CN111784190A - Construction method and application of forest stand spatial structure evaluation model - Google Patents
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Abstract
The invention relates to forest stand spatial structure evaluation, in particular to a construction method and application of a forest stand spatial structure evaluation model. The scheme of the invention comprises the following steps: (1) determining a forest stand space structure unit; (2) constructing a forest stand spatial structure evaluation index system; (3) dividing forest stand space structure levels; (4) establishing a corresponding relation between the index value interval and the evaluation level; (5) and establishing a forest stand space structure evaluation standard model. The method provides a new thought and method for evaluating the reasonability of the forest stand spatial structure based on the application of the entropy weight-cloud model method, realizes the uncertainty mapping between the evaluation index and the evaluation grade, and has more objective and accurate evaluation conclusion than the forest stand spatial structure index method based on the multiplication-division method principle.
Description
Technical Field
The invention relates to forest stand space structure evaluation, in particular to a construction method and application of a forest stand space structure evaluation model.
Background
For natural forests or artificial forests, forest stand structures are important characteristics of forest stands and are also the basis of forest management and management, and are always the focus and emphasis of research. The forest stand space structure research mainly comprises three aspects of determination of forest stand space structure units, evaluation index selection and quantification, evaluation method selection and the like. The method for determining the forest stand space structure unit comprises a structure four-group method, an adjacent tree method, a method for determining the nearest neighbor of the central tree by using a Voronoi diagram, a method for determining the nearest neighbor of the central tree by using a weighted Voronoi diagram and the like. The evaluation index selection and quantification mainly comprises a size ratio, an angular scale, a degree of mixing, a forest layer index, a degree of openness, a degree of aggregation and the like, the evaluation indexes are numerous, all the evaluation indexes are mutually dependent or exclusive, the existing research mainly analyzes a single index, but the weight of the index is not considered in the evaluation, and the evaluation conclusion is difficult to avoid.
Disclosure of Invention
In order to comprehensively evaluate the rationality of the forest stand spatial structure, an index system of the forest stand spatial structure is urgently needed to be constructed, the index weight is scientifically assigned, and the problem of uncertain mapping between quantitative indexes and qualitative evaluation results is solved.
The present study shows that: scientific evaluation of forest stand space structure rationality requires solving several problems: (1) scientifically constructing an evaluation index system, wherein a plurality of indexes for evaluating the spatial structure of the forest stand exist, and part of the indexes are contained or repeated and cannot be seen all over, so that the evaluation index system is determined after comprehensive analysis and consideration are needed; (2) the influence of each index on the spatial structure of the forest stand is different, so index weights need to be distinguished, and an objective weight method is adopted as far as possible to reduce the influence of human factors; (3) in the forest stand space structure rationality evaluation, the problem of mutual conversion between quantitative indexes and qualitative evaluation grades is solved. In the research, a forest stand spatial structure index system model is constructed according to a forest stand spatial structure theoretical principle, the weight of each index is calculated by using an entropy weight method, and finally the reasonability level of the forest stand spatial structure is evaluated by using a cloud model.
The invention provides a novel construction method and application of a forest stand spatial structure evaluation model, aiming at overcoming the defect of single index in the prior art. The construction method of the forest stand space structure evaluation model comprises the following steps:
(1) determining a forest stand space structure unit;
(2) constructing a forest stand spatial structure evaluation index system;
(3) dividing forest stand space structure levels;
(4) establishing a corresponding relation between the index value interval and the evaluation level;
(5) and establishing a forest stand space structure evaluation standard model.
Another preferable scheme of the invention is that the step (1) of determining the forest stand space structure unit adopts a Thiessen polygon to determine the nearest neighbor wood of the central wood.
Another preferable scheme of the invention is that in the step (2), a forest stand spatial structure evaluation index system is constructed by adopting 5 indexes of full mixing degree, magnitude ratio, cross angle competition index, opening ratio, angle scale and the like.
According to another preferable scheme of the invention, when the forest stand spatial structure evaluation index system is constructed in the step (2), the index weight is also assigned.
According to another preferable scheme of the invention, a normal cloud model is further constructed for each index when the forest stand spatial structure evaluation index system is constructed in the step (2).
Another preferable scheme of the present invention is that the step (3) divides the spatial structure of the forest stand into five levels.
