Method for rapidly judging artificial disturbance erosion intensity based on remote sensing data and medium

Abstract

The invention belongs to the technical field of remote sensing data application, and relates to a remote sensing data-based artificial disturbance erosion intensity rapid discrimination method and a medium, and the method comprises the following steps: preprocessing a remote sensing image to generate surface reflectance; according to the surface reflectance, generating a vegetation coverage grading layer by calculating NDVI and FVC and a grading rule; identifying artificially disturbed land through visual interpretation to generate a land class refined classification layer; generating an average gradient grid map layer according to artificial disturbance and gradient data, and generating an average gradient grading map layer according to a grading rule; integrating the average gradient grading map layer, the vegetation coverage grading map layer and the land category refining classification map layer to generate an artificial disturbance land erosion intensity judgment map layer, and judging artificial disturbance land erosion intensity through a lookup table; the method has the advantages of being easy and convenient to operate and high in applicability, standardization and unification of erosion strength evaluation are achieved, and evaluation efficiency and precision are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote sensing data applications, and particularly relates to a method and medium for rapidly discriminating the intensity of human-induced disturbance erosion based on remote sensing data. Background Art

[0002] With the rapid economic development and the accelerating urbanization process, the demand for land use and resource development in production and construction projects and activities (hereinafter referred to as "human-induced disturbances") continues to increase. However, human-induced disturbances often cause varying degrees of damage to the original landform and ecological environment. Without effective supervision, it will lead to the exacerbation of soil erosion problems and have a profound impact on the ecological environment quality and the sustainable utilization of soil and water resources. At present, for the determination of soil erosion intensity in human-induced disturbance areas, most methods use on-site measurement of individual projects and evaluate by obtaining the soil erosion modulus of the project area. However, this method has great limitations and is difficult to meet the needs of large-scale and rapid assessment. In addition, although existing remote sensing technology discrimination methods have certain spatial advantages, due to relying on qualitative means such as manual visual assessment, they often have deficiencies in accuracy and efficiency and are difficult to provide reliable support for the scientific assessment of soil erosion.

[0003] In view of the problems of strong subjectivity, low efficiency, and poor timeliness in traditional human-induced soil erosion assessment methods, there is an urgent need to construct a method for rapidly discriminating the intensity of human-induced disturbance erosion on a large scale based on remote sensing data. It can realize standardized and unified erosion intensity assessment, significantly improve the assessment efficiency and accuracy, and provide a scientific basis for soil and water conservation monitoring and management. By timely identifying potential soil erosion risks, this method can effectively support decision-makers to formulate targeted prevention and control measures, contribute to ecological environment protection and the sustainable utilization of soil and water resources, and ultimately achieve the coordinated unity of economic development and ecological protection.

[0004] With the rapid development of remote sensing technology, the patent document: "A Remote Sensing Intelligent Extraction Method for the Disturbance Range of Large-Scale Human-Induced Soil Erosion" (CN202310604455.3) proposed a remote sensing intelligent extraction method for the disturbance range of large-scale human-induced soil erosion, but it is only limited to the identification of the disturbance area range and does not evaluate the erosion intensity; the patent document: "A Method for Determining the Risk Level of Soil Erosion in Production and Construction Projects" (CN202410573180.6) mainly determines the risk level of soil erosion in individual production and construction projects and fails to achieve large-scale determination based on remote sensing images; the patent document: "A Method for Evaluating the Risk of Soil Erosion in Production and Construction Projects Based on Quantitative Index Operations" (CN202310290276) constructs quantitative indicators for the basic layer, risk layer, and benefit layer and determines weights and calibration parameters through the scale of production and construction projects, on-site risk characteristics, and the benefits of soil and water conservation measures, but its application scope is limited to the risk assessment of individual projects.

