AHP-based big tree health rapid evaluation method

Abstract

The invention discloses a big tree health rapid evaluation method based on AHP. According to the method, six indexes are selected from leaves, branches, trunks and roots to construct an evaluation system, the weight coefficient of each index is determined, scores are calculated according to the weights, and the health level of the big tree is divided into four grades. The system comprises an index selection module, a system construction module, a weight determination module and an evaluation grading module. The evaluation system is high in pertinence, reasonable in index and scientific in weight, complex instruments are not needed, the health condition of the big tree can be rapidly and accurately evaluated, and powerful support is provided for big tree protection.

Description

Technical Field

[0001] The invention relates to the technical field of large tree protection, and in particular to a large tree health rapid evaluation method based on AHP. Background Art

[0002] Big trees (specifically trees with a trunk diameter of more than 40cm) are living organisms of natural plant life, resources and wealth left by nature to mankind, and carriers of human history and civilization. The quality of big tree protection is a touchstone for measuring the management level of a modern city; and with the acceleration of urbanization, the protection of big tree resources is also facing huge challenges. Accurately judging the health status of big trees is an important prerequisite for doing a good job in big tree protection.

[0003] In terms of big tree protection, experts and scholars have conducted a lot of research, and various parts of the country have also formulated corresponding technical standards or procedures for growth monitoring, maintenance and rejuvenation, such as the "Technical Regulations for Growth and Environmental Monitoring of Ancient and Famous Trees", "Technical Specifications for Maintenance and Rejuvenation of Urban Ancient and Famous Trees", "Technical Regulations for Management of Ancient and Famous Trees", etc., which regulate and require the growth monitoring, maintenance and rejuvenation of big trees. However, for the health evaluation of big trees, only the scope of judgment of growth potential is generally defined, and a systematic and complete indicator system has not been established.

[0004] Theoretically, the more indicators for evaluating the health of large trees, the wider the coverage of tree characteristics, and the more accurate the judgment of the growth of trees. For example, experts and scholars such as Chen Junqi have included multiple indicators such as large tree morphological characteristics, site conditions, and physical and chemical properties of leaves into the health evaluation system. This seems scientific, but in actual operation, there are many measuring instruments involved, the statistical amount is too large, and the weight of each indicator is difficult to accurately grasp. Especially when there are many large trees, the measurement and judgment are cumbersome and complicated, and the practicality is lacking; but if too few quantitative indicators are selected, the growth of trees cannot be fully evaluated, and the scientific integrity may be questionable. In 2014, Beijing issued a local standard - "Technical Regulations for Rapid Health Diagnosis of Ancient and Famous Trees", which has important guiding significance for the rapid health diagnosis of ancient and famous trees in Beijing. However, due to the differences in climate and environment between the north and the south, there are also obvious differences in the types and growth habits of ancient trees and large trees, which are not suitable for rapid health diagnosis of ancient trees and other large trees in the south. Therefore, the rapid health diagnosis technology of large trees is still in its infancy, and it is still necessary to conduct in-depth analysis and discussion based on previous research and combined with actual scenarios and applications. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a method for rapid tree health evaluation based on AHP, comprising the following steps:

[0006] S1. Analyze the factors that affect the health of large trees, clarify the principles of indicator system construction according to national standards and combined with the actual standard requirements of the region, determine the indicators that affect various factors, build a large tree health evaluation indicator system, and formulate the characteristic coding of large tree health evaluation indicators;

[0007] S2, determine the weight of the evaluation index system, and obtain the weight of each index of the large tree health evaluation system through the judgment matrix;

[0008] S3, based on the determined weight coefficients of each evaluation index, determine the overall evaluation ranking, based on the overall evaluation ranking, establish a comprehensive evaluation model, score the health of the trees through various indicators and weights, divide the health of the trees into four levels according to the scores, and determine the health level of the trees.

