Black land erosion gully development risk prediction method and device and storage medium

Abstract

The invention relates to the technical field of soil erosion monitoring, and particularly provides a black land erosion gully development risk prediction method and device and a storage medium, and the method comprises the following steps: obtaining multi-source remote sensing data of a sample region, and carrying out the recognition of an erosion gully; extracting predictive factors from the multi-source remote sensing data, wherein the predictive factors comprise static environment factors and dynamic environment factors; decomposing the dynamic environment factors into four independent seasonal dynamic environment factors according to a winter snow accumulation period, a spring bare land period, a crop growth season and an autumn bare land period; and constructing an erosion gully development risk prediction model, carrying out model training by using the prediction factors, carrying out erosion gully development risk probability estimation on the to-be-analyzed region by using the trained erosion gully development risk prediction model, and generating an erosion gully development risk grade distribution map. According to the method, the defects of a traditional method in dynamic process description and scale adaptability are overcome, and the prediction accuracy is improved.

Description

Technical Field

[0001] This invention belongs to the field of soil erosion monitoring technology, and in particular relates to a method, device and storage medium for predicting the development risk of erosion gullies in black soil. Background Technology

[0002] Gullies are an extreme form of soil hydraulic erosion, posing a significant threat to land degradation and sustainable agricultural production. Current gully development risk prediction methods largely rely on static factors, such as topography, lithology, and soil indicators, or annual average climate and vegetation variables, making it difficult to characterize the key driving role of seasonal dynamic processes in gully development. Existing methods have three main shortcomings: First, they are highly dependent on static variables, often using static factors such as topography and lithology, and climate and vegetation variables are often taken as annual averages, ignoring seasonal variations; second, they fail to adequately characterize the seasonal development process of gullies, failing to reflect the concentrated precipitation periods, dynamic changes in vegetation cover, and the triggering effects of freeze-thaw and snowmelt processes on gully formation; third, they have poor applicability at the regional scale, with significant differences in the controlling factors between regional and watershed scales, and existing models lack factor weight adjustments based on scale effects. The Northeast Black Soil region is located in mid-to-high latitudes, where the alternation of winter snow accumulation, spring snowmelt, summer rainfall, and crop growing seasons has a significant impact on gully development. Existing gully development risk prediction models are severely inadequate in characterizing dynamic processes and scale adaptability. Summary of the Invention

[0003] In view of this, the present invention aims to provide a method, device and storage medium for predicting the development risk of erosion gullies in black soil, which solves the shortcomings of existing methods in dynamic process characterization and scale adaptability, improves prediction accuracy, and provides support for soil and water conservation in agricultural watersheds under different climate zones and agricultural production management models.

[0004] To achieve the above objectives, the technical solution created by this invention is implemented as follows: The first aspect of this invention provides a method for predicting the development risk of erosion gullies in black soil, comprising: Acquire multi-source remote sensing data for the sample area; Based on quantitative discrimination rules, erosion gullies are identified, and a list of spatial distribution of erosion gullies in the sample area is constructed. The list of spatial distribution of erosion gullies includes erosion gully locations and non-erosion gully locations. Predictive factors for erosion gully locations and non-erosion gully locations were extracted from multi-source remote sensing data. These predictive factors included static and dynamic environmental factors. The dynamic environmental factors were decomposed into four independent seasonal dynamic environmental factors based on the winter snow cover period, spring bare land period, crop growing season, and autumn bare land period. The static and seasonal dynamic environmental factors of erosion gully locations were used as positive samples, and the static and seasonal dynamic environmental factors of non-erosion gully locations were used as negative samples. The training dataset consisted of positive and negative samples. A risk prediction model for erosion gully development was constructed, and the training dataset was divided into a training set and a validation set, which were then input into the erosion gully development risk prediction model for training and validation. The trained gully development risk prediction model was used to estimate the probability of gully development risk in the area to be analyzed. The natural breakpoint method was used to divide the probability value of development risk into multiple risk levels and generate a distribution map of gully development risk levels.

