Method for determining vegetation growth threshold in karst area based on multi-source data fusion

Abstract

This application relates to the field of ecological environment treatment technology, specifically to a method for determining vegetation growth thresholds in karst regions based on multi-source data fusion. The method includes: acquiring multi-source data for each grid unit within a target area; determining the monthly soil material supply index and vegetation growth index observations for each grid unit within the target area during the observation period based on the multi-source data; performing Granger causality analysis on the monthly soil material supply index and vegetation growth index observations for each grid unit during the observation period to select sample grid units; and performing nonlinear analysis and mutation point identification on the monthly soil material supply index and vegetation growth index observations for the sample grid units during the observation period to determine the geological material supply limitation threshold for the target area. This method can accurately locate the critical position where ecological restoration resistance increases exponentially, providing a scientific and precise quantitative basis for vegetation restoration in karst regions.

Description

Technical Field

[0001] This application relates to the field of ecological environment treatment technology, specifically to a method for determining vegetation growth threshold in karst areas based on multi-source data fusion. Background Technology

[0002] The ecosystems in karst regions are fragile, and the effectiveness of their ecological restoration is closely constrained by the complex geological background and variable hydrological environment. When carrying out ecological restoration projects in such areas, such as rocky desertification control, the primary prerequisite is to accurately assess the vegetation restoration potential of different plots, distinguishing between manageable areas and areas that are difficult to restore naturally. This avoids ineffective investment of engineering resources and allows for seizing appropriate intervention opportunities.

[0003] In existing technologies, single-indicator statistical or linear regression methods are typically used to assess the restoration potential of a region. For example, rainfall data can be used directly to assess moisture limitations, or rock exposure rates can be used to assess geological limitations.

[0004] However, due to the extremely fragmented karst surface and the existence of a large number of local microhabitats not controlled by geological background, single-index statistics cannot eliminate these noise samples caused by micro-topography or human factors, resulting in distorted geological or water limits in the calculations, and thus failing to provide accurate scientific basis for vegetation restoration and ecological protection in karst areas. Summary of the Invention

[0005] To address the technical challenge of accurately assessing geological or moisture limitations in karst regions, this application aims to provide a method for determining vegetation growth thresholds in karst areas based on multi-source data fusion. The specific technical solution adopted is as follows: This application provides a method for determining vegetation growth thresholds in karst regions based on multi-source data fusion, comprising: acquiring multi-source data for each grid cell within a target area, including geological data, meteorological data, and vegetation data; determining the monthly soil material supply index and vegetation growth index observations for each grid cell within the target area during the observation period based on the multi-source data, wherein the soil material supply index characterizes the soil material supply capacity under the geological background, and the vegetation growth index observations characterize the productivity level, tree height, and plant diversity of the ecosystem within the grid cell; performing Granger causality analysis on the monthly soil material supply index and vegetation growth index observations for each grid cell during the observation period to screen for sample grid cells, wherein the sample grid cells are those where geological conditions have a significant temporal predictive impact on vegetation growth; and performing nonlinear analysis and mutation point identification on the monthly soil material supply index and vegetation growth index observations for the sample grid cells during the observation period to determine the geological material supply limitation threshold for the target area, wherein the geological material supply limitation threshold characterizes the physical critical point at which the ecosystem's self-sustaining capacity undergoes abrupt changes.

[0006] Optionally, the geological data includes monthly rock density, rock molar mass, and acid-insoluble matter content during the observation period, and the meteorological data includes monthly average temperature and average precipitation during the observation period. Based on this multi-source data, the soil material supply index for each grid cell within the target area is determined monthly during the observation period, including: determining the monthly carbonate mineral dissolution balance constant for each grid cell based on the monthly average temperature and temperature balance constant function relationship; determining the monthly potential chemical dissolution rate for each grid cell based on the monthly carbonate mineral dissolution balance constant, average precipitation, rock density, and rock molar mass; and determining the monthly soil material supply index for each grid cell based on the monthly potential chemical dissolution rate and acid-insoluble matter content.

[0007] Optionally, the vegetation data includes monthly net primary productivity, tree height, and vegetation diversity during the observation period. Based on this multi-source data, the monthly vegetation growth index observation values ​​for each grid unit during the observation period are determined, including: weighting and fusing the monthly net primary productivity, tree height, and vegetation diversity of each grid unit to obtain the monthly vegetation growth index observation values ​​for each grid unit.

[0008] Optionally, the above-mentioned Granger causality analysis of the monthly soil material supply index and vegetation growth index observations for each grid unit during the observation period, and the selection of sample grid units, includes: constructing the soil material supply index time series and vegetation growth index time series for each grid unit based on the monthly soil material supply index and vegetation growth index observations for each grid unit during the observation period; performing Granger causality tests on the soil material supply index time series and vegetation growth index time series for each grid unit to obtain the significance probability value of each grid unit, which is used to characterize the confidence level of the Granger causal influence of the soil material supply index on the vegetation growth index; and selecting sample grid units from all grid units based on the significance probability value of each grid unit.

[0009] Optionally, the above-mentioned selection of sample grid cells from all grid cells based on the significance probability value of each grid cell includes: identifying grid cells with significance probability values ​​less than a preset significance threshold; determining grid cells with significance probability values ​​less than the preset significance threshold as sample grid cells when the number of grid cells with significance probability values ​​less than the preset significance threshold is greater than or equal to a preset quantity threshold; and selecting a preset proportion of grid cells as sample grid cells in descending order of significance probability values ​​when the number of grid cells with significance probability values ​​less than the preset quantity threshold is less than the preset quantity threshold.

[0010] Optionally, the above-mentioned nonlinear analysis and mutation point identification of the monthly soil material supply index and vegetation growth index observations of the sample grid units during the observation period to determine the geological material supply limitation threshold of the target area includes: determining the monthly average soil material supply index and monthly average vegetation growth index observations of each grid unit based on the monthly soil material supply index and vegetation growth index observations of each grid unit during the observation period; normalizing the monthly average soil material supply index of each grid unit to obtain the normalized supply index of each grid unit; and fitting the ideal vegetation growth rate based on the normalized supply index and monthly average vegetation growth index observations of the sample grid units. The theoretical upper limit curve is used to characterize the maximum potential value of vegetation growth indicators that can be supported by a normalized supply index within the target area. Based on the theoretical upper limit curve, the normalized supply index of each grid unit, and the monthly average vegetation growth index observations, the unit supply deficit index of each grid unit is determined. This unit supply deficit index is used to characterize the ecological restoration resistance borne by the unit geological material supply capacity. Curvature analysis is performed on the trend of the unit supply deficit index with the normalized supply index, and the normalized supply index corresponding to the maximum curvature point is identified. The normalized supply index is then denormalized to obtain the geological material supply limitation threshold.

