Forest dynamic growth model construction method and system

Abstract

The invention discloses a forest dynamic growth model construction method and system, and belongs to the technical field of forestry digital twinning. According to the invention, through constructing the live-action three-dimensional model and carrying out refined modeling on the single plant, refined simulation and multi-dimensional prediction of the forest growth process are realized; a digital twinborn platform is utilized to integrate a forest simulation model and basic forest information, forest stand structure parameters are extracted, an arbor layer growth dynamic model is established, and then the species diversity and natural update density of a shrub layer are dynamically simulated. According to the method, the limitation of single plant scale is broken through, co-evolution of the arbor layer, the shrub layer and the natural updating layer is integrated for the first time, multi-dimensional prediction and visualization are supported, and scientific support is provided for forestry digital twinning.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry digital twins, and more specifically, to a method and system for constructing a forest dynamic growth model. Background Art

[0002] Currently, digital twin technology has been relatively maturely applied in fields such as industry, water conservancy, and smart cities. By real-time monitoring the state parameters of the twin objects, it provides a basis for formulating reasonable management measures and promotes the intelligent development of related industries.

[0003] However, in the forestry industry, due to the combined effects of the characteristics of forest and grass individuals and external environmental factors such as light, water, and soil, the growth process is extremely complex, and existing digital twin technologies are difficult to be directly applied. Current forest dynamic growth models are mostly at the single-tree scale, built based on the measured data and real-shot data of the growth status of single trees in multiple seasons, and it is difficult to construct an overall dynamic growth model of the tree layer from the stand scale. Moreover, most of them only focus on the dynamic changes in the growth of the forest tree layer, ignoring the dynamic changes in the species diversity of the shrub layer and the natural regeneration of the stand, which makes it difficult to realize the dynamic simulation of different levels of the forest ecosystem and leads to a lack of important technical support for truly realizing the functions of forestry digital twins.

[0004] Therefore, how to propose a method and system for constructing a forest dynamic growth model to achieve refined simulation, multi-dimensional prediction, and visual presentation of the forest growth process is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for constructing a forest dynamic growth model.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] On the one hand, the present invention discloses a method for constructing a forest dynamic growth model, including the following steps:

[0008] Obtain multi-source forest data; construct a real-scene three-dimensional model based on the multi-source forest data, and perform refined modeling on single trees on the basis of the real-scene three-dimensional model to obtain a forest simulation model; integrate the forest simulation model and the basic information of forest trees into a digital twin platform;

[0009] Obtain stand structure parameters from the digital twin platform according to the basic information of forest trees;

[0010] Establish a dynamic growth model of the tree layer at the stand scale by using the relationship between the productivity increment and the stand structure parameters;

[0011] Calculate the stand comprehensive structure index and the shrub layer diversity comprehensive index based on the output of the arbor layer growth dynamic model, and conduct dynamic simulation of shrub layer species diversity according to the stand comprehensive structure index and the shrub layer diversity comprehensive index;

[0012] Conduct dynamic simulation of natural regeneration density based on the stand structure parameters.

[0013] Preferably, the forest multi-source data includes airborne lidar data, hyperspectral data, orthophotos, ground-based lidar data, and artificial survey data;

[0014] The stand structure parameters include diameter at breast height, tree height, crown width, crown length, the first branch height, and stand density, openness, competition index, storey index, spatial density index, angular scale, diameter at breast height ratio, mingling degree, basal area at breast height, stand density, herb cover, total crown area, main canopy crown area, and lower canopy crown area.

[0015] Preferably, build a real-scene three-dimensional model based on the forest multi-source data, including:

[0016] Obtain multi-angle images through oblique photography flight, and conduct aerial triangulation and point piercing in combination with ground control point data to complete control network adjustment and air triangulation accuracy inspection;

[0017] Based on the verified control point data, use modeling software to reconstruct the model and generate an initial three-dimensional model of the whole stand including topographic features, vegetation, buildings, etc.;

[0018] Conduct geometric structure correction on the initial three-dimensional model of the whole stand to obtain a real-scene three-dimensional model.

