A provincial range rural black and odorous water body satellite remote sensing identification method and system

Abstract

The application discloses a kind of provincial range rural black and odorous water body satellite remote sensing identification method and system, it is related to environmental remote sensing technical field;A kind of provincial range rural black and odorous water body satellite remote sensing identification method, comprising the following steps: obtaining data, data processing, data image inlaying, obtaining water body distribution data, data cropping, obtaining index data product, obtaining classification grid, preliminary obtaining black and odorous water body distribution, obtaining the final distribution of black and odorous water body;The present application utilizes multi-source satellite remote sensing image data, in combination with water quality parameters, constructs the remote sensing identification model of provincial range rural area water body using NIR, NDWI, SWI index and slope data etc., then uses BOI, NDBWI etc. Index constructs rural area suspected black and odorous water body extraction model, in combination with the relevant data of water body field measurement in monitoring area determines the extraction threshold of water body and black and odorous water body, realizes the automatic identification and extraction of rural area black and odorous water body, and develops relevant software system.

Description

Technical Field

[0001] This invention relates to the field of environmental remote sensing technology, specifically to a satellite remote sensing identification method and system for black and odorous water bodies in rural areas within a provincial scope. Background Technology

[0002] Black and odorous water pollution is a serious water environment problem affecting the quality of life of urban and rural residents, directly impacting their sense of well-being. Currently, the remediation of black and odorous water bodies has become a major component of environmental protection work. In practical water environment management, black and odorous water bodies are divided into urban and rural black and odorous water bodies, each managed by different government departments. Urban black and odorous water body remediation began earlier and has a smaller scope of investigation, allowing for methods such as manual on-site verification, supplemented by water quality testing, satellite and drone remote sensing for precise identification and location. Compared to urban built-up areas, rural areas cover a larger area, with more dispersed and numerous water bodies, making investigation more difficult.

[0003] In most areas, the monitoring of black and odorous water bodies in rural areas still relies primarily on manual investigation, supplemented by certain water quality monitoring methods. This involves measuring parameters such as ammonia nitrogen, dissolved oxygen, and transparency, and, after identifying black and odorous water bodies according to relevant guidelines and standards, reporting them layer by layer by management agencies to establish a unified record. This method is inefficient, time-consuming, labor-intensive, and prone to omissions, making efficient dynamic monitoring impossible. New technologies and methods are urgently needed. In the past decade, with the widespread application of high spatial resolution satellite data, research on remote sensing monitoring of black and odorous water bodies in rural areas has gradually progressed. Because black and odorous water bodies differ from normal water bodies in color, odor, and composition, their spectral characteristics also differ. By analyzing and comparing the differences in the spectra of black and odorous water bodies and normal water bodies in relevant bands, starting with indicators that may cause differences between the two in the visible to near-infrared bands, and by constructing different remote sensing identification models for black and odorous water bodies such as difference and ratio, and then determining the corresponding thresholds through image feature analysis and field investigation, the distribution range of black and odorous water bodies in rural areas can be extracted. In response to the above problems, the inventors proposed a satellite remote sensing identification method and system for black and odorous water bodies in rural areas within a province to solve the above problems. Summary of the Invention

[0004] To address the shortcomings in rural water environment management practices in most areas, the investigation of black and odorous water bodies in rural areas primarily relies on on-site verification by investigators, with some water bodies requiring questionnaires and water quality monitoring. This approach is inefficient, time-consuming, and labor-intensive. Furthermore, relying on local investigators to conduct investigations and report results in the potential for overlooking some black and odorous water bodies, impacting the unified supervision effectiveness of provincial environmental management departments. Therefore, new technologies are urgently needed to improve efficiency and effectiveness. This invention, based on the actual needs of black and odorous water body management in provincial rural areas, utilizes multi-source satellite remote sensing data to develop a method and process for identifying and extracting black and odorous water bodies in rural areas within a provincial scope. It also develops a remote sensing identification system for black and odorous water bodies in rural areas, which can significantly improve the efficiency of related work. The purpose of this invention is to provide a satellite remote sensing identification method and system for black and odorous water bodies in rural areas within a provincial scope.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: a satellite remote sensing identification method for rural black and odorous water bodies within a provincial area, comprising the following steps:

[0006] S1. Data Acquisition: Acquire the latest raw multi-source satellite remote sensing image data or data products after relevant corrections for the monitored area, and acquire the digital elevation model (DEM) data and urban built-up area data for the area;

[0007] S2. Data Processing: The acquired satellite images are processed according to relevant procedures, including radiometric calibration, atmospheric correction, orthorectification, geometric fine correction and image fusion, ultimately producing high spatial resolution reflectivity data products after geometric correction.

