Forest fire monitoring method based on remote sensing satellite data and polar orbit satellite data

Abstract

The present invention discloses a forest fire monitoring method based on remote sensing satellite data and polar-orbiting satellite data. The method comprises: acquiring remote sensing data of a target area via a remote sensing satellite, acquiring first polar-orbit image data of the target area via a polar-orbiting satellite, processing the data to obtain standard data and polar-orbit data, extracting the regional forest area from the polar-orbiting data, removing flare pixel data to obtain second polar-orbiting image data, detecting and removing interference data from the second polar-orbiting image data to obtain third polar-orbiting image data, calculating a burning index to obtain the fire location, and masking the regional forest area with the fire location to obtain forest fire distribution information. By utilizing the high temporal resolution of remote sensing satellite data and the high spatial resolution of first polar-orbiting satellite image data to complement each other, the timeliness and fire identification of forest fire satellite monitoring can be improved. The method can be widely applied to the field of satellite data processing technology.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite data processing, and in particular to a forest fire point monitoring method based on remote sensing satellite data and polar orbit satellite data. Background Art

[0002] Forest fires are a major disaster that damages natural resources. They are often sudden and have wide-ranging impacts. Once a forest fire breaks out, it can severely harm forest resources and even the lives and property of the public. Forest fire monitoring is a prerequisite and a crucial step in fire suppression and rescue efforts. Satellite remote sensing technology, with its wide monitoring range, rapid acquisition speed, and rich information, has become a key tool for forest fire monitoring.

[0003] Existing satellite remote sensing technologies, such as the method for extracting fire points based on Himawari-9 data, only use Himawari-9 data. The spatial resolution of Himawari-9 data is not high, and it can only achieve continuous dynamic monitoring of large-scale fires. It has low sensitivity to small fire points in the early stages of a fire, resulting in low timeliness of fire monitoring and fire point identification. Summary of the Invention

[0004] In view of this, an embodiment of the present invention aims to provide a forest fire point monitoring method based on remote sensing satellite data and polar-orbiting satellite data, which can improve the timeliness and fire point identification of forest fire satellite monitoring.

[0005] In a first aspect, an embodiment of the present invention provides a forest fire monitoring method based on remote sensing satellite data and polar-orbiting satellite data, comprising the following steps:

[0006] Acquiring remote sensing data of a target area through a remote sensing satellite, and performing a first preprocessing on the remote sensing data to obtain standard data, wherein the standard data includes angle data, brightness temperature data, a plurality of first band data, and first reflectivity data corresponding to the first band data;

[0007] Acquiring first polar orbit image data of the target area through a polar orbit satellite, and performing a second preprocessing on the first polar orbit image data to obtain polar orbit data, wherein the polar orbit data includes a plurality of second band data and second reflectivity data corresponding to the second band data;

[0008] Extracting the regional forest area in the polar orbit data based on supervised classification method;

[0009] Acquiring angle data from the standard data, and removing flare pixel data from the polar orbit data based on the angle data to obtain second polar orbit image data;

[0010] detecting interference data in the second polar orbit image data using a preset detection model, and removing the interference data to obtain third polar orbit image data;

[0011] Calculating a first combustion index based on a plurality of first reflectivity data, calculating a second combustion index based on a plurality of second reflectivity data, extracting a fire point combustion area based on the first combustion index and the second combustion index, obtaining brightness temperature data of each pixel in the fire point combustion area, and extracting a first fire point position in the fire point combustion area based on the brightness temperature data and a brightness temperature threshold of each pixel;

[0012] determining a second fire point location based on the first fire point location and the third polar track image data;

[0013] The forest area is masked with the second fire point position to obtain forest fire point distribution information.

[0014] Optionally, the performing a first preprocessing on the remote sensing data specifically includes:

[0015] Acquire a plurality of first-band data of the remote sensing data, the plurality of first-band data including a first band and a second band, read first data block information of the first band, and read second data block information of the second band;

[0016] Obtaining the first reflectivity data by calculating according to the first data block information and a reflectivity calculation formula;

[0017] The brightness temperature data is obtained by calculation according to the second data block information and the brightness temperature calculation formula;

[0018] Performing geometric correction on the remote sensing data according to the first data block information and the second data block information;

[0019] The angle data is obtained by performing angle calculation on the remote sensing data according to the first data block information and the second data block information.

[0020] Optionally, performing geometric correction on the remote sensing data according to the first data block information and the second data block information specifically includes:

[0021] Acquire latitude and longitude information, row offset information, and column offset information from the first data block information and the second data block information;

[0022] The longitude and latitude of the remote sensing data are converted into row and column numbers according to the longitude and latitude information, the row offset information, and the column offset information, thereby converting the full disk projection of the remote sensing data into a Mercator projection.

[0023] Optionally, performing angle calculation on the remote sensing data according to the first data block information and the second data block information to obtain the angle data specifically includes:

[0024] Acquire imaging time information, sun position information during imaging, and satellite coordinate information from the first data block information and the second data block information;

[0025] The angle data is obtained by performing angle calculation according to the imaging time information, the sun position information at the time of imaging, and the satellite coordinate information.

[0026] Optionally, extracting the regional forest range in the polar orbit data based on a supervised classification method specifically includes:

[0027] Obtaining vegetation index, DEM data, and texture features of the polar orbit data, wherein the texture features are calculated according to a gray level co-occurrence matrix method;

[0028] Establishing a feature image layer based on the vegetation index, the DEM data and the texture features;

[0029] The regional forest area in the feature image layer is extracted based on a supervised classification method.

