A method and system for evaluating the health of a plantation stand

Abstract

The application discloses a method and system for evaluating the health degree of artificial forest, and belongs to the technical field of forest ecological evaluation. The method comprises the following steps: firstly, collecting multi-dimensional original index data of the artificial forest, and constructing a hierarchical index system for evaluating the health degree of the forest; secondly, calculating subjective weights and objective weights of the indexes by using the analytic hierarchy process and the entropy weight method respectively, and obtaining comprehensive weights by fusing the subjective weights and the objective weights; thirdly, standardizing the original index data, combining the comprehensive weights, constructing a comprehensive evaluation model, calculating a comprehensive index of the health degree of the forest, and completing grade evaluation. The method combines subjective and objective weighting methods to improve the accuracy of the weights, is comprehensive in evaluation dimensions, objective and reliable in results, can accurately determine the health state of the artificial forest in the loess hilly area, and provides data support and decision basis for the reconstruction of low-efficiency forests and the sustainable management of artificial forests.

Description

Technical Field

[0001] This invention relates to the field of forest ecological assessment technology, and in particular to a method and system for assessing the health of plantation stands. Background Technology

[0002] The Loess Plateau is a key area for ecological restoration in my country, and large-scale plantations have become the core carriers for soil and water conservation and ecological protection in the region. The health of the forest stands directly determines the effectiveness of the ecological functions of the plantations. At present, the evaluation methods for the health of plantation stands are mainly divided into two categories: a single subjective evaluation method and a single objective evaluation method. Subjective evaluation mainly uses the analytic hierarchy process (AHP), which relies on expert experience to assign values ​​to determine the weights of indicators. It is simple to operate but easily affected by subjective human factors. Objective evaluation is represented by the entropy weight method, which calculates weights based on the variation characteristics of the original data. It is highly dependent on data and lacks industry experience support.

[0003] Current conventional evaluation index systems are often one-dimensional, focusing only on single dimensions such as tree growth or pests and diseases, without considering the regional characteristics of the Loess Plateau region, such as poor soil, water scarcity, and complex forest stand structure, to establish stratified indicators. Furthermore, existing evaluation models fail to integrate subjective and objective weights, resulting in insufficient weighting rationality and significant bias in the final evaluation results. In addition, existing evaluation results only provide simple numerical values, failing to accurately distinguish the health differences among different types of plantations, and are insufficient to guide practical work such as the classification and transformation of inefficient ecological forests and the tending and management of plantations. The following problems exist:

[0004] (1) The weight calculation method is too simple: subjective weighting is too subjective and not scientific enough, while objective weighting ignores the professional experience of the forestry industry. The weight allocation of indicators is unreasonable and affects the accuracy of the evaluation.

[0005] (2) Poor adaptability of the indicator system: The existing indicators are not constructed in layers according to the site conditions and characteristics of artificial forest communities in the Loess Hilly Area, and cannot fully reflect the core health elements such as forest productivity, anti-interference ability, soil environment, and forest structure.

[0006] (3) Insufficient systematization of the evaluation process: The connection between data processing, weight calculation, model construction and result judgment is poor, and a standardized and feasible complete evaluation process has not been formed, making it difficult to apply in practice.

[0007] (4) The evaluation results are not very instructive: they only output a single evaluation value, which cannot distinguish the health levels of different plantations and cannot provide a targeted basis for the classification and transformation of inefficient forests and the management planning of plantations. Summary of the Invention

[0008] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for evaluating the health of plantation stands.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] This invention provides a method for evaluating the health of plantation stands, comprising the following steps: S1, Data Collection and Indicator System Construction: Collect multi-dimensional raw indicator data of the plantation to be tested, and construct a hierarchical indicator system for stand health evaluation based on a hierarchical principle. The hierarchical indicator system includes, from top to bottom, a target layer, a criterion layer, and an indicator layer. The target layer is the stand health of the plantation, the criterion layer includes four dimensions: stand productivity, resistance and resilience, soil nutrients, and stand structure, and the indicator layer consists of subdivided detection indicators set for each criterion layer; S2, Weight Calculation: Calculate the subjective weight of each indicator in the indicator layer using the analytic hierarchy process (AHP), and simultaneously calculate the objective weight of each indicator in the indicator layer using the entropy weight method. The subjective weights are then combined with the objective weights of the objective weights. S3. Data Standardization: Differentiate between positive and negative indicators in the original indicator data, and use the range standardization method to perform dimensionless standardization on the original indicator data to obtain standardized indicator scores; S4. Construct a comprehensive evaluation model and calculate the comprehensive index: Combine the comprehensive weights and the standardized indicator scores to construct a comprehensive evaluation model for stand health, substitute the data into the model for calculation, and obtain the comprehensive health index of the plantation to be tested; S5. Health Level Determination: Retrieve the preset stand health level classification standard, combine it with the calculated comprehensive health index of the stand, determine the corresponding stand health level of the plantation to be tested, and complete the stand health evaluation of the plantation.

