Calculation method and system for vegetation carrying capacity of loess-desert transition zone wind erosion correction type

Abstract

This invention discloses a wind-erosion-corrected vegetation carrying capacity calculation method and system for the loess-desert transition zone, belonging to the field of vegetation carrying capacity calculation technology. The method establishes a multi-source spatiotemporal database by acquiring vegetation data, moisture data, topographic data, meteorological data, soil data, and land use data of the study area. Based on the multi-source spatiotemporal database, vegetation response units (VRUs) are divided. Within each VRU, a regression model of soil moisture and vegetation cover is established, and the maximum value of historical simulated vegetation cover is used as the base soil moisture vegetation carrying capacity (VCC) of that VRU. water Construct a wind erosion sensitivity correction coefficient for the basic soil moisture and vegetation carrying capacity (VCC). water Corrections are made to generate wind erosion-corrected vegetation carrying capacity (VCC). final This invention enables precise assessment of vegetation carrying capacity under the dual stresses of water and wind erosion on a large regional scale in the loess-desert transition zone, providing a scientific, quantitative, and directly applicable basis for regional ecological restoration and spatial decision-making.

Description

Technical Field

[0001] This invention belongs to the field of vegetation carrying capacity calculation technology, and relates to a wind erosion-corrected vegetation carrying capacity calculation method and system for the loess-desert transition zone. Background Technology

[0002] Vegetation carrying capacity (VCC) is a core indicator for assessing ecosystem stability and sustainability. It is defined as the maximum density or cover of vegetation an ecosystem can maintain under specific site conditions, limited by key environmental factors such as water and nutrients. In arid and semi-arid regions, soil moisture is the dominant factor limiting vegetation growth and distribution. Therefore, soil moisture-vegetation carrying capacity—the maximum vegetation cover that can be maintained under conditions where soil moisture consumption does not exceed recharge—has become an important theoretical basis for ecological restoration and vegetation management in these areas. Currently, soil moisture-vegetation carrying capacity is mainly calculated through mathematical model simulations and based on the principle of soil moisture balance.

[0003] Mathematical modeling based on statistical laws belongs to empirical statistical models. Its core is to establish statistical relationships between vegetation growth indicators (such as canopy cover and biomass) and water factors based on long-term monitoring data, thereby deriving the theoretical maximum vegetation cover. The limitation of this method is that it is essentially a "black box" model, reflecting only the correlation between data points, and typically assumes that water is the only limiting factor, ignoring the synergistic effects of other environmental factors such as meteorology, topography, and soil texture. In complex transitional zones with significant spatial heterogeneity, this assumption often leads to systematic biases in the assessment results.

[0004] Methods based on soil moisture balance belong to the category of physical process models. Their core is based on the law of conservation of mass, establishing detailed soil moisture input-output balance equations and using the constraint of non-persistent water deficit to inversely deduce the maximum allowable transpiration water consumption of the ecosystem and the corresponding vegetation density. However, this model heavily relies on a large amount of high-precision measured data, such as saturated hydraulic conductivity, soil porosity, and plant physiological parameters, which are often difficult to obtain over large areas in regions with fragmented terrain and complex underlying surfaces, such as the Loess-Desert transition zone. Furthermore, directly extrapolating models based on single points or small scales to regional scales introduces scale-effect errors due to neglecting lateral runoff and spatial soil variability.

[0005] The loess-desert transition zone is characterized by drought, strong winds, abundant sand, and weak surface erosion resistance. Existing technologies, whether statistical or physical models, share a fundamental flaw: they generally adhere to the assumption that "water is the only or dominant limiting factor," assuming a spatially stable and consistent "water-vegetation" relationship. However, in this region, intense wind erosion is another crucial and undeniable ecological stressor. Wind erosion not only directly strips away topsoil and damages plant roots but also exacerbates ineffective soil moisture evaporation, significantly weakening the actual carrying capacity of the ecosystem. Therefore, traditional models, when assessing vegetation carrying capacity in the loess-desert transition zone, fail to characterize the "weakening effect" of wind erosion, leading to a systematic overestimation of the theoretical vegetation cover threshold in high-risk wind erosion areas (such as windward slopes and shifting sand dunes). Ecological restoration practices based on such biased assessment results are highly susceptible to the risks of "over-afforestation" and "over-restoration," causing large-scale vegetation death or the formation of "small old trees" due to the dual pressures of water and wind erosion, ultimately leading to the failure of ecological projects and waste of resources.

