A method for delineating dynamic and static inhibition zones of vegetation growth in karst areas

Abstract

This invention relates to the field of ecological restoration technology, specifically to a method for delineating dynamic and static vegetation growth inhibition zones in karst regions. It solves the technical problem of low accuracy in identifying vegetation growth inhibition zones in karst regions using existing technologies. The method includes: obtaining the soil potential index for each spatial unit of the target area; obtaining the ecological response characteristics of each spatial unit; determining the comprehensive soil limiting index for each spatial unit based on its soil potential index, amplitude characteristics, and morphological characteristics; and determining the inhibition zone dominated by soil limitation in the target area based on the comprehensive soil limiting index. This invention is used for identifying vegetation growth inhibition zones in karst regions.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration technology, specifically to a method for delineating dynamic and static inhibition zones for vegetation growth in karst areas. Background Technology

[0002] Karst ecosystems, due to their unique carbonate rock and soil background, are characterized by shallow soil layers, high rock exposure rates, and extremely slow soil formation rates, earning them the name "soil-dependent arid zones." In ecological restoration projects targeting these areas, such as desertification control and reforestation, accurately identifying the limiting factors for vegetation growth is a core prerequisite for developing restoration strategies. In non-karst regions, vegetation growth is typically driven primarily by meteorological factors such as precipitation. Therefore, existing engineering assessment methods often determine whether a region is water-limited by calculating the linear correlation between vegetation indices and precipitation over long-term series. If the correlation is significant, the region is identified as water-limited, and artificial afforestation or water replenishment is implemented accordingly. However, due to the complex soil background and highly heterogeneous surface environment in karst regions, there is a significant discrepancy between the assessment results of linear assessment methods and actual restoration needs, resulting in low accuracy in identifying vegetation growth inhibition zones in karst areas. Summary of the Invention

[0003] To address the technical problem that existing technologies suffer from low accuracy in identifying vegetation growth inhibition zones in karst areas due to the complex soil background and highly heterogeneous surface environment, resulting in significant discrepancies between conventional linear assessment methods and actual remediation needs, the present invention aims to provide a method for delineating dynamic and static vegetation growth inhibition zones in karst areas. The specific technical solution adopted is as follows: In a first aspect, the present invention provides a method for delineating dynamic and static inhibition zones of vegetation growth in karst regions. The method includes: obtaining a soil potential index for each spatial unit of a target region; the soil potential index is used to characterize the potential ability of rocks in a spatial unit to weather into soil; obtaining ecological response characteristics for each spatial unit; the ecological response characteristics include at least: amplitude characteristics and morphological characteristics; the amplitude characteristics are used to characterize the degree of deficit in vegetation growth relative to water supply; the morphological characteristics are used to characterize the degree of morphological matching between the vegetation growth sequence and the water supply sequence; determining a comprehensive soil limitation index for each spatial unit based on the soil potential index, amplitude characteristics, and morphological characteristics; the comprehensive soil limitation index is used to comprehensively assess the intensity of soil-related limitations on the spatial unit; and determining the inhibition zone dominated by soil limitations in the target region based on the comprehensive soil limitation index.

[0004] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: acquiring vegetation biomass observation data and soil moisture content observation data for each spatial unit within a preset time series; the preset time series consists of the months with active vegetation growth in each of multiple consecutive years; standardizing the vegetation biomass observation data and soil moisture content observation data for each spatial unit to determine a normalized vegetation sequence and a normalized water sequence; determining amplitude characteristics based on the normalized vegetation sequence and the normalized water sequence; and determining morphological characteristics based on the normalized vegetation sequence and the normalized water sequence.

[0005] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: constructing a dominant sample set based on the dominant spatial units in each spatial unit; the dominant spatial unit is a spatial unit in each spatial unit whose soil potential index meets a first preset condition and whose mean vegetation biomass meets a second preset condition; fitting the normalized vegetation sequence and normalized water sequence of the dominant spatial units in the dominant sample set to determine the ideal growth response relationship; fitting the normalized vegetation sequence and normalized water sequence of each spatial unit to determine the actual growth response relationship; and determining the amplitude feature based on the difference between the ideal growth response relationship and the actual growth response relationship.

[0006] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: determining the normalized water sequence of each spatial unit as a reference sequence, and determining the normalized vegetation sequence of each spatial unit as a test sequence; determining the minimum cumulative cost required to align the test sequence with the reference sequence based on a target algorithm; the target algorithm is an algorithm that quantifies the degree of morphological matching between sequences; and determining the minimum cumulative cost as a morphological feature.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: acquiring rock property data, average rainfall, and average temperature within a preset time period for each spatial unit; determining the theoretical soil formation rate for each spatial unit based on the rock property data, average rainfall, average temperature within the preset time period, and the chemical kinetics principle of rock weathering into soil; and normalizing the theoretical soil formation rate for each spatial unit to determine the soil potential index.

[0008] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: normalizing the amplitude feature and the morphological feature respectively to determine the normalized amplitude value and the normalized morphological value; weighting the normalized amplitude value and the normalized morphological value to determine the ecological anomaly degree; determining the soil constraint coefficient based on the soil potential index; the soil potential index is positively correlated with the soil constraint coefficient; and determining the comprehensive soil constraint index based on the correlation between the ecological anomaly degree and the soil constraint coefficient.

[0009] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: determining a classification threshold based on the statistical distribution characteristics of the soil limiting composite index of each spatial unit; and identifying spatial units in each spatial unit where the soil limiting composite index is greater than the classification threshold as inhibition zones dominated by soil limiting.

[0010] In conjunction with the first aspect mentioned above, in one possible implementation, the method further includes: determining an amplitude benchmark value based on the statistical distribution characteristics of the amplitude characteristics of each spatial unit; and identifying spatial units that do not belong to the soil-controlled suppression zone and whose amplitude characteristics are greater than the amplitude benchmark value as human-induced interference-dominated zones.

[0011] In conjunction with the first aspect mentioned above, in one possible implementation, the method further includes: identifying spatial units that do not belong to the soil-dominated inhibition zone and whose amplitude characteristics are not greater than the amplitude reference value as water regulation and recovery zones.

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, the vegetation biomass observation data includes at least: net primary productivity data and normalized vegetation index data obtained from remote sensing images; the soil moisture content observation data includes at least: soil moisture data.

[0013] The present invention has the following beneficial effects: This invention constructs a comprehensive soil constraint index by integrating the amplitude and morphological characteristics of the soil potential index and ecological response features. This index comprehensively assesses the intensity of soil-related limitations on spatial units and delineates soil-dominated inhibition zones. Compared to existing technologies that rely solely on the linear correlation analysis between vegetation indices and precipitation, this method integrates static soil background information with dynamic vegetation and water response relationships in a multidimensional way. This effectively removes the implicit static soil constraints behind seemingly complex ecological fluctuations, significantly improving the accuracy and scientific rigor of identifying vegetation growth inhibition zones in karst regions. It provides a reliable basis for precise site selection in ecological restoration projects, thus solving the technical problem of low accuracy in identifying vegetation growth inhibition zones in karst regions due to the complex soil background and highly heterogeneous surface environment, resulting in significant discrepancies between the assessment results of conventional linear assessment methods and actual restoration needs. Attached Figure Description

[0014] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating a method for delineating dynamic and static inhibition zones of vegetation growth in karst areas, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of a device for delineating dynamic and static inhibition zones of vegetation growth in karst areas, provided as an embodiment of the present invention. Detailed Implementation

[0016] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for delineating dynamic and static inhibition zones of vegetation growth in karst areas according to the present invention. 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.