[0011] Optionally, the meteorological data includes monthly soil volumetric water content during the observation period. The method further includes: determining the hydrological fluctuation coefficient of each grid cell in the target area based on the monthly average soil volumetric water content of each vegetation growing season during the observation period. The hydrological fluctuation coefficient is used to characterize the instability of the water environment within the grid cell. The target area is then divided into engineering zones based on the geological material supply limitation threshold of the target area and the hydrological fluctuation coefficient of each grid cell.

[0012] Optionally, the above-mentioned determination of the hydrological fluctuation coefficient of each grid unit in the target area based on the monthly average soil volumetric water content of each vegetation growing season during the observation period includes: constructing a soil moisture time series for each grid unit based on the monthly average soil volumetric water content of each vegetation growing season during the observation period; and determining the hydrological fluctuation coefficient of each grid unit based on the standard deviation and arithmetic mean of the soil moisture time series for each grid unit.

[0013] Optionally, the above-mentioned engineering zoning of the target area based on the geological material supply limitation threshold and the hydrological fluctuation coefficient of each grid unit includes: identifying grid units with a soil material supply index lower than the geological material supply limitation threshold as high-risk areas for geological supply; identifying grid units with a soil material supply index greater than or equal to the geological material supply limitation threshold and a hydrological fluctuation coefficient greater than the high-risk threshold for hydrological fluctuation as water-restricted areas; and identifying grid units with a soil material supply index greater than or equal to the geological material supply limitation threshold and a hydrological fluctuation coefficient less than or equal to the high-risk threshold for hydrological fluctuation as geologically and hydrologically suitable areas.

[0014] Optionally, the method further includes: determining the mean and standard deviation of the hydrological fluctuation coefficients of all grid cells; and determining the sum of the mean and standard deviation as the high-risk threshold for hydrological fluctuations.

[0015] This application has the following beneficial effects: By using multi-source data, the monthly soil material supply index and vegetation growth index of each grid unit during the observation period were determined, transforming the multi-source data into engineering evaluation indicators with clear physical meaning. Granger causality analysis was performed on the monthly soil material supply index and vegetation growth index of each grid unit to screen sample grid units where geological conditions have a significant temporal predictive impact on vegetation growth. This automatically identifies and eliminates non-zonal noise samples affected by local micro-topography or human interference, ensuring that subsequent analyses are based only on valid data that conforms to geological control laws. Nonlinear analysis and mutation point identification were performed on the monthly soil material supply index and vegetation growth index of the sample grid units to determine the physical critical points characterizing abrupt changes in the ecosystem's self-sustaining capacity. This allows for precise location of the critical position where ecological restoration resistance increases exponentially, providing a scientific and precise quantitative basis for vegetation restoration and ecological protection in karst regions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a method for determining vegetation growth threshold in karst regions based on multi-source data fusion, provided as an embodiment of this application; Figure 2 This is a flowchart illustrating another method for determining vegetation growth threshold in karst regions based on multi-source data fusion, provided as an embodiment of this application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for determining vegetation growth thresholds in karst regions based on multi-source data fusion proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] Karst ecosystems are extremely vulnerable, with discontinuous topsoil distribution and very thin soil layers. The effectiveness of ecological restoration projects is often constrained by both the supply capacity of geological materials and the stability of the hydrological environment. During the planning stage of ecological projects such as rocky desertification control, engineers need to accurately identify the vegetation restoration potential of different plots to avoid ineffective investment in areas lacking soil-forming conditions or missing engineering intervention opportunities in areas limited only by water availability.

[0021] Current engineering practices often employ single-indicator statistical or linear regression methods to assess a region's restoration potential. For example, rainfall data is directly used to assess water limitations, or rock exposure rates are used to assess geological limitations. However, these conventional assessment methods present two significant engineering application challenges in karst habitats: First, there is a mismatch between spatiotemporal scales and physical properties. Existing assessments often directly correlate the current rock weathering rate with the current vegetation biomass. However, from an engineering geology perspective, the material basis for vegetation growth (soil) is a long-term accumulated stock over geological history, not a current instantaneous weathering flow. Ignoring the dynamic balance mechanism of soil loss and replenishment and assessing solely based on instantaneous rates can lead to misjudgments of geological carrying capacity.

[0022] Second, there is interference from non-zonal ecological heterogeneous noise. Karst surfaces are extremely fragmented, containing numerous local microhabitats not controlled by geological background. For example, in contiguous barren bedrock areas, local solution channels or depressions may possess deep soil due to gravity deposition, creating the illusion of abundant rocks and lush vegetation; conversely, some areas with favorable geological conditions may appear to have abundant soil but poor vegetation due to excessive human cultivation. Conventional statistical methods cannot eliminate these noise samples caused by micro-topography or human factors, leading to distorted calculated geological constraint thresholds and failing to provide accurate basis for large-scale engineering site selection.

[0023] Therefore, there is an urgent need for a threshold determination method that can isolate non-zonal noise interference and quantitatively distinguish between geological capacity constraints and hydrological process constraints based on engineering geological assumptions.

[0024] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method for determining vegetation growth threshold in karst regions based on multi-source data fusion provided in this application.

[0025] Please see Figure 1 The diagram illustrates a flowchart of a method for determining vegetation growth threshold in karst regions based on multi-source data fusion, provided in one embodiment of this application.

[0026] like Figure 1 As shown, the method for determining vegetation growth threshold in karst areas based on multi-source data fusion includes S101-S104.

[0027] S101. Obtain multi-source data for each grid cell within the target area.

[0028] The multi-source data includes geological data, meteorological data, and vegetation data. The geological data includes monthly rock density, rock molar mass, and acid-insoluble matter content during the observation period. The meteorological data includes monthly average temperature, average precipitation, and monthly soil volumetric water content during the observation period. The vegetation data includes monthly net primary productivity, tree height, and vegetation diversity during the observation period.

[0029] In this embodiment of the application, the target area is a local area to be detected in a karst region.

[0030] It should be understood that, since geological data, meteorological data, and vegetation data come from different sources, have different spatial resolutions, and their physical properties are not directly comparable, it is first necessary to establish a unified spatial computing benchmark and divide each grid cell into multi-source data.

[0031] Optionally, the target area can be divided into several grid cells of equal area. The size of the grid cells is determined according to the total area of ​​the target area, the data spatial resolution, and the required analysis accuracy, so as to ensure that each grid cell can accurately represent the local geographic and ecological characteristics.

[0032] For example, the size of a grid cell can be 250 meters × 250 meters.