[0006] Therefore, there is an urgent need for a technical method that can explicitly introduce the influence of wind erosion disturbance into the soil moisture and vegetation carrying capacity model, be applicable to large-scale areas of the Loess-Desert transition zone, take into account multiple environmental factors other than water, and realize localized correction and spatial zoning of the model, so as to improve the scientificity and applicability of the carrying capacity assessment results in the Loess-Desert transition zone. Summary of the Invention

[0007] The purpose of this invention is to provide a wind-erosion-corrected vegetation carrying capacity calculation method and system for the loess-desert transition zone, so as to solve the technical problem that the evaluation results of vegetation carrying capacity in the loess-desert transition zone are not accurate enough in the existing technology.

[0008] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a method for calculating the wind erosion-corrected vegetation bearing capacity in the loess-desert transition zone, comprising the following steps: Acquire vegetation, water, topography, meteorological, soil, and land use data for the study area to establish a multi-source spatiotemporal database; Vegetation Response Units (VRUs) are defined based on the multi-source spatiotemporal database; A regression model of soil moisture versus vegetation cover was established within each VRU, and the maximum value of historical simulated vegetation cover was used as the baseline soil moisture-vegetation carrying capacity (VCC) of the VRU. water ; Construct a wind erosion sensitivity correction coefficient for the basic soil moisture and vegetation carrying capacity (VCC). water Corrections are made to generate wind erosion-corrected vegetation carrying capacity (VCC).final .

[0009] Furthermore, the step of acquiring vegetation data, moisture data, topographic data, meteorological data, soil data, and land use data of the study area to establish a multi-source spatiotemporal database specifically includes: Vegetation data were collected, and Normalized Difference Vegetation Index (NDVI) data with a spatial resolution of 1 km from MODIS remote sensing imagery were obtained. The vegetation cover (FVC) was calculated using the following formula:

[0010] In the formula, NDVI represents the pixel NDVI value; NDVI soil This represents the NDVI value of bare soil; NDVI veg This represents the NDVI value of pure vegetation. Moisture data were collected, and 1km single-layer soil moisture raster data reconstructed based on AMSR-E and AMSR2 data were obtained. The annual average value was calculated as the soil moisture index for the corresponding year. Collect terrain data and extract elevation, slope, and aspect using 1km resolution terrain data; Collect meteorological data to obtain average wind speed, maximum wind speed and prevailing wind direction data of meteorological stations during the same period; Collect soil data and obtain 1km resolution soil texture data from the Earth Resources Data Cloud; Collect land use data and obtain land use data with a 1km resolution. The vegetation data, water data, topographic data, meteorological data, soil data, and land use data are subjected to unified projection, spatial resolution matching, and study area cropping. Outliers are removed or smoothed to establish a multi-source spatiotemporal database.

[0011] Furthermore, the step of dividing vegetation response units (VRUs) based on the multi-source spatiotemporal database specifically includes: Soil texture, slope, aspect, and land use type are classified and numbered respectively; The grid overlay formula is used to perform overlay calculations to generate VRU grids with unique codes, thus completing the division of vegetation response units (VRUs). The grid overlay formula is: soil × 1000 + land use × 100 + slope × 10 + aspect.