[0019] Preferably, based on the real-scene three-dimensional model, adopt different three-dimensional modeling methods for refined modeling of individual trees for different scenarios;

[0020] The different three-dimensional modeling methods include: three-dimensional modeling method based on the results of real-scene three-dimensional model construction, three-dimensional modeling method based on point cloud data, three-dimensional modeling method based on digital photogrammetry system, three-dimensional modeling method based on fitting algorithm, and three-dimensional modeling method based on artificial intelligence.

[0021] Preferably, use the relationship between productivity increment and the stand structure parameters to establish an arbor layer growth dynamic model at the stand scale, including:

[0022] Select standard trees and measure the historical radial growth information of the standard trees;

[0023] Based on the historical radial growth information, calculate the historical productivity increment of forest trees using a univariate collection equation and infer the productivity increment of the stand;

[0024] Construct a relationship model between the growth increment of the stand productivity and the stand structure parameters, and correct the relationship model with the current site index level;

[0025] Combined with the current site index level, estimate the growth increment of the stand productivity under other site index level conditions;

[0026] According to the growth increment of the stand productivity, inversely estimate the diameter at breast height and height growth of each tree, and combined with the basic information of the trees, simulate the number and distribution of the updated seedlings entering the arbor layer to complete the dynamic growth modeling of the arbor layer at the stand scale.

[0027] Preferably, calculate the stand comprehensive structure index and the shrub layer diversity comprehensive index based on the output of the arbor layer growth dynamic model, and conduct dynamic simulation of the shrub layer species diversity according to the stand comprehensive structure index and the shrub layer diversity comprehensive index, including:

[0028] Construct a shrub species database;

[0029] Based on the output of the arbor layer growth dynamic model, predict the stand structure parameters, and calculate the stand comprehensive structure index S and the shrub layer diversity comprehensive index R. The formulas are as follows:

[0030] S = 0.81 * diameter at breast height + 0.91 * crown length + 0.74 * storey index - 0.21 * competition index + 0.37 * spatial density index + 0.1 * angle scale - 0.23 * stand density;

[0031] R = 0.70 * Shannon-Wiener index + 0.35 * Margalef index + 0.94 * Pielou index + 0.93 * Simpson index;

[0032] Obtain the simulated shrub layer species number according to the relationship formula of the stand comprehensive structure index S and the shrub layer diversity comprehensive index R;

[0033] Randomly select shrubs in the shrub species database with the same number as the simulated shrub layer species for simulation to complete the simulation of the shrub layer diversity.

[0034] Preferably, conduct dynamic simulation of the natural regeneration density based on the stand structure parameters, including:

[0035] Establish an arbor regeneration species database, and complete the real-scene modeling to form a regeneration seedling model library;

[0036] Calculate the stand regeneration density Y. The formula is as follows:

[0037] Y = 3600 + 3982.99 * Dominant tree species diameter at breast height ratio - 1160.60 / Ratio of main canopy area to lower canopy area - 493.696;

[0038] After obtaining the stand regeneration density, randomly select a regeneration seedling model from the regeneration seedling model library according to the number of seedlings for simulation.

[0039] Preferably, a method for constructing a forest dynamic growth model further includes optimizing the dynamic simulation of the tree layer, shrub layer, and natural regeneration density.

[0040] On the other hand, the present invention also discloses a system for constructing a forest dynamic growth model, including:

[0041] A forest simulation construction module for obtaining multi-source forest data; constructing a real-scene three-dimensional model based on the multi-source forest data, performing refined modeling on individual trees on the basis of the real-scene three-dimensional model to obtain a forest simulation model; integrating the forest simulation model and the basic information of forest trees into a digital twin platform;

[0042] A parameter extraction module for obtaining stand structure parameters from the digital twin platform according to the basic information of forest trees;

[0043] A tree layer simulation module for establishing a dynamic growth model of the tree layer at the stand scale by using the relationship between productivity growth and the stand structure parameters;

[0044] A shrub layer simulation module for calculating the stand comprehensive structure index and the shrub layer diversity comprehensive index based on the output of the tree layer dynamic growth model, and performing dynamic simulation of shrub layer species diversity according to the stand comprehensive structure index and the shrub layer diversity comprehensive index;

[0045] A natural regeneration density simulation module for performing dynamic simulation of natural regeneration density based on the stand structure parameters;

[0046] A simulation improvement module for optimizing the dynamic simulation of the tree layer, shrub layer, and natural regeneration density.

