Partitioned treatment method and device for soil erosion, storage medium and electronic equipment

Abstract

The invention discloses a subarea treatment method and device for soil erosion, a storage medium and electronic equipment, and the method comprises the steps: carrying out the preprocessing of collected multi-source data needed by landscape pattern research, and obtaining the preprocessed data; calculating an equation factor of the general soil loss equation by using the preprocessed data, and deducing a soil erosion modulus based on the equation factor inversion; determining a landscape index space layer of the landscape pattern, inputting the landscape index space layer and the soil erosion modulus into a space-time geographically weighted regression model, and outputting a local regression coefficient on each space-time unit; and dividing treatment stages based on the local regression coefficient and generating a partitioning scheme. By means of the method and device, the problem that in the related technology, due to the fact that correlation analysis between the landscape pattern and soil erosion is insufficient, the zoning treatment effect of the soil erosion is poor is solved.

Description

Technical Field

[0001] This application relates to the field of ecological environment monitoring and soil and water conservation technology, and more specifically, to a method, apparatus, storage medium and electronic equipment for zoned treatment of soil erosion. Background Technology

[0002] Among the relevant technologies: 1. Traditional USLE / RUSLE models do not consider the local characteristics of the Loess Plateau, such as the erosibility of loess soil and the hilly and gully terrain, and ignore the regulatory effect of landscape patterns (such as PD and CONTAG) on erosion, resulting in large simulation errors; 2. There is no unified standard for the selection of landscape indices, which is prone to analytical distortion due to multicollinearity (such as the redundancy of LPI and DIVISION); 3. Linear regression (OLS / PLSR) cannot capture spatial heterogeneity (such as the difference in PD-erosion relationship between the east and west of the Sanchuan River Basin) and temporal dynamics (such as the 2005 natural-human-dominated inflection point); 4. There is a lack of phased erosion regulation thresholds for the "ecological engineering period to urbanization period" of small watersheds, resulting in a disconnect between theory and governance practice.

[0003] There is currently no effective solution to the problem that the zoned management of soil erosion is not effective due to insufficient analysis of the relationship between landscape pattern and soil erosion in related technologies. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, storage medium, and electronic device for zoned management of soil erosion, in order to solve the problem that the zoned management of soil erosion is ineffective due to insufficient analysis of the correlation between landscape pattern and soil erosion in related technologies.

[0005] To achieve the above objectives, according to a first aspect of this application, a method for zoned management of soil erosion is provided. The method includes: preprocessing multi-source data collected for landscape pattern research to obtain preprocessed data, wherein the multi-source data includes: remote sensing data, meteorological data, and soil data; calculating equation factors of a general soil loss equation using the preprocessed data, and deriving a soil erosion modulus based on the equation factors; determining a landscape index spatial layer of the landscape pattern, and inputting the landscape index spatial layer and the soil erosion modulus into a spatiotemporal geographic weighted regression model, outputting local regression coefficients for each spatiotemporal unit, wherein the local regression coefficients are used to describe the non-stationary relationship between the landscape index and soil erosion; and dividing the management stages based on the local regression coefficients and generating a zoning scheme.

[0006] Furthermore, the equation factors of the general soil loss equation are calculated using the preprocessed data, and the soil erosion modulus is derived based on the equation factors. The equation factors include: rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover factor, and soil and water conservation measures factor. The preprocessed data is substituted into the corresponding logical calculation formula to obtain the equation factors; the equation factors are multiplied to obtain the soil erosion modulus.

[0007] Further, the spatial layer of landscape indices for determining the landscape pattern includes: selecting a preset number of initial candidate indices from patch level, type level, and landscape level; filtering the initial candidate indices through a dual screening mechanism to obtain the filtered indices; and spatially calculating the filtered indices using the moving window method to obtain the spatial layer of landscape indices.

[0008] Furthermore, before determining the landscape index spatial layer of the landscape pattern and inputting the landscape index spatial layer and soil erosion modulus into the spatiotemporal geographic weighted regression model to output the local regression coefficients on each spatiotemporal unit, the method also includes: extracting the landscape spatiotemporal dataset of each spatial unit in the landscape index spatial layer; extracting the soil spatiotemporal dataset of each observation sample in the soil erosion modulus; and matching the landscape spatiotemporal dataset of each spatial unit with the soil spatiotemporal dataset to obtain a spatiotemporal panel dataset, wherein the spatiotemporal panel dataset includes: spatial coordinates, time coordinates, landscape index values, and erosion modulus values.

[0009] Furthermore, before dividing the governance stages based on local regression coefficients and generating a partitioning scheme, the method also includes: calculating the spatiotemporal distance between corresponding sample points based on the spatial and temporal coordinates of the spatiotemporal panel dataset input by Euclidean distance; and inputting the spatiotemporal distance into a Gaussian decay function to obtain the spatiotemporal weights.

[0010] Furthermore, a spatial layer of landscape indices representing the landscape pattern is determined, and the spatial layer of landscape indices and the soil erosion modulus are input into a spatiotemporal weighted regression model to output local regression coefficients for each spatiotemporal unit. This includes: combining spatiotemporal weights with spatiotemporal panel datasets to extract target sample data related to the spatiotemporal unit from the spatial layer of landscape indices and the soil erosion modulus; and inputting the target sample data into the spatiotemporal weighted regression model to obtain the local regression coefficients of landscape indices on soil erosion for each spatiotemporal unit.

[0011] Furthermore, the governance stages are divided and zoning schemes are generated based on local regression coefficients, including: determining key time inflection points based on the spatiotemporal characteristics of the regression coefficients; dividing the ecological engineering stage and the urbanization stage based on the key time inflection points, extracting the landscape index control threshold for each stage, and generating zoning governance schemes based on the landscape index control thresholds.

[0012] To achieve the above objectives, according to a second aspect of this application, a zoning management device for soil erosion is provided. The device includes: a processing unit for preprocessing multi-source data collected for landscape pattern research to obtain preprocessed data, wherein the multi-source data includes: remote sensing data, meteorological data, and soil data; a modulus calculation unit for calculating equation factors of a general soil loss equation based on the preprocessed data, and deriving a soil erosion modulus based on the equation factors; a first determining unit for determining a landscape index spatial layer of the landscape pattern, and inputting the landscape index spatial layer and the soil erosion modulus into a spatiotemporal geographic weighted regression model, outputting local regression coefficients for each spatiotemporal unit, wherein the local regression coefficients describe the non-stationary relationship between the landscape index and soil erosion; and an ecological zoning unit for dividing management stages based on the local regression coefficients and generating a zoning scheme.