[0011] Furthermore, in step S1, the index layer indicators corresponding to the forest stand productivity include plant height and productivity; the index layer indicators corresponding to the resistance and resilience include fire disturbance degree, disease degree, insect pest degree, and regeneration seedling density; the index layer indicators corresponding to the soil nutrients include soil available nitrogen, soil available phosphorus, and soil organic matter; and the index layer indicators corresponding to the forest stand structure include herb richness index, herb diversity index, herb cover, litter cover, stand vertical structure, shrub cover, and tree canopy closure.

[0012] Furthermore, the specific steps for calculating subjective weights using the analytic hierarchy process in step S2 are as follows: S201, based on the hierarchical index system, construct a judgment matrix for pairwise comparison of the indicators layer by layer; S202, perform a consistency check on each judgment matrix. If the consistency check result meets the preset qualification standard, proceed to the next step; if it does not meet the standard, revise the judgment matrix until it passes the check; S203, solve for the largest eigenvalue and the corresponding eigenvector of the qualified judgment matrix, normalize the eigenvector, and obtain the subjective weights corresponding to each indicator.

[0013] Furthermore, in step S202, the consistency check includes calculating the consistency index and the consistency ratio. When the consistency ratio is less than or equal to a preset threshold, the judgment matrix is ​​deemed to have passed the check.

[0014] Furthermore, the specific steps for calculating the objective weight using the entropy weight method in step S2 are as follows: S211, normalize the standardized index scores obtained after processing in step S3 to obtain index probability values; S212, calculate the information entropy corresponding to each index based on the index probability values; S213, calculate the coefficient of variation of each index based on the information entropy, and then calculate the proportion based on the coefficient of variation of all indicators to obtain the objective weight corresponding to each index.

[0015] Furthermore, the formula for calculating the overall weight in step S2 is as follows:

[0016] , i=1,…,n

[0017] In the formula:

[0018] —The overall weight of the i-th indicator;

[0019] W i —The weights of each stability index obtained by the analytic hierarchy process;

[0020] Mi—the weights of each stability index obtained by the entropy weight method;

[0021] S i —Combined weight of the two methods.

[0022] Furthermore, the calculation method for the range standardization method in step S3 is as follows:

[0023] For positive indicators where larger values ​​equate to better performance, the standardized formula is:

[0024] ;

[0025] For negative indicators where smaller values ​​equate to better performance, the standardized formula is:

[0026] );

[0027] In the formula:

[0028] x ij —The j-th type of plantation corresponding to the i-th stability index;

[0029] y ij —Stability index value after standardization.

[0030] Furthermore, the expression for the comprehensive evaluation model of forest stand health in step S4 is as follows:

[0031]

[0032] In the formula:

[0033] E – A comprehensive index of plantation stability;

[0034] i — the i-th stability;

[0035] S i —The weight of the i-th stability evaluation index;

[0036] y i — The score for stability of the i-th digit.

[0037] This invention also provides a plantation stand health evaluation system for use in any of the aforementioned plantation stand health evaluation methods, comprising: a data acquisition module for collecting various raw indicator data of the plantation to be tested in the field and transmitting the raw indicator data to an indicator system construction module and a data processing module; an indicator system construction module connected to the data acquisition module for receiving the raw indicator data and constructing a hierarchical indicator system for stand health evaluation; and a weight calculation module connected to the indicator system construction module, comprising a subjective weight calculation unit, an objective weight calculation unit, and a comprehensive weight fusion unit; wherein the subjective weight calculation unit is used to calculate the subjective weight of the indicators using the analytic hierarchy process (AHP), and the objective ... The entropy weight method calculates the objective weights of the indicators. The comprehensive weight fusion unit is used to fuse subjective weights and objective weights to obtain a comprehensive weight. The data processing module, connected to the data acquisition module and the weight calculation module, is used to distinguish between positive and negative indicators and to perform dimensionless processing on the original indicator data using the range standardization method, outputting standardized indicator scores. The comprehensive evaluation module, connected to the weight calculation module and the data processing module, is used to call the comprehensive weights and standardized indicator scores, run the comprehensive evaluation model to calculate the comprehensive index of forest stand health. The grade determination module, connected to the comprehensive evaluation module, is used to retrieve the preset health grade standard, combine it with the comprehensive index of forest stand health to determine the health grade of the plantation forest stand, and output the final evaluation result.

