Method for assessing the risk of soil erosion

Abstract

The application relates to a soil erosion risk assessment method, which comprehensively considers the influence of vegetation, land use, topography and rainfall and other factors on soil erosion by acquiring multi-source ecological data of each grid area in a target area. First, the vegetation coverage is determined according to the vegetation index, and the initial erosion risk level is determined in combination with the land use type to evaluate the basic erosion risk under different land use types and vegetation coverage conditions. Then, the risk correction parameters are determined by using the terrain slope and rainfall data to correct the initial erosion risk level, and the comprehensive erosion risk level is obtained to more accurately reflect the actual erosion risk. Then, the sensitive coefficient is introduced, the management priority index is calculated in combination with the comprehensive erosion risk level, and the erosion sensitivity difference of different land types is fully considered. Finally, the management priority of each grid area is determined according to the management priority index, and a scientific and reasonable decision basis is provided for soil erosion management.

Description

Technical Field

[0001] This application relates to the field of ecological and environmental protection technology, and in particular to a method for assessing soil erosion risk. Background Technology

[0002] Soil erosion, a key indicator of ecological degradation, poses a serious threat to land resource security, agricultural production stability, and ecosystem balance. To ensure sustainable ecological development, conducting soil erosion risk assessments and prioritizing remediation efforts is crucial. However, significant shortcomings remain in the current fields of soil erosion risk assessment and remediation prioritization.

[0003] Existing assessment methods mostly focus on calculating single erosion risks, failing to adequately consider the inherent differences between land use types. Different land use types exhibit varying soil characteristics, vegetation cover, and resistance to erosion. Using only a single erosion risk calculation method results in assessments lacking specificity and failing to accurately reflect the actual soil erosion risk under different land use types. Furthermore, some schemes rely solely on fixed thresholds when determining risk, neglecting to adequately consider the differences in erosion sensitivity among different land types. These problems lead to a severe disconnect between assessment results and actual governance needs, failing to provide an effective basis for scientific and rational governance decisions. Summary of the Invention

[0004] Based on this, the purpose of this application is to provide a soil erosion risk assessment method that can accurately assess soil erosion risk and reasonably classify remediation priorities.

[0005] The soil erosion risk assessment method described in this application includes the following steps:

[0006] Acquire multi-source ecological data for each grid area within the target area; the multi-source ecological data includes vegetation index, land use type, terrain slope, and rainfall data; For any of the grid areas, the corresponding vegetation cover is determined based on the vegetation index; the corresponding initial erosion risk level is determined based on the land use type and the vegetation cover. Based on the terrain slope and rainfall data of the grid area, the corresponding risk correction parameters are determined; the initial erosion risk level is corrected based on the risk correction parameters to obtain the corresponding comprehensive erosion risk level; Obtain the sensitivity coefficient of the grid area; based on the comprehensive erosion risk level and the sensitivity coefficient, obtain the treatment priority index of the grid area; based on the treatment priority index of each grid area in the target area, determine the treatment priority corresponding to each grid area.

[0007] This application first acquires multi-source ecological data, including vegetation index, land use type, topographic slope, and rainfall, for each grid area within the target region. Based on the vegetation index, the vegetation cover of the corresponding grid area is determined, and the initial erosion risk level is determined in conjunction with the land use type. This step comprehensively considers land use differences and the fundamental impact of vegetation on soil erosion, making the initial assessment results more consistent with the actual conditions of different regions. Next, risk correction parameters are determined using topographic slope and rainfall data to correct the initial erosion risk level, resulting in a comprehensive erosion risk level. Topographic slope and rainfall are key natural factors affecting soil erosion. This correction process dynamically reflects the impact of changes in natural conditions on erosion risk, avoiding the problem of assessment results not matching reality due to relying on a single factor or fixed threshold, thus improving the accuracy of the comprehensive erosion risk level assessment. Then, the sensitivity coefficient of the grid area is obtained, and a governance priority index is calculated in conjunction with the comprehensive erosion risk level. The introduction of the sensitivity coefficient fully considers the differences in erosion sensitivity among different land types, enabling the governance priority index to more accurately reflect the urgency and importance of governance in each region. Finally, the governance priority of each grid area is determined based on the governance priority index. This application provides a complete and accurate system for assessing soil erosion risk and prioritizing remediation efforts. This system provides a scientific, reasonable, and highly targeted basis for decision-making in soil erosion remediation work, which helps improve remediation efficiency and ensure the sustainable development of the ecological environment.

[0008] To better understand and implement this application, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating the soil erosion risk assessment method according to an embodiment of this application. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings. Wherein, when the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements.

[0011] It should be understood that the embodiments described below do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application.

[0012] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application are also intended to include the plural forms unless the context clearly indicates otherwise. Furthermore, in the description of this application, unless otherwise stated, “a plurality” means two or more. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items, for example, A and / or B, which can represent: A alone, A and B together, and B alone; the character “ / ” generally indicates that the preceding and following objects are in an “or” relationship.

[0013] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, this information should not be limited to these terms, and these terms are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. Depending on the context, the word "if" as used in this application can be interpreted as "when," "when," or "in response to determination."

[0014] Please refer to Figure 1 The soil erosion risk assessment method described in this application includes the following steps: S101: Acquire multi-source ecological data for each grid area within the target area; the multi-source ecological data includes vegetation index, land use type, terrain slope, and rainfall data; S102: For any of the grid areas, determine the corresponding vegetation coverage based on the vegetation index; determine the corresponding initial erosion risk level based on the land use type and the vegetation coverage. S103: Determine the corresponding risk correction parameters based on the terrain slope and rainfall data of the grid area; correct the initial erosion risk level based on the risk correction parameters to obtain the corresponding comprehensive erosion risk level; S104: Obtain the sensitivity coefficient of the grid area; obtain the treatment priority index of the grid area based on the comprehensive erosion risk level and the sensitivity coefficient; determine the treatment priority corresponding to each grid area based on the treatment priority index of each grid area in the target area.