[0047] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for constructing a forest dynamic growth model. By constructing a real-scene three-dimensional model and performing refined modeling of individual trees, it realizes the refined simulation and multi-dimensional prediction of the forest growth process; uses the digital twin platform to integrate the forest simulation model and the basic information of forest trees, extracts stand structure parameters, establishes a dynamic growth model of the tree layer, and then performs dynamic simulation of shrub layer species diversity and natural regeneration density. The present invention breaks through the limitation of the single-tree scale, integrates the co-evolution of the tree layer, shrub layer, and natural regeneration layer for the first time, supports multi-dimensional prediction and visualization, and provides scientific support for forestry digital twin. Brief Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to the provided drawings.

[0049] Figure 1 It is a flowchart of the method provided by the present invention;

[0050] Figure 2 It is a flowchart for constructing the initial three-dimensional model of the whole forest stand provided by the present invention;

[0051] Figure 3 It is a schematic diagram of the three-dimensional modeling method flow based on the results of the real-scene three-dimensional model construction provided by the present invention;

[0052] Figure 4 It is a schematic diagram of the three-dimensional modeling method flow based on point cloud data provided by the present invention;

[0053] Figure 5 It is a schematic diagram of the three-dimensional modeling method flow based on the digital photogrammetry system provided by the present invention. Detailed Description of the Embodiments

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0055] On the one hand, the embodiments of the present invention disclose a method for constructing a forest dynamic growth model, as Figure 1 shown, including the following steps:

[0056] S1. Obtain multi-source forest data; construct a real-scene three-dimensional model based on the multi-source forest data, perform refined modeling on individual trees on the basis of the real-scene three-dimensional model to obtain a forest simulation model; integrate the forest simulation model and the basic information of forest trees into the digital twin platform.

[0057] S11. The multi-source forest data includes airborne lidar data, hyperspectral data, orthophoto images, ground-based lidar data, and manual survey data.

[0058] The airborne data is divided into lidar data, hyperspectral data, and orthophoto images, and is realized by relying on an unmanned aerial vehicle.

[0059] After the lidar data is processed, first, a digital elevation model of the forest farm is obtained. Second, combined with CCD images, vegetation point clouds and non-vegetation point clouds are obtained. The non-vegetation point clouds are used to reconstruct building structures, and the vegetation point clouds are used for individual tree segmentation to extract individual tree structure information.

[0060] After the hyperspectral data is processed, it mainly provides information such as spectral characteristics and vegetation indices of different tree species, which is used for individual tree classification, etc.

[0061] After the orthophoto is processed, it mainly provides richer texture information and is combined with lidar data for stand information analysis.

[0062] A ground-based lidar is used to scan the forest at the plot scale to mainly obtain canopy information and structural information, obtain ground point clouds, register them with airborne point cloud data, and be used for the construction of a 1:1 three-dimensional tree species model in the later stage and to improve the accuracy of branch reconstruction.

[0063] Ground-based manual survey data includes individual tree positions, tree heights, tree species information, on-site photos, etc., which provide verification data for individual tree segmentation and individual tree species classification results.

[0064] S12. Construct a real-scene three-dimensional model based on multi-source forest data, including:

[0065] Obtain multi-angle images through oblique photography aerial flights, perform aerial triangulation and point piercing in combination with ground control point data, complete control network adjustment and aerial triangulation accuracy inspection; based on the verified control point data, use modeling software for model reconstruction to generate an initial three-dimensional model of the entire stand including ground features such as terrain, vegetation, and buildings. The specific process is referred to Figure 2 .