The method adopts an entropy weight method to calculate the weight so as to determine the influence of each index factor on the space structure of the forest stand. The entropy weight method calculates the entropy weight of each index by using the information entropy according to the index value of each single tree in the sample plot, and then corrects the weight of each index through the entropy weight, thereby obtaining the objective index weight of the forest stand spatial structure. In the process of calculating the membership degree of the forest stand spatial structure grade of the sample plot by using the cloud model, the boundary of each forest stand spatial structure index grade is converted into 'cloud', and the final average membership degree is obtained by repeatedly operating the forward cloud generator for many times. Theoretically and practically, the method provided by the invention utilizes an entropy weight-cloud-based model to evaluate the reasonability of the forest stand space structure, and is more objective and scientific.
Drawings
FIG. 1 is a 'angle scale' membership cloud picture
Detailed Description
The invention respectively sets 5 fixed research sample plots in three areas, namely a large country nature protection area in an entrance ring lake, a dragon tiger mountain forest farm, a reed head country forest farm and the like, of representative forest stands. An evaluation index system consisting of 5 indexes such as full mixing degree, size ratio, opening ratio, angle scale and angle competition index is constructed; the method comprises the steps of objectively calculating the weight of an evaluation index by adopting an entropy weight method, comprehensively evaluating the reasonability level of the forest stand spatial structure based on a cloud model method, and comparing the evaluation result with a forest stand spatial structure index method based on a multiplication-division method principle. According to the comparison result, as for the single index, except that the index grade of one sample is in the second stage (II) to the fifth stage (V), the other samples are all distributed between the second stage (II) and the fourth stage (IV), and the third stage (III) is more than one, and accounts for 52% of the total grade, and the second stage (II) and the fourth stage (IV) account for 46.7% of the total grade, which indicates that most index grades are in the middle grade. From the comprehensive evaluation result, the reasonability grades of the spatial structures of the forest stands of all the sample plots are a third grade (III) and a fourth grade (IV), wherein the third grade (III) accounts for 2/3 of all the sample plots, and the result shows that the spatial structures of the natural forest stands of the Hongting lake are reasonable as a whole and are at a medium upper grade. Therefore, the application of the entropy weight-cloud model method provides a new thought and method for forest stand space structure rationality evaluation, uncertainty mapping between evaluation indexes and evaluation levels is achieved, and evaluation conclusion is more objective and accurate than a forest stand space structure index method based on a multiplication-division principle.
The scheme of the invention is explained in detail by the following embodiments, and the scheme of the embodiments of the invention comprises the following steps:
(1) determining a forest stand space structure unit;
(2) constructing a forest stand spatial structure evaluation index system;
(3) dividing forest stand space structure levels;
(4) establishing a corresponding relation between the index value interval and the evaluation level;
(5) and establishing a forest stand space structure evaluation standard model.
Furthermore, the spatial structure of the forest stand is evaluated by using the model.
Study area and data overview
Overview of the region of investigation
The method is characterized in that a large country natural protection area in an Huandong lake, a Longhu mountain land forest land and a Lutou national forest land are taken as researches, 5 fixed research sample plots are arranged on each forest land according to different altitudes by selecting representative forest stands, the sample plot area is 20mx20m, 765 trees with DBH larger than 5cm are researched in total, 53 kinds of trees belong to 25 families and 40 families. Five broad-leaf tree species including camphor, sweetgum, cedar, cyclobalanopsis glauca and castanopsis sclerophylla are widely distributed in 3 investigation areas. The examination results are shown in table 1.
TABLE 1 overview of sample survey plots
1 detailed description of the invention
1.1 entropy weight method
The entropy weight method adopted by the invention is an objective weighting method, the weight is determined according to the information provided by each index observation value, the deviation caused by artificial subjective factors can be avoided, and the calculation steps are as follows:
(1) and constructing a judgment matrix. M evaluation objects, n evaluation indexes, rijThe values of the j (j) th evaluation index representing the i (i) th evaluation object (1, 2, …, m) th evaluation object (1, 2, …, n) th evaluation index are used to establish a normalized matrix (r)ij)m*n。
(2) And (5) index standardization treatment. Because the selected evaluation index units are different, non-dimensionalization processing is required, and the positive index and the negative index of the range difference method are quantized to be between 0 and 1 in the research.
