A forest stand quality assessment method and quality assessment system based on enclosure domain

Abstract

The present invention provides a stand quality assessment method and quality assessment system based on an enclosure domain, and relates to the technical field of stand quality assessment. The quality assessment method provided by the present invention includes: obtaining a global model based on the geographic information modeling of the stand to be assessed using low-altitude technology; randomly selecting multiple enclosures within the global model and performing typing based on the combination of low-altitude visual traits to obtain a typing combination; investigating the survey values ​​of the assessment indicators within the enclosure under the same typing combination; weighting the survey values ​​based on the typing combination to obtain typing unit assessment values; and performing a global assessment of the global model based on the typing unit assessment values ​​to obtain a stand quality assessment result. The method provided by the present invention can fully consider the heterogeneous characteristics within the stand, eliminate spatial heterogeneity bias, and thereby effectively improve the accuracy and convenience of stand quality assessment, which is conducive to rapid quality assessment and analysis in complex forest areas.

Description

Technical Field

[0001] The present invention relates to the technical field of forest stand quality assessment, and in particular to a forest stand quality assessment method and quality assessment system based on an enclosed domain. Background Art

[0002] Stand quality is a core indicator for measuring the comprehensive status of forest ecosystems, which includes tree species structure, functional characteristics and health level. From the perspective of tree species structure analysis, key parameters include tree species composition, forest layer configuration, diameter distribution and canopy density. From the perspective of functional characteristics analysis, it includes carbon sequestration capacity, soil and water conservation, water conservation and windbreak and sand fixation, soil ecological restoration, forest health care, landscape recreation, etc., while from the perspective of health level analysis, it includes pest and disease infection rate, natural regeneration capacity and stress resistance. In addition, by accurately evaluating the specific functions of the forest stand, it is possible to effectively improve the introduction and implementation rate of targeted scientific measures in the forest management process, increase the scientific nature and sustainable development capacity of forest stand management, for example, evaluating soil and water conservation capacity can effectively identify high-risk areas for soil and water loss, thereby providing targeted positioning for subsequent restoration.

[0003] Current stand quality assessment technology has developed into a multi-layered system that combines ground surveys, remote sensing detection, and model analysis. This system uses ground surveys as a foundation and combines remote sensing technology to invert stand quality information. Machine learning algorithms are then used to fuse multi-source data in order to improve the accuracy of quality assessment results. In existing techniques, when conducting ground surveys, sampling points are often randomly set within the target stand. The standard plot method is used to set sample plots with an area of ​​0.06 hectares to 1 hectare. Total stations, laser rangefinders, and other measuring equipment are used to locate and define the sample plot boundaries. Measuring tools such as measuring rods, rulers, and girder rulers are used to measure the physical parameters of each tree. Auxiliary indicators such as soil physical and chemical properties are measured at random points within the sample plot.

[0004] Although the random point distribution method can ensure spatial representativeness in a statistical sense, it still has many defects in practical application. When faced with the quality assessment of forest stands in areas with complex terrain, the accessibility of sample plots obtained by random sampling is poor, and range random sampling easily ignores the heterogeneous characteristics within the forest stand. In addition, the location differences in the layout of sample plots (slope aspect, soil type, forest stand density) will lead to inconsistent evaluation results, which greatly affects the accuracy and convenience of forest stand quality assessment. Therefore, there is an urgent need to provide a solution to improve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a stand quality assessment method and quality assessment system based on enclosed domain, which can fully consider the heterogeneous characteristics within the stand and eliminate spatial heterogeneity bias, thereby effectively improving the accuracy and convenience of stand quality assessment, and facilitating rapid quality assessment and analysis in complex forest areas.

[0006] In the first aspect, the present invention provides a stand quality assessment method based on an enclosure, comprising: obtaining a global model by acquiring geographic information modeling of the stand to be assessed based on low-altitude technology; randomly selecting multiple enclosures within the global model and performing typing based on a combination of low-altitude visible traits to obtain a typing combination; investigating the survey values ​​of the assessment indicators within the enclosure under the same typing combination; weighting the survey values ​​based on the typing combination to obtain typing unit assessment values; performing a global assessment of the global model based on the typing unit assessment values ​​to obtain a stand quality assessment result; the assessment indicators include canopy 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.

[0007] Optionally, the low-altitude visibility trait combination includes one of deciduous and evergreen, fast-growing and non-fast-growing, broad-leaved and needle-leaved trees.

