A method for evaluating forest vegetation restoration effect

Abstract

This invention discloses a method for evaluating the effect of forest vegetation restoration, comprising: determining the study area for evaluating the effect of forest vegetation restoration; calculating the vegetation community index and ecological function index of the control area based on time series; calculating the extreme values ​​of fluctuation of the vegetation community index and ecological function index; calculating the influence weights of environmental factors and disturbance factors on vegetation community and ecological function; constructing a calculation model for calculating the forest vegetation restoration index based on the influence weights; uniformly selecting several sampling areas within the study area; calculating the forest vegetation restoration index of each sampling area using the calculation model; constructing a semi-variogram function; and evaluating the effect of forest vegetation restoration in the study area. This invention, starting from a four-dimensional index system of vegetation community-ecological function-environmental factors-disturbance factors, comprehensively evaluates the effect of forest vegetation restoration, obtains the spatial differences in the effect of forest vegetation restoration, and provides a basis for differentiated restoration management.

Description

Technical Field

[0001] This invention relates to the field of vegetation restoration, and more specifically to a method for evaluating the effectiveness of forest vegetation restoration. Background Technology

[0002] Forest vegetation restoration is a core means of addressing ecological degradation and enhancing ecosystem service functions, and its effectiveness evaluation is a crucial step in guiding the optimization of restoration projects and verifying the achievement of restoration goals. Currently, existing methods for evaluating the effectiveness of forest vegetation restoration mainly suffer from the following shortcomings:

[0003] 1. Relying too much on single structural indicators such as vegetation cover and biomass, while ignoring the synergistic assessment of ecological functions (such as carbon sequestration and soil and water conservation) and environmental responses (such as soil quality improvement), leads to one-sided assessment results that fail to reflect the comprehensive recovery level of the ecosystem.

[0004] 2. Most of the assessments use snapshots of a single point in time, failing to consider dynamic changes over time and failing to couple in disturbances such as climate fluctuations (e.g., temperature and precipitation variations) and human activities (e.g., road construction and agricultural reclamation), thus failing to truly reflect the dynamic adaptability of the recovery process.

[0005] 3. The ability to assess spatial differences in restoration effects is weak, and it can only provide a partial reflection of the overall forest vegetation restoration effect, resulting in poor assessment accuracy. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a method for evaluating the effect of forest vegetation restoration.

[0007] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0008] A method for evaluating the effect of forest vegetation restoration is provided, which includes the following steps:

[0009] S1: Determine the study area for evaluating the effect of forest vegetation restoration, and collect vegetation community data, ecological function data, environmental factor data and disturbance factor data from adjacent control areas, and calculate the vegetation community index and ecological function index of the control area based on time series.

[0010] S2: Calculate the extreme values ​​of the fluctuation of the vegetation community index and the ecological function index based on the time series, and obtain the environmental factor data and disturbance factor data of the time before the collection time corresponding to the extreme value of the fluctuation, and calculate the influence weight of environmental factors and disturbance factors on vegetation community and ecological function.

[0011] S3: Construct a calculation model for calculating the forest vegetation restoration index based on the influence weights, and fit the calculation model using vegetation community data, ecological function data, environmental factor data and disturbance factor data collected in the control area, and output the well-fitted calculation model.

[0012] S4: Take several sampling areas evenly within the study area, collect vegetation community data, ecological function data, environmental factor data, and disturbance factor data in each sampling area, input them into the fitted calculation model, calculate the forest vegetation restoration index for each sampling area, construct a semi-variogram function, and evaluate the forest vegetation restoration effect in the study area.

[0013] Further, step S1 includes:

[0014] S11: Determine the study area for evaluating the effect of forest vegetation restoration, and select a control area from the forest vegetation restoration area adjacent to the study area. Obtain vegetation community data, ecological function data, environmental factor data and disturbance factor data recorded at different times during the forest vegetation restoration process in the control area.