[0030] Optionally, detecting interference data in the second polar orbit image data using a preset detection model and removing the interference data to obtain third polar orbit image data specifically includes:

[0031] Establishing a water vapor detection model, a fog detection model, and a cloud detection model based on the standard data and the second polar orbit image data;

[0032] The second polar orbit image data is respectively input into the water vapor detection model, the fog detection model and the cloud detection model to obtain water vapor pixels, fog pixels and cloud pixels, and the water vapor pixels, fog pixels and cloud pixels are eliminated to obtain the third polar orbit image data.

[0033] Optionally, determining the second fire point position according to the first fire point position and the third polar orbit image data specifically includes: extracting the second fire point position from the first fire point position using the third polar orbit image data and an extraction rule, wherein the extraction rule is as follows:

[0034] band20>310K and band20-band21>10and band4<0.3

[0035] Among them, band20 represents the radiation value and reflectivity of band 20 in the third polar image data, band21 represents the radiation value and reflectivity of band 21 in the third polar image data, and band4 represents the radiation value and reflectivity of band 4 in the third polar image data.

[0036] In a second aspect, an embodiment of the present invention provides a forest fire monitoring system based on remote sensing satellite data and polar-orbiting satellite data, comprising:

[0037] A first module is configured to acquire remote sensing data of a target area via a remote sensing satellite, and perform a first preprocessing on the remote sensing data to obtain standard data, wherein the standard data includes angle data, brightness temperature data, a plurality of first waveband data, and first reflectivity data corresponding to the first waveband data;

[0038] A second module is configured to acquire first polar orbit image data of the target area through a polar orbit satellite, and perform a second preprocessing on the first polar orbit image data to obtain polar orbit data, wherein the polar orbit data includes a plurality of second band data and second reflectivity data corresponding to the second band data;

[0039] The third module is used to extract the regional forest area in the polar track data based on the supervised classification method;

[0040] A fourth module is configured to obtain angle data from the standard data, and remove flare pixel data from the polar orbit data based on the angle data to obtain second polar orbit image data;

[0041] a fifth module, configured to detect interference data in the second polar orbit image data using a preset detection model, and obtain third polar orbit image data after removing the interference data;

[0042] A sixth module is configured to calculate a first combustion index based on a plurality of first reflectivity data, calculate a second combustion index based on a plurality of second reflectivity data, extract a fire point combustion area based on the first combustion index and the second combustion index, obtain brightness temperature data of each pixel in the fire point combustion area, and extract a first fire point position in the fire point combustion area based on the brightness temperature data and a brightness temperature threshold of each pixel;

[0043] A seventh module is configured to determine a second fire point location based on the first fire point location and the third polar orbit image data;

[0044] The eighth module is used to mask the second fire point position of the forest area in the region to obtain forest fire point distribution information.

[0045] In a third aspect, an embodiment of the present invention provides a forest fire monitoring device based on remote sensing satellite data and polar-orbiting satellite data, comprising:

[0046] at least one processor;

[0047] at least one memory for storing at least one program;

[0048] When the at least one program is executed by the at least one processor, the at least one processor implements the method described above.

[0049] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing a program executable by a processor, wherein the program executable by the processor is used to perform the method described above when executed by the processor.

[0050] The implementation of the embodiment of the present invention includes the following beneficial effects: The embodiment of the present invention provides a forest fire monitoring method based on remote sensing satellite data and polar-orbit satellite data, including: acquiring remote sensing data of a target area through a remote sensing satellite, performing a first preprocessing on the remote sensing data to obtain standard data, wherein the standard data includes angle data, brightness temperature data, multiple first band data and first reflectivity data corresponding to the first band data; acquiring first polar-orbit image data of the target area through a polar-orbit satellite, performing a second preprocessing on the first polar-orbit image data to obtain polar-orbit data, wherein the polar-orbit data includes multiple second band data and second reflectivity data corresponding to the second band data; extracting the regional forest range in the polar-orbit data based on a supervised classification method; acquiring angle data in the standard data, and performing a second preprocessing on the first polar-orbit image data based on the supervised classification method; The method comprises the following steps: removing the flare pixel data in the polar orbit data by using the angle data to obtain the second polar orbit image data; detecting the interference data in the second polar orbit image data by using a preset detection model, and obtaining the third polar orbit image data after removing the interference data; calculating the first combustion index according to the plurality of the first reflectivity data, calculating the second combustion index according to the plurality of the second reflectivity data, extracting the fire point combustion area according to the first combustion index and the second combustion index, obtaining the brightness temperature data of each pixel in the fire point combustion area, extracting the first fire point position in the fire point combustion area according to the brightness temperature data and the brightness temperature threshold of each pixel; determining the second fire point position according to the first fire point position and the third polar orbit image data; masking the forest range of the area with the second fire point position to obtain forest fire point distribution information. By acquiring remote sensing data of the target area and the first polar-orbit image data of the polar-orbit satellite through remote sensing satellites, processing the remote sensing data and the first polar-orbit image data, and finally obtaining the forest fire point distribution information, the high temporal resolution of the remote sensing satellite remote sensing data and the high spatial resolution of the first polar-orbit image data of the polar-orbit satellite are utilized to coordinate and complement each other, thereby improving the timeliness of forest fire satellite monitoring and the fire point identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1This is a schematic flow chart of the steps of a forest fire monitoring method based on remote sensing satellite data and polar-orbiting satellite data provided by an embodiment of the present invention;