[0009] Furthermore, the tree health evaluation index system includes three layers, namely:

[0010] The first layer is the target layer, including the rapid evaluation value A of tree health;

[0011] The second layer is the structural layer, including the influence of leaves B1, branches B2, trunks B3 and roots B4;

[0012] The third layer is the indicator layer, which includes sub-influencing factors of leaves B1, branches B2, trunks B3 and roots B4, including normal leaf rate C1, dead and broken branch ratio C2, new shoot sprouting ratio C3, trunk inclination C4, trunk damage degree C5 and effective range of root growth C6, a total of 6 indicators.

[0013] Furthermore, in step S2, constructing the judgment matrix also includes performing a consistency check on the judgment matrix; if the consistency check passes, determining the weight of each item, calculating the indicator value according to the plan, and calculating the evaluation result; if the consistency check fails, reconstructing the judgment matrix.

[0014] Furthermore, the normal leaf rate C1 is to randomly select a standard branch in the southeast, northwest, northeast and southwest directions of the crown, and count the proportion of normal leaves to the total number of leaves; the dead and broken branch ratio C2 is counted by on-site observation, and the proportion of dead or broken branches to the crown during the normal growing season; the new shoot germination ratio C3 is to observe the overall new leaves or new shoots germination of the crown, and calculate the percentage of the sprouting part area to the total crown area, or randomly select four branches in the east, west, south and north, and calculate the proportion of the sprouting leaf area to the whole branch area; the trunk inclination C4 is to measure the angle between the center line of the tree trunk and the vertical direction of the ground, which can reflect the degree of inclination of the tree trunk; the trunk damage degree C5 is to approximate each missing bark as a rectangle, take its longest and widest part to estimate the area of ​​the rectangle, approximate the trunk as a cylinder, calculate the side area of ​​the cylinder as the trunk area, and take the ratio as the trunk damage degree; the effective root growth range C6 is to measure the ratio of the tree hole area to the crown projection range.

[0015] Furthermore, characteristic codes for large tree health evaluation indicators are formulated, including marking them as different characteristic codes according to the specific proportions of normal leaf rate, dead and broken branch ratio, new shoot sprouting ratio, trunk inclination, trunk damage degree, and effective root growth range.

[0016] Furthermore, the characteristic coding of normal leaf rate is as follows: 5 points for healthy leaves above 95%, 4 points for healthy leaves between 95% and 80%, 3 points for healthy leaves between 80% and 60%, 2 points for healthy leaves between 60% and 30%, and 1 point for healthy leaves below 30%. The characteristic coding of dead and broken branch ratio is as follows: 5 points for dead branches less than 5% or almost no dead branches or scattered small dead branches, 5 points for dead branches with obvious small dead branches or dead and broken branches in the crown. %-10% is marked as 4 points, 1 point is marked as 10%-20% of the main branches above level 2 withered or broken branches, 2 points are marked as 20%-30% of the main branches above level 2 withered or broken branches, and 1 point is marked as more than 30% of the crown area of ​​the main branches above level 1 withered or broken branches; the characteristic coding of the new shoot germination ratio is: 5 points for the new shoot germination ratio of 90%, 4 points for the new shoot germination ratio of 80%, and 5 points for the new shoot germination ratio of 10%-10%. 70% is marked as 3 points, the new shoot germination ratio is 50% is marked as 2 points, and the new shoot germination ratio is less than 50% is marked as 1 point; the characteristic coding of trunk inclination is: trunk inclination ≤10° is marked as 5 points, trunk inclination ≤15° is marked as 4 points, trunk inclination ≤20° is marked as 3 points, trunk inclination ≤30° is marked as 2 points, and trunk inclination ≥30° is marked as 1 point; the characteristic coding of trunk damage degree is: trunk no damage is marked as 5 points, trunk damage degree ≤ 5% is marked as 4 points, trunk damage degree ≤15% is marked as 3 points, trunk damage degree ≤30% is marked as 2 points, and trunk damage degree greater than 30% is marked as 1 point; the characteristic coding of the effective range of root growth is specifically: effective range of root growth ≥95% is marked as 5 points, effective range of root growth ≥70% is marked as 4 points, effective range of root growth ≥45% is marked as 3 points, effective range of root growth ≥20% is marked as 2 points, and effective range of root growth below 20% is marked as 1 point.