[0038] Compared with the prior art, the technical solution disclosed in this invention has the following beneficial effects:

[0039] (1) This invention integrates the analytic hierarchy process (subjective weighting) and the entropy weighting method (objective weighting) to calculate the comprehensive weight of the indicators. It not only incorporates the professional experience of forestry experts, but also relies on the objective variation characteristics of the original data. This solves the problem of unreasonable weight allocation in a single weighting method, greatly improves the scientificity and accuracy of the indicator weights, and ensures the reliability of the evaluation results from the source.

[0040] (2) This invention combines the growth environment and community characteristics of artificial forests in the Loess Hilly Area to construct an indicator system in layers that includes four dimensions: forest productivity, resistance and resilience, soil nutrients and forest structure. It covers the core elements of forest health such as tree growth, resistance to disturbance, soil environment and community structure. The indicator system is highly targeted and comprehensive, and is suitable for the evaluation needs of regional artificial forests.

[0041] (3) The present invention designs a standardized evaluation process for the entire process of data collection, indicator construction, weight calculation, data standardization, model operation and grade determination. Each step is logically coherent and closely connected, forming a complete technical system. It is standardized in operation, highly implementable, and easy to promote and apply on a large scale.

[0042] (4) This invention quantifies the health of forest stands through a comprehensive evaluation model and classifies health levels. The evaluation results are intuitive and clear, and can accurately distinguish the health differences of artificial forests of different tree species and different afforestation models. It can provide a quantitative basis for artificial forest tending, low-efficiency forest classification and transformation, and forestry management planning in the Loess Hilly Area, and has high practical application value.

[0043] (5) All data processing, weight calculation and model operation in this invention are based on standardized formulas, with less human intervention, and the evaluation process is objective and fair. It can continuously conduct batch evaluations of multiple batches and multiple regions of artificial forests, and has high work efficiency. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the process for evaluating the health of plantation stands provided in an embodiment of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] This embodiment evaluates nine typical plantations in the Ningnan Loess Hilly Area: *Prunus armeniaca*, *Prunus persica*, *Hippophae rhamnoides*, *Caragana korshinskii*, *Picea spp.*, mixed forests of *Prunus armeniaca* and *Hippophae rhamnoides*, mixed forests of *Prunus armeniaca* and *Caragana korshinskii*, mixed forests of *Robinia pseudoacacia* and *Picea spp.*, and mixed forests of *Prunus armeniaca*, *Prunus persica*, and *Alfalfa*. Figure 1 As shown, a method for evaluating the health of plantation stands specifically includes:

[0049] Step S1: Data Collection and Indicator System Construction

[0050] Operation process:

[0051] Data collection: Field measurements were conducted on various raw indicators of nine types of plantations, covering four major categories: tree growth, disaster disturbance, soil nutrients, and community structure.

[0052] Constructing a hierarchical indicator system: Following the principles of scientific rigor, hierarchy, and regional adaptability, a three-tiered evaluation indicator system is established:

[0053] Target Layer A: Health of plantation stands (overall evaluation target);

[0054] Criterion Layer B: Four dimensions are set: B1 forest stand productivity, B2 resistance and resilience, B3 soil nutrients, and B4 forest stand structure;

[0055] Indicator Layer C: Matches sub-indicators to each criterion layer, totaling 16 items.

[0056] B1 stand productivity: C11 plant height, C12 productivity;

[0057] B2 Resistance and resilience: C21 Fire damage level, C22 Disease severity, C23 Pest severity, C24 Renewal seedling density;

[0058] B3 Soil Nutrients: C31 Available Nitrogen in Soil, C32 Available Phosphorus in Soil, C33 Soil Organic Matter;

[0059] B4 Stand Structure: C41 Herbaceous Richness Index, C42 Herbaceous Diversity Index, C43 Herbaceous Cover, C44 Litter Cover, C45 Stand Vertical Structure, C46 Shrub Cover, C47 Tree Canopy Closure.

[0060] This step takes into account the regional characteristics of the Loess Plateau region, such as drought and water scarcity, poor soil, pests and diseases in plantations, and significant differences in stand structure, to set indicators that comprehensively cover the core factors affecting stand health. The hierarchical indicator system has a clear logic, setting a unified standard for subsequent weight calculations and data processing. Compared to single-indicator evaluation, this indicator system can comprehensively reflect the stand growth status, resistance to disturbance, site conditions, and community stability, ensuring the integrity of the evaluation dimensions from the source.