[0005] Therefore, a rapid discrimination method and medium for the intensity of large-scale anthropogenic disturbance erosion based on remote sensing data are proposed, which have important theoretical significance and practical value and play an important supporting role in promoting ecological civilization construction and regional sustainable development. Summary of the Invention

[0006] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a rapid discrimination method and medium for the intensity of anthropogenic disturbance erosion based on remote sensing data.

[0007] In order to achieve the purpose of the present invention, the following technical solutions are adopted for implementation.

[0008] A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data includes the following steps:

[0009] S1. Preprocess the obtained multi-spectral remote sensing images to generate surface reflectance data; wherein: the multi-spectral remote sensing images include remote sensing images in the red, green, blue, and near-infrared bands, and the spatial resolution of the remote sensing images is 2m; the preprocessing includes radiometric calibration and atmospheric correction;

[0010] S2. Calculate the normalized difference vegetation index NDVI according to the surface reflectance data, calculate the fractional vegetation cover FVC using the vegetation index NDVI, and divide the fractional vegetation cover grades according to the fractional vegetation cover grading rules to generate a fractional vegetation cover grading layer;

[0011] S3. Identify anthropogenic disturbance land on the obtained multi-spectral remote sensing images through visual interpretation to obtain anthropogenic disturbance patch vector data, and refine the land use classification of the anthropogenic disturbance patch vector data according to surface features to generate a refined land use classification layer;

[0012] S4. According to the anthropogenic disturbance patch vector data and the resampled slope data, statistically calculate the average slope of the anthropogenic disturbance land to generate an average slope raster layer, and divide the average slope grades according to the average slope grading rules to generate an average slope grading layer;

[0013] S5. By integrating the average slope grading layer, the fractional vegetation cover grading layer, and the refined land use classification layer, calculate and generate an anthropogenic disturbance land erosion intensity determination layer, and determine the anthropogenic disturbance land erosion intensity through an anthropogenic soil and water loss intensity determination lookup table.

[0014] As a preferred solution of the present invention, the anthropogenic disturbance land erosion intensity is verified for accuracy and the determination result of the anthropogenic disturbance land erosion intensity is corrected based on the results of field investigations.

[0015] As a preferred embodiment of the present invention, the accuracy verification and correction are performed by analyzing the field investigation results of human-disturbed land to determine the actual erosion intensity of human-disturbed land, verifying the accuracy of the determined erosion intensity of human-disturbed land, and correcting the misjudged erosion intensity of human-disturbed land.

[0016] As a preferred embodiment of the present invention, the radiometric calibration is to convert the DN value in the multi-spectral remote sensing image data into the surface reflectance to obtain the true surface reflectance data.

[0017] As a preferred embodiment of the present invention, the atmospheric correction is to perform correction processing on the atmospheric influence in the multi-spectral remote sensing image data, eliminate the interference of atmospheric scattering and absorption effects, restore the true spectral characteristics of surface targets, and improve the authenticity and reliability of the image data.

[0018] As a preferred embodiment of the present invention, the slope data is obtained by calculating the digital elevation model (DEM) in the terrain data into slope and resampling it to a 2m spatial resolution.

[0019] As a preferred embodiment of the present invention, the average slope classification rule is to set the average slope less than 5° as level 1, the average slope of 5° - 15° as level 2, the average slope of 15° - 30° as level 3, and the average slope greater than 30° as level 4.

[0020] As a preferred embodiment of the present invention, the calculation formula of the normalized difference vegetation index (NDVI) is:

[0021] NDVI = (b nir - b red ) / (b nir + b red )

[0022] Where: b nir represents the reflectance data of the near-infrared band in the remote sensing image; b red represents the reflectance data of the red band in the remote sensing image.

[0023] As a preferred embodiment of the present invention, the calculation formula of the fractional vegetation cover (FVC) is:

[0024] FVC = (NDVI - NDVI soil ) / (NDVI veg - NDVI soil )

[0025] Where: NDVI is the normalized difference vegetation index; NDVI veg is the NDVI value of the pure vegetation pixel in the human-disturbed area; NDVI soil is the NDVI value of the pure bare soil pixel in the human-disturbed area.

