Forest quality evaluation method based on average value method and multi-threshold method

Abstract

The invention relates to the field of forest quality evaluation, in particular to a forest quality evaluation method based on an average value method and a multi-threshold method, and the method comprises the following steps: S1, obtaining the forest composition of a sample plot, the forest composition comprising indicator species; s2, constructing a first sensor network; s3, obtaining a quality index according to the sensor network; s4, reconstructing the sensor network according to the quality index; s5, obtaining a second sensor network with unequal latitudes as variables; a third sensor network with the preset settlement number as a variable at the same latitude is obtained; obtaining a fourth sensor network taking a quality index as a variable; and S6, respectively calculating quality score sets under the first to fourth sensor networks to obtain comprehensive evaluation under multiple thresholds and an average value. According to the method, multiple indexes are integrated into one index, the data are then used for calculating the quality score of each evaluation area, and then the scores are gathered to evaluate the quality of the whole forest.

Description

Technical Field

[0001] The invention relates to the field of forest quality evaluation, and in particular to a forest quality evaluation method based on an average value method and a multi-threshold method. Background Art

[0002] Forest ecosystems are one of the most important ecosystems on Earth, providing key ecological services such as carbon fixation, oxygen production, water conservation, and biodiversity protection. With the intensification of global climate change and human activities, the health of forest ecosystems has been threatened unprecedentedly. Therefore, accurate assessment of forest quality is crucial to guide forest management and formulate conservation strategies.

[0003] Traditional forest quality assessment methods often focus on a single ecological indicator, such as forest cover, biomass, or the abundance of certain specific species. However, these methods cannot fully reflect the complexity and multifunctionality of forest ecosystems. For example, a single forest cover indicator cannot distinguish the level of biodiversity within the forest, the health of ecological processes, or the resistance and resilience of the ecosystem to disturbances. In addition, with the increasing awareness of the value of ecosystem services, a more comprehensive approach is needed to assess the quality of forest ecosystems.

[0004] In this context, this paper aims to propose a new method for assessing forest quality by comprehensively considering the composition, structure and function of forests. This method can not only quantify the current state of forest ecosystems, but also identify the main factors affecting the quality, thus providing a scientific basis for forest protection and sustainable management. Summary of the invention

[0005] In view of the problems existing in the prior art, the purpose of the present invention is to provide a forest quality evaluation method based on average value and multi-threshold method.

[0006] The purpose of the present invention is achieved by the following technical solutions:

[0007] A forest quality evaluation method based on average method and multi-threshold method comprises the following steps:

[0008] S1. Select a sample plot and obtain the forest composition of the sample plot, wherein the forest composition includes indicator species;

[0009] S2. construct a first sensor network, wherein the sensor network includes at least a primary network and a secondary network;

[0010] The primary network includes a central node and an edge node, wherein the central node is arranged in an area where the indicated species has a preset number of settlements at equal latitudes, and the edge nodes are arranged in an area where the non-indicated species has a preset number of settlements directly adjacent to the central node, and each of the edge nodes is connected to the central node and each of the edge nodes is not directly connected to each other;

[0011] The secondary network is a subnetwork of each edge node, which includes a plurality of mesh nodes connected to each other, and each mesh node is arranged in an area where a single forest with a preset number of settlements is located at different latitudes;

[0012] Each of the first-level networks is connected via the respective central nodes to form the first sensor network;

[0013] S3, obtaining a quality indicator according to the sensor network;

[0014] S4, reconstructing the sensor network according to the quality index, and reconstructing the mesh nodes in the secondary network to be arranged in areas with the same quality index;

[0015] Reconstructing the preset number of settlements at equal latitudes in the primary network into a percentage setting number of the preset number of settlements at unequal latitudes;

[0016] S5, obtaining a second sensor network with unequal latitudes as variables;

[0017] A third sensor network is obtained with respect to a preset number of settlements at the same latitude as a variable;

[0018] A fourth sensor network with the quality indicator as a variable is obtained;

[0019] S6. Calculate the quality indicator sets under the first to fourth sensor networks respectively, and obtain a comprehensive evaluation under multiple thresholds and average values.

