Method for comprehensively quantifying shrub-herb-biological crust three-dimensional structure

Abstract

The invention belongs to the technical field of ecological restoration, and particularly relates to a method for comprehensively quantifying a shrub-herb-biological crust three-dimensional structure, which comprises the following steps of: collecting sample data, selecting a plurality of shrub, herb and biological crust coexisting sample plots, arranging a plurality of quadrats, respectively obtaining horizontal and vertical indexes of vegetation communities in the quadrats, and calculating the three-dimensional structure of the shrub-herb-biological crust. Representative description indexes of horizontal indexes and vertical indexes of different levels of vegetation are screened out and preprocessed; constructing a model, and determining the weight of each vegetation level and the path weight of the representative description index; and calculating a shrub-herb-biological crust three-dimensional structure index, performing deviation normalization processing on the standard representative description index, and calculating the shrub-herb-biological crust three-dimensional structure index in combination with the path weight of the standard representative description index. According to the method, the three-dimensional structure characteristics of the shrub-herb-biological crust multi-layer vegetation community can be comprehensively and quantitatively represented, and the morphological characteristics of the vegetation community of a forest stand space structure can be better represented.

Description

Technical Field

[0001] The invention belongs to the technical field of ecological restoration, and in particular relates to a method for comprehensively quantifying the three-dimensional structure of shrubs-herbs-biological crusts. Background Art

[0002] In arid and semi-arid areas, vegetation patches with a mosaic distribution of shrubs and herbs are often formed. As an important soil interface, biological crusts are often distributed in the gaps between vegetation patches and coexist with shrubs and herbs to form a shrub-herb-biological crust vegetation community. This vegetation community has obvious morphological and structural characteristics. In the horizontal structure, the shrubs, herbs and biological crust components are mosaic-distributed with different quantitative characteristics; in the vertical structure, the community forms three layers from top to bottom, namely the shrub layer, the herb layer and the biological crust layer, with obvious hierarchical relationships. Structure is the basis of function. Both the horizontal and vertical structures in the three-dimensional structure of shrubs-herbs-biological crusts have a profound impact on their ecological functions. Therefore, it is particularly important to quantify their morphological structure.

[0003] At present, the research on quantitative characterization of morphological characteristics of vegetation communities focuses on the spatial structure of forest stands. In order to describe the spatial distribution pattern of forest stands, forest layer structure, mixed situation and the dominance of individual sizes, spatial and quantitative indicators such as spatial density index, forest layer index, total mixed degree, and size ratio are introduced. What is relatively new is that some scholars have introduced structural equation models, combined with forest stand spatial indicators, to evaluate and grade forest structures from the two dimensions of horizontal and vertical structures. This is a bold attempt and progress in the study of quantitative characterization of morphological characteristics of vegetation communities to a certain extent, but it is still limited to a single vegetation type. The interlaced and overlapping distribution patterns between different layers of vegetation, such as the tree layer, shrub layer, herb layer, and biological crust layer, make the forest stand spatial structure parameters unable to well characterize the morphological characteristics of the multi-layered vegetation community of shrub-herb-biological crust. At present, most of the methods for evaluating and grading the community morphological structure focus on the single structural characteristics of the community (cover, height) or provide qualitative descriptions, and lack comprehensive consideration of its horizontal and vertical indicators. There is also no quantitative comprehensive indicator to reflect the three-dimensional structure of the multi-layered vegetation community, which hinders people from deeply understanding the ecological function of the shrub-herb-biological crust community from the perspective of community spatial structure. However, it is still difficult to comprehensively consider horizontal and vertical indicators and accurately describe its three-dimensional structural characteristics with quantitative indicators. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the purpose of the present invention is to provide a method for comprehensively quantifying the three-dimensional structure of shrubs, herbs and biological crusts, which can comprehensively and quantitatively characterize the three-dimensional structural characteristics of shrubs, herbs and biological crusts multilayer vegetation communities, and can better characterize the morphological characteristics of vegetation communities in the spatial structure of forest stands, and provide more reasonable reference data for evaluating the soil and water conservation effects of shrub, herb and biological crust symbiotic slopes.