[0008] S3. Data Image Mosaic: Mosaic the DEM data, calculate the slope of the monitoring area, generate a slope raster image, and resample it to keep the spatial resolution and reflectance data consistent.

[0009] S4. Obtain water body distribution data: Based on the above data products, a water body extraction model is constructed using near-infrared single-band index (NIR), normalized water body index (NDWI), shadowed water body index (SWI) and slope grid. According to the relevant knowledge base, appropriate extraction thresholds are set for different regions and different satellite data sources to extract water body distribution data.

[0010] S5. Data cropping: The above water body distribution data is cropped using vector data of urban built-up areas within the monitoring range to obtain water body distribution data of rural areas within the monitoring range;

[0011] S6. Obtain index data products: Based on the data products produced in S2, perform algebraic calculations on raster images using water body index models such as BOI and NDBWI to obtain the corresponding index data products.

[0012] S7. Obtain the classification raster: Based on the prior knowledge base, set appropriate extraction thresholds for different regional ranges and different remote sensing data sources, and perform binary classification on the above raster data products based on the set thresholds to obtain the classification raster.

[0013] S8. Preliminary acquisition of black and odorous water body distribution: Overlay the rural water body distribution data obtained in S5 and the classification grid obtained in S7, and perform an AND operation. The result is the preliminary distribution range grid of suspected black and odorous water bodies in the rural areas of the monitoring area.

[0014] S9. Obtain the final distribution of black and odorous water bodies: Overlay the above-mentioned raster data of suspected black and odorous water bodies in rural areas with the reflectance data products obtained in S2 and perform visual interpretation. Based on their spatial distribution characteristics, exclude suspected artificial aquaculture, fishing ponds and other water bodies to obtain the final distribution range of suspected black and odorous water bodies in rural areas.

[0015] The raw multi-source satellite remote sensing imagery in S1 includes domestic Gaofen-2, Gaofen-1, Gaofen-6, or other remote sensing imagery with similar spatial resolution, and its panchromatic spatial resolution should be better than 2 meters; the corrected data products of the multi-source satellite remote sensing imagery include Sentinel-2B satellite L1B or L2A level imagery products.

[0016] The DEM data in S1 uses SRTM 30-meter elevation data, and the urban built-up area uses the GUB (Global Urban Boundary) dataset released by the relevant research team of Tsinghua University.

[0017] Radiometric calibration in S2 uses the following formula: L(λ)=gain(λ)*DN(λ)+offset(λ), where L(λ) is the radiometric calibration result of the λ band, DN(λ) is the original pixel value of the λ band, and gain(λ) and offset(λ) are the gain and offset values ​​of the λ band of the satellite remote sensing sensor used, which can be obtained from the website of China Resources Satellite Application Center or relevant satellite remote sensing data providers; atmospheric correction uses the Flaash model or relative radiometric correction model of ENVI remote sensing software, and orthorectification uses the gdal_translate command-line tool of GDAL library; image fusion uses the NNdiffusePansharpen algorithm or the pan_sharpen command-line tool of GDAL library;

[0018] In S2, geometric fine correction uses Sentinel L1B or L2A data as reference imagery and performs batch automatic processing using the arosics library. To achieve faster processing speed and higher correction accuracy, the parameters are set as follows:

[0019] (1) Set max_shift to 100;

[0020] (2) Set grid_res to 800 and window_size to 512 for 1-meter resolution images;

[0021] (3) For 2-meter resolution images, set grid_size to 200 and window_size to 256;

[0022] In S3, the slope calculation based on the DEM uses the following formula:

[0023] slope=ATAN(√([dz / dx]2+[dz / dy]2))*57.29578;

[0024] dx is the distance difference between two pixels along the X-axis, dy is the distance difference between two pixels along the Y-axis, and dz is the calculated result of the elevation difference between two pixels in degrees.

[0025] The water index used in S4 is calculated using the following formula:

[0026] NIR = R(nir);

[0027] NDWI=(R(green)-R(nir)) / (R(green)+R(nir));

[0028] SWI=R(blue)+R(green)-R(nir);

[0029] In the formula, R(blue) is the blue band reflectance, R(green) is the green band reflectance, and R(nir) is the near-infrared band reflectance.