[0026] As a preferred embodiment of the present invention, the vegetation coverage classification rule is to set the vegetation coverage less than 0.3 as level 1, the vegetation coverage between 0.3 and 0.45 as level 2, the vegetation coverage between 0.45 and 0.60 as level 3, the vegetation coverage between 0.6 and 0.75 as level 4, and the vegetation coverage greater than 0.75 as level 5.

[0027] As a preferred embodiment of the present invention, the content of visual interpretation includes production and construction projects, reserved land, and production and construction activities.

[0028] As a preferred embodiment of the present invention, the content of refined land type classification includes: disturbed ground surface, surface hardening, surface covering, and water bodies.

[0029] As a preferred embodiment of the present invention, the lookup table for determining the intensity of human-induced soil and water loss is established through systematic literature research and expert consultation to establish an evaluation rule for human-induced soil and water loss based on three indicators: average slope, vegetation coverage, and the classification code of the land type of human-induced disturbance. The evaluation rule for human-induced soil and water loss is shown in the following table:

[0030]

[0031] As a preferred embodiment of the present invention, the calculation formula for the layer of determining the erosion intensity of human-induced disturbed land is:

[0032] The layer of determining the erosion intensity of human-induced disturbance = the layer of average slope classification * 100 + the layer of vegetation coverage classification * 10 + the layer of refined classification of human-induced disturbed plots.

[0033] A storage medium for storing computer-executable instructions, where the computer-executable instructions are set as any one of the methods for quickly discriminating the erosion intensity of human-induced disturbance based on remote sensing data.

[0034] As a preferred embodiment of the present invention, when the computer-executable instructions are executed, they implement a method for quickly discriminating the erosion intensity of human-induced disturbance based on remote sensing data as described in any one of claims 1-7.

[0035] Beneficial effects

[0036] The method of the present invention has the characteristics of simple operation and strong applicability, can realize the standardization and unification of erosion intensity assessment, significantly improve the assessment efficiency and accuracy, provide a scientific basis for the monitoring and management of soil and water conservation, and contribute to strengthening the prevention and control of soil and water loss at the regional scale and the sustainable utilization of resources. Description of the drawings

[0037] Figure 1 Average slope classification map of the case area;

[0038] Figure 2 Vegetation coverage classification map of the case area;

[0039] Figure 3 Refined classification map of human-disturbed plots in the case area;

[0040] Figure 4 Erosion intensity determination layer for human-disturbed plots in the case area;

[0041] Figure 5 Result map of human-induced soil and water loss intensity in the case area;

[0042] Figure 6 It is the overall structure view of the present invention. Specific implementation manners

[0043] The present invention will be further described in conjunction with embodiments and the accompanying drawings.

[0044] As Embodiment 1 of the present invention, as Figure 6 shown, a rapid discrimination method for human-disturbed erosion intensity based on remote sensing data includes the following steps:

[0045] S1. Remote sensing data preprocessing: First, multi-source remote sensing image data in the study area are acquired, including red, green, blue, and near-infrared bands, with a spatial resolution of 2 m. To ensure the standardization and applicability of the remote sensing image data, radiometric calibration and atmospheric correction are performed on the image data to generate surface reflectance data, providing a high-quality standardized data basis for subsequent remote sensing analysis and model construction;

[0046] S2. Average slope classification: The average slope within the human-disturbed land is statistically analyzed, and the average slope is classified into four grades: slope less than 5° is Grade 1, slope 5° - 15° is Grade 2, slope 15° - 30° is Grade 3, and slope greater than 30° is Grade 4;

[0047] S3. Vegetation coverage level division: First, the normalized difference vegetation index (NDVI) is calculated based on the remote sensing image, and then the fractional vegetation cover (FVC) is further calculated. According to the FVC value, the coverage level is divided into five grades: FVC less than 0.3 is Grade 1 (low coverage), FVC between 0.3 and 0.45 is Grade 2 (medium-low coverage), FVC between 0.45 and 0.60 is Grade 3 (medium coverage), FVC between 0.6 and 0.75 is Grade 4 (medium-high coverage), and FVC greater than 0.75 is Grade 5 (high coverage);