[0012] Furthermore, a regression model of soil moisture and vegetation cover is established within each VRU, and the maximum value of historical simulated vegetation cover is used as the basic soil moisture and vegetation carrying capacity (VCC) of the VRU. water The steps specifically include: Historical average soil moisture (SM) was calculated within each VRU. i,tCompared with the mean vegetation cover i,t ; Establish a regression equation between vegetation cover and soil moisture. ; Substituting the annual soil moisture values ​​of the study area into the regression equation, the simulated vegetation cover for each year was obtained, and the maximum value was taken as the baseline soil moisture vegetation carrying capacity (VCC) of the VRU. water .

[0013] Furthermore, the constructed wind erosion sensitivity correction coefficient is applied to the basic soil moisture and vegetation carrying capacity (VCC). water Corrections are made to generate wind erosion-corrected vegetation carrying capacity (VCC). final The steps specifically include: Construct wind field intensity factor and surface roughness factor to calculate wind erosion sensitivity. The specific calculation formula is as follows:

[0014]

[0015]

[0016] In the formula, Indicates the wind field intensity factor; This represents the average annual wind speed; This indicates the number of days with strong sandstorms starting in the year; N ( ) indicates normalization. Represents the surface roughness factor; This represents the height of the i-th cell within the window; Indicates the average elevation within the window; n Indicates the number of pixels in the window The wind erosion sensitivity is normalized to obtain the correction coefficient. :

[0017] In the formula, The elastic coefficient under wind erosion stress; Using correction factors to assess the basic soil moisture and vegetation carrying capacity (VCC) water Corrections are made to generate wind erosion-corrected vegetation carrying capacity:

[0018] In the formula, This indicates the carrying capacity of wind-erosion-corrected vegetation.

[0019] Furthermore, the method also includes: Wind erosion-corrected vegetation carrying capacity (VCC) final Compared with the current vegetation cover (FVC)curr The vegetation carrying capacity index I is obtained by performing interpolation calculation. The specific calculation formula is as follows:

[0020] Ecological restoration functional zones are delineated based on I values.

[0021] Furthermore, the ecological restoration functional zone includes an ecological surplus zone, an ecological balance zone, and an ecological overload zone; the vegetation carrying capacity index I>0 in the ecological surplus zone, the vegetation carrying capacity index I=0 in the ecological balance zone, and the vegetation carrying capacity index I<0 in the ecological overload zone.

[0022] Secondly, the present invention provides a wind erosion-corrected vegetation bearing capacity calculation system for the loess-desert transition zone, comprising: The data acquisition module is used to acquire vegetation data, water data, topographic data, meteorological data, soil data and land use data of the study area, and establish a multi-source spatiotemporal database; VRU partitioning module, used to partition vegetation response units (VRUs) based on the multi-source spatiotemporal database; The regression calculation module is used to establish a regression model of soil moisture and vegetation cover within each VRU, and uses the maximum value of historical simulated vegetation cover as the base soil moisture and vegetation carrying capacity (VCC) of the VRU. water ; The correction module is used to construct the wind erosion sensitivity correction coefficient for the basic soil moisture and vegetation carrying capacity (VCC). water Corrections are made to generate wind erosion-corrected vegetation carrying capacity (VCC). final .

[0023] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for calculating the wind erosion-corrected vegetation carrying capacity in the loess-desert transition zone as described above.

[0024] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for calculating the wind erosion-corrected vegetation carrying capacity in the loess-desert transition zone.

[0025] Compared with the prior art, the present invention has the following beneficial effects: This invention discloses a wind erosion-corrected vegetation carrying capacity calculation method and system for the loess-desert transition zone. By dividing the complex loess-desert transition zone into several microenvironments with uniform natural attributes through the division of Vegetation Response Units (VRUs), this method effectively removes background noise from topography and soil, improving the fitting degree of the established "water-vegetation" regression equation and ensuring the scientific validity of the basic carrying capacity calculation. Furthermore, a wind erosion sensitivity correction factor is introduced into the soil moisture-vegetation carrying capacity model, systematically and parameterizing the key ecological stress factor of wind erosion disturbance at the model level. This addresses the fundamental deficiency of existing technologies that "only consider water and ignore wind erosion." This enables the assessment model to accurately reflect the actual ecological mechanism of the dual "water-wind" stress in the loess-desert transition zone, avoiding the traditional bias of systematically overestimating vegetation carrying capacity in high-wind erosion risk areas. It provides a scientific, quantitative, and directly applicable basis for regional ecological restoration and spatial decision-making.