[0052] Figure 2 This is a flowchart of another forest fire monitoring method based on remote sensing satellite data and polar-orbiting satellite data provided by an embodiment of the present invention;

[0053] Figure 3 4 is a graph showing the reading result of the fifth data block provided by an embodiment of the present invention;

[0054] Figure 4 7. This is a graph showing the calculation results of brightness temperature values ​​for band seven of Himawari-9 remote sensing data provided by an embodiment of the present invention;

[0055] Figure 5 This is a graph of NDVI calculation results provided by an embodiment of the present invention;

[0056] Figure 6 This is a forestland extraction vector distribution map provided by an embodiment of the present invention;

[0057] Figure 7 is a cloud detection map provided by an embodiment of the present invention;

[0058] Figure 8 This is a forest fire burning area map provided by an embodiment of the present invention;

[0059] Figure 9 This is a structural block diagram of a forest fire monitoring system based on remote sensing satellite data and polar-orbiting satellite data provided by an embodiment of the present invention;

[0060] Figure 10 This is a structural block diagram of a forest fire monitoring device based on remote sensing satellite data and polar-orbiting satellite data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0062] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention.

[0063] In the description of the present invention, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0064] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0065] like Figure 1 As shown, an embodiment of the present invention provides a forest fire point monitoring method based on remote sensing satellite data and polar-orbiting satellite data, which includes the following steps.

[0066] S100. Acquire remote sensing data of a target area through a remote sensing satellite, and perform a first preprocessing on the remote sensing data to obtain standard data, wherein the standard data includes angle data, brightness temperature data, multiple first band data, and first reflectivity data corresponding to the first band data.

[0067] The Himawari-9 remote sensing satellite currently produces two data types: standard data in DAT format and primary data in NC format. Using NC format data to extract fire points is a common method, but this method suffers from information lag and cannot achieve effective real-time monitoring. Another method for extracting fire points based on Himawari-9 standard data, however, only uses the Himawari-9 standard data and fails to consider its low spatial resolution.

[0068] Reference Figure 2 In a specific embodiment, Himawari-9 remote sensing data (using standard data) is parsed, including reading data fields, inverting reflectance and brightness temperature, performing geometric correction, and calculating angle data. Each strip of remote sensing data in each band consists of 12 data blocks. After selecting a band, the data block fields of all strips are read.

[0069] Optionally, the performing a first preprocessing on the remote sensing data specifically includes:

[0070] S110, acquiring a plurality of first-band data of the remote sensing data, the plurality of first-band data including a first band and a second band, reading first data block information of the first band, and reading second data block information of the second band;

[0071] S120: Obtain the first reflectivity data by calculating according to the first data block information and a reflectivity calculation formula;

[0072] S130, obtaining the brightness temperature data by calculation according to the second data block information and a brightness temperature calculation formula;

[0073] S140, performing geometric correction on the remote sensing data according to the first data block information and the second data block information;

[0074] S150: Perform angle calculation on the remote sensing data according to the first data block information and the second data block information to obtain the angle data.

[0075] Specifically, the first band and the second band of the remote sensing data are obtained. The first band and the second band each include multiple bands. In a specific embodiment, the first band is the 1st to 6th band of the remote sensing data, and the second band is the 7th to 16th band of the remote sensing data. The first reflectivity data is calculated based on the first data block information read from the 1st to 6th band, and the brightness temperature data is calculated based on the second data block information read from the 7th to 16th band. Figure 3 , wherein the formula for calculating the first reflectivity data is as follows:

[0076] Radiance=Slope×Count+Intercept

[0077] Where Slope represents the slope read from the correction information block of the 5th data block, Intercept represents the intercept read from the correction information block of the 5th data block, and Count is the pixel value.

[0078] Reference Figure 4 , the formula for calculating the brightness temperature data is as follows:

[0079]

[0080] T b =C0+C1T e +C2T e 2

[0081] Where, T e represents effective brightness temperature; I represents radiance; λ represents central wavelength; h represents Planck constant; c represents speed of light, and ln represents natural logarithm. b Represents brightness temperature. C0, C1, and C2 are correction coefficients for converting radiance to brightness temperature. The speed of light and correction coefficients can be read from the data block.

[0082] The remote sensing data includes latitude and longitude information read from the first data block information and the second data block information, and the remote sensing data is geometrically corrected by latitude and longitude conversion. The remote sensing data includes angle calculation parameters read from the first data block information and the second data block information, and the angle calculation parameters are used to obtain the angle data by performing angle calculation.

[0083] Optionally, performing geometric correction on the remote sensing data according to the first data block information and the second data block information specifically includes:

[0084] S141. Acquire latitude and longitude information, row offset information, and column offset information from the first data block information and the second data block information;

[0085] S142. Convert the longitude and latitude of the remote sensing data into row and column numbers according to the longitude and latitude information, the row offset information, and the column offset information, thereby converting the full disk projection of the remote sensing data into a Mercator projection.