[0015] This application first acquires multi-source ecological data, including vegetation index, land use type, topographic slope, and rainfall, for each grid area within the target region. Based on the vegetation index, the vegetation cover of the corresponding grid area is determined, and the initial erosion risk level is determined in conjunction with the land use type. This step comprehensively considers land use differences and the fundamental impact of vegetation on soil erosion, making the initial assessment results more consistent with the actual conditions of different regions. Next, risk correction parameters are determined using topographic slope and rainfall data to correct the initial erosion risk level, resulting in a comprehensive erosion risk level. Topographic slope and rainfall are key natural factors affecting soil erosion. This correction process dynamically reflects the impact of changes in natural conditions on erosion risk, avoiding the problem of assessment results not matching reality due to relying on a single factor or fixed threshold, thus improving the accuracy of the comprehensive erosion risk level assessment. Then, the sensitivity coefficient of the grid area is obtained, and a governance priority index is calculated in conjunction with the comprehensive erosion risk level. The introduction of the sensitivity coefficient fully considers the differences in erosion sensitivity among different land types, enabling the governance priority index to more accurately reflect the urgency and importance of governance in each region. Finally, the governance priority of each grid area is determined based on the governance priority index. This application provides a complete and accurate system for assessing soil erosion risk and prioritizing remediation efforts. This system provides a scientific, reasonable, and highly targeted basis for decision-making in soil erosion remediation work, which helps improve remediation efficiency and ensure the sustainable development of the ecological environment.

[0016] The soil erosion risk assessment method described in this application uses computer equipment as the execution subject, and the following describes each step.

[0017] For step S101, multi-source ecological data of each grid area within the target area is obtained; the multi-source ecological data includes vegetation index, land use type, terrain slope and rainfall data.

[0018] The target area refers to the entire geographical research scope for which soil erosion-related monitoring, analysis, and evaluation will be carried out; it is a defined continuous geographic spatial unit. Specifically, it can be a specific area such as an administrative jurisdiction, watershed area, or ecological control zone, and serves as the unified research boundary for all raster data collection, index calculation, and classification in this method.

[0019] A raster region is a basic geographic computing unit that discretizes a target area according to a uniform spatial resolution, serving as the smallest carrier for spatial data processing. Raster regions have a regular grid shape, mostly square grids. Various types of ecological data, distance measurements, coefficient calculations, and risk assessments are all performed using individual raster regions as independent computational objects. The rasterization of the target area in this application's embodiments enables more detailed and accurate management and analysis of various ecological information within the area, facilitating both local and overall assessments of soil erosion.

[0020] Vegetation indices are a set of quantitative indicators obtained through remote sensing band calculations, used to quantify the growth status and coverage of surface vegetation. They generally include commonly used remote sensing indicators such as the Normalized Difference Vegetation Index (NDVI), and are obtained through inversion of multi-source remote sensing data. They are the core basic data for inverting vegetation coverage and determining erosion risk.

[0021] Land use type is a land category characterization index that classifies and categorizes the development, utilization, and cover attributes of land surface. It covers various land use categories, including human use and natural cover. In the embodiments of this application, land use types include at least cultivated land, forest land, grassland, bare land, and construction land. Different land use types correspond to differentiated erosion assessment rules and sensitivity coefficient assignment standards.

[0022] Topographic slope, a quantitative topographic parameter characterizing the inclination of a land surface, is a fundamental geographical factor reflecting the topographic undulation characteristics of a region. Topographic slope is a significant factor influencing soil erosion; a steeper slope results in faster water flow, stronger erosion force on the soil, and a higher risk of soil erosion. Topographic slope can be calculated based on digital elevation data, with different slope ranges corresponding to varying degrees of erosion impact. Specifically, in one embodiment, according to preset grading rules, corresponding topographic factor risk correction parameters are matched to correct for initial erosion risk.

[0023] Rainfall data refers to the amount of rainwater that falls to the ground within a certain period of time, and is an important indicator reflecting rainfall conditions. The amount and distribution of rainfall directly affect rainfall erosivity, and thus the degree of soil erosion. This application's embodiments record time-series meteorological observation data on regional precipitation changes. Specifically, in one embodiment, the rainfall data includes daily measured rainfall data for the target area throughout the year. Semi-monthly rainfall erosivity can be statistically generated, and the annual rainfall erosivity can be accumulated to determine the rainfall erosivity factor risk correction parameter, thereby achieving a secondary correction of erosion risk.

[0024] This step forms the foundation of the entire soil erosion risk assessment. Data on vegetation indices, land use types, topographic slope, and rainfall are acquired for each grid area within the target region using various methods, including satellite remote sensing and ground observations. This multi-source ecological data covers several key factors influencing soil erosion, providing comprehensive and accurate information for subsequent assessments. For example, vegetation indices reflect vegetation growth, land use types reflect the impact of human activities on the land, and topographic slope and rainfall data are two of the most influential natural factors on soil erosion.

[0025] For step S102, for any of the grid areas, the corresponding vegetation coverage is determined according to the vegetation index; the corresponding initial erosion risk level is determined according to the land use type and the vegetation coverage.

[0026] The initial erosion risk level is a preliminary assessment of the degree of soil erosion risk based on land use type and vegetation cover. Different combinations of land use types (such as forests, farmland, and construction land) and vegetation cover correspond to different initial erosion risk levels, used to preliminarily assess the likelihood of soil erosion. This application's embodiments combine differentiated judgment criteria for different land use types with corresponding vegetation cover thresholds to classify different grid areas to their corresponding initial erosion risk levels, providing a basic classification basis for subsequent multi-factor correction.

[0027] This step calculates the vegetation cover of the grid area based on the obtained vegetation index. Vegetation cover refers to the percentage of the vertical projection area of ​​vegetation on the ground to the total area of ​​the statistical area. It reflects the degree of protection that vegetation provides to the soil; the higher the vegetation cover, the lower the likelihood of soil erosion. Then, combining the land use type of the grid area with the calculated vegetation cover, the corresponding initial erosion risk level is determined using pre-set assessment criteria or models. Different combinations of land use type and vegetation cover correspond to different initial erosion risk levels. For example, forests with high vegetation cover have a lower initial erosion risk level, while bare land or farmland with low vegetation cover has a higher initial erosion risk level.