[0015] S12: Construct a time-series-based vegetation community dataset for the control region. Ecological function dataset Environmental factor dataset and interference factor dataset ; i Assign species numbers to the vegetation community data. For the first time collected N Individual vegetation community data, For the first N Each collection time, j Number the types of ecological function data. For the first time collected N Individual ecological function data, k Number the types of environmental factor data. For the first time collected N Data on environmental factors, u Number the types of interference factor data. For the first time collected N Data on several interference factors;

[0016] S13: Calculate the vegetation community index corresponding to different collection times in the control area. ;

[0017] ;

[0018] in, For the first n Each collection time, IFor the number of species in the vegetation community data, For the first i Ideal values ​​for vegetation community data For the first i Weights of vegetation community data;

[0019] Calculate the ecological function index corresponding to different collection times in the control area. ;

[0020] ;

[0021] in, J This refers to the number and types of ecological function data. For the first j Ideal values ​​for ecological function data, For the first j Weighting of ecological function data.

[0022] Further, step S2 includes:

[0023] S21: Obtain N Individual vegetation community indices and ecological function indices, based on ecological function indices and vegetation community index Screening extreme values ​​of ecological function index fluctuations Extreme values ​​of vegetation community index fluctuations ;

[0024] ;

[0025] ;

[0026] in, for N A set composed of vegetation community indices for N A set of ecological function indices;

[0027] S22: Obtaining Extreme Values Corresponding collection time and extreme values Corresponding collection time Calculate the collection time Previous collection time Corresponding environmental factor data weight Interference factor data weight Calculate the collection time Previous collection time Corresponding environmental factor data weight Interference factor data weight ;

[0028] ;

[0029] ;

[0030] S23: Weight These are respectively used as the weights of the influence of environmental factors and disturbance factors on the vegetation community. These are respectively used as the weights of the impact of environmental factors and disturbance factors on ecological functions.

[0031] Further, step S3 includes:

[0032] S31: Constructing and calculating the forest vegetation restoration index based on influence weights DRI The computational model;

[0033] ;

[0034] in, These are the weighting factors for vegetation community data and ecological function data, respectively. These are the influence coefficients of environmental factors and interference factors, respectively. These are the influence coefficients for different vegetation community data and ecological function data, respectively.

[0035] S32: Input the collected vegetation community dataset, ecological function dataset, environmental factor dataset, and disturbance factor dataset into the calculation model, fit the influence coefficients and weight correction factors in the calculation model, and take the average value of the influence coefficients and weight correction factors to obtain the well-fitted calculation model.

[0036] Further, step S4 includes:

[0037] S41: Take several sampling areas evenly within the study area and obtain the distance between each sampling area. h The data includes vegetation community data, ecological function data, environmental factor data, and disturbance factor data collected within the sampling area. These data are then input into a fitted calculation model to calculate the forest vegetation restoration index for different sampling areas. , This is the location label for the sampling area. c The sampling area is numbered;

[0038] S42: Forest vegetation restoration index based on different sampling areas Construct a semi-mutation function;

[0039] ;

[0040] in, Sampling interval h The semivariogram under the given conditions, Sampling interval h Logarithm of the sampling region under the given conditions To be consistent with the sampling area c Forest vegetation restoration index of adjacent sampling areas;

[0041] S43: The semi-variogram function is fitted using a spherical model;

[0042] ;

[0043] in, The nugget value is a semi-variogram. The structural variance of the semivariogram. a The threshold value for the sampling region spacing is set.

[0044] S44: Based on nugget value and structural variance Calculate the sill value of forest vegetation restoration effect in the study area. And set the threshold value for the sill. ,like If the spatial differences in forest vegetation restoration effects are large, the forest vegetation restoration effect in the study area is poor, and local remediation is needed; otherwise, if the spatial differences in forest vegetation restoration effects are small, the forest vegetation restoration effect in the study area is good.