[0017] 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 invention pertains.

[0018] The following describes in detail, with reference to the accompanying drawings, a specific scheme for the method of delineating dynamic and static inhibition zones of vegetation growth in karst areas provided by the present invention.

[0019] Please see Figure 1 The diagram illustrates a flowchart of a method for delineating dynamic and static inhibition zones of vegetation growth in karst areas according to an embodiment of the present invention. The method includes the following steps S101-S104, which will be described in detail below.

[0020] S101. Obtain the soil potential index for each spatial unit in the target area.

[0021] Among them, the soil potential index is used to characterize the potential ability of rocks in a spatial unit to weather into soil.

[0022] In one possible implementation, for each spatial unit within the target area, its soil potential index is determined based on its rock property data, multi-year average rainfall, and multi-year average temperature.

[0023] For example, based on the chemical kinetics of rock weathering into soil, the theoretical soil-forming rate of each spatial unit can be calculated by integrating lithology correction coefficients, the nonlinear promoting effect of rainfall on dissolution, and the exponential accelerating effect of temperature on the reaction rate. Then, the theoretical soil-forming rates of all spatial units within the target area are normalized to a range and mapped to a preset numerical interval. This interval can be set according to actual application needs, for example, from 1 to 10, where 1 represents the worst soil-forming conditions and 10 represents the best. The mapped value is used as the soil potential index of that spatial unit. A higher soil potential index indicates better soil-forming conditions in that unit, and vice versa.

[0024] S102. Obtain the ecological response characteristics of each spatial unit.

[0025] Among them, ecological response characteristics include at least: amplitude characteristics and morphological characteristics; amplitude characteristics are used to characterize the degree of deficit of vegetation growth relative to water supply; morphological characteristics are used to characterize the degree of morphological matching between vegetation growth time series and water supply time series.

[0026] In one possible implementation, for each spatial unit within the target area, its ecological response characteristics are determined based on its vegetation biomass observation data and soil moisture content observation data within a preset time series.

[0027] For example, firstly, vegetation biomass observation data and soil moisture content observation data for each spatial unit are acquired within a preset time series consisting of the months with active vegetation growth in each of multiple consecutive years; then, the vegetation biomass observation data and soil moisture content observation data for each spatial unit are standardized to eliminate the influence of absolute dimensions caused by differences in vegetation type and soil background between different spatial units, resulting in a normalized vegetation sequence and a normalized water sequence; then, based on the normalized vegetation sequence and the normalized water sequence, amplitude characteristics and morphological characteristics are determined respectively.

[0028] S103. Based on the soil potential index, amplitude characteristics, and morphological characteristics of each spatial unit, determine the comprehensive soil limiting index for each spatial unit.

[0029] In one possible implementation, for each spatial unit within the target area, a comprehensive soil limiting index is constructed based on the soil potential index, amplitude characteristics, and morphological characteristics of the spatial unit determined in the aforementioned steps; the comprehensive soil limiting index is used to comprehensively assess the intensity of the spatial unit's limitation by local soil conditions.

[0030] For example, firstly, the amplitude and morphological features are normalized to obtain normalized amplitude and normalized morphological values, enabling their fusion on the same dimensional scale. Then, the ecological anomaly information represented by the normalized amplitude and morphological values ​​is aggregated to obtain the ecological anomaly degree, reflecting the degree of vegetation growth anomaly in the spatial unit. Simultaneously, a soil constraint coefficient is determined based on the soil potential index, which is positively correlated with the soil potential index and used to characterize the ability of local soil conditions to suppress ecological anomalies. Finally, based on the correlation between the ecological anomaly degree and the soil constraint coefficient, a comprehensive soil constraint index for the spatial unit is generated.

[0031] S104. Based on the comprehensive soil limiting index, identify the inhibition zone in the target area that is dominated by soil limiting.

[0032] In one possible implementation, for each spatial unit within the target area, based on the comprehensive soil limitation index of the spatial unit determined in the aforementioned steps, and combined with the index distribution characteristics of all spatial units within the target area, it is determined whether the spatial unit belongs to a suppression zone dominated by soil limitation.

[0033] For example, firstly, the soil limiting composite index of all spatial units within the target area is statistically analyzed to obtain its statistical distribution characteristics; based on the statistical distribution characteristics, a classification threshold is determined, which is used to distinguish spatial units with significantly higher soil limiting levels; then, the soil limiting composite index of each spatial unit is compared with the classification threshold, and spatial units with soil limiting composite indices greater than the classification threshold are identified as inhibition zones dominated by soil limiting.

[0034] The technical solution provided in the above embodiments can bring at least the following beneficial effects: This embodiment constructs a comprehensive soil constraint index by integrating the amplitude and morphological characteristics of the soil potential index and ecological response characteristics, which can comprehensively assess the intensity of spatial unit limitation by local soil conditions, and delineate the inhibition zone dominated by soil limitation accordingly. Compared with the existing technology that only relies on the linear correlation analysis between vegetation index and precipitation, this method integrates static soil background information with dynamic vegetation and water response relationships in a multidimensional way. It can effectively peel away the implicit static soil constraints behind the seemingly complex ecological fluctuations, thereby significantly improving the accuracy and scientific nature of identifying vegetation growth inhibition zones in karst areas. This provides a reliable decision-making basis for the precise site selection of ecological restoration projects, thus solving the technical problem that the existing technical solutions have low accuracy in identifying vegetation growth inhibition zones in karst areas due to the complex soil background and highly heterogeneous surface environment in karst areas, where the evaluation results of conventional linear assessment methods deviate significantly from the actual restoration needs.

[0035] In one possible implementation, the process of obtaining the soil potential index of each spatial unit of the target area can be specifically implemented through the following S201-S204, which will be described in detail below.

[0036] S201. Obtain vegetation biomass observation data and soil moisture content observation data for each spatial unit within a preset time series.

[0037] In one possible implementation, for each spatial unit within the target area, vegetation biomass observation data and soil moisture content observation data are obtained according to a preset time series range. The preset time series consists of the months with active vegetation growth in each of the several consecutive years, which is used to characterize the relationship between water supply and growth response during the period of vigorous vegetation physiological activity.

[0038] For example, the research period is first set to multiple consecutive years (e.g., 2000 to 2020), and within each year, the months with active vegetation growth are selected (e.g., April to October). These active months constitute a pre-defined time series, and each spatial unit contains multiple time observation points within this series. Then, for each spatial unit, the vegetation biomass and soil moisture content observations at each time observation point are obtained: the vegetation biomass observations can be obtained based on remote sensing image products and are used to characterize the vegetation growth status of the unit at the corresponding time; the soil moisture content observations can be obtained based on a land surface data assimilation system and are used to characterize the water supply status of the unit at the corresponding time.

[0039] S202. The vegetation biomass observation data and soil moisture content observation data of each spatial unit are standardized to determine the normalized vegetation sequence and normalized water sequence.

[0040] In one possible implementation, for each spatial unit within the target area, standardization processing is performed based on its vegetation biomass observation data and soil moisture content observation data within a preset time series. This eliminates the influence of absolute dimensions caused by differences in vegetation type and soil background between different spatial units, resulting in dimensionless normalized vegetation sequences and normalized water sequences.