[0008] Optionally, when the geographic information of the forest stand to be evaluated is obtained based on low-altitude technology, the geographic information includes the coordinates of the trees, the low-altitude visible characteristics information of the trees, and the environmental information of the forest stand to be evaluated.

[0009] Optionally, the low-altitude technology includes low-altitude drones.

[0010] Optionally, the forest stand to be evaluated includes an artificial mixed forest or a natural forest.

[0011] Optionally, when multiple enclosed domains are randomly selected in the global model, the number of the enclosed domains is:

[0012]

[0013] in, is the total number of enclosed domains in the global model, is the orthographic projection area of ​​the global model, 、 are adjustment coefficients, and Greater than 1.

[0014] Optionally, the enclosed area is formed by three adjacent and non-collinear trees, and there are no other trees in the enclosed area.

[0015] Optionally, when selecting the enclosed area, two adjacent reference trees are pre-selected and trees to be selected are calibrated around the reference trees. Based on the fact that the sum of adjacent internal angle differences in the enclosed area is minimized, enclosed trees are selected from the trees to be selected to form the enclosed area.

[0016] Optionally, when typing is performed based on a combination of low-altitude visible traits to obtain typing combinations, the number of typing combinations is 1 to 4.

[0017] Optionally, when typing is performed based on low-altitude visible trait combinations to obtain typing combinations, the number of each typing combination is greater than or equal to 10%.

[0018] Optionally, typing includes manual annotation of typing or typing using machine learning.

[0019] Optionally, the enclosed area is classified based on a combination of low-altitude visual traits and according to the evaluation direction.

[0020] Optionally, when investigating the survey values ​​of the evaluation indicators in the enclosed area under the same classification combination, manual investigation is carried out by entering the forest stand to be evaluated.

[0021] Optionally, when weighting survey values ​​based on a combination of subtypes, include:

[0022]

[0023] in, For the The evaluation value of the typing unit of the enclosed domain under the typing combination, For the The total number of enclosed domains under each classification combination, For the Under the classification combination The survey value of the evaluation index of the enclosed domain, For the Under the classification combination The orthographic projection area of ​​the enclosed domain.

[0024] Optionally, when performing a global assessment of the global model based on the classification unit assessment value, the following is included:

[0025]

[0026] in, is the total amount of stand quality assessment of the assessment indicators of the stand to be assessed, For the The evaluation value of the typing unit of the enclosed domain under the typing combination, For the The total number of enclosed domains under each classification combination, is the number of typing combinations, is the total number of enclosed domains in the global model, is the total area of ​​the forest stand to be assessed.

[0027] Optionally, when the enclosed area is classified based on the deciduousness and evergreenness of trees, the classification combinations include: three deciduous trees; two deciduous trees and one evergreen tree; one deciduous tree and two evergreen trees; three evergreen trees.

[0028] Optionally, when the enclosed area is classified based on the fast-growing and non-fast-growing properties of trees, the classification combinations include: three fast-growing trees; two fast-growing trees and one non-fast-growing tree; one fast-growing tree and two non-fast-growing trees; three non-fast-growing trees.

[0029] Optionally, when the enclosed area is classified based on the broad-leaved and coniferous nature of the trees, the classification combinations include: three broad-leaved trees; two broad-leaved trees and one coniferous tree; one broad-leaved tree and two coniferous trees; three coniferous trees.

[0030] In a second aspect, the present invention further provides a stand quality assessment system for implementing any of the above-mentioned optional quality assessment methods, comprising:

[0031] The map construction module uses low-altitude technology to obtain the geographic information of the forest stand to be evaluated and build a global model;

[0032] Select a typing module, randomly select multiple enclosed domains in the global model, and perform typing based on the combination of low-altitude visible traits to obtain a typing combination;

[0033] The indicator survey module investigates the survey values ​​of the evaluation indicators in the enclosed area under the same classification combination, including canopy density, mixed degree, volume, branch and leaf biomass, VOCs content of near-surface air plants, carbon sink, water conservation, nitrogen fixation, mixed ratio, and negative oxygen ion concentration;

[0034] Weighted analysis module, which weights the survey values ​​based on the classification combination to obtain the classification unit evaluation value;

[0035] The global assessment module conducts a global assessment of the global model based on the classification unit assessment values ​​to obtain the stand quality assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flowchart of a stand quality assessment method based on an enclosure domain provided by the present invention;

[0037] Figure 2 Schematic diagram of the global model constructed and obtained in step S1 of the present invention;

[0038] Figure 3 A schematic diagram of all enclosed domains generated in the global model in step S2 of the present invention;

[0039] Figure 4 This is a structural diagram of a forest stand quality assessment system provided by the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, 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 part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be the common meanings understood by people with ordinary skills in the field to which the present invention belongs.

