Secondary forest development stage division method based on forest stand state characteristics

Abstract

The invention discloses a secondary forest development stage division method based on forest stand state characteristics, and belongs to the technical field of forest development stage division. Comprising the following steps: selecting an initial index set, performing index forward and standardization processing on sample plot survey data, and forming a data set; classifying the data by adopting a system clustering analysis method, checking and analyzing a state index influencing a classification result, and selecting plt; the index of 0.05 constitutes a secondary forest development stage division index system; carrying out weight assignment, then correcting the weight by adopting an entropy evaluation method, and determining the comprehensive weight of the index by utilizing a Lagrange multiplier method; the sum of the dimension-removed value of the division index and the comprehensive weight value product is a development stage value; according to an equidistant method, the development stage of the secondary forest is divided into a forest gap stage, a renewal stage, a differentiation stage, a built-up stage and a stable stage. Depending on forest age is avoided, and the method is suitable for multi-tree mixed secondary forests; the indexes are easy to obtain, and the division result can accurately guide forest management.

Description

Technical Field

[0001] The present invention relates to the technical field of forest development stage division, and in particular to a method for dividing secondary forest development stages based on stand state characteristics. Background Art

[0002] Forest growth and development exhibit dynamic characteristics over time, with distinct stages. Given that different growth stages require different forest management strategies, scientifically classifying growth and development stages is crucial for achieving sustainable forest management. Early stage classification methods primarily relied on forest appearance and structural characteristics. Subsequently, researchers gradually incorporated comprehensive indicators such as species composition dynamics, dominant species succession sequences, and ecosystem service functions, resulting in a variety of classification systems.

[0003] The most direct approach to studying forest growth and development stages is to classify forests based on stand age. Stand age classification is a simple and intuitive method for categorizing forest development stages, widely used in large-scale forest management and ecological function assessment. Its close correlation with forest growth characteristics makes it highly operational. The theoretical basis for stand age classification can be traced back to the comprehensive silvicultural works of the German scientist Cotta in the mid-19th century. In his study of plant community succession, Clements (1916) proposed that stand age could be used as an important indicator for determining forest succession stages. Subsequently, Oliver divided the forest development process after a major disturbance into four stages: initial stand development, disturbance removal, understory regeneration, and old age, and systematically described the characteristics of each stage. In actual forest surveys, stands are typically divided into five stages: overmature, mature, near-mature, middle-aged, and young. Specifically, stands younger than five years old are generally considered to be in the early succession stage, those aged between six and 15 years are in the middle succession stage, and those older than 15 years are in the late succession stage.

[0004] Analyzing forest structural characteristics, such as tree density, canopy layers, tree height, and diameter distribution, to classify forest development stages is a more comprehensive research approach. This approach not only focuses on stand age but also emphasizes the dynamic changes in forest physical structure, providing a more comprehensive reflection of the successional process and developmental state of forest ecosystems. As early as 1925, ASWat used the forest cycle model to describe the sequential phases of forest transition from gap formation to regeneration, community establishment, maturity, and aging. Oliver and Larson (1996), in their study of forest community dynamics, comprehensively considered factors such as stand age, structure, and function and proposed a method for classifying forest development stages based on the forest life cycle and community succession. This can be simply summarized as the primary stage, transition stage, maturity stage, decline stage, and regeneration and recovery stage. Emborg, by analyzing the structure of the uppermost canopy and the diameter at breast height (DBH), divides forests into four stages: gap formation, sapling regeneration, maturity, and aging. Based on field data from Altamira and Bragança, Tucker et al. (1998) proposed a complex index system for classifying forest succession stages. The core index is the ratio of the basal area at breast height of trees within a certain diameter at breast height range to the total basal area at breast height. Moran et al. (2000) defined forest development stages based on average stand height and basal area at breast height. They found significant overlap between these two indicators for stages 2 and 3 of succession, making their distinction difficult. In a regional study, Lu Yuanchang (2006) classified six developmental succession stages based on forest characteristics in Beijing: establishment, quality formation, growth and tending, target tree growth, stock growth, and persistent forest. Goodell (2007) used a combination of tree species composition, stand density, and diameter structure to classify state-owned forests in northern New York into five types: young forest, pole forest, mature forest, transitional forest, and old forest. Wang Xiaoping et al. (2008) proposed dividing forests into five stages based on regional characteristics and management conditions, namely, the community establishment stage, the competitive growth stage, the quality selection stage, the near-natural forest stage, and the natural persistent forest stage.