[0066] In the initial three-dimensional model of the entire stand established based on the modeling software, there are situations such as building deformation (texture stretching, structural distortion, broken surfaces, missing surfaces, etc.), suspended objects, and missing components caused by incorrect image matching or poor geometric postures. In this embodiment, a real-scene three-dimensional model is obtained by using software such as 3dsMax to correct the geometric structure of the initial three-dimensional model of the entire stand.

[0067] S13. On the basis of the real-scene three-dimensional model, different three-dimensional modeling methods are used for fine-grained modeling of individual trees for different scenarios. The fine-grained modeling method for individual trees is as follows:

[0068] (1) The three-dimensional modeling method based on the results of the real-scene three-dimensional model construction is as Figure 3As shown in the figure, based on the reconstructed real - scene 3D model as the basic reference, combined with ground - shot images, the individual - object modeling is completed through several links such as local separation and editing, fine revision and reconstruction, and update and merging. This modeling method makes full use of the 3D model results. Without the need for auxiliary equipment, operators can manually complete the individual - object modeling based on the real - world restored by the 3D model.

[0069] (2) The 3D modeling method based on point - cloud data is as Figure 4 shown.

[0070] 1) Obtain the oblique data and perform pre - processing. Denoise and correct distortion of the oblique images to improve the image quality.

[0071] Perform image equalization and color matching to ensure consistent image tone, which is convenient for subsequent texture mapping.

[0072] Obtain the position and attitude information of the images through the POS system carried by the UAV.

[0073] 2) Obtain the laser point - cloud data and perform pre - processing

[0074] Use a ground laser scanner (such as LiDAR) or an airborne laser scanner to obtain high - precision 3D point - cloud data. Laser scanning obtains the 3D coordinates of ground objects through the ranging principle, and the point - cloud density is usually dozens to hundreds of points per square meter.

[0075] Perform denoising, filtering, classification, etc. on the original point - cloud data to remove invalid points (such as vegetation, vehicles, etc.). Register the point - cloud data with the oblique image data to ensure spatial consistency.

[0076] Edit the point - cloud data, repair missing areas, and optimize the point - cloud quality.

[0077] Generate an irregular triangular network model (TIN) based on the point - cloud data to construct the geometric framework of the ground surface and buildings.

[0078] Generate a digital surface model (DSM) based on the point - cloud data to reflect the elevation information of the ground object surface.

[0079] 3) Solve the individual 3D model

[0080] Combine the oblique images and laser point - cloud data, and use 3D modeling software (such as ContextCapture, Agisoft Metashape) to reconstruct the model. Generate a 3D model with real textures. Perform operations such as denoising, simplification, and repair on the model to improve the model quality. Generate models with different levels of detail according to requirements (such as LOD1, LOD2, LOD3).

[0081] 4) Solve the ground 3D model

[0082] Generate a Digital Terrain Model (DTM) based on laser point cloud data to reflect the elevation information of the ground surface.

[0083] Fuse the ground surface model with the building model to generate a complete three-dimensional scene.

[0084] 5) Modify and improve the model data

[0085] Manually edit the generated three-dimensional model to repair loopholes and optimize the geometric structure. Optimize the model texture to ensure clear and distortion-free texture.

[0086] Verify the geometric accuracy of the model using Ground Control Points (GCPs) and Check Points.

[0087] This method uses high-precision laser point cloud as the data source for the model Mesh network. It scans the ground objects with lasers and obtains relatively accurate three-dimensional point cloud data through the principle of laser ranging. On this basis, a triangular network model is constructed. Some studies have shown that when the point cloud density is less than 10 points per square meter, the highest level of modeling fineness reaches LOD2. Therefore, if more refined three-dimensional results are required, the point cloud density needs to be increased. For example, the point cloud density for ultra-fine modeling needs to reach more than two hundred points per square meter.