(3) And calculating the entropy value of each index. Let the entropy of the jth index be HjThe calculation method is as follows:
in the formula:
the specific gravity of each index is shown, and m represents an evaluation target.
(4) Calculating the entropy weight of the jth evaluation index:
wherein n represents an evaluation index, WjNamely the weight set of the jth evaluation index.
1.2 cloud model
The Li Deyi Hospital provides an uncertainty conversion model-cloud model between qualitative concepts and quantitative values aiming at the defects of probability theory and fuzzy mathematics in the aspect of processing uncertainty, researches the relevance between ambiguity and randomness, and integrates the ambiguity and the randomness together to form mutual mapping between the qualitative concept and the quantitative concept. The positive Taiyang cloud model is the most important cloud model, and all cloud models constructed by the method are based on the normal cloud model.
The invention sets U as a quantitative discourse domain represented by an accurate numerical value, C is a qualitative concept on U, if the quantitative value x ∈ U and x is a random realization of the qualitative concept C, if x-N (Ex, En '2), wherein En' N (En, He2) and the membership degree to C satisfies:
the distribution over the domain of discourse U is said to be a normal cloud. The normal cloud model is characterized by three values of expected Ex, entropy En and super-entropy He [11, 14]. Ex is expected to represent the central value of the concept within the domain of interest; the entropy En represents a cloud drop value interval of a qualitative concept in a domain of discourse; the super-entropy He is the entropy of the entropy En, representing the degree of dispersion of cloud droplets. From the concept of cloud theory, the eigenvalues (Ex) of the normal cloud model can be determinedij，Enij，Heij) Characteristic value Exij、Enij、HeijIs shown below, HeijIn generalDetermined by empirical values.
Heij＝k (5)
1.3 principle idea of multiplication and division
The basic idea based on the multiplication-division principle: if x is the decision vector, when in m targets f (x)1)，…，f(xm) In, there are k f (x)1)，…，f(xk) Require achievement of the maximum, remaining f (x)k+1)，...，f(xm) It is desirable to achieve a minimum with f (x)1)，…，f(xm) If > 0, then the evaluation function Q (x) is used as the objective function, and the calculation formula of the objective function Q (x) is shown below.
2 construction of forest stand spatial structure evaluation system
2.1 determination of spatial building blocks of forest stands
The forest stand space structure unit is a basis for calculating a space structure index and analyzing forest stand space structure characteristics, and the number of the nearest adjacent trees is the most key problem for determining the forest stand space structure unit. Comprehensively analyzing the existing research results, the method adopts the Thiessen polygon to determine the nearest neighbor wood of the central wood, in order to facilitate modeling in ArcGIS, edge correction is carried out by setting a buffer area, and in a sample plot after edge correction, forest trees in the buffer area only serve as edge wood to participate in calculation of each index, but not serve as the central wood. Considering that the sample plot range is small, the buffer area range set by the research is 2 meters, survey sample plot data is subjected to plane display through XY coordinates, corresponding Thiessen polygons are generated, and the IDs of adjacent trees are found through spatial connection by utilizing the characteristic that a single Thiessen polygon only contains a unique forest and is adjacent to a polygon generated by surrounding forests.
2.2 construction of forest stand spatial structure evaluation index System
The forest stand space structure generally refers to three aspects of forest spatial distribution patterns, tree species mixing, forest competition and the like. The tree species mixture is usually expressed by the mixture degree, and the full mixture degree adopted by the invention is the best choice for describing the forest mixture. The spatial distribution of the forest is divided into horizontal distribution and vertical distribution, the horizontal distribution is described by an angular scale, and the vertical distribution is described by an open index. The wood competition includes not only the size of the trees around the trees, but also the covering of the competitive trees on the upper part of the object wood and the extrusion of the side wings, which are expressed by the size ratio and the crossing angle competition index respectively. Combining the above analysis, the research uses 5 indexes of full mixing degree, magnitude ratio, cross angle competition index, open ratio, angle scale and the like to construct a forest stand spatial structure evaluation index system, namely, the factor universe of the research U ═ U { (U {)1，u2，u3，u4，u5}. The description of each index is shown in table 2.