[0028] In one embodiment, step S102, which involves determining the corresponding vegetation cover based on the vegetation index, includes: Step S1021: The lower percentile value of the vegetation index in the target area is determined as the vegetation index value of the pure bare soil grid area; the higher percentile value of the vegetation index in the target area is determined as the vegetation index value of the pure vegetation grid area.

[0029] Among them, the bare soil grid area is a grid area within the target area that has no vegetation cover at all.

[0030] A pure vegetation grid area is a grid area within the target area that is completely covered by vegetation.

[0031] Within the target area, vegetation indices exhibit diverse numerical distributions. Lower percentile values ​​indicate that a small subset of raster areas within this area have low vegetation indices, and these areas are highly likely to be bare soil regions. Therefore, the lower percentile values ​​of the vegetation indices within the target area are used as the vegetation index values ​​for the bare soil raster areas. In one embodiment, the 5% lower percentile values—that is, the 5% of raster areas with the lowest vegetation indices within the target area—are selected, and their average or specific vegetation index values ​​are taken as the vegetation index values ​​for the bare soil raster areas.

[0032] Similarly, high percentile values ​​indicate that a small portion of the raster areas within the target region have a high vegetation index. These areas are typically vegetated areas. Therefore, the high percentile values ​​of the vegetation index within the target region are used as the vegetation index values ​​for vegetated raster areas. In one embodiment, the 95th percentile values, i.e., the top 5% of raster areas with the highest vegetation index within the target region, are selected, and their average or specific vegetation index values ​​are taken as the vegetation index values ​​for vegetated raster areas. This method can more accurately define the vegetation index characteristics of bare soil and vegetated areas.

[0033] Step S1022: Determine the initial vegetation coverage of the grid area based on the vegetation index of the grid area, the vegetation index of the bare soil grid area, and the vegetation index of the pure vegetation grid area.

[0034] The initial vegetation cover is a preliminary calculation based on the vegetation index of the grid area and the vegetation index values ​​of the pure bare soil and pure vegetation grid areas.

[0035] During soil erosion, vegetation cover significantly reduces the intensity of hydraulic erosion by intercepting rainfall, buffering raindrop splash, slowing surface runoff velocity, increasing infiltration, and retaining topsoil particles. It is a key parameter in soil erosion assessment for measuring the protective effect of vegetation; higher cover indicates stronger soil protection. This method further estimates vegetation cover based on the Normalized Difference Vegetation Index (NDVI), calculated using the following formula:

[0036] Where FVC represents vegetation cover; NDVIsoil is the NDVI value of the bare soil grid area ( ); NDVIveg represents the NDVI value of a purely vegetated raster region ( ); Indicates the target area The value of the first percentile.

[0037] Step S1023: When the initial vegetation coverage meets the preset normal value range, the initial vegetation coverage is determined as the final vegetation coverage; when the initial vegetation coverage does not meet the preset normal value range, the initial vegetation coverage is constrained to the preset normal value range to obtain the final vegetation coverage.

[0038] The preset normal value range is a pre-defined reasonable range of vegetation coverage values ​​that conforms to the actual situation, used to verify and correct the initial vegetation coverage.

[0039] In this step, the preset normal value range is a reasonable interval set based on actual geographical conditions and vegetation growth patterns, for example, 0%-100%. If the calculated initial vegetation cover falls within this range, the calculation result matches the actual situation, and the initial vegetation cover is directly determined as the final vegetation cover. If the initial vegetation cover is not within the preset normal value range, it may be due to data errors, special geographical environments, or other factors. In this case, it is necessary to constrain the initial vegetation cover to the preset normal value range. Refer to the following constraint formula for details:

[0040] If the initial vegetation coverage is greater than 100%, it is corrected to 100%; if the initial vegetation coverage is less than 0%, it is corrected to 0%, thus obtaining the final vegetation coverage and ensuring the rationality and accuracy of the vegetation coverage data.

[0041] This embodiment accurately defines the vegetation index values ​​for pure bare soil and pure vegetated areas using a scientific and reasonable method. The initial vegetation cover is calculated using linear interpolation, which accurately reflects the actual vegetation cover situation in the grid area. Simultaneously, the initial vegetation cover is verified and corrected by pre-setting a normal value range, effectively avoiding outliers caused by data errors or special circumstances, thus ensuring the accuracy and rationality of the final vegetation cover data.

[0042] In one embodiment, step S102, which determines the corresponding initial erosion risk level based on the land use type and the vegetation cover, includes: Step S1024: Determine the corresponding erosion risk level assessment system based on the land use type; wherein, different land use types correspond to different erosion risk level assessment systems. An erosion risk rating system is a set of rules used to assess and classify the degree of soil erosion risk. Different land use types have different mechanisms of impact on soil erosion, therefore, the erosion risk rating systems also differ.

[0043] Step S1025: Based on the erosion risk level assessment system and the vegetation coverage, determine the initial erosion risk level of the grid area.

[0044] After establishing an erosion risk assessment system for a specific land use type, vegetation cover data for each grid area within the region is incorporated into the corresponding assessment system. The system then comprehensively evaluates the vegetation cover according to predetermined criteria. For example, the system might specify that when vegetation cover falls within a certain range, it corresponds to a specific erosion risk score, which is used to determine the initial erosion risk level for that grid area. This approach fully considers the combined impact of land use type and vegetation cover on soil erosion, leading to a more accurate assessment of the initial erosion risk level.

[0045] In this embodiment, the land use types participating in the evaluation are defined as five categories: cultivated land, forest land (including forests and shrubs), grassland, bare land, and construction land. Let Lu be the land use type corresponding to each grid area within the target area, then its set of values ​​is... .

