Forest stand mixing optimization method and system based on tree cluster units

Abstract

The invention provides a forest stand mixing optimization method and optimization system based on tree cluster units, and relates to the technical field of forest management. The method provided by the invention comprises the following steps: acquiring arbor planting point information in an investigation forest, selecting a plurality of tree cluster units, investigating forest stand indexes of the tree cluster units, and constructing a tree species database; constructing a correlation model of the forest stand index and each pair of relative traits, and obtaining the optimal mixing ratio of each pair of relative traits under the optimal forest stand index; obtaining geographic information of a to-be-planted land parcel and randomly selecting to-be-planted points; and carrying out character assignment on the to-be-planted points based on the optimal mixing ratio of each pair of relative characters, and then matching arbor tree species to carry out tree species mixing. The method can directly guide the construction of artificial mixed forests from zero to existing in the to-be-planted land parcels, and facilitates the improvement of the forest management efficiency and replicability.

Description

Technical Field

[0001] The present invention relates to the technical field of forest management, and particularly relates to a method and system for optimizing stand mixed planting based on tree group units. Background Art

[0002] A mixed forest is a composite forest ecosystem formed by two or more tree species through specific spatial configurations, which can break the homogeneous distribution of a single tree species. Compared with pure forests, mixed forests have significant advantages in ecological functions and economic values. They can not only enhance the stability of forest ecosystems, but also improve the utilization efficiency of soil resources, light resources, etc. At the same time, mixed forests perform better in environmental benefits such as carbon sequestration capacity, soil and water conservation, and forest health. However, mixed forests without tree species optimization, scientific proportioning, and layout planning are prone to interspecific competition imbalance and will also cause great waste of resources due to the unreasonable spatial configuration of growers. Therefore, efficient optimization of mixed forests has become an inevitable choice to improve forest quality and management benefits. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for optimizing stand mixed planting based on tree group units, which can directly guide the construction of artificial mixed forests from scratch in the to-be-planted plots, and is beneficial to improving forest management efficiency and replicability.

[0004] In a first aspect, a method for optimizing stand mixed planting based on tree group units provided by the present invention includes: obtaining the information of tree planting points of arbors in the surveyed forest to construct a survey model and selecting multiple tree group units; investigating the stand indexes of the tree group units and extracting the species and traits of arbors to construct a tree species database; respectively constructing an association model between the stand indexes and each pair of relative traits within the tree group units; respectively obtaining the optimal mixed ratio of each pair of relative traits under the optimal stand indexes based on the association model; obtaining the geographical information of the to-be-planted plot to construct a planting map and randomly selecting to-be-planted points; after assigning trait values to the to-be-planted points based on the optimal mixed ratio of each pair of relative traits, matching arbor tree species in the tree species database to obtain a planting blueprint; and performing tree species mixed planting on the to-be-planted plot based on the planting blueprint.

[0005] Optionally, the surveyed forest and the to-be-planted plot are in the same spatio-temporal environment.

[0006] Optionally, the tree group unit is a combination of 3 to 7 non-collinear and adjacent arbors.

[0007] Optionally, the figure formed by the tree group units is a convex polygon.

[0008] Optionally, the information of tree planting points of arbors in the surveyed forest is obtained based on low-altitude technology.

[0009] Optionally, the surveyed forest includes artificial mixed forests or natural forests.

[0010] Optionally, the arbor planting point information includes arbor coordinates, leaf traits, and root shapes.

[0011] Optionally, the relative traits include broad-leaved traits and needle-leaved traits, deciduous traits and evergreen traits, deep-root traits and shallow-root traits, root endophyte-dominated traits and root ectophyte-dominated traits.

[0012] Optionally, the stand indicators include canopy density, mixture ratio, volume, foliage biomass, near-surface air plant VOCs content, carbon sink, water conservation, nitrogen fixation, mixture ratio, and negative oxygen ion concentration.