[0041] For example, for the vegetation biomass observation data of each spatial unit, its mean and standard deviation over time are first calculated. Then, for the vegetation biomass observation value of that unit at each time observation point, the difference between the observed value and the mean is calculated, and this difference is divided by the sum of the standard deviation and a preset correction factor to obtain the normalized vegetation value for that time observation point. The normalized vegetation values ​​of all time observation points constitute the normalized vegetation sequence of that unit. Similarly, for the soil moisture content observation data of each spatial unit, its normalized moisture value at each time observation point is calculated in the same way, and the normalized moisture values ​​of all time observation points constitute the normalized moisture sequence of that unit. The preset correction factor is a very small positive number (e.g., 0.001) to prevent division by zero errors caused by a zero standard deviation, ensuring the numerical stability of the calculation process.

[0042] S203. Based on the normalized vegetation sequence and the normalized water sequence, the amplitude characteristics were determined.

[0043] In one possible implementation, for each spatial unit within the target area, the amplitude characteristics of that spatial unit are determined based on its normalized vegetation sequence and normalized water sequence; the amplitude characteristics are used to characterize the degree of deficit in vegetation growth relative to water supply.

[0044] For example, firstly, a reference baseline for measuring vegetation growth efficiency is constructed. This baseline is determined based on time-series data of spatial units within the target area with superior soil conditions and good vegetation growth, representing the ideal growth response level achievable under current climatic conditions per unit water supply. Then, for each spatial unit, the actual response level of its normalized vegetation sequence relative to the normalized water sequence is analyzed. This actual response level is compared with the reference baseline, and the amplitude characteristic of the spatial unit is determined based on the degree of difference between the two. When the actual response level is lower than the reference baseline, the amplitude characteristic takes a positive value, and the larger the difference, the more severe the growth efficiency deficit of the unit; when the actual response level is not lower than the reference baseline, the amplitude characteristic takes a zero value.

[0045] S204. Based on normalized vegetation sequence and normalized water sequence, morphological characteristics were determined.

[0046] In one possible implementation, for each spatial unit within the target area, the morphological characteristics of the spatial unit are determined based on its normalized water sequence and normalized vegetation sequence; the morphological characteristics are used to characterize the degree of morphological matching between the vegetation growth sequence and the water supply sequence.

[0047] For example, the normalized water sequence of each spatial unit is used as the reference sequence, and the normalized vegetation sequence is used as the test sequence. The waveform matching degree between the two sequences is quantified by analyzing the morphological changes of the test sequence relative to the reference sequence on the time axis. Specifically, a method that can measure the morphological similarity between time series is used to calculate the total cost required to align the test sequence with the reference sequence. This total cost reflects the overall difference between the two sequences in morphological features such as peaks, troughs, rising edges, and falling edges. This total cost is used as the morphological feature of the spatial unit. The smaller the morphological feature value, the more similar the waveforms of the vegetation growth sequence and the water supply sequence are, reflecting that the soil in this unit has a strong buffering capacity for water and the vegetation response is stable. The larger the morphological feature value, the greater the difference between the two waveforms, reflecting that the soil buffering function in this unit is weak and the vegetation exhibits a disordered, impulsive response to water changes.

[0048] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment acquires the observation data of vegetation biomass and soil moisture content of spatial units within a preset time series, and performs standardization processing to obtain normalized vegetation sequences and normalized water sequences, and then determines amplitude features and morphological features based on these sequences. This achieves standardized processing of multi-source raw observation data, eliminates the influence of absolute dimensions caused by differences in vegetation type, soil background, etc., between different spatial units, and enables subsequent amplitude and morphological feature extraction to focus on the relative fluctuation trend and morphological features of the relationship between vegetation and water, laying a solid data foundation for subsequent accurate quantification of the degree of ecological response anomalies.

[0049] In one possible implementation, the process of determining the amplitude characteristics based on the normalized vegetation sequence and the normalized water sequence can be specifically implemented through the following S301-S304, which will be explained in detail below.

[0050] S301. A superior sample set is formed based on the superior spatial units in each spatial unit.

[0051] In one possible implementation, for each spatial unit within the target area, spatial units that perform well in both local soil conditions and actual growth status are selected based on their soil potential index and average vegetation biomass. These units constitute a superior sample set. The superior sample set is used to construct an ideal reference system representing the best growth potential under the current climatic conditions.

[0052] For example, firstly, the soil potential index of all spatial units within the target area is sorted in descending order, and the top-ranked spatial units are extracted as a candidate set of superior soil potential. The soil potential index characterizes the potential ability of a unit's rocks to weather into soil, and a higher ranking indicates superior soil formation conditions for that unit. Simultaneously, the mean of the normalized vegetation sequence for each spatial unit is calculated to obtain the mean vegetation biomass of that unit. The mean vegetation biomass of all spatial units within the target area is then sorted in descending order, and the top-ranked spatial units are extracted as a candidate set of superior vegetation growth. The mean vegetation biomass reflects the average growth level of that unit over time, and a higher ranking indicates good actual vegetation growth in that unit. Furthermore, the intersection of the candidate set of superior soil potential and the candidate set of superior vegetation growth is taken, and spatial units that simultaneously meet both screening criteria are identified as dominant spatial units. All dominant spatial units together constitute the dominant sample set.

[0053] S302. Fit the normalized vegetation sequence and normalized water sequence of the dominant spatial unit in the dominant sample set to determine the ideal growth response relationship.

[0054] In one possible implementation, for a set of dominant spatial units, the normalized vegetation sequence and normalized water sequence of all dominant spatial units are collected, and the overall fitting is performed based on these time-series data to determine the ideal growth response relationship; the ideal growth response relationship is used to characterize the response law of vegetation growth with water supply changes under optimal habitat conditions, and serves as a unified benchmark for measuring the overall growth efficiency.

[0055] For example, firstly, the normalized vegetation sequences of all dominant spatial units in the dominant sample set are used as the dependent variable dataset, and the corresponding normalized water sequences are used as the independent variable dataset. Then, linear regression analysis is used to fit the overall relationship between the dependent and independent variables, determining a unified response slope that describes the trend of vegetation growth with water supply changes. This response slope is taken as the ideal growth response relationship. This ideal response slope reflects the magnitude of vegetation growth response stimulated by a unit water supply in an ideal habitat with superior local soil conditions and good actual growth, embodying the theoretically optimal efficiency of vegetation growth under current climatic conditions.

[0056] S303. Fit the normalized vegetation sequence and normalized water sequence for each spatial unit to determine the actual growth response relationship.

[0057] In one possible implementation, for each spatial unit within the target area, a regression analysis method is used to determine the response parameter between its normalized vegetation sequence and normalized water sequence, and this response parameter is used as the actual growth response relationship of the unit. The actual growth response relationship is used to characterize the actual response level of the unit's vegetation growth under the current conditions as water supply changes, reflecting the true water use efficiency of the unit.

[0058] For example, for each spatial unit, its normalized vegetation sequence is used as the dependent variable, and its normalized water sequence is used as the independent variable. The response slope between the two is determined through linear regression analysis, and this response slope is taken as the actual growth response relationship of the unit. This actual response slope describes the magnitude of vegetation growth response that can be stimulated by a unit of water supply under the combined effects of specific soil background, soil conditions, and human disturbances in the unit, reflecting the true growth efficiency of the unit under the current environment.