[0017] Further, in step S2, constructing a judgment matrix specifically includes: comparing each element with each other and determining the weight of each structural layer to the target layer, that is, judging the indicators of the structural layer in pairs; constructing an N*N judgment matrix for the structural layer, in which the diagonal is the judgment of each indicator itself, and its importance is 1. If two elements have the same importance, the scale is 1, the former is slightly more important than the latter, the scale is 3, the former is obviously more important than the latter, the scale is 5, the former is extremely important than the latter, the scale is 7, the former is strongly important than the latter, and the intermediate value scale of the above adjacent judgments is 2, 4, 6, 8; the importance scale of the corresponding two factors exchanged in order is the reciprocal of 1 to 9

[0018] Furthermore, in step S2, the weights of various indicators are obtained by constructing a judgment matrix; the weights are calculated using the square root method, specifically:

[0019] Calculate the mth power of the product of each row to obtain an m-dimensional vector;

[0020]

[0021] Normalizing the vector gives the weight vector:

[0022]

[0023] After obtaining the weight matrix, calculate the maximum characteristic root, the formula is:

[0024]

[0025] Where n is the number of dimensions, Aw is the weight of the judgment matrix after standardization, and then the accumulated value is calculated by row;

[0026] Solve the CR value based on the CI and RI values ​​to determine whether its consistency is passed;

[0027]

[0028] When CI = 0, it is determined that the matrix is ​​completely consistent at this time; when the CI value is larger, the matrix consistency is lower. A random consistency index RI is introduced to weaken the negative impact of the order n. The consistency ratio CR is:

[0029]

[0030] When CR<0.1, it indicates that the consistency of the judgment matrix A is considered to be within the allowable range, and the eigenvector of A can be used to calculate the weight vector; when CR≥0.1, it indicates that a logical error occurs when constructing the matrix, and the judgment matrix needs to be corrected.

[0031] Furthermore, the comprehensive evaluation model in step S3 is: HI = ∑A i K i

[0032] In the formula, HI is the tree health evaluation score, A i is the evaluation score of the ith indicator, K i is the evaluation weight of the i-th indicator.

[0033] Furthermore, the four levels of step S are: 4≤HI≤5, indicating healthy; 3≤HI<4, indicating frail; 2≤HI<3, indicating weak; and HI<2, indicating endangered.

[0034] The present invention has the following beneficial effects:

[0035] (1) The evaluation system constructed by the present invention is highly targeted and suitable for rapid evaluation of the health of large trees in southern China, which can effectively solve the problem that the existing technology is not applicable to local areas.

[0036] (2) Six indicators that can directly reflect the health status of trees are selected from the four aspects of leaves, branches, trunks, and roots, including normal leaf rate, dead and broken branch ratio, new shoot germination ratio, trunk inclination, trunk damage degree, and effective root growth range. This can not only comprehensively cover the key factors affecting the health of large trees, but also avoid the cumbersome operation caused by too many indicators.

[0037] (3) This study started from reality and comprehensively selected multiple indicators of leaves, branches, trunks and roots that can directly reflect the health status of trees. The statistical method is standardized, the data is relatively easy to obtain, and the operability is strong, which is convenient for screening out large trees and famous trees that are in urgent need of rescue, and provides a basic basis for the next step of targeted comprehensive diagnosis and protection. It has strong practical significance, does not require complex instruments, is easy to operate, and is convenient for large-scale tree surveys and health grading, providing strong guidance for large tree protection practices. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is the overall flow chart of the large tree health rapid evaluation method based on AHP of the present invention; DETAILED DESCRIPTION

[0039] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0040] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections.

[0041] In order to simplify the drawings, only the parts related to the present invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, in order to simplify the drawings and facilitate understanding, in some figures, only one of the parts with the same structure or function is schematically drawn or marked. In this article, "one" not only means "only one", but also means "more than one".