[0013] Furthermore, the modulus calculation unit includes: a first determining module, used to input the preprocessed data into the corresponding logical calculation formula to obtain the equation factors; and a processing module, used to multiply the equation factors to obtain the soil erosion modulus.

[0014] Furthermore, the first determining unit includes: a selection module, used to select a preset number of initial candidate indices from patch level, type level, and landscape level; a filtering module, used to filter the initial candidate indices through a dual filtering mechanism to obtain the filtered indices; and a second determining module, used to perform spatial calculations on the filtered indices using the moving window method to obtain a landscape index spatial layer.

[0015] Furthermore, before determining the landscape index spatial layer of the landscape pattern and inputting the landscape index spatial layer and soil erosion modulus into the spatiotemporal geographic weighted regression model to output the local regression coefficients on each spatiotemporal unit, the device also includes: an extraction unit for extracting the spatial coordinates of each spatial unit in the landscape index spatial layer and extracting the temporal coordinates of each observation sample in the soil erosion modulus; and a matching unit for matching the landscape spatiotemporal dataset with the soil spatiotemporal dataset one by one to obtain a spatiotemporal panel dataset, wherein the spatiotemporal panel dataset includes: spatial coordinates, temporal coordinates, landscape index values, and erosion modulus values.

[0016] Furthermore, before dividing the governance stages based on local regression coefficients and generating a partitioning scheme, the method also includes: a distance calculation unit, used to calculate the spatiotemporal distance between corresponding sample points based on the spatial and temporal coordinates of the spatiotemporal panel dataset input based on Euclidean distance; and a second determination unit, used to input the spatiotemporal distance into a Gaussian decay function to obtain the spatiotemporal weights.

[0017] Furthermore, the ecological zoning unit includes: an extraction module, used to extract target sample data related to the spatiotemporal unit from the landscape index spatial layer and soil erosion modulus by combining spatiotemporal weights and spatiotemporal panel datasets; and a third determination module, used to input the target sample data into the spatiotemporal geographic weighted regression model to obtain the local regression coefficients of landscape index on soil erosion under each spatiotemporal unit.

[0018] Furthermore, the ecological zoning unit includes: a fourth determination module, used to determine key time inflection points based on the spatiotemporal characteristics of local regression coefficients; and a generation module, used to divide the ecological engineering stage and the urbanization stage based on the key time inflection points, extract the landscape index control threshold for each stage, and generate a zoning governance plan based on the landscape index control threshold.

[0019] According to a third aspect of the embodiments of this application, an electronic device is provided, including: a processor; and a memory storing computer-readable instructions, which, when executed by the processor, implement the soil erosion zoning management method described above.

[0020] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements a method for zoned management of soil erosion according to any one of the above.

[0021] This application employs the following steps: preprocessing the multi-source data collected for landscape pattern research to obtain preprocessed data, including remote sensing data, meteorological data, and soil data; calculating the equation factors of a general soil loss equation using the preprocessed data, and deriving the soil erosion modulus based on the equation factors; determining the landscape index spatial layer of the landscape pattern, and inputting the landscape index spatial layer and the soil erosion modulus into a spatiotemporal geographic weighted regression model, outputting local regression coefficients for each spatiotemporal unit, where the local regression coefficients describe the non-stationary relationship between the landscape index and soil erosion; and dividing the treatment stages and generating zoning schemes based on the local regression coefficients. This application solves the problem in related technologies where insufficient analysis of the correlation between landscape pattern and soil erosion leads to inadequate zoning treatment effects for soil erosion. By optimizing the equation factors and determining the spatiotemporal weights, the accuracy of zoning treatment for soil erosion is improved. Attached Figure Description

[0022] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0023] Figure 1This is a flowchart of a zoned treatment method for soil erosion provided in the embodiments of this application;

[0024] Figure 2 This is a schematic diagram of the spatiotemporal variation trend of the landscape pattern index of the Sanchuan River Basin provided according to the embodiments of this application;

[0025] Figure 3 This is a schematic diagram of a zoned treatment device for soil erosion according to an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of the network architecture of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0027] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0028] 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 of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first," "second," etc., 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 data can be interchanged where appropriate for the embodiments of this application 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.

[0030] According to an embodiment of this application, a method for zoned management of soil erosion is provided.

[0031] Figure 1 This is a flowchart of a zoned treatment method for soil erosion according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0032] Step S101: Preprocess the multi-source data collected for the landscape pattern study to obtain preprocessed data. The multi-source data includes: remote sensing data, meteorological data, and soil data.

[0033] Specifically, the multi-source data in this paper includes remote sensing data, meteorological data, and soil data, and may also include DEM (Digital Elevation Model) data. The preprocessed data includes corrected imagery, spatialized meteorological raster data, standardized soil vector data, a unified coordinate system dataset, and a basic dataset for landscape pattern analysis. This application utilizes professional data conversion tools (such as remote sensing TIFF, meteorological CSV, and soil SHP) to convert between multiple sources and achieves batch integration of multi-source data through a visualization workflow, reducing human error.

[0034] Step S102: Calculate the equation factor of the general soil loss equation using the preprocessed data, and derive the soil erosion modulus based on the equation factor.

[0035] The model expression for the Universal Soil Loss Equation (USLE), which calculates the soil erosion modulus A, is as follows:

[0036] Among them, equation factors , , , , , , respectively representing rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover and management factor, and soil and water conservation measures factor.

[0037] Specifically, the equation factors of the general soil loss equation are calculated using preprocessed data, and the soil erosion modulus is derived based on the equation factors. This can be achieved through the following steps. The equation factors include: rainfall erosivity factor, soil erodibility factor, slope length factor, slope gradient factor, vegetation cover factor, and soil and water conservation measures factor. The preprocessed data is substituted into the corresponding logical calculation formula to obtain the equation factors. The equation factors are then multiplied to obtain the soil erosion modulus.