[0046] Because different land use types exhibit significant differences in surface roughness, root system soil-fixing capacity, and runoff conditions, this application does not adopt a unified FVC grading standard. Instead, it constructs an initial erosion risk level based on vegetation cover and land use type zoning. The risk level is divided into five levels (1-5), representing extremely low risk, low risk, medium risk, high risk and extremely high risk, respectively. The land use type Lu and its vegetation cover FVC of the raster area are jointly determined, and their functional relationship is expressed as follows: For different Lu values, this application designs differentiated FVC threshold determination rules, as follows:

[0047]

[0048]

[0049]

[0050]

[0051] This application's embodiments determine the corresponding erosion risk level assessment system based on land use type, fully considering the differences in the impact of different land use types on soil erosion, making the assessment system more targeted and accurate. Different land use types have different surface characteristics and human activity patterns, and the mechanisms by which these factors affect soil erosion are complex and diverse. A specially designed assessment system can more accurately capture these influences and avoid the assessment bias that may be caused by using a single assessment system. At the same time, the initial erosion risk level of the grid area is determined by combining the important factor of vegetation cover. Vegetation cover directly reflects the degree of protection of the soil by surface vegetation and is one of the key factors affecting soil erosion. The organic combination of land use type and vegetation cover in the assessment can comprehensively consider multiple important aspects affecting soil erosion, thereby more realistically and accurately reflecting the possibility of soil erosion in different grid areas within the target area. This provides a reliable foundation for further analysis of soil erosion status and the formulation of targeted remediation measures, helping to improve the scientificity and effectiveness of soil erosion control work and better protect land resources and the ecological environment.

[0052] For step S103, the corresponding risk correction parameters are determined based on the terrain slope and rainfall data of the grid area; the initial erosion risk level is corrected based on the risk correction parameters to obtain the corresponding comprehensive erosion risk level.

[0053] Risk correction parameters are quantitative correction indices used to characterize the impact of natural environmental factors on soil erosion intensity and are used to calibrate the basic risk level. In the embodiments of this application, they mainly include topographic factor risk correction parameters and rainfall erosivity factor risk correction parameters, which correspond to the risk adjustment effects brought about by topographic conditions and rainfall erosion, respectively.

[0054] In this step, risk correction parameters are determined based on the acquired terrain slope and rainfall data. Generally, the steeper the terrain slope and the greater the rainfall, the higher the value of the risk correction parameter, indicating a higher risk of soil erosion. The determined risk correction parameters are then used to correct the initial erosion risk level. While the initial erosion risk level only considers land use type and vegetation cover, the risk correction parameters comprehensively consider the impact of terrain slope and rainfall on soil erosion, resulting in a more accurate overall erosion risk level that reflects the actual soil erosion risk.

[0055] In one embodiment, the risk correction parameters include topographic factor risk correction parameters and rainfall erosivity factor risk correction parameters; Step S103, which involves determining the corresponding risk correction parameters based on the terrain slope and rainfall data of the grid area, includes: Step S1031: Determine the corresponding terrain factor risk correction parameters based on the terrain slope of the grid area and the preset slope risk classification rules.

[0056] Slope risk grading rules are pre-defined rules that classify different risk levels based on the magnitude of the terrain slope. By dividing the terrain slope into different intervals and assigning a corresponding risk level and correction parameter value to each interval, the terrain factor risk correction parameters can be determined based on the actual terrain slope.

[0057] This step follows a pre-defined slope risk classification rule, which divides the terrain slope into several distinct intervals. For each interval, a corresponding terrain factor risk correction parameter value is pre-set. These values ​​are determined based on extensive research and practical experience, reflecting the degree of influence of different slope intervals on soil erosion risk. Finally, the terrain slope of each grid area is compared with the slope risk classification rule to determine its corresponding slope interval, and thus the corresponding terrain factor risk correction parameter is determined. In one embodiment, this step determines the terrain factor risk correction parameter based on the following slope risk classification rule. :

[0058] in, The slope angle is in degrees.

[0059] Step S1032: Determine the annual rainfall erosivity of the grid area based on the rainfall data of the grid area; determine the corresponding rainfall erosivity factor risk correction parameter based on the annual rainfall erosivity of the grid area and the preset rainfall erosivity risk classification rule.

[0060] Annual rainfall erosivity is an indicator that measures the potential of rainfall to erode soil. It takes into account factors such as rainfall intensity and duration, and reflects the overall effect of rainfall on soil erosion throughout the year.

[0061] Rainfall erosivity risk grading rules are pre-defined rules that classify different risk levels based on the magnitude of annual rainfall erosivity. Annual rainfall erosivity is divided into different intervals, and a corresponding risk level and correction parameter value are determined for each interval. These are used to determine the risk correction parameters for rainfall erosivity factors based on the actual annual rainfall erosivity.

[0062] This step is based on a pre-defined rainfall erosivity risk classification rule, which divides annual rainfall erosivity into different intervals. Each interval corresponds to a pre-set risk correction parameter value for the rainfall erosivity factor, reflecting the differences in the impact of different annual rainfall erosivity levels on soil erosion risk. Finally, the annual rainfall erosivity of each grid area is compared with the rainfall erosivity risk classification rule to determine its interval, and thus the corresponding rainfall erosivity factor risk correction parameter is determined. In one embodiment, this step determines the rainfall erosivity factor risk correction parameter based on the following rainfall erosivity risk classification rule. :

[0063] in, and These are the threshold values ​​for mild and severe erosivity based on the multi-year average rainfall erosivity of the target area, respectively. .

