Wild land-farmland boundary region division method, system, medium and equipment

Abstract

The invention relates to the technical field of remote sensing image processing, and provides a field-farmland boundary region zoning method and system, a medium and equipment, and the method comprises the steps: converting a farmland grid into a vector polygon, obtaining a plurality of farmland patches, filtering the farmland patches with the area smaller than a first threshold value, constructing an external expansion annular buffer region for each reserved farmland patch, and carrying out the external expansion annular buffer region; marking the externally expanded annular buffer area with the natural vegetation coverage greater than a second threshold value as a potential wild land-farmland junction area; the method comprises the following steps: dividing a potential field-farmland junction domain into four quadrants by taking a geometric center point of a farmland patch as an original point, calculating the natural vegetation coverage of each quadrant sub-region, and recognizing a mixed field-farmland junction domain and an interface type field-farmland junction domain according to a quadrant-level natural vegetation coverage threshold and quadrant standard number constraints. The method effectively solves the problem of quantitative identification of the agriculture and forestry interlaced region, can effectively identify the field-farmland junction region, and provides a basis for field fire risk assessment of an agriculture-dominated region.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote sensing image processing, and in particular relates to a method, system, medium and equipment for demarcating a wildland-farmland boundary area. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Against the backdrop of intensifying global climate change and the expansion of human activity, the frequency and destructiveness of wildfires are becoming increasingly prominent. To address the high risk of wildfires in urban fringe areas, the concept of the Wildland-Urban Interface (WUI) has been proposed. It is defined as the transitional zone where natural vegetation ecosystems intersect or intermingle with highly urbanized areas. The core risk of the WUI stems from the close contact or high spatial inlay between natural combustible vegetation and buildings and facilities in human settlements. This greatly increases the probability of fires caused by natural or human factors. Once a fire ignites, it can easily spread to buildings, causing serious casualties and property damage. The driving force behind this regional expansion is primarily the disorderly expansion of urban space and the increase in vegetation cover in surrounding areas. This results in the region being generally characterized by high building density, large areas of combustible vegetation coverage, and fragmented distribution of natural vegetation patches. Fire behavior exhibits significant regional clustering and complex dynamics.

[0004] However, the unique fire risk pattern in agriculturally dominated areas is primarily characterized by: In terms of urban structure, settlements at the urban-rural fringe are mostly high-density, concentrated, and contiguous. Their perimeters are isolated from the natural environment by planning or buffer zones, while relatively few areas within them are characterized by densely packed building complexes and a high degree of intermingling with natural vegetation. In contrast, a prominent feature of the region's landscape pattern is the widespread presence of large, contiguous farmland, which directly abuts natural vegetation areas such as forests, grasslands, and shrublands, forming a broad contact boundary.

[0005] More importantly, traditional agricultural fire practices, such as straw burning, persist in agricultural production. Agricultural-dominated regions generally face a wildfire risk pattern characterized by large areas of farmland directly adjacent to wildlands. However, the WUI concept and its related technical systems are based on an urban-rural structure dominated by low-density single-family homes. Directly applying them to agricultural-dominated regions with significantly different spatial patterns and farming traditions presents fundamental limitations. It is difficult to effectively cover and reflect the primary sources of wildfire risk, and it is difficult to accurately identify and quantify the highly heterogeneous wildland-farmland interlaced structure and its inherent risk-forming mechanisms. This results in insufficiently targeted risk assessment and prevention measures. Summary of the Invention

[0006] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a method, system, medium and equipment for wildland-farmland boundary zoning, creates a multi-level progressive spatial verification architecture, effectively solves the problem of quantitative identification of agricultural and forestry interlaced areas, and can effectively identify wildland-farmland boundaries that adapt to the characteristics of agricultural areas, providing a basis for wildfire risk assessment in agricultural-dominated areas.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: A first aspect of the present invention provides a method for demarcating a wildland-farmland boundary, comprising: Obtain remote sensing images of the target area, obtain wild areas and farmland areas through image segmentation, and encode the wild areas and farmland areas into rasters respectively; The farmland raster is converted into vector polygons to obtain several farmland patches. The farmland patches with an area smaller than a first threshold are filtered out. An outward expansion ring buffer is constructed for each retained farmland patch. The natural vegetation cover of the outward expansion ring buffer of each farmland patch is calculated. The outward expansion ring buffer with a natural vegetation cover greater than a second threshold is marked as a potential wildland-farmland interface. The potential wildland-farmland interface was divided into four quadrants with the geometric center of the farmland patch as the origin. The natural vegetation cover was calculated for each quadrant sub-area. Based on the quadrant-level natural vegetation cover threshold and the quadrant-standard number constraint, mixed wildland-farmland interface areas and interface wildland-farmland interface areas were identified in the potential wildland-farmland interface area. The natural vegetation coverage is the ratio of the number of wild land grids in the area to the total number of grids in the area.