[0005] Preferably, the quantification discrimination rules include the following four rules. If any point in the sample area satisfies at least two of the following four rules, then that point is considered an erosion gully: The normalized vegetation index of the erosion gully is less than or equal to 0.3, the normalized vegetation index of the outer area of ​​the erosion gully is greater than or equal to 0.5, and the texture contrast between the erosion gully and its outer area is greater than or equal to 15. The slope angle of the erosion gully wall is greater than 30 degrees; The aspect ratio of the erosion trench is visually measured based on true-color images, and the aspect ratio of the erosion trench is within a preset range; The tail end of the erosion gully is a fan-shaped depositional zone.

[0006] Preferably, the winter snow cover period is defined as from November to March of the following year; The spring bare ground period is defined as April to May; The crop growing season is defined as June to September; The autumn bare ground period is defined as October.

[0007] Preferably, static environmental factors include at least one of the following: altitude, slope, aspect, plane curvature, profile curvature, topographic location index, flow direction, distance from river, topographic humidity index, soil type, and land use type. Dynamic environmental factors include at least one of the following: Normalized Difference Vegetation Index (NDVI), Rainfall Index (PDSI), and Drought Index (PDSI).

[0008] Preferably, the process of constructing the training dataset also includes: using the variance inflation factor (VIF) to perform a multicollinearity test on all predictors; if the variance inflation factor (VIF) of any two predictors is greater than a preset value, then one of the two predictors is removed.

[0009] Preferably, the gully development risk prediction model is a random forest model, whose model parameters include: 1000 trees, 10 maximum depth, 30 minimum number of split samples, and 5 minimum number of leaf nodes.

[0010] Preferably, the prediction model for the development risk of erosion gullies is a gradient boosting tree or a deep learning convolutional neural network.

[0011] Preferably, the natural breakpoint method is used to divide the developmental risk probability value into five risk levels: extremely low, low, medium, high, and extremely high.

[0012] The second aspect of this invention provides a device for predicting the development risk of erosion gullies in black soil, comprising: At least one processor; And, a memory communicatively connected to at least one processor; wherein, The memory stores instructions that can be executed by at least one processor, which enables the at least one processor to perform a method for predicting the development risk of black soil erosion gullies.

[0013] The third aspect of this invention provides a storage medium storing computer instructions for causing a computer to execute a method for predicting the development risk of black soil erosion gullies.

[0014] Compared with the prior art, the present invention can achieve the following beneficial effects: This invention decomposes dynamic variables such as climate and vegetation into four key seasonal periods: winter snow cover period, spring bare soil period, crop growing season, and autumn bare soil period. Features are extracted from each period to accurately capture short-term, high-intensity erosion-driving events such as spring snowmelt, summer severe convective rainstorms, and autumn bare soil rainfall. This overcomes the signal smoothing and masking effects caused by using annual averages in existing technologies, significantly improving the ability to identify key triggering moments for erosion gully development and fundamentally enhancing the accuracy and reliability of the prediction model. The final output of this invention is an erosion gully development risk level distribution map, clearly marking extremely low, low, medium, high, and extremely high risk areas in an intuitive spatial visualization.

[0015] This invention can construct a multi-scale gully development risk prediction model, distinguish between regional and watershed scales, study the importance of different predictive factors at different model scales, identify and learn the dominant environmental factors that control gully development at different spatial scales, improve the applicability of the model at different spatial scales, and avoid the bias or erroneous extrapolation of single-scale models.

[0016] This invention introduces an erosion gully identification system based on a series of quantitative rules such as NDVI difference, texture contrast, aspect ratio, and sedimentation zone morphology. This changes the traditional subjective and inefficient model that relies on expert experience for manual visual interpretation, and effectively improves the objectivity and scientific nature of erosion gully identification. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a framework diagram of the black soil erosion gully development risk prediction method provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of the spatial distribution of erosion trenches provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the static environmental factor distribution for erosion gully development risk assessment provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the dynamic environmental factor distribution for erosion gully development risk assessment provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and do not constitute a limitation thereof. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the invention. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, some operations related to the invention are not shown or described in the specification. This is to avoid obscuring the core parts of the invention with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined to form various implementations. Furthermore, the order of the steps or actions in the method description can be changed or adjusted in a manner readily apparent to those skilled in the art. Therefore, the various orders in the specification and drawings are merely for the clear description of a particular embodiment and do not imply a mandatory order, unless otherwise stated that a particular order must be followed.