[0041] See also Figure 1 The present invention provides a stand quality assessment method based on an enclosure domain, comprising the following steps:

[0042] S1. Obtain geographic information of the forest stand to be assessed based on low-altitude technology to obtain a global model;

[0043] S2. Randomly select multiple enclosed domains in the global model and perform typing based on the low-altitude visible trait combination to obtain a typing combination;

[0044] S3. Investigate the survey values ​​of the evaluation indicators within the enclosed domain under the same classification combination;

[0045] S4. Weight the survey values ​​based on the classification combination to obtain the classification unit evaluation value;

[0046] S5. Conduct a global assessment of the global model based on the classification unit assessment values ​​to obtain the forest stand quality assessment results.

[0047] In fact, the stand quality assessment method provided by this invention, by combining low-altitude technical modeling with trait typing sampling, not only improves the safety and comprehensiveness of information collection, but also fully considers the heterogeneity within the stand, correcting for location differences, thereby facilitating improved accuracy and convenience in quality assessment. Furthermore, the use of enclosed domains for assessment surveys fully considers interspecific relationships within the stand and factors associated with the stand and the environment, further improving survey accuracy and enhancing the correlation between assessment survey results and environmental factors.

[0048] In some embodiments, the low-altitude technology used in step S1 includes a low-altitude drone. In practice, by mapping the forest stand to be evaluated using low-altitude technology, geographic information of the forest stand to be evaluated can be obtained, and by processing the geographic information, a global model corresponding to the forest stand to be evaluated can be obtained, which is conducive to randomly selecting an enclosed domain on the computer side.

[0049] Furthermore, the geographic information acquired using low-altitude technology in step S1 includes topographical variations, vegetation cover, environmental characteristics (e.g., slopes, water flows, etc.) within the forest stand to be assessed, as well as the low-altitude visual characteristics and locations of trees within the forest stand to be assessed. In practice, using low-altitude technology for surveying and mapping can improve the accuracy and convenience of data acquisition, while also effectively enhancing the comprehensiveness of information acquired from a low-altitude perspective.

[0050] Specifically, after obtaining geographic information of the forest stand to be assessed using low-altitude technology in step S1, a global model can be constructed using commonly used map-building methods in the field. In practice, when constructing a global model, topographic variations within the forest stand to be assessed can be ignored, and a simplified model can be constructed focusing on the location information of the trees within the forest stand to be assessed, thereby reducing method complexity and modeling efficiency.

[0051] In some embodiments, in step S1, a simple global model is obtained by modeling after obtaining geographic information based on low-altitude technology. Figure 2 The schematic diagram shown, Figure 2 The circle in the figure represents a tree in the stand to be evaluated. The size of the circle provides a visual representation of the projected area of ​​each tree from top to bottom. Furthermore, the global model allows for a visual representation of the relative positions of trees within the stand to be evaluated.

[0052] In some embodiments, the forest stand to be evaluated in step S1 is an artificial mixed forest or a natural forest. In fact, by conducting a stand quality survey on an artificial mixed forest, the management status of the artificial mixed forest can be effectively understood, which is conducive to adjusting the interplanting strategy during artificial interplanting. In addition, when conducting a stand survey on a natural forest, the stand quality of the stand that reaches a balanced state under natural conditions can be understood, which can provide a basis and direction for forest stand management.

[0053] In practice, in step S2, when randomly selecting multiple enclosed domains within the global model, the selected domains are formed by three adjacent, non-collinear trees, with no other trees within them. Specifically, the enclosed domains are triangular, and sampling surveys of randomly selected enclosed domains within the global model effectively improve assessment accuracy. Furthermore, based on the typing results, the visual characteristics of the enclosed domains can be correlated with the assessment results, effectively improving the relevance of the results with stand information.

[0054] In some embodiments, when randomly selecting multiple enclosed areas through the global model in step S2, all trees can be associated in advance in the global model to form enclosed areas, and the required number of enclosed areas can be randomly selected from all the enclosed areas for sampling survey, such as Figure 3 In fact, generating all enclosed domains is beneficial to improving the comprehensiveness of sampling.