[0008] Furthermore, the step of identifying mixed wildland-farmland boundaries and interface wildland-farmland boundaries in potential wildland-farmland boundaries includes: for a potential wildland-farmland boundary, if the number of quadrant sub-areas with natural vegetation coverage greater than the third threshold meets the standard, then the potential wildland-farmland boundary is determined to be a mixed wildland-farmland boundary; otherwise, the wildland raster is converted into a vector polygon to obtain a number of wildland patches, and the wildland patches with an area less than the fourth threshold are filtered out, a buffer zone is constructed for each retained wildland patch, the spatial intersection relationship between the farmland patches corresponding to the potential wildland-farmland boundary and the buffer zone of the wildland patches is detected, and the intersecting farmland patches are retained. For the outer expansion ring buffer zone of a certain intersecting farmland patch, if the number of quadrant sub-areas with natural vegetation coverage greater than the fifth threshold meets the standard, then the potential wildland-farmland boundary is determined to be an interface wildland-farmland boundary.

[0009] Furthermore, the image segmentation step includes: For the remote sensing images of the target area, feature extraction is performed through standard convolution to obtain high-resolution features; After downsampling the remote sensing image of the target area, feature extraction is performed through dilated convolution to obtain downsampling features; After upsampling the downsampled features, the normalized vegetation index is calculated to obtain the enhanced features. The gray-level co-occurrence matrix is ​​applied to the enhanced features to calculate the texture roughness and near-infrared reflectance intensity to obtain the texture features and reflectance features. After feature fusion of high-resolution features, downsampling features, enhancement features, texture features and reflection features, shrub, moss, mangrove, grassland, herbaceous wetland and woodland areas are obtained through classifier.

[0010] Furthermore, the image segmentation step further includes: For the remote sensing image of the target area, the normalized water index is calculated. The area where the normalized water index exceeds the set value is regarded as a herbaceous wetland, and the intersection with the herbaceous wetland obtained by the classifier is calculated to obtain the final herbaceous wetland. Combine shrubs, mosses, mangroves, grasslands, herbaceous wetlands, and woodland areas into wildland areas; For the remote sensing images of the target area, the areas except the wild areas are regarded as the pre-classified farmland areas.

[0011] Furthermore, the image segmentation step further includes: for the pre-classified farmland area, quantifying the consistency of the ridge direction based on the directional gradient histogram, and screening out the farmland area.

[0012] A second aspect of the present invention provides a wildland-farmland boundary zoning system, comprising: An image segmentation module is configured to: obtain a remote sensing image of a target area, obtain a wild area and a farmland area through image segmentation, and encode the wild area and the farmland area into rasters respectively; A preliminary demarcation module is configured to: convert the farmland raster into vector polygons to obtain a number of farmland patches, filter out farmland patches with an area smaller than a first threshold, construct an outward expansion ring buffer for each retained farmland patch, calculate the natural vegetation coverage of the outward expansion ring buffer of each farmland patch, and mark the outward expansion ring buffer with a natural vegetation coverage greater than a second threshold as a potential wildland-farmland interface area; The final partitioning module is configured to: divide the potential wildland-farmland interface area into four quadrants with the geometric center point of the farmland patch as the origin, calculate the natural vegetation cover for each quadrant sub-area, and identify mixed wildland-farmland interface areas and interface wildland-farmland interface areas in the potential wildland-farmland interface area based on the quadrant-level natural vegetation cover threshold and the quadrant compliance number constraint; The natural vegetation coverage is the ratio of the number of wild land grids in the area to the total number of grids in the area.

[0013] Furthermore, the step of identifying mixed wildland-farmland boundaries and interface wildland-farmland boundaries in potential wildland-farmland boundaries includes: for a potential wildland-farmland boundary, if the number of quadrant sub-areas with natural vegetation coverage greater than the third threshold meets the standard, then the potential wildland-farmland boundary is determined to be a mixed wildland-farmland boundary; otherwise, the wildland raster is converted into a vector polygon to obtain a number of wildland patches, and the wildland patches with an area less than the fourth threshold are filtered out, a buffer zone is constructed for each retained wildland patch, the spatial intersection relationship between the farmland patches corresponding to the potential wildland-farmland boundary and the buffer zone of the wildland patches is detected, and the intersecting farmland patches are retained. For the outer expansion ring buffer zone of a certain intersecting farmland patch, if the number of quadrant sub-areas with natural vegetation coverage greater than the fifth threshold meets the standard, then the potential wildland-farmland boundary is determined to be an interface wildland-farmland boundary.