[0042] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0043] In the embodiments shown in the drawings, the indications of directions (such as up, down, left, right, front and back) used to explain the structure and movement of the various components of the present invention are not absolute but relative. These descriptions are appropriate when these components are in the positions shown in the drawings. If the descriptions of the positions of these components change, the indications of these directions also change accordingly.

[0044] In addition, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0046] Wuhan is located in the middle and lower reaches of the Yangtze River plain, in the eastern part of the Jianghan Plain, at 113°41′-115°05′ east longitude and 29°58′-31°22′ north latitude. It is a subtropical monsoon humid climate zone, with abundant rainfall, sufficient sunshine, four distinct seasons, high temperature and concentrated precipitation in summer, and slightly cool and humid winter. In a year, the lowest average temperature is 3.0℃ in January, the highest average temperature is 29.3℃ in July, the summer lasts 135 days, and the spring and autumn seasons are about 60 days each. The rainfall is concentrated in the early summer plum rain season, with an annual precipitation of 1205 mm.

[0047] Therefore, this embodiment uses the ancient trees in Wuhan as a sample of large trees for evaluation. As a historical and cultural city, Wuhan attaches great importance to the protection of ancient trees and other large trees, and has organized experts and scholars to conduct resource surveys and statistics on existing ancient trees and other large trees many times. According to the 2023 Greening Bulletin of the Wuhan Municipal Bureau of Landscape and Forestry, there are currently 1,803 ancient trees and other large trees registered in Wuhan, including 58 first-level ancient trees (over 500 years old), 176 second-level ancient trees (over 300 years old), and 1,569 third-level ancient trees. Native tree species such as hackberry, Pistacia chinensis, and Gleditsia sinensis account for a large proportion.

[0048] Figure 1 This is the overall flow chart of the AHP-based rapid tree health evaluation method in this embodiment. Specifically, an index evaluation system is first established:

[0049] In order to improve diagnostic efficiency and effectively guide practice, we referred to the national industry standard "Technical Specifications for Maintenance and Rejuvenation Engineering of Urban Ancient and Famous Trees" (GBT 51168-2016) and the Beijing local standard "Technical Regulations for Rapid Diagnosis of Health of Ancient and Famous Trees" (DB11 / T 1113-2014), combined with the actual situation in Wuhan, to construct a rapid tree health evaluation system in Wuhan.

[0050] The rapid evaluation value of tree health is set as the target layer A; the leaves, branches, trunks and roots are set as the structural layer B; the normal leaf rate, the proportion of dead and broken branches, the proportion of new shoots, the inclination of the trunk, the degree of trunk damage, and the effective range of root growth, a total of 6 specific indicators that can directly reflect the health status of trees are selected as the indicator layer C, as shown in Table 1:

[0051] Table 1 Rapid evaluation system for tree health in Wuhan

[0052]

[0053] Secondly, indicator selection and determination:

[0054] Normal leaf rate, the color, shape and number of tree leaves can directly reflect the health status of trees. Normal leaves refer to leaves with normal color, no obvious loss of green or yellowing; no mosaic, wrinkling or curling; no obvious diseases or insect pests. The statistics are calculated by on-site observation. A standard branch is randomly selected in the four directions of the crown of the tree, and the proportion of normal leaves to the total number of leaves is calculated, which is the normal leaf rate.

[0055] The proportion of dead or broken branches is calculated by on-site observation, which is the proportion of dead or broken branches to the tree crown during the normal growing season.

[0056] The proportion of new shoots sprouting, the sprouting and growth of new shoots of trees, can effectively reflect the renewal of trees and is an important manifestation of the growth vitality of large trees. Therefore, the proportion of new shoots sprouting is also an important indicator for evaluating the health of trees. The on-site observation method is used to observe the sprouting of new leaves or new shoots in the crown as a whole, and calculate the percentage of the sprouting area to the total crown area; or randomly select four branches in the east, west, south and north to calculate the proportion of the sprouting leaf area to the whole branch area.