[0064] This embodiment comprehensively considers topography and rainfall, two factors that significantly influence soil erosion, by separately determining risk correction parameters for topographic factors and rainfall erosivity factors. Topographic slope directly affects water flow velocity and scouring force; the risk of soil erosion varies significantly under different slopes. The topographic factor risk correction parameters determined based on topographic slope accurately reflect the degree of influence of topography on erosion risk, making the assessment results more consistent with actual topographic characteristics. Rainfall is the driving force of soil erosion, and annual rainfall erosivity comprehensively reflects the potential capacity of rainfall to erode soil. By calculating annual rainfall erosivity using rainfall data and determining the corresponding rainfall erosivity factor risk correction parameters, the role of rainfall is fully considered. Applying these two risk correction parameters to subsequent erosion risk assessments can effectively correct the initial assessment results, improving the accuracy and reliability of soil erosion risk assessments.

[0065] In one embodiment, the rainfall data includes daily rainfall data throughout the year; Step S1032, determining the annual rainfall erosivity of the grid area based on the rainfall data of the grid area, includes: Step S10321: Calculate the rainfall erosion force for each half-month period of the year based on the rainfall data of the grid area.

[0066] A bi-monthly period divides the year into several consecutive half-month periods to allow for a more detailed analysis of the impact of rainfall on soil erosion at different time scales. Because the distribution and characteristics of rainfall vary significantly across seasons and months, using a bi-monthly period allows for a more accurate capture of the dynamic changes in rainfall erosivity. This step uses a bi-monthly rainfall erosivity model to calculate the rainfall erosivity for each bi-monthly period throughout the year.

[0067] This step uses a bi-weekly rainfall erosivity model for calculation. This model reflects the seasonal distribution of erosivity over a bi-weekly period, with rainfall and erosivity exhibiting a power function relationship. Annual rainfall erosivity is the cumulative value of bi-weekly rainfall erosivity, as shown in the following formula:

[0068]

[0069] in, Erosion force of rainfall in the j-th half-month (MJ) mm hm-2 h-1 yr-1); Let be the daily rainfall on day d of year i that is greater than or equal to 12 mm. If Rainfall exceeding the threshold of 12 mm is considered corrosive rainfall; otherwise, Equals 0; m represents the number of days in a half-month that experience erosive precipitation; and is the regression coefficient; Pd10 is the multi-year average (mm) of daily rainfall greater than or equal to 12 mm; Py10 is the multi-year average (mm) of the total annual daily rainfall greater than or equal to 12 mm.

[0070] Step S10322: The rainfall erosion force of each half-month period throughout the year is accumulated to obtain the annual rainfall erosion force of the grid area.

[0071] The calculation formula for this step is as follows:

[0072] in, Annual rainfall erosivity (MJ) mm hm-2 h-1 yr-1); It is the sequence number of the half-month of the year (there are 24 half-months in a year).

[0073] This embodiment utilizes daily rainfall data throughout the year to first calculate the rainfall erosivity for each half-month period, and then sums them to obtain the annual rainfall erosivity. On one hand, calculating in half-month periods allows for a more detailed capture of the characteristics and variations of rainfall across different time periods, fully considering the impact of seasonal differences and short-term fluctuations in rainfall on soil erosion. Rainfall intensity, frequency, and duration vary significantly across different seasons; dividing the time into half-month periods more accurately reflects these changes, making the calculated rainfall erosivity more consistent with reality. On the other hand, by summing the rainfall erosivity for each half-month period to obtain the annual rainfall erosivity, the cumulative effect of rainfall throughout the year is comprehensively considered, avoiding errors caused by considering only partial periods or using simple averaging methods. The annual rainfall erosivity calculated in this way can more comprehensively and accurately measure the potential capacity of rainfall for soil erosion, providing reliable data support for determining risk correction parameters for rainfall erosivity factors based on rainfall erosivity.

[0074] In one embodiment, step S103, which involves correcting the initial erosion risk level based on the risk correction parameter to obtain the corresponding comprehensive erosion risk level, includes: Step S1033: The initial erosion risk level is corrected by assigning the topographic factor risk correction parameter to obtain the first erosion risk level; the first erosion risk level is corrected by assigning the rainfall erosivity factor risk correction parameter to obtain the second erosion risk level.

[0075] The initial erosion risk level is typically represented by a numerical value or a scale, such as a number from 1 to 5 (1 being the lowest and 5 the highest). The topographic risk correction parameter is also a numerical value, which is added to the value corresponding to the initial erosion risk level. For example, if the initial erosion risk level is 3 (medium risk) and the topographic risk correction parameter is 0.5, then the value corresponding to the first erosion risk level is 3 + 0.5 = 3.5. After obtaining the first erosion risk level, the rainfall erosivity factor risk correction parameter is also assigned to adjust the value corresponding to the first erosion risk level. Assuming the rainfall erosivity factor risk correction parameter is 0.3 and the value corresponding to the first erosion risk level is 3.5, then the value corresponding to the second erosion risk level is 3.5 + 0.3 = 3.8.

[0076] The terrain factor risk correction parameter in this step Through formula (ERI1 maximum value is 5) Corrects the initial erosion risk level. Rainfall erosivity factor risk correction parameter. Through formula Revise the first erosion risk level.

[0077] Step S1034: When the second erosion risk level meets the preset risk level range constraint, the second erosion risk level is determined as the comprehensive erosion risk level; when the second erosion risk level does not meet the preset risk level range constraint, the second erosion risk level is constrained to the preset risk level range to obtain the comprehensive erosion risk level.

[0078] The preset risk level range constraint is a pre-defined, reasonable range of soil erosion risk levels used to constrain and adjust the second erosion risk level after two revisions, ensuring that the final comprehensive erosion risk level falls within a range that conforms to the actual situation and assessment requirements. In one embodiment, considering the upper limit of the risk level setting, a boundary constraint mechanism needs to be introduced: This ensures that the final erosion risk level is within the closed range of 1-5.

[0079] In one example, the preset risk level range is 1-5 (corresponding to low to high risk). If the value corresponding to the second erosion risk level is 3.2, falling within the 1-5 range, then this second erosion risk level is directly determined as the comprehensive erosion risk level. If the second erosion risk level exceeds the preset risk level range, for example, if the calculated value of the second erosion risk level is 6, exceeding the 1-5 range, then it needs to be constrained to the preset range. There are several methods for constraint, a common one being the truncation method: if the value is greater than the upper limit of the range, it is set as the upper limit value; if the value is less than the lower limit of the range, it is set as the lower limit value. In this example, 6 is constrained to 5, resulting in a comprehensive erosion risk level value of 5. This constraint ensures that the final comprehensive erosion risk level is within a reasonable and controllable range, facilitating subsequent analysis, decision-making, and management.