[0013] Optionally, when selecting multiple tree group units to construct an investigation model, the number of the tree group units is:

[0014] where is the total number of tree group units in the investigation model, is the orthographic projection area of the investigation model, and are both adjustment coefficients, and is greater than 1.

[0015] Optionally, multiple tree group units are randomly selected within the investigation model.

[0016] Optionally, the tree species database includes tree species and the corresponding tree traits of the tree species. The tree traits at least include the traits of the arbors within the tree group unit, and the tree species at least include the tree species of the arbors within the tree group unit.

[0017] Optionally, there are no other arbors in the figure formed by the tree group units.

[0018] Optionally, when constructing an association model between the stand indicators and each pair of relative traits within the tree group unit, it includes: statistically sorting the mixture ratios of each relative trait within all tree group units to obtain a mixture ratio sequence; comprehensively obtaining the comprehensive stand indicator value of the tree group units under the same mixture ratio; constructing an association model based on the mixture ratio sequence and the comprehensive stand indicator value.

[0019] Optionally, the number of tree group units under each same mixture ratio is greater than or equal to the minimum threshold.

[0020] Optionally, when constructing an association model based on the mixture ratio sequence and the comprehensive stand indicator value, construct a mapping relationship of the comprehensive stand indicator value to the mixture ratio sequence and fit to obtain the association model.

[0021] Optionally, the deviation of the number of tree group units under each mixture ratio is less than or equal to 15%.

[0022] Optionally, when integrating the stand indicators of the tree group units at the same mixing ratio, it includes:

[0023] Among them, is the comprehensive value of the stand indicators of the tree group unit at the -th mixing ratio, is the total number of tree group units at the -th mixing ratio, is the stand indicator of the -th tree group unit at the -th mixing ratio, is the stand age constraint coefficient of the -th tree group unit at the -th mixing ratio, is the orthographic projection area of the -th tree group unit at the -th mixing ratio.

[0024] In a second aspect, the present invention also provides a stand mixing optimization system based on tree group units, including: A basic construction module for obtaining the information of the tree planting points of the arbors in the surveyed forest to construct a survey model, and obtaining the geographical information of the plot to be planted to construct a planting map; A selection and survey module for selecting multiple tree group units in the survey model and surveying the stand indicators of the tree group units, and randomly selecting the points to be planted in the planting map; A database construction module for extracting the types and traits of the arbors in the tree group units to construct a tree species database; An information association module for constructing an association model between the stand indicators and each pair of relative traits within the tree group unit; A mixing analysis module for respectively obtaining the optimal mixing ratios of each pair of relative traits under the optimal stand indicators based on the association model; A trait assignment module for assigning traits to the points to be planted based on the optimal mixing ratios of each pair of relative traits; A blueprint construction module for matching the arbor tree species for the points to be planted with assigned traits in the tree species database to obtain a planting blueprint; A mixing planting module for carrying out tree species mixing on the plot to be planted based on the planting blueprint. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flowchart of a stand mixing optimization method based on tree group units provided by the present invention; Figure 2 is a structural schematic diagram of a stand mixing optimization system based on tree group units provided by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein shall have the ordinary meanings understood by those of ordinary skill in the art belonging to the field of the present invention.

[0027] See Figure 1 , the embodiments of the present invention provide a method for optimizing stand mixture based on tree group units, including the following steps: S1. Obtain the information of tree planting points of arbors in the surveyed forest to construct a survey model and select multiple tree group units; S2. Survey the stand indexes of the tree group units and extract the species and traits of arbors to construct a tree species database; S3. Respectively construct the association models between the stand indexes and each pair of relative traits within the tree group units; S4. Based on the association models, respectively obtain the optimal mixture ratios of each pair of relative traits under the optimal stand indexes; S5. Obtain the geographical information of the plot to be planted to construct a planting map and randomly select the points to be planted; S6. Assign trait values to the points to be planted based on the optimal mixture ratios of each pair of relative traits; S7. Match the arbor tree species in the tree species database to obtain a planting blueprint; S8. Conduct tree species mixture on the plot to be planted based on the planting blueprint.