[0038] For example, rainfall erosivity factor The calculation formula is: R = 0.053 × Pd = 1.655

[0039] Where R is the rainfall erosivity factor, MJ mm hm -2 h-1 a -1 Pd represents the average annual precipitation, in mm.

[0040] Soil erodibility factors The calculation formula is:

[0041] Where K is the soil erodibility factor t·h MJ -1 mm -1 Sa represents the soil sand content (%); S i SN represents the soil silt content (%), C represents the soil clay content (%), and OM represents the soil organic carbon content (%). SN can be obtained by dividing the soil organic matter content by the conversion factor 1.724. SN = 1 - Sa · 100 -1 0.1317 is the conversion factor from US units to International Standard Metric (SI).

[0042] slope length factor The calculation formula is:

[0043] Where λ is the projected horizontal slope length (m), and m is a variable slope index.

[0044] Slope factor The calculation formula is:

[0045] The formula for calculating vegetation cover and management factor C is:

[0046] Where C represents the vegetation cover and management factor, NDVI min The minimum NDVI value within the study area, NDVI max This represents the maximum NDVI value within the study area.

[0047] Soil and water conservation measures factors , including cultivated land =0.624; Grassland / Unused Land =0.363; Forest land =1; water area =0; Urban land =0.

[0048] It should be noted that the data parameters required for the above factor calculations are the data preprocessed in this application.

[0049] In summary, the soil erosion modulus A can be obtained based on the above formulas, where A is the amount of soil erosion per unit area (t hm²). -2a -1 Based on relevant ecological research and analysis, the soil erosion in the study area was divided into two categories: no significant erosion (<1000 t km). -2 a -1 Minor erosion (1000~2500 t km) -2 a -1 Moderate erosion (2500~5000 t km) -2 a -1 Intensity erosion (5000~15000 t km) -2 a -1 Severe erosion (>15000 t km) -2 a -1 There are 5 levels in total.

[0050] This case study eliminates various interferences in the original data through preprocessed multi-source data, providing a high-quality foundation for the calculation of equation factors, directly reducing the error of the results, and making the calculation of soil erosion modulus more scientific, accurate and in line with the actual situation of the study area.

[0051] Step S103: Determine the spatial layer of landscape indices for the landscape pattern, and input the spatial layer of landscape indices and soil erosion modulus into the spatiotemporal geographic weighted regression model, outputting the local regression coefficients on each spatiotemporal unit. The local regression coefficients are used to describe the non-stationary relationship between landscape indices and soil erosion.

[0052] Among them, the landscape index layer refers to the layer data that combines the landscape index with the geospatial location to intuitively display the spatial distribution of landscape pattern characteristics. The spatiotemporal weight is the core output of the spatiotemporal geographic weighted regression model. In essence, it is an "influence coefficient" that changes with time and spatial location. It is used to quantitatively describe the difference in the degree of influence of independent variables (such as landscape index) on dependent variables (such as soil erosion modulus) at different spatiotemporal points, thereby revealing the "non-stationarity" of the relationship between the two.

[0053] Specifically, the spatial layer of landscape indices for determining the landscape pattern can be obtained through the following steps: selecting a preset number of initial candidate indices from patch level, type level, and landscape level; filtering the initial candidate indices through a dual screening mechanism to obtain the filtered indices; and spatially calculating the filtered indices using the moving window method to obtain the spatial layer of landscape indices.

[0054] For example, combining existing research, erosion mechanisms, and data availability, 12 landscape pattern indices were selected as initial candidate indices for this application at the microscale (patch level), mesoscale (type level), and macroscale (landscape level). Pearson correlation analysis was then performed to remove highly correlated values ​​greater than 0.9. Then, the VIF (variance inflation factor) test was used to remove collinear indices with VIF > 7.5 (such as removing SHDI). Finally, 7 typical landscape indices were selected as the indices after screening for this application. Based on Fragstats 4.2 software, the moving window method was used to calculate the corresponding local statistics or indicators of the screened indices, and the calculation results were correlated with the center position of the window to generate a landscape index result layer reflecting local spatial characteristics.

[0055] This application employs a dual screening mechanism to ensure that the resulting landscape index spatial layer is more accurate, more targeted, and better reflects spatial heterogeneity.

[0056] Specifically, before determining the landscape index spatial layer of the landscape pattern and inputting the landscape index spatial layer and soil erosion modulus into the spatiotemporal geographic weighted regression model to output the local regression coefficients on each spatiotemporal unit, the method also includes: extracting the landscape spatiotemporal dataset of each spatial unit in the landscape index spatial layer and extracting the soil spatiotemporal dataset of each observed sample in the soil erosion modulus; matching the landscape spatiotemporal dataset and the soil spatiotemporal dataset one by one to obtain the spatiotemporal panel dataset, wherein the spatiotemporal panel dataset includes: spatial coordinates, time coordinates, landscape index values, and erosion modulus values.

[0057] Among them, the Spatiotemporal Geographically Weighted Regression (GTWR) model is an important extension of the traditional Geographically Weighted Regression (GWR) model for "spatiotemporally coupled data". GTWR embeds temporal subtleties into the regression parameters, enabling it to simultaneously capture the non-stationary changes in variable relationships in both space and time. Assuming the research objective is to analyze the spatiotemporal correlation between the "patch density index (landscape index, reflecting landscape fragmentation)" and the "soil erosion modulus" in a watershed, the GTWR model ultimately outputs local regression coefficients.

[0058] For example, the landscape index spatial layer can be "2010, 2015, 2020 X watershed patch density index layer" (raster format, 30 m × 30 m resolution). Each raster cell (spatial cell) corresponds to a "patch density value" (e.g., in 2010, raster A had a value of 5.2 patches / hm², indicating 5.2 landscape patches per hectare in the area, indicating high fragmentation), and includes its own spatial coordinates (e.g., latitude and longitude: 110.5°E, 30.2°N). The time coordinate of each observation sample in the soil erosion modulus can be the "soil erosion modulus data of watershed X for 2010, 2015, and 2020" calculated by the USLE model. Each observation sample includes an "erosion modulus value" (e.g., in 2010, point A had a value of 3500 t / (km²·a), indicating moderate erosion) and an "observation time" (e.g., 2010, 2015, and 2020, i.e., time coordinates). The spatiotemporal panel dataset includes: the spatiotemporal coordinates of each sample, the landscape index value of each sample, and the soil erosion modulus value of each sample.