[0033] Optionally, geological data can be obtained from the geological vector map of the target area (scale, for example, 1:200,000). The vector map patches are converted into raster data using nearest neighbor interpolation, ensuring that each grid cell receives a unique rock type label, including carbonate rocks such as limestone and dolomite. Simultaneously, the system has a pre-installed rock physical property database, matching the corresponding rock density, molar mass, and acid-insoluble content to the lithology label of each grid cell.

[0034] Among them, rock density is used to characterize the mass of rock per unit volume, with the unit being grams per cubic centimeter; molar mass is used to characterize the molecular weight of carbonate minerals, with the unit being grams per mole; acid insoluble content is the acid insoluble content in the rock, used to characterize the proportion of substances that can remain after the rock weathers to form soil, with the unit being percentage.

[0035] Optionally, meteorological data can be obtained from meteorological reanalysis data of the target area (such as ERA5-Land products). This meteorological reanalysis data contains temperature and precipitation monitoring records of the target area during the observation period (e.g., the last 15 months). Then, the original low-resolution (e.g., 9km) temperature and precipitation fields are resampled to 250m resolution using bilinear interpolation, and monthly average temperature and monthly average precipitation are extracted for each grid cell.

[0036] It should be understood that average temperature is used to characterize the thermal conditions of a grid cell, and the unit is degrees Celsius; average precipitation is used to characterize the total amount of moisture input to a grid cell, and the unit is millimeters.

[0037] It is understandable that the observation period refers to the specific time span covered by continuous data collected for analysis. The specific number of months needs to be determined based on the actual length of available data and the research objectives.

[0038] For example, an observation period can be 5 or 10 years. A longer period of time can eliminate the interference of abnormal climate in a single year (such as extreme drought or flood), thereby obtaining vegetation growth indicators and soil moisture data that can represent the long-term average state and stable patterns of the target area, ensuring that the geological material supply limit thresholds calculated subsequently are robust and representative.

[0039] Optionally, the vegetation net primary productivity remote sensing product (such as MODIS data) includes monthly vegetation net primary productivity monitoring records for the target area during the statistical observation period. The original resolution vegetation data is aligned to a 250-meter grid using bilinear interpolation, and the monthly vegetation net primary productivity for each grid cell is extracted during the observation period. The tree heights of each grid cell at the same time each month are extracted using a spaceborne radar (such as GEDI), and the average of all tree heights at the same time each month in each grid cell is determined as the tree height of that grid cell for that month. The spectral diversity index of each grid cell during the growing season is obtained through multispectral satellites, and this spectral diversity index is determined as vegetation diversity.

[0040] It should be understood that net primary productivity of vegetation is used to characterize the total amount of organic matter produced by vegetation through photosynthesis per unit area minus the amount consumed by autotrophic respiration, and the unit is grams of carbon per square meter per month.

[0041] Through the above processing, the lithology, rock density, rock molar mass, acid insoluble content, monthly average temperature, monthly average precipitation, and monthly net primary productivity, tree height, and vegetation diversity of each grid cell were obtained.

[0042] S102. Based on multi-source data, determine the monthly soil material supply index and vegetation growth index observation values ​​for each grid unit within the target area during the observation period.

[0043] Among them, the soil material supply index is used to characterize the soil material supply capacity under geological background, and the vegetation growth index observation value is used to characterize the productivity level of the ecosystem within the grid unit.

[0044] In this embodiment of the application, by combining static geodetic data with dynamic meteorological data, a soil material supply index is constructed based on the engineering assumption of dynamic balance between soil loss and replenishment in karst ecosystems (i.e., only areas where the geological material supply capacity can offset the soil loss rate have sustainable vegetation carrying capacity in the long term). The resulting soil material supply index reflects the long-term potential of soil material provided by weathering in geological history, rather than the current instantaneous weathering rate, thereby enabling a more accurate assessment of the upper limit of the region's geological carrying capacity.

[0045] It should be understood that the larger the value of the soil material supply index, the greater the potential of the grid unit to provide soil material through weathering during geological history, the more favorable the geological background conditions of the grid unit, and the more robust the material basis supporting vegetation growth. The higher the net primary productivity of vegetation, the taller the tree, and the higher the vegetation diversity, the larger the observed value of vegetation growth index.

[0046] S103. Granger causality analysis was performed on the monthly soil material supply index and vegetation growth index of each grid unit during the observation period to select sample grid units.

[0047] Among them, the sample grid cell is the grid cell in which geological conditions have a significant temporal predictive impact on vegetation growth.

[0048] It should be understood that in fragmented karst habitats, there exists a significant amount of non-zonal ecological heterogeneity. For example, in contiguous barren bedrock areas, localized rock gullies or karst depressions may possess deep soil due to gravity deposition, leading to lush vegetation (exhibiting an anomaly of low geological supply and high vegetation output); conversely, some areas with favorable geological conditions may experience sparse vegetation due to excessive human cultivation (exhibiting an anomaly of high geological supply and low vegetation output). This noise data obscures the true control of geological background over ecological restoration.

[0049] In order to obtain accurate geological constraint thresholds, the above-mentioned noisy data must be removed, and only grid cells in which geological conditions have a significant temporal predictive impact on vegetation growth must be retained.

[0050] In one alternative implementation, the soil material supply index time series and vegetation growth index time series for each grid cell can be constructed based on the monthly soil material supply index and vegetation growth index observations during the observation period. Granger causality tests are then performed on the soil material supply index time series and vegetation growth index time series for each grid cell to obtain the significance probability value for each grid cell. This significance probability value is used to characterize the confidence level of the Granger causal influence of the soil material supply index on the vegetation growth index. Based on the significance probability value of each grid cell, sample grid cells are selected from all grid cells.

[0051] Optionally, the monthly soil material supply index of each grid unit is arranged by time to form a soil material supply index time series, and the observed values ​​of vegetation growth indicators are arranged by time to form a vegetation growth indicator time series.

[0052] It should be noted that the soil material supply index time series reflects the impact of monthly climate fluctuations on the geological material supply capacity; the vegetation growth index time series reflects the actual output changes of the ecosystem during the observation period.

[0053] Optionally, the Granger causality test algorithm can be used to perform Granger causality tests on the time series of soil material supply index and vegetation growth index for each grid cell.

[0054] In this embodiment, when performing the Granger causality test using the Granger causality test algorithm, the maximum lag period is determined based on the response cycle of vegetation to changes in geological conditions. In the karst region of Southwest China, the response of vegetation to changes in soil moisture and nutrients typically lags by 6-10 months. Therefore, in this embodiment, the maximum lag period is 8. The constrained model is used to predict the current value based on the lag term of the vegetation growth index itself, while the unconstrained model is used to predict the current value by simultaneously using the lag term of the vegetation growth index itself and the lag term of the soil material supply index.