[0005] The technical solution of the present invention is:

[0006] A method for comprehensively quantifying the three-dimensional structure of shrub-herb-biological crusts comprises the following steps:

[0007] Step 1: Sample data collection and preprocessing: select multiple sample plots where shrubs, herbs and biological crusts coexist, randomly arrange multiple sample plots in each sample plot, obtain the horizontal and vertical indicators of shrubs-herbs-biological crusts in the sample plots, screen out representative descriptive indicators of the horizontal and vertical indicators of different shrubs, herbs and biological crusts, that is, the representative descriptive indicators of the three-dimensional structure of shrubs-herbs-biological crusts, and preprocess them to convert the data in the representative descriptive indicators of the three-dimensional structure of shrubs-herbs-biological crusts into standard normal distribution to obtain standard representative descriptive indicators;

[0008] Step 2: Model construction of shrub-herb-biological crust three-dimensional structure indicators. The shrub layer, herb layer and biological crust layer are selected as endogenous latent variables. The three-dimensional structure indicators formed by the horizontal and vertical indicators of shrub-herb-biological crust are selected as exogenous latent variables. The representative descriptive indicators of the three-dimensional structure of shrub-herb-biological crust obtained by screening are used as observed variables to construct a model of shrub-herb-biological crust three-dimensional structure indicators. The weights of each vegetation layer in the representative descriptive indicators of the three-dimensional structure of shrub-herb-biological crust and the path weights of the representative descriptive indicators of the horizontal and vertical indicators of vegetation at different levels are determined.

[0009] Step 3: Calculation of the three-dimensional structural index of shrubs-herbs-biological crusts. The deviation of the pre-processed standard representative descriptive index is normalized, and the path weights of the representative descriptive indexes of the horizontal and vertical indicators of vegetation at different levels are combined to calculate the three-dimensional structural index of shrubs-herbs-biological crusts.

[0010] Preferably, the horizontal structure indicators include:

[0011] Shrub projection cover, root base cover, and plant density;

[0012] Projected cover, basal cover, shrub and grass species richness, and biological crust cover of herbaceous plants.

[0013] Preferably, the vertical structure indicators include shrub height, crown width and herb height.

[0014] Preferably, the method for screening representative descriptive indicators of the shrub-herb-biological crust three-dimensional structure comprises the following steps:

[0015] By performing factor analysis and correction on the horizontal and vertical structural indicators of vegetation at different levels, representative descriptive indicators describing the three-dimensional structure of shrubs, herbs and biological crusts were screened and obtained, and the factor loading matrix of the representative descriptive indicators in the horizontal and vertical indicators was calculated to verify whether the screening was reasonable; shrub projection cover, herb projection cover and biological crust cover were selected as representative descriptive indicators of the horizontal structure; the product of shrub height and shrub crown width and herb height were selected as representative descriptive indicators of the vertical structure.

[0016] Preferably, the preprocessing method for the representative descriptive indexes of the three-dimensional structure of the shrub-herb-biological crust obtained by screening includes:

[0017] The representative descriptive indicators of the three-dimensional structure of the screened shrub-herb-biological crust were standardized by Z-Score.

[0018] Preferably, the construction of the shrub-herb-biological crust three-dimensional structure index model also includes a reliability and validity test of the shrub-herb-biological crust three-dimensional structure index model, and the test method is as follows:

[0019] Reliability test: The Cronbach's coefficient was calculated using the data standardized by Z-Score. If the Cronbach's coefficient was greater than 0.5, it indicated that the reliability test was qualified.

[0020] Validity test: Use the data standardized by Z-Score to calculate the KMO coefficient. The KMO coefficient should be greater than 0.6, indicating that the validity test is qualified.

[0021] Preferably, the confirmatory factor analysis and correction method of the shrub-herb-biological crust three-dimensional structure index model is as follows:

[0022] On the basis that the reliability and validity tests of the Z-Score standardized data were qualified, the Z-Score standardized data were input into the constructed shrub-herb-biological crust three-dimensional structure index model for identification and confirmatory factor analysis was performed, including structural validity, convergent validity and discriminant validity tests;

[0023] Structural validity is to test the fitness of the model, which is characterized by model significance, chi-square freedom ratio, goodness of fit index, standardized fit index, approximate root mean square error, and Akaike information criterion. It stipulates that the model significance is greater than 0.05, the chi-square freedom ratio is between 1 and 2, the goodness of fit index is greater than or equal to 0.9, the standardized fit index is greater than or equal to 0.9, the approximate root mean square error is less than 0.75, and the Akaike information criterion should be as small as possible;

[0024] Convergent validity refers to the degree of concentration of observed indicators on endogenous latent variables, which is reflected by combined validity and average coefficient of variation extraction, where combined validity must be greater than 0.5 and average coefficient of variation extraction must be greater than 0.5;

[0025] Discriminant validity characterizes the degree of difference between different endogenous latent variables, and is represented by the square root of the average coefficient of variation extracted. The specific calculation is that the correlation coefficient between the endogenous latent variables must be less than the square root of the average coefficient of variation extracted;

[0026] If the validity tests in the constructed shrub-herb-biological crust three-dimensional structure index model do not meet the requirements, the model path needs to be corrected, and the corrected model should be repeatedly subjected to confirmatory factor analysis until the validity tests are met.

