A fire monitoring and identification method for the Yili River Valley based on the FY-4B meteorological satellite

Abstract

The invention discloses a fire monitoring and identification method for the Yili River Valley based on the FY-4B meteorological satellite, comprising: downloading the FY-4B AGRI L1 data set from the Fengyun satellite remote sensing data service network, including band images and radiation calibration files corresponding to each band; acquiring the attribute information of the FY-4B L1 data set, performing radiation calibration, and calculating the longitude and latitude data according to the row and column numbers for geometric correction; performing band analysis on the processed L1 data set and calculating the standard deviation in different bands, determining the mid-infrared band that has a strong reaction to the fire point, and constructing a candidate combination band in combination with the thermal infrared band; calculating the band combination OIF index, excluding the band combination with a correlation coefficient between two bands exceeding 0.9 in the candidate combination, and obtaining the best combination band; and realizing the identification task of the fire point and the burned area by combining the best combination band and the brightness temperature map of the band sensitive to the surface temperature.

Description

Technical Field

[0001] The invention relates to the technical field of fire monitoring, and in particular to, but not limited to, a fire monitoring and identification method for the Yili River Valley based on a FY-4B meteorological satellite. Background Art

[0002] Forests and grasslands are important components of the biosphere and are widely distributed on the land surface. Forest fire is a major factor that disturbs forest and grassland ecosystems and has become one of the most serious natural disasters worldwide.

[0003] The issue of forest fire prevention has become a hot topic of concern to all walks of life around the world. my country's grasslands are very vast, accounting for about 40% of the country's land area. Since the mid-1980s, meteorological satellite remote sensing technology has provided a large amount of forest and grassland fire monitoring information for fire prevention departments. Through the development of satellite remote sensing fire monitoring technology and experience accumulation over the past decade, the accuracy of fire point identification has reached a considerable level, and fire points of a few acres in size can be identified. Despite the progress made, it is still necessary to strengthen the research of key technologies, the transformation of scientific research results, and the improvement of satellite fire monitoring business systems in order to build a sky-air-ground integrated forest fire early warning and monitoring technology system that serves the construction of ecological civilization.

[0004] A single satellite data source may be affected by environmental factors such as clouds and rain, and the joint use of multi-source satellite remote sensing data can improve the accuracy and timeliness of monitoring. FY-4B is a new generation of China's geostationary meteorological satellite with high temporal and spatial resolution and mid-infrared sensitivity, suitable for forest fire monitoring. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a fire monitoring and identification method for the Ili River Valley based on the FY-4B meteorological satellite, aiming to provide timely forest and grassland fire risk monitoring services to relevant departments and users, improve fire prevention and disaster relief capabilities, and reduce the harm caused by forest and grassland fires.

[0006] The technical solution of the embodiment of the present invention is achieved as follows:

[0007] An embodiment of the present invention provides a method for monitoring and identifying fire conditions in the Yili River Valley based on the FY-4B meteorological satellite, the method comprising:

[0008] Download the FY-4B AGRI L1 dataset from the Fengyun satellite remote sensing data service website, including band images and radiometric calibration files corresponding to each band; obtain the attribute information of the FY-4B L1 dataset, perform radiometric calibration, and calculate the longitude and latitude data according to the row and column numbers for geometric correction; perform band analysis on the processed L1 dataset and calculate the standard deviation in different bands to determine the mid-infrared band that reacts strongly to the fire point, and construct a candidate combination band in combination with the thermal infrared band; calculate the band combination OIF index, exclude the band combination with a correlation coefficient between two bands exceeding 0.9 in the candidate combination, and obtain the best combination band; combine the best combination band with the brightness temperature map of the band sensitive to the surface temperature to realize the identification task of the fire point and the burned area.

[0009] In some possible embodiments, the method further includes: calculating NDVI, NDWI and CDI and setting corresponding thresholds to respectively establish mask areas for vegetation, water bodies and clouds to remove non-target pixels and achieve ground object classification.

[0010] In some possible embodiments, the obtaining of attribute information of the FY-4B L1 dataset, performing radiation calibration, and calculating longitude and latitude data according to row and column numbers for geometric correction include: using the HDFVIEW tool to read the original HDF format AGRI L1 data and record the required attribute information; using the ENVI software to open the FY-4B remote sensing image data, identifying the image data layer and calibration table in the L1 dataset, and using the calibration table to convert the digital quantization value in the image data layer into a physically meaningful albedo or brightness temperature; downloading the GEO dataset corresponding to the L1 dataset from the official website, which includes the row and column numbers of the disk; calculating the longitude and latitude corresponding to each position according to the row and column numbers to generate a longitude and latitude dataset; and using the ENVI software to perform geometric correction on the longitude and latitude dataset by constructing a geographic positioning file GLT.