[0020] Preferably, the quality index is calculated by the following method:

[0021] S3.1. Select preset quantitative indicators involving trees, shrubs, herbaceous plants and indicator species, including species richness, biomass, and number of key species;

[0022] S3.2, normalize the selected indicators to quantify them into values ​​between 0 and 1;

[0023] S3.3: Integrate the normalized indicators to obtain quality indicators.

[0024] Preferably, the S3.4 is specifically:

[0025] The indicators are assigned equal weights according to the average method, as follows:

[0026]

[0027] Normalize the obtained values ​​between 0 and 1;

[0028] Where FQ represents forest quality, Xi represents the standardized value of the i-th indicator, and N represents the number of indicators.

[0029] Preferably, when calculating the quality index by the multi-threshold method, multiple groups of forest quality thresholds are set, and the following steps are performed for each group of forest quality thresholds:

[0030] S6.1. Obtain forest data sampled by different first sensor networks, and calculate forest quality at equal latitudes;

[0031] S6.2, obtaining forest data sampled by the second sensor network, and calculating forest quality at different latitudes;

[0032] S6.3, obtaining forest data sampled by the third sensor network, and calculating forest quality with different numbers of settlements as variables at equal latitudes;

[0033] S6.4, obtaining forest data sampled by the fourth sensor network, and calculating the forest quality with each species quality index as a variable;

[0034] The forest quality is a ratio of a specified species in the sampled sensor network that is greater than the forest quality threshold.

[0035] Preferably, after executing S6, verification is performed periodically at preset time intervals.

[0036] Preferably, the step of performing verification includes:

[0037] After the indicator species is replaced, the non-indicator species determined in S2 is used as a new indicator species, and the corresponding mesh node is reconstructed as a central node;

[0038] Disconnect the reconstructed mesh node in S2 from all other nodes and set it as the central node, and re-verify it according to the primary network of S2, and delete all the data of the original indicator during the re-verification process;

[0039] Execute S3-S5, and delete all data of the original indication species during execution;

[0040] The difference between the comprehensive evaluations before and after the indicator species is changed is calculated, and if the difference is within a preset range, the evaluation is passed.

[0041] Preferably, after executing the verification, multiple verifications are further included, and each time the verification is executed, the same steps as those for executing the verification are executed;

[0042] During multiple verifications, the indicator species deleted in each verification are the indicator species in the adjacent previous verification, and the original indicator species between non-adjacent verifications are retained in the current verification.

[0043] Preferably, after the verification is performed, forest quality estimation and prediction are also included, which specifically includes the following steps:

[0044] Get the time change rate of forest quality of all nodes;

[0045] Obtain different forest qualities of all sensor networks obtained through multiple thresholds as in S6, and calculate the time change rate of forest quality of each sensor network under different variables;

[0046] Estimate the forest quality and predicted changes of each indicator species;

[0047] If the predicted change rate is outside the range of the average forest quality time change rate of each node, the settings of each sensor network are checked in turn.

[0048] The beneficial effects of the present invention are:

[0049] The present invention integrates multiple indicators into one indicator, and these data are then used to calculate the quality score of each assessment area, and then these scores are combined to evaluate the quality of the entire forest. By selecting multiple preset quantitative indicators related to trees, shrubs, herbaceous plants and indicator species, such as species richness, biomass, number of key species, etc., it can comprehensively cover various elements in the forest ecosystem, avoid the one-sidedness caused by relying on only a single or a few indicators to evaluate forest quality, and make the evaluation results more accurately reflect the true quality status of the forest.