[0014] Furthermore, the image segmentation step includes: For the remote sensing images of the target area, feature extraction is performed through standard convolution to obtain high-resolution features; After downsampling the remote sensing image of the target area, feature extraction is performed through dilated convolution to obtain downsampling features; After upsampling the downsampled features, the normalized vegetation index is calculated to obtain the enhanced features. The gray-level co-occurrence matrix is ​​applied to the enhanced features to calculate the texture roughness and near-infrared reflectance intensity to obtain the texture features and reflectance features. After feature fusion of high-resolution features, downsampling features, enhancement features, texture features and reflection features, shrub, moss, mangrove, grassland, herbaceous wetland and woodland areas are obtained through classifier.

[0015] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for demarcating the wildland-farmland boundary as described above.

[0016] The fourth aspect of the present invention provides a computer device comprising a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, wherein when the processor executes the program, the steps in the method for demarcating the wildland-farmland boundary area as described above are implemented.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention creates a multi-level progressive spatial verification architecture, which effectively solves the problem of quantitative identification of agricultural and forestry interlaced areas: first, a global buffer zone analysis is used to realize the initial screening of macro-risk areas, and secondly, a four-quadrant rigid geometric segmentation method along the north-south / east-west axis is innovatively introduced. Combined with the quadrant-level vegetation coverage threshold and the quadrant compliance number constraint, the mixed WCI risk characteristics of deep infiltration within the farmland are accurately analyzed. Finally, through the continuous wild patch boundary identification and single-quadrant coverage verification, the interface conduction WCI area is locked; the defect of the traditional radial buffer zone in insufficient perception of heterogeneous space is overcome, and for the first time, the physical separation of the two types of disaster mechanisms of "spatial penetration" and "boundary contact" is realized in the landscape fragmentation area.

[0018] The present invention constructs a raster-vector dual-engine collaborative mechanism at the spatial computing level, uses raster pixel statistics to ensure the accuracy of microscopic coverage calculations, and realizes dynamic analysis of farmland geometric characteristics through vector space operations; introduces a geometric constraint mechanism in coverage calculations, and adopts a rigid four-quadrant division along the north-south and east-west axes to replace the traditional free direction division, eliminating spatial structure distortion errors and forming a portable farm-field interactive quantification tool chain.

[0019] This invention constructs a theoretical framework for the wildland-farmland interface that adapts to the characteristics of agricultural areas, innovates the traditional urban-centered wildfire risk cognition system, and provides a new theoretical basis for wildfire risk assessment in agriculture-dominated areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0021] Figure 1 This is a flow chart of a method for demarcating a wildland-farmland boundary area according to a first embodiment of the present invention; Figure 2 This is a structural diagram of a multi-factor semantic segmentation and recognition model based on the HRNetV2 network according to the first embodiment of the present invention; Figure 3 This is a flowchart of determining the wildland-farmland interface (WCI) according to the first embodiment of the present invention; Figure 4 It is a structural diagram of a computer device according to the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0022] To make the objectives, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0023] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0024] Example 1 This embodiment provides a method for demarcating wildland-farmland boundary areas.

[0025] This example provides a method for zoning wildland-cropland interfaces. It addresses high-risk wildfire areas unique to agriculturally dominated regions—the wildland-cropland interface (WCI). Within WCIs, fires in natural vegetation or straw burning within farmland can easily break through the boundaries and ignite adjacent combustible materials. Of particular note, vast farmland can serve as a pathway for wildfires to spread rapidly over long distances, threatening more distant vegetation areas or communities. This poses a serious threat not only to important regional ecological resources such as forests and grasslands, but also to valuable farmland resources, agricultural production facilities, and food security.

[0026] This embodiment provides a method for demarcating wildland-farmland interfaces. It innovatively proposes a multi-level progressive spatial verification architecture and a hybrid / interface WCI grading system. These methods aim to accurately characterize the different modes of spatial interaction between farmland and wildland, thereby achieving physical separation of risk mechanisms and providing direct and effective technical support for the scientific identification and delineation of WCI, fire risk assessment, and regional planning and management.

[0027] This embodiment provides a method for demarcating the boundary between wildland and farmland, such as Figure 1 As shown, the following steps are included: Step 1: If Figure 2 As shown in the figure, the GF-2 remote sensing image of the target area is obtained, and image preprocessing is performed. The trained high-resolution network (High-Resolution Representations for Labeling Pixels and Regions, HRNetV2) model is used to perform semantic segmentation on the remote sensing image of the target area to achieve land classification and merging, and obtain the wild land and farmland segmentation results.

