A forest quality evaluation method based on the average value method and the multi-threshold method

Abstract

The present invention relates to the field of forest quality evaluation, and specifically to a forest quality evaluation method based on the average value method and the multi-threshold method, comprising the following steps: S1, obtaining the forest composition of the sample plot, wherein the forest composition includes indicator species; S2, constructing a first sensor network; S3, obtaining quality indicators according to the sensor network; S4, reconstructing the sensor network according to the quality indicators; S5, obtaining a second sensor network with unequal latitudes as variables; obtaining a third sensor network with the number of preset settlements at the same latitude as variables; obtaining a fourth sensor network with quality indicators as variables; S6, respectively calculating the quality score sets under the first to fourth sensor networks to obtain a comprehensive evaluation under multi-thresholds and average values. The present invention realizes the integration of multiple indicators into one indicator, and these data are subsequently used to calculate the quality scores of each evaluation area, and then these scores are aggregated to evaluate the quality of the entire forest.

Description

Technical Field

[0001] The present invention relates to the field of forest quality evaluation, and specifically relates to a forest quality evaluation method based on the average value method and the 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 for humans. With the intensification of global climate change and human activities, the health of forest ecosystems has been threatened unprecedentedly. Therefore, accurately evaluating forest quality is crucial for guiding forest management and formulating protection 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 comprehensively reflect the complexity and multifunctionality of forest ecosystems. For example, a single forest cover indicator cannot distinguish the biodiversity level within the forest, the health status of ecological processes, or the resistance and resilience of the ecosystem to disturbances. In addition, with the increasing understanding of the value of ecosystem services, a more comprehensive method is needed to evaluate the quality of forest ecosystems.

[0004] In this context, the present invention aims to propose a new method for evaluating 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 quality, thereby providing a scientific basis for forest protection and sustainable management. Summary of the Invention

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

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

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

[0008] S1. Select sample plots and obtain the forest composition of the sample plots, where the forest composition includes indicator species;

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

[0010] The first-level network includes a central node and edge nodes. The central node is set in the area where the indicator species with a preset number of settlements is located at the same latitude. The edge nodes are set in the areas of non-indicator species with a preset number of settlements directly adjacent to the central node. Each of the edge nodes is connected to the central node, and the edge nodes are not directly connected to each other;

[0011] The second-level network is a sub-network of each of the edge nodes, which includes a plurality of interconnected mesh nodes. Each of the mesh nodes is set in the 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 through its respective central node to form the first sensor network;

[0013] S3. Obtain the quality indicators according to the sensor network;

[0014] S4. Reconstruct the sensor network according to the quality indicators, and reconstruct the mesh nodes in the second-level network to be set in the areas with the same quality indicators;

[0015] Reconstruct the preset number of settlements at the same latitude in the first-level network to the percentage setting number of the preset number of settlements at different latitudes;

[0016] S5. Obtain the second sensor network with different latitudes as variables;

[0017] Obtain the third sensor network with the preset number of settlements at the same latitude as variables;

[0018] Obtain the fourth sensor network with the quality indicators as variables;

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

[0020] Preferably, the quality indicators are calculated by the following method:

[0021] S3.1. Select preset quantity indicators related to trees, shrubs, herbs and indicator species, including species richness, biomass, and the 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 the quality indicators.

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

[0025] Equal weights are assigned to the indicators according to the average value method as follows:

[0026]

[0027] Normalize the obtained value 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 indicator by the multi-threshold method, multiple sets of forest quality thresholds are set, and the following steps are performed for each set of the forest quality thresholds:

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

[0031] S6.2. Obtain the forest data sampled by the second sensor network, and calculate the forest quality at unequal latitudes;

[0032] S6.3. Obtain the forest data sampled by the third sensor network, and calculate the forest quality with the variable of unequal settlement numbers at equal latitudes;

[0033] S6.4. Obtain the forest data sampled by the fourth sensor network, and calculate the forest quality with the variable of the quality indicators of each species;

[0034] wherein, the forest quality is the proportion value of the indicator species in the sampled sensor network being greater than the forest quality threshold.

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

[0036] Preferably, the steps of performing verification include:

[0037] After replacing the indicator species, use the non-indicator species determined in S2 as the new indicator species, and reconstruct the corresponding mesh node into the central node;

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

[0039] Perform S3 - S5, and delete all data of the original indicator species when performing;

[0040] Calculate the difference between the comprehensive evaluations before and after replacing the indicator species. If the difference is within the preset range, the evaluation passes.