[0027] Preferably, the weights of the vegetation levels and the path weights of the horizontal and vertical indicators of vegetation at different levels are determined by the following formula:

[0028]

[0029] Where P i is the path weight; p i is the path coefficient output by the model, i==1,2…,n.

[0030] Preferably, the standard representative descriptive index deviation normalization process is determined by the following formula:

[0031]

[0032] In the formula, X i is the normalized index value; x i is the representative descriptive index value of the three-dimensional structure of shrubs-herbs-biological crusts obtained in each sample plot before pretreatment; x min 、x max The minimum and maximum values ​​in the data respectively.

[0033] Preferably, the three-dimensional structural index of the shrub-herb-biological crust is determined by the following formula:

[0034]

[0035] Where S is the three-dimensional structure index of shrub-herb-biological crust, and the value range of S is [0,1]; C i is the weight of each indicator; X i is the normalized index value.

[0036] Compared with the prior art, the method of comprehensively quantifying the three-dimensional structure of shrubs, herbs and biological crusts of the present invention has the following beneficial effects:

[0037] This method is not limited to the spatial structure of a single vegetation type, but integrates multiple layers of vegetation such as shrub layer, herb layer and biological crust layer, introduces the model construction of shrub-herb-biological crust three-dimensional structure index, effectively integrates the horizontal structure parameters and vertical structure parameters of different layers of shrub layer, herb layer and biological crust layer, and proposes the concept of "shrub-herb-biological crust three-dimensional structure index". It comprehensively and quantitatively characterizes the three-dimensional structural characteristics of shrub-herb-biological crust multilayer vegetation community, and can better characterize the morphological characteristics of vegetation community with shrub-herb-biological crust spatial structure, and provide more reasonable reference data for evaluating the soil and water conservation effect of shrub, herb and biological crust symbiotic slopes. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is an overall flow chart of the construction of the three-dimensional structure index of shrubs-herbs-biological crusts in the embodiment of the present invention;

[0039] Figure 2 A flowchart for constructing the three-dimensional structure index of shrubs-herbs-biological crusts in an embodiment of the present invention;

[0040] Figure 3 The theoretical model of the three-dimensional structural index of shrub-herb-biological crust in the embodiment of the present invention is constructed;

[0041] Figure 4 is the regression relationship between the vegetation interlaced coverage and the shrub-herb-biological crust three-dimensional structure index in the application case of the present invention; wherein: y is the vegetation interlaced coverage; χ is the shrub-herb-biological crust three-dimensional structure index; R 2 is the coefficient of determination, and P is the significance level;

[0042] Figure 5 Middle: Regression relationship of shrub-herb-biological crust three-dimensional structure index; (a): y is shrub projection coverage, (b): y is medium shrub height, (c): y is medium shrub crown width, (d): y is medium herb projection coverage, (e): y is medium herb height, (f): y is medium biological crust coverage; χ is shrub-herb-biological crust three-dimensional structure index; R 2 is the determination coefficient; P is the significance level. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0044] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0045] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0046] The interlaced and overlapping distribution patterns between different layers of vegetation, such as the tree layer, shrub layer, herb layer, and biological crust layer, make the forest stand spatial structure parameters unable to well characterize the morphological characteristics of the multi-layered vegetation community of shrub-herb-biological crust. At present, most of the methods for evaluating and grading the community morphological structure focus on the single structural characteristics of the community (cover, height) or provide qualitative descriptions, and lack comprehensive consideration of its horizontal and vertical indicators. There is also no quantitative comprehensive indicator to reflect the three-dimensional structure of the multi-layered vegetation community, which hinders people from deeply understanding the ecological function of the shrub-herb-biological crust community from the perspective of community spatial structure. However, it is still difficult to comprehensively consider horizontal and vertical indicators and accurately describe its three-dimensional structural characteristics with quantitative indicators.