[0028] Step 101: retrieve Gaofen-2 multispectral image data covering the target area from the Gaofen Satellite Data Center, and the cloud coverage of the remote sensing image of the target area must be less than 10%.

[0029] Step 102: Use the satellite-provided radiation calibration coefficient file to convert the original pixel brightness (DN) value into apparent radiance.

[0030] Step 103: For the remote sensing image of the target area, atmospheric correction (FLAASH) is used. The imaging time, geographic location, and aerosol model are input, and the radiation transfer model is combined to eliminate the atmospheric scattering effect and output the surface reflectance data.

[0031] Step 104: Normalize the pre-processed GF-2 remote sensing image data using the four-band blue / green / red / near-infrared bands. Using the Normalized Difference Water Index (NDWI) (NDWI = (Green - NIR) / (Green + NIR)), initially identify areas with an NDWI greater than 0.3 as herbaceous wetland pixels. The near-infrared reflectance of these herbaceous wetland pixels is enhanced to 1.5 times the original value. Green represents the green band, and NIR represents the near-infrared band.

[0032] Step 105: Initialize the dual-stream HRNetV2 model and perform dual-process processing on the remote sensing image of the target area. The main branch processes the 512×512 original resolution image to capture details, and the secondary branch processes the 128×128 downsampled image to recognize large-scale features.

[0033] In step 106, the high-resolution branch (i.e., the main branch) extracts shrub and moss features through a 3×3 standard convolution kernel; the downsampling branch (i.e., the secondary branch) uses a dilated convolution with a dilation rate of 3 to capture wetland, mangrove, and grassland features.

[0034] The remote sensing image of the target area has four bands: blue, green, red, and near-infrared. In step 105, the weights of the first four bands are 1:1:1:1. In the main and sub-branch convolutional layers, the weight of the near-infrared band (NIR) is increased by 1.25 times to 1:1:1:1.25, and the weight of the near-infrared channel is increased by 25% to enhance the vegetation response.

[0035] Step 107: After upsampling the features extracted by the secondary branch to 512×512, an NDVI (normalized difference vegetation index) feature layer is added to calculate the NDVI enhanced feature: NDVI = (NIR-R) / (NIR+R), to highlight the herbaceous wetland. NIR is the near-infrared band and R is the red band. The result obtained after adding the NDVI feature layer (NDVI enhanced feature) is applied to the gray-level co-occurrence matrix to calculate the texture roughness and near-infrared reflectance intensity to obtain the texture feature and reflectance feature. The calculation formula is as follows: ; Where C is the texture roughness, P(i,j) is the probability of gray levels i and j co-occurring in the gray level co-occurrence matrix, and N is the number of quantized gray levels; ; Among them, NIR iis the near infrared reflection intensity, NIR p is the near-infrared reflectance value at pixel P, and k is the total number of pixels in the calculation window.

[0036] Step 108: The high-resolution features extracted by the main branch, the downsampling features extracted by the sub-branch, the NDVI enhancement features, the texture features, and the reflection features are fused through a connection operation. For the fused features, shrubs, mosses, mangroves, grasslands, herbaceous wetlands, and woodland areas are obtained through a classifier.

[0037] Step 109 : Intersect the herbaceous wetland pixels with NDWI>0.3 in step 104 with the herbaceous wetland pixels obtained in step 108 , and mark them as herbaceous wetlands.

[0038] Step 110: After subtracting the shrubs, mosses, mangroves, grasslands, and woodlands obtained in step 108 and the herbaceous wetland areas obtained in step 109 from the original image, the remaining area is considered as the farmland pre-classification area. In the farmland pre-classification area, the consistency of the ridge direction is quantified based on the histogram of oriented gradients (HOG). Regular strips with more than 75% uniform direction are marked as farmland. The specific calculation formula is as follows: ; Where R is the concentration ratio in the main direction, i is the number of directions (the present invention uses 10° intervals as one direction, with a total of 36 directions), H i is the gradient amplitude sum of the i-th direction interval, max(H i ) is the amplitude sum of the main direction interval. When R≥0.75, it means that there is a dominant direction in the area, so it is judged as farmland.

[0039] Step 111: Merge the six land features (woodland, grassland, shrub, herbaceous wetland, mangrove, and moss and lichen) into a single "wild land" layer, retaining the original classification attribute fields.

[0040] Step 112: A 5×5 window median filter is applied to the merged wildland-farmland binary classification map to eliminate salt and pepper noise, and small patches smaller than 100 square meters are removed by patch area calculation.