[0020] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0021] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] Please see Figure 1 In one embodiment of the present invention, a method for predicting the development risk of erosion gullies in black soil is provided, comprising the following steps: S1: Acquire multi-source remote sensing data for the sample area; S2: Identify erosion trenches based on quantitative discrimination rules, and construct a spatial distribution list of erosion trenches in the sample area. The spatial distribution list of erosion trenches includes erosion trench locations and non-erosion trench locations. S3: Extract prediction factors for the erosion gully locations and the non-erosion gully locations from the multi-source remote sensing data. The prediction factors include static environmental factors and dynamic environmental factors. Decompose the dynamic environmental factors into four independent seasonal dynamic environmental factors according to the winter snow cover period, spring bare land period, crop growing season, and autumn bare land period. Use the static environmental factors and seasonal dynamic environmental factors of the erosion gully locations as positive samples and the static environmental factors and seasonal dynamic environmental factors of the non-erosion gully locations as negative samples. The positive and negative samples constitute the training dataset.

[0024] S4: Construct an erosion gully development risk prediction model, divide the training dataset into a training set and a validation set, and input them into the erosion gully development risk prediction model for training and validation; S5: Using the trained gully development risk prediction model, estimate the probability of gully development risk in the area to be analyzed. Use the natural breakpoint method to divide the probability value of development risk into multiple risk levels and generate a distribution map of gully development risk levels.

[0025] As an optional embodiment, in step S1, multi-source remote sensing data of the sample area is first acquired, including but not limited to remote sensing image features of erosion gullies, UAV aerial images, on-site landscape maps, and specific geological data. In this embodiment of the invention, the sample area is selected within a certain region and five agricultural watersheds for risk prediction and accuracy verification. The five agricultural watersheds are designated as Agricultural Watershed 1, Agricultural Watershed 2, Agricultural Watershed 3, Agricultural Watershed 4, and Agricultural Watershed 5. The multi-source remote sensing data includes satellite remote sensing images, high-resolution remote sensing images, UAV aerial survey data, and field survey data.

[0026] After acquiring multi-source remote sensing data, it is preprocessed to standardize and align the data, resulting in a multi-source data layer that is completely uniform in terms of spatial reference, resolution, temporal phase, and numerical format.

[0027] As an optional implementation, in step S2, a high-precision list of erosion gullies' spatial distribution is established to provide a basis for model validation and accuracy assessment. This list includes erosion gully locations and non-erosion gully locations. Specifically, for a sample area, its boundaries are imported into an erosion gully calibration platform. Visual interpretation and erosion gully boundary extraction are performed by combining the high reflectance spectrum characteristics of bare land, the linear shadow characteristics of the gully walls, and the fragmented texture characteristics of patches. The erosion gully boundary features are verified by comparing remote sensing interpretation results with high-resolution imagery. Field verification is conducted using GPS positioning to record the coordinates of the gully head, middle section, and mouth. To meet the requirements of subsequent model training, point sampling is performed on the sample area using the erosion gully calibration platform. Specifically, 11,081 erosion gully locations are collected and identified, serving as positive samples. 10,636 non-erosion gully locations are identified and used as control samples, i.e., negative samples.

[0028] Four quantitative discrimination rules are specifically set. These rules are used to identify erosion trenches through visual interpretation. If any point in the sample area satisfies at least two of the following four rules, then that point is considered an erosion trench: Rule 1: The sense of separation between the gully head and walls and the surrounding environment is used as the criterion for judgment. The judgment criteria are initially quantified through visual interpretation combined with differences in vegetation cover and texture contrast. Specifically, the Normalized Difference Vegetation Index (NDVI) of the sampling point is calculated using ArcGIS. When the NDVI of the sampling point is less than or equal to 0.3, and the NDVI value of the surrounding area is greater than or equal to 0.5; simultaneously, the texture contrast between the sampling point and its surrounding area is calculated based on the gray-level co-occurrence matrix of satellite imagery. When the texture contrast between the gully head or walls of the sampling point and the surrounding area is greater than or equal to 15 (the texture contrast value ranges from 0 to 100, with higher values ​​indicating more significant differences), meeting both conditions is considered a "significant sense of separation."