[0050] The present invention adopts a multi-threshold method to calculate the quality index, sets multiple groups of forest quality thresholds, and calculates the forest quality under corresponding variables for different sensor networks. The proportion of the designated species in each sensor network that is greater than the forest quality threshold is used as the basis for measuring forest quality. This method can adapt to the complex and diverse actual conditions of forest ecosystems, better capture the subtle differences in forest quality under different states and conditions, and improve the adaptability and sensitivity of the evaluation method to different forest types and different development stages. In addition, a verification step is set, by replacing the indicator species, reconstructing the nodes, disconnecting the connection, and deleting the relevant data of the original indicator species, and re-evaluating and calculating, and comparing the differences between the comprehensive evaluation before and after replacing the indicator species, so as to test the stability and reliability of the evaluation method, timely discover possible deviations or unreasonableness in the evaluation process, ensure that the evaluation results can withstand scrutiny, and provide solid data support for subsequent forest management, planning and other work based on the evaluation results.

[0051] After verification, it also has the function of estimating and predicting forest quality. By obtaining the time change rate of forest quality under different variables of all nodes and sensor networks, it can grasp the dynamic change trend of forest quality in the time dimension, and then make reasonable estimates and predictions on the forest quality of each indicator species and its future changes, and gain insights into possible changes in forest quality in advance, providing forward-looking decision-making basis for the sustainable development and scientific management of forest resources, such as timely adjustment of forest cultivation and protection strategies to prevent the occurrence of problems such as forest quality decline. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0053] Figure 1 is a flow chart of a method provided by an embodiment of the present invention;

[0054] Figure 2 It is a schematic diagram of the relationship between latitude and forest quality under different thresholds provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is described below through specific examples. It should be understood that the one or more method steps mentioned in the present invention do not exclude the existence of other method steps before and after the combination step or the insertion of other method steps between these explicitly mentioned steps; it should also be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. Moreover, unless otherwise specified, the numbering of each method step is only a convenient tool for identifying each method step, and is not intended to limit the order of arrangement of each method step or to limit the scope of the present invention. The change or adjustment of the relative relationship thereof shall also be regarded as the scope of the present invention without substantially changing the technical content.

[0056] In order to better understand the above technical scheme, the exemplary embodiments of the present invention are described in more detail below. Although exemplary embodiments of the present invention are shown, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0057] The present invention will be further described below in conjunction with the following examples.

[0058] Embodiment 1

[0059] The embodiment of the present disclosure provides a forest quality evaluation method based on the average method and the multi-threshold method, and the process is as follows: Figure 1 As shown, the following steps are included:

[0060] S1. Select a sample plot and obtain the forest composition of the sample plot, wherein the forest composition includes indicator species;

[0061] S2. construct a first sensor network, wherein the sensor network includes at least a primary network and a secondary network;

[0062] The primary network includes a central node and an edge node, wherein the central node is arranged in an area where the indicated species has a preset number of settlements at equal latitudes, and the edge nodes are arranged in an area where the non-indicated species has a preset number of settlements directly adjacent to the central node, and each of the edge nodes is connected to the central node and each of the edge nodes is not directly connected to each other;

[0063] The secondary network is a subnetwork of each edge node, which includes a plurality of mesh nodes connected to each other, and each mesh node is arranged in an area where a single forest with a preset number of settlements is located at different latitudes;

[0064] Each of the first-level networks is connected via the respective central nodes to form the first sensor network;

[0065] S3, obtaining a quality indicator according to the sensor network;

[0066] S4, reconstructing the sensor network according to the quality index, and reconstructing the mesh nodes in the secondary network to be arranged in areas with the same quality index;

[0067] Reconstructing the preset number of settlements at equal latitudes in the primary network into a percentage setting number of the preset number of settlements at unequal latitudes;

[0068] S5, obtaining a second sensor network with unequal latitudes as variables;

[0069] A third sensor network is obtained with respect to a preset number of settlements at the same latitude as a variable;

[0070] A fourth sensor network with the quality indicator as a variable is obtained;

[0071] S6. Calculate the quality indicator sets under the first to fourth sensor networks respectively, and obtain a comprehensive evaluation under multiple thresholds and average values.