[0061] Step S2: Weight Calculation (Subjective Weight + Objective Weight + Comprehensive Weight)

[0062] This step is divided into three parts: calculating subjective weights using the analytic hierarchy process, calculating objective weights using the entropy weight method, and calculating the comprehensive weights by fusion.

[0063] 2.1 Calculating Subjective Weights using the Analytic Hierarchy Process (AHP)

[0064] Constructing the judgment matrix: Following the 9-level scaling rule of the Analytic Hierarchy Process (AHP) and combining the experience of forestry experts, pairwise comparison judgment matrices are constructed layer by layer for the target layer-criteria layer and the criterion layer-indicator layer.

[0065] Consistency check: Calculation formula: ,

[0066] in, As a consistency indicator, The consistency ratio, This is the average random consistency index. The industry-standard acceptance level is CR ≤ 0.1.

[0067] Weight calculation: In this embodiment, all judgment matrices CR are less than 0.1, which is a qualified result. The maximum eigenvalue and eigenvector of the matrix are solved and normalized to obtain the subjective weight Wi. The results are shown in Tables 10-1 to 10-5 of the document.

[0068] As shown in Table 10-1, the CR of the plantation stand health assessment matrix (A) is 0.0444, which is less than 0.1, indicating that the assessment matrix has good consistency and is statistically significant. Therefore, the calculated weight W... i Effective. The weights of stand productivity B1, resistance and resilience B2, soil nutrients B3, and stand structure B4 are 0.0569, 0.1219, 0.2633, and 0.5579, respectively.

[0069] Table 10-1 Calculation results from layer A to layer B of the judgment matrix

[0070]

[0071] As shown in Table 10-2, the CR of the stand productivity judgment matrix (B1) is 0.0000, which is less than 0.1, indicating that the judgment matrix has good consistency and is statistically significant. Therefore, the calculated weight W... i Effective. Among them, the weights of plant height C11 and productivity C12 are 0.1250 and 0.8750, respectively.

[0072] Table 10-2 Calculation results of the judgment matrix from layer B1 to layer C

[0073]

[0074] As shown in Table 10-3, the CR of the forest resistance and resilience judgment matrix (B2) is 0.0277, which is less than 0.1, indicating that the judgment matrix has good consistency and is statistically significant. Therefore, the calculated weight W... i Effective. The weights of fire disturbance level C21, disease level C22, insect damage level C23, and seedling density C24 are 0.0637, 0.1542, 0.1542, and 0.6279, respectively.

[0075] Table 10-3 Calculation results of the judgment matrix from layer B2 to layer C

[0076]

[0077] As shown in Table 10-4, the CR of the forest soil condition judgment matrix (B3) is 0.0000, which is less than 0.1, indicating that the judgment matrix has good consistency and is statistically significant. Therefore, the calculated weight W... i Effective. The weights of soil available nitrogen (C31), available phosphorus (C32), and organic matter (C33) were 0.1111, 0.1111, and 0.7778, respectively.

[0078] Table 10-4 Calculation results of judgment matrix from layer B3 to layer C

[0079]

[0080] As shown in Table 10-5, the CR of the stand structure judgment matrix (B4) is 0.0171, which is less than 0.1, indicating that the judgment matrix has good consistency and is statistically significant. Therefore, the calculated weight W... i Effective. The weights of herb richness index C41, herb diversity index C42, herb cover C43, litter cover C44, stand vertical structure C45, shrub cover C46, ​​and tree canopy closure C47 are 0.0284, 0.0284, 0.0924, 0.1190, 0.1578, 0.2026, and 0.3714, respectively.

[0081] Table 10-5 Calculation results of judgment matrix from layer B4 to layer C

[0082]

[0083] Technical benefits: The Analytic Hierarchy Process (AHP) relies on the experience of industry experts to assign values, fully combining practical experience in the management of artificial forests in the Loess Hilly Area, ensuring that the weights conform to professional understanding in the forestry field; the consistency test can eliminate unreasonable judgment matrices, avoid human judgment errors, and improve the rationality of subjective weights.

[0084] 2.2 Calculation of Objective Weights using the Entropy Weight Method

[0085] Data normalization: Based on the data after preliminary standardization in step S3, calculate the probability values ​​of the indicators.

[0086] ;

[0087] Calculate information entropy: Formula ;

[0088] Calculating objective weights: Calculating the coefficient of variation based on information entropy 1- Ultimately, the objective weight is obtained. The results are shown in Table 10-6 of the document.