[0029] Rule 2: The slope angle of the ditch wall at the sampling point is used as the criterion. Specifically, the slope angle of the ditch wall at the sampling point is identified. If the slope angle is greater than 30 degrees, the condition is considered met. In ArcGIS, the sloped area of ​​the ditch wall at the sampling point is a shaded area, appearing in a dark tone. By visual interpretation combined with ArcGIS's "Raster Calculation" tool, the proportion of the sloped area can be used as an effective indirect indicator of the steepness of the surface slope. The higher the proportion of shaded area, the more pronounced the slope of the ditch wall. The slope angle of the ditch wall can be estimated based on the proportion of the shaded area and the color contrast.

[0030] Rule 3: The width-to-depth ratio of the sampling point is used as the criterion. Specifically, if the width-to-depth ratio of the sampling point is within a preset range, it is considered to meet the condition for being classified as an erosion trench. The cross-sectional shape of an erosion trench is usually U-shaped, V-shaped, trapezoidal, or approximately regular. If the width-to-depth ratio of the sampling point falls within any of the following morphological ranges, it is considered to meet Rule 3: For erosion trenches with a U-shaped cross-section, the width-to-depth ratio is within... Within the interval, the width-to-depth ratio of the V-shaped erosion trench is at Within the interval, the width-to-depth ratio of the trapezoidal cross-section of the erosion trench is... Within the interval, the width-to-depth ratio of the square-shaped erosion trenches is... Within the range.

[0031] Rule 4: The shape of the trench tail region at the sampling point is used as the criterion. Specifically, if the trench tail at the sampling point is determined to be a fan-shaped depositional area, and the following three conditions are met simultaneously, then Rule 4 is considered satisfied: The area of ​​the sedimentary zone at the tail of the sampling point is greater than or equal to the width of the tail outlet × 20m. The outlet width is obtained by visual measurement of the true color image, and the visual measurement error is usually less than or equal to 0.5m. 20m is the typical sedimentary correlation length in the direction of the extension of the erosion gully outlet in the Northeast Black Soil Region.

[0032] Using the outlet of the gully tail as the vertex (the midpoint of the outlet can be directly taken), extract the edge of the sedimentary zone at the rear of the gully tail. Based on the edge contour line, approximately estimate the included angle between the two edges of the sedimentary zone. This included angle should be within... Within the range.

[0033] The texture uniformity of the sedimentary area is greater than or equal to 0.6. The texture uniformity of the sedimentary area is calculated based on visual inspection combined with the gray-level co-occurrence matrix. The value range of the texture uniformity of the sedimentary area is... A higher uniformity value indicates a more homogeneous sedimentary area. When the texture uniformity is greater than or equal to 0.6, it conforms to the loose sedimentary characteristics of the outlet of the main erosion gully. After the above visual interpretation, field sampling and drone aerial photography were conducted in October 2024 and May and June 2025 based on the marked erosion gully maps.

[0034] The spatial distribution list of erosion trenches in the sample area can be obtained through the above process, as follows: Figure 2 As shown.

[0035] As an optional embodiment, in step S3, variable screening and feature construction are performed to select predictive factors. Traditional predictive factors used in predicting the development risk of gullies are usually static factors, such as topography, lithology, and soil indicators; or annual average climate and vegetation variables. These are insufficient to characterize the key driving role of seasonal dynamic processes in gully development and cannot reflect the risk of gully development caused by climate and environmental changes during seasonal transitions. Therefore, this embodiment of the invention innovatively designs predictive factors, which include both static and dynamic environmental factors. Static environmental factors include topographic, hydrological, geological, soil, and land features, specifically characterized by altitude, slope, aspect, lithology, geomorphology, plan curvature, profile curvature, topographic location index, flow direction, distance from river, river power index, topographic humidity index, soil type, land use type, population density, or distance from road. One or more of the above static environmental factors can be selected according to actual needs. As for dynamic environmental factors, this embodiment of the invention uses the Normalized Difference Vegetation Index (NDVI), rainfall index, and drought index (PDSI). One or more of the above dynamic environmental factors can be selected according to actual needs. To ensure the accuracy of the subsequent gully development risk prediction model in depicting the dynamic process, this invention innovatively decomposes dynamic environmental factors into four independent seasonal dynamic environmental factors: winter snow cover period, spring bare land period, crop growing season, and autumn bare land period, instead of using the annual average as a single feature. Based on the actual seasonal variation characteristics of Northeast China, the winter snow cover period is defined as November to March of the following year; the spring bare land period as April to May; the crop growing season as June to September; and the autumn bare land period as October. This invention systematically classifies each environmental factor to serve the sensitivity modeling of gully development. Among them, naturally continuous variables are divided into five levels using the natural breakpoint method in the ArcGIS platform; non-continuous variables are classified using the assignment method. The specific classification basis and standards are as follows: Elevation, as a basic topographic factor, such as Figure 3 As shown in (a), it is divided into five levels: 95–172m, 172–237m, 237–315m, 315–408m, and 408–954m.