[0059] Specifically, before dividing the governance phases based on local regression coefficients and generating a partitioning scheme, the method also includes: calculating the spatiotemporal distance between corresponding sample points based on the spatial and temporal coordinates of the spatiotemporal panel dataset input by Euclidean distance; and inputting the spatiotemporal distance into a Gaussian decay function to obtain the spatiotemporal weights.

[0060] In other words, this case constructs an improved GTWR model, introduces a spatial factor λ (0.6~0.8) and a temporal factor μ (0.2~0.4), and uses a Gaussian decay function to calculate the spatiotemporal weights. The optimal bandwidth h can be determined to be 0.11~0.15km / yr, and the spatiotemporal correlation between the core landscape index and soil erosion can be accurately analyzed.

[0061] Specifically, this study uses a Gaussian decay function based on Euclidean distance to calculate the spatiotemporal weights.

[0062] The Euclidean distance formula is:

[0063] in, λ represents the spatiotemporal distance; λ and μ are the spatial and temporal factors, respectively.

[0064] Calculate the spatiotemporal weights The formula is: , where h is the bandwidth.

[0065] The spatiotemporal weights calculated in this case not only improve the interpretability of the results but also enhance the prediction accuracy of the model. In other words, the weight calculation fully considers the attenuation effect of spatiotemporal distance on the influence, and the model can more accurately capture the spatiotemporal heterogeneity of variable relationships.

[0066] Specifically, the landscape index spatial layer of the landscape pattern is determined, and the landscape index spatial layer and soil erosion modulus are input into the spatiotemporal weighted regression model to output the local regression coefficients of each spatiotemporal unit. This can be obtained through the following steps: combining spatiotemporal weights and spatiotemporal panel datasets, target sample data related to the spatiotemporal unit are extracted from the landscape index spatial layer and soil erosion modulus; the target sample data are input into the spatiotemporal weighted regression model to obtain the local regression coefficients of the landscape index on soil erosion under each spatiotemporal unit.

[0067] For example, the spatiotemporal weight of each sample to the target unit is calculated using a Gaussian decay function (weight range 0~1, weight ≥0.3 is considered a "high-weight sample", i.e., target sample data). The algorithm formula of the spatiotemporal geographic weighted regression model is as follows: ,

[0068] in, It is the dependent variable of the sample point (i,t) (e.g., the amount of soil erosion at point i at time t); It is the k-th explanatory variable of the sample point (i,t); Let be the spatial coordinates of the i-th sample point at time t; This is the intercept value; For the k-th independent variable (such as the landscape index) at the sample point (i,t), which is also the local regression coefficient in this case; This is the random error term.

[0069] The local regression coefficients obtained in this application can accurately reflect the specificity of the impact of landscape index on soil erosion in different spatiotemporal units, breaking through the limitation of the traditional global regression model that "a single coefficient represents the overall relationship", thus revealing the spatiotemporal dynamic law of the relationship between the two in a more objective and detailed way.

[0070] Step S104: Divide the governance stages based on local regression coefficients and generate a zoning scheme.

[0071] The local regression coefficients obtained in this application can accurately reflect the specificity of the impact of landscape index on soil erosion in different spatiotemporal units, breaking through the limitation of the traditional global regression model that "a single coefficient represents the overall relationship", thus revealing the spatiotemporal dynamic law of the relationship between the two in a more objective and detailed way.

[0072] Specifically, the process of dividing governance stages and generating zoning schemes based on local regression coefficients can be achieved through the following steps: determining key time inflection points based on the spatiotemporal characteristics of local regression coefficients; dividing ecological engineering stages and urbanization stages based on key time inflection points, extracting landscape index control thresholds for each stage, and generating zoning governance schemes based on landscape index control thresholds.

[0073] For example, from 2000 to 2010, the absolute value of the C coefficient increased slowly (from 0.25 to 0.30), indicating a gradual change in the inhibitory effect of vegetation on erosion. In 2012, the absolute value of the C coefficient surged from 0.30 to 0.55, a sudden change exceeding 80%. From 2012 to 2020, the absolute value of the coefficient remained above 0.55, and the growth slowed down again. Therefore, 2012 was a critical inflection point for the X watershed. After that year, the inhibitory effect of vegetation on erosion significantly increased, suggesting that large-scale ecological intervention measures may have been initiated in the watershed. Combining the key inflection point (2012) and the actual development background of the watershed (verified through statistical yearbooks and policy documents), the period from 2000 to 2020 is divided into two stages, while the core characteristics of each stage are clearly defined: for example, when the vegetation coverage index of the upstream forest land is less than 60%, the absolute value of the C coefficient is < 0.3, indicating insufficient erosion suppression and a high risk of moderate erosion. Based on this, a zoning management plan is generated, for example, a zoning plan is output based on spatial heterogeneity (such as "adding ecological corridors in the western high PD area and promoting terraced fields in the eastern cultivated area"). This case ensures that the plan has a scientific basis and is feasible by quantifying thresholds and inflection points, ultimately improving the efficiency and effectiveness of ecological governance.

[0074] Optionally, based on the above research in this application, this study investigated the soil erosion modulus of the Sanchuan River from 1990 to 2023. The soil erosion modulus of the Sanchuan River from 1990 to 2023 generally showed a trend of first decreasing and then increasing, with an average erosion modulus of 584 t / km². -2 a -1 There is no obvious erosion, but the maximum erosion intensity has reached 19,339 t km. -2 a -1 The erosion was severe. From 1990 to 2015, the overall trend was downward, reaching a low of 446 t km² in 2015. -2 a -1 Compared to 1990, it decreased by 60.04%; from 2020 to 2023, it began to rise again, reaching an erosion modulus of 652 t km² by 2023. -2 a -1 This represents a 31.60% increase compared to 2015.