TABLE 2 forest stand spatial structure index description
Wherein, the formula of the competition index of the intersection angle is as follows:
2.3 dividing forest stand spatial structure rationality level
According to the indexes of the forest stand spatial structure described in table 2, with reference to the division standard of Cao Xiao Yu on the evaluation level of the forest stand spatial structure, a forest stand spatial structure evaluation level table as shown in table 3 is constructed, and the reasonability of the forest stand spatial structure is divided into five levels from low to high, namely, the comment domain V ═ { V ═ V of the research1，v2，v3，v4，v5}。
TABLE 3 forest stand spatial structure evaluation grading
2.4 establishment of corresponding relation between index value interval and evaluation grade
According to the forest stand space structure rationality levels defined in table 3, each evaluation index value is divided into intervals to correspond to the corresponding levels. In the evaluation index system, because the optimal value of the angular scale is randomly distributed and is close to 0.5, in order to better match the evaluation grade, the value range [0, 1] of the angular scale is subtracted by 0.5, and then the absolute value of the results of the parts of [ -0.5, 0] is taken, so that the value range of the angular scale is changed into [0, 0.5], the final optimal value is close to 0, and the finally constructed index partition grade matrix is shown in table 4.
TABLE 4 forest stand space structure index partition matrix
3 study of excess syndrome
3.1 calculation of spatial structure index value of forest stand in same plot and determination of grade
Table 5 shows the results obtained by calculating specific numerical values of the evaluation indexes of 15 plots from the descriptions of the forest stand spatial structure indexes in table 2, and comparing the index values of the plots with the interval values of the evaluation index levels in table 4 to obtain the levels corresponding to the indexes of the plots. As can be seen from table 5, the evaluation indexes all have the second to fifth ranks (II) to (V), and mainly have the third to fourth ranks (III) to (IV).
TABLE 5 stand space structure index grade
3.2 entropy weight-cloud model forest stand spatial structure evaluation
3.2.1 index weight calculation
The indexes representing the reasonability of the forest stand space structure are more, the indexes are mutually dependent or exclusive, and the forest stand space structure is influenced by the indexes in different sizes, so that when a plurality of indexes are comprehensively adopted for evaluating the reasonability of the forest stand space structure, the indexes need to be assigned, and the objectivity of an evaluation result is directly influenced by a weight assignment method. The research has better objectivity in calculating the weight by using an entropy weight method, and can avoid subjective factors in the assignment process. The weights of the indexes of the forest stand space structure obtained by applying the entropy weight method are shown in the table 6.
TABLE 6 forest stand spatial structure evaluation index weight matrix
3.2.2 evaluation index cloud model parameter calculation
Cloud model parameters of 5 indexes in an evaluation index system corresponding to grades are obtained according to a positive Taiwanese model digital feature calculation formula, a forest stand space structure evaluation index normal cloud model feature parameter matrix shown in a table 7 is constructed, and the super entropy in the research is determined according to experience.
TABLE 7 forest stand spatial structure evaluation index normal cloud model characteristic parameter matrix
3.2.3 fuzzy membership matrix calculation
And (3) calculating the membership degree of each evaluation index by using the normal cloud model parameters corresponding to each evaluation grade in the table 7 and by using the forward generator, repeatedly operating the forward generator 1000 times to obtain higher accuracy due to randomness of results, and finally obtaining the membership degree of each evaluation index, wherein the membership cloud graphs of each grade are obtained by taking the angular scale as an example and are respectively shown in the table 8 and the figure 1.
TABLE 8A 01 sample evaluation index membership matrix
3.2.4 comprehensive evaluation results
Taking an a01 sample plot as an example, multiplying the membership matrix Z in table 8 by the index weight matrix W in table 6 to obtain the membership of the rational level of each forest stand spatial structure corresponding to the a01 sample plot, and according to the maximum membership principle, the rational level of the forest stand spatial structure of the a01 sample plot is level III, as shown in table 9.
Table 9A 01 sample rationality level membership and evaluation results
The results of the evaluation of the sample a01 are shown in table 10, which shows the respective levels of rationality of the spatial structure of all samples.