[0030] The water body extraction thresholds described in S4 are determined based on a pre-established threshold lookup table. The relevant values ​​in the water body threshold lookup table are determined according to the selected satellite and the actual situation of the monitoring area, based on the relevant characteristics of the image histogram and the field survey. The recommended values ​​are as follows: For GF1 and GF2 satellites, the following settings are recommended: NIR < 1800; NDWI < -0.50; SWI > -500; slope < 20. For GF1B, C, and D satellites, the following settings are recommended: NIR < 1500; NDWI < -0.60; SWI > -1000; slope < 20.

[0031] The water extraction described in S4 involves performing an AND operation between the classification results of the three indices NIR, NDWI, and SWI and the SLOPE calculation results.

[0032] The calculation formulas for the NDBWI and BOI black and odorous water body indices mentioned in S6 are as follows:

[0033] NDBWI=(R(green)-R(red)) / (R(green)+R(red));

[0034] BOI=(R(green)-R(red)) / (R(blue)+R(green)+R(red));

[0035] In the formula, R(blue) is the blue band reflectance, R(green) is the green band reflectance, and R(red) is the red band reflectance.

[0036] The extraction threshold for black and odorous water bodies mentioned in S7 is determined based on a pre-set lookup table for black and odorous water body extraction thresholds. The relevant values ​​in the black and odorous water body threshold lookup table are determined based on the selected satellite and the on-site investigation of normal water bodies and black and odorous water bodies in the monitoring area. The recommended values ​​are as follows:

[0037] For GF1 and GF2 satellites, the following settings can be configured: NDBWI < 0.050; BOI < 0.054.

[0038] The following settings can be configured for GF1B, C, and D satellites: NDBWI < 0.026; BOI < 0.028.

[0039] A system for satellite remote sensing identification of black and odorous water bodies in rural areas within a province includes the following steps:

[0040] Step 1: Satellite image raw data decompression module: Decompress the raw images to the target folder according to the set raw image folder and decompression target folder;

[0041] Step 2: Satellite Image Indexing Module: Extract satellite image metadata (time, date, cloud cover, etc.) and the coverage area of ​​each image (latitude and longitude of the upper left corner, upper right corner, lower right corner, and lower left corner) to create a spatial index for the high-resolution image data. The index file is stored in shapefile format, and the coordinate system is the same as that used in the original satellite image data.

[0042] Step 3: Image Processing Module: Automatically processes satellite images, including radiometric calibration, atmospheric correction, orthorectification, image fusion, and geometric correction (performing fine geometric correction based on existing reference images).

[0043] Step 4: Water body extraction module: Automatically extract water body data. Using the method model described in S4, automatically extract the water body distribution range of the monitoring area based on the corrected image according to the set threshold.

[0044] Step 5: Suspected Black and Odorous Water Body Extraction Module: Automatically extracts suspected black and odorous water bodies in rural areas. Using the black and odorous water body calculation model described in S6, it automatically extracts suspected black and odorous water bodies according to the set threshold.

[0045] Step Six: Navigation QR Code Generation Module: By calling the relevant APIs of the map software, navigation QR codes for Gaode Map and Baidu Map are automatically generated based on the latitude and longitude of the center of the suspected black and odorous water patches. Users can scan the QR code using the corresponding mobile navigation app to plan the route, making it convenient to quickly reach the location during on-site verification.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] 1. This invention utilizes multi-source satellite remote sensing imagery data, including domestic high-resolution satellite imagery and foreign Sentinel2B satellite imagery, combined with on-site measurements of spectral characteristics, dissolved oxygen, ammonia nitrogen, and transparency of normal and black and odorous water bodies. It constructs a remote sensing identification model for rural water bodies within a provincial area using NIR, NDWI, SWI indices, and slope data. Then, it constructs an extraction model for suspected black and odorous water bodies in rural areas using BOI, NDBWI, and other indices. Finally, it determines the extraction thresholds for water bodies and black and odorous water bodies by combining relevant on-site measurements of water bodies in the monitoring area. This achieves automatic identification and extraction of black and odorous water bodies in rural areas, and a related software system has been developed.