[0028] In fact, by using the surveyed forest as a sample to select tree group units for investigation to obtain stand indexes, and by constructing the association relationship between the stand indexes and the trait mixture ratios, the optimal mixture ratios of each relative trait under the optimal stand indexes are obtained, and these optimal mixture ratios are used to generate a planting blueprint, so that the overall planting blueprint conforms to the optimal mixture ratios of all relative traits, which is beneficial to improving the artificial mixed forest to reach the optimal management level. In addition, through the planting blueprint, it is possible to directly guide the construction of an artificial mixed forest from scratch on the plot to be planted, which is beneficial to improving the forest management efficiency and replicability.

[0029] In fact, the surveyed forest used in step S1 is an artificial mixed forest or a natural forest. Since the natural forest has undergone natural growth for a long time in a natural state, it can form a multi-layered and stable vegetation structure through the cooperation of top-layer trees, middle-layer shrubs, and bottom-layer herbs. The artificial mixed forest, on the other hand, is a mixed forest with a definite forest age obtained by artificial planting. In fact, at present, when constructing an artificial mixed forest, the artificial pure forest is usually mixed and transformed. Therefore, choosing the artificial mixed forest as the surveyed forest can further extend and develop on the basis of the existing work. In addition, the artificial mixed forest constructed by the method of the present invention can also play a role in the stand mixing of another plot to be planted.

[0030] Specifically, the surveyed forest used in step S1 and the plot to be planted in step S5 are in the same spatio-temporal environment. In fact, in the same spatio-temporal environment, the surveyed forest and the plot to be planted are in the same climate environment, and the stand structure in the surveyed forest is the result of long-term adaptation to this climate environment. Therefore, stand mixing based on the surveyed forest can reduce the growth and death problems of the forest land caused by climate inadaptability after planting. At the same time, in the same spatio-temporal environment, the soil matching and the ecological system succession process are similar, which can ensure the normal growth of the mixed forest on the mixed plot.

[0031] In some embodiments, the same spatio-temporal environment is specifically an area that is geographically in the same or adjacent location, and within the same river basin range with similar water systems and similar component soils, and also needs to be in the same or similar climate zone. Further, an artificial mixed forest or a natural forest can be found as the surveyed forest within a circular range with a radius of 300 km centered on the plot to be planted. Specifically, the difference between the forest land area of the surveyed forest and the area of the plot to be planted is less than or equal to 20%.

[0032] In some embodiments, in step S1, the information of the tree planting points in the surveyed forest can be obtained based on low-altitude technology. In fact, low-altitude unmanned aerial vehicle equipment can be used to fly inside and above the surveyed forest to capture image information for identification, and the tree coordinates can be directly obtained by combining positioning methods such as GPS and Beidou, and the leaf traits can be obtained through the image information. In addition, when obtaining the root traits of the trees, the species of the trees can be obtained through image recognition, and their root traits can be distinguished by their species.

[0033] Specifically, the tree planting point information obtained in step S1 includes tree coordinates, leaf traits, and root traits. The tree coordinates include the position of the tree itself and the relative positions between the current tree and other trees, enabling the accurate position coordinates of all trees in the surveyed forest to be comprehensively obtained. More specifically, the leaf traits of the tree include multiple pairs of relative traits, such as broad-leaved traits and needle-leaved traits, deciduous traits and evergreen traits. Similarly, the root traits of the tree include multiple pairs of relative traits, such as deep-root traits and shallow-root traits, root endophyte-dominated traits and root ectophyte-dominated traits.

[0034] In some embodiments, after obtaining the image information of the surveyed forest based on low-altitude technology in step S1, a mathematical model can be constructed using a neural network. In the initial stage, the tree species in the tree pictures are manually labeled as the training set, and after training the mathematical model with the training set, the model is enabled to distinguish the tree species from the tree pictures, and the corresponding leaf traits and root trait information are matched using the distinguished tree species.