[0072] Preferably, the quality index is calculated by the following method:

[0073] S3.1. Select preset quantitative indicators involving trees, shrubs, herbaceous plants and indicator species, including species richness, biomass, and number of key species;

[0074] S3.2, normalize the selected indicators to quantify them into values ​​between 0 and 1;

[0075] S3.3: Integrate the normalized indicators to obtain quality indicators.

[0076] Preferably, the S3.4 is specifically:

[0077] The indicators are assigned equal weights according to the average method, as follows:

[0078]

[0079] Normalize the obtained values ​​between 0 and 1;

[0080] Where FQ represents forest quality, Xi represents the standardized value of the i-th indicator, and N represents the number of indicators.

[0081] Preferably, when calculating the quality index by the multi-threshold method, multiple groups of forest quality thresholds are set, and the following steps are performed for each group of forest quality thresholds:

[0082] S6.1. Obtain forest data sampled by different first sensor networks, and calculate forest quality at equal latitudes;

[0083] S6.2, obtaining forest data sampled by the second sensor network, and calculating forest quality at different latitudes;

[0084] S6.3, obtaining forest data sampled by the third sensor network, and calculating forest quality with different numbers of settlements as variables at equal latitudes;

[0085] S6.4, obtaining forest data sampled by the fourth sensor network, and calculating the forest quality with each species quality index as a variable;

[0086] The forest quality is a ratio of a specified species in the sampled sensor network that is greater than the forest quality threshold.

[0087] Preferably, after executing S6, verification is performed periodically at preset time intervals.

[0088] Preferably, the step of performing verification includes:

[0089] After the indicator species is replaced, the non-indicator species determined in S2 is used as a new indicator species, and the corresponding mesh node is reconstructed as a central node;

[0090] Disconnect the reconstructed mesh node in S2 from all other nodes and set it as the central node, and re-verify it according to the primary network of S2, and delete all the data of the original indicator during the re-verification process;

[0091] Execute S3-S5, and delete all data of the original indication species during execution;

[0092] The difference between the comprehensive evaluations before and after the indicator species is changed is calculated, and if the difference is within a preset range, the evaluation is passed.

[0093] Preferably, after executing the verification, multiple verifications are further included, and each time the verification is executed, the same steps as those for executing the verification are executed;

[0094] During multiple verifications, the indicator species deleted in each verification are the indicator species in the adjacent previous verification, and the original indicator species between non-adjacent verifications are retained in the current verification.

[0095] Preferably, after the verification is performed, forest quality estimation and prediction are also included, which specifically includes the following steps:

[0096] Get the time change rate of forest quality of all nodes;

[0097] Obtain different forest qualities of all sensor networks obtained through multiple thresholds as in S6, and calculate the time change rate of forest quality of each sensor network under different variables;

[0098] Estimate the forest quality and predicted changes of each indicator species;

[0099] If the predicted change rate is outside the range of the average forest quality time change rate of each node, the settings of each sensor network are checked in turn.

[0100] The disclosed embodiment integrates multiple indicators into one indicator, and these data are then used to calculate the quality score of each assessment area, and then these scores are combined to evaluate the quality of the entire forest. By selecting multiple preset quantitative indicators related to trees, shrubs, herbaceous plants and indicator species, such as species richness, biomass, number of key species, etc., it can comprehensively cover various elements in the forest ecosystem, avoid the one-sidedness caused by relying on only a single or a few indicators to evaluate forest quality, and make the evaluation results more accurately reflect the true quality status of the forest.