[0088] (3) Three-dimensional modeling method based on digital photogrammetry system

[0089] The digital photogrammetry system is one of the essential equipment for large-scale topographic map production. The theoretical core of digital photogrammetry is the collinearity equation. The so-called collinearity equation means that the projection center, the image coordinates (x, y) of the image point, and the corresponding ground point are located on the same straight line. Based on the principle of the collinearity equation, the necessary point coordinates of the model can be measured in a stereoscopic environment, and thus a three-dimensional stereoscopic model can be constructed. Its technical route is as Figure 5 shown. The key step is to collect the feature lines of ground objects in a stereoscopic environment and combine them with a fine digital elevation model to complete the construction of the white model of ground objects.

[0090] The collection content mainly includes the contour lines of buildings, road edges, water system boundaries, terrain feature lines (such as ridge lines, valley lines), etc. Superimpose the feature lines of ground objects on the DEM in a three-dimensional environment to ensure that the elevation information of the feature lines is consistent with the DEM, correct the elevation of the feature lines to eliminate errors. Extract the contours from the feature lines and assign heights, widths, and local refinement values according to information such as DEM or additional height information, road grades, or measured data, and finally fuse them with the fine digital elevation model to generate the white model of ground objects.

[0091] (4) Three-dimensional modeling method based on fitting algorithm

[0092] Based on point cloud data and combined with a model library, automated 3D modeling of the model is completed. First, a forest tree model library is constructed; then, using the model library as the basic unit, the transformation parameters of the spatial transformation (translation, rotation, or scaling) between models are obtained by automatically extracting or manually collecting the feature points of the 3D model construction method of forest trees, and finally, the modeling is completed.

[0093] (5) 3D modeling method based on artificial intelligence

[0094] Based on the 3D tree point cloud, dense point cloud data covering all elements is formed, and based on this, the point clouds of the trunks, branches, and leaves of forest trees are obtained. Through artificial intelligence (AI) technology, combined with on-site photos, the bark material corresponding to the field is given to generate the tree trunk, and after pasting the corresponding leaves, a 1:1 restored 3D tree species model is made to complete the reconstruction of individual trees.

[0095] The forest simulation model not only includes the spatial position distribution information of each forest tree, but also includes the basic information of forest trees, and all these basic information can be integrated into the digital twin platform.

[0096] S2. Obtain the stand structure parameters from the digital twin platform according to the basic information of forest trees.

[0097] Based on the basic information of forest trees, using the data processing and analysis software (stand spatial structure parameter calculation formula) of the digital twin platform, various stand structure parameters related to the dynamic growth of the forest are obtained, including diameter at breast height, tree height, crown width, crown length, height to the first live branch, and stand density, openness, competition index, storey index, spatial density index, angular scale, diameter at breast height ratio, mingling, basal area at breast height, stand density, herb cover, total crown area, main crown layer crown area, lower crown layer crown area, etc.

[0098] Furthermore, in this embodiment, by consulting data, the stand age information is obtained, combined with the tree height parameter, and through comprehensive analysis of the platform, the site index level of different plots is obtained as the correction coefficient of the dynamic growth model. The correction coefficient for a site index level of 14 is 1, and for each increase (decrease) of 1 level, the correction coefficient increases (decreases) by 0.2.

[0099] S3. Use the relationship between the productivity increment and the stand structure parameters to establish a growth dynamic model of the tree layer (trees with a diameter at breast height greater than or equal to 5 cm) at the stand scale.

[0100] In the target forest stand, 8 - 12 standard trees are selected for different tree species respectively. Core samples are taken from the trees using an increment borer. After pretreatment, cross-dating is carried out using a dendrometer to obtain the radial growth information of the trees in the past 5 years. The productivity increment of the trees in the past 5 years is calculated using a univariate collection equation. The productivity increment of the forest stand is deduced. Combining with the parameter information in S2, a relationship model between these parameters and the productivity increment of the forest stand is established. Based on this relationship model, combined with the site index level of the current forest stand, the productivity increment of the forest stand in the next 5 years under other site index levels is further deduced. The growth of each tree and the number of forest stand regeneration are estimated by backtracking based on the productivity increment in the next 5 years. Specifically, the growth information of forest stand regeneration seedlings is obtained based on S1. Calculated according to the fact that 40% of the trees with a diameter at breast height less than 5 cm will enter the tree layer after 5 years, it is randomly simulated into the simulation model.