[0041] Step 113: Encode the merged wildland layer (containing attribute fields for six land features: woodland, grassland, shrub, herbaceous wetland, mangrove, and moss and lichen) and the farmland layer to generate a two-channel GeoTIFF (Geographic Tagged Image File Format) raster with a spatial resolution consistent with the original resolution of the Gaofen-2 remote sensing image (channel 1: wildland classification / channel 2: farmland mask).

[0042] Step 2: Convert the farmland raster data obtained by image segmentation into vector polygons, establish an outward expansion buffer for each farmland patch, calculate the vegetation coverage within the buffer, and mark it as a potential WCI area.

[0043] Step 201: Convert the farmland raster into vector polygons to obtain a number of farmland patches, automatically filter out farmland patches with an area less than 0.1 square kilometers, and generate a unique ID and spatial attribute table (including fields such as area and perimeter) for each farmland patch.

[0044] Step 202: Construct a 2.4 km outer ring buffer for each retained farmland patch.

[0045] Step 203: Calculate the natural vegetation coverage for each farmland patch buffer. The coverage calculation uses the binary wildland raster as input. The statistical formula is defined as: (Number of wildland pixels in the buffer zone / total number of pixels in the buffer zone) × 100%; It should be noted that pixels and rasters are the same concept.

[0046] During the statistical process, the original resolution of Gaofen-2 was used to count, and buffer zones with natural vegetation coverage ≥ 25% within the buffer zone boundary were screened and marked as potential WCI areas. The rest were considered non-intersection areas and removed from the processing flow.

[0047] Step 3: If Figure 3 As shown in the figure, the buffer zone of the potential WCI area is divided into four quadrants, and the vegetation cover is calculated quadrant by quadrant to determine the mixed WCI area.

[0048] Step 301: The farmland buffer zone marked as potential WCI is divided into four quadrants (B1, B2, B3, and B4) based on the geometric center point of the farmland patch and the directions of due north (N, 0°), due east (E, 90°), due south (S, 180°), and due west (W, 270°).

[0049] Step 302: Calculate the vegetation coverage of each quadrant sub-area independently. The coverage calculation method is the same as that of step 203. That is, the vegetation coverage within the four-quadrant polygon is counted.

[0050] Step 303: When the coverage of three or more quadrants in the buffer zone corresponding to a single farmland patch is ≥50%, the farmland patch buffer zone is determined to be a mixed WCI. Finally, a mixed WCI vector set is output and an attribute identification field is added. Otherwise, it is determined to be a potential non-mixed WCI farmland patch.

[0051] Step 4: Spatial intersection detection was performed on large wildland patches with potential non-mixed WCI farmland patches through a 2.4 km buffer zone. After retaining the intersecting patches, a four-quadrant coverage calculation was performed to determine the interface WCI area.

[0052] Step 401: Convert the wildland raster data after image segmentation and merging into vector polygons to obtain a number of wildland patches. Calculate the area of ​​each wildland patch and retain the area ≥ 5km 2 Large continuous wildland patches.

[0053] Step 402: Construct a 2.4 km buffer zone for the retained large wildland patches, detect their spatial intersection with the farmland patches in the potential WCI area that are not marked as mixed WCI (i.e., potential non-mixed WCI farmland patches), and retain the intersecting farmland patches. That is, check polygon by polygon whether they are located in an area ≥ 5 km 2 Near dense vegetation areas.

[0054] Step 403: Perform four-quadrant coverage calculation on the retained intersecting farmland patches, and the calculation method is consistent with step 203.

[0055] Step 404: When the coverage of one or more quadrants in the buffer zone corresponding to a single farmland patch is ≥50%, the farmland patch buffer zone is determined to be an interface-type WCI, and an interface-type WCI vector set is finally output and an attribute identification field is added.

[0056] Step 5: Integrate the spatial distribution data of mixed WCI and interface WCI to generate a wildland-farmland interface zoning map.

[0057] This example provides a method for zoning wildland-farmland interfaces and constructs a theoretical framework for the wildland-farmland interface (WCI) adapted to the characteristics of agricultural regions, revolutionizing the traditional urban-centered wildfire risk perception system. This framework systematically explains for the first time the dual-hazard nature of farmland ecosystems: they serve as both a medium for the outward spread of wildfires through agricultural activities such as straw burning, and as a core hazard-bearing body for wildfires invading from natural areas. Compared to traditional models, this theory focuses on fire dynamics mechanisms dominated by spatial connectivity, revealing the key role of farmland as a physical channel in the spread of cross-border fires, and providing a new theoretical basis for wildfire risk assessment in agricultural-dominated regions.