[0045] The beneficial effects of this invention are as follows: This invention uses a four-dimensional index system of vegetation community, ecological function, environmental factors, and disturbance factors to comprehensively evaluate the effect of forest vegetation restoration, and constructs a forest vegetation restoration index based on the data system of restored areas. DRI The computational model used in this study ensures high reliability of the forest vegetation restoration index calculation results for the research area, quantifies the impact of disturbance factors on the restoration effect, and enables dynamic tracking of the restoration process. Furthermore, the forest vegetation restoration index is analyzed based on semi-variogram functions. DRI The spatial distribution characteristics of forest vegetation restoration can be used to obtain spatial differences in the effects of forest vegetation restoration, providing a basis for differentiated restoration management. Attached Figure Description

[0046] Figure 1 A flowchart for evaluating the effectiveness of forest vegetation restoration. Detailed Implementation

[0047] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0048] like Figure 1 As shown, a method for evaluating the effectiveness of forest vegetation restoration includes the following steps:

[0049] S1: Determine the study area for assessing the effectiveness of forest vegetation restoration, and collect vegetation community data, ecological function data, environmental factor data, and disturbance factor data from adjacent control areas. Calculate the vegetation community index and ecological function index of the control areas. Step S1 specifically includes:

[0050] S11: Determine the study area for evaluating the effect of forest vegetation restoration, and select a control area from the forest vegetation restoration area adjacent to the study area. Obtain vegetation community data, ecological function data, environmental factor data and disturbance factor data recorded at different times during the forest vegetation restoration process in the control area.

[0051] The vegetation community data in this embodiment includes species richness. S Vegetation diversity index H Dominance Index D , vegetation diameter at breast height G and seedling renewal density N Ecological function data includes vegetation carbon sequestration. C Soil and water conservation capacity W and litter decomposition rate L Environmental factor data include soil organic carbon (SOC), soil pH, soil moisture content (SWC), and normalized difference vegetation index (NDVI); interference factor data includes annual mean temperature variation coefficient. CV T Annual precipitation variation coefficient CV P Human activity intensity index HAI ;

[0052] Neighboring areas with restored forest vegetation share similar climatic, ecological, and disturbance factors, and their forest vegetation growth environments are similar, which can provide a reference for assessing the effectiveness of forest vegetation restoration in the study area.

[0053] S12: Construct a time-series-based vegetation community dataset for the control region. Ecological function dataset Environmental factor dataset and interference factor dataset ; i Assign species numbers to the vegetation community data. For the first time collected N Individual vegetation community data, For the first N Each collection time, j Number the types of ecological function data. For the first time collected N Individual ecological function data, k Number the types of environmental factor data. For the first time collected N Data on environmental factors, u Number the types of interference factor data. For the first time collected N Data on several interference factors;

[0054] S13: Calculate the vegetation community index corresponding to different collection times in the control area. ;

[0055] ;

[0056] in, For the first n Each collection time, I For the number of species in the vegetation community data, For the first i Ideal values ​​for vegetation community data For the first i Weights of vegetation community data;

[0057] This embodiment I =5, the weights corresponding to different vegetation community data. The values ​​are selected based on the region's forest vegetation restoration requirements. For example, vegetation growth varies significantly at different altitudes; in high-altitude areas, vegetation growth is difficult, and the vegetation diversity index... H Seedling renewal density N More importantly, and secondarily, among the remaining indicators, the vegetation diversity index can be used. H Seedling renewal density N The weight of each indicator is 0.35, while the weight of the other three indicators is 0.1.

[0058] Calculate the ecological function index corresponding to different collection times in the control area. ;

[0059] ;

[0060] in, JThis refers to the number and types of ecological function data. For the first j Ideal values ​​for ecological function data, For the first j Weighting of ecological function data;

[0061] This embodiment J =3, weights of different ecological function data The value is determined based on the region's forest vegetation restoration requirements, such as the soil and water conservation capacity in desert areas. W More importantly, the weight of soil and water conservation capacity is 0.4, while the weights of other factors such as litter decomposition rate and vegetation carbon sequestration are both 0.3.