[0041] Preferably, after performing the verification, multiple verifications are further included. Each time a verification is performed, the same steps as those in the said verification are executed;

[0042] During multiple verifications, the types of indicators deleted each time are the adjacent types of indicators in the previous verification. The original types of indicators between non - adjacent verifications are retained in the current verification.

[0043] Preferably, after performing the verification, forest quality estimation and prediction are further included, which specifically include the following steps:

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

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

[0046] Estimate the forest quality and predicted change amount 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, verify the settings of each sensor network in sequence.

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

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

[0050] The present invention calculates the quality index by using the multi-threshold method, sets multiple groups of forest quality thresholds, and calculates the forest quality for different sensor networks under corresponding variables respectively. The proportion value of the indicator species greater than the forest quality threshold in each sensor network is used as the basis for measuring the forest quality. This method can adapt to the complex and diverse actual situations 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. And a verification step is set. After replacing the indicator species, reconstructing nodes, disconnecting connections, and deleting the relevant data of the original indicator species, the evaluation calculation is carried out again. By comparing the differences in the comprehensive evaluation before and after replacing the indicator species, the stability and reliability of the evaluation method are tested, and possible deviations or unreasonable points in the evaluation process can be found in time to ensure that the evaluation results can stand the test and provide solid data support for subsequent forest management, planning and other work based on the evaluation results.

[0051] After the verification is completed, it also has the function of forest quality estimation and prediction. By obtaining the forest quality time change rates of all nodes and each sensor network under different variables, the dynamic change trend of forest quality in the time dimension can be grasped. Furthermore, the forest quality of each indicator species and its future change amount can be reasonably estimated and predicted, and the possible changes in forest quality can be insighted in advance, providing a forward-looking decision-making basis for the sustainable development and scientific management of forest resources. For example, timely adjusting forest cultivation, protection and other strategies to prevent problems such as the decline of forest quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the following drawings without creative efforts.

[0053] Figure 1 It is a schematic flowchart of the method provided by the embodiment of the present invention;

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

[0055] The technical solution of the present invention will be described below through specific specific examples. It should be understood that one or more method steps mentioned in the present invention do not exclude the existence of other method steps before and after the combined steps or the insertion of other method steps between these clearly mentioned steps; it should also be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. Moreover, unless otherwise specified, the numbers of each method step are only convenient tools for identifying each method step, rather than limiting the arrangement order of each method step or the scope in which the present invention can be implemented. The change or adjustment of their relative relationship, without substantial change in the technical content, should also be regarded as the scope in which the present invention can be implemented.

[0056] To better understand the above technical solution, the exemplary embodiments of the present invention will be described in more detail below. Although the 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 to enable a more thorough understanding of the present invention and to be able to fully convey the scope of the present invention to those skilled in the art.

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

[0058] Embodiment 1

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

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

[0061] S2. Construct a first sensor network, and the sensor network includes at least a first-level network and a second-level network;

[0062] The first-level network includes a central node and edge nodes. The central node is set in the area where the indicator species with a preset settlement number is located under the same latitude. The edge nodes are set in the area where the non-indicator species with a preset settlement number directly adjacent to the central node is located. Each edge node is connected to the central node and no two edge nodes are directly connected to each other;

[0063] The second-level network is a sub-network of each edge node, and it includes a plurality of mesh nodes connected to each other. Each mesh node is set in the area where a single forest composition with a preset settlement number is located under different latitudes;

[0064] Each first-level network is connected through its respective central node to form the first sensor network;

[0065] S3. Obtain quality indicators based on the sensor network;

[0066] S4. Reconstruct the sensor network according to the quality indicators, and reconstruct the mesh nodes in the secondary network to be set in the area with the same quality indicators;

[0067] Reconstruct the preset settlement quantity at the same latitude in the primary network to the percentage setting quantity of the preset settlement quantity at different latitudes;

[0068] S5. Obtain a second sensor network with different latitudes as variables;

[0069] Obtain a third sensor network with the preset settlement quantity at the same latitude as a variable;

[0070] Obtain a fourth sensor network with quality indicators as variables;

[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 indicators are calculated by the following method:

[0073] S3.1. Select preset quantity indicators related to trees, shrubs, herbs and indicator species, including species richness, biomass, and the 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 specific content of S3.4 is as follows:

[0077] Allocate equal weights to the indicators according to the average value 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 quality indicators by the multi-threshold method, set multiple groups of forest quality thresholds, and perform the following steps for each group of the forest quality thresholds:

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

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

[0084] S6.3. Obtain the forest data sampled by the third sensor network, and calculate the forest quality with different settlement numbers as variables at the same latitude;

[0085] S6.4. Obtain the forest data sampled by the fourth sensor network, and calculate the forest quality with the quality indicators of each species as variables;

[0086] Wherein, the forest quality is the proportion value of the indicator species in the sampled sensor network that is greater than the forest quality threshold.

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

[0088] Preferably, the steps of performing verification include:

[0089] After replacing the indicator species, use the non-indicator species determined in S2 as the new indicator species, and reconstruct the corresponding mesh node into the central node;

[0090] After disconnecting the reconstructed mesh node in S2 from all other nodes and setting it as the central node, re-verify according to the first-level network of S2, and delete all data of the original indicator species during the re-verification;

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

[0092] Calculate the difference between the comprehensive evaluations before and after replacing the indicator species. If the difference is within the preset range, the evaluation passes.

[0093] Preferably, after performing verification, multiple verifications are also included. Each time verification is performed, the same steps as those for performing verification are executed;

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

[0095] Preferably, after performing verification, forest quality estimation and prediction are also included, specifically including the following steps:

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

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

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

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

[0100] The embodiments of the present disclosure integrate multiple indicators into one indicator. These data are then used to calculate the quality score of each evaluation area, and then these scores are aggregated to evaluate the quality of the entire forest. By selecting multiple preset quantity indicators related to trees, shrubs, herbaceous plants and indicator species, such as species richness, biomass, the number of key species, etc., it can comprehensively cover various elements in the forest ecosystem, avoid one-sidedness caused by relying solely on 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 uses the multi-threshold method to calculate the quality index, sets multiple groups of forest quality thresholds, and calculates the forest quality respectively for different sensor networks under corresponding variables. The proportion value of the indicator species greater than the forest quality threshold in each sensor network is used as the basis for measuring the forest quality. This method can adapt to the complex and diverse actual situations of the forest ecosystem, 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. And a verification step is set. By replacing the indicator species, reconstructing the nodes, disconnecting the connections and deleting the relevant data of the original indicator species and then re-performing the evaluation calculation, comparing the differences between the comprehensive evaluations before and after replacing the indicator species, so as to test the stability and reliability of the evaluation method, timely discover possible deviations or unreasonable points in the evaluation process, ensure that the evaluation results can stand scrutiny, and provide solid data support for subsequent forest management, planning and other work based on the evaluation results.

[0102] After the verification is completed, it also has the functions of forest quality estimation and prediction. By obtaining the time change rate of the forest quality of all nodes and each sensor network under different variables, the dynamic change trend of the forest quality in the time dimension can be grasped, and then the forest quality of each indicator species and its future change amount can be reasonably estimated and predicted, so as to anticipate in advance the possible changes in the forest quality, and provide forward-looking decision-making basis for the sustainable development and scientific management of forest resources, such as timely adjusting forest cultivation, protection and other strategies to prevent problems such as the decline of forest quality.

[0103] Embodiment 2

[0104] The embodiment of the present disclosure is a specific embodiment carried out after selecting actual sample plots on the basis of Embodiment 1.

[0105] Plot surveys were conducted in natural forests in Northeast China. There were a total of 44 plots. Each tree in the plots was georeferenced, and the species name, diameter at breast height, total height, crown width, and crown length were recorded. In addition, the data of shrubs and herbs in the plots were also recorded in the embodiments of the present disclosure. For each plot, the species richness of shrubs and herbs and the shrub coverage were recorded in the embodiments of the present disclosure.

[0106] Based on the published research and the definitions related to forest quality, the embodiments of the present disclosure selected 14 indicators reflecting the basic attributes of the forest ecosystem to formulate the basic framework of the forest quality evaluation system of the embodiments of the present disclosure as shown in Table 1. These indicators represent the composition, structure, and function of the forest.