[0080] This embodiment modifies the initial erosion risk level step by step by assigning and correcting the risk correction parameters for topographic factors and rainfall erosivity factors. This allows for a comprehensive and detailed consideration of topography and rainfall, two factors that significantly influence soil erosion, ensuring that the overall erosion risk level more accurately reflects the actual situation. Simultaneously, a preset risk level range constraint mechanism is introduced to prevent the risk level from exceeding a reasonable range due to parameter adjustments, thus guaranteeing the reliability and practicality of the assessment results.

[0081] For step S104, obtain the sensitivity coefficient of the grid area; obtain the treatment priority index of the grid area based on the comprehensive erosion risk level and the sensitivity coefficient; determine the treatment priority of each grid area based on the treatment priority index of each grid area in the target area.

[0082] Sensitivity coefficients are quantitative evaluation parameters used to characterize the inherent attributes and spatial location importance of a land parcel, and are used to represent the urgency of regional governance. They comprehensively consider factors such as soil properties, ecological functions, and socio-economic value of the land parcel, reflecting the urgency of regional governance; a higher sensitivity coefficient indicates that the area requires priority for governance. In the embodiments of this application, the sensitivity coefficients mainly include land use sensitivity coefficients and distance sensitivity coefficients, which respectively reflect the inherent erosion sensitivity of land types and the locational correlation influence between the grid area and surrounding sensitive targets.

[0083] The governance priority index is a comprehensive quantitative index calculated by integrating multiple dimensions of indicators such as erosion risk and sensitivity attributes. It is used to quantitatively distinguish the order of governance. Specifically, it is obtained by weighted summation of comprehensive erosion risk level and sensitivity coefficient. The index value directly corresponds to the priority of soil erosion governance in different grid areas.

[0084] This step obtains the sensitivity coefficient of the grid area. The sensitivity coefficient comprehensively considers the inherent attributes of the land parcel and its spatial location importance, reflecting the urgency of regional governance. Based on the comprehensive erosion risk level and the sensitivity coefficient, a governance priority index is obtained. This index combines soil erosion risk with the urgency of regional governance, providing a more comprehensive reflection of the priority of governance for the grid area. Finally, based on the governance priority index of each grid area within the target area, the corresponding governance priority for each grid area is determined. Grid areas with higher governance priority indices are prioritized for governance, thus ensuring the rational allocation of governance resources and improving governance efficiency.

[0085] In one embodiment, the sensitivity coefficient includes a land use sensitivity coefficient and a distance sensitivity coefficient; the land use sensitivity coefficient is determined based on the land use type of the grid area and a preset sensitivity level assignment rule; the distance sensitivity coefficient is determined based on the distance from the grid area to sensitive targets within the target area.

[0086] The land use sensitivity coefficient is determined based on the land use type of the grid area according to a preset sensitivity level assignment rule. Different land use types have different sensitivities to soil erosion, and this coefficient reflects the impact of this difference on the priority of governance. For example, forest land may have a stronger resistance to soil erosion and a lower sensitivity coefficient, while bare land or construction land may be more prone to soil erosion and have a higher sensitivity coefficient.

[0087] The distance sensitivity coefficient is determined based on the distance from the grid area to sensitive targets within the target area. Sensitive targets are areas or facilities that would suffer severe consequences if affected by environmental problems such as soil erosion, such as water sources, residential areas, and ecological protection zones. The closer a grid area is to a sensitive target, the greater the potential harm to the sensitive target should soil erosion occur; therefore, a higher distance sensitivity coefficient indicates a higher priority for remediation. In one embodiment, sensitive targets include core water systems, water sources, densely populated residential areas, important transportation corridors, and ecological protection boundaries.

[0088] In one embodiment, the land use sensitivity coefficient is as follows: ; Distance sensitivity coefficient:

[0089] Where d is the Euclidean distance from the grid area to the nearest sensitive target; dmin is the minimum distance from all effective grid areas within the target area to the core water system; and dmax is the maximum distance from all effective grid areas within the target area to the core water system.

[0090] Step S104, which involves obtaining the governance priority index of the grid area based on the comprehensive erosion risk level and the sensitivity coefficient, includes: Step S1041: The comprehensive erosion risk level, the land use sensitivity coefficient, and the distance sensitivity coefficient are weighted and summed according to preset weights to obtain the governance priority index of the grid area.

[0091] The governance priority index is a comprehensive indicator that is calculated by combining the comprehensive erosion risk level and sensitivity coefficient. It is used to determine the priority order of grid areas in soil erosion control work. The higher the index, the higher the governance priority.

[0092] In this step, based on the Comprehensive Erosion Risk Index (ERI), a Governance Priority Index (GPI) is further constructed, which integrates the comprehensive erosion risk index, land use sensitivity coefficient, and distance sensitivity coefficient with weights. The preset weights are set according to the degree of influence of each factor on the priority of soil erosion control. The setting of weights needs to comprehensively consider multiple factors and be verified and adjusted through actual data to ensure its rationality and scientific validity. The formula for calculating the grid area GPI is as follows:

[0093] in, As a governance priority index; Based on the overall erosion risk level; Land use sensitivity coefficient; This is the distance sensitivity coefficient; Weighting coefficients: .

[0094] In one embodiment, .

[0095] This embodiment calculates the governance priority index by weighting and summing the comprehensive erosion risk level, land use sensitivity coefficient, and distance sensitivity coefficient according to preset weights. This comprehensively considers multiple key factors, including soil erosion risk itself, land use type, and distance from sensitive targets. It can more accurately reflect the importance and urgency of each grid area in soil erosion control, avoiding assessment bias caused by considering only a single factor. Simultaneously, the governance priority index provides a quantitative basis for decision-making in soil erosion control work. Based on the magnitude of the governance priority index, the governance sequence of each grid area can be rationally determined.