[0048] 2. The related technical methods and systems of the present invention fully utilize the natural advantages of remote sensing technology, enabling comprehensive and periodic monitoring of black and odorous water bodies in rural areas within a province. This provides spatial distribution data of black and odorous water body locations for rural water environment management, facilitating on-site verification and treatment, greatly improving the efficiency of black and odorous water body investigation, and saving manpower and resources. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of the overall technical process of the present invention;

[0051] Figure 2 This is a schematic diagram of the spectral curve of a turbid river in rural areas according to the present invention;

[0052] Figure 3 This is a schematic diagram of the spectral curve of the black and smelly pond water body according to the present invention;

[0053] Figure 4This is a schematic diagram of the initial interface of the software system of the present invention;

[0054] Figure 5 This is a schematic diagram of the water body distribution in rural areas of city B in this embodiment of the invention;

[0055] Figure 6 This is a distribution map of suspected black and odorous water bodies in rural areas of city B, as described in this embodiment of the invention. Detailed Implementation

[0056] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] like Figure 1-6 As shown, this invention provides a satellite remote sensing identification method for black and odorous water bodies in rural areas within a provincial area, comprising the following steps:

[0058] S1. Data Acquisition: Acquire the latest raw multi-source satellite remote sensing image data or data products after relevant corrections for the monitored area, and acquire the digital elevation model (DEM) data and urban built-up area data for the area;

[0059] S2. Data Processing: The acquired satellite images are processed according to relevant procedures, including radiometric calibration, atmospheric correction, orthorectification, geometric fine correction and image fusion, ultimately producing high spatial resolution reflectivity data products after geometric correction.

[0060] S3. Data Image Mosaic: Mosaic the DEM data, calculate the slope of the monitoring area, generate a slope raster image, and resample it to keep the spatial resolution and reflectance data consistent.

[0061] S4. Obtain water body distribution data: Based on the above data products, a water body extraction model is constructed using near-infrared single-band index (NIR), normalized water body index (NDWI), shadowed water body index (SWI), and slope grid. According to the relevant knowledge base, appropriate extraction thresholds are set for different regions and different satellite data sources to extract water body distribution data.

[0062] S5. Data cropping: The above water body distribution data is cropped using vector data of urban built-up areas within the monitoring range to obtain water body distribution data of rural areas within the monitoring range;

[0063] S6. Obtain index data products: Based on the data products produced in S2, perform algebraic calculations on raster images using water body index models such as BOI and NDBWI to obtain the corresponding index data products.

[0064] S7. Obtain the classification raster: Based on the prior knowledge base, set appropriate extraction thresholds for different regional ranges and different remote sensing data sources, and perform binary classification on the above raster data products based on the set thresholds to obtain the classification raster.

[0065] S8. Preliminary acquisition of black and odorous water body distribution: Overlay the rural water body distribution data obtained in S5 and the classification grid obtained in S7, and perform an AND operation. The result is the preliminary distribution range grid of suspected black and odorous water bodies in the rural areas of the monitoring area.

[0066] S9. Obtain the final distribution of black and odorous water bodies: Overlay the above-mentioned raster data of suspected black and odorous water bodies in rural areas with the reflectance data products obtained in S2 and perform visual interpretation. Based on their spatial distribution characteristics, exclude suspected artificial aquaculture, fishing ponds and other water bodies to obtain the final distribution range of suspected black and odorous water bodies in rural areas.

[0067] The raw multi-source satellite remote sensing imagery in S1 includes domestic Gaofen-2, Gaofen-1, Gaofen-6, or other remote sensing imagery with similar spatial resolution, and its panchromatic spatial resolution should be better than 2 meters; the corrected data products of the multi-source satellite remote sensing imagery include Sentinel-2B satellite L1B or L2A level imagery products.

[0068] The DEM data in S1 uses SRTM 30-meter elevation data, and the urban built-up area uses the GUB (Global Urban Boundary) dataset released by a relevant research team at Tsinghua University.

[0069] In S2, radiometric calibration is performed using the following formula: L(λ) = gain(λ) * DN(λ) + offset(λ), where L(λ) is the radiometric calibration result of the λ band, DN(λ) is the original pixel value of the λ band, and gain(λ) and offset(λ) are the gain and offset values ​​of the λ band of the satellite remote sensing sensor used, which can be obtained from the website of the China Resources Satellite Application Center or relevant satellite remote sensing data providers. Atmospheric correction uses the Flaash model or relative radiometric correction model of ENVI remote sensing software, and orthorectification uses the gdal_translate command-line tool of the GDAL library. Image fusion uses the NNdiffusePansharpen algorithm or the pan_sharpen command-line tool of the GDAL library.

[0070] In S2, geometric fine correction uses Sentinel L1B or L2A data as reference imagery and performs batch automatic processing using the arosics library. To achieve faster processing speed and higher correction accuracy, the parameters are set as follows:

[0071] (1) Set max_shift to 100;

[0072] (2) Set grid_res to 800 and window_size to 512 for 1-meter resolution images;

[0073] (3) For 2-meter resolution images, grid_size is set to 200 and window_size is set to 256.

