A method and system for identifying damaged areas of soil in a coal mine area
By dividing the coal mining area into image patches and extracting vegetation and bare soil features, combined with soil nutrient and microbial information, the damaged areas can be identified by benchmarking. This solves the problem of misjudgment in soil damage identification in coal mining areas using remote sensing methods and achieves high-precision soil damage assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA UNIV OF MINING & TECH
- Filing Date
- 2026-04-24
- Publication Date
- 2026-07-21
Smart Images
Figure CN122435447A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of soil remediation technology, and in particular to a method and system for identifying soil damage areas in coal mining areas. Background Technology
[0002] While large-scale coal mining supports economic and social development, it also causes severe damage to the soil environment of mining areas and their surroundings. Coal mining activities often lead to the degradation of surface vegetation, destruction of soil structure, nutrient loss, and imbalance of microbial communities, resulting in soil damage areas of varying degrees. These damaged areas not only weaken the ecological function of the soil but may also trigger secondary environmental problems such as soil erosion and pollutant migration. Therefore, accurate identification and assessment of soil damage areas in coal mining areas are prerequisites for carrying out targeted ecological restoration.
[0003] Currently, among the methods for identifying soil damage in coal mining areas, monitoring methods based on remote sensing technology mainly utilize satellite or aerial remote sensing imagery to indirectly reflect soil conditions through surface features such as vegetation indices. Although remote sensing methods have the advantages of large-scale and periodic monitoring, they are easily limited by factors such as weather and resolution. Furthermore, relying solely on surface vegetation information often fails to fully reveal the damage to the internal structure and microbial ecology of the soil, especially in areas with uneven vegetation cover or bare soil, which can easily lead to misjudgments. Summary of the Invention
[0004] The purpose of this invention is to address the shortcomings of the prior art by providing a method and system for identifying soil damage areas in coal mining areas, thereby solving the problems in the prior art.
[0005] The present invention specifically provides the following technical solution: A method for identifying soil damage zones in coal mining areas, comprising: Acquire vegetation index and bare soil images of coal mining areas as soil image information for coal mining areas; The coal mining area is divided into n×n image patches with different vegetation and bare soil characteristics. Based on the soil image information of the coal mining area, different vegetation and bare soil characteristics of each image patch are extracted. Based on the different vegetation and bare soil characteristics, each image patch is classified into m sampling areas. Soil samples were taken from m sampling areas in the coal mining area to obtain soil nutrient and microbial information. The soil nutrient and microbial information were compared with the baseline values of undamaged soil to determine the size of the soil damage area in the sampling area. All soil damage areas of different sizes were then fused with n×n image patches to obtain the degree of damage of n×n soil areas in the coal mining area.
[0006] Preferably, the soil nutrients include the content of organic matter, total nitrogen, total phosphorus, and total potassium; the microbial information includes the diversity index and abundance of microorganisms, bacteria, and fungi.
[0007] Preferably, the degree of damage to the soil in the coal mining area is expressed as a percentage, and the damage level is divided according to a preset threshold range.
[0008] Preferably, when fusing all soil damage areas of different sizes with n×n image patches, the calculation result for each patch includes the degree of damage to nutrients and microorganisms. If any index is lower than the value of the undamaged area, it is considered a soil damage area.
[0009] Preferably, when fusing all soil damage areas of different sizes with n×n image patches, the method further includes: the calculation result of each patch has confidence information, wherein the confidence depends on the similarity and spatial distance between the pixel spectral features and the training sample features.
[0010] Preferably, the step of comparing soil nutrient and microbial information with the baseline values of undamaged soil to determine the size of the soil damage area in the sampling area specifically involves: By comparing the soil nutrient and microbial information of the sampling area with the values of the undamaged soil, the relative proportions of each indicator are calculated to obtain the size of the damaged area of the soil. The weights of each indicator are determined, and a damage index is calculated to represent soil damage.
[0011] Preferably, the vegetation indices include: cover and biomass, vegetation stress and health status; the bare soil imagery includes: surface composition, soil moisture / water information, and signs of erosion and subsidence.
