data, combined with historical climate data for calibration. Sensitivity analysis is used to adjust model parameters to adapt to different environmental prediction scenarios. After the model runs, it generates predicted data for each future time point, showing the possible location of the forest boundary under assumed climate change conditions. This data provides a foundation for further analysis, while the dynamic prediction data provides policymakers with possible forest management and protection strategies to adapt to predicted environmental changes.

[0034] By using dynamic forest boundary prediction data and combining it with visualization tools, we can reveal the future movement trend and range of change of forest boundaries and generate a dynamic change prediction map of forest boundaries. Geographic Information System (GIS) technology, combined with the latest dynamic forecast data of forest boundaries, was applied to perform data visualization. Various GIS tools and techniques, such as spatial analysis and layer overlay, were used to construct dynamic maps representing time-series data. The forecast maps not only show the expected movement of forest boundaries but also demonstrate the extent of impact under different environmental scenarios. The visualization results help managers and researchers intuitively understand the potential changing trends of forest boundaries over the next few decades. The graphics can also be used for public reporting and scientific research, raising public and policymaker awareness of the impacts of climate change on forest ecosystems. Furthermore, the detailed visual information provided by the dynamic charts supports the planning of more effective forest management and conservation measures.

[0035] Please see Figure 7 The steps for 3D visualization are as follows: By calling the forest boundary dynamic change prediction map and combining it with forest spatial distribution data, the data is merged to generate comprehensive forest spatial data; The process of retrieving dynamic forest boundary change prediction maps and forest spatial distribution data from a geographic information system requires meticulous handling of the format and alignment issues of each data layer to ensure accurate correspondence of data points during the merging process. Furthermore, the merging steps include data validation, deduplication, and spatial alignment to ensure that the merged dataset meets the accuracy and completeness requirements for subsequent 3D modeling. In this way, a new dataset of integrated forest spatial data is generated, which reflects in detail the current boundary conditions of the forest and their dynamic changes over time. This provides accurate foundational data for subsequent 3D modeling and visualization.

[0036] Three-dimensional modeling was performed based on comprehensive forest spatial data. The model accuracy and scale were adjusted to highlight the spatial distribution and boundary changes of the forest, and preliminary modeling results were obtained. In this process, the following formula is used: =( / ) 100%, of which, This represents the percentage of accuracy in representing forest data in a 3D model. This refers to the number of verified data points, while This refers to the total number of data points in the model. The model parameters are adjusted to achieve the required accuracy. In a target forest region, there are 500 total data points, of which 480 have been validated. , The calculation process is as follows: ; The results show that the model has an accuracy of 96%, reflecting its accuracy and reliability, and can be used for further visualization processing.

[0037] The preliminary modeling results are visualized in 3D by setting the viewpoint and lighting, and then rendering the 3D scene to obtain the preliminary 3D visualization results. The preliminary modeling results were imported into 3D visualization software. First, the format and compatibility of the model file were checked to ensure that it could be loaded correctly in the visualization software. Next, the rendering settings in the software, such as lighting angle, intensity, and camera view, were adjusted to finely control the scene display effect. These settings are crucial for accurately displaying the forest's hierarchical structure and spatial distribution. By previewing and adjusting the results in real time, the display effect was continuously optimized to more realistically reflect the natural state and ecological characteristics of the forest. This not only improved the visual effect of the model but also enabled the model to play a greater role in ecological research and resource management. Finally, the preliminary 3D visualization display results showed the three-dimensional layout of the forest and its dynamic changes in detail.

[0038] Please see Figure 8 The steps to obtain a 3D visualization model of forest spatial structure are as follows: Analyze the preliminary 3D visualization results, identify key forest areas and tree species, calibrate the tree data of each layer, and generate calibrated spatial distribution data of forest trees; By analyzing the current 3D visualization results using 3D visualization software, key forest areas and tree species are identified. Spatial data calibration is performed on the identified forest areas. During the calibration process, GIS technology is used to accurately measure the tree species and quantity in each area. The measurement data will be used to generate a more detailed tree distribution map. At the same time, the coverage and growth density of each type of tree are calculated. Based on this data, the calibrated spatial distribution data of forest trees is generated, providing important scientific basis for forest management and protection.