[0086] Specifically, the longitude and latitude information of the sub-satellite point in the remote sensing data is read from the first data block information and the second data block information, and then the row offset information and column offset information for converting the longitude and latitude into row and column numbers are obtained from the longitude and latitude information. The longitude and latitude are converted into row and column numbers based on the longitude and latitude information of the sub-satellite point, the row offset information, and the column offset information, and the full disk projection in the remote sensing data of the remote sensing satellite Himawari-9 is converted into a Mercator projection.

[0087] Optionally, performing angle calculation on the remote sensing data according to the first data block information and the second data block information to obtain the angle data specifically includes:

[0088] S151, acquiring imaging time information, sun position information during imaging, and satellite coordinate information from the first data block information and the second data block information;

[0089] S152: Perform angle calculation according to the imaging time information, the sun position information at the time of imaging, and the satellite coordinate information to obtain the angle data, thereby obtaining the angle data.

[0090] Specifically, angle data calculation primarily involves obtaining information such as the solar zenith angle and the satellite zenith angle, which reflect the intensity of energy received by the sensor throughout the day. The required angle data is calculated by reading parameters such as the imaging time, the sun's position at the time of imaging, and the satellite's coordinates from the first and second data blocks of remote sensing data.

[0091] S200. Acquire first polar-orbit image data of the target area through a polar-orbit satellite, and perform second preprocessing on the first polar-orbit image data to obtain polar-orbit data, wherein the polar-orbit data includes a plurality of second band data and second reflectivity data corresponding to the second band data.

[0092] Reference Figure 1-2 Specifically, the preprocessing of polar-orbiting satellite first-orbit image data mainly includes band combination, radiometric calibration, atmospheric correction, orthorectification, image mosaicking, and geometric correction. Atmospheric correction converts radiometric brightness values ​​into actual surface reflectivity to eliminate errors caused by atmospheric scattering, absorption, and reflection. Orthorectification eliminates deformation caused by terrain or camera orientation, ultimately generating a planar orthophoto.

[0093] In a specific embodiment, the polar-orbiting satellites include multiple different types of polar-orbiting satellites. By utilizing the different functions and data of different polar-orbiting satellites, data processing is coordinated to obtain more accurate fire location information. During preprocessing, data from polar-orbiting satellites such as Sentinel-2, MODIS, FY-3D, and Landsat are subjected to a second preprocessing step.

[0094] S300: Extracting the regional forest area in the polar orbit data based on a supervised classification method.

[0095] Specifically, the first polar orbit image data is subjected to a first preprocessing to obtain polar orbit data, which includes geographic feature information and vegetation information, etc. The regional forest range in the polar orbit data is obtained through a supervised classification method.

[0096] Optionally, extracting the regional forest area in the polar orbit data based on a supervised classification method specifically includes:

[0097] S310, obtaining vegetation index, DEM data, and texture features of the polar orbit data, wherein the texture features are calculated according to a gray level co-occurrence matrix method;

[0098] S320, establishing a feature image layer based on the vegetation index, the DEM data, and the texture features;

[0099] S330: Extracting the forest area in the feature image layer based on a supervised classification method.

[0100] Reference Figure 5-6Specifically, vegetation indices and texture features are calculated from polar-orbiting satellite data and combined with DEM (Digital Elevation Model) data. Forest distribution is then identified and extracted through supervised classification. Texture features are calculated using a gray-level co-occurrence matrix. Forestland extraction requires the establishment of a sample library. This sample data consists of real-world landforms, including forestland and non-forestland samples, mapped prior to supervised classification. Vegetation indices, texture features, and DEM data are fused with polar-orbiting satellite imagery as the classification input, and forestland extent is extracted using supervised classification.

[0101] Vegetation indices include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Atmospheric Impedance Vegetation Index (ARVI), Ratio Vegetation Index (RVI), and Soil Adjusted Vegetation Index (SAVI). The specific calculation formulas are as follows:

[0102]

[0103]

[0104]

[0105] RVNIR / Red

[0106]

[0107] Where NIR is the near-infrared band reflectance of the first polar-orbit image data of the polar-orbiting satellite, Red is the red light band reflectance of the first polar-orbit image data of the polar-orbiting satellite, Blue is the blue light band reflectance of the first polar-orbit image data of the polar-orbiting satellite, and L is the soil adjustment coefficient.

[0108] In a specific embodiment, band 8, band 4, band 3, and band 2 of the polar-orbiting satellite Sentinel-2's first polar-orbit image data are used to calculate vegetation indices including the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Atmospheric Impedance Vegetation Index (ARVI), the Ratio Vegetation Index (RVI), and the Soil Adjusted Vegetation Index (SAVI). The parameters in the above calculation formula are: NIR is band 8 of the Sentinel-2's first polar-orbit image data; Red is band 4 of the Sentinel-2's first polar-orbit image data; Blue is band 2 of the Sentinel-2's first polar-orbit image data; and L is the soil adjustment coefficient, which is 0.5.

[0109] In a specific embodiment, the gray level co-occurrence matrix method is used to calculate eight texture features, including Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, and Correlation, of Sentinel-2 image band8, band4, band3, and band2, respectively, and the processing window is set to 5x5.