[0012] This invention provides a system for identifying soil damage areas in coal mining areas, comprising: The data acquisition module is used to acquire vegetation index and bare soil images in coal mining areas, which serve as soil image information for coal mining areas. The segmentation and extraction module is used to segment the coal mining area into n×n image patches with different vegetation and bare soil features, and extract different vegetation and bare soil features of each image patch based on the soil image information of the coal mining area. Based on the different vegetation and bare soil features, each image patch is classified into m sampling regions. The sampling and damage acquisition module is used to sample soil in the coal mining area in m sampling areas, acquire soil nutrient and microbial information in the sampling areas, and compare the soil nutrient and microbial information with the baseline values of undamaged soil to obtain the size of the soil damage area in the sampling area. All soil damage areas of different sizes are fused with n×n image patches to obtain the damage degree of n×n areas of coal mining soil.
[0013] The present invention provides a computer device, including a memory and a processor. The memory stores a program, and when the program is executed by the processor, the processor performs the steps of the above-described method for identifying soil damage areas in coal mining areas.
[0014] The present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for identifying soil damage areas in coal mining areas.
[0015] Compared with the prior art, the present invention has the following significant advantages: This invention divides a coal mining area into n×n image patches and extracts vegetation and bare soil features to form m sampling zones. It establishes a direct mapping relationship between limited but precise soil nutrient and microbial detection data and macroscopic remote sensing images. Based on this, it divides vegetation and bare soil information into different patches and compares the damage size of sampling points with the baseline values of the n×n image patches to obtain the size of the soil damage area in the sampling region. This expands discrete, precise test data into a damage distribution map covering every patch in the entire mining area. The output results are not limited to a general qualitative judgment of damage / undamaged, but are refined to the specific damage degree of n×n patches, greatly improving the overall identification accuracy and analysis efficiency of soil damage areas in large mining areas. Attached Figure Description
[0016] Figure 1 This is a flowchart of a method for identifying soil damage areas in coal mining areas, as exemplified by this invention. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0018] Existing technologies rely on traditional methods of field investigation and sampling analysis, involving manually collecting soil samples and measuring their physical, chemical, and biological indicators (such as organic matter content and microbial activity) to assess soil condition. While accurate, this method suffers from inefficiency, high cost, and limited spatial coverage, making rapid assessment of large areas difficult. Addressing these shortcomings and the limitations of remote sensing methods, this embodiment discloses a method and system for identifying soil damage areas in coal mining areas, used for soil damage assessment in the impact zone of an open-pit coal mine. The flowchart of the system and method for identifying soil damage areas in coal mining areas is shown below. Figure 1 As shown.
[0019] The method for identifying soil damage areas in coal mining areas provided by this invention includes the following steps: Step S1: Obtain vegetation index and bare soil images of the coal mining area as soil image information of the coal mining area.
[0020] Vegetation indices include: cover and biomass, vegetation stress and health status; bare soil images include: surface composition, soil moisture / water information, and signs of erosion and subsidence.
[0021] First, a data acquisition module is constructed. The data acquisition module adopts a drone aerial photography unit, including a drone and a multispectral sensor, as a specific implementation of the industrial camera and multispectral remote sensing unit. The multispectral sensor can simultaneously acquire images in the blue light (475 nm), green light (560 nm), red light (668 nm), red edge (717 nm, 842 nm) and near-infrared (842 nm) bands.
[0022] Under favorable weather conditions, the UAV aerial photography unit was used to conduct aerial surveys of the target coal mining area, acquiring high-resolution multispectral images covering the entire study area. After preprocessing the raw images (including radiometric correction and orthorectification), the Normalized Difference Vegetation Index (NDVI) and Bare Soil Index (BSI) were calculated. Using an image segmentation method based on the watershed algorithm, the entire study area image was automatically divided into 300×300 (n=300) image patches with relatively uniform spectral characteristics. Each patch represents a basic assessment unit with specific vegetation cover and bare soil conditions.
[0023] Step S2: Divide the coal mining area into n×n image patches with different vegetation and bare soil characteristics, and extract different vegetation and bare soil characteristics for each image patch based on the soil image information of the coal mining area. Based on the different vegetation and bare soil characteristics, classify each image patch into m sampling areas; m is less than or equal to n.