[0074] In S3, the slope calculation based on the DEM uses the following formula:

[0075] slope=ATAN(√([dz / dx]2+[dz / dy]2))*57.29578;

[0076] dx is the distance difference between two pixels along the X-axis, dy is the distance difference between two pixels along the Y-axis, and dz is the calculated result of the elevation difference between two pixels in degrees.

[0077] The water index used in S4 is calculated using the following formula:

[0078] NIR = R(nir);

[0079] NDWI=(R(green)-R(nir)) / (R(green)+R(nir));

[0080] SWI=R(blue)+R(green)-R(nir);

[0081] In the formula, R(blue) is the blue band reflectance, R(green) is the green band reflectance, and R(nir) is the near-infrared band reflectance.

[0082] The water body extraction thresholds described in S4 are determined based on a pre-established threshold lookup table. The relevant values ​​in the water body threshold lookup table are determined according to the selected satellite and the actual situation of the monitoring area, based on the relevant features of the image histogram and the field survey. The recommended values ​​are as follows: For GF1 and GF2 satellites, the following settings are recommended: NIR < 1800; NDWI < -0.50; SWI > -500; slope < 20. For GF1B, C, and D satellites, the following settings are recommended: NIR < 1500; NDWI < -0.60; SWI > -1000; slope < 20.

[0083] The water extraction described in S4 involves performing an AND operation between the classification results of the three indices NIR, NDWI, and SWI and the SLOPE calculation results.

[0084] The calculation formulas for the NDBWI and BOI black and odorous water body indices mentioned in S6 are as follows:

[0085] NDBWI=(R(green)-R(red)) / (R(green)+R(red));

[0086] BOI=(R(green)-R(red)) / (R(blue)+R(green)+R(red));

[0087] In the formula, R(blue) is the blue band reflectance, R(green) is the green band reflectance, and R(red) is the red band reflectance.

[0088] The extraction threshold for black and odorous water bodies mentioned in S7 is determined based on a pre-set lookup table for black and odorous water body extraction thresholds. The relevant values ​​in the black and odorous water body threshold lookup table are determined based on the selected satellite and the on-site investigation of normal water bodies and black and odorous water bodies in the monitoring area. The recommended values ​​are as follows:

[0089] For GF1 and GF2 satellites, the following settings can be configured: NDBWI < 0.050; BOI < 0.054.

[0090] The following settings can be configured for GF1B, C, and D satellites: NDBWI < 0.026; BOI < 0.028;

[0091] The reflectance image is classified into binary values ​​according to the above conditions. Pixels that meet the criteria are set to 1, and those that do not are set to 0.

[0092] A system for satellite remote sensing identification of black and odorous water bodies in rural areas within a province is characterized by comprising the following steps:

[0093] Step 1: Satellite image raw data decompression module: Decompress the raw images to the target folder according to the set raw image folder and decompression target folder;

[0094] Step 2: Satellite Image Indexing Module: Extract satellite image metadata (time, date, cloud cover, etc.) and the coverage area of ​​each image (latitude and longitude of the upper left corner, upper right corner, lower right corner, and lower left corner) to create a spatial index for the high-resolution image data. The index file is stored in shapefile format, and the coordinate system is the same as that used in the original satellite image data.

[0095] Step 3: Image Processing Module: Automatically processes satellite images, including radiometric calibration, atmospheric correction, orthorectification, image fusion, and geometric correction (performing fine geometric correction based on existing reference images).

[0096] Step 4: Water body extraction module: Automatically extract water body data. Using the method model described in S4, automatically extract the water body distribution range of the monitoring area based on the corrected image according to the set threshold.

[0097] Step 5: Suspected Black and Odorous Water Body Extraction Module: Automatically extracts suspected black and odorous water bodies in rural areas. Using the black and odorous water body calculation model described in S6, it automatically extracts suspected black and odorous water bodies according to the set threshold.

[0098] Step Six: Navigation QR Code Generation Module: This module calls relevant APIs from map software to automatically generate navigation QR codes for Gaode Maps and Baidu Maps based on the latitude and longitude of the center of the suspected black and odorous water patches. Users can scan the QR code using the corresponding mobile navigation app to plan their route, facilitating quick arrival at the location during on-site verification.