[0101] The present invention adopts a multi-threshold method to calculate the quality index, sets multiple groups of forest quality thresholds, and calculates the forest quality under corresponding variables for different sensor networks. The proportion of the designated species in each sensor network that is greater than the forest quality threshold is used as the basis for measuring forest quality. This method can adapt to the complex and diverse actual conditions of forest ecosystems, better capture the subtle differences in forest quality under different states and conditions, and improve the adaptability and sensitivity of the evaluation method to different forest types and different development stages. In addition, a verification step is set, by replacing the indicator species, reconstructing the nodes, disconnecting the connection, and deleting the relevant data of the original indicator species, and re-evaluating and calculating, and comparing the differences between the comprehensive evaluation before and after replacing the indicator species, so as to test the stability and reliability of the evaluation method, timely discover possible deviations or unreasonableness in the evaluation process, ensure that the evaluation results can withstand scrutiny, and provide solid data support for subsequent forest management, planning and other work based on the evaluation results.

[0102] After verification, it also has the function of estimating and predicting forest quality. By obtaining the time change rate of forest quality under different variables of all nodes and sensor networks, it can grasp the dynamic change trend of forest quality in the time dimension, and then make reasonable estimates and predictions of the forest quality of each indicator species and its future changes, and gain early insights into possible changes in forest quality, providing forward-looking decision-making basis for the sustainable development and scientific management of forest resources, such as timely adjustment of forest cultivation and protection strategies to prevent problems such as forest quality decline.

[0103] Embodiment 2

[0104] The embodiment disclosed in the present invention is a specific embodiment carried out after selecting an actual sample site based on the first embodiment.

[0105] A sample plot survey was conducted in natural forests in Northeast China, with a total of 44 sample plots. Each tree in the sample plot was georeferenced and the species name, diameter at breast height, total height, crown width and crown length were recorded. In addition, the embodiment of the present disclosure also records the data of shrubs and herbaceous plants in the plot. For each plot, the embodiment of the present disclosure records the species richness of shrubs and herbaceous plants and the shrub coverage.

[0106] Based on published research and definitions related to forest quality, the present disclosure embodiment selects 14 indicators that reflect the basic attributes of forest ecosystems to develop the basic framework of the forest quality evaluation system of the present disclosure embodiment as shown in Table 1. These indicators represent the composition, structure and function of the forest.

[0107] Table 1. 14 candidate indicators for assessing forest quality

[0108]

[0109] The disclosed embodiment uses five indicators to explain forest composition. In addition to the species richness of woody, shrubby and herbaceous species, the disclosed embodiment also selects two other indicators: the number of Korean pine trees and the number of endemic species. Korean pine is an indicator species in Northeast China. Woody endemic species include Robinia, Pinus tabulaeformis, Willow, Sorbus, Rhus and Mulberry.

[0110] The disclosed embodiment includes three forest structure indicators: total tree biomass per plot, coefficient of variation of tree diameter at breast height, and shrub coverage.

[0111] Finally, the disclosed embodiment evaluates six indicators of ecosystem function. The first five indicators provide a comprehensive measure of leaf traits (including the leaf shape index Fdis, the community weighted average of the leaf area of ​​trees in each plot, the community weighted average of the specific leaf area of ​​trees in each plot, the community weighted mean of the leaf carbon content of each plot, and the community weighted median of the leaf nitrogen content of each plot). The carbon storage represents the carbon storage of the forest.

[0112] In order to comprehensively evaluate the forest quality in the study area of ​​the embodiment of the present disclosure, the embodiment of the present disclosure divides the 14 indicators into two categories according to their response: (1) positive index, where the higher the value, the higher the forest quality, and (2) negative index (all the indicators used in this article are positive indexes), where the higher the value, the lower the forest quality. Negative indexes are represented by multiplying the index value by -1. Subsequently, each indicator that constitutes forest quality is normalized to a range of 0 to 1.

[0113] The averaging method assigns equal weights to the indicators as follows:

[0114]

[0115] Where EI represents forest quality, Xi represents the standardized value of the i-th indicator, and N represents the number of indicators. To facilitate subsequent comparisons, the obtained values ​​are normalized between 0 and 1.