[0075] In other words, the soil erosion area in the basin decreased from 1341.40 km² in 1990 to 908.14 km² in 2023 over the past 30 years, a reduction of more than 30%. Specifically, the proportion of area with no significant erosion first increased and then decreased, from 67% to 90% and then fell back to 78%; the proportion of slight erosion decreased from 21% to 17%; the proportions of moderate and severe erosion both decreased significantly, from 7% to 4% and 4% to 1%, respectively; the area of ​​severe erosion was 0.04 km² in 1990, and the data for subsequent years are too small to be considered, indicating that severe erosion has been effectively controlled. Overall, although the soil erosion situation in the Sanchuan River Basin fluctuated from 1990 to 2023, it showed an overall improving trend.

[0076] In terms of soil erosion intensity across different land use types, the soil erosion modulus from 1990 to 2023 was: cultivated land > grassland > unused land > forest land > construction land > water area. Areas with no significant soil erosion were mainly distributed in forest land, construction land, and water area; moderate to slight soil erosion was concentrated in cultivated land, grassland, and unused land. The erosion modulus of cultivated land, grassland, construction land, and water area are shown below.

[0077] From the spatial distribution of soil erosion intensity, from 1990 to 2023, the western region of the Sanchuan River Basin was mainly characterized by moderate to slight erosion, while the eastern region was dominated by no significant erosion. From 1990 to 2015, the reduction in soil erosion was mainly in the southwestern region, while by 2023, the areas where soil erosion intensified in the Sanchuan River Basin were mainly concentrated in the western part of the basin, primarily radiating outwards from the Sanchuan River system.

[0078] Optionally, based on the above research in this application, this case also studies the landscape pattern indices of the Sanchuan River from 1990 to 2023, such as... Figure 2 As shown, PD exhibits a U-shaped trend of "decline-rise," with a significant decrease in 2023 compared to 1990; while CONTAG shows an inverted U-shaped trajectory of "rise-decline." It is noteworthy that 2010 served as a crucial turning point, with PD reaching its trough (10.89 x 100). -1 hm -2Meanwhile, CONTAG climbed to its peak (58.10%), showing a significant negative correlation between the two (r = -0.803, P < 0.05). The IJI value continued to increase during the study period (cumulative increase of 23.30%), indicating a gradual shift from random distribution to spatial clustering of patch types, and a significant improvement in the physical connectivity of patch adjacency relationships. This spatial reorganization trend is further corroborated by AWMSI: although the value fluctuated in stages, it showed an overall decreasing trend (cumulative decrease of 35.44%), revealing a continuous reduction in the complexity of patch edges and a trend towards more regular geometric shapes. This morphological optimization is beneficial for reducing ecological resistance and promoting key ecological processes such as species migration and energy flow. As core indicators characterizing landscape heterogeneity, SHDI and DIVISION maintained a dynamic equilibrium during the study period. This steady-state characteristic indicates that despite local patch reorganization, the overall landscape matrix of the watershed maintained a balanced pattern of type diversity and spatial separation, reflecting the landscape resilience driven by both natural succession and human activities.

[0079] Spatially, the landscape indices exhibit a clear east-west differentiation. The PD gradient shows a significant east-west difference, with low values ​​concentrated in the eastern part of the basin and high values ​​dominating the western part, gradually expanding from northwest to central. The LPI spatial pattern is significantly negatively correlated with PD, indicating a low degree of fragmentation in large patches where dominant species congregate; the stable spatial distribution of LPI confirms the ecological resilience of core patches. AWMSI also shows a west-to-east differentiation, with a continuously decreasing peak value, suggesting that patch edges are becoming more regularized. CONTAG shows a fragmented distribution but with gentle fluctuations, reflecting a dynamic equilibrium in landscape connectivity. IJI is dominated by low values ​​with a continuously rising minimum threshold and a gradually expanding high-value area, indicating a structural adjustment in the adjacency relationships between patches. DIVISION low values ​​are distributed in a strip along the eastern side, while high values ​​are widely distributed in the west, showing a dynamically stable pattern. The SHDI low-value area is highly coupled with the DIVISION low-value area, with their high values ​​radiating along the water system corridors. The explosive growth of SHDI high values ​​in 2023 suggests a sudden change in the ecological function of the water system. Overall, the low values ​​of the four indices PD, AWMSI, DIVISION, and SHDI in the eastern edge area are superimposed with high values ​​of LPI, indicating that the landscape structure in this area is simple, the patch types are uniform, and the edges are regular, which is consistent with the low and flat terrain in the eastern part of the basin. The southwest is a cluster of high values ​​of PD, AWMSI, and DIVISION, reflecting high patch density, complex shapes, and high landscape segmentation, which is consistent with the natural geographical conditions of steep terrain and dense gullies in the southwest of the basin.

[0080] Optionally, based on the above research in this application, this case also studies the impact of landscape pattern changes on soil erosion from 1990 to 2023. (1) From 1990 to 2023, the soil erosion intensity in the Sanchuan River Basin showed a trend of first decreasing and then increasing, with the eroded area decreasing from 1341.40 km² to 908.14 km². The erosion modulus of different land use types was as follows: cultivated land > grassland > unused land > forest land > construction land > water area. (2) From 1990 to 2023, the landscape pattern of the Sanchuan River Basin showed the evolution characteristics of increased patch aggregation and connectivity, reduced fragmentation, and a tendency of landscape heterogeneity to reach dynamic equilibrium. Spatially, the eastern edge area showed low PD, low AWMSI, low DIVISION, low SHDI, and high LPI, while the southwestern area showed heterogeneous characteristics of high PD, high AWMSI, and high DIVISION. (3) The impact of landscape indices on soil erosion exhibits significant spatiotemporal differentiation and dynamic evolution: a) Enhanced spatial heterogeneity: The areas of no significant influence of CONTAG and IJI have expanded significantly; while the positive and negative effects of PD and AWMSI have gradually shown a trend of strengthening and homogenization, indicating that the influence of landscape pattern on SE has shifted from "natural pattern dominance" to "human activity dominance". b) Temporal dynamic transformation: After 2005, the influence pattern of each index underwent a systematic transformation. Therefore, 2005-2010 constituted a key turning point in the dynamics of soil erosion and the evolution of landscape pattern in the watershed. The phenomenon of intensified soil erosion observed after 2010. The positive effect areas of PD and IJI have shifted and expanded to the central and western regions and the central region, respectively, while the negative influence areas of AWMSI and CONTAG have expanded significantly, suggesting that landscape fragmentation and decreased connectivity in the watershed may exacerbate the risk of soil erosion. c) Human disturbance response: During the study period, the exponential effect in the urbanization hotspots in the south-central part of the basin changed from weakly positive / positive to no significant impact, which is directly related to the landscape homogenization caused by the expansion of construction land; while the positive effect aggregation in the agricultural intensification area in the northwest points to the nonlinear driving of the erosion process by the change in farming patterns.