[0035] In some embodiments, after obtaining the tree planting point information of the trees in the surveyed forest in step S1, a digital map corresponding to the surveyed forest is constructed as a survey model. Through the survey model, tree cluster units can be quickly selected on a computer device, and at the same time, the selected tree cluster units together with their coordinate positions can be sent to the mobile terminal for surveyors to perform precise positioning and discrimination in the surveyed forest. Specifically, when constructing the survey model, the terrain changes in the surveyed forest can be ignored, and the focus can be on constructing a simple model based on the tree coordinates in the surveyed forest, which is beneficial to reducing the complexity of the model.

[0036] Actually, when selecting multiple tree cluster units after constructing the survey model in step S1, the selected tree cluster units are tree combinations composed of 3 to 7 non-collinear and adjacent trees. In addition, the figure formed by the tree cluster units is a convex polygon, and there are no other trees within the formed figure. Specifically, by selecting tree cluster units in the survey model, it is possible to avoid manually entering the surveyed forest to enclose survey plots, and at the same time, it is possible to increase the randomness of the tree cluster units in the survey model, which is beneficial to improving the credibility and accuracy of the survey results.

[0037] In some embodiments, multiple tree cluster units are randomly selected in step S1, and the number of selected tree cluster units is positively correlated with the total area of the surveyed forest. In fact, the more tree cluster units are selected in the survey model, the higher the survey accuracy of the surveyed forest, but the corresponding survey workload will also increase. Therefore, to balance the survey accuracy and survey workload, the number of selected tree cluster units in the survey model can be expressed as:

[0038] Where, is the total number of tree cluster units in the survey model, To investigate the orthographic projection area of the model, , are both adjustment coefficients, and is greater than 1.

[0039] In fact, when investigating the stand indicators of the tree cluster unit in step S2, the stand indicators include at least one forest management indicator. Specifically, the stand indicators include canopy density, mixture degree, volume, foliage biomass, content of plant VOCs in the near-surface air, carbon sequestration capacity, water conservation capacity, nitrogen fixation amount, mixture ratio, and negative oxygen ion concentration. In some embodiments, when calculating the stand indicators within the tree cluster unit, common calculation or investigation methods in the art can be used.

[0040] In fact, when extracting the types and traits of arbors in the tree cluster unit to construct a tree species database in step S2, it is beneficial to match arbor tree species in step S7. Specifically, since the surveyed forest and the plot to be planted are in the same spatio-temporal environment, it can be considered that the arbor tree species growing in the surveyed forest can also grow normally in the plot to be planted.

[0041] Furthermore, the tree species database includes arbor species and the corresponding arbor traits of the arbor species. For example, in the tree species database, there can be a mapping relationship of [arbor species, arbor traits], and the arbor species at least include the arbor species within the tree cluster unit, and the arbor traits at least include the traits of the arbors within the tree cluster unit. Specifically, when the arbor species within the tree cluster unit is A and the arbor traits include A, B, C, D, it can be represented as [A, (A|B|C|D)] in the tree species database.

[0042] In fact, establishing an association model between the stand indicators and the relative traits within the tree cluster unit in step S3 can associate the magnitude of the stand indicators with the mixture ratio of the relative traits within the tree cluster unit. In fact, different mixture ratios of arbor tree species within the tree cluster unit will significantly affect the size of the stand indicators. Taking the smallest tree cluster unit - the tree cluster unit composed of three arbors as an example, if all three arbors are deep-rooted tree species, it is difficult to stratify the soil roots in the enclosed area, which will have an obvious impact on the water storage capacity. However, if two deep-rooted arbors and one shallow-rooted arbor are paired, it is beneficial to improve the water storage capacity because the roots can be well stratified in the soil.

[0043] In some embodiments, when performing step S3, it includes the following sub-steps: S3.1. Statistically sort the mixture ratios of each relative trait within all tree cluster units to obtain a mixture ratio sequence; S3.2. Synthesize the stand indicators of the tree cluster units under the same mixture ratio to obtain a comprehensive stand indicator value; S3.3. Construct an association model based on the mixture ratio sequence and the comprehensive stand indicator value.