[0005] TWINSPAN (Two-Way Indicator Species Analysis) is a classification and analysis method used in community ecology. It primarily categorizes samples based on species distribution patterns and is widely used in grouping studies of ecological data. Proposed by Hill (1979), TWINSPAN is a multivariate analysis method based on species abundance data and widely used in vegetation and ecology. In recent years, researchers have used TWINSPAN to conduct an initial classification of mixed spruce-fir broadleaved forests, combining factors such as forest species composition, site conditions, and environmental factors. Zhou Mengli, for example, combined structural and growth analysis of each stand type to ultimately determine the developmental stage of mixed spruce-fir broadleaved forests. Jia Ke used the TWINSPAN classification method to identify seven successional stages in temperate forests in Wangqing County and summarized the characteristics of each successional stage. Based on field surveys, Zhao Rong used TWINSPAN to classify and ordinate Larix principis-rupprechtii communities in Shanxi Province, providing a reference for the classification of forest successional stages. Zhang Gaosheng used TWINSPAN and DCA ordination methods to quantitatively classify the modern Yellow River Delta plant communities, providing methods and theoretical basis for the division of forest development stages and forest management according to stages.

[0006] Ordered clustering is a method that clusters data and sorts them according to a specific criterion. Its core goal is to optimize the order of clusters while clustering to meet specific requirements. Data objects are grouped so that objects within the same group are highly similar and objects between different groups are highly different. This method also introduces order into the clustering results, creating a certain sequential relationship between clusters. Sneath et al. (1963) provided the theoretical basis for ordered clustering and other classification methods. Early scholars, both domestic and international, used ordered cluster analysis to classify forest succession development stages. Cong Zhefu (1989) used ordered clustering to classify the growth and development stages and age periods of Tianshan spruce forests (Picea schrenkiana var. tianschanica) into regeneration, young forest, pole timber forest, middle-aged forest, near-mature forest, mature forest, and overmature forest. Bao Qing et al. used Fisher's optimal partitioning method combined with ordered clustering of indicators to divide natural larch forests (Larix gmelinii Pupr.) in the Heihe region into five stages: seedling, sapling, rapid growth, stable growth, and maturity. Cai Xuelin et al. used ordered clustering to divide plantations of Chinese fir (Cunninghamia lanceolata) and Masson pine (Pinus massoniana Lamb.) into four stages: sapling, rapid growth, dry wood, and maturity. Regarding the applicability of ordered clustering to natural forests, Glatthorn (2018) verified the effectiveness of cluster analysis in identifying developmental stages in pristine beech forests.

[0007] Among existing methods, age-based classification is suitable for plantations. However, for mixed, uneven-aged forests with varying tree species composition, age is difficult to determine, limiting its applicability. Structural-based classification methods often use indicators such as age, tree height, and diameter at breast height (DBH), remaining grounded in the concept of plantation developmental stage classification and therefore less applicable. This is particularly true for secondary forests with mixed species, which require a comprehensive evaluation that integrates species composition, ecological function, and multiple indicators. The TWINSPAN method avoids reliance on stand age structure; however, in practical applications, misclassification of taxa may occur, necessitating appropriate manual adjustments by specialized researchers. Ordered clustering methods are suitable for analyzing data with natural order, such as time series and ecological succession, and have broad applications in fields such as ecology and geography. However, their computational complexity is high, and particularly for large datasets, they can require long computation times. Furthermore, these methods are sensitive to parameter settings and the choice of objective function, susceptible to subjective factors, and the validity and interpretability of the results often rely on domain expertise. Summary of the Invention

[0008] In order to solve the problems raised in the above-mentioned prior art, the purpose of the present invention is to provide a method for dividing the development stages of secondary forests based on forest stand status characteristics.

[0009] To achieve the above objectives, the present invention provides the following technical solutions:

[0010] The present invention provides a method for dividing secondary forest development stages based on stand state characteristics, comprising the following steps:

[0011] Step 1: Screening of stand status characteristic indicators

[0012] An initial set of indicators was selected based on the three dimensions of stand vitality, stand structure, and tree species diversity;

[0013] Carry out index positive processing and standardization on the sample site survey data and form a data set;

[0014] The mean distance method in the hierarchical cluster analysis was used to classify the normalized and standardized data. Based on the cluster analysis results, the Kruskal-Wallis nonparametric test was used to analyze the state indicators that affect the classification results. The indicators with a significant difference level of p < 0.05 were selected to form the secondary forest development stage classification index system.