[0116] The disclosed embodiment adopts a multi-threshold method to calculate forest quality. In order to evaluate how latitude affects forest quality, Figure 2 As shown, the embodiment of the present disclosure studies the slope of forest quality change at different thresholds in latitude, thereby exploring the impact of latitude on forest quality. The embodiment of the present disclosure plots the impact of each threshold of latitude from 5% to 99% on forest quality. The embodiment of the present disclosure selects the maximum latitude effect threshold (T mde ) to show the relationship between latitude and forest quality. mde Indicates the value where latitude has the strongest positive or negative influence. This results in forest quality values ​​between 0 and N (number of indicators) at this threshold. The obtained values ​​are also normalized between 0 and 1 to facilitate subsequent comparisons.

[0117] The disclosed embodiment obtains forest quality estimates calculated by the averaging method and the multi-threshold method. The averaging method assigns equal weight to each indicator, reflecting the level of aggregation. Compared with the averaging method, the multi-threshold method more comprehensively reflects the potential variability of various forest qualities under different thresholds. On the other hand, the multi-threshold method emphasizes the importance of each indicator.

[0118] For the forest quality of the average method, there is a negative correlation with latitude. However, the opposite result was obtained for the multi-threshold method at the 97% threshold.

[0119] The averaging approach assigns equal weight to each indicator, reflecting a level of aggregation that allows for intuitively clear assessments of forest quality. However, it does not easily account for trade-offs between functions. It is difficult to distinguish between cases where the two functions operate at extremes, suggesting a strong trade-off, and cases where the functions operate at intermediate levels. Therefore, it remains uncertain whether this result is driven solely by latitude or by several observed indicators if the averaging approach is not combined with single indicator analysis.

[0120] The multi-threshold approach emphasizes the importance of each indicator and comprehensively reflects the potential changes in various forest qualities under different thresholds. This approach provides more detailed information and flexibility, providing a more nuanced view of forest quality. However, it provides a set of indicators rather than a single indicator, and mainly describes a phenomenon rather than providing information that is critical for practical applications.

[0121] Both methods contribute to the understanding of the influences that determine forest quality. The average method is intuitively appealing, and the multi-threshold method provides a relatively comprehensive and clear summary of the relationship between latitude and forest quality. It effectively resolves many ambiguities and challenges that exist in previous methods. Both methods provide ways to assess the relationship between latitude and forest quality from different perspectives, and each method has its own advantages and limitations.

[0122] Table 2. Forest quality values ​​assessed using the mean value method and the multi-threshold method

[0123]

[0124]

[0125] Through the above implementation, the disclosed embodiment can comprehensively evaluate the health status of forest ecosystems and provide a scientific basis for forest management and protection. In addition, the method of the disclosed embodiment is also scalable and can be applied to other regions and different types of forest ecosystems, providing support for the assessment and protection of global forest quality.

[0126] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms should not be understood as necessarily being directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification.

[0127] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A forest quality evaluation method based on the average method and the multi-threshold method, characterized in that: The steps include: S1. Select a sample plot and obtain the forest composition of the sample plot, wherein the forest composition includes indicator species; S2. construct a first sensor network, wherein the sensor network includes at least a primary network and a secondary network; The primary network includes a central node and an edge node, wherein the central node is arranged in an area where the indicated species has a preset number of settlements at equal latitudes, and the edge nodes are arranged in an area where the non-indicated species has a preset number of settlements directly adjacent to the central node, and each of the edge nodes is connected to the central node and each of the edge nodes is not directly connected to each other; The secondary network is a subnetwork of each edge node, which includes a plurality of mesh nodes connected to each other, and each mesh node is arranged in an area where a single forest with a preset number of settlements is located at different latitudes; Each of the first-level networks is connected via the respective central nodes to form the first sensor network; S3, obtaining a quality indicator according to the sensor network; S4, reconstructing the sensor network according to the quality index, and reconstructing the mesh nodes in the secondary network to be arranged in areas with the same quality index; Reconstructing the preset number of settlements at equal latitudes in the primary network into a percentage setting number of the preset number of settlements at unequal latitudes; S5, obtaining a second sensor network with unequal latitudes as variables; A third sensor network is obtained with respect to a preset number of settlements at the same latitude as a variable; A fourth sensor network with the quality indicator as a variable is obtained; S6. Calculate the quality indicator sets under the first to fourth sensor networks respectively, and obtain a comprehensive evaluation under multiple thresholds and average values.