[0036] Slope directly controls runoff rate and erosion potential, such as Figure 3 As shown in (b), the levels are divided into five levels: 0–1°, 1–3°, 3–8°, 8–16°, and 16–56°.

[0037] Slope aspect influences microclimate and vegetation pattern by modulating solar radiation and local wind fields, such as Figure 3 As shown in (c), the area is divided into nine directions: flatland (F), north (N), northeast (NE), east (E), southeast (SE), south (S), southwest (SW), west (W), and northwest (NW).

[0038] Lithology, as an intrinsic factor controlling surface erosion resistance, is divided into 18 categories, including granite and basalt. Its structure and weathering characteristics directly affect erosion sensitivity. Figure 3 The numbers shown in (d) are denoted as A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R.

[0039] Geomorphic units primarily function by influencing the erosion base level and hydrodynamic conditions; this study involves, for example... Figure 3 Plains, hills, depressions, and gentle slopes are shown in (e).

[0040] Planar curvature characterizes the horizontal undulations of the Earth's surface, influencing the convergence and dispersion of runoff. Based on the natural discontinuity method, it is classified as follows: Figure 3 The following are shown in (f): plane, concave and convex surfaces.

[0041] The profile curvature reflects the change in water flow acceleration along the slope direction and is divided into, for example, Figure 3 As shown in (g): 0–1.3, 1.3–4.1, 4.1–8.9, 8.9–16.8, 16.8–50.6; both planar curvature and profile curvature are key indicators for describing topographic features.

[0042] The topographic location index identifies landform locations by comparing the difference between a pixel's elevation and the average elevation of its neighbors. Negative values ​​indicate easily eroded units such as valleys and runoff basins, while positive values ​​correspond to stable landforms such as ridges and plateaus. Figure 3 The values ​​shown in (h) are: 0.1–0.4, 0.4–0.6, 0.6–0.7, 0.7–1.0, and 1.0–1.9.

[0043] The water flow direction is extracted based on the D8 algorithm and divided into the following categories: Figure 3 The eight flow directions (N, NE, E, SE, S, SW, W, NW) shown in (i) are used to characterize surface runoff paths.

[0044] Distance from the river is used to indicate the potential impact of the river network on gully erosion development, expressed in Euclidean distances such as... Figure 3 As shown in (j), the range is divided into five levels: 0.00–0.09 km, 0.09–0.21 km, 0.21–0.35 km, 0.35–0.76 km, and 0.76–1.50 km.

[0045] The river power index, which combines slope and catchment area, is used to assess runoff erosion capacity. High-value areas represent concentrated catchment zones with significant erosion risk, and are divided into several categories. Figure 3 The values ​​shown in (k) are: -15.6–-13.4, -13.4–-11.4, -11.4–-9.4, -9.4–-6.4, and -6.4–9.2.

[0046] The topographic moisture index reflects the spatial differentiation of soil moisture; high-value areas indicate areas prone to latent erosion and can be categorized as follows: Figure 3 The values ​​shown in (L) are: -9.0–-3.9, -3.9–--1.4, -1.4–0.8, 0.8–3.7, and 3.7–18.4.