[0048] S4. Classification of human-disturbed land: The human-disturbed areas in the study area are interpreted based on the remote sensing image, and further refined classification is carried out within the disturbed areas. The interpretation results are divided into four land types according to surface characteristics: disturbed surface, surface hardening, surface covering, and water body;

[0049] S5. Determination of the erosion intensity of anthropogenic disturbance patches: By integrating the slope classification layer, the vegetation coverage classification layer, and the refined classification layer of anthropogenic disturbance land, calculate and generate the corresponding layer for determining the erosion intensity of anthropogenic disturbance land. Determine the erosion intensity of anthropogenic disturbance land through the lookup table for determining the intensity of anthropogenic soil and water loss. Using the vector data of anthropogenic disturbance land, statistically calculate the proportion of the area with different erosion intensities, and take the erosion intensity with the largest area proportion as the erosion intensity of the corresponding anthropogenic disturbance land.

[0050] S6. Verification of determination accuracy: By analyzing the field survey and measured data of anthropogenic disturbance land, determine the actual anthropogenic erosion intensity and verify the remote sensing comprehensive discrimination results. Evaluate the discrimination accuracy of quantity and area to further verify the reliability of the discrimination results. After the accuracy evaluation, correct the intensity of misjudged patches.

[0051] As an embodiment of the present invention, the preprocessing of remote sensing data includes radiometric calibration and atmospheric correction, where:

[0052] The radiometric calibration: Perform radiometric calibration processing on the remote sensing image data to convert the digital number (DN value) into the surface reflectance to obtain the true surface reflectance data;

[0053] Atmospheric correction: Perform correction processing on the atmospheric influence in the remote sensing image data to eliminate the interference of atmospheric scattering and absorption effects, restore the true spectral characteristics of surface targets, and improve the authenticity and reliability of the image data.

[0054] As an embodiment of the present invention, the process of average slope classification includes the following steps:

[0055] S21. Resampling of slope data: Calculate the slope from the digital elevation model (DEM) in the study area and resample it to a 2m spatial resolution, and spatially align the resampled slope data with the remote sensing image data to ensure the consistency of the slope data and the remote sensing image pixels;

[0056] S22. Statistics of average slope: Based on the interpreted anthropogenic disturbance patch data, statistically calculate the average slope at the patch scale;

[0057] S23. Average slope classification: According to the preset slope classification rules, perform reclassification processing on the generated raster layer of the average slope of patches to generate the average slope classification layer, which is one of the basic data for subsequent determination of the intensity of soil and water loss.

[0058] The average slope classification rules are shown in the following table

[0059] Slope grading rules Level Less than 5° 1 5-15° 2 15-30° 3 Greater than 30° 4

[0060] As an embodiment of the present invention, for the surface-exposed area, since it is mainly composed of bare soil, and may be mixed with some vegetation, and the vegetation boundary is relatively blurred and difficult to clearly define, the vegetation coverage is selected as the key index to characterize the vegetation status.

[0061] The calculation formula of the normalized difference vegetation index NDVI is:

[0062] NDVI = (b nir - b red ) / (b nir + b red )

[0063] In the formula: b nir represents the reflectance data of the near-infrared band in the remote sensing image; b red represents the reflectance data of the red band in the remote sensing image.

[0064] The calculation formula of the fractional vegetation cover FVC is:

[0065] FVC = (NDVI - NDVI soil ) / (NDVI veg - NDVI soil )

[0066] In the formula: NDVI is the normalized difference vegetation index; NDVI veg is the NDVI value of the pure vegetation pixel in the human-disturbed area; NDVI soil is the NDVI value of the pure bare soil pixel in the human-disturbed area.