[0099] Example: Cities A, B, C, and D in a certain province were selected as monitoring areas. Remote sensing was used to investigate black and odorous water bodies in the rural areas of these four cities. The specific process is as follows:

[0100] Step 1: Collect remote sensing data and other data. Domestic high-resolution satellite remote sensing data (Gaofen-1, Gaofen-1B, Gaofen-1C, Gaofen-1D, and Gaofen-6) were collected, all with a spatial resolution of 8 meters for multispectral and 2 meters for panchromatic. Sentinel-2B satellite data were also collected for geometric correction. The following auxiliary data were collected and organized: (1) administrative boundary data of a certain province; (2) global city range data released by a team from Tsinghua University; (3) SRTM 30-meter dem data of a certain province; (4) location data of existing confirmed black and odorous water bodies in the above four cities. Using the software system developed in this invention, a spatial index was established for the satellite data. The satellite data was filtered according to administrative region, cloud cover, image acquisition time, etc. More than 200 high-resolution images and more than 30 Sentinel-2B images were selected.

[0101] Step 2: The software system provided by this invention is used to automatically process the high-resolution satellite image data, specifically including radiometric calibration, atmospheric correction, orthorectification, image fusion, and geometric fine correction. The radiometric calibration parameters use the calibration parameters of the satellite sensors published by the China Center for Resources Satellite Data and Application. The atmospheric correction calls the FLAASH model of ENVI software, and the average altitude in the model is obtained from SRTM 30-meter elevation data. The region is selected as "rural". The orthorectification calls the gdal_translate function of the GDAL library. The DEM used for orthorectification is SRTM 30-meter elevation data. The image fusion calls the NNDiffusePansharpen tool of ENVI software. The geometric fine correction calls the COREG_LOCAL function of the aromas library. The grid_res parameter is set to 200, the window_size parameter is set to 256, the max_shift parameter is set to 100, and the reflectance image product output format is set to GTiff.

[0102] Step 3: Based on the SRTM 30-meter DEM data, calculate the slope using the formula slope=ATAN(√([dz / dx]2+[dz / dy]2))*57.29578, generate slope raster data, and resample it at a 2-meter spatial resolution.

[0103] Step 4: Using the software system provided by this invention, based on the reflectance image product, calculate water body indices such as NIR, NDWI, and SWI according to the following formula, and generate water body index raster data;

[0104] NIR = R(nir);

[0105] NDWI=(R(green)-R(nir)) / (R(green)+R(nir));

[0106] SWI=R(blue)+R(green)-R(nir);

[0107] Step 5: Using the software system provided by this invention, based on the above-mentioned water index and slope raster data, automatically align and perform binary classification, setting the following threshold:

[0108] GF1: NIR<1800; NDWI<-0.50; SWI>-500; slope<20;

[0109] GF1B, C, and D satellite settings: NIR < 1500; NDWI < -0.60; SWI > -1000;

[0110] slope < 20;

[0111] Pixels that satisfy the condition are set to 1, and those that do not are set to 0.

[0112] Step 6: Using the software system provided by this invention, the above classification results are processed by calling the relevant NumPy functions to perform an AND operation to generate a water body distribution raster image. According to a rough count, more than 6,000 water body patches of various types, such as rivers, ponds and ditches, were extracted from the four cities. After excluding the urban built-up areas, there are approximately 5,000 water bodies in the rural areas of the four cities.

[0113] Step 7: Based on the reflectivity product produced in Step 2, calculate the NDBWI and BOI black and odorous water indices according to the following formula, and generate the corresponding result raster.

[0114] NDBWI=(R(green)-R(red)) / (R(green)+R(red));

[0115] BOI=(R(green)-R(red)) / (R(blue)+R(green)+R(red));

[0116] Step 8: Perform binary classification on the raster images generated in Step 7 according to the following thresholds to obtain the classified raster images:

[0117] The GF1 satellite can be configured with: NDBWI < 0.050; BOI < 0.054;

[0118] The following settings can be configured for GF1B, C, and D satellites: NDBWI < 0.026; BOI < 0.028;

[0119] Step 9: Overlay the above classification result raster image with the water body distribution raster obtained in Step 6 to obtain the distribution raster image of suspected black and odorous water bodies in the four cities.

[0120] Step 10: Use the data of the built-up areas of the four cities to cut the above-mentioned grid of suspected black and odorous water bodies. The suspected black and odorous water body patches outside the built-up areas of the cities are the suspected black and odorous water bodies in the rural areas of the four cities.