[0055] Optionally, after obtaining the significance probability value of each grid cell, grid cells with significance probability values ​​less than a preset significance threshold can be identified first; if the number of grid cells with significance probability values ​​less than the preset significance threshold is greater than or equal to a preset quantity threshold, the grid cells with significance probability values ​​less than the preset significance threshold are identified as sample grid cells; if the number of grid cells with significance probability values ​​less than the preset significance threshold is less than the preset quantity threshold, a preset proportion of grid cells are selected as sample grid cells in descending order of significance probability values.

[0056] It is understandable that grid cells with a significance probability value less than the preset significance threshold have a reliable causal relationship in statistics, and their geological conditions can significantly predict vegetation growth.

[0057] For example, the value of the preset significance threshold can be determined according to the confidence level requirements of spatial statistical analysis. In ecological engineering, for areas with high accuracy requirements, it can be 0.05, which means that the soil material supply index has a significant Granger causal effect on vegetation growth indicators at a 95% confidence level.

[0058] It should be understood that the preset quantity threshold is used to ensure that there is sufficient data for subsequent nonlinear analysis and mutation point identification. In this embodiment, it is taken as 5% of the total number of grid cells in the target area.

[0059] In this embodiment of the application, when the number of grid cells with a significance probability value less than the preset significance threshold is sufficient (i.e. greater than or equal to the preset number threshold), all grid cells with a significance probability value less than the preset significance threshold can be directly determined as sample grid cells.

[0060] If the number of grid cells with a significance probability value less than the preset significance threshold is small (i.e. less than the preset number threshold), it indicates that the number of available samples under the normal significance screening conditions is insufficient. At this time, the fallback screening strategy is activated: sort all grid cells in descending order of significance probability value, and then select the grid cells with a preset proportion before sorting as sample grid cells.

[0061] It should be understood that the number of grid cells in the preset ratio should be greater than the preset number threshold to ensure that there are enough samples (even if the quality is slightly low) to support subsequent analysis, even if the sample quality is low.

[0062] For example, the preset ratio can be 20%.

[0063] The aforementioned method for selecting sample grid cells integrates the dynamic changes in geological background with the temporal response of vegetation growth into a unified analytical framework by constructing time series data of soil material supply index and vegetation growth index for each grid cell. Based on this, a Granger causality test is performed on each grid cell to quantify the confidence level of whether a temporal predictive relationship exists between the soil material supply index and the vegetation growth index using a significance probability value. Sample grid cells are then selected based on this significance probability value. This temporal causality-based selection mechanism can more accurately eliminate spurious samples caused by accidental spatial clustering or short-term climate fluctuations, ensuring that the selected sample grid cells truly reflect the long-term driving effect of geological background on vegetation growth. This provides a data foundation with clear causal logic for subsequent nonlinear analysis and threshold determination.

[0064] S104. Perform nonlinear analysis and mutation point identification on the monthly soil material supply index and vegetation growth index of the sample grid units during the observation period to determine the geological material supply limitation threshold of the target area.

[0065] Among them, the geological material supply limitation threshold is used to characterize the physical critical point at which the self-sustaining capacity of an ecosystem undergoes a sudden change.

[0066] In one implementation of this application, the monthly average soil material supply index and the monthly average vegetation growth index of each grid cell can be determined based on the monthly soil material supply index and vegetation growth index observations during the observation period. The monthly average soil material supply index of each grid cell is normalized to obtain the normalized supply index of each grid cell. Based on the normalized supply index and the monthly average vegetation growth index observations of the sample grid cells, a theoretical upper limit curve for vegetation growth is fitted. Based on the theoretical upper limit curve, the normalized supply index of each grid cell, and the monthly average vegetation growth index observations, the unit supply deficit index of each grid cell is determined. The curvature analysis of the unit supply deficit index with the normalized supply index is performed, and the normalized supply index corresponding to the maximum curvature point is identified. The normalized supply index is then denormalized to obtain the geological material supply limitation threshold.

[0067] Among them, the theoretical upper limit curve is used to characterize the maximum potential value of vegetation growth index that can be supported by a normalized supply index in the target area; the unit supply deficit index is used to characterize the ecological restoration resistance borne by the unit geological material supply capacity.

[0068] Optionally, the monthly average soil material supply index of each grid cell can be determined by the ratio between the sum of the monthly soil material supply indices of each grid cell during the observation period and the number of months in the observation period; and the monthly average vegetation growth index of each grid cell can be determined by the ratio between the sum of the monthly vegetation growth index observations of each grid cell during the observation period and the number of months in the observation period.

[0069] Optionally, all grid cells within the target area can be traversed to obtain the global maximum and minimum values ​​of the monthly average soil material supply index. Then, based on the maximum-minimum normalization method, the monthly average soil material supply index of each grid cell can be mapped to the interval between 0 and 1 to obtain the normalized supply index of each grid cell.

[0070] Understandably, through normalization, the monthly average soil material supply index is converted into a dimensionless relative index, the magnitude of which only reflects the relative level of the grid cell in the entire target area, while the distribution characteristics of the original data are completely preserved.

[0071] In this embodiment of the application, considering that the response of an ecosystem to resource supply typically exhibits characteristics of low-level linear growth and high-level saturation, a nonlinear model with saturation characteristics (such as the Michaelis-Menten equation) is preferred for fitting.

[0072] Optionally, when the fitted model parameters do not satisfy the physical meaning (such as the half-saturation constant included in the regression coefficients of the Michaelis-Menten equation being negative), the model is automatically switched to a linear quantile regression model for fitting: the normalized supply index of the sample grid cell is used as the independent variable, and the monthly average vegetation growth index observation value of the sample grid cell is used as the dependent variable, and the quantile regression algorithm is used to fit the theoretical upper limit curve of vegetation growth.

[0073] In this study, quantile regression was performed using the 0.95 quantile to capture the boundary features at the top of the sample, ensuring that the fitted curve represents the upper limit of vegetation growth indicators that the geological conditions can support, rather than the average level.

[0074] It should be understood that the unit supply deficit index is used to characterize the ecological restoration resistance borne by the unit geological material supply capacity. The larger the value, the heavier the restoration pressure faced by the unit geological supply capacity.

[0075] Optionally, for each grid cell, firstly, based on its normalized supply index, the monthly average vegetation growth index observation value corresponding to the normalized supply index is found from the theoretical upper limit curve, and the monthly average vegetation growth index observation value is determined as the theoretical upper limit value of the vegetation growth index; then, the ratio of the monthly average vegetation growth index observation value of the grid cell to the theoretical upper limit value is calculated, and the relative ecological deficit is obtained by subtracting the ratio from 1; finally, the relative ecological deficit is divided by the normalized supply index of the grid cell to obtain the unit supply deficit index of the grid cell.