[0026] Furthermore, this invention compares the calculated wind erosion-corrected vegetation carrying capacity with the existing vegetation cover, calculates the vegetation carrying capacity index, and divides the overloaded area and surplus area, which provides a reliable basis for actual replanting. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the system of the present invention; Figure 3 This is a schematic diagram of the architecture of the loess-desert transition zone vegetation carrying capacity assessment system according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating the calculation of vegetation bearing capacity in the loess-desert transition zone according to an embodiment of the present invention. Figure 5 This is a schematic diagram illustrating the construction principle of the vegetation response unit in an embodiment of the present invention; Figure 6 This is the calculation process for the wind erosion sensitivity correction coefficient in an embodiment of the present invention; Figure 7 This is a load-bearing capacity surplus / deficit diagram according to an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] It should be understood that in the embodiments of this application, "at least one" means one or more, and "more than one" means two or more. "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the related objects before and after it are in an "or" relationship. "Contains A, B and / or C" means containing any one, two, or three of A, B, and C.

[0032] It should be understood that in the embodiments of this application, "B corresponding to A", "B corresponding to A", "A corresponds to B" or "B corresponds to A" means that B is associated with A, and B can be determined based on A. Determining B based on A does not mean that B is determined solely based on A; B can also be determined based on A and / or other information.

[0033] The wind-erosion-corrected vegetation carrying capacity calculation method for the loess-desert transition zone provided by this invention can be executed by an electronic device, such as a terminal or server. The terminal can be a smartphone, tablet, laptop, or other similar device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. It is understood that this invention does not limit the specific entity executing this wind-erosion-corrected vegetation carrying capacity calculation method for the loess-desert transition zone.

[0034] The technical solution of this application will be described in detail below through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments described below are used to explain the technical solution of this application and are not intended to limit actual use.

[0035] See Figure 1 and Figure 4 This invention, using Yulin City, Shaanxi Province as the specific implementation area, and with a data time span of 2010 to 2020 (as the model training period) and 2024 (as the current status assessment period), discloses a wind erosion-corrected vegetation carrying capacity calculation method for the loess-desert transition zone, including the following steps: S1. Acquire vegetation data, water data, topographic data, meteorological data, soil data, and land use data of the study area to establish a multi-source spatiotemporal database; Vegetation data: Normalized Difference Vegetation Index (NDVI) data at a 1km spatial resolution were acquired from MODIS remote sensing imagery. Annual average NDVI values ​​were calculated using ArcGIS software and converted into Free Vegetation Cap (FVC) values, which serve as an indicator of vegetation cover for the corresponding year. The main calculation formulas are as follows:

[0036] Where: FVC: vegetation cover, value [0,1]; NDVI: pixel NDVI value; NDVI soil NDVI value of bare soil; NDVI veg NDVI value of pure vegetation; Moisture data: 1km single-layer soil moisture raster data reconstructed based on AMSR-E and AMSR2 data were acquired, and the annual average value was calculated as the soil moisture index for the corresponding year. Topographic data: Using 1km resolution topographic data, extract elevation, slope, and aspect; Meteorological data: Obtain average wind speed, maximum wind speed, and prevailing wind direction data from meteorological stations during the same period; Soil data: Acquire 1km resolution soil texture data from the Earth Resources Data Cloud; Land use data: Acquire land use data at a 1km resolution.

[0037] The above data were subjected to unified projection, spatial resolution matching, and study area cropping, and outliers were removed or smoothed.