[0121] Step 11: Load the raster images of suspected black and odorous water bodies in the rural areas of the above four cities into ArcGIS software, and overlay the Tianditu WMTS image and image annotation layer. Interpret the images according to the following spatial distribution characteristics; those meeting the following characteristics are not considered black and odorous water bodies:

[0122] (1) The water body is a regular quadrilateral shape;

[0123] (2) There is a guardhouse by the water;

[0124] (3) The water bodies are distributed in contiguous areas, clearly belonging to aquaculture areas;

[0125] (4) The water body is located in a park, scenic area, etc.;

[0126] Step 12: Using the software system provided by this invention, the relevant functions of the Fiona and Rasterio libraries are called to vectorize the suspected raster, obtaining vector data on the distribution of suspected black and odorous water bodies in rural areas of the four cities. ArcGIS software is then used to edit the data, calculating the coordinates of the center point of each water body polygon. A total of 100 suspected black and odorous water bodies were identified in the four cities.

[0127] Step 13: Using the software system provided by the invention, call the Gaode Map API to generate a Gaode Map Gaohang QR code according to the above central store coordinates. After scanning with the Gaode Map mobile APP or WeChat, a route to the suspected black and odorous water body can be planned.

[0128] The remote sensing identification of suspected black and odorous water bodies in rural areas of four cities (City A, etc.) in a certain province has now been completed.

[0129] This embodiment describes the main features and basic process of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

[0130] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A satellite remote sensing identification method for black and odorous water bodies in rural areas within a provincial scope, characterized in that, Includes the following steps: S1. Data Acquisition: Acquire the latest raw multi-source satellite remote sensing image data or data products after relevant correction for the monitored area, and acquire the digital elevation model data and urban built-up area data for the area; S2. Data Processing: The acquired satellite images are processed according to relevant procedures, including radiometric calibration, atmospheric correction, orthorectification, geometric fine correction and image fusion, ultimately producing high spatial resolution reflectivity data products after geometric correction. S3. Data Image Mosaic: Mosaic the DEM data, calculate the slope of the monitoring area, generate a slope raster image, and resample it to keep the spatial resolution and reflectance data consistent. S4. Obtain water body distribution data: Based on the above data products, a water body extraction model is constructed using near-infrared single-band index (NIR), normalized water body index (NDWI), shadowed water body index (SWI), and slope grid. According to the relevant knowledge base, appropriate extraction thresholds are set for different regions and different satellite data sources to extract water body distribution data. S5. Data cropping: The above water body distribution data is cropped using vector data of urban built-up areas within the monitoring range to obtain water body distribution data of rural areas within the monitoring range; S6. Obtain index data products: Based on the data products produced in S2, perform raster image algebraic calculations using the BOI and NDBWI black and odorous water body index models to obtain the corresponding index data products. S7. Obtain the classification raster: Based on the prior knowledge base, set appropriate extraction thresholds for different regional ranges and different remote sensing data sources, and perform binary classification on the above index data products based on the set thresholds to obtain the classification raster. S8. Preliminary acquisition of black and odorous water body distribution: Overlay the rural water body distribution data obtained in S5 and the classification grid obtained in S7, and perform an "AND" operation. The result is the preliminary distribution range grid of suspected black and odorous water bodies in the rural areas of the monitoring area. S9. Obtain the final distribution of black and odorous water bodies: Overlay the raster data of suspected black and odorous water bodies in the above-mentioned rural areas with the reflectance data products obtained in S2 for visual interpretation. Based on their spatial distribution characteristics, exclude suspected artificial aquaculture and fishing ponds to obtain the final distribution range of suspected black and odorous water bodies in rural areas.

2. The satellite remote sensing identification method for rural black and odorous water bodies within a provincial area as described in claim 1, characterized in that: The raw multi-source satellite remote sensing imagery in S1 includes domestically produced Gaofen-2, Gaofen-1, and Gaofen-6 satellites. The corrected data products of the multi-source satellite remote sensing imagery include Sentinel-2B satellite L1B or L2A level imagery products. The DEM data in S1 uses SRTM 30-meter elevation data, and the urban built-up area uses the GUB dataset released by the relevant research team of Tsinghua University.

3. The satellite remote sensing identification method for rural black and odorous water bodies within a provincial area as described in claim 1, characterized in that: The radiation calibration in S2 uses the following formula: In the formula, for Radiometric calibration results for the band. The original pixel value of the band. and Gain and offset values ​​for the satellite remote sensing sensor bands used can be obtained from the China Center for Resources Satellite Data and Applications website or relevant satellite remote sensing data providers; atmospheric correction uses the Flaash model or relative radiometric correction model of ENVI remote sensing software, and orthorectification uses the gdal_translate command-line tool of the GDAL library; image fusion uses the NNdiffusePansharpen algorithm or the pan_sharpen command-line tool of the GDAL library. In S2, geometric fine correction uses Sentinel L1B or L2A data as reference imagery and performs batch automatic processing using the arosics library. To achieve faster processing speed and higher correction accuracy, the parameters are set as follows: (1) Set max_shift to 100; (2) Set grid_res to 800 and window_size to 512 for 1-meter resolution images; (3) For 2-meter resolution images, grid_size is set to 200 and window_size is set to 256.