[0057] The inclination of the trunk is measured by measuring the angle between the center line of the tree trunk and the vertical direction of the ground, which can reflect the degree of inclination of the tree trunk.

[0058] The degree of trunk damage. During the growth of trees, various types of decay, breakage, and even holes may appear on the trunk due to factors such as natural lightning strikes or human damage. The degree of damage is from shallow to deep in the bark (including phloem), cambium, and xylem. When calculating the degree of trunk damage, the focus should be on the missing or damaged bark on the trunk, and the proportion of the area of ​​missing or damaged bark to the trunk area should be calculated. For ease of measurement, each missing bark can be approximated as a rectangle, and the area of ​​the rectangle can be estimated by taking its longest and widest part; the trunk can be approximated as a cylinder, and the side area of ​​the cylinder can be calculated as the trunk area; the ratio is the degree of trunk damage. Effective root growth space

[0059] The root growth range generally corresponds to the vertical projection range of the crown, so in practice the vertical projection range of the crown is often regarded as the effective growth space of the root system. On this basis, the ratio of the actual tree pit area to the vertical projection area of ​​the crown is calculated. The larger the ratio, the larger the tree pit area, the fewer unfavorable factors such as hard pavement, the wider the effective root growth space, and the more beneficial it is to the healthy growth of trees, and vice versa.

[0060] Formulate characteristic coding for tree health evaluation indicators:

[0061] Table 2 Characteristic coding of rapid health evaluation indicators for large and famous trees

[0062]

[0063]

[0064] Determination of evaluation index weight coefficient:

[0065] The proportion of each indicator is weighed comprehensively, and the selected indicators are compared pairwise with reference to expert opinions to establish a judgment matrix. The scale value and meaning of the judgment matrix are shown in Table 3. Then, the weight coefficient of each indicator is determined in turn using the hierarchical analysis method, as shown in Table 4. The weight objectively reflects the relative importance of each indicator to a certain factor in the indicator layer.

[0066] Table 3 Establishment of judgment matrix for pairwise comparison

[0067]

[0068]

[0069] The square root method is used to calculate the weight, specifically:

[0070] Calculate the mth power of the product of each row to obtain an m-dimensional vector;

[0071]

[0072] Normalizing the vector gives the weight vector:

[0073]

[0074] After obtaining the weight matrix, calculate the maximum characteristic root, the formula is:

[0075]

[0076] Where n is the number of dimensions, Aw is the weight of the judgment matrix after standardization, and then the accumulated value is calculated by row;

[0077] Solve the CR value based on the CI and RI values ​​to determine whether its consistency is passed;

[0078]

[0079] Table 4 Weight table of indicators in C-Ai structure layer

[0080]

[0081] Calculated by the square root method:

[0082] λ max =4.205; CI = 0.068; CR = 0.071 < 0.10. Therefore, it can be judged that the matrix consistency in Table 4 meets the requirements.

[0083] Table 5 is the consistency test result of the branch index judgment matrix. By calculating λ max =2, CI=0, RI=0, CR=0.000<0.10, it can be judged that the consistency of the branch index judgment matrix meets the requirements.

[0084] Table 5 Consistency test of branch index judgment matrix

[0085]

[0086] Table 6 shows the consistency test results of the trunk index judgment matrix. By calculating λ max =2, CI=0, RI=0, CR=0.000<0.10, it can be judged that the consistency of the trunk index judgment matrix meets the requirements.

[0087] Table 6 Consistency test of tree trunk index judgment matrix

[0088]

[0089] Overall evaluation ranking:

[0090] According to the calculated weight coefficients of each evaluation index, the overall evaluation ranking is listed, and the results are shown in Table 7:

[0091] Table 7. Overall ranking of each indicator layer

[0092]

[0093] The calculation results show that C1, i.e. the normal leaf rate, and C5, the degree of trunk damage, each with a weight of 0.2700, have the highest weight, indicating that the health of the leaves and the degree of trunk damage directly reflect the health of the tree and are important indicators in health evaluation; followed by C2, the proportion of dead and broken branches, and C3, the proportion of new shoots, each with a weight of 0.1250, which also account for a large proportion in tree health evaluation. C5, the inclination of trees, is more closely related to tree safety, and accounts for a relatively low proportion in health evaluation.