[0038] S2, see S2. Figure 5 Vegetation Response Units (VRUs) are defined based on the multi-source spatiotemporal database. To eliminate the interference of factors such as micro-topography and soil background differences on regression analysis, VRUs were divided according to the principle of "site condition consistency".

[0039] Indicator Classification: Soil texture: Soil texture data in Yulin City was classified into six types: silty clay, loam, loamy clay, sandy loam, sandy clay, and silty loam. These were then reclassified using ArcGIS software, sequentially assigned numbers 1-6.

[0040] Slope classification: Based on the slopes calculated from the DEM data of Yulin City, the slopes were divided into flat land (≤0.05°), slightly sloping land (0.05°-2°), gentle slopes (2°-5°), and steep slopes (>5°). These were then reclassified using ArcGIS software and numbered 1-4.

[0041] Slope aspect classification: It is divided into five types: flat slope, shady slope (315°-45°), semi-sunny slope (45°-135°), sunny slope (135°-225°), and semi-shady slope (225°-315°). They are then reclassified using ArcGIS software and numbered 1-5.

[0042] Land use: Extract cultivated land, forest land, grassland, and unused map patches, and reclassify these four types, numbering them 1-4 in sequence.

[0043] Spatial overlay: Use the raster calculator in ArcGIS software to overlay the above layers. The overlay formula is: Soil × 1000 + Land Use × 100 + Slope × 10 + Aspect.

[0044] S3. Establish a regression model of soil moisture and vegetation cover within each VRU, and use the maximum value of historical simulated vegetation cover as the basic soil moisture and vegetation carrying capacity (VCC) of the VRU. water ; Based on the assumption that "soil moisture is the main driving force for vegetation growth", regression analysis was performed within each VRU.

[0045] Statistical aggregation: In ArcGIS software, using the zoning statistics tool, for the i-th type of VRU, extract the annual mean soil moisture SM for all cells within that region from 2010 to 2020. i,t Compared with the mean vegetation cover i,t .

[0046] Regression modeling: Construct a univariate regression model for each VRU and select the optimal function form (linear, logarithmic, exponential, or polynomial):

[0047] Threshold extrapolation: Using 1km pixels as the basic unit, and based on the regression equation of the VRU region where the pixel is located, the annual average soil moisture value of the study area is substituted into the regression equation as the independent variable to obtain the vegetation cover value of the same location from 2010 to 2020. The maximum value is selected as the basic soil moisture vegetation carrying capacity (VCC) value of that pixel. water ).

[0048] S4, see S4 Figure 6 Construct a wind erosion sensitivity correction coefficient; This embodiment introduces wind erosion factors for spatial correction, taking into account the characteristics of the loess-desert transition zone.

[0049] S401, Select evaluation indicators: 1) Wind field intensity factor (W): This factor combines the annual average wind speed and the number of days with strong winds. The higher the wind speed, the greater the correction should be.

[0050] V avg Annual average wind speed (unit: m / s).

[0051] D gale Number of days with strong sandstorms since the beginning of the year (unit: days).

[0052] Note: Days with wind speeds ≥ 6 m / s are defined as days with strong sandstorms.

[0053] Since wind speed and the number of days have different dimensions, they cannot be added directly and must be mapped to the interval [0, 1].

[0054] Using the range standardization formula:

[0055]

[0056] Where Vi,Di are the actual values ​​of the i-th grid or station, and V max Vmin D max D min These represent the maximum and minimum values ​​within the study area.

[0057] Constructing wind field intensity index W :

[0058] Where N() represents the normalization process, V avg D is the average annual wind speed. gale The cumulative number of days per year with a speed greater than 6 m / s.

[0059] 2) Surface roughness factor (R): Surface roughness is calculated using a DEM. The flatter and more open the terrain, the lower the wind erosion resistance and the higher the sensitivity; complex terrain (gullies) has a windproof effect.