[0047] Soil types are classified into 17 categories based on texture and soil-forming process, such as northern paddy soil and bentonite. Different soil types have significantly different permeability and erosion resistance, such as... Figure 3 The numbers (m) are denoted as A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q.

[0048] Land use and vegetation cover regulate erosion processes through canopy and root action, such as Figure 3 The land (n) is divided into six categories: cultivated land, forest land, grassland, lakes, urban land and unused land.

[0049] Population density, as an indirect indicator of the intensity of human activity, may influence erosion processes through land cover change, such as... Figure 3 The population density (O) is divided into three levels: 0–3575 people / km², 3575–17480 people / km², and 17480–50754 people / km².

[0050] Road systems exacerbate erosion risks by altering local catchment paths and human disturbance. Based on a Chinese highway dataset, buffer zones are generated using Euclidean distance. Figure 3 As shown in (p), the distance is divided into five levels: 0.00–0.02 km, 0.02–0.04 km, 0.04–0.07 km, 0.07–0.12 km, and 0.12–0.14 km.

[0051] Dynamic factors were selected as follows: Figure 4 The NDVI (a, b, c, d), precipitation (e, f, g, h), and PDSI (i, j, k, L) are divided into snow cover period, spring bare period, crop growing period, and autumn bare period according to the unique climatic characteristics of Northeast China, thus constructing a basic dataset for dynamic risk prediction of gully development.

[0052] After identifying all predictor factors, the specific data values ​​of all predictor factors are spatially unified to form a resampled raster format with a resolution of 30m. The data is then standardized, and categorical variables are converted to numerical values, transforming geospatial data into numerical features that machine learning models can efficiently learn and understand. Due to the large number of selected predictor factors, to reduce the computational burden of subsequent models, the correlation between predictor factors is studied, and predictor factors with similar predictive contributions are eliminated. Specifically, the variance inflation factor (VIF) is used to test for multicollinearity among all predictor factors. If the VIF of any two predictor factors exceeds a preset value, one of these two predictor factors is eliminated.

[0053] After the above processing, corresponding static environmental factors and seasonal dynamic environmental factors were obtained for both erosion gully sites and non-erosion gully sites. The static and seasonal dynamic environmental factors of the erosion gully sites were used as positive samples, and the static and seasonal dynamic environmental factors of the non-erosion gully sites were used as negative samples. The positive and negative samples were combined into a training dataset. The samples in the training dataset were then randomly divided into a training set (70%, 15202 samples) and a validation set (30%, 6515 samples) for subsequent model training and validation.

[0054] As an optional embodiment, in step S4, an erosion gully development risk prediction model is constructed. To meet the prediction needs of regions of different sizes, multiple scale erosion gully development risk prediction models can be established during the model construction process. Typically, models can be built for both land area scale and watershed scale, respectively, to study the mapping relationship between erosion gully development risk and prediction factors in different regions. The erosion gully development risk prediction model can be a model commonly used in the field, such as a random forest model, gradient boosting tree, or deep learning convolutional neural network. In this embodiment of the invention, a random forest model is specifically used, and its model parameters include: The number of trees is 500-1500, and 1000 is selected specifically; The maximum depth is 5-15, but 10 is selected specifically. The minimum number of split samples is 20-40, specifically 30; The minimum number of leaf nodes is 3-7, and 5 is specifically chosen.

[0055] The model sets the developmental risk probability to 0-1.

[0056] The samples in the training set are input into the erosion gully development risk prediction model for training. When the model converges, the training results are verified using the samples in the validation set.

[0057] As an optional embodiment, in step S5, the trained gully development risk prediction model is used to estimate the probability of gully development risk in the area to be analyzed. Multi-source remote sensing data of the area to be analyzed is acquired, and the corresponding prediction factors are extracted and input into the trained gully development risk prediction model. The model then outputs the corresponding gully development risk probability. The natural breakpoint method is further used to divide the development risk probability values ​​into multiple risk levels, specifically into five risk levels: extremely low (0-0.2), low (0.2-0.4), medium (0.4-0.6), high (0.6-0.8), and extremely high (0.8-1). An gully development risk level distribution map is generated based on the output results.