[0110] Mean represents the average grayscale value of an image. In texture difference analysis, the average grayscale values ​​of different regions or images can be compared to observe overall texture brightness differences. Variance indicates the dispersion of the grayscale values ​​in an image. A higher variance means that the image has more grayscale variation, which can be used to observe texture detail and differences in grayscale distribution. Homogeneity reflects the degree of similarity between pairs of pixels with similar grayscale levels in an image. Higher homogeneity indicates a more uniform texture in the image, while lower homogeneity indicates a more heterogeneous texture. Contrast indicates the grayscale difference between adjacent pixels in an image. Higher contrast means that the texture in the image is clearer and more prominent, while lower contrast means that the texture is more blurred or smoother. Dissimilarity indicates the grayscale difference between different pixels in an image. Higher dissimilarity indicates that the texture in the image is more diverse and rich, while lower dissimilarity indicates that the texture in the image is more similar or monotonous. Entropy indicates the uncertainty or amount of information in an image. Higher entropy means that the texture in the image is more complex and random, while lower entropy means that the texture in the image is simpler and more regular. Second Moment measures the average value of the grayscale distribution of an image. A higher second moment indicates that the texture in the image is richer and more dispersed, while a lower second moment indicates that the texture in the image is more concentrated and centralized. Correlation reflects the linear correlation between pixels in the image. A higher correlation indicates that the texture in the image is more coherent and consistent, while a lower correlation indicates that the texture in the image is more incoherent and inconsistent.

[0111] A feature image layer was established based on "Sentinel-2 first polar orbit image data + vegetation index + texture features + terrain data". The supervised classification method was used on the basis of sample data to extract regional forest vectors, and then the regional forest range was obtained.

[0112] S400 , obtaining angle data from the standard data, and removing flare pixel data from the polar data based on the angle data to obtain second polar image data.

[0113] Specifically, the angle data of the sun in the standard data is obtained, and then the flare pixels are judged according to the reflectivity of the wavelength of the polar orbit data of the polar satellite in the same period, and then the flare pixels are removed to avoid affecting the detection.

[0114] In a specific embodiment, the azimuth angle of the sun relative to Himawari-9 is between 170° and 210°, and the reflectivity of band 1 and band 2 of the polar-orbiting satellite MODIS are both greater than 0.32 or the reflectivity of band 3 and band 4 of the polar-orbiting satellite FY-3D are both greater than 0.2 during the same period, and the pixel is determined to be a flare pixel.

[0115] S500: Detect interference data in the second polar orbit image data using a preset detection model, and obtain third polar orbit image data after removing the interference data.

[0116] Reference Figure 7 Specifically, after establishing a detection model based on the second polar orbit image data, polar orbit satellites are used to identify clouds. The conditions for cloud detection are: the sum of the reflectivity of the polar orbit satellite band data a and b at wavelengths of 0.62-0.67μm and 0.84-0.88μm is not less than 0.7, or the temperature value of the band data c at wavelengths of 11.77-13.48μm is not higher than 265K, or the sum of the reflectivity of the band data a and b at wavelengths of 0.62-0.67μm and 0.84-0.88μm is not less than 0.7, and the temperature value of the band data c at wavelengths of 11.77-13.48μm is not higher than 285K.

[0117] Conditions for water vapor detection: The reflectivity of polar-orbiting satellite data in the 0.84-0.88μm band b is no greater than 0.15, the reflectivity of data in the 2.105-2.135μm band d is no greater than 0.05, and the NDVI is less than 0.

[0118] Fog detection conditions: If the reflectance of the polar-orbiting satellite wavebands at 0.41-0.42μm, 0.44-0.45μm, and 2.1-2.2μm, e, f, and d, meets the following conditions, it is considered fog:

[0119] and and and E>0.09

[0120] Where E represents the reflectivity of band e, F represents the reflectivity of band f, and D represents the reflectivity of band d.

[0121] Optionally, detecting interference data in the second polar orbit image data using a preset detection model and removing the interference data to obtain third polar orbit image data specifically includes:

[0122] S510: Establishing a water vapor detection model, a fog detection model, and a cloud detection model according to the standard data and the second polar orbit image data;

[0123] S520. Input the second polar orbit image data into the water vapor detection model, the fog detection model, and the cloud detection model respectively to obtain water vapor pixels, fog pixels, and cloud pixels, and remove the water vapor pixels, fog pixels, and cloud pixels to obtain the third polar orbit image data.

[0124] In a specific embodiment, cloud detection conditions are: the sum of the reflectivity of band 1 and band 2 of the polar-orbiting satellite MODIS is greater than 0.9, or the sum of the reflectivity of band 3 and band 4 of FY-3D is greater than 1.2, or the radiation values ​​of band 32, band 33 of MODIS and band 25 of FY-3D are lower than 260K, or the sum of the reflectivity of band 1 and band 2 of MODIS or band 3 and band 4 of FY-3D is greater than 0.7, and at the same time, the radiation values ​​of band 32, band 33 of MODIS and band 25 of FY-3D are lower than 280K;

[0125] The water vapor detection conditions are: the reflectivity of MODIS band 2 or FY-3D band 4 is less than 0.15, the reflectivity of MODIS band 7 or FY-3D band 7 is less than 0.05, and the NDVI is less than 0;

[0126] Fog detection: If the reflectance of bands 7, 8, and 9 of FY-3D or bands 7, 8, and 9 of MODIS meets the following conditions, it is considered fog:

[0127] and and and band8>0.09

[0128] Wherein, band7 represents the reflectivity of band 7, band8 represents the reflectivity of band 8, and band9 represents the reflectivity of band 9.