[0059] S304. Determine the amplitude characteristics based on the difference between the ideal growth response relationship and the actual growth response relationship.

[0060] In one possible implementation, for each spatial unit within the target area, the amplitude characteristics of the unit are determined based on the comparison between the actual growth response relationship of the unit and the ideal growth response relationship determined based on the dominant sample set; the amplitude characteristics are used to characterize the degree of deficit in vegetation growth relative to water supply in that unit.

[0061] For example, firstly, the response parameters corresponding to the actual growth response relationship of the spatial unit are obtained, and simultaneously, the response parameters corresponding to the ideal growth response relationship determined based on the dominant sample set are obtained. The response parameters are used to characterize the amplitude of the vegetation growth response that can be stimulated by a unit water supply. Then, the difference between the ideal response parameters and the actual response parameters is calculated, and this difference is used as the amplitude characteristic of the unit. When the actual response parameters are lower than the ideal response parameters, the difference is positive, and the larger the difference, the more severe the deficiency in the growth efficiency of the unit relative to the ideal baseline; when the actual response parameters are not lower than the ideal response parameters, the amplitude characteristic is zero.

[0062] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment selects spatial units with excellent soil potential index and high average vegetation biomass as the dominant sample set from the target area, and obtains the ideal growth response relationship based on this sample set. Then, it determines the amplitude characteristics according to the difference between the actual growth response relationship and the ideal relationship of each spatial unit. The dual benchmark screening mechanism takes into account both the theoretical potential of the soil and the actual state of the vegetation, and constructs an ideal growth model that is more in line with the actual karst environment. It avoids the misjudgment that a single benchmark may lead to theoretically excellent but actually poor or actually excellent but unsustainable soil conditions. This allows the calculated amplitude characteristics to truly reflect the degree of growth efficiency deficit of the spatial unit caused by soil or human factors.

[0063] In one possible implementation, the process of determining morphological characteristics based on the normalized vegetation sequence and the normalized water sequence can be specifically implemented through the following S401-S403, which will be described in detail below.

[0064] S401. The normalized water sequence of each spatial unit is determined as the reference sequence, and the normalized vegetation sequence of each spatial unit is determined as the test sequence.

[0065] In one possible implementation, for each spatial unit within the target area, after standardizing the vegetation biomass observation data and soil moisture observation data to obtain normalized vegetation sequences and normalized water sequences, the data roles are pre-defined for subsequent quantification of the morphological matching degree between the two sequences.

[0066] For example, for each spatial unit, its normalized water sequence is set as the reference sequence, and its normalized vegetation sequence is set as the test sequence. The reference sequence serves as a baseline template, representing the fluctuation pattern of water supply over time in that unit; the test sequence serves as the object to be matched, representing the fluctuation pattern of vegetation growth over time in that unit. By aligning and analyzing the test sequence with the reference sequence, the response characteristics of vegetation growth relative to water supply over time can be quantified.

[0067] S402. Determine the minimum cumulative cost required to align the test sequence with the reference sequence based on the target algorithm.

[0068] In one possible implementation, for each spatial unit within the target area, a dynamic time warping algorithm capable of measuring the morphological similarity between time series is used, based on a pre-defined reference sequence and a test sequence, to calculate the minimum cumulative cost required to align the test sequence with the reference sequence; the minimum cumulative cost is used to characterize the degree of morphological matching between the vegetation growth time series and the water supply time series.

[0069] For example, firstly, a distance matrix is ​​constructed between the reference sequence and the test sequence. Each element in this matrix represents the degree of difference between the value at a certain time point in the reference sequence and the value at a certain time point in the test sequence. Then, a dynamic programming method is used to search for an optimal path from the starting point to the ending point in the distance matrix. This path satisfies the monotonicity and continuity constraints of the time index and minimizes the sum of the difference values ​​corresponding to all positions on the path. This minimum cumulative difference value is used as the minimum cumulative cost required to align the test sequence with the reference sequence. The smaller the minimum cumulative cost, the more similar the waveforms of the two sequences are, and the more stable the response of vegetation growth to water supply. Conversely, the larger the minimum cumulative cost, the greater the difference in waveforms between the two sequences, and the more disordered the response of vegetation growth to water supply.

[0070] S403. Determine the minimum cumulative cost as a morphological feature.

[0071] In one possible implementation, for each spatial unit within the target area, based on the minimum cumulative cost required to align the test sequence with the reference sequence calculated by the target algorithm, the minimum cumulative cost is directly used as the morphological feature of the spatial unit; the morphological feature is used to characterize the degree of morphological matching between the vegetation growth sequence and the water supply sequence.

[0072] For example, for each spatial unit, the minimum cumulative cost is assigned to the morphological characteristic parameter of that unit. This morphological characteristic parameter retains both the numerical value and physical meaning of the minimum cumulative cost: when the morphological characteristic value is small, it indicates that the waveform similarity between the normalized vegetation sequence and the normalized water sequence of that unit is high, and the vegetation growth responds smoothly and orderly to water supply, reflecting that the soil medium of that unit has a good buffering function; when the morphological characteristic value is large, it indicates that the waveforms of the two sequences are significantly different, and the vegetation growth exhibits a disordered, pulse-like response to water supply, reflecting that the soil medium of that unit is deficient or has extremely weak buffering function.

[0073] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment uses the normalized water sequence as the reference sequence and the normalized vegetation sequence as the test sequence, and employs an algorithm for quantifying the degree of morphological matching between the sequences to calculate the minimum cumulative cost required for alignment, and uses this cost as a morphological feature. It can specifically identify disordered impulse responses of vegetation caused by the absence of soil media. In such areas, vegetation growth is sensitive to changes in water but its morphology is disordered, resulting in a high matching cost with the water sequence. In areas with soil buffers, however, the vegetation response is relatively stable and exhibits a regular lag, resulting in a low matching cost. This morphological feature effectively distinguishes between the normal lag response with soil and the abnormal impulse response without soil from a waveform perspective.

[0074] In one possible implementation, the process of obtaining the soil potential index of each spatial unit of the target area can be specifically implemented through the following S501-S503, which will be described in detail below.

[0075] S501. Obtain rock property data, average rainfall, and average temperature within a preset time period for each spatial unit.

[0076] In one possible implementation, for each spatial unit within the target area, the corresponding soil background data and climate background data are obtained as the basic input parameters for subsequent calculation of the soil potential index; rock property data are used to characterize the lithological characteristics of the spatial unit and its content of weatherable material, and average rainfall and average temperature are used to characterize the long-term hydrothermal climate conditions of the unit.

[0077] For example, firstly, rock property data for each spatial unit is obtained based on a soil lithology map. This data can be further converted into a lithology correction coefficient to reflect the proportion of materials in the rock that can weather into soil. For instance, pure carbonate rock areas correspond to a lower lithology correction coefficient, argillaceous carbonate rock areas correspond to a medium coefficient, and clastic rock areas correspond to a higher coefficient. Simultaneously, based on long-term meteorological observation data or meteorological reanalysis data, the multi-year average rainfall and multi-year average temperature for each spatial unit within a preset time period (e.g., 2000 to 2020) are obtained. The preset time period is a long-term observation period consisting of multiple consecutive years, used to stably characterize the climate characteristics of the unit and avoid the random influence of meteorological fluctuations in a single year.