[0062] S2: Calculate the extreme values ​​of the vegetation community index and ecological function index based on time-series data, and obtain the environmental factor data and disturbance factor data of the time preceding the data collection time corresponding to the extreme values. Calculate the influence weights of environmental factors and disturbance factors on the vegetation community and ecological function. Step S2 specifically includes:

[0063] S21: Obtain N Individual vegetation community indices and ecological function indices, based on ecological function indices and vegetation community index Screening extreme values ​​of ecological function index fluctuations Extreme values ​​of vegetation community index fluctuations ;

[0064] ;

[0065] ;

[0066] in, for N A set composed of vegetation community indices for N A set of ecological function indices;

[0067] In this embodiment, the principle of selecting two extreme values ​​is as follows: by calculating the average value of the difference between consecutive ecological function indices and vegetation community indices, the ecological function index and vegetation community index corresponding to the largest ratio of the difference to the average value are selected, indicating that the change between the two adjacent indices is the largest, and the index corresponding to the latter is the extreme value of fluctuation; during this period, environmental factors and disturbance factors have the greatest impact on the ecological function index and vegetation community index.

[0068] S22: Obtaining Extreme Values Corresponding collection time and extreme values Corresponding collection time Calculate the collection time Previous collection time Corresponding environmental factor data weight Interference factor data weight Calculate the collection time Previous collection time Corresponding environmental factor data weight Interference factor data weight ;

[0069] ;

[0070] ;

[0071] S23: Weight These are respectively used as the weights of the influence of environmental factors and disturbance factors on the vegetation community. These are respectively used as the weights of the impact of environmental factors and disturbance factors on ecological functions.

[0072] S3: Construct a calculation model for the forest vegetation restoration index based on influence weights, and fit the calculation model using vegetation community data, ecological function data, environmental factor data, and disturbance factor data collected from the control area, outputting the well-fitted calculation model. Step S3 specifically includes:

[0073] S31: Constructing and calculating the forest vegetation restoration index based on influence weights DRI The computational model;

[0074] ;

[0075] in, These are the weighting factors for vegetation community data and ecological function data, respectively. These are the influence coefficients of environmental factors and interference factors, respectively. These are the influence coefficients for different vegetation community data and ecological function data, respectively.

[0076] In the computational model, weighting factors For a known quantity, generally take The parameters can be reasonably set according to the requirements of the vegetation community and ecological function during the forest vegetation restoration process in the study area. The influence coefficients of environmental factors, disturbance factors, vegetation community data, and ecological function data are all unknowns and need to be fitted with data collected from the control area to be used for the forest vegetation restoration index of the subsequent study area. DRI The calculation.

[0077] S32: Input the collected vegetation community dataset, ecological function dataset, environmental factor dataset, and disturbance factor dataset into the calculation model, fit the influence coefficients and weight correction factors in the calculation model, and take the average value of the influence coefficients and weight correction factors to obtain the well-fitted calculation model.

[0078] In this embodiment, the calculation model has 6 unknown coefficients, and at least 6 sets of data must be collected from the control area to ensure that the 6 unknown coefficients can be solved. The solution process can be carried out by solving the system of equations using MATLAB software.

[0079] S4: Within the study area, several sampling areas are uniformly selected. Vegetation community data, ecological function data, environmental factor data, and disturbance factor data are collected from each sampling area. These data are input into the fitted computational model to calculate the forest vegetation restoration index for each sampling area. A semi-variogram is then constructed to evaluate the forest vegetation restoration effect in the study area. Step S4 specifically includes:

[0080] S41: Take several sampling areas evenly within the study area and obtain the distance between each sampling area. h The data includes vegetation community data, ecological function data, environmental factor data, and disturbance factor data collected within the sampling area. These data are then input into a fitted calculation model to calculate the forest vegetation restoration index for different sampling areas. , This is the location label for the sampling area. c The sampling area is numbered;