[0015] Step 2: Determine the comprehensive weight of indicators

[0016] According to the significance of non-parametric tests of each stand status index, the weights were assigned using the analytic hierarchy process, and then the entropy method was used to correct them to reduce the influence of subjective factors in the index assignment process. The comprehensive weights of the indicators were determined using the Lagrange multiplier method.

[0017] Step 3: Calculate the comprehensive value of the stand development stage

[0018] The sum of the product of the dimensionless value of the division index and the comprehensive weight value is the development stage value;

[0019] Step 4: Divide Developmental Stages

[0020] Based on the calculated secondary forest development stage values ​​of stand status characteristics, the secondary forest development stage is divided into five stages: gap period, regeneration period, differentiation period, establishment period and stable period according to the equal interval method.

[0021] Furthermore, the indicators screened in step 1 include: stand vitality index, stand structure index and tree species diversity index;

[0022] The stand vitality indicators include the proportion of seedling trees, natural regeneration level, dominance of dominant species, hectare basal area and volume of stock;

[0023] The stand structure indicators include non-spatial structure indicators and spatial structure indicators. Among them, non-spatial structure indicators include stand diameter distribution q value, average tree height and stand crowding. Spatial structure indicators include horizontal structure and vertical structure. Specific indicators include intermixing degree, angular scale and forest layer.

[0024] The tree species diversity indices include the Shannon-Wiener index, the Margalef richness index, the Pielou evenness index and the Simpson diversity index.

[0025] Furthermore, the formula for the forward processing is:

[0026] x ‘ =Cx (1)

[0027] In the formula, x is the original data and C is a fixed value;

[0028] The formula for the standardization process is:

[0029]

[0030] min(x) and max(x) are the minimum and maximum values ​​of the data set, respectively.

[0031] Furthermore, in step 2, the calculation formula of the comprehensive weight is:

[0032]

[0033] Where: j is the comprehensive weight of the jth indicator, W j is the subjective weight calculated by AHP method, w j is the objective weight calculated by the entropy method, and m is the total number of indicators.

[0034] Furthermore, in step 3, the calculation formula for the developmental stage value is:

[0035]

[0036] Where: W is the comprehensive value of the forest stand state, which is a number between [0, 1], x j is the standardized value of the j-th indicator.

[0037] Furthermore, in step 4, the comprehensive threshold value of the gap period is W∈[0-0.2), the comprehensive threshold value of the renewal period is W∈[0.2-0.4), the comprehensive threshold value of the differentiation period is W∈[0.4-0.6), the comprehensive threshold value of the construction period is W∈[0.6-0.8), and the comprehensive threshold value of the stable period is W∈[0.8-1].

[0038] Furthermore, the stand status characteristics at each development stage are as follows:

[0039] Gap stage: The forest window area is large, the individuals of the dominant species are scattered, there are few regeneration seedlings / saplings, the trees are clustered, and the degree of intermixing is low;

[0040] Regeneration period: Regeneration in forest gaps is intensive, the number of founder species sprouts increases, pioneer species appear occasionally, and the degree of intergrowth transitions to intensive.

[0041] Differentiation stage: The dominance of the dominant species increases, the trees compete fiercely, the trees are clustered or randomly distributed, and the intensity of intermingling is high;

[0042] Establishment period: The dominance of the dominant species decreases, the species diversity is high, the distribution is mainly random, and the degree of intermingling reaches an extreme intensity;

[0043] Stable period: There are many large-diameter trees, pioneer species disappear, the accumulation volume is high, the distribution is random or slightly aggregated, and the degree of intermingling ≥ intensity.

[0044] Based on the above technical solution, the embodiments of the present invention can produce at least the following technical effects:

[0045] 1. The distinction between forest succession and forest development stages is clearly defined. Forest succession emphasizes the process of community replacement in forests across spatial and temporal scales, involving species turnover and environmental evolution. Different successional stages have relatively distinct boundaries. Developmental stages, on the other hand, emphasize the series of ecological processes that a forest undergoes throughout its life cycle, from formation and development to maturity and even decline, within a specific successional stage. Compared to the long-term and directional nature of succession, developmental stages focus more on the dynamic changes in community composition and structure under specific environmental conditions. Unlike community succession, developmental stages do not have the relatively clear boundaries between them.