[0076] It should be noted that, in order to prevent the denominator from being zero, a preset background soil maintenance constant can be added to the denominator when dividing by the normalized supply index of the grid cell.

[0077] Alternatively, the baseline soil maintenance constant can be a small positive number (e.g., 10). -4 This represents the weak replenishment of soil by non-geological sources (such as humus from fallen leaves and branches).

[0078] Alternatively, the unit supply deficit index satisfies the following formula: in, Represents grid cells The unit supply deficit index, Represents grid cells The monthly average vegetation growth index observation value, Represents grid cells The normalized supply index, The normalized supply index represents the theoretical upper limit curve. The corresponding theoretical upper limit of vegetation growth indicators, This represents the baseline soil maintenance constant. for The function represents the larger of the two values ​​within the parentheses.

[0079] In this formula, This indicates the achievement rate of vegetation growth indicators, representing the proportion of the current vegetation growth indicator to the theoretical upper limit of the vegetation growth indicator. The value range is usually between 0 and 1, and is calculated by subtracting 1 from the actual value. The relative ecological deficit is obtained. The larger the relative ecological deficit, the further the current vegetation growth index is from the theoretical upper limit of the vegetation growth index, the more urgent the need for ecological restoration, and the larger the unit supply deficit index. This represents the geological supply capacity of the grid unit. The stronger the geological supply capacity (the larger the denominator), the smaller the unit supply deficit index corresponding to the same degree of ecological deficit.

[0080] It should be noted that the theoretical upper limit curve was fitted using the 0.95 quantile, allowing a small number of sample points to have observed vegetation growth index values ​​higher than the curve. Therefore, there may be individual points with negative relative ecological deficit values. In this case, based on... The function sets negative values ​​to zero.

[0081] Next, a curvature analysis is performed on the trend of the unit supply deficit index as a function of the normalized supply index.

[0082] First, the range of the normalized supply index is divided into K intervals at equal intervals (K is a positive integer, for example, K is 100). The median of the normalized supply index in each interval is taken as the representative value of the normalized supply index in that interval. Based on the normalized supply index of each grid cell, the grid cells falling into each interval and the unit supply deficit index of each grid cell in each interval are counted. Finally, the median of the unit supply deficit index in each interval is counted as the representative value of the unit supply deficit index in each interval. The representative value of the normalized supply index and the representative value of the unit supply deficit index in an interval are taken as a two-dimensional data point. The two-dimensional data points of multiple intervals constitute a discrete trend sequence.

[0083] Secondly, the discrete trend sequence is smoothed using a Savitzky-Golay filter to obtain a continuously differentiable trend function. This trend function is used to characterize the unit supply deficit index as a function of the normalized supply index, and the curvature of the trend function is calculated.

[0084] It should be noted that the Savitzky-Golay filter is a smoothing method based on local polynomial fitting, which can remove noise while preserving the peak and valley characteristics of the data. In this embodiment, the window length is 5 and the order is 2.

[0085] It should be understood that curvature is a mathematical quantity that describes the degree of curvature of a curve, and its calculation formula is the absolute value of the second derivative divided by the square of the first derivative plus 1 to the power of 1.5.

[0086] Optionally, the curvature at a location in the trend function satisfies the following formula: in, Normalized supply index in trend function The curvature value at that point, Trend function right The second derivative, This means taking the absolute value of the second derivative to ensure that the curvature value is positive. Trend function right The first derivative.

[0087] Finally, based on this formula, the maximum curvature value in the trend function is identified, and then the normalized supply index corresponding to the maximum curvature value point is determined.

[0088] It should be understood that the point of maximum curvature corresponds to the position where the trend function is most curved, and in engineering terms, it corresponds to the point of abrupt change where the ecological deficit index begins to rise exponentially.

[0089] Understandably, the inverse normalization operation is the process of restoring the normalized value to its original dimensions. Specifically, it involves determining the difference between the global maximum and minimum values ​​of the monthly average soil material supply index, multiplying it by the normalized supply index corresponding to the point of maximum curvature, and adding the global minimum value to obtain the monthly average soil material supply index in its original dimensions. This monthly average soil material supply index in its original dimensions is then determined as the geological material supply constraint threshold.

[0090] It should be understood that this geological material supply limitation threshold is used to characterize the physical critical point at which the self-sustaining capacity of an ecosystem undergoes abrupt changes. When the soil material supply index is below this geological material supply limitation threshold, it means that regardless of water conditions, the geological background cannot provide sufficient material basis to offset soil loss, and the ecosystem enters a period of irreversible degradation. When the soil material supply index is above this geological material supply limitation threshold, it indicates that the geological background has the material basis to support vegetation growth, and its ecological limitation may come from other factors (such as water fluctuations).

[0091] The methods provided in S101-S104 above determine the monthly soil material supply index and vegetation growth index observation values ​​for each grid unit during the observation period using multi-source data, transforming the multi-source data into engineering evaluation indicators with clear physical meaning. By performing Granger causality analysis on the monthly soil material supply index and vegetation growth index observation values ​​for each grid unit, sample grid units with significant temporal predictive influence of geological conditions on vegetation growth are screened out. This automatically identifies and eliminates non-zonal noise samples affected by local micro-topography or human interference, ensuring that subsequent analysis is based only on valid data that conforms to geological control laws. By performing nonlinear analysis and mutation point identification on the monthly soil material supply index and vegetation growth index observation values ​​of sample grid units, the physical critical points characterizing abrupt changes in the ecosystem's self-sustaining capacity are determined. This accurately locates the critical positions where ecological restoration resistance increases exponentially, providing a scientific and precise quantitative basis for vegetation restoration and ecological protection in karst areas.

[0092] Combination Figure 1 ,like Figure 2 As shown, in one implementation of this application embodiment, the method for determining the soil material supply index of each grid cell within the target area based on multi-source data includes S201-S203.

[0093] S201. Based on the monthly average air temperature and temperature balance constant function relationship of each grid cell, determine the monthly carbonate mineral dissolution balance constant of each grid cell.

[0094] Among them, the carbonate mineral dissolution equilibrium constant is used to characterize the equilibrium constant when the dissolution reaction of carbonate minerals (calcite or dolomite) in water reaches equilibrium under specific temperature conditions. This value increases with increasing temperature.

[0095] Optionally, the temperature equilibrium constant function relationship is a function relationship constructed based on thermodynamic equations. The temperature equilibrium constant function relationship can be found in geochemical simulation software (such as PHREEQC and MINTEQ). It is used to characterize the functional relationship between temperature and carbonate mineral dissolution equilibrium constant. It can output the corresponding carbonate mineral dissolution equilibrium constant based on the input temperature value.