[0067] Vegetation coverage classification: According to the preset vegetation coverage classification rules, the generated vegetation coverage raster layer is reclassified. After reclassification, the vegetation coverage classification layer is obtained.

[0068] The vegetation coverage classification rules are shown in the following table.

[0069] Vegetation coverage level Rule Level Low coverage Less than 0.3 1 Medium-low coverage 0.3～0.45 2 Medium coverage 0.45～0.6 3 Medium-high coverage 0.6～0.75 4 High coverage Greater than 0.75 5

[0070] As an embodiment of the present invention, the classification of human-disturbed land includes:

[0071] Interpretation of human-disturbed patches: Based on the remote sensing image data, visual interpretation of the human-disturbed patches in the study area is carried out. The interpretation content includes human disturbances such as production and construction projects, reserved land, and production and construction activities, and the vector data of the human-disturbed patches corresponding to the remote sensing time phase is obtained.

[0072] Refined land classification: Based on the interpretation results of anthropogenic disturbance patches, the patches are further classified according to surface characteristics. The classification content includes: disturbed surface (bare surface with a small amount of vegetation), surface hardening (hardened roads, buildings, sites, etc. within the disturbed area), surface covering (dust-proof nets or other covering measures laid on the bare surface), and water bodies (water bodies within the disturbed area). After classification, a raster layer with a spatial resolution of 2m is generated based on land type codes. At the same time, the land type layer is pixel-aligned with the remote sensing image data to ensure data consistency.

[0073] The land classification rules are shown in the following table.

[0074] Land use category Category code Disturbed ground surface 1 Surface hardening 2 Surface covering 3 Water body 4

[0075] As an embodiment of the present invention, the determination of the erosion intensity of anthropogenic disturbance patches includes:

[0076] Formulation of the lookup table for determining the erosion intensity of anthropogenic disturbances: Through systematic literature research and expert consultation, an evaluation rule for anthropogenic soil and water loss is established based on three indicators: average slope, vegetation coverage, and refined land types of anthropogenic disturbance plots.

[0077] The lookup rules for determining the erosion intensity of anthropogenic disturbances are shown in the following table

[0078]

[0079] Generation of the layer for determining the erosion intensity of anthropogenic disturbances: By integrating the average slope classification layer, vegetation coverage classification layer, and refined classification layer of anthropogenic disturbance plots, the calculation formula is as follows:

[0080] Layer for determining the erosion intensity of anthropogenic disturbances = average slope classification layer * 100 + vegetation coverage classification layer * 10 + refined classification layer of anthropogenic disturbance plots

[0081] As an embodiment of the present invention, the verification of the determination accuracy includes:

[0082] Determination accuracy assessment: Based on the field investigation results, the accuracy of the results of remotely sensed comprehensive discrimination of the intensity of anthropogenic soil and water loss is evaluated. By counting the number of misjudged patches and the area of misjudged patches, the accuracy rate is then calculated. The calculation formula is as follows:

[0083] Accuracy rate of patch quantity determination = (total number of patches - number of misjudged patches) / total number of patches * 100

[0084] Accuracy rate of patch area determination = (total patch area - misjudged patch area) / total patch area * 100

[0085] Result correction: Based on the field investigation results, the intensity of misjudged patches is corrected.

[0086] A method and medium for quickly discriminating the intensity of large - scale human - induced erosion based on remote sensing data, where the medium stores computer - executable instructions, and the computer - executable instructions are set to the method for quickly discriminating the intensity of large - scale human - induced erosion based on remote sensing data as described above.

[0087] As an embodiment of the present invention, when the computer - executable instructions are executed, the method for quickly discriminating the intensity of large - scale human - induced erosion based on remote sensing data as described above is implemented.