TABLE 10 membership degree and comprehensive evaluation grade of all sample plot index grades
3.3 evaluation of forest stand spatial structure index based on multiplication and division method
3.3.1 forest stand spatial structure evaluation index calculation method
According to the analysis, in the forest stand space structure evaluation index system, the full mixing degree and the opening ratio are preferably selected, and the minimum ratio, the cross angle competition index and the angle scale are preferably selected. The calculation formula for determining the evaluation index of the spatial structure according to the multiplication-division principle is as follows:
in formula (7): mx，Kx，Ux，UCIxAnd WxRespectively the total mixing degree, the opening ratio, the size ratio, the crossing angle competition index and the angle scale of the single woodM，σK，σU，σUCIAnd σWRespectively, the full mixing degree, the open ratio, the size ratio, the cross angle competition index and the standard deviation of the angle scale.
3.3.2 evaluation criteria and calculation results of spatial Structure
In order to analyze and compare the evaluation indexes of the various samples, the values are converted into a [0, 1] interval by a normalization method.
In formula (8): l' and L respectively represent values before and after forest stand spatial structure evaluation index normalization; l ismax，LminRespectively representing the maximum and minimum values in the sample data.
And mapping the calculation result to the evaluation index grade of the forest stand space structure by adopting an equal-interval division method, taking 0.2 as a segmentation point, taking the value smaller than 0.2 as an I grade, and taking the value larger than 0.8 as a V grade, and performing normalization to obtain the same sample evaluation result shown in the table.
TABLE 11 forest stand space structure evaluation results based on multiplication-division principle
3.4 comparative analysis of evaluation results
In order to verify the feasibility of the entropy weight-cloud model method in the forest stand space structure rationality evaluation, the single-factor minimum level, the single-factor median level and the forest stand space structure index evaluation result based on the multiplication method in the forest stand space structure index system of the research are compared and analyzed with the forest stand space structure evaluation structure based on the entropy weight-cloud model, and the obtained comparative analysis conditions are shown in table 8.
TABLE 12 forest stand space structure rationality evaluation structure comparison
In table 8, the single factor lowest level samples the lowest level of the five index levels, and the single factor median level samples the middle level of the five index levels. In 15 sampling samples, the evaluation grade obtained by the forest stand space structure index method based on the multiplication and division principle is even lower than the lowest grade of single-factor evaluation, such as A05 and A09, which obviously has the disadvantage of not conforming to the conventional treatment; in the entropy weight-cloud model-based forest stand spatial structure evaluation structure, 12 sample plots are higher than a single-factor lowest level, the other 3 sample plots are equal to the single-factor lowest level, 11 sample plot levels are equal to a single-factor median level, 3 evaluation levels in the other 4 sample plots are higher than the single-factor median level, and the level of 1 sample plot is lower than the single-factor median level, which is obviously closer to the real situation.
4 results and analysis
As can be seen from table 5, the indexes of the same plots except for the index grades of the plot a06 distributed from the second stage (II) to the fifth stage (V), and the indexes of the same plots distributed from the second stage (II) to the fourth stage (IV) and accounted for 52% of the total grades by the third stage (III), whereas the indexes of the second stage (II) and the fourth stage (IV) accounted for 46.7% of the total grades, and all the indexes of any one plot were not high or low. In the tree species mixing aspect, the mixing state of most sample plots is in the middle level, which occupies 2/3, the fourth level (IV) occupies 1/5 of the sample plots, and the mixing degree of the rest sample plots belongs to the second level (II), which indicates that the mixing degree of the lake region of the ringworm is in the middle state as a whole. The tree competition aspect represents that the magnitude ratio of the horizontal competition level is in two grades of a second grade (II) and a third grade, wherein the second grade accounts for 3/5 and indicates that the competition wood is weaker with the adjacent wood; the competition index of intersection angles belongs to the third level (III) and the fourth level (IV), and the third level occupies 4/5, which indicates that the pressure of the tree, which is covered by the upper part and squeezed by the side wings, is moderate, and the tree can not be seriously hindered from growing. In the aspect of tree spatial distribution, the angular scales for expressing horizontal distribution are in the third level (III) and the fourth level (IV), and the fourth level accounts for 4/5, which indicates that the horizontal distribution of trees is reasonable; the open ratio for expressing the vertical distribution is in the second grade (II) to the fifth grade (V), which is the only evaluation index spanning four grades, but the third grade (III) and the fourth grade (IV) account for 4/5, which shows that the vertical distribution is ideal, the open degree of A06 is better, and the growth of trees is greatly influenced and needs to be adjusted by taking corresponding measures when A08 and A14 are in a relatively sheltered state. The above analysis shows that, for a single index, in any sample plot, some indexes are ranked higher and other indexes are ranked lower, so that it is not intuitive to use a single index to assess the rationality of the forest stand space structure of the whole sample plot.