[0024] In one embodiment, band reflectance and texture features of vegetation and bare soil are extracted for each image patch. Machine learning methods are used to reduce the dimensionality of the features, extract similar features, and classify them into 50 sampling regions with spatial and typological representativeness.
[0025] Step S3: Sampling of soil in the coal mining area is carried out in m sampling areas to obtain soil nutrient and microbial information. The soil nutrient and microbial information are compared with the baseline values of undamaged soil to obtain the size of the soil damage area in the sampling area. All soil damage areas of different sizes are fused with n×n image patches to obtain the degree of damage of n×n areas of coal mining soil.
[0026] Precise field soil sampling was conducted based on the geographical center coordinates of 50 identified sampling areas. At each sampling point, a five-point sampling method was used to collect topsoil from 0-20 cm depth, which was then divided into two parallel samples using a quartering method. Soil nutrient and microbial information for that area was tested and obtained. Simultaneously, soil samples were collected from non-mining agricultural land as a baseline for undamaged soil control.
[0027] Soil nutrients include organic matter, total nitrogen, total phosphorus, and total potassium. Soil organic matter was determined by potassium dichromate titration method; total nitrogen was determined by Kjeldahl method; total phosphorus was determined by alkali fusion-molybdenum antimony colorimetric method; and total potassium was determined by alkali fusion-flame photometry method.
[0028] Microbial information mainly includes the diversity and abundance of bacteria and fungi. PCR amplification and sequencing were performed on the V4 region of the bacterial 16S rRNA gene and the ITS1 region of the fungi using the Illumina platform. The sequencing data were analyzed using a bioinformatics workflow to calculate the Shannon diversity index of bacterial and fungal communities and to analyze the relative abundance of key taxa (such as Actinobacteria and Ascomycota).
[0029] In the sampling and damage acquisition module, the degree of damage at the sampling points is quantified: the soil nutrient and microbial information of the 50 sampling areas obtained from the test is compared with the values of the undamaged soil, and the relative proportion of each indicator is calculated to obtain the size of the damaged area of the soil. If the relative proportion of any indicator is less than 1 (i.e. the measured value is lower than the benchmark value), it is considered that there is a damage signal.
[0030] The weights of each indicator are determined, and a "damage index" between 0% and 100% is calculated. The higher the value, the more severe the soil damage at that point.
[0031] Secondly, the acquired damage index is aligned and fused with the information of 300×300 image patches. Using 15 image features of each image patch as independent variables and the "damage index" of multiple sampling points in its spatial location as dependent variables, a local weighted regression is performed to predict the damage degree of all 300×300 image patches.
[0032] During the fusion calculation, the calculation result of each patch has confidence information. This confidence depends on the similarity between the pixel spectral features and the training sample features, as well as the spatial geographic distance between the patch and the neighboring sampling points. The higher the similarity and the closer the distance, the higher the confidence.
[0033] The final damage index is expressed as a percentage, and the damage area level is divided into four levels according to the preset threshold range: healthy (0%-30%), mild damage (>30%-50%), moderate damage (>50%-70%), and severe damage (>70%-100%).
[0034] This embodiment utilizes a method for identifying soil damage areas in coal mining areas, making the identification of damaged areas in coal mining areas faster and more accurate.
[0035] This invention proposes a system for identifying soil damage zones in coal mining areas, comprising: The data acquisition module is used to acquire vegetation index and bare soil images of the coal mining area as soil image information. The segmentation and extraction module is used to segment the coal mining area into n×n image patches with different vegetation and bare soil characteristics, and extract different vegetation and bare soil characteristics of each image patch based on the coal mining area soil image information. Based on the different vegetation and bare soil characteristics, each image patch is classified into m sampling areas. The sampling and damage acquisition module is used to sample the coal mining area soil in the m sampling areas respectively, acquire the soil nutrient and microbial information of the area, calculate the soil nutrient and microbial information, compare it with the baseline value of undamaged soil, acquire the size of the damaged area of the soil, and fuse all damaged areas of different sizes with the n×n image patches to obtain the damage degree of the n×n areas of the coal mining area soil.
[0036] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a program, and when the program is executed by the processor, the processor performs the steps of a method for identifying soil damage areas in coal mining areas.