[0078] S502. Based on rock property data, average rainfall and average temperature within a preset time period, and the chemical kinetics of rock weathering into soil, determine the theoretical soil formation rate for each spatial unit.

[0079] In one possible implementation, for each spatial unit within the target area, the theoretical soil formation rate of that spatial unit is calculated based on its rock property data, multi-year average rainfall, and multi-year average temperature, combined with the chemical kinetics principle of rock weathering into soil. The theoretical soil formation rate is used to characterize the absolute rate of rock weathering into soil in that unit, reflecting the potential capacity for soil formation in the soil context.

[0080] For example, firstly, a lithology correction coefficient is extracted from the rock property data. This coefficient characterizes the proportion of weatherable material (i.e., acid-insoluble matter that can be converted into soil) in the rock. Different lithological types correspond to different correction coefficients; for example, pure carbonate rocks have a lower regional coefficient, argillaceous carbonate rocks have a medium regional coefficient, and clastic rocks have a higher regional coefficient. Then, based on the principles of chemical kinetics, a preset reaction kinetics exponential factor is used. Taking into account the nonlinear driving effect of rainfall on the dissolution process and the exponential accelerating effect of temperature on the chemical reaction rate, the lithology correction coefficient, multi-year average rainfall, and multi-year average temperature are used as input parameters. The theoretical soil formation rate for each spatial unit is then calculated using a dissolution kinetics model. The reaction kinetics pre-exponential factor is an empirical constant determined based on experimental data from typical carbonate rock samples in the study area, used to correct the basic reaction rate. The nonlinear driving effect of rainfall on the dissolution process is characterized by the rainfall effect index, which reflects the diminishing marginal contribution of water input to the dissolution rate. The exponential acceleration effect of temperature on the reaction rate is characterized by an exponential term including activation energy and a gas constant. The activation energy ranges from 40 kJ / mol to 80 kJ / mol, and is set to 55 kJ / mol in this embodiment. The gas constant is taken as the standard value of 8.314 J / (mol·K). This exponential term is dimensionless, and its value increases with increasing temperature, reflecting the accelerating effect of temperature on the chemical reaction rate. The theoretical soil formation rate is positively correlated with the lithology correction coefficient, rainfall, and temperature; that is, the larger the lithology correction coefficient, the greater the rainfall, and the higher the temperature, the greater the theoretical soil formation rate.

[0081] For example, the theoretical soil formation rate of the i-th spatial unit Satisfy the following formula 1: Formula 1 in, For a preset reaction kinetics pre-exponential factor (exemplary, ), used to correct the basic reaction rate, where λ is an empirical constant with specific dimensions, its dimensions being coordinated according to the value of the rainfall effect index β, so that The overall dimension is length / time; This represents the multi-year average rainfall, reflecting the intensity of water input. The rainfall effect index characterizes the nonlinear contribution of moisture to dissolution. It is a natural exponential function, representing the exponential effect of temperature on the reaction rate; The activation energy of a chemical reaction is the energy barrier that the reaction needs to overcome. It is the ideal gas constant; The average temperature over many years is expressed in degrees Kelvin and reflects thermal energy conditions. This is the lithology correction factor, which characterizes the proportion of weatherable material in a rock.

[0082] As a rainfall-driven term, the greater the rainfall, the higher the dissolution rate. The exponent β is usually less than 1, reflecting the diminishing marginal returns effect (for example, β = 0.85). As a temperature factor, the higher the temperature, the larger the exponential value and the faster the rate of increase. The lithology coefficient is the lithology coefficient; the more weatherable material there is, the higher the rate. All three are positively correlated, and the soil formation rate is obtained by multiplying and accumulating them. It is the theoretical soil formation rate of each unit based on climate and lithology, reflecting the soil formation potential in the soil context. The higher the value, the more favorable the soil conditions.

[0083] S503. Normalize the theoretical soil formation rate for each spatial unit to determine the soil potential index.

[0084] In one possible implementation, for each spatial unit within the target area, based on the determination of its theoretical soil formation rate, the rate is normalized by combining the overall distribution characteristics of the theoretical soil formation rates of all spatial units within the target area to obtain a dimensionless soil potential index. The soil potential index is used to characterize the potential ability of the spatial unit to weather rocks into soil, serving as a quantitative representation of the local soil conditions in subsequent comprehensive assessments.

[0085] For example, firstly, all spatial units within the target area are traversed to obtain the global maximum and minimum theoretical soil formation rates. Then, for each spatial unit, based on the relative position of its theoretical soil formation rate with respect to the global maximum and minimum, a range normalization method is used to map the rate to a preset numerical interval. The mapped value is then used as the soil potential index for that unit. This soil potential index is a dimensionless value, and its magnitude is positively correlated with the theoretical soil formation rate: when the theoretical soil formation rate of a spatial unit equals the global minimum, its soil potential index takes the minimum value of the preset interval; when the theoretical soil formation rate equals the global maximum, its soil potential index takes the maximum value of the preset interval; the index values ​​of other units are linearly determined based on their relative positions between the minimum and maximum values. The preset numerical interval can be set according to actual application needs, for example, a range of 1 to 10, where 1 represents the worst soil formation conditions and 10 represents the best soil formation conditions.

[0086] For example, by traversing all spatial units within the target area, the maximum theoretical soil formation rate is obtained statistically. and minimum value Then, the rate value of each unit is mapped to the range normalization method. Within the range, the soil potential index The following formula 2 is satisfied: in, This represents the theoretical soil formation rate. The minimum soil formation rate for the target area; The maximum soil formation rate in the target area; range normalized linear transformation: first calculate the relative position. We get [0, 1], then multiply by 9 to get [0, 9], and finally add 1 to get [1, 10]. This transformation preserves the original order and compresses the value range to an interval that is convenient for subsequent calculations; It is a dimensionless index; the larger the value, the better the soil formation conditions (10 is the best, 1 is the worst). It serves as the denominator weight for subsequent comprehensive indices, implementing the soil rejection logic: the worse the soil conditions (G is close to 1), the larger the comprehensive index. Its value range is [1, 10], and it is dimensionless.

[0087] Understandably, if If all units have the same soil formation rate, normalization is not possible. In practice, the range is usually not zero. If the range is usually zero, then a uniform assumption should be made. .

[0088] The technical solution provided in the above embodiments can bring at least the following beneficial effects: This embodiment obtains rock property data, multi-year average rainfall, and multi-year average temperature of spatial units, determines the theoretical soil formation rate based on the chemical kinetics principle of rock weathering into soil, and normalizes it to obtain a soil potential index. This transforms the soil formation potential, which is difficult to observe directly, into a quantifiable and comparable dimensionless index, providing core parameters that can characterize the soil's background constraint for subsequent comprehensive assessment.

[0089] In one possible implementation, the process of determining the comprehensive soil limiting index of each spatial unit based on the soil potential index, amplitude characteristics, and morphological characteristics of each spatial unit can be specifically implemented through the following S601-S604, which will be explained in detail below.

[0090] S601. Normalize the amplitude and morphological features respectively to determine the normalized amplitude value and normalized morphological value.