[0096] S202. Based on the monthly carbonate mineral dissolution equilibrium constant, average precipitation, rock density, and rock molar mass of each grid cell, determine the monthly potential chemical dissolution rate of each grid cell.

[0097] It should be understood that the potential chemical dissolution rate is used to characterize the volume of carbonate rock dissolved per unit area per unit time in a grid cell under given climatic conditions and lithological background through chemical dissolution.

[0098] In this embodiment, the potential chemical dissolution rate can be determined by combining the regional effective infiltration coefficient and the atmospheric carbon dioxide partial pressure. The regional effective infiltration coefficient is used to characterize the proportion of rainfall that can penetrate into rock fissures and participate in the dissolution reaction. In typical scenarios of the Southwest Karst region, it is usually taken as 0.8 based on empirical averages. In implementation scenarios with different geological structures, this preset regional effective infiltration coefficient can also be adjusted. The atmospheric carbon dioxide partial pressure refers to the portion of the total atmospheric pressure contributed solely by carbon dioxide gas. Since carbon dioxide dissolves in water to form carbonic acid, the hydrogen ions dissociated from carbonic acid are direct reactants for dissolving carbonate minerals. Therefore, the higher the atmospheric carbon dioxide partial pressure, the more carbon dioxide dissolves in water, the higher the acidity of the water, and the stronger the dissolution ability of carbonate rocks. In this embodiment, the atmospheric carbon dioxide partial pressure is taken as the standard atmospheric value of 0.035 atm.

[0099] Alternatively, the potential chemical dissolution rate satisfies the following formula: in, Represents grid cells Potential chemical dissolution rate, Represents grid cells The molar mass of the rock, expressed in grams per mole (g / mol). Indicates based on grid cells temperature The calculated dissolution equilibrium constant of carbonate minerals, This represents the partial pressure of carbon dioxide in the atmosphere. Represents grid cells rock density, Represents grid cells The average monthly precipitation, Indicates the effective infiltration coefficient of the area. This represents a conversion factor used to scale the calculation result to a reasonable numerical range. Its value can be set in conjunction with the target unit (such as cubic meters / square kilometers / month). In this embodiment of the application, the exemplary value is 100.

[0100] In this formula, This indicates the promoting effect of carbon dioxide concentration on dissolution. This reflects the effect of temperature on the dissolution equilibrium. Multiplying the two together represents the driving force of hydrochemical conditions (i.e., under specific temperature and carbon dioxide partial pressure conditions) on dissolution. Multiplying this by... This transforms the "molar-level reaction driving force" into the "mass-level reaction product quantity". As a whole, it represents the dissolution mass potential of a unit mole of rock at a specific temperature and carbon dioxide partial pressure; This represents the monthly dissolution volume potential per unit volume of rock, multiplied by a conversion factor. This is used to adjust the result to a suitable numerical range.

[0101] The effective amount of water that actually participates in the dissolution reaction is represented by the amount of precipitation and the infiltration rate. The greater the precipitation and the higher the infiltration rate, the more water can participate in the dissolution. The monthly dissolution volume potential per unit volume of rock is multiplied by the monthly effective amount of water to obtain the monthly dissolution volume per unit area, which is the potential chemical dissolution rate.

[0102] S203. Based on the potential chemical dissolution rate and acid-insoluble content of each grid cell per month, determine the soil material supply index for each grid cell per month.

[0103] In one alternative implementation, the soil material supply index for each grid cell can be obtained by multiplying the potential chemical dissolution rate by the acid-insoluble content and then dividing by a preset soil reference bulk density.

[0104] It should be understood that the higher the content of acid-insoluble matter, the more substances can be formed into soil after the rocks weather. The soil reference bulk density is used to characterize the reference density of the generated soil. Its value can be determined based on the measured soil data of the target area. In this embodiment, the empirical value of 1.3 tons / cubic meter is taken.

[0105] Alternatively, the soil material supply index may satisfy the following formula: in, Represents grid cells The soil material supply index, Represents grid cells Potential chemical dissolution rate, Represents grid cells The content of acid-insoluble matter, This indicates the reference bulk density of the soil.

[0106] In this formula, Representing the rate of soil-forming material volume formation, under given geological and climatic conditions, the grid cell... The natural weathering process can "produce" a volume of material suitable for soil formation from rocks per unit time. Multiplying the rate of soil formation volume production by the soil reference bulk density converts the volume flux of soil-forming material into mass flux, i.e., the rate of soil formation mass production. This eliminates the influence of differences in soil physical properties (compactness) in different regions on the characterization of geological supply capacity, ultimately yielding... This indicates that after considering the proportion of acid-insoluble matter in the rock that can form soil and the bulk density of the resulting soil, the grid cell... The mass of soil material (in tons) that can be provided per unit area per month through rock weathering. This value quantifies the ability of the geological background to replenish the soil on a long-term scale.

[0107] The methods provided in S201-S203 above determine the carbonate mineral dissolution equilibrium constant corresponding to the average air temperature of each grid cell through the temperature equilibrium constant function relationship. They also calculate the potential chemical dissolution rate by combining average precipitation, rock density, and rock molar mass, and finally determine the soil material supply index by combining the acid insoluble content. This calculation process is based on the engineering assumption of dynamic balance between soil loss and replenishment in karst ecosystems. That is, on a long-term scale, only areas where the geological material supply capacity can offset the soil loss rate have sustainable vegetation carrying capacity. This allows the calculated soil material supply index to characterize the long-term potential of providing soil materials in geological history, rather than the current instantaneous weathering rate. This avoids misjudgment of geological carrying capacity caused by ignoring the dynamic balance mechanism of soil loss and replenishment, and provides standardized geological background data for subsequent Granger causality analysis and determination of geological material supply limitation thresholds.

[0108] In one implementation of this application, the method for determining the vegetation growth index observation value of each grid cell is as follows: the monthly net primary productivity, tree height and vegetation diversity of each grid cell are weighted and fused to obtain the monthly vegetation growth index observation value of each grid cell.

[0109] In one alternative implementation, the net primary productivity (NPMP), tree height, and vegetation diversity of each grid cell in a given month can be normalized, mapping each indicator to the interval between 0 and 1, to obtain the normalized NMPMP, normalized tree height, and normalized vegetation diversity for each grid cell in that month. Then, the normalized NMPMP, normalized tree height, and normalized vegetation diversity are weighted and summed to obtain the monthly vegetation growth index observation values ​​for each grid cell.

[0110] Optionally, the weighting coefficients can be set according to the regional ecological characteristics. In this embodiment, the weights are 0.5 for net primary productivity of vegetation, 0.25 for tree height, and 0.25 for vegetation diversity.