[0047] See also Figures 1 to 3 As shown, in order to comprehensively and quantitatively characterize the three-dimensional structural characteristics of the shrub-herb-biological crust multilayer vegetation community, to provide more reasonable reference data in the evaluation of the soil and water conservation effect of the symbiotic slope of shrubs, herbs and biological crusts, and to better characterize the morphological characteristics of the vegetation community with a spatial structure of shrubs-herbs-biological crusts, this embodiment provides a method for comprehensively quantifying the three-dimensional structure of shrubs-herbs-biological crusts, including the following steps:

[0048] (1) Sample data collection and preprocessing

[0049] Sample plots where shrubs, herbs and biological crusts coexist were selected, and more than 3 5*5m plots were randomly arranged in each plot (with a plot interval of more than 20m). The plots need to be covered with vegetation of different densities and have different structural characteristics to ensure the representativeness and diversity of the plot selection. The survey content includes: ① Basic information of the plot: longitude and latitude, slope, slope position, slope aspect, and altitude. ② Vegetation morphological and structural characteristic indicators in the plot: horizontal indicators include shrub projection cover, root base cover, plant density, herbaceous plant projection cover, root base cover, abundance and biological crust cover; vertical indicators include shrub height, crown width and herb height; shrub and herb species. In addition, the staggered cover of vegetation in the plot was calculated, and the calculation formula is as follows:

[0050] C alternate =|C T -(C PS +C PH +C PB )|

[0051] Where: C alternate is the vegetation interlaced coverage, in %; C T is the total vegetation coverage in the sample plot, in %; C PS , C PH , C PB are the projected covers of shrubs, herbs and biological crusts, respectively, in %.

[0052] Before each plot survey, a DJI Air2S drone was used to shoot vertically from 7.0 m above the plot to obtain photos of the complete plot. ArcGIS 10.2 software was used to process the plot photos later to obtain the morphological and structural characteristic indicators of different vegetation levels in the plot, that is, the vegetation community coverage in the plot, and the projection coverage and root base coverage of shrubs and herbs were determined by visual inspection. Fifteen 25*25 cm plots were evenly arranged in each plot to investigate the coverage of biological crusts. The height and crown width of shrubs and grasses were measured with a tape measure and a breast diameter ruler. Representative descriptive indicators of horizontal and vertical indicators of vegetation at different levels, that is, representative descriptive indicators of the three-dimensional structure of shrubs, herbs and biological crusts were screened out, and preprocessed to convert data with different characteristics into standard normal distribution to obtain standard representative descriptive indicators. The types of shrubs and grasses were identified with reference to the Flora of the Loess Plateau. The density of shrubs and the abundance of herbs were investigated by counting.

[0053] (2) Model construction of shrub-herb-biological crust three-dimensional structural indicators

[0054] There are latent variables and observed variables in the model construction of three-dimensional structure indicators. The latent variables include exogenous latent variables and endogenous latent variables. Exogenous latent variables are variables without measurement errors, endogenous latent variables are dependent variables or intermediate variables, and observed variables are actual measured values. The three-dimensional structure indicators formed by the horizontal and vertical indicators of shrubs-herbs-biological crusts are selected as exogenous latent variables to reflect the morphological and structural characteristics of shrubs-herbs-biological crusts communities; the shrub layer, herb layer and biological crust layer can use different observation indicators to indirectly reflect their respective horizontal and vertical structures. Therefore, the shrub layer, herb layer and biological crust layer are selected as endogenous latent variables, and the representative descriptive indicators of the horizontal and vertical indicators of vegetation at different levels are used as observed variables (that is, the representative descriptive indicators of the three-dimensional structure of shrubs-herbs-biological crusts obtained by screening are used as observed variables). The constructed theoretical model is as follows: Figure 3 shown.

[0055] Building the model also includes:

[0056] A: Screening and pretreatment of representative descriptive indicators for the three-dimensional structure of shrubs, herbs and biological crusts

[0057] The representative descriptive indicators of the three-dimensional structure of shrubs, herbs and biological crusts were screened and obtained by combining the morphological and structural significance of each indicator in the shrub-herb-biological crust community. Vegetation projection cover can intuitively reflect the horizontal coverage of shrubs, herbs and biological crusts. Therefore, shrub projection cover, herb projection cover and biological crust cover were selected to describe the horizontal structure of vegetation at different levels. Height is an important indicator reflecting the vertical structure of vegetation. However, in the shrub layer, the crown width can also reflect the vertical distribution characteristics of shrubs to a certain extent, describing its horizontal extension length to the surrounding space. Therefore, the shrub height and the shrub crown width are multiplied to reflect the vertical structure of the shrub layer. The crown width of herbaceous plants is small, so the herb height is selected to describe the vertical structure of the herbaceous layer. The vertical characteristics of biological crusts in shrub-herb-biological crust vegetation communities are small, so they are ignored. After that, the screened indicators were normalized by normal distribution. After all of them met the normal distribution, they were standardized by Z-Score (standard score) to obtain standard representative descriptive indicators.