[0044] In fact, when performing step S3.1 to count the hybridization ratios of each relative trait within all tree cluster units, assuming that the relative traits in the tree cluster unit are A and a, the corresponding hybridization ratios of trait A and trait a can be as shown in Table 1 below.

[0045] Table 1 Hybridization Ratios within Tree Cluster Units

[0046]

[0047] In some embodiments, when sorting to obtain a hybridization ratio sequence after counting the hybridization ratios of each relative trait, the sorting can be performed according to any one of each pair of relative traits. For example, sorting according to trait A in Table 1 is as shown in Table 2 below.

[0048] Table 2 Hybridization Ratio Sequence of Relative Traits (A / a)

[0049]

[0050] In fact, it can be seen from Table 2 that when the hybridization ratio of relative traits is (0, 1) or (1, 0), it is considered that there is no tree species hybridization in the currently selected tree cluster unit, and the species of multiple arbors constituting the tree cluster unit are the same or at least the traits are the same. Therefore, (0, 1) or (1, 0) can be excluded when constructing the hybridization ratio sequence.

[0051] In some embodiments, the number of tree cluster units under each same hybridization ratio in step S3.1 is greater than or equal to the minimum threshold. By setting the minimum threshold, it can be ensured that the stand indices of the tree cluster units under the current hybridization ratio have sufficient reference value, which is beneficial to improving the accuracy of the obtained optimal hybridization ratio. Specifically, the number of tree cluster units under each hybridization ratio needs to be approximately the same, and the deviation is less than or equal to 15%.

[0052] In fact, when performing step S1 to select multiple tree cluster units within the survey model, the hybridization ratio of the relative traits in the tree cluster unit can be pre-calculated to determine whether it meets the above constraint conditions. If not, re-selection can be performed within the survey model to meet the constraint conditions and improve the accuracy of the optimal hybridization ratio. Specifically, when pre-calculating whether the hybridization ratio of the relative traits in the tree cluster unit meets the constraint conditions, 1 to 4 pairs of relative traits can be selected for calculation according to the optimization direction.

[0053] In some embodiments, when performing step S3.2 to synthesize the stand indices of tree cluster units with the same mixing ratio, the credibility and accuracy of the stand indices can be effectively improved by synthesizing the stand indices within multiple tree cluster units. In fact, due to natural elimination and artificial selective cutting during the growth process of natural forests and artificial mixed forests, as well as the different numbers of arbors selected when choosing tree cluster units, the areas and sizes of the regions enclosed by the formed tree cluster units are also different. At the same time, the tree cluster units are located in different regions of the surveyed forest. Therefore, by synthesizing the stand indices, the influence brought by environmental differences and the differences in the sizes of tree cluster units can be effectively overcome.

[0054] In some embodiments, when performing step S3.2 to synthesize the stand indices of tree cluster units with the same mixing ratio, arbors of different tree ages will have an obvious impact on the stand indices. For example, when the arbors are young, their root systems and foliage systems are relatively tender and it is difficult to have an impact on the stand indices. However, the impact of arbors on the stand indices tends to be stable after they grow into shape. Therefore, when comprehensively calculating the stand indices, adjustments can be made according to the forest age of the tree cluster units.

[0055] Specifically, the formula for comprehensively calculating the stand indices when performing step S3.2 can be:

[0056] Among them, is the comprehensive value of the stand indices of the th tree cluster unit under the th mixing ratio, is the total number of tree cluster units under the th mixing ratio, is the stand index of the th tree cluster unit under the th mixing ratio, is the forest age constraint coefficient of the th tree cluster unit under the th mixing ratio, is the orthographic projection area of the th tree cluster unit under the

[0057] In some embodiments, when retrieving tree cluster units in step S1, the forest age of the tree cluster units can be used as a constraint condition. When the forest age within the selected tree cluster unit does not meet the constraint condition, it is excluded and reselected. At this time, the forest age constraint coefficient in the formula is fixed at 1.