[0055] In other embodiments, when selecting an enclosed domain within the global model in step S2, two adjacent reference trees may be randomly selected in advance within the global model, and candidate trees may be calibrated around the reference trees. Based on minimizing the sum of adjacent internal angle differences within the enclosed domain, enclosed trees are selected from the candidate trees to form the enclosed domain. In practice, by forming an enclosed domain based on minimizing the sum of adjacent internal angle differences, an enclosed domain with three acute angles can be preferentially obtained, which better reflects the interaction between the trees.

[0056] In some embodiments, when randomly selecting enclosed domains within the global model in step S2, the number of enclosed domains is positively correlated with the total area of ​​the forest stand to be assessed. In practice, the greater the number of enclosed domains set within the global model, the higher the theoretical accuracy, but the associated survey workload also increases exponentially. Therefore, the number of enclosed domains within the global model can be expressed as:

[0057]

[0058] in, is the total number of enclosed domains in the global model, is the orthographic projection area of ​​the global model (which can be directly measured by low-altitude technology in step S1), 、 are adjustment coefficients, and Greater than 1.

[0059] In some embodiments, when typing is performed based on a combination of low-altitude visible traits in step S2, the low-altitude visible trait combination is a combination of tree traits that can be directly observed with the naked eye. Specifically, it can be directly acquired in step S1 using low-altitude technology and directly annotated using machine learning combined with a neural network model. More specifically, the low-altitude visible trait combination can be represented as A and a (A and a are a set of relative traits), and can further include one of the following: deciduous versus evergreen, fast-growing versus non-fast-growing, or broad-leaved versus coniferous.

[0060] In a further embodiment, after typing based on a combination of low-altitude visible traits in step S2, the number of typing combinations is one to four, specifically "AAA," "AAa," "Aaa," and "aaa." Furthermore, when typing one combination in the enclosed domain based on deciduous vs. evergreen, fast-growing vs. non-fast-growing, or broad-leaved vs. coniferous, the types of the enclosed domain are shown in Table 1 below. When typing based on two combinations of deciduous vs. evergreen, or broad-leaved vs. coniferous, the types of trees are shown in Table 2, and the types of the enclosed domain are shown in Table 3 below.

[0061] Table 1 Types of enclosed domains when typing based on a combination of low-altitude visible traits

[0062]

[0063] Table 2 Tree types based on deciduous vs. evergreen, broad-leaved vs. coniferous

[0064]

[0065] Table 3 Types of enclosed domains when categorizing tree types based on Table 2

[0066]

[0067] In some embodiments, when categorizing tree types based on low-altitude visible traits in step S2 to obtain categorical combinations, the number of each categorical combination is greater than or equal to 10% of the total number. In practice, by controlling the lower limit of the number of species in the enclosed domain, it is possible to more comprehensively assess the relationships between trees under various relative traits in mixed forests (artificial mixed forests or natural forests), quantify the contribution of tree relationships to the evaluation results, and mitigate assessment bias.

[0068] In some embodiments, after selecting and categorizing enclosed domains within the global model, if the number of enclosed domains for one or more types does not meet a preset standard, it is necessary to reselect enclosed domains within the global model. Furthermore, if multiple (up to 5-10) attempts to select enclosed domains within the global model still fail to meet the preset standard, the categorization results of the last selected enclosed domain will be used for investigation.

[0069] In some embodiments, when typing based on the combination of low-altitude visible traits in step S2, machine learning combined with a neural network model can be used to type the combinations in the selected enclosed domain. Specifically, a binary typing method can be used. For example, a certain trait is represented as "1" and the corresponding shape is represented as "0", then the type of the enclosed domain can be represented as "111", "110", "100", or "000".

[0070] In some embodiments, in step S2, the enclosed area can be classified based on a combination of low-altitude visible traits and evaluation directions. In practice, the evaluation direction is related to the evaluation index to determine which type of index should be focused on when evaluating stand quality. When based on the combination of deciduous and evergreen tree traits, the enclosed area can be surveyed for corresponding indicators based on different evaluation directions.

[0071] In some embodiments, step S1 can be combined with step S2. After obtaining geographic information and modeling using low-altitude technology, the enclosed domain is directly selected in the global model and the characteristics are classified to obtain the classification results of the enclosed domain, and the coordinates of the selected enclosed domain are sent to forestry personnel so that the forestry personnel can enter the forest stand to be evaluated and conduct artificial forestry surveys on the area enclosed by the enclosed domain.

[0072] In practice, when investigating the evaluation indicators within the enclosed domains under the same genotypic combination in step S3, commonly used survey methods in the field can be used to obtain survey values ​​for the same evaluation indicators across different enclosed domains under the same genotypic combination. Specifically, by pre-sampling enclosed domains and surveying the selected enclosed domains, the sampling unevenness associated with traditional surveys can be reduced. Furthermore, when surveying enclosed domains under the same genotypic combination, their internal indicators tend to converge to a certain extent, effectively reducing the number of sample plots.