[0096] In one embodiment, the distance sensitivity coefficient in step S104 is determined through the following steps: Step S1042: Determine the sensitive targets within the target area.

[0097] Step S1043: Obtain the Euclidean distance from each of the grid regions to their respective nearest sensitive target.

[0098] Step S1044: Determine the minimum Euclidean distance and the maximum Euclidean distance of the entire area based on the Euclidean distances corresponding to all grid areas within the target area.

[0099] The minimum Euclidean distance for the entire area is the minimum Euclidean distance among all grid areas within the target area to their nearest sensitive target, representing the closest possible distance between a grid area and a sensitive target within the entire target area.

[0100] The maximum Euclidean distance across the entire area is the maximum value among all grid areas within the target area to their nearest sensitive target, representing the farthest distance between a grid area and a sensitive target within the entire target area.

[0101] Step S1045: Normalize the calculation based on the Euclidean distance corresponding to each grid region, the minimum Euclidean distance of the entire region, and the maximum Euclidean distance of the entire region to obtain the distance sensitivity coefficient of the corresponding grid region.

[0102] Normalization is a mathematical transformation that maps data of different dimensions to a specific interval (usually [0,1]) to facilitate comparison and analysis between different data. In this embodiment, normalization converts Euclidean distance into a distance sensitivity coefficient, allowing the distance sensitivity coefficient to intuitively reflect the relative distance between the grid area and the sensitive target.

[0103] This embodiment identifies sensitive targets within the target area, accurately calculates the Euclidean distance from each grid area to the nearest sensitive target, further determines the minimum and maximum Euclidean distances for the entire area, and finally performs normalization calculations to obtain the distance sensitivity coefficient. This series of steps ensures that the distance sensitivity coefficient scientifically and accurately reflects the relative distance between each grid area and the sensitive target. In subsequent soil erosion control work, when calculating the control priority index by combining the comprehensive erosion risk level and the land use sensitivity coefficient, the distance sensitivity coefficient can fully consider the degree of impact of soil erosion on sensitive targets. The closer a grid area is to a sensitive target, the higher its distance sensitivity coefficient, and the greater its weight in the control priority index, thus placing it higher in the control sequence. This embodiment can rationally allocate control resources, prioritize the control of areas that pose a greater threat to sensitive targets, effectively reduce the adverse impact of soil erosion on sensitive targets such as core water systems, water sources, densely populated areas, important transportation corridors, and ecological protection boundaries, improve the targeting and effectiveness of soil erosion control, and ensure the ecological environment security and sustainable socio-economic development of the region.

[0104] In one embodiment, step S104, which determines the governance priority of each grid region based on the governance priority index of each grid region within the target area, includes: Step S1046: Determine several index intervals based on the distribution of governance priority indices of all grid areas within the target area.

[0105] The index range is a range of values ​​divided according to the distribution of the governance priority index of all grid areas in the target area. Each range corresponds to a different governance priority. By classifying the governance priority index into the corresponding range, the governance priority of each grid area can be clearly defined.

[0106] This step determines several index intervals according to certain rules. For example, if the governance priority index is relatively evenly distributed, it can be divided into several equal intervals using an equally spaced approach; if the data exhibits obvious clustering characteristics, intervals can be divided based on the clustering, ensuring that each interval contains a relatively reasonable number of raster regions. By determining the index intervals, a classification basis is provided for the subsequent determination of governance priorities.

[0107] Step S1047: Determine the governance priority of the corresponding grid area based on the index interval corresponding to the governance priority index of each grid area.

[0108] The priority of treatment is the classification of the importance of each grid area in the soil erosion treatment sequence within the target area. Areas with higher priority are given priority in treatment measures to rationally allocate treatment resources, improve treatment efficiency, and minimize the damage caused by soil erosion.

[0109] After determining the index intervals, this step compares the governance priority index of each grid region with the various index intervals to determine which interval the index falls into. Then, according to pre-defined rules, a corresponding governance priority is assigned to each index interval.

[0110] This embodiment first determines a reasonable index range based on the distribution of governance priority indices across all grid areas within the target region, and then determines the governance priority based on the index range corresponding to the governance priority index of each grid area, thus achieving a scientific and rational allocation of governance resources. Because the governance priority index comprehensively considers factors such as the overall erosion risk level, land use sensitivity coefficient, and distance sensitivity coefficient, it accurately reflects the importance and urgency of each grid area in soil erosion control. The index ranges and governance priorities determined based on this ensure that governance work is targeted, prioritizing areas with high soil erosion risk, significant threats to sensitive targets, and sensitive land use types. This avoids the blindness and arbitrariness of governance work and improves governance efficiency.

[0111] In one embodiment, step S1046, which determines several index intervals based on the distribution of governance priority indices across all grid areas within the target area, includes: Step S10461: Statistically analyze the governance priority index of all grid areas within the target area, and determine at least two graded thresholds using the percentile method; based on the at least two graded thresholds, divide the area into several index intervals.

[0112] The Governance Priority Index (GPI) values ​​of all grid areas in the target region were statistically analyzed, and two dynamic thresholds were determined using the percentile method. Based on this, three levels of governance actions are defined as follows:

[0113] Among them, Level I is the key treatment area, Level II is the priority treatment area, and Level III is the general prevention and control area.

[0114] This embodiment scientifically determines the grading thresholds and divides the index intervals using the percentile method, achieving precise prioritization of governance. The governance priority index itself comprehensively considers multiple dimensions such as soil erosion risk, land use sensitivity, and distance from sensitive targets, fully reflecting the governance needs of each grid area. Using the percentile method to determine the grading thresholds further ensures that the index interval division conforms to actual distribution characteristics and effectively distinguishes the governance urgency of different areas, based on the objective laws of data distribution. This interval division method, combining statistical data characteristics and comprehensive evaluation indicators, makes the governance priority determination process more scientific and rational, guiding governance resources to be prioritized for the areas most in need, avoiding resource waste or governance blind spots, thereby significantly improving the efficiency and effectiveness of soil erosion control.