[0129] S600. Calculate a first combustion index based on multiple first reflectivity data, calculate a second combustion index based on multiple second reflectivity data, extract a fire point combustion area based on the first combustion index and the second combustion index, obtain brightness temperature data of each pixel in the fire point combustion area, and extract the first fire point position in the fire point combustion area based on the brightness temperature data and brightness temperature threshold of each pixel.

[0130] Specifically, the first burning index is calculated based on band 3 (in the red light band), band 4 (in the near infrared band), band 6 (in the shortwave infrared band), and band 14 (in the thermal infrared band) of the Himawari-9 standard data. The second burning index of the same area as that photographed by the Himawari-9 is calculated based on the red light band, near infrared band (0.76-0..9μm), shortwave infrared band (2.08-2.35μm), and thermal infrared band (10.4-12.5μm) of the third polar-orbit image data of the polar-orbiting satellite. The burning area of ​​the same area is extracted based on the first burning index of the Himawari-9 and the second burning index of the polar-orbiting satellite. The location of the first fire point is preliminarily extracted by reading the brightness temperature data and brightness temperature threshold of each pixel in the burning area of ​​the fire point. The calculation method of each burning index is as follows:

[0131]

[0132]

[0133]

[0134] Where Red uses the 3-band reflectance of the Himawari-9 data or the red band reflectance of a polar-orbiting satellite, NIR uses the 4-band reflectance of the Himawari-9 data or the near-infrared band reflectance of a polar-orbiting satellite, SWIR uses the 6-band reflectance of the Himawari-9 data or the shortwave infrared band reflectance of a polar-orbiting satellite, and Thermal uses the 14-band radiance value of the Himawari-9 data or the thermal infrared band radiance value of a polar-orbiting satellite. Burned areas have high BAI (Burn Area Index) and low NBR (Normalized Burn Ratio) and NBRT (Normalized Burn Ratio-Thermal).

[0135] In a specific embodiment, the combustion index is calculated using bands 3, 4, 6, and 14 of Himawari-9 and bands 3 (in the red light band), 4 (in the near infrared band), 6 (in the shortwave infrared band), and 7 (in the thermal infrared band) of the polar-orbiting satellite Landsat, to extract the burning area of ​​the fire point and then extract the position of the first fire point.

[0136] S700: Determine a second fire point position according to the first fire point position and the third polar-rail image data.

[0137] Specifically, after obtaining the first fire point location, the third polar orbit image data from the polar-orbiting satellite is used to locate the fire point location. The fire point is accurately extracted based on the following rules: the polar-orbiting satellite wavelength range is in the band data g of 3.92-3.98μm, the band data h of 10.78-11.28μm, and the band data b of 0.84-0.88μm:

[0138] G>310K and GH>10and B<0.3

[0139] Where G represents the radiation value and reflectivity of band g, H represents the radiation value and reflectivity of band h, and B represents the radiation value and reflectivity of band b.

[0140] Optionally, determining the second fire point location according to the first fire point location and the third polar orbital image data specifically includes: extracting the second fire point location from the first fire point location using the third polar orbital image data and an extraction rule, wherein the extraction rule is as follows:

[0141] band20>310K and band20-band21>10and band4<0.3

[0142] Among them, band20 represents the radiation value and reflectivity of band 20 in the third polar image data, band21 represents the radiation value and reflectivity of band 21 in the third polar image data, and band4 represents the radiation value and reflectivity of band 4 in the third polar image data.

[0143] In a specific embodiment, the second fire point position is located using the band 20, band 24, and band 4 data of FY-3D.

[0144] S800, masking the second fire point position by the forest area to obtain forest fire point distribution information;

[0145] Reference Figure 8 Specifically, the fire point information is screened and sorted, and the fire point data within the forest area is extracted using the forest vector mask to obtain the forest fire point distribution information.

[0146] The implementation of the embodiment of the present invention includes the following beneficial effects: The embodiment of the present invention provides a forest fire monitoring method based on remote sensing satellite data and polar-orbit satellite data, including: acquiring remote sensing data of a target area through a remote sensing satellite, performing a first preprocessing on the remote sensing data to obtain standard data, wherein the standard data includes angle data, brightness temperature data, multiple first band data and first reflectivity data corresponding to the first band data; acquiring first polar-orbit image data of the target area through a polar-orbit satellite, performing a second preprocessing on the first polar-orbit image data to obtain polar-orbit data, wherein the polar-orbit data includes multiple second band data and second reflectivity data corresponding to the second band data; extracting the regional forest range in the polar-orbit data based on a supervised classification method; acquiring angle data in the standard data, and performing a second preprocessing on the first polar-orbit image data based on the supervised classification method; The method comprises the following steps: removing the flare pixel data in the polar orbit data by using the angle data to obtain the second polar orbit image data; detecting the interference data in the second polar orbit image data by using a preset detection model, and obtaining the third polar orbit image data after removing the interference data; calculating the first combustion index according to the plurality of the first reflectivity data, calculating the second combustion index according to the plurality of the second reflectivity data, extracting the fire point combustion area according to the first combustion index and the second combustion index, obtaining the brightness temperature data of each pixel in the fire point combustion area, extracting the first fire point position in the fire point combustion area according to the brightness temperature data and the brightness temperature threshold of each pixel; determining the second fire point position according to the first fire point position and the third polar orbit image data; masking the forest range of the area with the second fire point position to obtain forest fire point distribution information. By acquiring remote sensing data of the target area from remote sensing satellites and first-orbit imagery from polar-orbiting satellites, these data are processed to derive forest fire distribution information. By leveraging the high temporal resolution of remote sensing satellite data and the high spatial resolution of first-orbit imagery from polar-orbiting satellites, the timeliness of forest fire satellite monitoring and fire identification are improved. Directly processing remote sensing data in DAT format reduces time lag and improves monitoring efficiency. Introducing supervised classification methods into forest area extraction addresses the issue of inaccurate forest fire monitoring due to the lack of timely land use data.