[0089] Table 10-6 Information Entropy and Weights of Various Forest Stand Health Indicators Using the Entropy Weight Method

[0090]

[0091] The entropy weight method relies entirely on the degree of dispersion of the original data to assign values. The greater the data variation and the stronger the ability of the indicator to distinguish, the higher the weight. This avoids human subjective bias and makes the weight fit the actual data characteristics.

[0092] 2.3 Integrated Calculation of Comprehensive Weight

[0093] Using formula , i=1,…,n

[0094] In the formula:

[0095] W i —The weights of each stability index obtained by the analytic hierarchy process;

[0096] Mi—the weights of each stability index obtained by the entropy weight method;

[0097] S i —Combined weight of the two methods.

[0098] By combining subjective and objective weights, the final comprehensive weight is obtained. The results are shown in Table 10-7 of the document.

[0099] Table 10-7 Determination of the comprehensive weights of various evaluation indicators for the health of nine types of plantation forests

[0100] Forest stand productivity B1 0.0569 0.0881 0.0401 Resistance and recovery B2 0.1219 0.3090 0.2447 Soil nutrient B3 0.2633 0.1874 0.2575 Forest stand structure B4 0.5579 0.4156 0.4577 Plant height C11 0.0071 0.0419 0.0040 Productivity C12 0.0498 0.0462 0.0312 Fire interference level C21 0.0078 0.0461 0.0049 Disease severity C22 0.0188 0.0725 0.0185 Infestation severity C23 0.0188 0.0279 0.0071 Update seedling density C24 0.0765 0.1624 0.1688 Soil alkaline nitrogen C31 0.0293 0.0530 0.0210 Available phosphorus C32 in soil 0.0293 0.0735 0.0292 Soil organic matter C33 0.2048 0.0610 0.1695 Herb richness index C41 0.0162 0.0481 0.0106 Herbaceous diversity index C42 0.0183 0.0445 0.0111 Herbaceous layer coverage C43 0.0503 0.0329 0.0225 Litter cover C44 0.0655 0.0493 0.0439 Vertical stand structure C45 0.0875 0.0569 0.0676 Shrub cover C46 0.1128 0.0993 0.1521 Canopy closure of the tree layer: C47 0.2074 0.0845 0.2379

[0101] By integrating subjective and objective weights, the professional experience of experts is preserved while respecting the objective laws of on-site monitoring data. This addresses the shortcomings of a single weighting method and makes the allocation of indicator weights more scientific and balanced.

[0102] Step S3: Data Standardization Processing

[0103] Operation process:

[0104] Indicator attribute classification: In this embodiment, plant height, productivity, regeneration seedling density, soil nutrients, various diversity indices, canopy cover, canopy closure, and vertical structure are positive indicators (the higher the value, the higher the stand health); fire disturbance degree, disease degree, and insect infestation degree are negative indicators (the higher the value, the lower the stand health).

[0105] Range standardization: The corresponding formulas were used to perform dimensionless processing on the 16 raw data of the 9 types of plantations to eliminate differences in the dimensions and numerical ranges of different indicators, and to obtain the standardized indicator scores. .

[0106] Different indicators have vastly different units and numerical magnitudes (e.g., plant height in meters, seedling density in plants per hm²), making them unsuitable for direct weighted calculations. Range standardization renders all data dimensionless, mapping them uniformly to the 0-1 range, ensuring subsequent model calculations can proceed normally while preserving the relative differences of the original data.

[0107] Step S4: Construct a comprehensive evaluation model and calculate the comprehensive index.

[0108] Operation process:

[0109] Constructing a comprehensive evaluation model: A model for evaluating the stability of plantation ecosystems was adopted.

[0110] In the formula:

[0111] E – A comprehensive index of plantation stability;

[0112] i — the i-th stability;

[0113] S i —The weight of the i-th stability evaluation index;

[0114] y i — The score for stability of the i-th element;

[0115] The comprehensive weight S of the 16 indicators i With standardized score y i Multiply the products one-to-one and then sum them.

[0116] Data Substitution and Calculation: Substitute the standardized scores and comprehensive weights of the nine types of plantations into the model to calculate the comprehensive health index E of each type of plantation. The calculation results are shown in Table 10-8 of the document.