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

[0108]

[0109]

[0110] The embodiments of the present disclosure used five indicators to explain the forest composition. In addition to the species richness of woody, shrub, and herb species, the embodiments of the present disclosure also selected 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 pseudoacacia, Pinus tabuliformis, Salix babylonica, Sorbus pohuashanensis, Toxicodendron vernicifluum, and Morus alba.

[0111] The embodiments of the present disclosure include three forest structure indicators: the total tree biomass per plot, the coefficient of variation of tree diameter at breast height, and the shrub coverage.

[0112] Finally, the embodiments of the present disclosure evaluated six indicators of ecosystem function. The first five indicators provided a comprehensive measure of leaf traits (including the leaf shape index Fdis, the community-weighted mean of tree leaf area per plot, the community-weighted mean of specific leaf area of trees per plot, the community-weighted mean of leaf carbon content per plot, and the community-weighted median of leaf nitrogen content per plot). And the carbon storage represents the carbon storage of the forest.

[0113] To comprehensively evaluate the forest quality in the research area of the embodiments of the present disclosure, the 14 indicators were divided into two categories according to their responses: (1) positive indices, where a higher value indicates higher forest quality, and (2) negative indices (all the indicators used in this article are positive indices), where a higher value indicates lower forest quality. The negative index is represented by multiplying the index value by -1. Subsequently, each indicator constituting the forest quality was normalized to the range of 0 to 1.

[0114] The averaging method assigns equal weights to the indicators as follows:

[0115]

[0116] Among them, EI represents the forest quality, Xi represents the standardized value of the i-th index, and N represents the number of indexes. For the convenience of subsequent comparison, the obtained values are normalized between 0 and 1.

[0117] The embodiments of the present disclosure adopt a multi-threshold method to calculate the forest quality. To evaluate how latitude affects the forest quality, as Figure 2 shown, the embodiments of the present disclosure study the change slope of the forest quality in latitude under different thresholds, so as to explore the influence of latitude on the forest quality. The embodiments of the present disclosure plot the influence of each threshold with latitude from 5% to 99% on the forest quality. The embodiments of the present disclosure select the maximum latitude effect threshold (T mde ) to show the relationship between latitude and forest quality. T mde represents the value at which latitude has the strongest positive or negative impact. This results in the forest quality value under this threshold being between 0 and N (the number of indexes). For the convenience of subsequent comparison, the obtained values are also normalized between 0 and 1.

[0118] The embodiments of the present disclosure obtain the forest quality estimation values calculated by the average method and the multi-threshold method. The average method assigns equal weights to each index, reflecting the aggregation level. Compared with the average 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 index.

[0119] For the forest quality of the average method, it is negatively correlated with latitude. However, for the multi-threshold method, the opposite result is obtained at the 97% threshold.

[0120] The average method assigns equal weights to each index, reflecting an aggregation level, and can intuitively and clearly evaluate the forest quality. However, it cannot easily make trade-offs between functions. It is difficult to distinguish the situations where two functions operate in extreme cases, which indicates a strong trade-off, as well as the situations where the functions operate at the intermediate level. Therefore, if the average method is not combined with single-index analysis, it is still uncertain whether this result is driven only by latitude or by several observed indexes.

[0121] The multi-threshold method emphasizes the importance of each index and comprehensively reflects the potential changes of various forest qualities under different thresholds. This method provides more detailed information and flexibility, providing a more detailed view of the forest quality. However, it provides a set of indexes rather than a single index, mainly describing a phenomenon rather than providing information crucial for practical applications.

[0122] Both of these methods contribute to understanding the factors determining forest quality. The averaging 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 addresses many ambiguities and challenges present in previous methods. Both methods offer ways to evaluate the relationship between latitude and forest quality from different perspectives, each with its own advantages and limitations.

[0123] Table 2. Forest quality values evaluated separately by the averaging method and the multi-threshold method

[0124]

[0125]

[0126] Through the above implementation manners, the embodiments of the present disclosure 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 embodiments of the present disclosure is also scalable and can be applied to other regions and different types of forest ecosystems to provide support for the assessment and protection of global forest quality.

[0127] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification.