[0039] Based on the calibrated spatial distribution data of forest trees, the height, angle and zoom level of the viewing angle are adjusted to reveal the spatial layout and dynamic changes of the forest from multiple angles, and data on the actual viewing effect are obtained. The following formula is used in the process: ; Calculate the relationship between the observer's perspective and the forest hierarchy, where Indicates the viewing angle. Represents the viewing height (the height from the ground to the observation point). This represents the horizontal distance from the observation point to the forest. In a practical application, rice, Meters, substituting into the formula, yields: ; This angle was calculated based on actual measurements of height and distance, and was used to adjust camera settings for multi-view presentations to ensure the observer receives the best visual experience. Results showed that by adjusting the viewing angle to 18.43 degrees, the observer could best observe the forest's layered structure and tree distribution.

[0040] Based on the actual perspective of the data, the visual effects are optimized by applying 3D rendering, revealing the dynamic changes of the forest ecology and generating a 3D visualization model of the forest spatial structure. After showcasing the overall effect data, 3D rendering technology is used to enhance the model's visual performance. First, by adjusting rendering parameters such as lighting, shadows, and texture details, the model's visual consistency and realism under different perspectives are ensured. Then, 3D animation processing is performed to showcase the ecological dynamics of the forest. For example, the animation simulates the effect of trees swaying in the wind and the impact of seasonal changes on forest colors. The resulting 3D visualization model not only shows the spatial structure of the forest but also dynamically displays changes in the forest ecosystem, which is of great value for environmental education and ecological research.

[0041] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A three-dimensional visualization system for forest ecology, characterized in that, The system includes: The spatiotemporal raster modeling module collects and integrates multi-time period raster data of forests, records forest topography, climate, soil and vegetation information, aligns the data and unifies geographic coordinates to obtain spatiotemporal raster data of forests. Based on the aforementioned forest spatiotemporal raster data, the forest spatial distribution analysis module extracts the spatial distribution of forest vegetation under different environmental conditions, analyzes spatial differences over time, reveals the spatial changes of forest over different time periods based on the spatial difference analysis results, identifies forest ecological distribution patterns, and outputs forest spatial distribution characteristics. The dynamic forest boundary prediction module assesses the changing trend of forest boundaries at different time points based on the spatial distribution characteristics of the forest, analyzes the dynamic positional changes of forest boundaries under different environmental conditions in the future based on the assessment results, and constructs a dynamic change prediction map of forest boundaries. The forest spatial structure visualization module, based on the predicted map of dynamic changes in forest boundaries, displays the spatial distribution characteristics of the forest and the prediction results of the boundaries in three dimensions. Based on the three-dimensional visualization results, it highlights the spatial distribution of forest trees and the information on the forest hierarchical structure, displays the spatial layout and dynamic changes of the forest from multiple perspectives, and outputs a three-dimensional visualization model of the forest spatial structure.

2. The forest ecology three-dimensional visualization system according to claim 1, characterized in that, The steps for acquiring the forest spatiotemporal raster data are as follows: Based on multi-time satellite imagery and ground observation data, information on forest topography, climate, soil, and vegetation is extracted to generate a preliminary forest feature dataset. The preliminary forest feature dataset is cleaned and normalized to correct data biases and fill in missing values, using the following formula: ； Calculate the data quality index for each grid point. This yields a quality-controlled forest feature dataset, in which... Representing the Data for a specific time period This represents the average value of data across all time periods. This represents the reliability score of the data. The number of points in time; Using the quality-controlled forest feature dataset, spatial alignment is performed, and the data is adjusted to match unified geographic coordinates to generate spatiotemporal raster data of the forest.