[0060]

[0061] R: Surface roughness index h i The height of the i-th pixel within the window Average elevation within the window n: Number of pixels in the window S402, Calculate wind erosion sensitivity (ESI):

[0062] S403, Generate correction coefficient K: Normalize the ESI values ​​to the [0,1] interval and construct a correction function:

[0063] in The elastic coefficient under wind erosion stress is set based on empirical values ​​from field investigations. In this embodiment, it is taken as... =0.1 means that the bearing capacity of the strongest wind erosion zone has decreased by 10%.

[0064] S5, based on the basic soil moisture and vegetation carrying capacity (VCC) water Corrections are made to generate wind erosion-corrected vegetation carrying capacity (VCC). final .

[0065] Pixel-scale correction of foundation bearing capacity is performed using correction factors:

[0066] In areas with high wind erosion risk, by lowering the theoretical bearing capacity threshold, some moisture is reserved to resist the additional water loss due to wind erosion and to prevent excessively dense vegetation from dying due to wind and sand.

[0067] S6, see S6. Figure 7 Calculate the vegetation carrying capacity index I and delineate ecological restoration functional zones; Using the current vegetation cover (FVC) in 2024 curr With the corrected bearing capacity VCC final The vegetation carrying capacity index I is obtained by performing interpolation.

[0068] Based on the I value and its sign, it is divided into three repair functional areas: Ecological surplus zone (I>0): The current vegetation has not reached its carrying capacity limit, and the risk of wind erosion has been considered through correction factors. Replanting and supplementary planting can be carried out, and suitable vegetation should be selected according to the specific environmental conditions.

[0069] Ecological balance zone (I=0): The current vegetation is basically in line with the environmental carrying capacity. The main focus is on protection through enclosure and natural restoration, minimizing human disturbance.

[0070] Ecological overshoot zone (I<0): The current vegetation density has exceeded the carrying capacity limit under the dual constraints of "water and wind erosion," posing a risk of deep soil drying and vegetation degradation. New afforestation is strictly prohibited. Existing overly dense plantations should be pruned and thinned appropriately to reduce density and maintain the long-term stability of the community.

[0071] See Figure 2 and Figure 3 This invention discloses a wind erosion-corrected vegetation carrying capacity calculation system for the loess-desert transition zone, comprising a data acquisition module, a VRU partitioning module, a regression calculation module, and a correction module; details are as follows: The data acquisition module is used to acquire vegetation, moisture, topography, meteorological, soil, and land use data of the study area to establish a multi-source spatiotemporal database. The data required for this invention (NDVI, DEM, soil moisture, and meteorological data) can all be obtained from publicly available satellite remote sensing products or meteorological stations. The calculation process is based on mature GIS spatial analysis technology. This makes the method applicable not only to Yulin, Shaanxi, but also to similar agro-pastoral ecotones or sandy edge areas such as Ordos in Inner Mongolia and Yanchi in Ningxia, demonstrating broad application prospects.

[0072] The VRU partitioning module is used to partition vegetation response units (VRUs) based on the multi-source spatiotemporal database. This invention deconstructs the complex loess-desert transition zone into several microenvironments with uniform natural attributes through VRU partitioning. This processing method effectively removes background noise from topography and soil, improving the fitting degree of the established "water-vegetation" regression equation and ensuring the scientific validity of the foundation bearing capacity calculation.

[0073] The regression calculation module is used to establish a regression model of soil moisture and vegetation cover within each VRU, and uses the maximum value of historical simulated vegetation cover as the basic soil moisture and vegetation carrying capacity (VCCwater) of the VRU. The calibration module is used to construct a wind erosion sensitivity correction coefficient to correct the basic soil moisture and vegetation carrying capacity (VCCwater), generating a wind erosion-corrected vegetation carrying capacity (VCCfinal). Addressing the characteristics of the Yulin region—strong winds and abundant sand—this invention goes beyond water carrying capacity and creatively introduces a wind erosion sensitivity correction factor. This technological improvement corrects the systematic bias of traditional models in overestimating carrying capacity in high wind erosion areas such as windward slopes and mobile sand dunes. Through "dimensionality reduction," the calculation results are more conservative and secure, avoiding the phenomenon of "small, old trees" or large-scale vegetation death caused by blindly pursuing high vegetation coverage.