[0094] Construction of a rapid health diagnosis model and evaluation of tree health levels:

[0095] Construct a rapid tree health diagnosis model, the model is, HI = ∑A i K i

[0096] Where HI is the health evaluation score of large trees, A i is the evaluation score of the ith indicator, K i is the evaluation weight of the i-th indicator.

[0097] The rapid evaluation score of tree health is calculated through various indicators and weights, and its health level is divided into 4 levels. The corresponding scores and evaluation criteria of each level are shown in Table 8.

[0098] Table 8 Comprehensive score of tree health

[0099]

[0100] For large trees assessed as "healthy", daily inspections and care are sufficient to prevent accidental damage caused by emergencies; for large trees assessed as "weak", daily maintenance should be strengthened, and measures such as fertilizer and water management, disease and pest control, crown trimming, and dead branch cleaning can be taken according to the actual growth conditions. Attention should be paid to increasing the number of inspections, paying attention to growth conditions, and preventing further deterioration; for trees judged to have a growth potential level below severe (including severe), a comprehensive health diagnosis including soil testing, root detection, trunk cavities, chlorophyll content determination, and other measures should be immediately carried out, and a special protection plan should be issued based on the comprehensive health diagnosis, and special rejuvenation protection should be implemented; large trees assessed as "endangered" should be reported immediately, and it is recommended that emergency protection should be carried out within at least 1 week. In the event of extreme weather such as high temperature and heavy rain, rescue should be carried out immediately and on-site protection should be provided.

[0101] Using the above health assessment method, a rapid health diagnosis was conducted on some large trees in Donghu Hi-Tech Zone, Dongxihu District and Wuchang District. The results are as follows:

[0102] Table 9 Summary of health evaluation of large trees in Wuhan

[0103]

[0104]

[0105]

[0106] Through the rapid health assessment of a total of 43 large trees in Wuchang District, Dongxihu District and Donghu High-tech Zone, it was found that 24 trees were in the weak level, accounting for the highest proportion of 55.81%, followed by 14 trees in the healthy level, accounting for 32.56%; 3 trees were seriously weak, accounting for 6.98%; the number of endangered trees was the least, with only 2 trees in an endangered state, accounting for 4.65%. According to the provisions of 3.0.6 of the "Technical Specifications for the Maintenance and Rejuvenation of Ancient and Famous Trees in Cities" (GBT 51168-2016), large and famous trees that are seriously weak, endangered or have safety hazards should be rejuvenated. Therefore, among the 43 large trees surveyed this time, 5 large trees that need special protection are seriously weak or above; the remaining 38 large trees are well maintained and inspected on a daily basis, and the overall maintenance condition is good.

[0107] Among them, the normal leaf rate and the degree of trunk damage reflect the health status of trees most directly. There are 40 trees with a normal leaf rate of more than 80%, and 2 trees with a normal leaf rate of less than 60%, which is basically consistent with the overall health evaluation. Affected by the freezing rain in Wuhan in early 2024, this survey found that the number of dead and broken branches of large and famous trees increased significantly. Only 9 trees had no obvious dead and broken branches. Most trees, especially camphor trees, had obvious dead and broken branches. There were 7 large trees with broken main branches. Among them, the main branch of a hackberry tree located in the East Lake High-tech Zone was severely torn, causing serious damage to the trunk. There are 43 trees with a new shoot germination ratio of more than 80%, which is basically consistent with the normal leaf rate distribution.