[0081] S42: Forest vegetation restoration index based on different sampling areas Construct a semi-mutation function;

[0082] ;

[0083] in, Sampling interval h The semivariogram under the given conditions, Sampling interval h Logarithm of the sampling region under the given conditions To be consistent with the sampling area c Forest vegetation restoration index of adjacent sampling areas;

[0084] S43: The semi-variogram function is fitted using a spherical model;

[0085] ;

[0086] in, The nugget value is a semi-variogram. The structural variance of the semivariogram. aThe threshold value for the sampling region spacing is set.

[0087] Gold Value Random variation at a small scale (such as within a sampling area) may originate from micro-topographical differences, local fluctuations in soil fertility, or sampling errors. For example, the nugget value within a sampling area. =0.03, indicating that the difference in vegetation restoration effect caused by micro-topography within the sampling area is small, and the sampling design is reasonable; if the nugget value within a certain sampling area is... If the value is 0.08, then the sampling area needs to be encrypted to reduce random errors.

[0088] sill value This represents the total variation (random variation + structural variation) of vegetation restoration effects within the study area. A larger sill value indicates more significant spatial differences in restoration effects across the entire study area. For example, the sill value in areas converted from farmland to forest. =0.12, sill value of the mine restoration area = 0.25, indicating that the spatial fluctuation of restoration effect is more intense in the mine restoration area due to the large differences in soil conditions.

[0089] The semi-variogram introduced in this invention plays a role by quantifying spatial heterogeneity. It assesses the spatial uniformity of forest vegetation restoration effect in the study area by measuring the spatial differences in forest vegetation restoration index in each sampling area.

[0090] S44: Based on nugget value and structural variance Calculate the sill value of forest vegetation restoration effect in the study area. And set the threshold value for the sill. ,like If the spatial differences in forest vegetation restoration effects are large, the forest vegetation restoration effect in the study area is poor, and local remediation is needed; otherwise, if the spatial differences in forest vegetation restoration effects are small, the forest vegetation restoration effect in the study area is good.

[0091] This invention uses a four-dimensional index system—vegetation community, ecological function, environmental factors, and disturbance factors—to comprehensively evaluate the effect of forest vegetation restoration, and constructs a forest vegetation restoration index based on data from restored areas. DRI The computational model used in this study ensures high reliability of the forest vegetation restoration index calculation results for the research area, quantifies the impact of disturbance factors on the restoration effect, and enables dynamic tracking of the restoration process. Furthermore, the forest vegetation restoration index is analyzed based on semi-variogram functions. DRI The spatial distribution characteristics of forest vegetation restoration can be used to obtain spatial differences in the effects of forest vegetation restoration, providing a basis for differentiated restoration management.