[0111] In one implementation of this application, after obtaining the geological material supply limitation threshold of the target area, S301-S302 can also be executed.

[0112] S301. Based on the monthly average soil volumetric water content of each vegetation growing season during the observation period, determine the hydrological fluctuation coefficient of each grid unit in the target area.

[0113] The hydrological fluctuation coefficient is used to characterize the instability of the water environment within a grid cell.

[0114] In one alternative implementation, the soil moisture time series for each grid cell can be constructed based on the monthly average soil volumetric water content for each vegetation growing season during the observation period; and the hydrological fluctuation coefficient for each grid cell can be determined based on the standard deviation and arithmetic mean of the soil moisture time series for each grid cell.

[0115] For example, assuming the observation period is the last 10 months and the vegetation growing season is from April to October, the average monthly soil volumetric water content for each month from April to October is extracted and arranged in chronological order to obtain the soil moisture time series.

[0116] It should be understood that the length of this soil moisture time series is the product of the number of months in the vegetation growing season and the number of months observed. This soil moisture time series fully records the dynamic changes in water conditions of this grid cell during the multi-month vegetation growing season.

[0117] It should be understood that the larger the hydrological fluctuation coefficient, the more drastic the monthly or seasonal fluctuations in water conditions, and the higher the risk of water stress faced by vegetation; the smaller the hydrological fluctuation coefficient, the more stable the water conditions.

[0118] Optionally, the hydrological fluctuation coefficient satisfies the following formula: in, Represents grid cells Hydrological fluctuation coefficient, Represents grid cells Soil moisture time series, The standard deviation of soil moisture over time. The arithmetic mean of the soil moisture time series. For the denominator correction term, take the smallest positive number (e.g., 10). -5 This is used to prevent situations where the denominator is zero in extremely arid or bare rock areas (where average soil moisture approaches zero), making the calculation meaningless.

[0119] Based on this formula, it should be understood that the standard deviation measures the dispersion of each value in a soil moisture time series relative to the mean. The larger the standard deviation, the more drastic the fluctuations in moisture conditions over time; that is, sometimes very wet and sometimes very dry. The greater the fluctuation amplitude, the larger the hydrological fluctuation coefficient. The mean represents the baseline moisture supply level of the grid cell. The higher the mean, the wetter the grid cell as a whole; the lower the mean, the drier the grid cell as a whole. Dividing the standard deviation by the arithmetic mean yields the hydrological fluctuation coefficient, which quantifies the instability of the moisture environment and comprehensively reflects the average moisture supply level and the degree of fluctuation.

[0120] S302. Based on the geological material supply limitation threshold of the target area and the hydrological fluctuation coefficient of each grid unit, the target area is divided into engineering zones.

[0121] In one alternative implementation, grid cells with a monthly average soil material supply index less than the geological material supply limitation threshold are identified as high-risk areas for geological supply; grid cells with a monthly average soil material supply index greater than or equal to the geological material supply limitation threshold and a hydrological fluctuation coefficient greater than the high-risk threshold for hydrological fluctuation are identified as water-restricted areas; and grid cells with a monthly average soil material supply index greater than or equal to the geological material supply limitation threshold and a hydrological fluctuation coefficient less than or equal to the high-risk threshold for hydrological fluctuation are identified as geologically and hydrologically suitable areas.

[0122] It should be understood that the high-risk threshold for hydrological fluctuations is used to distinguish between areas of normal fluctuations and areas of high-risk fluctuations.

[0123] Optionally, the mean and standard deviation of the hydrological fluctuation coefficients of all grid cells can be determined; then the sum of the mean and standard deviation can be determined as the high-risk threshold for hydrological fluctuations.

[0124] Understandably, when the monthly average soil material supply index of a grid cell is less than the geological material supply limitation threshold, it indicates that the geological background of that grid cell can no longer provide the material basis required to maintain the ecosystem. The marginal cost of ecological restoration has increased exponentially, and it belongs to an irreversible degradation area. Therefore, this grid cell is identified as a high-risk area for geological supply. In such areas, regardless of water conditions, the geological background does not have the potential to support vegetation self-sustaining; the essence of its ecological limitation lies in insufficient capacity.

[0125] When the monthly average soil material supply index of a grid cell is greater than or equal to the geological material supply limitation threshold, and its hydrological fluctuation coefficient is greater than the high-risk threshold for hydrological fluctuation, it indicates that although the grid cell has soil formation potential, its water environment is extremely unstable, and vegetation growth is strongly constrained by water fluctuations. The essence of its ecological limitation lies in process instability, so the grid cell is identified as a water-restricted zone. In such areas, the geological supply capacity is higher than the threshold, providing the material basis to support vegetation growth; however, the hydrological fluctuation coefficient is significantly higher, indicating poor soil water holding capacity or severe impact from rainfall fluctuations.

[0126] When the monthly average soil material supply index of a grid cell is greater than or equal to the geological material supply limitation threshold, and its hydrological fluctuation coefficient is less than or equal to the high-risk threshold for hydrological fluctuation, it indicates that the grid cell has sufficient geological supply and a stable water environment, with no significant physical limiting factors. The habitat conditions are suitable for vegetation growth, and therefore the grid cell is designated as a geologically and hydrologically suitable zone. In such areas, neither the geological background nor hydrological conditions pose limitations, and the ecosystem has the potential for self-sustaining and recovery.

[0127] In one alternative implementation, after completing the engineering zoning determination of all grid cells, raster data or vector graphics containing three types of engineering zoning—high-risk areas for geological supply, areas with limited water retention, and geologically-hydrologically suitable areas—can be generated to show the spatial distribution of different engineering zoning in the target area, providing a scientific basis for the planning and implementation of ecological projects such as rocky desertification control.

[0128] The methods provided in S301-S302 above, based on determining the geological material supply limitation threshold, further introduce a hydrological fluctuation coefficient to classify the target area for engineering purposes, achieving a synergistic assessment of geological capacity limitations and hydrological process limitations. A soil moisture time series is constructed based on the monthly average soil volumetric water content of each vegetation growing season during the observation period, and the hydrological fluctuation coefficient is determined based on the standard deviation and arithmetic mean of this time series. This ensures that the hydrological fluctuation coefficient reflects both the average water supply level and its fluctuation amplitude, accurately identifying areas with poor soil water holding capacity or those severely affected by rainfall fluctuations. By performing a binary cross-determination between the soil material supply index and the geological material supply limitation threshold, and between the hydrological fluctuation coefficient and the high-risk threshold for hydrological fluctuations, the target area is divided into high-risk geological supply areas, water retention-limited areas, and geologically and hydrologically suitable areas, effectively transforming multi-source monitoring data into engineering decision-making schemes.