[0108] In this survey, about 50% of the trees had some trunk damage. Among them, there were 3 trees with a damage ratio of more than 30%. The most serious ones were the Chinese mulberry and bitter paper located in the East Lake High-tech Zone, with damage areas of 73.39% and 69.70% respectively. Regular trunk repair and observation are needed to prevent further damage. There are 28 trees with an effective root growth range of more than 95%, accounting for 62.2%. This is mainly because the proportion of trees in remote urban areas in this survey is relatively high. Most trees grow in the countryside and the proportion of hard pavement is low; there are 7 trees with an effective space of less than 20%, and the lowest two are 3.58% and 3.81% respectively. The tree pit area is only 5-9 square meters, which limits the growth of the root system. Most of the trees (31 trees) have no tilt in the trunk, 8 trees have a tilt within 15 degrees, and 2 trees have a tilt greater than 30 degrees, one in Zuoling and one in Dongshan. Support and protection have been built.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A rapid tree health evaluation method based on AHP, characterized in that: The steps include: S1. Analyze the factors that affect the health of large trees, clarify the principles of indicator system construction according to national standards and combined with the actual standard requirements of the region, determine the indicators that affect various factors, build a large tree health evaluation indicator system, and formulate the characteristic coding of ancient large tree health evaluation indicators; S2, construct a judgment matrix, and obtain the weights of each indicator of the tree health evaluation system through the judgment matrix; S3, based on the determined weight coefficients of each evaluation index, determine the overall evaluation ranking, based on the overall evaluation ranking, establish a comprehensive evaluation model, score the health of the trees through various indicators and weights, divide the health of the trees into four levels according to the scores, and determine the health level of the trees.

2. The evaluation method according to claim 1, characterized in that: The tree health evaluation index system consists of three layers: The first layer is the target layer, including the rapid evaluation value A of tree health; The second layer is the structural layer, including the influence of leaves B1, branches B2, trunks B3 and roots B4; The third layer is the indicator layer, which includes sub-influencing factors of leaves B1, branches B2, trunks B3 and roots B4, including normal leaf rate C1, dead and broken branch ratio C2, new shoot sprouting ratio C3, trunk inclination C4, trunk damage degree C5 and effective range of root growth C6, a total of 6 indicators.

3. The evaluation method according to claim 1, characterized in that: In step S2, constructing the judgment matrix also includes performing a consistency check on the judgment matrix; If the consistency test passes, determine the weight of each item, calculate the indicator value according to the plan, and calculate the evaluation result; If the consistency check fails, the judgment matrix is ​​reconstructed.

4. The evaluation method according to claim 2, characterized in that: The normal leaf rate C1 is to randomly select a standard branch in the southeast, northwest, northeast and southwest directions of the crown, and calculate the proportion of normal leaves to the total number of leaves; the dead and broken branch ratio C2 is calculated by on-site observation, and the proportion of dead or broken branches to the crown during the normal growing season; the new shoot germination ratio C3 is to observe the overall new leaves or new shoots of the crown, and calculate the percentage of the sprouting area to the total crown area, or randomly select four branches in the east, west, south and north to calculate the proportion of the sprouting leaf area to the whole branch area; the trunk inclination C4 is to measure the angle between the center line of the tree trunk and the vertical direction of the ground, which can reflect the degree of inclination of the tree trunk; the trunk damage degree C5 is to approximate each missing bark as a rectangle, take its longest and widest part to estimate the area of ​​the rectangle, approximate the trunk as a cylinder, calculate the side area of ​​the cylinder as the trunk area, and take the ratio as the trunk damage degree; the effective root growth range C6 is to measure the ratio of the tree hole area to the crown projection range.

5. The evaluation method according to claim 1, characterized in that: The development of characteristic codes for large tree health evaluation indicators includes marking them as different characteristic codes according to the specific proportions of normal leaf rate, dead and broken branch ratio, new shoot sprouting ratio, trunk inclination, trunk damage degree, and effective root growth range.