Table 10 shows the comprehensive evaluation results based on the entropy weight-cloud model, and finally 15 sample plot evaluation results are obtained as a third-level (III) and a fourth-level (IV), wherein the third-level (III) accounts for 2/3 of all sample plot samples, which indicates that the spatial structure of the forest stand of the ringdongting lake is relatively reasonable as a whole and is at a medium upper level, which is consistent with the overall health condition of the current ringdongting lake forest stand, and indicates the scientificity and feasibility of the entropy weight-cloud model method for evaluating the rationality of the spatial structure of the forest stand.
5 conclusion
The invention constructs a forest stand spatial structure evaluation index system consisting of 5 indexes, and assigns the forest stand spatial structure evaluation index for the first time by an entropy weight assignment method, namely an objective weight assignment method. In order to realize uncertain mapping between quantitative evaluation indexes and qualitative evaluation grades, a cloud model is firstly applied to forest stand space structure rationality evaluation and is compared and analyzed with a comprehensive forest stand space structure evaluation method based on a multiplication-division method principle.
(1) The traditional forest stand spatial structure analysis mainly uses single index factor analysis, a traditional forest stand spatial structure homogeneity evaluation or forest stand spatial structure comprehensive index evaluation method based on a multiplication-division principle breaks through the traditional forest stand spatial structure analysis method, a plurality of index factors are adopted to comprehensively evaluate the forest stand spatial structure, the influence of indexes on the forest stand spatial structure is not considered, multiplication-division calculation is carried out only by taking the size as the best mode, and certain unscientific performance can be known by comparing the evaluation result with a single index evaluation structure.
(2) The entropy weight-cloud model evaluation method not only comprehensively considers a plurality of index factors influencing the spatial structure of the forest stand, but also adopts the entropy weight method to calculate the weight so as to determine the influence of each index factor on the spatial structure of the forest stand. The entropy weight method calculates the entropy weight of each index by using the information entropy according to the index value of each single tree in the sample plot, and then corrects the weight of each index through the entropy weight, thereby obtaining the objective index weight of the forest stand spatial structure. In the process of calculating the membership degree of the forest stand spatial structure grade of the sample plot by using the cloud model, the boundary of each forest stand spatial structure index grade is converted into 'cloud', and the final average membership degree is obtained by repeatedly operating the forward cloud generator for many times. Theoretically and practically, the method utilizes the entropy weight-cloud model to evaluate the reasonability of the forest stand space structure, and is more objective and scientific.
Claims (7)
1. A construction method of a forest stand spatial structure evaluation model is characterized by comprising the following steps:
(1) determining a forest stand space structure unit;
(2) constructing a forest stand spatial structure evaluation index system;
(3) dividing forest stand space structure levels;
(4) establishing a corresponding relation between the index value interval and the evaluation level;
(5) and establishing a forest stand space structure evaluation standard model.
2. The method for constructing a forest stand space structure evaluation model according to claim 1, wherein the step (1) of determining forest stand space structure units is to determine nearest neighbor trees of the central trees by using Thiessen polygons.
3. The method for constructing the forest stand space structure evaluation model according to claim 1, wherein 5 indexes of the full mixing degree, the magnitude ratio and the intersection angle competition index, the opening ratio and the angle scale are adopted in the step (2) to construct a forest stand space structure evaluation index system.
4. The method for constructing the forest stand space structure evaluation model according to claim 1, wherein index weights are also assigned when the forest stand space structure evaluation index system is constructed in the step (2).
5. The method for constructing the forest stand spatial structure evaluation model according to claim 1, wherein a normal cloud model is further constructed for each index when the forest stand spatial structure evaluation index system is constructed in the step (2).
6. The method for constructing a forest stand space structure evaluation model according to claim 1, wherein the forest stand space structure is divided into five levels.
7. The application of the method for constructing the forest stand space structure evaluation model according to claim 6, wherein the constructed forest stand space structure evaluation model is applied to the evaluation of the reasonability of the forest stand space structure.
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