[0058] In summary, the above description is merely a preferred embodiment of this specification and is not intended to limit the scope of protection of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of protection of this specification.

[0059] The systems, apparatuses, modules, or units described in one or more of the above embodiments may be implemented by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer. Specifically, a computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0060] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0061] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

Claims

1. A method for predicting the development risk of erosion gullies in black soil, characterized in that, include: Acquire multi-source remote sensing data for the sample area; Based on quantitative discrimination rules, erosion gullies are identified, and a spatial distribution list of erosion gullies in the sample area is constructed. The spatial distribution list of erosion gullies includes erosion gully locations and non-erosion gully locations. Predictive factors for the erosion gully locations and non-erosion gully locations are extracted from the multi-source remote sensing data. These predictive factors include static environmental factors and dynamic environmental factors. The dynamic environmental factors are decomposed into four independent seasonal dynamic environmental factors based on the winter snow cover period, spring bare land period, crop growing season, and autumn bare land period. The static environmental factors and seasonal dynamic environmental factors of the erosion gully locations are used as positive samples, and the static environmental factors and seasonal dynamic environmental factors of the non-erosion gully locations are used as negative samples. The training dataset is composed of positive and negative samples. A risk prediction model for erosion gully development is constructed, and the training dataset is divided into a training set and a validation set, which are then input into the erosion gully development risk prediction model for training and validation. The trained gully development risk prediction model is used to estimate the probability of gully development risk in the area to be analyzed. The natural breakpoint method is used to divide the probability value of development risk into multiple risk levels and generate a distribution map of gully development risk levels.

2. The method for predicting the development risk of black soil erosion gullies according to claim 1, characterized in that, The quantification discrimination rules include the following four rules. If any point in the sample area satisfies at least two of the following four rules, then that point is considered an erosion gully: The normalized vegetation index of the erosion gully is less than or equal to 0.3, the normalized vegetation index of the outer area of ​​the erosion gully is greater than or equal to 0.5, and the texture contrast between the erosion gully and its outer area is greater than or equal to 15. The slope angle of the erosion gully wall is greater than 30 degrees; The aspect ratio of the erosion trench is visually measured based on true-color images, and the aspect ratio of the erosion trench is within a preset range; The tail end of the erosion gully is a fan-shaped depositional zone.

3. The method for predicting the development risk of black soil erosion gullies according to claim 1, characterized in that, The winter snow cover period is defined as from November to March of the following year; The spring bare land period is defined as April to May; The crop growing season is defined as June to September; The autumn bare land period is defined as October.

4. The method for predicting the development risk of black soil erosion gullies according to claim 1, characterized in that, The static environmental factors include at least one of the following: altitude, slope, aspect, plane curvature, profile curvature, topographic location index, flow direction, distance from river, topographic humidity index, soil type, and land use type. The dynamic environmental factors include at least one of the following: Normalized Difference Vegetation Index (NDVI), Rainfall Index (PDSI), and Drought Index (PDSI).

5. The method for predicting the development risk of black soil erosion gullies according to claim 1, characterized in that, The process of constructing the training dataset also includes: using the variance inflation factor (VIF) to perform a multicollinearity test on all predictors; if the variance inflation factor (VIF) of any two predictors is greater than a preset value, then one of the two predictors is removed.

6. The method for predicting the development risk of black soil erosion gullies according to claim 1, characterized in that, The gully development risk prediction model is a random forest model, and its model parameters include: 1000 trees, 10 maximum depth, 30 minimum number of split samples, and 5 minimum number of leaf nodes.

7. The method for predicting the development risk of black soil erosion gullies according to claim 1, characterized in that, The erosion gully development risk prediction model is a gradient boosting tree or a deep learning convolutional neural network.

8. The method for predicting the development risk of black soil erosion gullies according to claim 1, characterized in that, The natural breakpoint method was used to classify the developmental risk probability value into five risk levels: extremely low, low, medium, high, and extremely high.

9. A device for predicting the development risk of erosion gullies in black soil, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the black soil erosion gully development risk prediction method according to any one of claims 1 to 8.

10. A storage medium, characterized in that, It stores computer instructions for causing the computer to execute the black soil erosion gully development risk prediction method according to any one of claims 1 to 8.

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