[0058] In some embodiments, when performing step S3.3, when constructing the mapping relationship between the comprehensive value of the stand indices and the mixing ratio sequence, it is specifically shown in Table 3.

[0059] Table 3 Mapping between the comprehensive value of the stand indices and the mixing ratio sequence

[0060]

[0061] In fact, when constructing the mapping relationship in step S3.3, the relative trait mixing ratio can be used as the horizontal axis and the comprehensive value of the stand index as the vertical axis for mapping. After linearly fitting the scatter points obtained from the mapping, an association model can be obtained, and from the association model, the change in the stand index brought about by the change in the trait mixing ratio can be intuitively known.

[0062] Specifically, in step S4, when obtaining the optimal mixing ratio of each pair of relative traits based on the association model under the optimal stand index, the change trend of the comprehensive value of the stand index with the mixing ratio in the association model can be calculated by mathematical methods, and the mixing ratio value when the optimal stand index is reached can be known. In fact, the calculated optimal mixing ratio may not necessarily be the mixing ratio actually existing in the tree group unit, and it can be a new mixing ratio value obtained through trend calculation.

[0063] In some embodiments, after calculating the optimal mixing ratio of each pair of relative traits in step S4, it can be expressed as [relative trait 1, X1, 1 - X1], [relative trait 2, X2, 1 - X2], [relative trait 3, X3, 1 - X3], etc. In fact, after obtaining the above set of optimal mixing ratios, it can be considered that under the stand index being explored currently, these mixing ratios can well improve the benefits of forest management.

[0064] In fact, when obtaining the geographical information of the plot to be planted in step S5, the same method as in step S1 can be used to obtain the geographical information of the plot to be planted by using low-altitude technology. Specifically, when the surveyed forest is close to the location of the plot to be planted, two types of information can be directly obtained through low-altitude equipment and an investigation model and a planting map can be constructed respectively, which can greatly improve the convenience and efficiency.

[0065] In some embodiments, the plot to be planted in step S5 is a plot without arbors on the surface or with arbors on the surface that do not need to be retained. Through the plot to be planted, the positions of the arbors in the planting process can be directly planned to avoid mutual influence among the arbors during the growth process. Specifically, when selecting the points to be planted in the planting map, they are selected based on the preset planting density, and the points to be planted can be relatively evenly distributed on the planting map.

[0066] In some embodiments, the geographical information of the plot to be planted is recorded in the planting map in step S5, such as factors that can affect the growth and distribution of arbors, such as slope, height, water area, watershed, etc. In fact, the preset planting density used when selecting the points to be planted can be set conventionally according to the common knowledge in the field and in combination with the planting density of the surveyed forest. In addition, the spacing between adjacent points to be planted can also be set when selecting the points to be planted.

[0067] Actually, the to-be-planted points generated in step S5 are blank nodes, which only record the coordinate information in the planting map for forestry personnel to accurately locate. In addition, by assigning traits to the to-be-planted points in step S6, the to-be-planted points can record the corresponding traits and then match the corresponding tree species, so that forestry personnel can plant the required tree species at the to-be-planted points.

[0068] Specifically, by assigning traits to all the to-be-planted points in the planting map, the overall mixing ratio of the tree species in the planting map can meet the optimal mixing ratio obtained in step S4, and further, the mixing ratio of the relative traits of the overall trees in the planting map can be optimized, which is beneficial to improving the stability of forestry indicators during the operation of the planting map and improving forestry indicators.

[0069] In some embodiments, when performing step S6, assuming that the relative traits for which trait assignment is required are: relative trait 1 (A / a), relative trait 2 (B / b), relative trait 3 (C / c), relative trait 4 (D / d), then after assignment, [relative trait 1, relative trait 2, relative trait 3, relative trait 4] are recorded in the to-be-planted points.