[0128] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A forest quality evaluation method based on the average value method and the multi-threshold method, characterized in that, It includes the following steps: S1. Select a sample plot and obtain the forest composition of the sample plot, where the forest composition includes indicator species; S2. Construct a first sensor network, and the sensor network includes at least a primary network and a secondary network; The primary network includes a central node and edge nodes. The central node is set in the area where the indicator species with a preset number of settlements is located under the same latitude. The edge nodes are set in the area where the non-indicator species with a preset number of settlements directly adjacent to the central node is located. Each of the edge nodes is connected to the central node and no two edge nodes are directly connected to each other; The secondary network is a sub-network of each of the edge nodes, and it includes a plurality of mutually connected mesh nodes. Each of the mesh nodes is set in the area where a single forest composition with a preset number of settlements is located under different latitudes; Each of the primary networks is connected through its respective central node to form the first sensor network; S3. Obtain a quality index according to the sensor network; S4. Reconstruct the sensor network according to the quality index, and reconstruct the mesh nodes in the secondary network to be set in the area with the same quality index; Reconstruct the preset number of settlements under the same latitude in the primary network to the percentage setting number of the preset number of settlements under different latitudes; S5. Obtain a second sensor network with different latitudes as variables; Obtain a third sensor network with the preset number of settlements under the same latitude as variables; Obtain a fourth sensor network with the quality index as a variable; S6. Calculate the quality index sets under the first to fourth sensor networks respectively, and obtain a comprehensive evaluation under multiple thresholds and the average value; When calculating the quality index by the multi-threshold method, set multiple groups of forest quality thresholds, and perform the following steps for each group of the forest quality thresholds: S6.

1. Obtain the forest data sampled by different first sensor networks, and calculate the forest quality under the same latitude; S6.

2. Obtain the forest data sampled by the second sensor network, and calculate the forest quality under different latitudes; S6.

3. Obtain the forest data sampled by the third sensor network, and calculate the forest quality with different settlement numbers as variables under the same latitude; S6.

4. Obtain the forest data sampled by the fourth sensor network, and calculate the forest quality with the quality indexes of each species as variables; Wherein, the forest quality is the proportion value that the indicator species in the sampled sensor network is greater than the forest quality threshold.

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

1. Select preset quantity indexes related to trees, shrubs, herbaceous plants and indicator species, including species richness, biomass, and the number of key species; S3.

2. Perform normalization processing on the selected indexes to quantify them into values between 0 and 1; S3.3: Integrate the normalized indexes to obtain a quality index.

3. The forest quality evaluation method based on the average value method and the multi-threshold method according to claim 2, wherein The specific content of S3.4 is: Allocate equal weights to the indexes according to the average value method as follows: Normalize the obtained value between 0 and 1; Where FQ represents forest quality, Xi represents the standardized value of the i-th index, and N represents the number of indexes.

4. The forest quality evaluation method based on the average value method and the multi-threshold method according to claim 1, characterized in that After executing S6, perform verification at a preset time interval.

5. The forest quality evaluation method based on the average value method and the multi-threshold method according to claim 4, characterized in that The steps of performing verification include: After replacing the indicated species, use the non-indicated species determined in S2 as the new indicated species, and reconstruct the corresponding mesh node into the central node; After disconnecting the reconstructed mesh node in S2 from all other nodes and setting it as the central node, perform re-verification according to the first-level network of S2, and delete all data of the original indicated species during the re-verification process; Execute S3 - S5, and delete all data of the original indicated species during execution; Calculate the difference between the comprehensive evaluations before and after replacing the indicated species. If the difference is within the preset range, the evaluation passes.

6. The forest quality evaluation method based on the average value method and the multi-threshold method according to claim 5, characterized in that After performing verification, multiple verifications are also included. Each time verification is performed, the same steps as those for performing verification are executed; During multiple verifications, the indicated species deleted each time is the indicated species in the previous adjacent verification, and the original indicated species between non-adjacent verifications is retained in the current verification.

7. The forest quality evaluation method based on the average value method and the multi-threshold method according to claim 4, wherein After performing verification, forest quality estimation and prediction are also included, specifically including the following steps: Obtain the forest quality time change rate of all nodes; Obtain the different forest qualities of all sensor networks obtained through multiple thresholds in S6, and calculate the forest quality time change rate of each sensor network under different variables; Estimate the forest quality and predicted change amount of each indicated species; If the predicted change rate is outside the range of the forest quality time change rate of each average node, verify the settings of each sensor network in turn.

Images