[0101] S4. Calculate the comprehensive structure index of the forest stand and the comprehensive diversity index of the shrub layer based on the output of the tree layer growth dynamic model, and conduct dynamic simulation of the shrub layer species diversity according to the comprehensive structure index of the forest stand and the comprehensive diversity index of the shrub layer.

[0102] S41. Investigate all shrub species growing naturally in the local area, establish a local shrub species database, and complete real-scene modeling to form a shrub species model library.

[0103] S42. Based on S2 and S3, obtain the forest stand structure parameters 5 years later for the forest stand, and construct the comprehensive structure index factor (S) of the forest stand = 0.81 * diameter at breast height + 0.91 * crown length + 0.74 * tree layer index - 0.21 * competition index + 0.37 * spatial density index + 0.1 * angle scale - 0.23 * forest stand density;

[0104] Construct the comprehensive diversity index (R) of the shrub layer = 0.70 * Shannon - Wiener index + 0.35 * Margalef index + 0.94 * Pielou index + 0.93 * Simpson index.

[0105] S43. Establish the relationship between S and R: R = 0.82 * S, calculate R from S; then calculate the Margalef index from R to obtain the number of shrub layer species simulated after 5 years.

[0106] After obtaining the number of shrub layer species, according to the shrub species model library obtained in S41, randomly select the corresponding number of shrubs for simulation to complete the simulation of the shrub layer diversity.

[0107] S5. Conduct dynamic simulation of the natural regeneration density based on the forest stand structure parameters, including:

[0108] S51. Investigate all arbor species growing naturally in the local area, establish a database of local arbor regeneration species, and complete real-scene modeling to form a regeneration seedling model library.

[0109] S52. Natural regeneration density (Y) in the next 5 years = 3600 + 3982.99 * ratio of breast diameter of dominant tree species - 1160.60 / ratio of main crown area to lower crown area - 493.696.

[0110] S53. After obtaining the stand regeneration density, randomly select regeneration seedling models from the regeneration seedling model library according to the number of seedlings for simulation.

[0111] S6. Optimize the dynamic simulation of the arbor layer, shrub layer, and natural regeneration density.

[0112] The optimization point of the dynamic growth simulation of the arbor layer is the number of regenerated seedlings entering the arbor layer and their growth information.

[0113] By dynamically adjusting the proportion of regenerated seedlings entering the arbor layer (such as optimizing the survival rate of seedlings with a breast diameter < 5 cm from 40% to a dynamic threshold based on site conditions), introducing a growth model (such as correcting the growth curves of breast diameter and tree height of regenerated seedlings based on environmental factors), combining the monitoring data (such as UAV remote sensing and manual surveys) real-time feedback by the digital twin platform, and using the random forest algorithm to optimize the site index correction coefficient, improve the adaptability of arbor layer growth prediction.

[0114] The optimization point of the dynamic simulation of shrub layer species diversity is the growth information such as the height and ground diameter of shrubs.

[0115] In the shrub layer diversity simulation, integrate environmental factors such as light intensity and soil moisture, dynamically correct the growth parameters of shrub height and ground diameter, optimize the spatial distribution of shrub species through the species competition model (such as adjusting the competition weight in the S index), and calibrate the coefficient of the diversity comprehensive index (R = 0.82 * S) combined with field survey data to enhance the ecological rationality of species diversity prediction.

[0116] The optimization point of the dynamic simulation of natural regeneration density is the growth information of regenerated seedlings.