[0088] As an embodiment of the present invention, as Figures 1 to 6 shown, a method for quickly discriminating the intensity of human - induced erosion based on remote sensing data includes the following steps:

[0089] S1. Image pre - processing: First, obtain multi - source remote sensing image data in the study area, including red, green, blue, and near - infrared bands, with a spatial resolution of 2m. To ensure the standardization and applicability of the remote sensing image data, radiometric calibration and atmospheric correction processing are performed on the image data to generate surface reflectance data, providing a high - quality standardized data basis for subsequent remote sensing analysis and model construction.

[0090] S2. Average slope grading:

[0091] Statistically analyze the average slope of the human - induced disturbance areas in the case area and perform slope grading according to a preset standard. The slope is divided into four grades: slope less than 5°, slope 5° - 15°, slope 15° - 30°, and slope greater than 30°. After statistics, there are a total of 1744 patches with a slope grading of level 1 (less than 5°) in the case area, accounting for 97.43% of the total number of human - induced patches, and its total area is 75.03 km 2 ; there are a total of 46 patches with a slope grading of level 2 (5° - 15°), accounting for 2.57% of the total number of human - induced patches, and its total area is 1.82 km 2 as Figure 1 shown.

[0092] S3. Vegetation coverage grading:

[0093] First, calculate the normalized difference vegetation index (NDVI) based on the remote sensing image, and then further calculate the fractional vegetation cover (FVC). According to the FVC value, the coverage level is divided into five grades: low coverage, medium - low coverage, medium coverage, medium - high coverage, and high coverage. After statistics, the total area with a vegetation coverage of level 1 in the case area is 49.82 km 2 , accounting for 64.84% of the total area of human - induced patches; the area of level 2 is 11.86 km 2 , accounting for 15.43% of the total area of human - induced patches; the area of level 3 is 10.47 km 2, accounting for 13.62% of the total area of artificial patches; the area of Grade 4 is 3.76 km 2 , accounting for 4.89% of the total area of artificial patches; the area of Grade 5 is 10.94 km 2 , accounting for 1.22% of the total area of artificial patches, as Figure 2 shown.

[0094] S4. Refined classification of human-disturbed land:

[0095] Based on remote sensing images, the human-disturbed areas in the case area are interpreted, and further refined classification is carried out within the disturbed areas. The interpretation results are divided into four land types according to surface characteristics: disturbed surface, surface hardening, surface covering, and water body. Statistical results show that the area of the disturbed surface in the case area is 43.41 km 2 , the area of surface hardening is 30.82 km 2 , the area of surface covering is 1.10 km 2 , the area of water body is 1.52 km 2 , as Figure 3 shown.

[0096] S5. Judgment of the intensity of human-induced soil and water loss:

[0097] By integrating the slope grading layer, vegetation coverage grading layer, and refined classification layer of human-disturbed plots, the corresponding erosion intensity judgment layer of human-disturbed plots is calculated and generated. Through the lookup table of human-induced soil and water loss intensity judgment, the erosion intensity of human-disturbed plots is determined. Using the vector data of human-disturbed plots, the area proportion of different erosion intensities is statistically analyzed, and the erosion intensity with the largest area proportion is taken as the erosion intensity of the corresponding human-disturbed plot.

[0098] According to the lookup table of human-induced soil and water loss intensity judgment, the proportion of each soil and water loss intensity within the human-disturbed patches is determined, and the intensity with the highest proportion is taken as the soil and water loss intensity of the human-disturbed patch. Through the remote sensing comprehensive discrimination method, it is calculated that the soil and water loss intensity of the human-disturbed patches in the demonstration area is mainly slight, accounting for 77.98% of the total area of human-disturbed patches; for the human-disturbed patches with soil and water loss, it is mainly mild intensity, accounting for more than 95% of the total soil and water loss area, as Figure 4 shown.

[0099] Table 1 Statistical table of human-induced soil and water loss intensity calculated by this method

[0100]

[0101] S6. Verification of judgment accuracy: Based on the field investigation results of 137 patches, from the perspective of the number of patches, this method misjudges 17 patches, and the accuracy rate is 87.59%. From the perspective of area, the misjudged area is 1.83 km2 with an accuracy rate of 90.22%, as shown in Figure 5 .