4. The satellite remote sensing identification method for rural black and odorous water bodies within a provincial area as described in claim 1, characterized in that: In S3, the slope is calculated based on the DEM using the following formula: slope = ATAN * 57.29578 dx is the distance difference between two pixels along the X-axis, dy is the distance difference between two pixels along the Y-axis, and dz is the calculated result of the elevation difference between two pixels in degrees.

5. The satellite remote sensing identification method for black and odorous water bodies in rural areas within a province as described in claim 1, characterized in that: The water index used in S4 is calculated using the following formula: NIR = R(nir); NDWI=(R(green)-R(nir)) / (R(green)+R(nir)); SWI=R(blue)+R(green)-R(nir); In the formula, R(blue) is the blue band reflectance, R(green) is the green band reflectance, and R(nir) is the near-infrared band reflectance.

6. The satellite remote sensing identification method for rural black and odorous water bodies within a provincial area as described in claim 4, characterized in that: The water extraction threshold in S4 is determined based on a pre-established threshold lookup table. The relevant values ​​in the water threshold lookup table are determined according to the selected satellite and the actual situation of the monitoring area, based on the relevant characteristics of the image histogram and the field survey. The recommended values ​​are as follows: For GF1 and GF2 satellites, the following settings are recommended: NIR < 1800; NDWI < -0.50; SWI > -500; slope < 20. For GF1 B, C, and D satellites, the following settings are recommended: NIR < 1500; NDWI < -0.60; SWI > -1000; slope < 20.

7. The satellite remote sensing identification method for black and odorous water bodies in rural areas within a province as described in claim 1, characterized in that: The water extraction described in S4 involves performing an AND operation between the classification results of the three indices NIR, NDWI, and SWI and the SLOPE calculation results.

8. The satellite remote sensing identification method for rural black and odorous water bodies within a provincial area as described in claim 1, characterized in that: The calculation formulas for the NDBWI and BOI black and odorous water body indices mentioned in S6 are as follows: NDBWI=(R(green)-R(red)) / (R(green)+R(red)); BOI=(R(green)-R(red)) / (R(blue)+R(green)+R(red)); In the formula, R(blue) is the blue band reflectance, R(green) is the green band reflectance, and R(red) is the red band reflectance.

9. The satellite remote sensing identification method for rural black and odorous water bodies within a provincial area as described in claim 1, characterized in that: The extraction threshold for black and odorous water bodies in S7 is determined based on a pre-set lookup table. The relevant values ​​in the lookup table are determined based on the selected satellite and the on-site investigation of normal and black and odorous water bodies in the monitoring area. Recommended values ​​are as follows: For GF1 and GF2 satellites, the following settings can be configured: NDBWI < 0.050; BOI < 0.

054. The following settings can be configured for GF1B, C, and D satellites: NDBWI < 0.026; BOI < 0.028; The reflectance image is classified into binary values ​​according to the above conditions. Pixels that meet the criteria are set to 1, and those that do not are set to 0.

10. The system used in the satellite remote sensing identification method for rural black and odorous water bodies within a provincial area as described in claim 1, characterized in that, Includes the following steps: Step 1: Satellite image raw data decompression module: Decompress the raw images to the target folder according to the set raw image folder and decompression target folder; Step 2: Satellite Image Indexing Module: Extract satellite image metadata and the coverage area of ​​each image to create a spatial index for the high-resolution image data. The index file is stored in shapefile format, and the coordinate system is the same as that used in the original satellite image data. Step 3: Image Processing Module: Automatically processes satellite images, including radiometric calibration, atmospheric correction, orthorectification, image fusion, and geometric correction; Step 4: Water body extraction module: Automatically extracts water bodies using the model described in S4 and automatically extracts the water body distribution range of the monitoring area based on the corrected image according to the set threshold. Step 5: Suspected Black and Odorous Water Body Extraction Module: Automatically extracts suspected black and odorous water bodies in rural areas. Using the black and odorous water body index model described in S6, it automatically extracts suspected black and odorous water bodies according to the set threshold. Step Six: Navigation QR Code Generation Module: Calls relevant APIs of map software to automatically generate QR codes for map software based on the latitude and longitude of the center of suspected black and odorous water patches. Users can scan the QR code using the corresponding mobile navigation app to plan a route, facilitating quick arrival at the location during on-site verification.

Images