[0147] like Figure 9 As shown, an embodiment of the present invention further provides a forest fire point monitoring system based on remote sensing satellite data and polar-orbiting satellite data, comprising:

[0148] A first module is configured to acquire remote sensing data of a target area via a remote sensing satellite, and perform a first preprocessing on the remote sensing data to obtain standard data, wherein the standard data includes angle data, brightness temperature data, a plurality of first waveband data, and first reflectivity data corresponding to the first waveband data;

[0149] A second module is configured to acquire first polar orbit image data of the target area through a polar orbit satellite, and perform a second preprocessing on the first polar orbit image data to obtain polar orbit data, wherein the polar orbit data includes a plurality of second band data and second reflectivity data corresponding to the second band data;

[0150] The third module is used to extract the regional forest area in the polar track data based on the supervised classification method;

[0151] A fourth module is configured to obtain angle data from the standard data, and remove flare pixel data from the polar orbit data based on the angle data to obtain second polar orbit image data;

[0152] a fifth module, configured to detect interference data in the second polar orbit image data using a preset detection model, and obtain third polar orbit image data after removing the interference data;

[0153] A sixth module is configured to calculate a first combustion index based on a plurality of first reflectivity data, calculate a second combustion index based on a plurality of second reflectivity data, extract a fire point combustion area based on the first combustion index and the second combustion index, obtain brightness temperature data of each pixel in the fire point combustion area, and extract a first fire point position in the fire point combustion area based on the brightness temperature data and a brightness temperature threshold of each pixel;

[0154] A seventh module is configured to determine a second fire point location based on the first fire point location and the third polar orbit image data;

[0155] The eighth module is used to mask the second fire point position of the forest area in the region to obtain forest fire point distribution information.

[0156] It can be seen that the contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0157] like Figure 10 As shown, an embodiment of the present invention further provides a forest fire point monitoring device based on remote sensing satellite data and polar orbit satellite data, comprising:

[0158] at least one processor;

[0159] at least one memory for storing at least one program;

[0160] When the at least one program is executed by the at least one processor, the at least one processor implements the method steps described in the above method embodiment.

[0161] It can be seen that the contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0162] In addition, the embodiments of the present application further disclose a computer program product or computer program, which is stored in a computer-readable storage medium. The processor of a computer device can read the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device performs the above-mentioned method. Similarly, the contents of the above-mentioned method embodiment are all applicable to the present storage medium embodiment, and the functions specifically implemented by the present storage medium embodiment are the same as those of the above-mentioned method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned method embodiment.

[0163] It is understood that all or some steps, systems in the disclosed method above can be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital information processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those of ordinary skill in the art, the term computer storage medium is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data) and is volatile and non-volatile, removable and non-removable media. Computer storage media includes but is not limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, disk storage or other magnetic storage device, or can be used to store desired information and any other medium that can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in modulated data information such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0164] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the scope of knowledge possessed by ordinary technicians in the technical field without departing from the spirit of the present invention.

Claims

1. A forest fire monitoring method based on remote sensing satellite data and polar orbit satellite data, characterized in that: include: Acquiring remote sensing data of a target area through a remote sensing satellite, and performing a first preprocessing on the remote sensing data to obtain standard data, wherein the standard data includes angle data, brightness temperature data, a plurality of first band data, and first reflectivity data corresponding to the first band data; Acquiring first polar orbit image data of the target area through a polar orbit satellite, and performing a second preprocessing on the first polar orbit image data to obtain polar orbit data, wherein the polar orbit data includes a plurality of second band data and second reflectivity data corresponding to the second band data; Extracting the regional forest area in the polar orbit data based on supervised classification method; Acquiring angle data from the standard data, and removing flare pixel data from the polar orbit data based on the angle data to obtain second polar orbit image data; detecting interference data in the second polar orbit image data using a preset detection model, and removing the interference data to obtain third polar orbit image data; A first combustion index is calculated based on the plurality of first reflectivity data, a second combustion index is calculated based on the plurality of second reflectivity data, and a fire point combustion area is extracted based on the first combustion index and the second combustion index; brightness temperature data of each pixel in the fire point combustion area is obtained, and a first fire point position in the fire point combustion area is extracted based on the brightness temperature data of each pixel and a brightness temperature threshold; wherein the combustion index is calculated as follows: Wherein, Red uses the 3-band reflectance of Himawari-9 data or the red band reflectance of polar-orbiting satellites, NIR uses the 4-band reflectance of Himawari-9 data or the near-infrared band reflectance of polar-orbiting satellites, SWIR uses the 6-band reflectance of Himawari-9 data or the shortwave infrared band reflectance of polar-orbiting satellites, Thermal uses the 14-band radiation value of Himawari-9 data or the thermal infrared band radiation value of polar-orbiting satellites, BAI is the burned area index, NBR is the normalized burn index, and NBRT is the improved normalized burn index. determining a second fire point location based on the first fire point location and the third polar track image data; The forest area is masked with the second fire point position to obtain forest fire point distribution information.