[0102] Table 2 Statistical Table of Discrimination Accuracy

[0103]

[0104] S7. Result Correction: The remote sensing determination results were corrected according to the field survey results. A total of 17 patches were corrected, with an area of 1.83 km 2 , among which, 9 patches were corrected in Wujiang District, with an area of 1.27 km 2 ; 4 patches were corrected in Qingpu District, with an area of 0.23 km 2 ; 4 patches were corrected in Jiashan County, with an area of 0.33 km 2 . After correction, the area of artificial soil and water loss in the case area is 15.41 km 2 , accounting for 20.05% of the area of artificial patches. Among them, the mild degree is 14.7 km 2 , and the moderate degree is 0.71 km 2 .

[0105] Table 3 Statistical Table of Field Survey Correction

[0106]

[0107]

[0108] Table 4 Statistical Table of Artificial Soil and Water Loss Intensity after Correction

[0109]

[0110] To sum up: The present invention discloses a method for quickly discriminating the intensity of large-scale artificial disturbance erosion based on remote sensing data and its related media. This method follows the principles of being applicable to large-scale regions and being easy to operate, and formulates a set of evaluation rules for artificial soil and water loss through literature research and expert experience method.

[0111] First, the average slope of the artificially disturbed area in the study area is statistically analyzed, and the slope is classified according to a preset standard, which is divided into four grades: slope less than 5°, slope 5° - 15°, slope 15° - 30°, and slope greater than 30°. Subsequently, the artificially disturbed range in the study area is interpreted based on remote sensing images, and further refined classification is carried out within the disturbed range. The interpretation results are divided into four land types according to surface characteristics: disturbed surface, surface hardening, surface covering, and water body.

[0112] Next, the Normalized Difference Vegetation Index (NDVI) is calculated based on remote sensing images, and the Fractional Vegetation Cover (FVC) is further calculated. According to the FVC values, they are divided into five coverage levels: low coverage, medium-low coverage, medium coverage, medium-high coverage, and high coverage.

[0113] Finally, by integrating the slope classification layer, the vegetation coverage classification layer, and the refined classification layer of human disturbance areas, the corresponding erosion intensity determination layer of human disturbed plots is calculated and generated, and combined with the lookup table for determining the intensity of human-induced soil and water loss, the erosion intensity of each disturbed plot is determined. This method provides a scientific basis and technical support for the assessment of the intensity of human-induced soil and water loss at the regional scale.

[0114] The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings, and thus do not limit the scope of rights of the embodiments of the present application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall fall within the scope of rights of the embodiments of the present application.

Claims

1. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data, characterized in that: It includes the following steps: S1. Preprocess the obtained multispectral remote sensing images to generate surface reflectance data. Among them: the multispectral remote sensing images include remote sensing images in the red, green, blue, and near-infrared bands, and the spatial resolution of the remote sensing images is 2m; the preprocessing includes radiometric calibration and atmospheric correction; S2. Calculate the normalized difference vegetation index NDVI based on the surface reflectance data, calculate the fractional vegetation cover FVC using the vegetation index NDVI, and divide the fractional vegetation cover grades according to the fractional vegetation cover grading rules to generate a fractional vegetation cover grading layer; S3. Identify the anthropogenic disturbance land on the obtained multispectral remote sensing images through visual interpretation to obtain the vector data of anthropogenic disturbance patches, and refine the land type classification of the vector data of anthropogenic disturbance patches according to the surface characteristics to generate a refined land type classification layer; S4. According to the vector data of anthropogenic disturbance patches and the resampled slope data, calculate the average slope of the anthropogenic disturbance land to generate an average slope raster layer, and divide the average slope grades according to the average slope grading rules to generate an average slope grading layer; S5. By integrating the average slope grading layer, the fractional vegetation cover grading layer, and the refined land type classification layer, calculate and generate a layer for determining the erosion intensity of anthropogenic disturbance land, and determine the erosion intensity of anthropogenic disturbance land through the lookup table for determining the intensity of anthropogenic soil and water loss.