[0074] Preferably, it also includes ecological restoration functional zones, used to calculate the vegetation carrying capacity index I, and to delineate ecological restoration functional zones based on the I value. This provides a quantitative decision-making basis for ecological restoration projects and vegetation structure adjustments.

[0075] This invention proposes a method for calculating soil moisture and vegetation carrying capacity over large areas: Previous water balance models require relatively detailed and complex data, and their calculation areas are limited to small regions, such as a single slope, making them unsuitable for calculating the carrying capacity of large areas like Yulin City. Furthermore, traditional mathematical models neglect the influence of factors such as topography and soil texture. Therefore, this invention, by considering environmental conditions and dividing the area into VRUs (Vascular Rules), can more accurately calculate the soil moisture and vegetation carrying capacity of large areas.

[0076] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a method for calculating the wind erosion-corrected vegetation carrying capacity in the loess-desert transition zone.

[0077] This invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for calculating the wind erosion-corrected vegetation carrying capacity in the loess-desert transition zone described in the above embodiments.

[0078] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0079] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0080] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0081] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating the wind erosion-corrected vegetation bearing capacity in the loess-desert transition zone, characterized in that, Includes the following steps: Acquire vegetation, water, topography, meteorological, soil, and land use data for the study area to establish a multi-source spatiotemporal database; Vegetation Response Units (VRUs) are defined based on the multi-source spatiotemporal database; A regression model of soil moisture versus vegetation cover was established within each VRU, and the maximum value of historical simulated vegetation cover was used as the baseline soil moisture-vegetation carrying capacity (VCC) of the VRU. water ; Construct a wind erosion sensitivity correction coefficient for the basic soil moisture and vegetation carrying capacity (VCC). water Corrections are made to generate wind erosion-corrected vegetation carrying capacity (VCC). final .

2. The method for calculating the wind erosion-corrected vegetation bearing capacity in the loess-desert transition zone according to claim 1, characterized in that, The steps of acquiring vegetation data, moisture data, topographic data, meteorological data, soil data, and land use data of the study area, and establishing a multi-source spatiotemporal database, specifically include: Vegetation data were collected, and Normalized Difference Vegetation Index (NDVI) data with a spatial resolution of 1 km from MODIS remote sensing imagery were obtained. The vegetation cover (FVC) was calculated using the following formula: In the formula, NDVI represents the pixel NDVI value; NDVI soil This represents the NDVI value of bare soil; NDVI veg This represents the NDVI value of pure vegetation. Moisture data were collected, and 1km single-layer soil moisture raster data reconstructed based on AMSR-E and AMSR2 data were obtained. The annual average value was calculated as the soil moisture index for the corresponding year. Collect terrain data and extract elevation, slope, and aspect using 1km resolution terrain data; Collect meteorological data to obtain average wind speed, maximum wind speed and prevailing wind direction data of meteorological stations during the same period; Collect soil data and obtain 1km resolution soil texture data from the Earth Resources Data Cloud; Collect land use data and obtain land use data with a 1km resolution. The vegetation data, water data, topographic data, meteorological data, soil data, and land use data are subjected to unified projection, spatial resolution matching, and study area cropping. Outliers are removed or smoothed to establish a multi-source spatiotemporal database.

3. The method for calculating the wind erosion-corrected vegetation bearing capacity in the loess-desert transition zone according to claim 1, characterized in that, The step of dividing vegetation response units (VRUs) based on the multi-source spatiotemporal database specifically includes: Soil texture, slope, aspect, and land use type are classified and numbered respectively; The grid overlay formula is used to perform overlay calculations to generate VRU grids with unique codes, thus completing the division of vegetation response units (VRUs). The grid overlay formula is: soil × 1000 + land use × 100 + slope × 10 + aspect.