[0037] According to the disclosed embodiments, the computer device can communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth communication, etc.) or with any device that enables the computing device to communicate with one or more other computing devices (e.g., router, demodulator, etc.).
[0038] The present invention also provides a storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps of a method for identifying soil damage areas in coal mining areas.
[0039] According to the disclosed embodiments, the storage medium can be a non-volatile computer-readable storage medium, such as, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, the storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0040] The above description, in conjunction with specific preferred embodiments, provides a more detailed explanation of the present invention. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered to fall within the scope of protection of the present invention.
Claims
1. A method for identifying soil damage zones in coal mining areas, characterized in that, include: Acquire vegetation index and bare soil images of coal mining areas as soil image information for coal mining areas; The coal mining area is divided into n×n image patches with different vegetation and bare soil characteristics. Based on the soil image information of the coal mining area, different vegetation and bare soil characteristics of each image patch are extracted. Based on the different vegetation and bare soil characteristics, each image patch is classified into m sampling areas. Soil samples were taken from m sampling areas in the coal mining area to obtain soil nutrient and microbial information. The soil nutrient and microbial information were compared with the baseline values of undamaged soil to determine the size of the soil damage area in the sampling area. All soil damage areas of different sizes were then fused with n×n image patches to obtain the degree of damage of n×n soil areas in the coal mining area.
2. The method for identifying soil damage areas in coal mining areas as described in claim 1, characterized in that, The soil nutrients include the content of organic matter, total nitrogen, total phosphorus, and total potassium; the microbial information includes the diversity index and abundance of microorganisms, bacteria, and fungi.
3. The method for identifying soil damage areas in coal mining areas as described in claim 1, characterized in that, The degree of damage to the soil in an n×n block of coal mining area is expressed as a percentage, and the damage level is divided according to a preset threshold range.
4. The method for identifying soil damage areas in coal mining areas as described in claim 1, characterized in that, When fusing all soil damage areas of different sizes with n×n image patches, the calculation result for each patch includes the degree of damage to nutrients and microorganisms. If any index is lower than the value of the undamaged area, it is considered a soil damage area.
5. The method for identifying soil damage areas in coal mining areas as described in claim 4, characterized in that, When fusing all soil damage areas of different sizes with n×n image patches, the method further includes: the calculation result of each patch has confidence information, wherein the confidence depends on the similarity and spatial distance between the pixel spectral features and the training sample features.
6. The method for identifying soil damage areas in coal mining areas as described in claim 1, characterized in that, The step of comparing soil nutrient and microbial information with baseline values of undamaged soil to determine the size of the soil damage area in the sampling area is as follows: By comparing the soil nutrient and microbial information of the sampling area with the values of the undamaged soil, the relative proportions of each indicator are calculated to obtain the size of the damaged area of the soil. The weights of each indicator are determined, and a damage index is calculated to represent soil damage.
7. The method for identifying soil damage areas in coal mining areas as described in claim 1, characterized in that, The vegetation indices include: cover and biomass, vegetation stress and health status; the bare soil images include: surface composition, soil moisture / water information, and signs of erosion and subsidence.
8. A system for identifying soil damage zones in coal mining areas, characterized in that, include: The data acquisition module is used to acquire vegetation index and bare soil images in coal mining areas, which serve as soil image information for coal mining areas. The segmentation and extraction module is used to segment the coal mining area into n×n image patches with different vegetation and bare soil features, and extract different vegetation and bare soil features of each image patch based on the soil image information of the coal mining area. Based on the different vegetation and bare soil features, each image patch is classified into m sampling regions. The sampling and damage acquisition module is used to sample soil in the coal mining area in m sampling areas, acquire soil nutrient and microbial information in the sampling areas, and compare the soil nutrient and microbial information with the baseline values of undamaged soil to obtain the size of the soil damage area in the sampling area. All soil damage areas of different sizes are fused with n×n image patches to obtain the damage degree of n×n areas of coal mining soil.
9. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores a program that, when executed by the processor, causes the processor to perform the steps of the method for identifying soil damage areas in a coal mining area as described in any one of claims 1 to 7.
10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for identifying soil damage areas in coal mining areas according to any one of claims 1 to 7.