[0073] In some embodiments, the evaluation indicators in step S3 include canopy density, intermixedness, standing volume, foliage biomass, VOC content of near-surface air plants, carbon sequestration, water conservation, nitrogen fixation, intermixedness ratio, and negative oxygen ion concentration. By investigating the relevant evaluation indicators within the enclosed area, the results can be extended to the entire forest stand to be evaluated, effectively improving the accuracy and efficiency of quality surveys of large or complex forest stands.

[0074] Specifically, in step S4, after weighting the survey values ​​based on the clade combination, the survey values ​​of multiple enclosures of different shapes under the same clade combination can be combined to obtain the clade unit assessment value of the enclosure under this clade combination. In practice, even enclosures with the same clade combination can vary in shape and area due to factors such as stand growth, natural selection, and artificial selection. Weighted integration of multiple enclosures can effectively improve accuracy.

[0075] In some embodiments, when the survey values ​​are weighted based on the typing combination in step S4, it can be expressed as:

[0076]

[0077] in, For the The evaluation value of the typing unit of the enclosed domain under the typing combination, For the The total number of enclosed domains under each classification combination, For the Under the classification combination The survey value of the evaluation index of the enclosed domain, For the Under the classification combination The orthographic projection area of ​​the enclosed domain.

[0078] In fact, in step S5, the global model can be evaluated based on the classification unit evaluation value, thereby obtaining the forest stand quality evaluation result. Specifically, it can be expressed as:

[0079]

[0080] in, is the total amount of stand quality assessment of the assessment indicators of the stand to be assessed, For the The evaluation value of the typing unit of the enclosed domain under the typing combination, For the The total number of enclosed domains under each classification combination, is the number of typing combinations, is the total number of enclosed domains in the global model, is the total area of ​​the forest stand to be assessed.

[0081] See also Figure 4 The present invention provides a stand quality assessment system for implementing any of the above-mentioned optional quality assessment methods, comprising:

[0082] A map construction module 100 is configured to obtain geographic information of the forest stand to be evaluated based on low-altitude technology to obtain a global model;

[0083] Selecting a typing module 200, randomly selecting multiple enclosed domains in the global model and performing typing based on the low-altitude visible trait combination to obtain a typing combination;

[0084] The indicator survey module 300 investigates the survey values ​​of the evaluation indicators within the enclosed area under the same classification combination; wherein the evaluation indicators include canopy density, intermixedness, volume, branch and leaf biomass, VOCs content of near-surface air plants, carbon sink, water conservation, nitrogen fixation, intermixed ratio, and negative oxygen ion concentration;

[0085] The weighted analysis module 400 is used to weight the survey values ​​based on the classification combination to obtain the classification unit evaluation value;

[0086] The global assessment module 500 performs a global assessment on the global model based on the classification unit assessment value to obtain a stand quality assessment result.

[0087] While the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations of these embodiments are possible. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as set forth in the claims. Furthermore, the invention described herein is susceptible to other embodiments and may be practiced or implemented in a variety of ways.

Claims

1. A stand quality assessment method based on enclosed domain, characterized in that: include: A global model is obtained by acquiring geographic information of the forest stand to be evaluated based on low-altitude technology, wherein the geographic information includes tree coordinates, low-altitude visible trait information of the tree, and environmental information of the forest stand to be evaluated; a plurality of enclosed domains are randomly selected within the global model and classified based on low-altitude visible trait combinations to obtain classification combinations, wherein the low-altitude visible trait combination includes one of deciduous and evergreen, fast-growing and non-fast-growing, broad-leaved and coniferous properties of the tree, the enclosed domain is formed by three adjacent and non-collinear trees, and there are no other trees within the enclosed domain, and the number of each classification combination is greater than or equal to 10% of the total; and the survey values ​​of the evaluation indicators within the enclosed domain under the same classification combination are investigated; The survey values ​​are weighted and scored based on the classification combination, including: ； in, For the The evaluation value of the typing unit of the enclosed domain under the typing combination, For the The total number of enclosed domains under each classification combination, For the Under the classification combination The survey value of the evaluation index of the enclosed domain, For the Under the classification combination The orthographic projection area of ​​the enclosed domain; the stand quality assessment results are obtained by conducting a full-domain assessment of the full-domain model based on the classification unit assessment value, including: ； in, is the stand quality assessment result of the assessment index of the stand to be assessed, For the The evaluation value of the typing unit of the enclosed domain under the typing combination, For the The total number of enclosed domains under each classification combination, is the number of typing combinations, is the total number of enclosed domains in the global model, is the total area of ​​the stand to be assessed; The evaluation indicators include density, mixedness, accumulation, branch and leaf biomass, VOCs content of near-surface air plants, carbon sequestration, water conservation, nitrogen fixation, mixed ratio, and negative oxygen ion concentration.