[0091] In one possible implementation, for each spatial unit within the target area, based on determining its amplitude and morphological characteristics, and combining the overall distribution characteristics of the amplitude and morphological characteristics of all spatial units within the target area, these two characteristics are normalized to obtain dimensionless indices with unified dimensions and consistent numerical ranges, namely normalized amplitude values ​​and normalized morphological values. The normalized amplitude values ​​and normalized morphological values ​​are used to achieve the fusion of characteristics of different dimensions in subsequent comprehensive index calculations.

[0092] For example, firstly, all spatial units within the target area are traversed to obtain the global maximum and minimum values ​​of the amplitude feature, as well as the global maximum and minimum values ​​of the morphological feature. Then, for each spatial unit, based on the relative position of its amplitude feature with respect to the global maximum and minimum values, a range normalization method is used to map the amplitude feature to a preset numerical range, and the mapped value is used as the normalized amplitude value of that unit. Similarly, based on the relative position of its morphological feature with respect to the global maximum and minimum values, the same range normalization method is used to map the morphological feature to a preset numerical range, and the mapped value is used as the normalized morphological value of that unit. The preset numerical range can be set according to actual application requirements, for example, a range from 0 to 1, where a value closer to 1 indicates a more severe degree of anomaly represented by the feature, and a value closer to 0 indicates a milder degree of anomaly. The specific calculation formula is given in Formula 2, and will not be elaborated here.

[0093] S602. Weight the normalized amplitude value and the normalized morphology value to determine the degree of ecological anomaly.

[0094] In one possible implementation, for each spatial unit within the target area, based on the obtained normalized amplitude value and normalized morphology value, the two dimensionless indicators are weighted and fused to obtain the ecological anomaly degree of the unit. The ecological anomaly degree is used to aggregate and characterize the comprehensive degree of anomaly of the unit in the two dimensions of growth amplitude and response morphology, reflecting the overall deviation level of vegetation growth from the ideal state.

[0095] For example, firstly, preset weighting coefficients are configured for the normalized amplitude value and the normalized morphological value. These weighting coefficients adjust the contribution ratio of amplitude and morphological features to the ecological anomaly degree. They can be set and adjusted according to the actual ecological characteristics of different regions or engineering application needs; for example, the weights of amplitude and morphological features can be set to be equal. Then, for each spatial unit, its normalized amplitude value is multiplied by the corresponding weighting coefficient, and its normalized morphological value is multiplied by the corresponding weighting coefficient. The two products are summed, and the summed value is used as the ecological anomaly degree of that unit. This ecological anomaly degree is a dimensionless value, and its range is jointly constrained by the normalized amplitude value, the normalized morphological value, and the weighting coefficients. The larger the ecological anomaly degree, the more severe the comprehensive anomaly degree in terms of both growth amplitude deficit and response morphological disorder in that unit.

[0096] S603. Determine the soil constraint coefficient based on the soil potential index.

[0097] In one possible implementation, for each spatial unit within the target area, based on its soil potential index, the soil constraint coefficient of that unit is determined according to the index. The soil constraint coefficient is used to characterize the ability of local soil conditions to suppress ecological anomalies and reflects the mitigating effect of soil formation potential on vegetation growth restriction. The soil potential index and the soil constraint coefficient are positively correlated.

[0098] For example, for each spatial unit, its soil potential index is... As input parameters, through a preset mapping relationship Determine the corresponding soil constraint coefficient , where e is the natural constant (approximately 2.718). The function is the natural logarithm. This mapping relationship ensures that the larger the soil potential index, the larger the determined soil constraint coefficient; and the smaller the soil potential index, the smaller the determined soil constraint coefficient. Specifically, when the soil potential index takes the minimum value of the preset numerical range, the corresponding soil constraint coefficient takes a smaller value; when the soil potential index takes the maximum value of the preset numerical range, the corresponding soil constraint coefficient takes a larger value; the soil constraint coefficient corresponding to other soil potential index values ​​is determined according to its positive correlation with the soil potential index. This soil constraint coefficient is a dimensionless value, and its value range can be set according to actual application needs, for example, set to a numerical range greater than 1 to ensure that it has an effective suppressive effect on ecological anomaly. S604. Based on the correlation between ecological anomaly and soil constraint coefficient, determine the comprehensive soil constraint index.

[0099] In one possible implementation, for each spatial unit within the target area, based on the determination of its ecological anomaly degree and soil constraint coefficient, a comprehensive soil constraint index is generated for that unit according to the correlation between the two. The comprehensive soil constraint index is used to comprehensively assess the intensity of the spatial unit's constraint by local soil conditions, reflecting the coupling effect between the static soil background and the dynamic ecological response.

[0100] For example, for each spatial unit, its ecological anomaly degree is used as the basic quantity, and its soil constraint coefficient is used as the adjustment factor. The comprehensive soil constraint index of the unit is calculated through a preset correlation between the two. This preset correlation ensures that the ecological anomaly degree and the comprehensive soil constraint index are positively correlated, that is, the greater the ecological anomaly degree, the greater the comprehensive soil constraint index; at the same time, it ensures that the soil constraint coefficient and the comprehensive soil constraint index are negatively correlated, that is, the greater the soil constraint coefficient, the smaller the comprehensive soil constraint index, and vice versa.

[0101] For example, the Soil Limiting Composite Index The following formula 3 is satisfied: Formula 3 in, Weighting coefficients (0≤ ≤1, for example =0.5); The normalized amplitude feature is [0, 1]. The normalized morphological features are [0, 1]; It is the natural logarithm function; Soil potential index [1, 10]; natural constant ≈2.718; For weighted summation, the degree of ecological anomaly (amplitude and form) is aggregated; the larger the value, the more abnormal the ecology. The denominator is... ,because ∈[1, 10, the denominator takes values ​​approximately in the range [1, 3, 3], and There is a positive correlation; therefore, the greater the soil potential, the larger the denominator and the smaller the comprehensive index, reflecting the suppression of ecological anomalies by soil constraints. The smaller the soil potential, the closer the denominator is to 1, and the ecological anomalies are preserved or even amplified. It is a dimensionless comprehensive index. The larger the value, the more severely the unit is limited by the soil background and the lower the potential for ecological restoration.

[0102] The technical solution provided in the above embodiments can bring at least the following beneficial effects: This embodiment obtains the ecological anomaly degree by normalizing and aggregating the amplitude and morphological features, and determines the soil constraint coefficient that is positively correlated with it based on the soil potential index. Then, a comprehensive soil constraint index is generated according to the correlation between the ecological anomaly degree and the soil constraint coefficient. This realizes the quantitative constraint of soil background on ecological response. In areas with low soil potential, the soil constraint coefficient is small, the inhibitory effect on ecological anomaly degree is weak, and the comprehensive soil constraint index is high, thus forcibly highlighting the soil veto logic in a mathematical mechanism. Conversely, in areas with high soil potential, the soil constraint coefficient can effectively suppress short-term ecological fluctuations. This makes the implicit static soil constraint explicit into a quantifiable high resistance indicator.

[0103] In one possible implementation, the process of determining the inhibition zone dominated by soil limitation in the target area based on the comprehensive soil limitation index can be specifically implemented through the following S701-S702, which will be described in detail below.

[0104] S701. Based on the statistical distribution characteristics of the soil limiting comprehensive index of each spatial unit, determine the classification threshold.

[0105] In one possible implementation, for all spatial units within the target area, based on the determination of the comprehensive soil limitation index for each spatial unit, an adaptive classification threshold is calculated according to the overall statistical distribution characteristics of these indices to distinguish areas with significantly higher soil limitation levels. The classification threshold is then used to subsequently divide the spatial units into suppressed and non-suppressed areas dominated by soil limitation.