2. The forest quality evaluation method based on the average method and the multi-threshold method according to claim 1 is characterized in that: The quality index is calculated by the following method: S3.

1. Select preset quantitative indicators involving trees, shrubs, herbaceous plants and indicator species, including species richness, biomass, and number of key species; S3.2, normalize the selected indicators to quantify them into values ​​between 0 and 1; S3.3: Integrate the normalized indicators to obtain quality indicators.

3. The forest quality evaluation method based on the average method and the multi-threshold method according to claim 2 is characterized in that: The S3.4 is specifically: The indicators are assigned equal weights according to the average method, as follows: Normalize the obtained values ​​between 0 and 1; Where FQ represents forest quality, Xi represents the standardized value of the i-th indicator, and N represents the number of indicators.

4. The forest quality evaluation method based on the average method and the multi-threshold method according to claim 1 is characterized in that: When calculating the quality index by the multi-threshold method, multiple groups of forest quality thresholds are set, and the following steps are performed for each group of forest quality thresholds: S6.

1. Obtain forest data sampled by different first sensor networks, and calculate forest quality at equal latitudes; S6.2, obtaining forest data sampled by the second sensor network, and calculating forest quality at different latitudes; S6.3, obtaining forest data sampled by the third sensor network, and calculating forest quality with different numbers of settlements as variables at equal latitudes; S6.4, obtaining forest data sampled by the fourth sensor network, and calculating the forest quality with each species quality index as a variable; The forest quality is a ratio of a specified species in the sampled sensor network that is greater than the forest quality threshold.

5. The forest quality evaluation method based on the average method and the multi-threshold method according to claim 4 is characterized in that: After executing S6, verification is performed at a preset time interval as a period.

6. The forest quality evaluation method based on the average method and the multi-threshold method according to claim 5 is characterized in that: The steps to perform the calibration include: After the indicator species is replaced, the non-indicator species determined in S2 is used as a new indicator species, and the corresponding mesh node is reconstructed as a central node; Disconnect the reconstructed mesh node in S2 from all other nodes and set it as the central node, and re-verify it according to the primary network of S2, and delete all the data of the original indicator during the re-verification process; Execute S3-S5, and delete all data of the original indication species during execution; The difference between the comprehensive evaluations before and after the indicator species is changed is calculated, and if the difference is within a preset range, the evaluation is passed.

7. The forest quality evaluation method based on the average method and the multi-threshold method according to claim 6 is characterized in that: After the verification is performed, multiple verifications are also included, and each time the verification is performed, the same steps as the verification are performed; During multiple verifications, the indicator species deleted in each verification are the indicator species in the adjacent previous verification, and the original indicator species between non-adjacent verifications are retained in the current verification.

8. The forest quality evaluation method based on the average method and the multi-threshold method according to claim 5 is characterized in that: After the verification is performed, forest quality estimation and prediction are also included, which includes the following steps: Get the time change rate of forest quality of all nodes; Obtain different forest qualities of all sensor networks obtained through multiple thresholds as in S6, and calculate the time change rate of forest quality of each sensor network under different variables; Estimate the forest quality and predicted changes of each indicator species; If the predicted change rate is outside the range of the average forest quality time change rate of each node, the settings of each sensor network are checked in turn.

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