[0070] Specifically, assuming that the optimal mixing ratio of relative trait 1 (A / a) is (0.46, 0.54), the optimal mixing ratio of relative trait 2 (B / b) is (0.53, 0.47), the optimal mixing ratio of relative trait 3 (C / c) is (0.61, 0.39), the optimal mixing ratio of relative trait 4 (D / d) is (0.66, 0.34), and the number of to-be-planted points in the planting map is 10,000, then the overall trees in the planting map need to have 4,600 A traits and 5,400 a traits, 5,300 B traits and 4,700 b traits, 6,100 C traits and 3,900 c traits, 6,600 D traits and 3,400 d traits. When assigning traits, the above traits are evenly distributed to the to-be-planted points, so that the overall planting map meets the above optimal mixing ratio.

[0071] Actually, when performing step S6, assuming that the number of relative traits for which trait assignment is required is t, then the number of trait combinations generated after trait assignment is 2 t ones. In fact, when assigning traits, special situations may occur where the corresponding tree species of the trait combination do not exist or are difficult to survive in the environment of the to-be-planted plot. Then, some trait combinations can be pre-labeled before assigning traits to avoid them. For example, if the trait combination [A, b, C, D] does not exist, then this combination is avoided when assigning traits.

[0072] In fact, after the trait assignment is performed in step S6, not only the geographical location information is recorded in the points to be planted, but also the trait combination of the arbor trees required at this location is recorded. Therefore, step S7 is executed to match the corresponding arbor tree species in the tree species database through the trait combination for forestry planting. Specifically, the tree species database used for matching tree species can also draw on the existing arbor database, and screen the environment where the plot to be planted is located and supplement it into the tree species database for tree species matching.

[0073] See Figure 2 , the present invention also provides a stand mixed planting optimization and cooperation system based on tree group units, including: The basic construction module 100 is used to obtain the arbor planting point information in the surveyed forest to construct a survey model, and obtain the geographical information of the plot to be planted to construct a planting map; The selection and survey module 200 is used to select multiple tree group units in the survey model and survey the stand indexes of the tree group units, and randomly select the points to be planted in the planting map; The database construction module 300 is used to extract the types and traits of the arbors in the tree group units to construct a tree species database; The information association module 400 is used to construct an association model between the stand indexes and each pair of relative traits in the tree group unit; The mixed planting analysis module 500 is used to obtain the optimal mixing ratio of each pair of relative traits under the optimal stand indexes respectively based on the association model; The trait assignment module 600 is used to assign traits to the points to be planted based on the optimal mixing ratio of each pair of relative traits; The blueprint construction module 700 is used to match arbor tree species for the points to be planted after trait assignment in the tree species database to obtain a planting blueprint; The mixed planting module 800 is used to perform tree species mixed planting on the plot to be planted based on the planting blueprint.

[0074] Although the embodiments of the present invention have been described in detail above, it is obvious to those skilled in the art that various modifications and changes can be made to these embodiments. However, it should be understood that such modifications and changes all fall within the scope and spirit of the present invention described in the claims. Moreover, the present invention described herein can have other embodiments and can be implemented or realized in various ways.

Claims

1. A mixed stand optimization method based on tree cluster units, characterized in that: include: Obtain the information of tree planting points in the survey forest to build a survey model and select multiple tree cluster units; Investigate the forest stand indicators of tree cluster units and extract the types and traits of trees to build a tree species database; construct association models between forest stand indicators and each pair of relative traits in tree cluster units; obtain the optimal intermixing ratio of each pair of relative traits under the optimal forest stand indicators based on the association models; obtain the geographic information of the plots to be planted to build a planting map and randomly select the points to be planted; after assigning traits to the planting points based on the optimal intermixing ratio of each pair of relative traits, match the tree species in the tree species database to obtain a planting blueprint; and mix tree species in the plots to be planted based on the planting blueprint.