[0106] For example, firstly, the soil limiting composite index of all spatial units within the target area is statistically analyzed to obtain its statistical distribution characteristic parameters. These parameters include statistics reflecting the central tendency and dispersion of the index. Then, based on the central tendency and dispersion statistics, and combined with preset adjustment parameters, a classification threshold is calculated. The method for determining this classification threshold ensures that it adapts to the overall distribution characteristics of the soil limiting composite index within the target area: when the index distribution is relatively concentrated, the classification threshold is relatively close to the central tendency value; when the index distribution is relatively dispersed, the classification threshold is relatively far from the central tendency value. By adjusting the value of the preset adjustment parameters, the strictness of the classification threshold can be controlled. For example, increasing the adjustment parameter value can raise the classification threshold, thereby screening out spatial units with more significant soil limiting.

[0107] For example, classification threshold The following formula 4 is satisfied: Formula 4 in, This represents the average soil constraint index for all spatial units within the target area. is the standard deviation of the soil limiting composite index for all spatial units within the target area; k is the adjustment coefficient (for example, a value of 1). Based on the principle of normal distribution, the mean plus k times the standard deviation is taken as the threshold to identify abnormally high values. When k=1, the top 16% of high value areas are selected. It is an adaptive threshold that varies with the data distribution and is used to divide the continuous composite index into high resistance zones (soil-limited dominant) and other zones.

[0108] S702. Spatial units in each spatial unit where the comprehensive soil limitation index is greater than the classification threshold are identified as inhibition zones dominated by soil limitation.

[0109] In one possible implementation, for each spatial unit within the target area, based on the determined classification threshold, its soil limitation comprehensive index is compared with the classification threshold, and the comparison result determines whether the unit belongs to the inhibition zone dominated by soil limitation. The inhibition zone dominated by soil limitation refers to those areas with extremely poor local soil conditions and vegetation growth that is irreversibly limited by soil factors, and should be protected as a natural enclosure area.

[0110] For example, for each spatial unit, the soil limiting composite index of that unit is obtained and compared numerically with a classification threshold. If the soil limiting composite index of the unit is greater than the classification threshold, the unit is identified as a soil-limited dominant inhibition zone; if the soil limiting composite index of the unit is not greater than the classification threshold, the unit is temporarily designated as a non-inhibition zone, pending further classification. All spatial units identified as soil-limited dominant inhibition zones together constitute the set of soil-limited dominant inhibition zones in the target area. This determination result can serve as a direct basis for subsequent engineering decisions, such as marking these areas as natural protection zones, strictly prohibiting large-scale artificial afforestation or reclamation activities.

[0111] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment determines the classification threshold based on the statistical distribution characteristics of the comprehensive soil limiting index of all spatial units within the target area, and identifies spatial units with an index greater than the threshold as inhibition zones dominated by soil limitation. This adaptive threshold determination method avoids the subjective bias caused by artificially setting a fixed threshold, and can objectively identify spatial units with significantly high comprehensive soil limiting indices. These units are areas with extremely low soil formation potential and severely abnormal ecological responses, and should be protected as natural afforestation areas to avoid ineffective artificial afforestation investment.

[0112] In one possible implementation, it is also necessary to determine the artificial interference-dominant area. This process can be specifically implemented through the following S801-S802, which will be described in detail below.

[0113] S801. Determine the amplitude reference value based on the statistical distribution characteristics of the amplitude characteristics of each spatial unit.

[0114] In one possible implementation, for all spatial units within the target area, based on the determination of the amplitude characteristics of each spatial unit, an amplitude benchmark value is calculated according to the overall statistical distribution characteristics of these amplitude characteristics to distinguish between high and low growth efficiency. The amplitude benchmark value serves as a reference standard for subsequent determination of areas dominated by human interference, and is used to identify areas where the local soil conditions are acceptable but the growth efficiency is significantly low.

[0115] For example, firstly, the amplitude characteristics of all spatial units within the target area are statistically analyzed to obtain their statistical distribution characteristic parameters. These parameters include statistics reflecting the central tendency of the amplitude characteristics, such as the arithmetic mean. Then, this central tendency statistic is determined as the amplitude baseline value. This baseline value is determined in a way that allows it to adapt to the overall distribution level of amplitude characteristics within the target area, reflecting the general state of vegetation growth efficiency deficit in that area. A larger baseline value indicates a more severe overall growth efficiency deficit within the target area; a smaller baseline value indicates that the overall growth efficiency is closer to the ideal level.

[0116] S802. Spatial units that do not belong to the soil-controlled inhibition zone and whose amplitude characteristics are greater than the amplitude benchmark value are identified as human-induced interference-dominated zones.

[0117] In one possible approach, for spatial units within the target area that have been excluded from the inhibition zone dominated by soil limitations, the amplitude characteristics of each spatial unit and the established amplitude benchmark value are combined to further identify those areas where vegetation growth is inhibited due to external human disturbances, and these areas are identified as human-dominated disturbance zones. Human-dominated disturbance zones refer to areas where the local soil conditions are not extremely poor, but the vegetation growth efficiency is significantly lower than the regional average level, and the disturbance is mainly caused by human activities such as overgrazing and firewood cutting. These areas should be designated as disturbance investigation areas and subject to key monitoring and human activity control.

[0118] For example, firstly, spatial units not identified as soil-dominated suppression zones are selected from all spatial units within the target area, forming a set of units to be classified. Then, for each spatial unit in this set, its amplitude characteristics are obtained and compared numerically with a baseline amplitude value. If the amplitude characteristic of a unit is greater than the baseline value, the unit is classified as a human-dominated disturbance zone; if the amplitude characteristic is not greater than the baseline value, the unit is temporarily reserved for further classification. All spatial units identified as human-dominated disturbance zones collectively constitute the set of human-dominated disturbance zones within the target area. This determination can serve as a direct basis for subsequent engineering decisions, such as marking these areas as disturbance investigation zones and implementing key human disturbance elimination measures such as fencing, grazing bans, and energy substitution to unleash their inherent natural restoration potential.

[0119] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment determines the amplitude benchmark value based on the statistical distribution characteristics of the amplitude characteristics of all spatial units within the target area, and identifies spatial units that do not belong to the soil-limited inhibition zone and whose amplitude characteristics are greater than the benchmark value as human-interference-dominated zones. Based on excluding soil-limited-dominated zones, it further identifies areas where the local soil conditions are acceptable but vegetation growth efficiency is significantly low.

[0120] In one possible implementation, it is also necessary to determine the moisture regulation and recovery zone. This process can be specifically implemented through the following S901, which will be described in detail below.

[0121] S901. Spatial units that do not belong to the soil-dependent inhibition zone and whose amplitude characteristics are not greater than the amplitude reference value are identified as water regulation and recovery zones.

[0122] In one possible implementation, for spatial units within the target area that have been excluded from the suppression zone dominated by soil limitations and the human-induced interference zone, based on the comparison results of their amplitude characteristics with the amplitude benchmark value, areas with good local soil conditions and vegetation growth efficiency close to the ideal level of the region are identified and designated as water regulation and restoration zones. Water regulation and restoration zones refer to meteorological water-deficient areas with good soil foundation, normal soil water holding capacity, vegetation growth efficiency close to the ideal benchmark, and mainly controlled by the current precipitation changes. These areas should be treated as artificially assisted restoration zones and restoration measures such as slope rainwater harvesting and replanting drought-resistant economic crops should be implemented.