[0129] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0130] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for determining a vegetation growth threshold in a karst region based on multi-source data fusion, characterized in that, include: Acquire multi-source data for each grid cell within the target area, including geological data, meteorological data, and vegetation data; Based on the multi-source data, the soil material supply index and vegetation growth index of each grid cell in the target area are determined monthly during the observation period. The soil material supply index is used to characterize the soil material supply capacity under the geological background, and the vegetation growth index is used to characterize the productivity level, tree height and plant diversity of the ecosystem within the grid cell. Granger causality analysis was performed on the monthly soil material supply index and vegetation growth index observations of each grid cell during the observation period to screen out sample grid cells. The sample grid cells are those grid cells in which geological conditions have a significant temporal predictive impact on vegetation growth. Nonlinear analysis and mutation point identification were performed on the monthly soil material supply index and vegetation growth index of the sample grid units during the observation period to determine the geological material supply limitation threshold of the target area. The geological material supply limitation threshold is used to characterize the physical critical point at which the self-sustaining capacity of the ecosystem undergoes abrupt changes.

2. The method of claim 1, wherein, The geological data includes monthly rock density, rock molar mass, and acid-insoluble matter content during the observation period. The meteorological data includes monthly average temperature and average precipitation during the observation period. Based on the multi-source data, the soil material supply index for each grid cell within the target area is determined monthly during the observation period, including: Based on the functional relationship between the monthly average air temperature and the temperature balance constant of each grid cell, the monthly carbonate mineral dissolution balance constant of each grid cell is determined. Based on the monthly carbonate mineral dissolution equilibrium constant, average precipitation, rock density, and rock molar mass of each grid cell, the potential chemical dissolution rate of each grid cell is determined monthly. The monthly soil material supply index for each grid cell is determined based on the potential chemical dissolution rate and acid-insoluble content of each grid cell.

3. The method of claim 1, wherein, The vegetation data includes monthly net primary productivity, tree height, and vegetation diversity during the observation period. Based on the multi-source data, the monthly vegetation growth index observations for each grid cell during the observation period are determined, including: The monthly net primary productivity, tree height, and vegetation diversity of each grid cell are weighted and fused to obtain the monthly vegetation growth index observation values ​​for each grid cell.

4. The method of claim 1, wherein, Granger causality analysis was performed on the monthly soil material supply index and vegetation growth index observations of each grid cell during the observation period to select sample grid cells, including: Based on the monthly soil material supply index and vegetation growth index observations of each grid cell during the observation period, a time series of soil material supply index and vegetation growth index for each grid cell are constructed. Granger causality tests were performed on the time series of soil material supply index and vegetation growth index for each grid cell to obtain the significance probability value of each grid cell. The significance probability value is used to characterize the confidence level of the existence of Granger causality between the soil material supply index and the vegetation growth index. Sample grid cells are selected from all grid cells based on the significance probability value of each grid cell.

5. The method of claim 4, wherein, The process of selecting sample grid cells from all grid cells based on the saliency probability value of each grid cell includes: Identify grid cells whose significance probability value is less than a preset significance threshold; If the number of grid cells with a significance probability value less than a preset significance threshold is greater than or equal to a preset number threshold, then the grid cells with a significance probability value less than the preset significance threshold are determined as sample grid cells. If the number of grid cells with a significance probability value less than a preset significance threshold is less than a preset quantity threshold, a preset proportion of grid cells are selected as sample grid cells in descending order of significance probability value.

6. The method of claim 1, wherein, The process involves nonlinear analysis and mutation point identification of the monthly soil material supply index and vegetation growth index observations of the sample grid cells during the observation period to determine the geological material supply limitation threshold of the target area, including: Based on the monthly soil material supply index and vegetation growth index observation values ​​of each grid cell during the observation period, the monthly average soil material supply index and monthly average vegetation growth index observation values ​​of each grid cell are determined. The monthly average soil material supply index of each grid cell was normalized to obtain the normalized supply index of each grid cell. Based on the normalized supply index and monthly average vegetation growth index observations of the sample grid cells, a theoretical upper limit curve for vegetation growth is fitted. The theoretical upper limit curve is used to characterize the maximum potential value of vegetation growth index that a normalized supply index can support within the target area. Based on the theoretical upper limit curve, the normalized supply index of each grid cell, and the monthly average vegetation growth index observation, the unit supply deficit index of each grid cell is determined. The unit supply deficit index is used to characterize the ecological restoration resistance borne by the unit geological material supply capacity. Curvature analysis was performed on the variation trend of the unit supply deficit index with the normalized supply index, and the normalized supply index corresponding to the point of maximum curvature was identified. The normalized supply index is denormalized to obtain the geological material supply restriction threshold.

7. The method of claim 1, wherein, The meteorological data includes monthly soil volumetric water content during the observation period, and the method further includes: Based on the monthly average soil volumetric water content of each vegetation growing season during the observation period, the hydrological fluctuation coefficient of each grid cell in the target area is determined. The hydrological fluctuation coefficient is used to characterize the degree of instability of the water environment within the grid cell. The target area is divided into engineering zones based on the geological material supply limitation threshold of the target area and the hydrological fluctuation coefficient of each grid unit.

8. The method of claim 7, wherein, The determination of the hydrological fluctuation coefficient for each grid cell within the target area, based on the monthly average soil volumetric water content of each vegetation growing season during the observation period, includes: Based on the monthly average soil volumetric water content of each vegetation growing season during the observation period, a soil moisture time series for each grid cell was constructed. The hydrological fluctuation coefficient for each grid cell is determined based on the standard deviation and arithmetic mean of the soil moisture time series for each grid cell.

9. The method of claim 7, wherein, The engineering partitioning of the target area based on the geological material supply limitation threshold of the target area and the hydrological fluctuation coefficient of each grid cell includes: Grid cells with a monthly average soil material supply index lower than the geological material supply limit threshold are identified as high-risk areas for geological supply. Grid cells whose monthly average soil material supply index is greater than or equal to the geological material supply limitation threshold and whose hydrological fluctuation coefficient is greater than the high-risk threshold for hydrological fluctuation are identified as water retention-limited areas. Grid cells whose monthly average soil material supply index is greater than or equal to the geological material supply limitation threshold and whose hydrological fluctuation coefficient is less than or equal to the high-risk threshold for hydrological fluctuation are identified as geologically and hydrologically suitable areas.

10. The method of claim 9, wherein, The method further includes: Determine the mean and standard deviation of the hydrological fluctuation coefficient for all grid cells; The sum of the mean and standard deviation is determined as the high-risk threshold for hydrological fluctuations.

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