[0117] Table 10-8 Evaluation values ​​and comprehensive evaluation of health status of nine types of planted forests

[0118] Forest stand productivity B1 0.0252 0.0220 0.0120 0.0182 0.0072 0.0178 0.0258 0.0401 0.0106 Resistance and recovery B2 0.0453 0.0465 0.1403 0.0532 0.0436 0.2113 0.0628 0.0127 0.0404 Soil nutrient B3 0.0855 0.1693 0.0596 0.2269 0.0752 0.0545 0.1351 0.0781 0.0882 Forest stand structure B4 0.1609 0.0800 0.1859 0.0971 0.1466 0.2801 0.2196 0.2379 0.2179 Plant height C11 0.0034 0.0024 0.0000 0.0006 0.0023 0.0015 0.0017 0.0040 0.0018 Productivity C12 0.0218 0.0147 0.0071 0.0128 0.0000 0.0163 0.0192 0.0312 0.0039 Fire interference level C21 0.0000 0.0049 0.0049 0.0049 0.0049 0.0000 0.0049 0.0049 0.0049 Disease severity C22 0.0093 0.0093 0.0185 0.0185 0.0185 0.0093 0.0093 0.0000 0.0093 Infestation severity C23 0.0071 0.0071 0.0036 0.0071 0.0071 0.0036 0.0071 0.0000 0.0071 Update seedling density C24 0.0289 0.0048 0.1013 0.0024 0.0000 0.1688 0.0265 0.0000 0.0096 Soil alkaline nitrogen C31 0.0000 0.0210 0.0116 0.0183 0.0102 0.0005 0.0135 0.0079 0.0085 Available phosphorus C32 in soil 0.0000 0.0043 0.0053 0.0069 0.0079 0.0292 0.0064 0.0048 0.0059 Soil organic matter C33 0.0167 0.1330 0.0345 0.1695 0.0657 0.0000 0.0820 0.0336 0.0559 Herb richness index C41 0.0048 0.0091 0.0025 0.0047 0.0017 0.0048 0.0106 0.0037 0.0000 Herbaceous diversity index C42 0.0068 0.0097 0.0015 0.0050 0.0045 0.0088 0.0111 0.0039 0.0000 Herbaceous layer coverage C43 0.0133 0.0175 0.0058 0.0225 0.0000 0.0091 0.0150 0.0183 0.0158 Litter cover C44 0.0439 0.0000 0.0154 0.0252 0.0033 0.0318 0.0165 0.0186 0.0165 Vertical stand structure C45 0.0338 0.0338 0.0338 0.0338 0.0000 0.0676 0.0676 0.0000 0.0676 Shrub cover C46 0.0000 0.0462 0.1521 0.0632 0.0000 0.1179 0.0444 0.0000 0.0427 Canopy closure of the tree layer: C47 0.1271 0.0000 0.0000 0.0000 0.1466 0.0945 0.1075 0.2379 0.1075

[0119] The comprehensive index results of the nine plantations in this example are as follows: Prunus armeniaca and Hippophae rhamnoides forest 0.5638, Prunus armeniaca and Caragana korshinskii forest 0.4433, Hippophae rhamnoides forest 0.3978, Caragana korshinskii forest 0.3954, Robinia pseudoacacia and spruce forest 0.3689, Prunus armeniaca, Prunus persica and Alfalfa forest 0.3572, Prunus persica forest 0.3178, Prunus armeniaca forest 0.3169, and spruce forest 0.2727.

[0120] The model is simple in structure, efficient in computation, and highly interpretable, making it suitable for comprehensive evaluation of forest ecology. The model quantifies a comprehensive index in the range of 0 to 1, with larger values ​​indicating better stand health, thus achieving a quantitative expression of stand health and facilitating horizontal comparisons of different plantations.

[0121] Step S5: Health Level Determination

[0122] Operation process:

[0123] Pre-set grading standards: Based on the actual condition of the regional plantations, four health levels are defined:

[0124] Level 1 (Healthy): Overall Index ≥ 0.5;

[0125] Level 2 (Sub-health): 0.4 ≤ Comprehensive Index < 0.5;

[0126] Level 3 (Inefficient): 0.3 ≤ Comprehensive Index < 0.4;

[0127] Level 4 (Severely Inefficient): Overall Index < 0.3.

[0128] Level determination: The classification is completed by combining the comprehensive index.

[0129] Level 1 (Healthy): Apricot and Seabuckthorn Forest;

[0130] Level 2 (Sub-health): Apricot and Caragana forest;

[0131] Level 3 (low efficiency): Sea buckthorn forest, Caragana forest, Robinia pseudoacacia forest, Prunus armeniaca forest, Prunus armeniaca forest, Prunus armeniaca forest;

[0132] Level 4 (Severely Inefficient): Spruce Forest.