3. The forest ecology three-dimensional visualization system according to claim 2, characterized in that, The analysis steps for the spatial differences over the time period are as follows: Based on the aforementioned forest spatiotemporal raster data, topographic and climate change data are integrated to generate an initial record of forest vegetation spatial distribution. Based on the initial forest vegetation spatial distribution records, spatial analysis was performed to compare the vegetation spatial distribution at different time points, using the following formula: ； Calculate the variation in vegetation distribution The spatial differences over time were obtained, among which, and The vegetation distribution data represent two different time points. Based on the spatial differences over the aforementioned time period, and considering relevant environmental factors, the following formula is used: ； Calculate the total change for each time period The results of the comprehensive spatial difference analysis over a period of time were obtained, among which, Indicates the number of time periods for evaluation. Indicates the first Differences in vegetation change over time periods.

4. A three-dimensional visualization system for forest ecology according to claim 3, characterized in that, The steps for obtaining the spatial distribution characteristics of the forest are as follows: Based on the comprehensive time-space difference analysis results, the correlation between the difference factors and vegetation changes is compared, and the correlation analysis between the change amount and the influencing factors is carried out to generate the correlation analysis results between the environment and vegetation. Based on the results of the environment and vegetation correlation analysis, the ecological distribution patterns of different forest areas are classified and analyzed to determine the key ecological distribution patterns and obtain the results of forest ecological distribution patterns. Based on the results of the forest ecological distribution pattern, the spatial distribution and diffusion trends of the differential ecological patterns are analyzed, and the spatial distribution characteristics of the forest are output.

5. A three-dimensional visualization system for forest ecology according to claim 4, characterized in that, The steps for assessing the forest boundary change trend at the aforementioned time points are as follows: Using the results of the forest spatial feature analysis, forest tree density, vegetation type and land use are recorded, and the data are summarized and integrated to generate a comprehensive forest spatial feature description record; By comparing and analyzing the comprehensive forest spatial feature description records with satellite image data at different time points, the actual location changes of forest boundaries at all time points are identified and recorded to obtain time series forest boundary change data; Based on the aforementioned time-series forest boundary change data, the following formula is used: ； Calculation time point and forest boundary change rate The analysis results of forest boundary change trends at different time points were generated, among which, and They represent time points respectively and The location of the forest boundary, and This indicates the corresponding point in time.

6. A three-dimensional visualization system for forest ecology according to claim 5, characterized in that, The steps for constructing the forest boundary dynamic change prediction map are as follows: Based on the analysis results of the forest boundary change trend at the aforementioned time points, combined with precipitation and temperature data, the potential impact of future environmental conditions on the forest boundary location is analyzed, and the future environmental impact analysis results are generated. Based on the results of the future environmental impact analysis, and combined with the vegetation growth dynamics and land use changes, the forest boundary location at the future prediction time point is analyzed to obtain dynamic prediction data of the forest boundary. By using the aforementioned dynamic forest boundary prediction data and combining it with visualization tools, the future movement trend and range of change of forest boundaries are revealed, and a dynamic change prediction map of forest boundaries is generated.

7. A three-dimensional visualization system for forest ecology according to claim 6, characterized in that, The steps for displaying the 3D visualization are as follows: The forest boundary dynamic change prediction map is called, and combined with the forest spatial distribution data, the data is merged to generate comprehensive forest spatial data; Based on the comprehensive spatial data of the forest, a three-dimensional model was created, and the model accuracy and scale were adjusted to highlight the spatial distribution and boundary changes of the forest, thus obtaining preliminary modeling results. The preliminary modeling results are then subjected to 3D visualization processing. The viewpoint and lighting are set, and a 3D scene is rendered to obtain preliminary 3D visualization results.

8. A three-dimensional visualization system for forest ecology according to claim 7, characterized in that, The steps for obtaining the three-dimensional visualization model of the forest spatial structure are as follows: Analyze the preliminary 3D visualization results, identify key forest areas and tree species, calibrate the tree data of each layer, and generate calibrated forest tree spatial distribution data; Based on the calibrated spatial distribution data of forest trees, the height, angle and zoom level of the viewing angle are adjusted to reveal the spatial layout and dynamic changes of the forest from multiple angles, and the actual viewing effect data is obtained. Based on the actual perspective display data, 3D rendering is applied to optimize the visual effects, reveal the dynamic changes in the forest ecosystem, and generate a 3D visualization model of the forest spatial structure.

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