4. The method for calculating the wind erosion-corrected vegetation bearing capacity in the loess-desert transition zone according to claim 1, characterized in that, A regression model of soil moisture and vegetation cover is established within each VRU, and the maximum value of historical simulated vegetation cover is used as the baseline soil moisture and vegetation carrying capacity (VCC) of the VRU. water The steps specifically include: Historical average soil moisture (SM) was calculated within each VRU. i,t Compared with the mean vegetation cover i,t ; Establish a regression equation between vegetation cover and soil moisture. ; Substituting the annual soil moisture values ​​of the study area into the regression equation, the simulated vegetation cover for each year was obtained, and the maximum value was taken as the baseline soil moisture vegetation carrying capacity (VCC) of the VRU. water .

5. The method for calculating the wind erosion-corrected vegetation bearing capacity in the loess-desert transition zone according to claim 1, characterized in that, The constructed wind erosion sensitivity correction coefficient is applied to the basic soil moisture and vegetation carrying capacity (VCC). water Corrections are made to generate wind erosion-corrected vegetation carrying capacity (VCC). final The steps specifically include: Construct wind field intensity factor and surface roughness factor to calculate wind erosion sensitivity. The specific calculation formula is as follows: In the formula, Indicates the wind field intensity factor; This represents the average annual wind speed; This indicates the number of days with strong sandstorms starting in the year; N ( ) indicates normalization. This represents the surface roughness factor. This represents the height of the i-th cell within the window; Indicates the average elevation within the window; n Indicates the number of pixels in the window The wind erosion sensitivity is normalized to obtain the correction coefficient. : In the formula, The elastic coefficient under wind erosion stress; Using correction factors to assess the basic soil moisture and vegetation carrying capacity (VCC) water Corrections are made to generate wind erosion-corrected vegetation carrying capacity: In the formula, This indicates the carrying capacity of wind-erosion-corrected vegetation.

6. The method for calculating the wind erosion-corrected vegetation bearing capacity in the loess-desert transition zone according to claim 1, characterized in that, Also includes: Wind erosion-corrected vegetation carrying capacity (VCC) final Compared with the current vegetation cover (FVC) curr The vegetation carrying capacity index I is obtained by performing interpolation calculation. The specific calculation formula is as follows: Ecological restoration functional zones are delineated based on I values.

7. The method for calculating the wind erosion-corrected vegetation bearing capacity in the loess-desert transition zone according to claim 6, characterized in that, The ecological restoration functional zones include ecological surplus zones, ecological balance zones, and ecological overload zones; the vegetation carrying capacity index I>0 in the ecological surplus zone, the vegetation carrying capacity index I=0 in the ecological balance zone, and the vegetation carrying capacity index I<0 in the ecological overload zone.

8. A wind erosion-corrected vegetation carrying capacity calculation system for the loess-desert transition zone, characterized in that, include: The data acquisition module is used to acquire vegetation data, water data, topographic data, meteorological data, soil data and land use data of the study area, and establish a multi-source spatiotemporal database; VRU partitioning module, used to partition vegetation response units (VRUs) based on the multi-source spatiotemporal database; The regression calculation module is used to establish a regression model of soil moisture and vegetation cover within each VRU, and uses the maximum value of historical simulated vegetation cover as the base soil moisture and vegetation carrying capacity (VCC) of the VRU. water ; The correction module is used to construct the wind erosion sensitivity correction coefficient for the basic soil moisture and vegetation carrying capacity (VCC). water Corrections are made to generate wind erosion-corrected vegetation carrying capacity (VCC). final .

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for calculating the wind erosion-corrected vegetation carrying capacity in the loess-desert transition zone as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for calculating the wind erosion-corrected vegetation carrying capacity in the loess-desert transition zone as described in any one of claims 1-7.

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