2. The quality assessment method according to claim 1, wherein: When obtaining geographic information of the forest stand to be evaluated based on low-altitude technology, the low-altitude technology includes low-altitude drones; the forest stand to be evaluated includes artificial mixed forests or natural forests.

3. The quality assessment method according to claim 1, wherein: When multiple enclosed domains are randomly selected in the global model, the number of the enclosed domains is: ； in, is the total number of enclosed domains in the global model, is the orthographic projection area of ​​the global model, 、 are adjustment coefficients, and greater than 1; When selecting the enclosed region, two adjacent reference trees are preselected and trees to be selected are calibrated around the reference trees. Based on the minimum sum of adjacent internal angle differences in the enclosed region, enclosed trees are selected from the trees to be selected to form the enclosed region.

4. The quality assessment method according to claim 1, wherein: When typing is performed based on a combination of low-altitude visible traits to obtain a typing combination, the types of the typing combination are 1 to 4; typing includes manual annotation typing or typing using machine learning; typing the enclosed domain based on the low-altitude visible trait combination and according to the evaluation direction.

5. The quality assessment method according to claim 1, wherein: When investigating the survey values ​​of the evaluation indicators in the enclosed area under the same classification combination, enter the forest stand to be evaluated to conduct manual investigation.

6. The quality assessment method according to claim 1, wherein: When the enclosed domain is classified based on the deciduousness and evergreenness of the trees, the classification combinations include: three deciduous trees; two deciduous trees and one evergreen tree; one deciduous tree and two evergreen trees; three evergreen trees; when the enclosed domain is classified based on the fast-growing and non-fast-growing nature of the trees, the classification combinations include: three fast-growing trees; two fast-growing trees and one non-fast-growing tree; one fast-growing tree and two non-fast-growing trees; three non-fast-growing trees; when the enclosed domain is classified based on the broad-leaved and coniferous nature of the trees, the classification combinations include: three broad-leaved trees; two broad-leaved trees and one coniferous tree; one broad-leaved tree and two coniferous trees; three coniferous trees.

7. A forest stand quality assessment system for implementing the quality assessment method according to any one of claims 1 to 6, characterized in that: include: A map construction module, which uses low-altitude technology to obtain geographic information of the forest stand to be evaluated and builds a global model. The geographic information includes tree coordinates, low-altitude visual characteristics of the trees, and environmental information of the forest stand to be evaluated. A typing module is selected, and multiple enclosed domains are randomly selected within the global model and typed based on low-altitude visible trait combinations to obtain typing combinations. The low-altitude visible trait combination includes one of deciduous and evergreen, fast-growing and non-fast-growing, broad-leaved and coniferous tree types. The enclosed domain is formed by three adjacent and non-collinear trees, and there are no other trees in the enclosed domain. The number of each typing combination is greater than or equal to 10% of the total. The indicator survey module investigates the survey values ​​of the evaluation indicators in the enclosed area under the same classification combination, including canopy density, mixed degree, volume, branch and leaf biomass, VOCs content of near-surface air plants, carbon sink, water conservation, nitrogen fixation, mixed ratio, and negative oxygen ion concentration; The weighted analysis module is used to weight the survey values ​​based on the classification combination to obtain the unit evaluation value, including: ； in, For the The evaluation value of the typing unit of the enclosed domain under the typing combination, For the The total number of enclosed domains under each classification combination, For the Under the classification combination The survey value of the evaluation index of the enclosed domain, For the Under the classification combination The orthographic projection area of ​​the enclosed domain; The global assessment module conducts a global assessment of the global model based on the classification unit assessment values ​​to obtain the stand quality assessment results, including: ； in, is the stand quality assessment result of the assessment index of the stand to be assessed, For the The evaluation value of the typing unit of the enclosed domain under the typing combination, For the The total number of enclosed domains under each classification combination, is the number of typing combinations, is the total number of enclosed domains in the global model, is the total area of ​​the forest stand to be assessed.

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