[0117] For the natural regeneration density formula, analyze the regeneration coefficient through historical data regression, introduce a dynamic model of seedling survival rate (adjust the survival rate threshold according to climate data), combine the differences between simulation results and actual observations, and use the gradient boosting tree algorithm to iteratively optimize the formula parameters to enhance the accuracy and environmental adaptability of regeneration density prediction.

[0118] On the other hand, the present invention also discloses a system for constructing a forest dynamic growth model, including:

[0119] Forest simulation construction module, which is used to obtain multi-source forest data; construct a real-scene three-dimensional model based on the multi-source forest data, refine the modeling of individual trees on the basis of the real-scene three-dimensional model to obtain a forest simulation model; integrate the forest simulation model and the basic information of forest trees into the digital twin platform;

[0120] Parameter extraction module, which is used to obtain stand structure parameters from the digital twin platform according to the basic information of forest trees;

[0121] Arbor layer simulation module, which is used to establish a growth dynamic model of the arbor layer at the stand scale by using the relationship between productivity growth and stand structure parameters;

[0122] Shrub layer simulation module, which is used to calculate the comprehensive stand structure index and the comprehensive shrub layer diversity index based on the output of the arbor layer growth dynamic model, and conduct dynamic simulation of shrub layer species diversity according to the comprehensive stand structure index and the comprehensive shrub layer diversity index;

[0123] Natural regeneration density simulation module, which is used to conduct dynamic simulation of natural regeneration density based on stand structure parameters;

[0124] Simulation improvement module, which is used to optimize the dynamic simulation of the arbor layer, shrub layer and natural regeneration density.

[0125] In the present specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and reference can be made to the description in the method part for related parts.

[0126] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a forest dynamic growth model, characterized in that, It includes the following steps: Obtain multi-source forest data; construct a real-scene three-dimensional model based on the multi-source forest data, and perform refined modeling on individual trees based on the real-scene three-dimensional model to obtain a forest simulation model; integrate the forest simulation model and the basic information of forest trees into a digital twin platform; Obtain stand structure parameters from the digital twin platform according to the basic information of forest trees; Establish a dynamic growth model of the arbor layer at the stand scale by using the relationship between productivity increment and the stand structure parameters; Calculate the stand comprehensive structure index and the shrub layer diversity comprehensive index based on the output of the dynamic growth model of the arbor layer, and perform dynamic simulation of shrub layer species diversity according to the stand comprehensive structure index and the shrub layer diversity comprehensive index; Perform dynamic simulation of natural regeneration density based on the stand structure parameters.

2. The method for constructing a forest dynamic growth model according to claim 1, characterized in that The multi-source forest data includes airborne lidar data, hyperspectral data, orthophotos, ground-based lidar data, and artificial survey data; The stand structure parameters include diameter at breast height, tree height, crown width, crown length, height to the first live branch, and stand density, openness, competition index, storey index, spatial density index, angular scale, diameter at breast height ratio, mingling degree, basal area at breast height, stand density, herb cover, total crown area, main crown layer crown area, and lower crown layer crown area.

3. The method for constructing a forest dynamic growth model according to claim 2, wherein, Constructing a real-scene three-dimensional model based on the multi-source forest data includes: Obtain multi-angle images through oblique photography aerial flight, perform aerial triangulation and point piercing in combination with ground control point data, and complete control network adjustment and aerial triangulation accuracy inspection; Based on the verified control point data, use modeling software to perform model reconstruction to generate an initial three-dimensional model of the whole stand including ground features such as terrain, vegetation, and buildings; Perform geometric structure correction on the initial three-dimensional model of the whole stand to obtain a real-scene three-dimensional model.

4. A method for constructing a forest dynamic growth model according to claim 1, characterized in that, Based on the real-scene three-dimensional model, use different three-dimensional modeling methods for refined modeling of individual trees in different scenarios; The different three-dimensional modeling methods include: three-dimensional modeling method based on the results of real-scene three-dimensional model construction, three-dimensional modeling method based on point cloud data, three-dimensional modeling method based on digital photogrammetry system, three-dimensional modeling method based on fitting algorithm, and three-dimensional modeling method based on artificial intelligence.