2. The rapid discrimination method of the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 1, characterized in that: The determination result of the erosion intensity of the anthropogenic disturbance land is verified for accuracy and corrected based on the results of field investigations.

3. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 2, characterized in that: The accuracy verification and correction are carried out by analyzing the results of field investigations of anthropogenic disturbance land to determine the actual erosion intensity of anthropogenic disturbance land, verifying the accuracy of the determined erosion intensity of anthropogenic disturbance land, and correcting the misjudged erosion intensity of anthropogenic disturbance land.

4. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 1, characterized in that: The radiometric calibration is to convert the DN value in the multispectral remote sensing image data into surface reflectance to obtain real surface reflectance data.

5. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 1, characterized in that: The atmospheric correction is to correct the atmospheric influence in the multispectral remote sensing image data, eliminate the interference of atmospheric scattering and absorption effects, restore the true spectral characteristics of surface targets, and improve the authenticity and reliability of the image data.

6. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 1, characterized in that: The slope data is obtained by calculating the digital elevation model DEM in the 12.5m terrain data into slope and resampling it to a 2m spatial resolution.

7. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 1, characterized in that: The average slope grading rules are: set the average slope less than 5° as level 1, the average slope of 5° - 15° as level 2, the average slope of 15° - 30° as level 3, and the average slope greater than 30° as level 4.

8. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 1, characterized in that: The calculation formula of the normalized difference vegetation index NDVI is: NDVI=(b nir -b red ) / (b nir +b red ) Where: b nir represents the reflectance data in the near-infrared band of the remote sensing image; b red represents the reflectance data in the red band of the remote sensing image.

9. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 1, characterized in that: The calculation formula of the fractional vegetation cover FVC is: FVC=(NDVI - NDVI soil ) / (NDVI veg - NDVI soil ) Where: NDVI is the Normalized Difference Vegetation Index; NDVI veg is the NDVI value of the pure vegetation pixel in the human disturbance area; NDVI soil is the NDVI value of the pure bare soil pixel in the human disturbance area.

10. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 1, characterized in that: The fractional vegetation cover grading rules are: set the fractional vegetation cover less than 0.3 as level 1, the fractional vegetation cover between 0.3 and 0.45 as level 2, the fractional vegetation cover between 0.45 and 0.60 as level 3, the fractional vegetation cover between 0.6 and 0.75 as level 4, and the fractional vegetation cover greater than 0.75 as level 5.

11. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 1, characterized in that: The content of the visual interpretation includes production construction projects, reserved land, and production construction activities.

12. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 1, characterized in that: The content of the refined classification of land types includes: disturbed ground surface, hardened ground surface, covered ground surface, and water bodies.

13. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 1, characterized in that: The lookup table for determining the intensity of human-induced soil and water loss is established through systematic literature research and expert consultation. An evaluation rule for human-induced soil and water loss is established based on three indicators: average slope, vegetation coverage, and classification code of land types with human disturbance. The evaluation rule for human-induced soil and water loss is shown in the following table:

14. A rapid discrimination method for the intensity of anthropogenic disturbance erosion based on remote sensing data according to claim 1, characterized in that: The calculation formula for the layer of determining the erosion intensity of land with human disturbance is: Layer of determining the erosion intensity of human disturbance = Layer of average slope classification * 100 + Layer of vegetation coverage classification * 10 + Layer of refined classification of human-disturbed plots.

15. A storage medium, characterized in that: The storage medium stores computer-executable instructions, and the computer-executable instructions are set to be a method for quickly discriminating the erosion intensity of human disturbance based on remote sensing data as described in any one of claims 1-14.

16. A storage medium according to claim 15, characterized in that: When the computer-executable instructions are executed, a method for quickly discriminating the erosion intensity of human disturbance based on remote sensing data as described in any one of claims 1-7 is implemented.

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