2. The method according to claim 1, characterized in that The performing a first preprocessing on the remote sensing data specifically includes: Acquire a plurality of first-band data of the remote sensing data, the plurality of first-band data including a first band and a second band, read first data block information of the first band, and read second data block information of the second band; Obtaining the first reflectivity data by calculating according to the first data block information and a reflectivity calculation formula; The brightness temperature data is obtained by calculation according to the second data block information and the brightness temperature calculation formula; Performing geometric correction on the remote sensing data according to the first data block information and the second data block information; The angle data is obtained by performing angle calculation on the remote sensing data according to the first data block information and the second data block information.

3. The method according to claim 2, characterized in that The performing geometric correction on the remote sensing data according to the first data block information and the second data block information specifically includes: Obtaining longitude and latitude information, row offset information, and column offset information from the first data block information and the second data block information; converting the longitude and latitude of the remote sensing data into row and column numbers based on the longitude and latitude information, the row offset information, and the column offset information, thereby converting the full disk projection of the remote sensing data into a Mercator projection.

4. The method according to claim 2, characterized in that The performing angle calculation on the remote sensing data according to the first data block information and the second data block information to obtain the angle data specifically includes: Acquire imaging time information, sun position information during imaging, and satellite coordinate information from the first data block information and the second data block information; The angle data is obtained by performing angle calculation according to the imaging time information, the sun position information at the time of imaging, and the satellite coordinate information.

5. The method according to claim 1, wherein The extracting of the regional forest range in the polar orbit data based on the supervised classification method specifically includes: Obtaining vegetation index, DEM data, and texture features of the polar orbit data, wherein the texture features are calculated according to a gray level co-occurrence matrix method; Establishing a feature image layer based on the vegetation index, the DEM data and the texture features; The regional forest area in the feature image layer is extracted based on a supervised classification method.

6. The method according to claim 1, characterized in that The detecting interference data in the second polar orbit image data by using a preset detection model and removing the interference data to obtain the third polar orbit image data specifically includes: A water vapor detection model, a fog detection model, and a cloud detection model are established based on the standard data and the second polar orbit image data; the second polar orbit image data is input into the water vapor detection model, the fog detection model, and the cloud detection model respectively to obtain water vapor pixels, fog pixels, and cloud pixels; and the water vapor pixels, fog pixels, and cloud pixels are eliminated to obtain the third polar orbit image data.

7. The method according to claim 1, characterized in that Determining the second fire point position according to the first fire point position and the third polar orbit image data specifically includes: extracting the second fire point position from the first fire point position using the third polar orbit image data and an extraction rule, wherein the extraction rule is as follows: band20>310K and band20-band21>10 and band4<0.3 Among them, band20 represents the radiation value and reflectivity of band 20 in the third polar image data, band21 represents the radiation value and reflectivity of band 21 in the third polar image data, and band4 represents the radiation value and reflectivity of band 4 in the third polar image data.

8. A forest fire monitoring system based on remote sensing satellite data and polar orbit satellite data, characterized in that: include: A first module is configured to acquire remote sensing data of a target area via a remote sensing satellite, and perform a first preprocessing on the remote sensing data to obtain standard data, wherein the standard data includes angle data, brightness temperature data, a plurality of first waveband data, and first reflectivity data corresponding to the first waveband data; A second module is configured to acquire first polar orbit image data of the target area through a polar orbit satellite, and perform a second preprocessing on the first polar orbit image data to obtain polar orbit data, wherein the polar orbit data includes a plurality of second band data and second reflectivity data corresponding to the second band data; The third module is used to extract the regional forest area in the polar track data based on the supervised classification method; A fourth module is configured to obtain angle data from the standard data, and remove flare pixel data from the polar orbit data based on the angle data to obtain second polar orbit image data; a fifth module, configured to detect interference data in the second polar orbit image data using a preset detection model, and obtain third polar orbit image data after removing the interference data; The sixth module is configured to calculate a first combustion index based on the plurality of first reflectivity data, calculate a second combustion index based on the plurality of second reflectivity data, extract a fire point combustion area based on the first combustion index and the second combustion index, obtain brightness temperature data for each pixel in the fire point combustion area, and extract the first fire point position in the fire point combustion area based on the brightness temperature data and brightness temperature threshold of each pixel; wherein the combustion index is calculated as follows: Wherein, Red uses the 3-band reflectance of Himawari-9 data or the red band reflectance of polar-orbiting satellites, NIR uses the 4-band reflectance of Himawari-9 data or the near-infrared band reflectance of polar-orbiting satellites, SWIR uses the 6-band reflectance of Himawari-9 data or the shortwave infrared band reflectance of polar-orbiting satellites, Thermal uses the 14-band radiation value of Himawari-9 data or the thermal infrared band radiation value of polar-orbiting satellites, BAI is the burned area index, NBR is the normalized burn index, and NBRT is the improved normalized burn index. A seventh module is configured to determine a second fire point location based on the first fire point location and the third polar orbit image data; The eighth module is used to mask the second fire point position of the forest area in the region to obtain forest fire point distribution information.

9. A forest fire monitoring device based on remote sensing satellite data and polar orbit satellite data, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is configured to perform the method according to any one of claims 1 to 7 when executed by the processor.

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