[0058] This embodiment provides a method for zoning the wildland-farmland interface, creates a multi-level progressive spatial verification framework, and effectively solves the problem of quantitative identification of agricultural and forestry interlaced areas. First, a 2.4km global buffer zone analysis is used to achieve preliminary screening of macro-risk areas (vegetation coverage ≥ 25%); secondly, a four-quadrant rigid geometric segmentation method along the north-south / east-west axis is innovatively introduced, combined with the quadrant-level vegetation coverage threshold (≥ 50%) and the quadrant compliance number constraint (≥ 3), to accurately analyze the mixed WCI risk characteristics of deep infiltration within farmland; finally, through ≥ 5km 2Continuous wildland patch boundaries are identified and single-quadrant coverage is verified (coverage of the boundary contact zone ≥ a threshold), pinpointing areas of interfacial conduction WCI. This method overcomes the shortcomings of traditional radial buffers in their inability to perceive heterogeneous spaces, and for the first time achieves a physical separation of the two disaster mechanisms of "spatial penetration" and "boundary contact" in landscape fragmentation.

[0059] This embodiment provides a method for zoning the boundary between wildland and farmland, deeply integrating deep learning and spatial topology computing technologies, and constructing a multi-scale collaborative parsing engine. The HRNetV2 deep learning model is used to achieve high-precision pixel-level synchronous segmentation of six types of natural features such as woodlands and grasslands, generating a semantically structured binary basis. A raster-vector dual-engine collaborative mechanism is constructed at the spatial computing level, and raster pixel statistics are used to ensure the accuracy of microscopic coverage calculations. Dynamic analysis of farmland geometric features is achieved through vector space operations. A geometric constraint mechanism is introduced in the coverage calculation, and a rigid four-quadrant segmentation along the north-south, east-west axes is used to replace the traditional free direction division, eliminating spatial structure distortion errors and forming a portable farm-field interaction quantification tool chain.

[0060] This example provides a method for zoning wildland-farmland interfaces and establishes a mixed / interface WCI grading system, providing precise spatial guidance for differentiated prevention and control strategies. To address the deep vegetation penetration characteristics of mixed-type areas, it recommends the construction of grid-based firebreaks and all-weather agricultural fire control. To address the contact characteristics of large combustible boundaries in interface-type areas, it designs boundary buffer zone flame retardant projects and a time-sharing scheduling mechanism for fire source activity. This technical framework enables a transition from homogeneous prevention and control to "risk mechanism identification and prevention and control strategy adaptation."

[0061] Example 2 This embodiment provides a wildland-farmland boundary zoning system, which specifically includes: An image segmentation module is configured to: obtain a remote sensing image of a target area, obtain a wild area and a farmland area through image segmentation, and encode the wild area and the farmland area into rasters respectively; A preliminary demarcation module is configured to: convert the farmland raster into vector polygons to obtain a number of farmland patches, filter out farmland patches with an area smaller than a first threshold, construct an outward expansion ring buffer for each retained farmland patch, calculate the natural vegetation coverage of the outward expansion ring buffer of each farmland patch, and mark the outward expansion ring buffer with a natural vegetation coverage greater than a second threshold as a potential wildland-farmland interface area; The final partitioning module is configured to: divide the potential wildland-farmland interface area into four quadrants with the geometric center point of the farmland patch as the origin, calculate the natural vegetation cover for each quadrant sub-area, and identify mixed wildland-farmland interface areas and interface wildland-farmland interface areas in the potential wildland-farmland interface area based on the quadrant-level natural vegetation cover threshold and the quadrant compliance number constraint; The spatial data integration module is configured to: integrate the spatial topology of hybrid WCI and interface WCI to generate standardized zoning maps and statistical reports.

[0062] The natural vegetation coverage is the ratio of the number of wild land grids in the area to the total number of grids in the area.

[0063] Among them, the step of identifying mixed wildland-farmland boundaries and interface wildland-farmland boundaries in potential wildland-farmland boundaries includes: for a potential wildland-farmland boundary, if the number of quadrant sub-areas with natural vegetation coverage greater than the third threshold meets the standard, then the potential wildland-farmland boundary is determined to be a mixed wildland-farmland boundary; otherwise, the wildland raster is converted into a vector polygon to obtain a number of wildland patches, and the wildland patches with an area less than the fourth threshold are filtered out, a buffer zone is constructed for each retained wildland patch, the spatial intersection relationship between the farmland patches corresponding to the potential wildland-farmland boundary and the buffer zone of the wildland patches is detected, and the intersecting farmland patches are retained. For the outer expansion ring buffer zone of a certain intersecting farmland patch, if the number of quadrant sub-areas with natural vegetation coverage greater than the fifth threshold meets the standard, then the potential wildland-farmland boundary is determined to be an interface wildland-farmland boundary.