[0123] For example, firstly, from all spatial units within the target area, spatial units that are not identified as either soil-dominated suppression zones or human-dominated zones are selected as the set of units to be classified. These units have passed the aforementioned two screenings: firstly, their comprehensive soil limitation index is not greater than the classification threshold, indicating that their local soil conditions are not extremely poor; secondly, they do not belong to human-dominated zones, indicating that their amplitude characteristics are not greater than the amplitude benchmark value, and their growth efficiency is not significantly lower than the regional average. Then, all spatial units in the set of units to be classified are directly identified as water regulation and restoration zones. All spatial units identified as water regulation and restoration zones together constitute the set of water regulation and restoration zones in the target area. This determination result can serve as a direct basis for subsequent engineering decisions, such as marking these areas as artificially assisted restoration zones, prioritizing the implementation of slope rainwater harvesting projects, replanting drought-resistant economic crops, and other artificial assistance measures, utilizing their excellent soil foundation to accelerate ecological output.

[0124] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment determines the spatial units that do not belong to the inhibition zone dominated by soil limitation and whose amplitude characteristics are not greater than the amplitude reference value as the water regulation and recovery zone, thereby completing the zoning of the entire target area.

[0125] In one possible implementation, the vegetation biomass observation data includes at least: net primary productivity data and normalized vegetation index data obtained from remote sensing images; the soil moisture content observation data includes at least: soil moisture data.

[0126] The technical solutions provided in the above embodiments can bring at least the following beneficial effects: This embodiment concretizes vegetation biomass observation data into net primary productivity data or normalized vegetation index data acquired based on remote sensing imagery, and concretizes soil moisture content observation data into soil moisture data acquired based on a land surface data assimilation system. These data sources have advantages such as wide coverage, low acquisition cost, long time series, and suitable spatial resolution, which can effectively support the needs of large-area, long-term karst ecological monitoring and assessment, and ensure the data accessibility and operability of this method in practical engineering applications.

[0127] Please see Figure 2 This document illustrates a schematic diagram of a karst vegetation growth dynamic and static inhibition zone delineation device 200 according to an embodiment of the present invention. The device includes: a communication unit 201 and a processing unit 202. The communication unit 201 is used to acquire the soil potential index of each spatial unit in the target area; the soil potential index characterizes the potential ability of the spatial unit's rocks to weather into soil. The communication unit 201 is used to acquire the ecological response characteristics of each spatial unit; the ecological response characteristics include at least: amplitude characteristics and morphological characteristics; the amplitude characteristics characterize the degree of deficit in vegetation growth relative to water supply; the morphological characteristics characterize the degree of morphological matching between the vegetation growth sequence and the water supply sequence. The processing unit 202 is used to determine the comprehensive soil limitation index of each spatial unit based on the soil potential index, amplitude characteristics, and morphological characteristics; the comprehensive soil limitation index is used to comprehensively assess the intensity of the spatial unit's limitation by local soil conditions. The processing unit 202 is used to determine the inhibition zone dominated by soil limitation in the target area based on the comprehensive soil limitation index.

[0128] It should be noted that the order of the above embodiments of the present invention 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.

[0129] 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 delineating dynamic and static inhibition zones of vegetation growth in karst areas, characterized in that, The method includes: Obtain the soil potential index of each spatial unit in the target area; obtain the ecological response characteristics of each spatial unit; determine the comprehensive soil limitation index of each spatial unit based on the soil potential index, the amplitude characteristics, and the morphological characteristics; determine the inhibition zone dominated by soil limitation in the target area based on the comprehensive soil limitation index. The acquisition of the ecological response characteristics of each spatial unit includes: Acquire vegetation biomass observation data and soil moisture content observation data for each spatial unit within a preset time series; perform standardization processing on the vegetation biomass observation data and soil moisture content observation data for each spatial unit to determine the normalized vegetation sequence and normalized water sequence. A dominant sample set is constructed based on the dominant spatial units in each spatial unit; the dominant spatial unit is the spatial unit in each spatial unit whose soil potential index meets a first preset condition and whose mean vegetation biomass meets a second preset condition; the normalized vegetation sequence and normalized water sequence of the dominant spatial units in the dominant sample set are fitted to determine the ideal growth response relationship; the normalized vegetation sequence and normalized water sequence of each spatial unit are fitted to determine the actual growth response relationship; the amplitude feature is determined based on the difference between the ideal growth response relationship and the actual growth response relationship. The normalized water sequence of each spatial unit is determined as the reference sequence, and the normalized vegetation sequence of each spatial unit is determined as the test sequence; the minimum cumulative cost required to align the test sequence with the reference sequence is determined based on the target algorithm; the target algorithm is an algorithm for quantifying the degree of morphological matching between sequences; the minimum cumulative cost is determined as the morphological feature. The process of obtaining the soil potential index for each spatial unit of the target area includes: obtaining rock property data, average rainfall, and average temperature within a preset time period for each spatial unit; determining the theoretical soil formation rate for each spatial unit based on the rock property data, the average rainfall, average temperature within the preset time period, and the chemical kinetics principle of rock weathering into soil; and normalizing the theoretical soil formation rate for each spatial unit to determine the soil potential index. The method for determining the soil-limited inhibition zone in the target area includes: determining a classification threshold based on the statistical distribution characteristics of the soil limitation comprehensive index of each spatial unit; and determining the spatial units in each spatial unit whose soil limitation comprehensive index is greater than the classification threshold as the soil-limited inhibition zone.

2. The method for delineating dynamic and static inhibition zones of vegetation growth in karst areas according to claim 1, characterized in that, The determination of the comprehensive soil limiting index for each spatial unit based on the soil potential index, the amplitude characteristics, and the morphological characteristics of each spatial unit includes: The amplitude feature and the morphological feature are normalized respectively to determine the normalized amplitude value and the normalized morphological value; The normalized amplitude value and the normalized morphological value are weighted to determine the degree of ecological anomaly. Based on the soil potential index, the soil constraint coefficient is determined; the soil potential index is positively correlated with the soil constraint coefficient. Based on the correlation between the ecological anomaly degree and the soil constraint coefficient, the comprehensive soil constraint index is determined.

3. The method for delineating dynamic and static inhibition zones of vegetation growth in karst areas according to claim 1, characterized in that, The method further includes: Based on the statistical distribution characteristics of the amplitude features of each spatial unit, an amplitude reference value is determined; Spatial units that do not belong to the soil-limited inhibition zone and whose amplitude characteristics are greater than the amplitude reference value are identified as human-induced interference-dominated zones.

4. The method for delineating dynamic and static inhibition zones of vegetation growth in karst areas according to claim 3, characterized in that, The method further includes: Spatial units that do not belong to the soil-dependent inhibition zone and whose amplitude characteristics are not greater than the amplitude reference value are identified as water regulation and recovery zones.

5. The method for delineating dynamic and static inhibition zones of vegetation growth in karst areas according to claim 1, characterized in that, The vegetation biomass observation data includes at least: net primary productivity data and normalized vegetation index data obtained from remote sensing images; the soil moisture content observation data includes at least: soil moisture data.

Images