[0046] 2. Avoid relying on the stand age structure in the division of secondary forest development stages, which provides a new perspective for the division of stand development stages. The age of trees in secondary forests cannot be accurately determined like that of artificial forests. This is especially true for mixed-species forests of varying ages. Using the average age method may result in stands of different ages having similar stand structures, while stands of the same age may also have significant differences in structure. In addition, the age-based method does not fully consider the influence of other factors (such as human logging interference, etc.), making it difficult to fully adapt to different types of forests and may lead to incomplete matching of the division results with the actual ecological stage.

[0047] 3. Using stand status indicators to divide development stages makes data easily accessible, the analysis method simple and easy to understand, and can guide the scientific formulation of management and restoration strategies, providing an important basis for accurately improving forest productivity and quality. Forest growth and development is a continuous, nonlinear process in time and space. The state characteristics of the stand characterize the natural properties of the actual stand, such as tree species composition, structure, and vitality, and reflect the long-term combined effects of various natural ecological processes and human activities in time and space. The indicators used to divide the development stages of secondary forests are all factors in conventional forest surveys, and they have obvious differences corresponding to different development stages. After dividing the development stages using this scheme, corresponding management plans can be formulated based on the stand status characteristics at each development stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0049] Figure 1 Phylogenetic plots using average linkage (between groups) were used for the present hierarchical clustering. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0051] The object of the present invention is achieved through the following technical solutions:

[0052] 1. Stand status characteristic indicators

[0053] The development of secondary forests is influenced by the type and intensity of disturbances, as well as environmental conditions. Stand status refers to the natural state of a stand and characterizes its natural attributes. The current state of a stand is a comprehensive reflection of the forest's long-term development, after experiencing both natural and human disturbances. It contains a wealth of information and serves not only as an important basis for demarcating developmental stages but also as a crucial guide for the restoration and management of secondary forests. Generally speaking, stand status can be characterized by three dimensions: stand vitality, stand structure, and tree species diversity. Stand vitality indicators include the proportion of seedlings, natural regeneration grade, dominance of dominant species, basal area per hectare, and standing volume. Stand structure indicators include both non-spatial and spatial structural indicators. Non-spatial structural indicators include the q-value of stand diameter distribution, average tree height, and stand crowding. Spatial structural indicators include horizontal and vertical structure, including intermixing, angular scale, and layer. Species diversity indicators include the Shannon-Wiener index, Margalef richness index, Pielou evenness index, and Simpson diversity index.

[0054] 2. Screening of characteristic indicators of stand status based on developmental stages

[0055] Based on survey data from sample plots in Quercus acutissima secondary forests in Xiaolongshan, Gansu, Korean pine broad-leaved forests in Jiaohe, Jilin, and evergreen broad-leaved forests in Guizhou, we screened for stand status characteristic indicators for developmental stage classification. Considering the varying dimensions and ranges of stand status indicators, we first processed the indicator values ​​using positive transformations (Equation 1) and normalization (Equation 2) to form a dataset. We then used the mean distance method in hierarchical cluster analysis to classify the positively transformed and normalized data. Based on the cluster analysis results, we used the Kruskal-Wallis nonparametric test to analyze the status indicators that influenced the classification results. Indicators with a significant difference at a p-value level of <0.05 were selected to form an indicator system for secondary forest developmental stage classification.

[0056] Through the difference method, forward transformation and Min-Max standardization, see formula (1) (2),

[0057] x ‘ =Cx (1)

[0058]

[0059] Where x is the original data, C is a fixed value (depending on the actual situation), min(x) and max(x) are the minimum and maximum values ​​of the data set, respectively.

[0060] like Figure 1 As shown, Figure 1 Clustering results after normalization and standardization of stand status indicators for 15 plots in the Xiaolongshan forest region of Gansu Province. Selecting a = 17, the 15 plots were divided into four categories. Category 1 and Category 2 had the largest number of plots, with 6 and 5 plots, respectively. Category 3 had 3 plots, and Category 4 had 1 plot.

[0061] The Kruskal-Wallis nonparametric test was used to identify the main influencing factors affecting the classification results (Table 1). Several stand status indicators showed significant differences among different groups, including angular scale and dominance of dominant species, four tree species diversity indices, intermixedness, average tree height, diameter distribution q value, regeneration and stock volume (p < 0.05).