[0064] The image segmentation step includes: For the remote sensing images of the target area, feature extraction is performed through standard convolution to obtain high-resolution features; After downsampling the remote sensing image of the target area, feature extraction is performed through dilated convolution to obtain downsampling features; After upsampling the downsampled features, the normalized vegetation index is calculated to obtain the enhanced features. The gray-level co-occurrence matrix is ​​applied to the enhanced features to calculate the texture roughness and near-infrared reflectance intensity to obtain the texture features and reflectance features. After fusion of high-resolution features, downsampled features, enhanced features, texture features, and reflectance features, the shrub, moss, mangrove, grassland, herbaceous wetland, and woodland regions are obtained through the classifier. For the remote sensing image of the target area, the normalized water index is calculated. The area where the normalized water index exceeds the set value is regarded as a herbaceous wetland, and the intersection with the herbaceous wetland obtained by the classifier is calculated to obtain the final herbaceous wetland. Combine shrubs, mosses, mangroves, grasslands, herbaceous wetlands, and woodland areas into wildland areas; For the remote sensing images of the target area, the areas other than the wild areas are considered as the pre-classified farmland areas; For the pre-classified farmland areas, the ridge direction consistency is quantified based on the directional gradient histogram to screen out the farmland areas.

[0065] It should be noted here that the various modules in this embodiment correspond one-to-one to the various steps in Example 1, and the specific implementation processes are the same, which will not be repeated here.

[0066] Example 3 This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the method for demarcating the wildland-farmland boundary area as described in the first embodiment above are implemented.

[0067] Example 4 This embodiment provides a computer device, such as Figure 4 As shown, the present invention includes a computer-readable storage medium 1003, a processor 1001, a communication interface 1002, and a computer program stored on the computer-readable storage medium 1003 and executable on the processor 1001. The processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means. The communication interface 1002 is configured to receive and transmit data, and when the processor 1001 executes the program, the steps of the method for demarcating the wildland-farmland boundary as described in the first embodiment are implemented.

[0068] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for demarcating the boundary between wildland and farmland, characterized in that: include: Obtain remote sensing images of the target area, obtain wild areas and farmland areas through image segmentation, and encode the wild areas and farmland areas into rasters respectively; The farmland raster is converted into vector polygons to obtain several farmland patches. The farmland patches with an area smaller than a first threshold are filtered out. An outward expansion ring buffer is constructed for each retained farmland patch. The natural vegetation cover of the outward expansion ring buffer of each farmland patch is calculated. The outward expansion ring buffer with a natural vegetation cover greater than a second threshold is marked as a potential wildland-farmland interface. The potential wildland-farmland interface was divided into four quadrants with the geometric center of the farmland patch as the origin. The natural vegetation cover was calculated for each quadrant sub-area. Based on the quadrant-level natural vegetation cover threshold and the quadrant-standard number constraint, mixed wildland-farmland interface areas and interface wildland-farmland interface areas were identified in the potential wildland-farmland interface area. The natural vegetation coverage is the ratio of the number of wild land grids in the area to the total number of grids in the area.

2. A method for demarcating the boundary between wildland and farmland according to claim 1, characterized in that: The step of identifying mixed wildland-farmland boundaries and interface wildland-farmland boundaries in potential wildland-farmland boundaries includes: for a potential wildland-farmland boundary, if the number of quadrant sub-areas with natural vegetation coverage greater than a third threshold meets the standard, then the potential wildland-farmland boundary is determined to be a mixed wildland-farmland boundary; otherwise, the wildland raster is converted into a vector polygon to obtain a number of wildland patches, and the wildland patches with an area less than a fourth threshold are filtered out, a buffer zone is constructed for each retained wildland patch, the spatial intersection relationship between the farmland patches corresponding to the potential wildland-farmland boundary and the buffer zone of the wildland patches is detected, and the intersecting farmland patches are retained. For the outer expansion ring buffer zone of a certain intersecting farmland patch, if the number of quadrant sub-areas with natural vegetation coverage greater than a fifth threshold meets the standard, then the potential wildland-farmland boundary is determined to be an interface wildland-farmland boundary.