6. The evaluation method according to claim 5, characterized in that: The characteristic coding of normal leaf rate is as follows: 5 points for healthy leaves above 95%, 4 points for healthy leaves between 95% and 80%, 3 points for healthy leaves between 80% and 60%, 2 points for healthy leaves between 60% and 30%, and 1 point for healthy leaves below 30%. The characteristic coding of dead branches ratio is as follows: 5 points for dead branches less than 5% or almost no dead branches or scattered small dead branches, 5 points for obvious small dead branches or dead branches between 5% and 10% for healthy leaves. % is marked as 4 points, 1 point is marked as 10%-20% of the main branches above level 2 with dead branches or broken branches, 2 points are marked as 20%-30% of the main branches above level 2 with dead branches or broken branches, and 1 point is marked as more than 30% of the crown area of ​​the main branches above level 1 with dead branches or broken branches; the characteristic coding of the new shoot germination ratio is: 5 points for the new shoot germination ratio of 90%, 4 points for the new shoot germination ratio of 80%, and 5 points for the new shoot germination ratio of 70%. The characteristic coding of trunk inclination is as follows: 5 points for trunk inclination ≤10°, 4 points for trunk inclination ≤15°, 3 points for trunk inclination ≤20°, 2 points for trunk inclination ≤30°, and 1 point for trunk inclination ≥30°; the characteristic coding of trunk damage degree is as follows: 5 points for trunk without damage, 5 points for trunk damage degree ≤5% It is marked as 4 points, trunk damage degree ≤15% is marked as 3 points, trunk damage degree ≤30% is marked as 2 points, and trunk damage degree greater than 30% is marked as 1 point; the characteristic coding of the effective range of root growth is: effective range of root growth ≥95% is marked as 5 points, effective range of root growth ≥70% is marked as 4 points, effective range of root growth ≥45% is marked as 3 points, effective range of root growth ≥20% is marked as 2 points, and effective range of root growth below 20% is marked as 1 point.

7. The evaluation method according to claim 1, characterized in that: In step S2, constructing a judgment matrix specifically includes: comparing each element with each other and determining the weight of each structural layer to the target layer, that is, judging the indicators of the structural layer pairwise; constructing an N*N judgment matrix for the structural layer, in which the diagonal is the judgment of each indicator itself, and its importance is 1. If two elements have the same importance, the scale is 1, the former is slightly more important than the latter, the scale is 3, the former is obviously more important than the latter, the scale is 5, the former is extremely important than the latter, the scale is 7, the former is strongly important than the latter, and the intermediate value scale of the above adjacent judgments is 2, 4, 6, 8; the importance scale of the corresponding two factors exchanged in order is the reciprocal of 1 to 9.

8. The evaluation method according to claim 1, characterized in that: In step S2, the weights of various indicators are obtained by constructing a judgment matrix; the weights are calculated using the square root method, specifically: Calculate the mth power of the product of each row to obtain an m-dimensional vector; Normalizing the vector gives the weight vector: After obtaining the weight matrix, calculate the maximum characteristic root, the formula is: Where n is the number of dimensions, Aw is the weight of the judgment matrix after standardization, and then the accumulated value is calculated by row; Solve the CR value based on the CI and RI values ​​to determine whether its consistency is passed; When CI = 0, it is determined that the matrix is ​​completely consistent at this time; when the CI value is larger, the matrix consistency is lower. A random consistency index RI is introduced to weaken the negative impact of the order n. The consistency ratio CR is: When CR<0.1, it indicates that the consistency of the judgment matrix A is considered to be within the allowable range, and the eigenvector of A can be used to perform weight vector calculation; when CR≥0.1, it indicates that a logical error occurs when constructing the matrix, and the judgment matrix needs to be corrected.

9. The evaluation method according to claim 1, characterized in that: The comprehensive evaluation model in step S3 is: HI = ∑A i K i , Where HI is the health evaluation score of large trees, A i is the evaluation score of the ith indicator, K i is the evaluation weight of the i-th indicator.

10. The evaluation method according to claim 9, characterized in that: The four levels of step S are: 4≤HI≤5, indicating healthy; 3≤HI<4, indicating frail; 2≤HI<3, indicating weak; and HI<2, indicating endangered.

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