[0058] B: Reliability and validity test, confirmatory factor analysis and revision of the shrub-herb-biological crust three-dimensional structure index model

[0059] Reliability and validity tests were conducted based on Z-Score standardized data. Cronbach's α is the most commonly used method to measure test reliability. A value greater than 0.5 indicates that the data is highly reliable. The KMO coefficient (Kaiser-Meyer-Olkin) is an indicator used to compare the simple correlation coefficient and partial correlation coefficient between indicators. Its value is greater than 0.6, and the Bartlett sphericity test P is less than 0.05, indicating that the indicators are spherically distributed and suitable for confirmatory factor analysis. The details are as follows:

[0060] Reliability test: Cronbach's alpha is calculated using the data standardized by Z-Score. The Cronbach's alpha should be greater than 0.5, indicating that the reliability test is qualified. The calculation formula of Cronbach's alpha is In the formula, α is the Cronbach's coefficient, N is the total amount of data in the survey plot, is the average covariance between the data, s 2 is the total variance of the data

[0061] Validity test: Use the data standardized by Z-Score to calculate the KMO coefficient (Kaiser-Meyer-Olkin). The KMO coefficient should be greater than 0.6, indicating that the validity test is qualified. The calculation formula of the KMO coefficient is: In the formula, r ij is the correlation coefficient between all data in the survey plot, a ij is the partial correlation coefficient between all data within the survey sample.

[0062] The Z-Score standardized data were input into the model constructed in AMOS24.0 software for confirmatory factor analysis (CFA), including structural validity, convergent validity and discriminant validity tests. Among them, structural validity is a fitness test of the model to determine whether the data is suitable for the hypothesized measurement model. The model fitness indicators selected model significance (P), chi-square freedom ratio (Normed Chi-square), goodness of fit index (GFI), root mean square error (RMSEA), Akaike Information Criterion (AIC). Convergent validity is the degree of concentration of observation indicators on endogenous latent variables, which characterizes whether each observation indicator can accurately evaluate the endogenous latent variable, and is reflected by construct reliability (CR) and average variance extracted (AVE). Discriminant validity characterizes the degree of difference between different endogenous latent variables, which is reflected by the square root of the average variance extracted.

[0063] The significance of the model was obtained through the chi-square test. First, the chi-square value χ 2 ,

[0064]

[0065] O is the observed frequency and E is the expected frequency

[0066] df is the number of model parameters minus the number of constraints, and the significance P value is found using the chi-square value and degrees of freedom.

[0067] Chi-squared degrees of freedom ratio:

[0068] Goodness of fit indicators:

[0069] SSE is the residual sum of squares of the model, and SST is the total residual sum of squares; SSE represents the sum of squared errors between the model predicted values ​​and the actual observed values, while SST represents the sum of squared errors between the actual observed values ​​and their mean.

[0070] Canonical fit indices:

[0071] is the chi-square value without model, Chi-square value of the fitted model;

[0072] Akaike information criterion: AIC = 2k-2ln(L);

[0073] k is the number of parameters estimated in the model, and L is the maximum likelihood estimate of the model.

[0074] Combined validity:

[0075]

[0076] Factor loadings are the factor loadings of each observed indicator, and error variances are the error variances of each observed indicator.

[0077] Average variance extracted:

[0078]

[0079] factor loading 2 is the square of the factor loading of each observed indicator, and N is the total number of observed indicators.

[0080] Discriminant Validity:

[0081] and

[0082] Corr(X,Y) is the correlation coefficient between latent variables X and Y. and are the square roots of the AVE of the latent variables X and Y, respectively.

[0083] If the validity of each model test does not meet the requirements, the model needs to be modified according to the MI index of the AMOS24.0 output report. The modified model is repeated for confirmatory factor analysis, and the subsequent steps can be carried out only after the test meets the requirements.