[0133] The quantitative values ​​are transformed into intuitive health levels, and the evaluation results are easy to understand and can directly guide field work: healthy forest stands maintain the existing management model; sub-healthy forest stands carry out routine tending; low-efficiency forest stands implement targeted transformation measures such as replanting, pruning, and water and fertilizer improvement; severely low-efficiency forest stands focus on comprehensive transformation, realizing a closed loop of evaluation and application.

[0134] This invention also provides a system for evaluating the health of plantation stands, with the functions, connections, and workflows of each module as follows:

[0135] Data acquisition module: Connects to field monitoring equipment and manual record sheets, automatically / manually inputs raw data such as plant height, soil nutrients, pests and diseases, and forest stand structure, and synchronously transmits them to the indicator system construction module and data processing module.

[0136] Indicator system construction module: It has a built-in preset hierarchical indicator template and automatically matches the corresponding indicator system according to the evaluation area. In this embodiment, it calls the 16 indicator system of the Loess Hilly Area.

[0137] The weight calculation module consists of three units that work sequentially: the subjective weight calculation unit runs the analytic hierarchy process (AHP) program to automatically construct the judgment matrix, perform consistency checks, and solve for the subjective weights; the objective weight calculation unit runs the entropy weight method program to calculate the information entropy and objective weights based on standardized data; and the comprehensive weight fusion unit calls the fusion formula to output the final comprehensive weights.

[0138] Data processing module: Built-in positive / negative indicator judgment rules and range standardization formula, automatically completes dimensionless data processing, and outputs standardized scores.

[0139] Comprehensive evaluation module: It calls the comprehensive weight and standardized score, runs the linear weighted model, and automatically calculates and outputs the comprehensive health index of each type of plantation forest.

[0140] Level determination module: It has a built-in health level threshold standard. After receiving the comprehensive index, it automatically matches the level and generates and outputs a complete evaluation report containing the original data, weights, comprehensive index, health level, and improvement suggestions.

[0141] System technical benefits: This system proceduralizes the entire evaluation process, reducing manual calculation workload and human error. It supports simultaneous evaluation of large batches of plantation forest samples, is suitable for routine monitoring and regional surveys by forestry departments, and has a high degree of automation and practicality.

[0142] Overall Application Summary

[0143] This invention's method and system are tailored to the characteristics of plantations in the Loess Plateau region, employing a customized index system that integrates subjective and objective weighting methods to improve weight accuracy. The entire process is standardized and reproducible. The evaluation results clearly distinguish the health differences among plantations of different tree species and management patterns. For example, in this embodiment, the overall health of mixed forests is better than that of pure forests, while the health of pure spruce forests is the worst, consistent with actual ecological performance. Based on the evaluation results, replanting and mixed planting, as well as soil improvement, can be implemented in severely inefficient forests (spruce forests); pruning and thinning can be carried out on inefficient forests, truly achieving evaluation-guided plantation management and inefficient forest transformation, with strong technical applicability.

[0144] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0145] The block diagrams of devices, apparatuses, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0146] It should also be noted that in the apparatus, device, and method of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of the present invention.

[0147] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0148] It should be understood that the qualifying terms "first", "second", "third", "fourth", "fifth" and "sixth" used in the description of the embodiments of the present invention are only used to more clearly illustrate the technical solutions and are not intended to limit the scope of protection of the present invention.

[0149] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for evaluating the health of plantation forest stands, characterized in that, Includes the following steps: S1. Data Collection and Indicator System Construction: Collect multi-dimensional raw indicator data of the plantation to be tested, and construct a hierarchical indicator system for evaluating stand health based on the hierarchical principle. The hierarchical indicator system includes, from top to bottom, the target layer, the criterion layer, and the indicator layer. The target layer is the stand health of the plantation. The criterion layer includes four dimensions: stand productivity, resistance and resilience, soil nutrients, and stand structure. The indicator layer consists of detailed detection indicators set for each criterion layer. S2. Weight Calculation: Calculate the subjective weight of each indicator in the indicator layer using the analytic hierarchy process (AHP), and simultaneously calculate the objective weight of each indicator using the entropy weight method. Combine the subjective and objective weights to obtain the comprehensive weight of each indicator. S3. Data Standardization Processing: Distinguish between positive and negative indicators in the raw indicator data, and perform dimensionless standardization using the range standardization method to obtain standardized indicator scores. S4. Construct a comprehensive evaluation model and calculate the comprehensive index: Combine the comprehensive weight and the standardized index score to construct a comprehensive evaluation model for stand health. Substitute the data into the model to calculate the comprehensive index of stand health of the plantation to be tested. S5. Health Level Determination: Retrieve the preset stand health level classification standards, combine them with the calculated stand health comprehensive index, determine the stand health level of the plantation to be tested, and complete the plantation stand health evaluation.