5. A method for constructing a forest dynamic growth model according to claim 2, characterized in that, Establish a dynamic growth model of the arbor layer at the stand scale by using the relationship between productivity increment and the stand structure parameters, including: Select standard trees and measure the historical radial growth information of the standard trees; Based on the historical radial growth information, use a univariate collection equation to calculate the historical productivity increment of forest trees and deduce the productivity increment of the stand; Construct a relationship model between the productivity increment of the stand and the stand structure parameters, and correct the relationship model with the current site index level; Combined with the current site index level, deduce the productivity increment of the stand under other site index level conditions; Reverse deduce the diameter at breast height and tree height growth of each forest tree according to the productivity increment of the stand, and combined with the basic information of the forest trees, simulate the number and distribution of regenerated seedlings entering the arbor layer to complete the dynamic growth modeling of the arbor layer at the stand scale.

6. The method for constructing a forest dynamic growth model according to claim 5, wherein Calculate the stand comprehensive structure index and the shrub layer diversity comprehensive index based on the output of the arbor layer growth dynamic model, and conduct dynamic simulation of shrub layer species diversity according to the stand comprehensive structure index and the shrub layer diversity comprehensive index, including: Construct a shrub species database; Predict the stand structure parameters based on the output of the arbor layer growth dynamic model, and calculate the stand comprehensive structure index S and the shrub layer diversity comprehensive index R. The formulas are as follows: S = 0.81 * DBH + 0.91 * crown length + 0.74 * storey index - 0.21 * competition index + 0.37 * spatial density index + 0.1 * angle gauge - 0.23 * stand density; R = 0.70 * Shannon-Wiener index + 0.35 * Margalef index + 0.94 * Pielou index + 0.93 * Simpson index; Obtain the simulated shrub layer species number according to the relationship between the stand comprehensive structure index S and the shrub layer diversity comprehensive index R; Randomly select shrubs with the same number as the simulated shrub layer species number from the shrub species database for simulation to complete the simulation of shrub layer diversity.

7. A method for constructing a forest dynamic growth model according to claim 2, characterized in that, Conduct dynamic simulation of natural regeneration density based on the stand structure parameters, including: Establish an arbor regeneration species database, complete real-scene modeling, and form a regeneration seedling model library; Calculate the stand regeneration density Y. The formula is as follows: Y = 3600 + 3982.99 * dominant species tree DBH size ratio - 1160.60 / ratio of main canopy area to lower canopy area - 493.696; After obtaining the stand regeneration density, randomly select regeneration seedling models from the regeneration seedling model library according to the number of seedlings for simulation.

8. A method for constructing a forest dynamic growth model according to claim 1, characterized in that, It also includes optimizing the dynamic simulation of the arbor layer, shrub layer, and natural regeneration density.

9. A forest dynamic growth model construction system, characterized in that, Including: A forest simulation construction module for obtaining multi-source forest data; Construct a real-scene three-dimensional model based on the multi-source forest data, conduct refined modeling of individual trees on the basis of the real-scene three-dimensional model to obtain a forest simulation model; integrate the forest simulation model and the basic information of forest trees into the digital twin platform; A parameter extraction module for obtaining stand structure parameters from the digital twin platform according to the basic information of forest trees; An arbor layer simulation module for establishing an arbor layer growth dynamic model at the stand scale by using the relationship between productivity growth and the stand structure parameters; A shrub layer simulation module for calculating the stand comprehensive structure index and the shrub layer diversity comprehensive index based on the output of the arbor layer growth dynamic model, and conducting dynamic simulation of shrub layer species diversity according to the stand comprehensive structure index and the shrub layer diversity comprehensive index; A natural regeneration density simulation module for conducting dynamic simulation of natural regeneration density based on the stand structure parameters.

10. A system for constructing a forest dynamic growth model according to claim 9, characterized in that, It also includes a simulation improvement module for optimizing the dynamic simulation of the arbor layer, shrub layer, and natural regeneration density.

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