[0084] C: Determination of weights of each indicator in the three-dimensional structure index of shrubs-herbs-biological crusts

[0085] The path coefficients output by the model are normalized to determine the weights of each vegetation layer in the three-dimensional structure index and the weights of the horizontal and vertical indicators of vegetation at different levels. The processing formula is as follows:

[0086]

[0087] In the formula: P i Path weight; p i is the path coefficient output by the model, i==1,2…,n.

[0088] (3) Calculation of three-dimensional structural indicators of shrubs, herbs and biological crusts

[0089] The standard representative descriptive index after preprocessing is normalized, and the processing formula is as follows:

[0090]

[0091] Where: X i is the normalized index value; x i is the representative descriptive index value of the three-dimensional structure of shrubs-herbs-biological crusts obtained in each sample plot before pretreatment; x min 、x max The minimum and maximum values ​​in the data respectively.

[0092] The normalized values ​​obtained by normalizing the deviations of the representative descriptive indicators of the shrub-herb-biological crust three-dimensional structure are used to calculate the shrub-herb-biological crust three-dimensional structure index in combination with the indicator weights. The calculation formula is as follows:

[0093]

[0094] Where: S is the three-dimensional structure index of shrub-herb-biological crust, [0,1]; C i is the weight of each indicator; X i is the normalized index value, i==1,2…,n.

[0095] In summary, the above method is based on the principles of scientificity, effectiveness and practicality, and is not limited to the spatial structure of a single vegetation type. Instead, it integrates multiple layers of vegetation such as the shrub layer, herb layer and biological crust layer, introduces the model construction of the shrub-herb-biological crust three-dimensional structure index, and innovatively integrates the horizontal and vertical structure parameters of different layers. The concept of "shrub-herb-biological crust three-dimensional structure index" is proposed, which comprehensively and quantitatively characterizes the three-dimensional structural characteristics of the shrub-herb-biological crust multilayer vegetation community, provides more reasonable reference data for evaluating the soil and water conservation effect of shrub, herb and biological crust symbiotic slopes, and can better characterize the morphological characteristics of vegetation communities in the spatial structure of forest stands.

[0096] The application cases of this method are as follows:

[0097] The basic information of the sample plot is shown in Table 1:

[0098] Table 1 Information table of three-dimensional structure indexes of shrubs, herbs and biological crusts (n=40)

[0099]

[0100] Based on the basic information of the sample plot, factor analysis was performed on the horizontal and vertical indicators, as shown in Table 2:

[0101] Table 2 Analysis of various index factors of shrub-grass-biological crust three-dimensional structure

[0102]

[0103]

[0104] Through factor analysis, it was found that there was redundancy between indicators, which led to unclear classification of indicators at each level of vegetation. Therefore, it was necessary to combine the morphological structure significance of each indicator in the shrub-herb-biological crust community to screen and obtain representative descriptive indicators of the three-dimensional structure of shrub-herb-biological crust. Vegetation projection cover can intuitively reflect the horizontal coverage of shrubs, herbs and biological crusts. Therefore, shrub projection cover, herb projection cover and biological crust cover were selected to describe the horizontal structure of vegetation at different levels. Height is an important indicator reflecting the vertical structure of vegetation. However, in the shrub layer, the crown width can also reflect the vertical distribution characteristics of shrubs to a certain extent, describing its horizontal extension length to the surrounding space. Therefore, the shrub height and the shrub crown width are multiplied to reflect the vertical structure of the shrub layer. The crown width of herbaceous plants is small, so the herb height is selected to describe the vertical structure of the herb layer. The vertical characteristics of biological crusts in shrub-herb-biological crust vegetation communities are small, so they are ignored. Then, the five indicators obtained by screening were reversely verified by factor analysis (Table 3). The results show that the cumulative contribution rate of the three common factors is 93.151%, and the classification of indicators represented by different common factors is clearer.

[0105] Table 3 Factor analysis of representative descriptive indicators of the three-dimensional structure of shrub-grass-biological crusts

[0106]

[0107] Based on the representative descriptive indicators screened, they were standardized by Z-Score, and then reliability and validity tests were performed. The results showed that the Cronbach's coefficient of the reliability test was 0.694; the KMO coefficient of the validity test was 0.678, and P was 0.000, and the Bartlett sphericity test results were good.