2. The method for evaluating the health of plantation stands according to claim 1, characterized in that, In step S1, the index layer indicators corresponding to the forest stand productivity include plant height and productivity; the index layer indicators corresponding to the resistance and resilience include fire disturbance degree, disease degree, insect pest degree, and regeneration seedling density; the index layer indicators corresponding to the soil nutrients include soil available nitrogen, soil available phosphorus, and soil organic matter; and the index layer indicators corresponding to the forest stand structure include herb richness index, herb diversity index, herb cover, litter cover, stand vertical structure, shrub cover, and tree canopy closure.

3. The method for evaluating the health of plantation stands according to claim 1, characterized in that, The specific steps for calculating subjective weights using the analytic hierarchy process (AHP) in step S2 are as follows: S201. Based on the hierarchical index system, construct a pairwise comparison judgment matrix for each index layer by layer; S202. Perform a consistency check on each judgment matrix. If the consistency check result meets the preset pass standard, proceed to the next step; if it does not pass, revise the judgment matrix until it passes the check; S203. Solve for the largest eigenvalue and the corresponding eigenvector of the pass judgment matrix, normalize the eigenvector, and obtain the subjective weights corresponding to each index.

4. The method for evaluating the health of plantation stands according to claim 3, characterized in that, In step S202, the consistency check includes calculating the consistency index and the consistency ratio. When the consistency ratio is less than or equal to the preset threshold, the judgment matrix check is deemed to be qualified.

5. The method for evaluating the health of plantation stands according to claim 1, characterized in that, The specific steps for calculating the objective weight using the entropy weight method in step S2 are as follows: S211, normalize the standardized index scores obtained after processing in step S3 to obtain the index probability values; S212, calculate the information entropy corresponding to each index based on the index probability values; S213, calculate the coefficient of variation of each index based on the information entropy, and then calculate the proportion based on the coefficient of variation of all indicators to obtain the objective weight corresponding to each index.

6. The method for evaluating the health of plantation stands according to claim 1, characterized in that, The formula for calculating the overall weight in step S2 is as follows: ,i=1,…,n In the formula: —The overall weight of the i-th indicator; W i —The weights of each stability index obtained by the analytic hierarchy process; Mi—the weights of each stability index obtained by the entropy weight method; S i —Combined weight of the two methods.

7. The method for evaluating the health of plantation stands according to claim 1, characterized in that, The calculation method for the range standardization method in step S3 is as follows: For positive indicators where larger values ​​equate to better performance, the standardized formula is: ； For negative indicators where smaller values ​​equate to better performance, the standardized formula is: ）； In the formula: x ij —The j-th type of plantation corresponding to the i-th stability index; y ij —Stability index value after standardization.

8. The method for evaluating the health of plantation stands according to claim 1, characterized in that, The expression for the comprehensive evaluation model of forest stand health in step S4 is: In the formula: E – A comprehensive index of plantation stability; i — the i-th stability; S i —The weight of the i-th stability evaluation index; y i — The score for stability of the i-th digit.

9. A system for evaluating the health of plantation stands, characterized in that, A method for evaluating the health of plantation stands according to any one of claims 1-8, comprising: a data acquisition module for collecting raw indicator data of various indicators of the plantation to be tested in the field and transmitting the raw indicator data to an indicator system construction module and a data processing module; an indicator system construction module connected to the data acquisition module for receiving the raw indicator data and constructing a hierarchical indicator system for evaluating stand health; and a weight calculation module connected to the indicator system construction module, comprising a subjective weight calculation unit, an objective weight calculation unit, and a comprehensive weight fusion unit; wherein the subjective weight calculation unit is used to calculate the subjective weights of the indicators using the analytic hierarchy process (AHP), and the objective weight calculation unit is used to calculate the objective weights of the indicators using the entropy weight method. The comprehensive weight fusion unit is used to fuse subjective and objective weights to obtain a comprehensive weight; the data processing module, connected to the data acquisition module and the weight calculation module respectively, is used to distinguish between positive and negative indicators, and to perform dimensionless processing on the original indicator data using the range standardization method, outputting standardized indicator scores; the comprehensive evaluation module, connected to the weight calculation module and the data processing module respectively, is used to call the comprehensive weight and standardized indicator scores, run the comprehensive evaluation model to calculate the comprehensive forest stand health index; the grade determination module, connected to the comprehensive evaluation module, is used to retrieve the preset health grade standard, combine it with the comprehensive forest stand health index to determine the health grade of the plantation forest stand, and output the final evaluation result.

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