[0062] Table 1 Significance test results of stand status indicators

[0063]

[0064] Based on the results of significance tests, we selected angular scale, dominant species dominance, intermixedness, average tree height, regeneration, diameter distribution q-value, Shannon-Wiener index, Margalef index, and volume as indicators for secondary forest developmental stage classification. The four diversity indices selected in this study all express species richness and evenness, but with different emphases. Therefore, for greater simplicity in constructing the developmental stage classification indicator system, the commonly used Shannon-Wiener index and Margalef index were used as two of the indicators.

[0065] 3. Weights and comprehensive values ​​of stand status characteristic indicators divided by developmental stage

[0066] Based on the significance of the nonparametric tests for each stand status indicator, weights were assigned using the analytic hierarchy process (AHP) method. These weights were then corrected using the entropy method (Table 2) to reduce the influence of subjective factors in the indicator assignment process. The Lagrange multiplier method was then used to determine the comprehensive weights of the indicators (Equation 3). The sum of the dedimensionalized values ​​of the division indicators and the product of the comprehensive weights was the developmental stage value (Equation 4), which is a number between 0 and 1.

[0067]

[0068] Where: j is the comprehensive weight of the jth indicator, W j is the subjective weight calculated by AHP method, w j is the objective weight calculated by the entropy method, and m is the total number of indicators.

[0069]

[0070] Where: W is the comprehensive value of the forest stand status, x j is the standardized value of the j-th indicator.

[0071] Table 2 Weights of indicators for forest stand development stages

[0072]

[0073] 4. Classification of secondary development stages based on stand characteristics

[0074] The threshold for the secondary forest development stage based on stand status characteristics is a number between 0 and 1. Using the equal-interval method, the secondary forest development stage is divided into five stages: gap stage (W∈[0-0.2]), regeneration stage (W∈[0.2-0.4]), differentiation stage (W∈[0.4-0.6]), establishment stage (W∈[0.6-0.8]), and stable stage (W∈[0.8-1]). Table 3 shows the basic status characteristics of each development stage.

[0075] Table 3 Characteristics of stand status at secondary forest development stages

[0076]

[0077] 5. Application examples of the secondary development stage division method based on stand status characteristics

[0078] (1) Data source

[0079] Fifteen long-term monitoring plots were established using a TOPCON total station (GTS-601) for per-tree location in typical secondary Quercus acutatus forests within the Xiaolongshan Forestry Protection Center in Gansu Province, including the Baihua, Guanyin, Longmen, Liziyuan, and Dangchuan forest farms. Trees with a diameter greater than 5 cm at breast height (h = 1.3 m) within the plots were located and labeled. Basic information such as species, diameter at breast height (DBH), height, crown width, origin, forest health, and forest layer were recorded. Historical management records of the forest block within the plots were also reviewed. Furthermore, five 5 m x 5 m plots were established at the four corners and center of the plots to count regeneration seedlings and saplings and investigate stand regeneration. A general overview of the plots is shown in Table 4.

[0080] Table 4 Overview of Quercus acuteserrata secondary forest plots

[0081]

[0082] (2) Index values ​​for the division of secondary forest development stages

[0083] The survey data of 15 forest stands were analyzed and the indicators for dividing the secondary forest development stages were calculated. The results are shown in Table 5.

[0084] Table 5 Stand status index values

[0085]

[0086]

[0087] (3) Results of the developmental stage division of Quercus acuteseri secondary forest

[0088] Based on the comprehensive weights of secondary forest development stage indicators and the dimensionless standard values, the comprehensive values ​​of the development stages of the 15 plots were calculated. The results are shown in Table 6.

[0089] Table 6 Comprehensive thresholds and development stages of Quercus acuteseri secondary forests

[0090]

[0091] Secondary forest is a type of forest formed after the primary forest is disturbed by natural or human factors. Its development process is affected by the type and intensity of disturbance and environmental conditions. Its species composition and structural changes may also be accelerated or deviate from the natural succession path due to human intervention (such as replanting and thinning), showing unique dynamic characteristics. Therefore, the division of secondary forest development stages cannot be completely based on the natural vegetation succession theory and the age-based division method of artificial forests, nor can it be completely separated from the succession process and actual state of the forest ecosystem. The present invention clearly defines the difference between forest succession and forest development stages. Forest succession emphasizes the community replacement process that occurs in forests on spatial and temporal scales, involving species replacement and environmental evolution. Different succession stages have relatively obvious boundaries; while development stages emphasize the series of ecological processes that a forest undergoes in its complete life cycle from formation, development to maturity and even decline within a specific succession stage. Compared with the long-term and directionality of succession, development focuses more on the dynamic changes in community composition and structure under specific environmental conditions. The development stages do not have relatively clear boundaries like community replacement.