[0081] In summary, the zoning management method for soil erosion provided in this application preprocesses multi-source data required for landscape pattern research to obtain preprocessed data. The multi-source data includes remote sensing data, meteorological data, and soil data. The method then uses the preprocessed data to calculate the equation factors of a general soil loss equation and derives the soil erosion modulus based on these factors. Next, it determines a spatial layer of landscape indices representing the landscape pattern and inputs the spatial layer of landscape indices and the soil erosion modulus into a spatiotemporal weighted regression model, outputting local regression coefficients for each spatiotemporal unit. These local regression coefficients describe the non-stationary relationship between the landscape index and soil erosion. Finally, it divides the management stages based on the local regression coefficients and generates a zoning scheme. This application solves the problem in related technologies where insufficient analysis of the correlation between landscape pattern and soil erosion leads to inadequate zoning management effects. By optimizing the equation factors and determining the spatiotemporal weights, it improves the accuracy of zoning management of soil erosion.

[0082] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0083] This application also provides a zonal control device for soil erosion. It should be noted that the zonal control device for soil erosion in this application can be used to execute the zonal control method for soil erosion provided in this application. The zonal control device for soil erosion provided in this application is described below.

[0084] Figure 3 This is a schematic diagram of a zoned soil erosion control device 300 according to an embodiment of this application. Figure 3 As shown, the device includes: a processing unit 301, a modular calculation unit 302, a first determination unit 303, and an ecological zoning unit 304.

[0085] Specifically, the processing unit 301 is used to preprocess the multi-source data collected for landscape pattern research to obtain preprocessed data, wherein the multi-source data includes: remote sensing data, meteorological data, and soil data;

[0086] Modulus calculation unit 302 is used to calculate the equation factor of the general soil loss equation using the preprocessed data, and to derive the soil erosion modulus based on the equation factor.

[0087] The first determining unit 303 is used to determine the landscape index spatial layer of the landscape pattern, and input the landscape index spatial layer and soil erosion modulus into the spatiotemporal geographic weighted regression model, and output the local regression coefficients on each spatiotemporal unit. The local regression coefficients are used to describe the non-stationary relationship between the landscape index and soil erosion. The ecological zoning unit 304 is used to divide the governance stages based on the local regression coefficients and generate zoning schemes.

[0088] In summary, in the soil erosion zoning management device provided in this application embodiment, the processing unit 301 is used to preprocess the multi-source data collected for landscape pattern research to obtain preprocessed data, wherein the multi-source data includes: remote sensing data, meteorological data, and soil data; the modulus calculation unit 302 is used to calculate the equation factors of the general soil loss equation using the preprocessed data, and derive the soil erosion modulus based on the equation factors; the first determination unit 303 is used to determine the landscape index spatial layer of the landscape pattern, and input the landscape index spatial layer and the soil erosion modulus into the spatiotemporal geographic weighted regression model, and output the local regression coefficients on each spatiotemporal unit, wherein the local regression coefficients are used to describe the non-stationary relationship between the landscape index and soil erosion; and the ecological zoning unit 304 is used to divide the management stages based on the local regression coefficients and generate a zoning scheme.

[0089] This application addresses the problem in related technologies where insufficient analysis of the correlation between landscape pattern and soil erosion leads to inadequate regional control of soil erosion. By optimizing equation factors and determining spatiotemporal weights, the accuracy of regional control of soil erosion is improved.

[0090] Optionally, in the soil erosion zoning control device provided in the embodiments of this application, the modulus calculation unit includes: a first determining module, used to input the preprocessed data into the corresponding logical calculation formula to obtain the equation factors; and a processing module, used to multiply the equation factors to obtain the soil erosion modulus.

[0091] Optionally, in the soil erosion zoning control device provided in the embodiments of this application, the first determining unit includes: a selection module, used to select a preset number of initial candidate indices from patch level, type level, and landscape level; a screening module, used to screen the initial candidate indices through a dual screening mechanism to obtain the screened indices; and a second determining module, used to perform spatial calculation on the screened indices using the moving window method to obtain a landscape index spatial layer.

[0092] Optionally, in the soil erosion zoning control device provided in this application embodiment, before determining the landscape index spatial layer of the landscape pattern, inputting the landscape index spatial layer and the soil erosion modulus into the spatiotemporal geographic weighted regression model, and outputting the local regression coefficients on each spatiotemporal unit, the device further includes: an extraction unit, used to extract the spatial coordinates of each spatial unit in the landscape index spatial layer, and extract the time coordinates of each observation sample in the soil erosion modulus; and a matching unit, used to match the landscape spatiotemporal dataset with the soil spatiotemporal dataset one by one to obtain a spatiotemporal panel dataset, wherein the spatiotemporal panel dataset includes: spatial coordinates, time coordinates, landscape index values, and erosion modulus values.

[0093] Optionally, in the soil erosion zoning treatment device provided in this application embodiment, before dividing the treatment stages based on local regression coefficients and generating a zoning scheme, the method further includes: a distance calculation unit, used to calculate the spatiotemporal distance between corresponding sample points based on the spatial coordinates and time coordinates of the spatiotemporal panel dataset input based on Euclidean distance; and a second determination unit, used to input the spatiotemporal distance into a Gaussian decay function to obtain spatiotemporal weights.

[0094] Optionally, in the soil erosion zoning control device provided in this application embodiment, the ecological zoning unit includes: an extraction module, used to extract target sample data related to the spatiotemporal unit from the landscape index spatial layer and the soil erosion modulus by combining spatiotemporal weights and spatiotemporal panel datasets; and a third determination module, used to input the target sample data into the spatiotemporal geographic weighted regression model to obtain the local regression coefficients of the landscape index on soil erosion under each spatiotemporal unit.