[0108] The results of confirmatory factor analysis showed that the structural validity met the requirements (Table 4). In the convergent validity, the CRs of the latent variables shrub-grass-biological crust three-dimensional structure index, shrub layer and herb layer were 0.898, 0.914 and 0.844, respectively, all greater than 0.7; the AVEs were 0.752, 0.841 and 0.730, respectively, all greater than 0.5, indicating that the latent variables had a high ability to explain the observed variables. In the discriminant validity, the correlation coefficients between the shrub-grass-biological crust three-dimensional structure index, shrub layer and herb layer were all less than the square root of AVE, indicating that the discriminant validity between the latent variables was ideal.

[0109] Table 4 Model fitness index

[0110]

[0111] On the premise that the confirmatory factor analysis was passed, the path coefficients in the model were normalized, and the weights of each vegetation level in the shrub-herb-biological crust three-dimensional structure index and the path weights of the representative descriptive indicators of the horizontal and vertical indicators of vegetation at different levels were obtained (Table 5).

[0112] Table 5 Weights of various indicators of shrub-herb-biological crust three-dimensional structure

[0113]

[0114] The three-dimensional structural index of shrub-herb-biological crust was calculated by path coefficient. Figure 4 As shown in the figure, the shrub-herb-biological crust three-dimensional structure index ranges from 0 to 1. In the relationship between the shrub-herb-biological crust three-dimensional structure index and the vegetation interlaced coverage in the sample plot set as 40 plots, it can be found that with the increase of the three-dimensional structure index, the vegetation interlaced coverage increases linearly, indicating that the vertical overlap area between shrubs, herbs and biological crusts increases, and the three-dimensional structure becomes more complex.

[0115] See also Figure 5 The figure shows the relationship between the three-dimensional structural index of shrub-herb-biological crust and the various indexes of shrub-herb-biological crust community. It can be found that the various indexes in the community increase with the increase of the three-dimensional structural index, but the degree of increase is different. The shrub layer index (shrub projection coverage, shrub height, shrub crown width) increases exponentially, the herb layer index (herb projection coverage, herb height) increases linearly, and the biological crust layer index (biological crust coverage) increases logarithmically, indicating that with the increase of the three-dimensional structural index, the various indexes of the shrub-herb-biological crust community become more complex. Therefore, the three-dimensional structural index can effectively distinguish the change characteristics of different vegetation layers and can be used to describe the three-dimensional structure of shrub-herb-biological crust.

[0116] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for comprehensive quantification of the three-dimensional structure of shrub-herb-biological crusts, characterized in that: The following steps are involved: Step 1: Sample data collection and preprocessing: select multiple sample plots where shrubs, herbs and biological crusts coexist, randomly arrange multiple sample plots in each sample plot, obtain the horizontal and vertical indicators of shrubs-herbs-biological crusts in the sample plots, screen out representative descriptive indicators of the horizontal and vertical indicators of different shrubs, herbs and biological crusts, that is, the representative descriptive indicators of the three-dimensional structure of shrubs-herbs-biological crusts, and preprocess them to convert the data in the representative descriptive indicators of the three-dimensional structure of shrubs-herbs-biological crusts into standard normal distribution to obtain standard representative descriptive indicators; Step 2: Model construction of shrub-herb-biological crust three-dimensional structure indicators. The shrub layer, herb layer and biological crust layer are selected as endogenous latent variables. The three-dimensional structure indicators formed by the horizontal and vertical indicators of shrub-herb-biological crust are selected as exogenous latent variables. The representative descriptive indicators of the three-dimensional structure of shrub-herb-biological crust obtained by screening are used as observed variables to construct a model of shrub-herb-biological crust three-dimensional structure indicators. The weights of each vegetation layer in the representative descriptive indicators of the three-dimensional structure of shrub-herb-biological crust and the path weights of the representative descriptive indicators of the horizontal and vertical indicators of vegetation at different levels are determined. Step 3: Calculation of the three-dimensional structural index of shrubs-herbs-biological crusts. The deviation of the pre-processed standard representative descriptive index is normalized, and the path weights of the representative descriptive indexes of the horizontal and vertical indicators of vegetation at different levels are combined to calculate the three-dimensional structural index of shrubs-herbs-biological crusts.

2. A method for comprehensive quantification of shrub-herb-biological crust three-dimensional structure according to claim 1, characterized in that: The horizontal structural indicators include: Shrub projection cover, root base cover, and plant density; Projected cover, basal cover, shrub and grass species richness, and biological crust cover of herbaceous plants.

3. A method for comprehensive quantification of shrub-herb-biological crust three-dimensional structure according to claim 2, characterized in that: The vertical structure indicators include shrub height, crown width and herb height.