2. The mixed forest stand optimization method according to claim 1, characterized in that: The survey forest and the land to be planted are in the same space-time environment; and / or, the tree cluster unit is a tree combination consisting of 3 to 7 trees that are not in the same straight line and are adjacent to each other; and / or, the figure formed by the tree cluster unit is a convex polygon; and / or, the information of tree planting points in the survey forest is obtained based on low-altitude technology; and / or, the survey forest includes artificial mixed forests or natural forests.

3. The mixed forest stand optimization method according to claim 1, characterized in that: The tree planting point information includes tree coordinates, leaf traits, and root shape; and / or, the relative traits include broad-leaved traits and coniferous traits, deciduous traits and evergreen traits, deep root traits and shallow root traits, and the dominant traits of root endophytes and dominant traits of root ectophytes; and / or, the forest stand indicators include density, intermixing degree, accumulation, branch and leaf biomass, VOCs content of near-surface air plants, carbon sink, water conservation, nitrogen fixation, intermixing ratio, and negative oxygen ion concentration.

4. The method for optimizing mixed forest stands according to claim 1, characterized in that: When constructing a survey model and selecting multiple tree cluster units, the number of tree cluster units is: ； in, is the total number of tree cluster units in the survey model, is the orthographic projection area of ​​the survey model, , are adjustment factors, and Greater than 1; And / or, multiple tree cluster units are randomly selected within the survey model; and / or, the tree species database includes tree species and tree traits corresponding to the tree species, the tree traits at least include the traits of the trees in the tree cluster units, and the tree species at least include the species of the trees in the tree cluster units; and / or, there are no other trees in the graph formed by the tree cluster units.

5. The method for optimizing mixed forest stands according to claim 1, characterized in that: When constructing the association model between stand indicators and each pair of relative traits in tree cluster units, it includes: counting the intermixing ratios of each relative trait in all tree cluster units and sorting them to obtain the intermixing ratio sequence; integrating the stand indicators of tree cluster units under the same intermixing ratio to obtain the comprehensive value of the stand indicators; and constructing the association model based on the intermixing ratio sequence and the comprehensive value of the stand indicators.

6. The method for optimizing mixed forest stands according to claim 5, characterized in that: The number of tree cluster units under each same intermixing ratio is greater than or equal to the minimum threshold; and / or, when constructing an association model based on the intermixing ratio sequence and the comprehensive value of the stand index, a mapping relationship between the comprehensive value of the stand index and the intermixing ratio sequence is constructed and the association model is fitted; and / or, the deviation of the number of tree cluster units under each intermixing ratio is less than or equal to 15%; And / or, when the forest stand indicators of tree cluster units with the same mixed ratio are combined, they include: ； in, For the The comprehensive value of stand index of tree cluster units under mixed ratio, For the The total number of tree cluster units under the mixed ratio, For the Mixed ratio The stand index of each tree cluster unit, For the Mixed ratio The forest age constraint coefficient of each tree cluster unit is For the Mixed ratio The orthographic projection area of ​​a tree cluster unit.

7. An optimization system for implementing the stand mixed forest optimization method according to any one of claims 1 to 6, characterized in that: include: The basic construction module is used to obtain the information of tree planting points in the survey forest to build a survey model, and obtain the geographic information of the plots to be planted to build a planting map; A selection survey module is used to select multiple tree cluster units in the survey model and investigate the forest stand indicators of the tree cluster units, and randomly select the planting points in the planting map; Database construction module, used to extract the types and characteristics of trees in tree cluster units to construct a tree species database; The information association module is used to construct the association model between the stand index and each pair of relative traits in the tree cluster unit; The mixed interplanting analysis module obtains the optimal mixed interplanting ratio of each pair of relative traits under the optimal forest stand index based on the association model; The trait assignment module assigns trait values ​​to the planting sites based on the optimal intermixing ratio of each pair of relative traits; The blueprint construction module matches the tree species to be planted at the planting site after the trait is assigned in the tree species database to obtain a planting blueprint; The mixed planting module mixes tree species in the planting plots based on the planting blueprint.

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