[0115] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of this application, and this application also intends to include these modifications and variations.

Claims

1. A method for assessing soil erosion risk, characterized in that, Includes the following steps: Acquire multi-source ecological data for each grid area within the target area; the multi-source ecological data includes vegetation index, land use type, terrain slope, and rainfall data; For any of the grid areas, the corresponding vegetation cover is determined based on the vegetation index; the corresponding initial erosion risk level is determined based on the land use type and the vegetation cover. Based on the terrain slope and rainfall data of the grid area, the corresponding risk correction parameters are determined; The initial erosion risk level is corrected based on the risk correction parameters to obtain the corresponding comprehensive erosion risk level. Obtain the sensitivity coefficient of the grid region; Based on the comprehensive erosion risk level and the sensitivity coefficient, the treatment priority index of the grid area is obtained; based on the treatment priority index of each grid area in the target area, the treatment priority corresponding to each grid area is determined.

2. The soil erosion risk assessment method according to claim 1, characterized in that, The step of determining the corresponding vegetation cover based on the vegetation index includes: The lower percentile value of the vegetation index within the target area is determined as the vegetation index value of the pure bare soil grid area; the higher percentile value of the vegetation index within the target area is determined as the vegetation index value of the pure vegetation grid area. The initial vegetation coverage of the grid area is determined based on the vegetation index of the grid area, the vegetation index of the bare soil grid area, and the vegetation index of the pure vegetation grid area. When the initial vegetation coverage meets the preset normal value range, the initial vegetation coverage is determined as the final vegetation coverage; when the initial vegetation coverage does not meet the preset normal value range, the initial vegetation coverage is constrained to the preset normal value range to obtain the final vegetation coverage.

3. The soil erosion risk assessment method according to claim 1, characterized in that, The steps for determining the corresponding initial erosion risk level based on the land use type and the vegetation cover include: Based on the land use type, a corresponding erosion risk level assessment system is determined; different land use types correspond to different erosion risk level assessment systems. Based on the erosion risk level assessment system and the vegetation coverage, the initial erosion risk level of the grid area is determined.

4. The soil erosion risk assessment method according to claim 1, characterized in that, The risk correction parameters include topographic factor risk correction parameters and rainfall erosivity factor risk correction parameters; The steps for determining the corresponding risk correction parameters based on the terrain slope and rainfall data of the grid area include: Based on the terrain slope of the grid area and the preset slope risk classification rules, the corresponding terrain factor risk correction parameters are determined. Based on the rainfall data of the grid area, the annual rainfall erosivity of the grid area is determined; based on the annual rainfall erosivity of the grid area and the preset rainfall erosivity risk classification rules, the corresponding rainfall erosivity factor risk correction parameters are determined.

5. The soil erosion risk assessment method according to claim 4, characterized in that, The step of correcting the initial erosion risk level based on the risk correction parameters to obtain the corresponding comprehensive erosion risk level includes: The initial erosion risk level is corrected by assigning the risk correction parameter of the topographic factor to obtain the first erosion risk level; the first erosion risk level is corrected by assigning the risk correction parameter of the rainfall erosivity factor to obtain the second erosion risk level. When the second erosion risk level meets the preset risk level range constraint, the second erosion risk level is determined as the comprehensive erosion risk level; when the second erosion risk level does not meet the preset risk level range constraint, the second erosion risk level is constrained to the preset risk level range to obtain the comprehensive erosion risk level.

6. The soil erosion risk assessment method according to claim 4, characterized in that, The rainfall data includes daily rainfall data throughout the year; The step of determining the annual rainfall erosivity of the grid area based on the rainfall data of the grid area includes: Based on the rainfall data of the grid area, the rainfall erosivity for each half-month period throughout the year is calculated; The annual rainfall erosion force of the grid area is obtained by summing up the rainfall erosion forces of each half-month period throughout the year.

7. The soil erosion risk assessment method according to claim 1, characterized in that, The sensitivity coefficients include land use sensitivity coefficients and distance sensitivity coefficients; the land use sensitivity coefficients are determined based on the land use type of the grid area and preset sensitivity level assignment rules; the distance sensitivity coefficients are determined based on the distance from the grid area to sensitive targets within the target area. The step of obtaining the governance priority index of the grid area based on the comprehensive erosion risk level and the sensitivity coefficient includes: The comprehensive erosion risk level, the land use sensitivity coefficient, and the distance sensitivity coefficient are weighted and summed according to preset weights to obtain the governance priority index of the grid area.

8. The soil erosion risk assessment method according to claim 7, characterized in that, The distance sensitivity coefficient is determined through the following steps: Identify sensitive targets within the target area; Obtain the Euclidean distance from each of the grid regions to its nearest sensitive target; Based on the Euclidean distances corresponding to all grid areas within the target area, determine the minimum and maximum Euclidean distances for the entire area. The distance sensitivity coefficient of the corresponding grid region is obtained by normalizing the Euclidean distance of each grid region, the minimum Euclidean distance of the entire region, and the maximum Euclidean distance of the entire region.

9. The soil erosion risk assessment method according to claim 1, characterized in that, The step of determining the governance priority of each grid area based on the governance priority index of each grid area within the target area includes: Based on the distribution of the governance priority index of all grid areas within the target area, several index intervals are determined; The governance priority of each grid region is determined based on the index range corresponding to the governance priority index of each grid region.

10. The soil erosion risk assessment method according to claim 9, characterized in that, The steps for determining several index intervals based on the distribution of governance priority indices across all grid areas within the target region include: The governance priority index of all grid areas within the target area is statistically analyzed, and at least two graded thresholds are determined using the percentile method; based on the at least two graded thresholds, several index intervals are obtained.

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