Claims

1. A method for evaluating the effect of forest vegetation restoration, characterized in that, Includes the following steps: S1: Determine the study area for evaluating the effect of forest vegetation restoration, and collect vegetation community data, ecological function data, environmental factor data and disturbance factor data from adjacent control areas, and calculate the vegetation community index and ecological function index of the control area based on time series. S2: Calculate the extreme values ​​of the fluctuation of the vegetation community index and the ecological function index based on the time series, and obtain the environmental factor data and disturbance factor data of the time before the collection time corresponding to the extreme value of the fluctuation, and calculate the influence weight of environmental factors and disturbance factors on vegetation community and ecological function. S3: Construct a calculation model for calculating the forest vegetation restoration index based on the influence weights, and fit the calculation model using vegetation community data, ecological function data, environmental factor data and disturbance factor data collected in the control area, and output the well-fitted calculation model. S4: Take several sampling areas evenly within the study area, collect vegetation community data, ecological function data, environmental factor data and disturbance factor data in each sampling area, input them into the fitted calculation model, calculate the forest vegetation restoration index of each sampling area, construct a semi-variogram function, and evaluate the forest vegetation restoration effect in the study area. Step S1 includes: S11: Determine the study area for evaluating the effect of forest vegetation restoration, and select a control area from the forest vegetation restoration area adjacent to the study area. Obtain vegetation community data, ecological function data, environmental factor data and disturbance factor data recorded at different times during the forest vegetation restoration process in the control area. S12: Construct a time-series-based vegetation community dataset for the control region. Ecological function dataset Environmental factor dataset and interference factor dataset ; i Assign species numbers to the vegetation community data. For the first time collected N Individual vegetation community data, For the first N Each collection time, j Number the types of ecological function data. For the first time collected N Individual ecological function data, k Number the types of environmental factor data. For the first time collected N Data on environmental factors, u Number the types of interference factor data. For the first time collected N Data on several interference factors; S13: Calculate the vegetation community index corresponding to different collection times in the control area. ; ； in, For the first n Each collection time, I For the number of species in the vegetation community data, For the first i Ideal values ​​for vegetation community data For the first i Weights of vegetation community data; Calculate the ecological function index corresponding to different collection times in the control area. ; ； in, J This refers to the number and types of ecological function data. For the first j Ideal values ​​for ecological function data, For the first j Weighting of ecological function data; Step S2 includes: S21: Obtain N Individual vegetation community indices and ecological function indices, based on ecological function indices and vegetation community index Screening extreme values ​​of ecological function index fluctuations Extreme values ​​of vegetation community index fluctuations ; ； ； in, for N A set composed of vegetation community indices for N A set of ecological function indices; S22: Obtaining Extreme Values Corresponding collection time and extreme values Corresponding collection time Calculate the collection time Previous collection time Corresponding environmental factor data weight Interference factor data weight Calculate the collection time Previous collection time Corresponding environmental factor data weight Interference factor data weight ; ； ； S23: Weight These are respectively used as the weights of the influence of environmental factors and disturbance factors on the vegetation community. These are respectively used as the weights of the impact of environmental factors and disturbance factors on ecological functions; Step S3 includes: S31: Constructing and calculating the forest vegetation restoration index based on influence weights DRI The computational model; ； in, These are the weighting factors for vegetation community data and ecological function data, respectively. These are the influence coefficients of environmental factors and interference factors, respectively. These are the influence coefficients for different vegetation community data and ecological function data, respectively. S32: Input the collected vegetation community dataset, ecological function dataset, environmental factor dataset, and disturbance factor dataset into the calculation model, fit the influence coefficients and weight correction factors in the calculation model, and take the average value of the influence coefficients and weight correction factors to obtain the well-fitted calculation model.

2. The method for evaluating the effect of forest vegetation restoration according to claim 1, characterized in that, Step S4 includes: S41: Take several sampling areas evenly within the study area and obtain the distance between each sampling area. h The data includes vegetation community data, ecological function data, environmental factor data, and disturbance factor data collected within the sampling area. These data are then input into a fitted calculation model to calculate the forest vegetation restoration index for different sampling areas. , This is the location label for the sampling area. c The sampling area is numbered; S42: Forest vegetation restoration index based on different sampling areas Construct a semi-mutation function; ； in, Sampling interval h The semivariogram under the given conditions, Sampling interval h Logarithm of the sampling region under the given conditions To be consistent with the sampling area c Forest vegetation restoration index of adjacent sampling areas; S43: The semi-variogram function is fitted using a spherical model; ； in, The nugget value is a semi-variogram. The structural variance of the semivariogram. a The threshold value for the sampling region spacing is set. S44: Based on nugget value and structural variance Calculate the sill value of forest vegetation restoration effect in the study area. And set the threshold value for the sill. ,like If the spatial differences in forest vegetation restoration effects are large, the forest vegetation restoration effect in the study area is poor, and local remediation is needed; otherwise, if the spatial differences in forest vegetation restoration effects are small, the forest vegetation restoration effect in the study area is good.

Images