3. The method for demarcating the boundary between wildland and farmland according to claim 1, wherein: The step of image segmentation comprises: For the remote sensing images of the target area, feature extraction is performed through standard convolution to obtain high-resolution features; After downsampling the remote sensing image of the target area, feature extraction is performed through dilated convolution to obtain downsampling features; After upsampling the downsampled features, the normalized vegetation index is calculated to obtain the enhanced features. The gray-level co-occurrence matrix is ​​applied to the enhanced features to calculate the texture roughness and near-infrared reflectance intensity to obtain the texture features and reflectance features. After feature fusion of high-resolution features, downsampling features, enhancement features, texture features and reflection features, shrub, moss, mangrove, grassland, herbaceous wetland and woodland areas are obtained through classifier.

4. A method for demarcating the boundary between wildland and farmland according to claim 3, characterized in that: The step of image segmentation also includes: For the remote sensing image of the target area, the normalized water index is calculated. The area where the normalized water index exceeds the set value is regarded as a herbaceous wetland, and the intersection with the herbaceous wetland obtained by the classifier is calculated to obtain the final herbaceous wetland. Combine shrubs, mosses, mangroves, grasslands, herbaceous wetlands, and woodland areas into wildland areas; For the remote sensing images of the target area, the areas except the wild areas are regarded as the pre-classified farmland areas.

5. A method for demarcating the boundary between wildland and farmland according to claim 4, characterized in that: The image segmentation step further includes: quantifying the consistency of ridge directions in the pre-classified farmland areas based on the directional gradient histogram, and screening out the farmland areas.

6. A wildland-farmland boundary zoning system, characterized by: include: An image segmentation module is configured to: obtain a remote sensing image of a target area, obtain a wild area and a farmland area through image segmentation, and encode the wild area and the farmland area into rasters respectively; A preliminary demarcation module is configured to: convert the farmland raster into vector polygons to obtain a number of farmland patches, filter out farmland patches with an area smaller than a first threshold, construct an outward expansion ring buffer for each retained farmland patch, calculate the natural vegetation coverage of the outward expansion ring buffer of each farmland patch, and mark the outward expansion ring buffer with a natural vegetation coverage greater than a second threshold as a potential wildland-farmland interface area; The final partitioning module is configured to: divide the potential wildland-farmland interface area into four quadrants with the geometric center point of the farmland patch as the origin, calculate the natural vegetation cover for each quadrant sub-area, and identify mixed wildland-farmland interface areas and interface wildland-farmland interface areas in the potential wildland-farmland interface area based on the quadrant-level natural vegetation cover threshold and the quadrant compliance number constraint; The natural vegetation coverage is the ratio of the number of wild land grids in the area to the total number of grids in the area.

7. The wildland-farmland boundary zoning system according to claim 6, characterized in that: The step of identifying mixed wildland-farmland boundaries and interface wildland-farmland boundaries in potential wildland-farmland boundaries includes: for a potential wildland-farmland boundary, if the number of quadrant sub-areas with natural vegetation coverage greater than a third threshold meets the standard, then the potential wildland-farmland boundary is determined to be a mixed wildland-farmland boundary; otherwise, the wildland raster is converted into a vector polygon to obtain a number of wildland patches, and the wildland patches with an area less than a fourth threshold are filtered out, a buffer zone is constructed for each retained wildland patch, the spatial intersection relationship between the farmland patches corresponding to the potential wildland-farmland boundary and the buffer zone of the wildland patches is detected, and the intersecting farmland patches are retained. For the outer expansion ring buffer zone of a certain intersecting farmland patch, if the number of quadrant sub-areas with natural vegetation coverage greater than a fifth threshold meets the standard, then the potential wildland-farmland boundary is determined to be an interface wildland-farmland boundary.

8. The wildland-farmland boundary zoning system according to claim 6, characterized in that: The step of image segmentation comprises: For the remote sensing images of the target area, feature extraction is performed through standard convolution to obtain high-resolution features; After downsampling the remote sensing image of the target area, feature extraction is performed through dilated convolution to obtain downsampling features; After upsampling the downsampled features, the normalized vegetation index is calculated to obtain the enhanced features. The gray-level co-occurrence matrix is ​​applied to the enhanced features to calculate the texture roughness and near-infrared reflectance intensity to obtain the texture features and reflectance features. After feature fusion of high-resolution features, downsampling features, enhancement features, texture features and reflection features, shrub, moss, mangrove, grassland, herbaceous wetland and woodland areas are obtained through classifier.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of a method for demarcating a wildland-farmland boundary as described in any one of claims 1 to 5 are implemented.

10. A computer device comprising a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, wherein: When the processor executes the program, the steps of the method for demarcating the boundary between wildland and farmland as described in any one of claims 1 to 5 are implemented.

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