[0095] Optionally, in the soil erosion zoning management device provided in this application embodiment, the ecological zoning unit includes: a fourth determining module, used to determine key time inflection points based on the spatiotemporal characteristics of local regression coefficients; and a generating module, used to divide the ecological engineering stage and the urbanization stage based on the key time inflection points, extract the landscape index control threshold for each stage, and generate a zoning management plan based on the landscape index control threshold.

[0096] The soil erosion zoning control device includes a processor and a memory, namely the aforementioned processing unit 301, modular calculation unit 302, first determination unit 303, and ecological zoning unit 304.

[0097] All of these are stored as program units in memory, and the processor executes these program units stored in memory to implement the corresponding functions.

[0098] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and soil erosion can be addressed through zonal management by adjusting kernel parameters.

[0099] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0100] In an exemplary embodiment of this application, a computer storage medium capable of implementing the above-described methods is also provided. It stores a program product capable of implementing the methods described in this specification. In some possible embodiments, various aspects of this application can also be implemented as a program product, including program code. When the program product is run on a terminal device, the program code causes the terminal device to execute the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this application. For example, the following steps can be executed: preprocessing the multi-source data collected for landscape pattern research to obtain preprocessed data, wherein the multi-source data includes: remote sensing data, meteorological data, and soil data; calculating the equation factors of the general soil loss equation using the preprocessed data, and inverting the soil erosion modulus based on the equation factors; determining the landscape index spatial layer of the landscape pattern, and inputting the landscape index spatial layer and the soil erosion modulus into a spatiotemporal geographic weighted regression model, outputting the local regression coefficients on each spatiotemporal unit, wherein the local regression coefficients are used to describe the non-stationary relationship between the landscape index and soil erosion; dividing the governance stages based on the local regression coefficients and generating a zoning scheme.

[0101] In one optional implementation: the equation factors of the general soil loss equation are calculated using the preprocessed data, and the soil erosion modulus is derived based on the equation factors. The equation factors include: rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover factor, and soil and water conservation measures factor. The preprocessed data is substituted into the corresponding logical calculation formula to obtain the equation factors; the equation factors are multiplied together to obtain the soil erosion modulus.

[0102] In one optional implementation: a preset number of initial candidate indices are selected from patch level, type level, and landscape level; the initial candidate indices are filtered through a dual screening mechanism to obtain the filtered indices; the filtered indices are spatially calculated using the moving window method to obtain a landscape index spatial layer.

[0103] In one optional implementation: extract the landscape spatiotemporal dataset for each spatial unit in the landscape index spatial layer; extract the soil spatiotemporal dataset for each observed sample in the soil erosion modulus; match the landscape spatiotemporal dataset and the soil spatiotemporal dataset for each spatial unit to obtain a spatiotemporal panel dataset, wherein the spatiotemporal panel dataset includes: spatial coordinates, time coordinates, landscape index values, and erosion modulus values.

[0104] In one alternative implementation: the spatiotemporal distance between corresponding sample points is calculated based on the spatial and temporal coordinates of the input spatiotemporal panel dataset using Euclidean distance; the spatiotemporal distance is then input into a Gaussian decay function to obtain the spatiotemporal weights.

[0105] In one alternative implementation: combining spatiotemporal weights and spatiotemporal panel datasets, target sample data related to spatiotemporal units are extracted from the landscape index spatial layer and soil erosion modulus; the target sample data are input into a spatiotemporal geographic weighted regression model to obtain the local regression coefficients of landscape index on soil erosion under each spatiotemporal unit.

[0106] In one alternative implementation: key time inflection points are determined based on the spatiotemporal characteristics of the regression coefficients; ecological engineering stages and urbanization stages are divided based on the key time inflection points, and landscape index control thresholds for each stage are extracted; and zonal governance schemes are generated based on the landscape index control thresholds.

[0107] In an optional embodiment, the present application may further include a program product for implementing the above-described method. This program product may be a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0108] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0109] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0110] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0111] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0112] Furthermore, in an exemplary embodiment of this application, an electronic device capable of implementing the above-described method is also provided.

[0113] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0114] The following reference Figure 4 To describe an electronic device 400 according to this embodiment of the present application. Figure 4 The electronic device 400 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0115] like Figure 4 As shown, the electronic device 400 is manifested in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one storage unit 420, a bus 430 connecting different system components (including storage unit 420 and processing unit 410), and a display unit 440.

[0116] The storage unit stores program code, which can be executed by the processing unit 410, causing the processing unit 410 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this application. For example, the processing unit 410 can perform the following steps: preprocessing the multi-source data collected for landscape pattern research to obtain preprocessed data, wherein the multi-source data includes: remote sensing data, meteorological data, and soil data; calculating the equation factors of the general soil loss equation using the preprocessed data, and deriving the soil erosion modulus based on the equation factors; determining the landscape index spatial layer of the landscape pattern, and inputting the landscape index spatial layer and the soil erosion modulus into a spatiotemporal geographic weighted regression model, outputting the local regression coefficients on each spatiotemporal unit, wherein the local regression coefficients are used to describe the non-stationary relationship between the landscape index and soil erosion; dividing the governance stages based on the local regression coefficients and generating a zoning scheme.

[0117] In one optional implementation: the equation factors of the general soil loss equation are calculated using the preprocessed data, and the soil erosion modulus is derived based on the equation factors. The equation factors include: rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover factor, and soil and water conservation measures factor. The preprocessed data is substituted into the corresponding logical calculation formula to obtain the equation factors; the equation factors are multiplied together to obtain the soil erosion modulus.

[0118] In one optional implementation: a preset number of initial candidate indices are selected from patch level, type level, and landscape level; the initial candidate indices are filtered through a dual screening mechanism to obtain the filtered indices; the filtered indices are spatially calculated using the moving window method to obtain a landscape index spatial layer.

[0119] In one optional implementation: extract the landscape spatiotemporal dataset for each spatial unit in the landscape index spatial layer; extract the soil spatiotemporal dataset for each observed sample in the soil erosion modulus; match the landscape spatiotemporal dataset and the soil spatiotemporal dataset for each spatial unit to obtain a spatiotemporal panel dataset, wherein the spatiotemporal panel dataset includes: spatial coordinates, time coordinates, landscape index values, and erosion modulus values.