[0092] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for dividing secondary forest development stages based on stand state characteristics, characterized in that: The following steps are involved: Step 1: Screening of stand status characteristic indicators An initial set of indicators was selected based on the three dimensions of stand vitality, stand structure, and tree species diversity; Carry out index positive processing and standardization on the sample site survey data and form a data set; The mean distance method in the hierarchical cluster analysis was used to classify the normalized and standardized data. Based on the cluster analysis results, the Kruskal-Wallis nonparametric test was used to analyze the state indicators that affect the classification results. The indicators with a significant difference level of p < 0.05 were selected to form the secondary forest development stage classification index system. Step 2: Determine the comprehensive weight of indicators According to the significance of non-parametric tests of each stand status index, the weights were assigned using the analytic hierarchy process, and then the entropy method was used to correct them to reduce the influence of subjective factors in the index assignment process. The comprehensive weights of the indicators were determined using the Lagrange multiplier method. Step 3: Calculate the comprehensive value of the stand development stage The sum of the product of the dimensionless value of the division index and the comprehensive weight value is the development stage value; Step 4: Divide developmental stages Based on the calculated secondary forest development stage values ​​of stand status characteristics, the secondary forest development stage is divided into five stages: gap period, regeneration period, differentiation period, establishment period and stable period according to the equal interval method.

2. The method for dividing secondary forest development stages based on stand state characteristics according to claim 1, characterized in that: The indicators screened in step 1 include: stand vitality index, stand structure index and tree species diversity index; The stand vitality indicators include the proportion of seedling trees, natural regeneration level, dominance of dominant species, hectare basal area and volume of stock; The stand structure indicators include non-spatial structure indicators and spatial structure indicators. Among them, non-spatial structure indicators include stand diameter distribution q value, average tree height and stand crowding. Spatial structure indicators include horizontal structure and vertical structure. Specific indicators include intermixing degree, angular scale and forest layer. The tree species diversity indices include the Shannon-Wiener index, the Margalef richness index, the Pielou evenness index and the Simpson diversity index.

3. The method for dividing secondary forest development stages based on stand state characteristics according to claim 1, characterized in that: The formula for the forward processing is: x ‘ =C-x (1) In the formula, x is the original data and C is a fixed value; The formula for the standardization process is: min(x) and max(x) are the minimum and maximum values ​​of the data set, respectively.

4. The method for dividing secondary forest development stages based on stand state characteristics according to claim 1, characterized in that: In step 2, the calculation formula of the comprehensive weight is: Where: j is the comprehensive weight of the jth indicator, W j is the subjective weight calculated by AHP method, w j is the objective weight calculated by the entropy method, and m is the total number of indicators.

5. The method for dividing secondary forest development stages based on stand state characteristics according to claim 1, characterized in that: In step 3, the calculation formula for the developmental stage value is: Where: W is the comprehensive value of the forest stand state, which is a number between [0, 1], x j is the standardized value of the j-th indicator.

6. The method for dividing secondary forest development stages based on stand state characteristics according to claim 5, characterized in that: In step 4, the comprehensive threshold value of the gap period is W∈[0-0.2), the comprehensive threshold value of the renewal period is W∈[0.2-0.4), the comprehensive threshold value of the differentiation period is W∈[0.4-0.6), the comprehensive threshold value of the establishment period is W∈[0.6-0.8), and the comprehensive threshold value of the stable period is W∈[0.8-1].

7. The method for dividing secondary forest development stages based on stand state characteristics according to claim 6, characterized in that: The characteristics of the stand status at each development stage are as follows: Gap stage: The forest window area is large, the individuals of the dominant species are scattered, there are few regeneration seedlings / saplings, the trees are clustered, and the degree of intermixing is low; Regeneration period: Regeneration in forest gaps is intensive, the number of founder species sprouts increases, pioneer species appear occasionally, and the degree of intergrowth transitions to intensive. Differentiation stage: The dominance of the dominant species increases, the trees compete fiercely, the trees are clustered or randomly distributed, and the intensity of intermixing is high; Establishment period: The dominance of the dominant species decreases, the species diversity is high, the distribution is mainly random, and the degree of intermingling reaches an extreme intensity; Stable period: There are many large-diameter trees, pioneer species disappear, the accumulation volume is high, the distribution is random or slightly aggregated, and the degree of intermingling ≥ intensity.

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