4. A method for comprehensive quantification of shrub-herb-biological crust three-dimensional structure according to claim 3, characterized in that: The method for screening representative descriptive indicators of the shrub-herb-biological crust three-dimensional structure comprises the following steps: By performing factor analysis and correction on the horizontal and vertical structural indicators of vegetation at different levels, representative descriptive indicators describing the three-dimensional structure of shrubs, herbs and biological crusts were screened and obtained, and the factor loading matrix of the representative descriptive indicators in the horizontal and vertical indicators was calculated to verify whether the screening was reasonable; shrub projection cover, herb projection cover and biological crust cover were selected as representative descriptive indicators of the horizontal structure; the product of shrub height and shrub crown width and herb height were selected as representative descriptive indicators of the vertical structure.

5. A method for comprehensive quantification of shrub-herb-biological crust three-dimensional structure according to claim 4, characterized in that: The preprocessing methods for the representative descriptive indicators of the three-dimensional structure of the screened shrub-herb-biological crust include: The representative descriptive indicators of the three-dimensional structure of the screened shrub-herb-biological crust were standardized by Z-Score.

6. A method for comprehensive quantification of shrub-herb-biological crust three-dimensional structure according to claim 5, characterized in that: The construction of the shrub-herb-biological crust three-dimensional structure index model also includes the reliability and validity test of the shrub-herb-biological crust three-dimensional structure index model, and the test method is as follows: Reliability test: The Cronbach's coefficient was calculated using the data standardized by Z-Score. If the Cronbach's coefficient was greater than 0.5, it indicated that the reliability test was qualified. Validity test: Use the data standardized by Z-Score to calculate the KMO coefficient. The KMO coefficient should be greater than 0.6, indicating that the validity test is qualified.

7. A method for comprehensive quantification of shrub-herb-biological crust three-dimensional structure according to claim 6, characterized in that: The confirmatory factor analysis and correction method of the shrub-herb-biological crust three-dimensional structure index model is as follows: On the basis that the reliability and validity tests of the Z-Score standardized data were qualified, the Z-Score standardized data were input into the constructed shrub-herb-biological crust three-dimensional structure index model for identification and confirmatory factor analysis was performed, including structural validity, convergent validity and discriminant validity tests; Structural validity is to test the fitness of the model, which is characterized by model significance, chi-square freedom ratio, goodness of fit index, standardized fit index, approximate root mean square error, and Akaike information criterion. It stipulates that the model significance is greater than 0.05, the chi-square freedom ratio is between 1 and 2, the goodness of fit index is greater than or equal to 0.9, the standardized fit index is greater than or equal to 0.9, the approximate root mean square error is less than 0.75, and the Akaike information criterion should be as small as possible; Convergent validity refers to the degree of concentration of observed indicators on endogenous latent variables, which is reflected by combined validity and average coefficient of variation extraction, where combined validity must be greater than 0.5 and average coefficient of variation extraction must be greater than 0.5; Discriminant validity characterizes the degree of difference between different endogenous latent variables, and is represented by the square root of the average coefficient of variation extracted. The specific calculation is that the correlation coefficient between the endogenous latent variables must be less than the square root of the average coefficient of variation extracted; If the validity tests in the constructed shrub-herb-biological crust three-dimensional structure index model do not meet the requirements, the model path needs to be corrected, and the corrected model should be repeatedly subjected to confirmatory factor analysis until the validity tests are met.

8. The method for comprehensive quantification of shrub-herb-biological crust three-dimensional structure according to claim 1, characterized in that: The weights of each vegetation layer and the path weights of the horizontal and vertical indicators of vegetation at different levels are determined by the following formula: Where P i is the path weight; p i is the path coefficient output by the model, i==1,2…,n.

9. A method for comprehensive quantification of shrub-herb-biological crust three-dimensional structure according to claim 8, characterized in that: The deviation normalization of the representative descriptive index of the standard is determined by the following formula: In the formula, X i is the normalized index value; x i is the representative descriptive index value of the three-dimensional structure of shrubs-herbs-biological crusts obtained in each sample plot before pretreatment; x min 、x max The minimum and maximum values ​​in the data respectively.

10. The method for comprehensive quantification of shrub-herb-biological crust three-dimensional structure according to claim 9, characterized in that: The three-dimensional structural index of shrub-herb-biological crust is determined by the following formula: Where S is the three-dimensional structure index of shrub-herb-biological crust, and the value range of S is [0,1]; C i is the weight of each indicator; X i is the normalized index value.

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