[0120] In one alternative implementation: the spatiotemporal distance between corresponding sample points is calculated based on the spatial and temporal coordinates of the input spatiotemporal panel dataset using Euclidean distance; the spatiotemporal distance is then input into a Gaussian decay function to obtain the spatiotemporal weights.

[0121] In one alternative implementation: combining spatiotemporal weights and spatiotemporal panel datasets, target sample data related to spatiotemporal units are extracted from the landscape index spatial layer and soil erosion modulus; the target sample data are input into a spatiotemporal geographic weighted regression model to obtain the local regression coefficients of landscape index on soil erosion under each spatiotemporal unit.

[0122] In one alternative implementation: key time inflection points are determined based on the spatiotemporal characteristics of the regression coefficients; ecological engineering stages and urbanization stages are divided based on the key time inflection points, and landscape index control thresholds for each stage are extracted; and zonal governance schemes are generated based on the landscape index control thresholds.

[0123] Storage unit 420 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 4201 and / or cache memory 4202, and may further include a read-only memory (ROM) 4203.

[0124] Storage unit 420 may also include a program / utility 4204 having a set (at least one) program module 4205, such program module 4205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0125] Bus 430 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0126] Electronic device 400 can also communicate with one or more external devices 500 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 400, and / or with any device that enables electronic device 400 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 450. Furthermore, electronic device 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 460. As shown, network adapter 460 communicates with other modules of electronic device 400 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0127] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this application.

[0128] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this application, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0129] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

Claims

1. A method for zoned management of soil erosion, characterized in that, include: The collected multi-source data required for landscape pattern research are preprocessed to obtain preprocessed data, wherein the multi-source data includes: remote sensing data, meteorological data, and soil data; The equation factors of the general soil loss equation are calculated using the preprocessed data, and the soil erosion modulus is derived based on the equation factors. Determine the landscape index spatial layer of the landscape pattern, and input the landscape index spatial layer and the soil erosion modulus into the spatiotemporal geographic weighted regression model, outputting the local regression coefficients on each spatiotemporal unit, wherein the local regression coefficients are used to describe the non-stationary relationship between the landscape index and soil erosion; The governance stages are divided based on the local regression coefficients, and a partitioning scheme is generated.

2. The method according to claim 1, characterized in that, The equation factors of the general soil loss equation are calculated using the preprocessed data, and the soil erosion modulus is derived based on these equation factors. The equation factors include: rainfall erosivity factor, soil erodibility factor, slope length factor, slope gradient factor, vegetation cover factor, and soil and water conservation measure factor. The preprocessed data is then substituted into the corresponding logical calculation formula to obtain the equation factors; The soil erosion modulus is obtained by multiplying the factors in the equation.

3. The method according to claim 1, characterized in that, Determining the spatial layer of landscape indices for the aforementioned landscape pattern includes: Select a predetermined number of initial candidate indices from the patch level, type level, and landscape level; The initial candidate indices are filtered through a dual screening mechanism to obtain the filtered indices; The filtered indices are spatially calculated using the moving window method to obtain the spatial layer of the landscape index.

4. The method according to claim 1, characterized in that, Before determining the landscape index spatial layer of the landscape pattern, and inputting the landscape index spatial layer and the soil erosion modulus into the spatiotemporal weighted regression model to output the local regression coefficients on each spatiotemporal unit, the method further includes: Extract the landscape spatiotemporal dataset of each spatial unit in the landscape index spatial layer, and extract the soil spatiotemporal dataset of each observation sample in the soil erosion modulus; The landscape spatiotemporal dataset is matched one-to-one with the soil spatiotemporal dataset to obtain a spatiotemporal panel dataset, wherein the spatiotemporal panel dataset includes: spatial coordinates, time coordinates, landscape index values, and erosion modulus values.

5. The method according to claim 4, characterized in that, Before dividing the governance phases based on the local regression coefficients and generating a partitioning scheme, the method further includes: The spatiotemporal distance between corresponding sample points is calculated based on the spatial and temporal coordinates of the spatiotemporal panel dataset input using Euclidean distance. The spatiotemporal distance is input into the Gaussian decay function to obtain the spatiotemporal weight.

6. The method according to claim 5, characterized in that, Determine the spatial layer of landscape indices for the landscape pattern, and input the spatial layer of landscape indices and the soil erosion modulus into a spatiotemporal weighted regression model, outputting the local regression coefficients for each spatiotemporal unit, including: By combining the spatiotemporal weights with the spatiotemporal panel dataset, target sample data related to the spatiotemporal unit are extracted from the landscape index spatial layer and the soil erosion modulus. The target sample data is input into the spatiotemporal geographic weighted regression model to obtain the local regression coefficients of the landscape index on soil erosion under each spatiotemporal unit.

7. The method according to claim 1, characterized in that, Based on the local regression coefficients, the governance stages are divided and a partitioning scheme is generated, including: The key time inflection points are determined based on the spatiotemporal characteristics of the local regression coefficients. Based on the key time inflection points, the ecological engineering stage and the urbanization stage are divided, and the landscape index control threshold of each stage is extracted. Based on the landscape index control threshold, a zoned governance plan is generated.

8. A zoned treatment device for soil erosion, characterized in that, include: The processing unit is used to preprocess the multi-source data collected for landscape pattern research to obtain preprocessed data, wherein the multi-source data includes: remote sensing data, meteorological data, and soil data; The modulus calculation unit calculates the equation factors of the general soil loss equation based on the preprocessed data, and derives the soil erosion modulus based on the equation factors. The first determining unit is used to determine the landscape index spatial layer of the landscape pattern, and input the landscape index spatial layer and the soil erosion modulus into the spatiotemporal geographic weighted regression model, and output the local regression coefficients on each spatiotemporal unit, wherein the local regression coefficients are used to describe the non-stationary relationship between the landscape index and soil erosion. Ecological zoning units are used to divide governance stages and generate zoning schemes based on the local regression coefficients.

9. A computer-readable storage medium, characterized in that, The storage medium includes a stored program, wherein the program executes the zonal management method for soil erosion as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: One or more processors, a memory, a display device, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing the zoned treatment method for soil erosion as described in any one of claims 1 to 7.

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