[0011] In some possible embodiments, the digital quantization value conversion is performed using the following radiation calibration formula:

[0012] L λ =Gain·DN λ +Offset;

[0013] Where L λ , Gain, and Offset are the converted physical quantity radiance, gain coefficient, and offset, respectively. λ is the initial pixel digital value; the gain coefficient and the offset are determined according to the calibration table provided by the L1 data set.

[0014] In some possible embodiments, the calculation formula of the inter-band correlation coefficient is as follows:

[0015]

[0016] The band combination OIF index is calculated by the following formula:

[0017]

[0018] in, is the covariance of two bands i and j; δ ii is the standard deviation of band i; δ jj is the standard deviation of band j; n is the number of band combinations.

[0019] In some possible embodiments, the mid-infrared band that reacts strongly to fire points is the B7 band, the candidate combination bands are {B7, B8, B12}, {B7, B10, B13}, {B7, B12, B13}; and the optimal combination band is {B7, B8, B12}.

[0020] In some possible embodiments, the combination of the optimal combination band and the brightness temperature map of the band sensitive to the surface temperature realizes the task of identifying the fire point and the burned area, including: refining the classification of the fire points according to the brightness temperature data of the optimal combination band and the mid-infrared band to obtain a fire point vector map layer; superimposing the fire point vector map layer on the true color composite image to intuitively judge the recognition effect.

[0021] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0022] In the embodiment of the present invention, first, the FY-4B AGRI L1 data saved in HDF format can be downloaded from the Fengyun satellite remote sensing data service website, and pre-processing operations such as obtaining data set attributes, performing radiation calibration on the data set, and calculating longitude and latitude data according to row and column numbers are performed; then, the band characteristics of the fire point samples, the correlation coefficient between bands, and the band combination OIF index are calculated for the 14 bands of the FY-4B remote sensing data, and the separability of the objects between the bands and the spectral characteristics of the fire points are observed, and the optimal band combination for distinguishing forest fires is screened out; finally, the brightness temperature map of the bands sensitive to the surface temperature is combined to realize the identification task of the fire point and the burned area. Thus, an automatic identification method for the forest and grassland fire points in the Ili River Valley is established, which can improve the ability of fire prevention and disaster relief and reduce the harm caused by forest and grassland fires. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 work, among which:

[0024] Figure 1 A schematic diagram of a flow chart of a method for fire monitoring and identification in the Yili River Valley based on the FY-4B meteorological satellite provided in an embodiment of the present invention;

[0025] Figure 2 A full disk image of the FY4-B geostationary radiation imager provided in an embodiment of the present invention;

[0026] Figure 3 A fire point monitoring image in the infrared band of the FY4-B geostationary orbit radiation imager provided in an embodiment of the present invention;

[0027] Figure 4 A comparison chart of the classification of FY-4B multi-band fire monitoring provided by an embodiment of the present invention; (a) is a fire monitoring chart synthesized by the mid-infrared high albedo band, infrared band 12, and 13 channels, (b) is a fire monitoring chart synthesized by the mid-infrared high albedo band, infrared band 10, and 13 channels, and (c) is a fire monitoring chart synthesized by the mid-infrared high albedo band, low albedo band, and far-infrared band;

[0028] Figure 5 The FY-4B geostationary satellite multi-channel fire point monitoring composite image provided by the embodiment of the present invention;

[0029] Figure 6 The present invention provides a true-color synthetic fire monitoring image of the FY-4B geostationary satellite in an embodiment. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0032] It should be pointed out that the terms "first\second\third" involved in the embodiments of the present invention are only used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described here can be implemented in an order other than that illustrated or described here.

[0033] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the embodiments of the present invention belong. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0034] The FY-4B satellite scanning imaging radiometer is mainly responsible for obtaining cloud images. It has a total of 14 channels, which is nearly three times the 5 channels of FY-2. It can clearly distinguish different phases of clouds and high and middle-layer water vapor. AGRI is one of the main payloads of the FY-4 geostationary meteorological satellite. It realizes accurate and flexible two-dimensional pointing through a precise dual scanning mirror mechanism, and can achieve minute-level rapid regional scanning. It adopts an off-axis three-reflection main optical system to obtain earth cloud images of more than 14 bands at high frequency, and uses the on-board black body for high-frequency infrared calibration to ensure the accuracy of the observation data. In addition, FY-4 produced color satellite cloud images for the first time, and generated regional observation images for research as quickly as once per minute.

[0035] The research area of ​​this invention is summarized as follows: the Yili River Valley is surrounded by mountains on the north, east and south. In the north, there are the Keguqin Mountain and the Boluokonu Mountain, which are northwest-southeast oriented; in the south, there are the Haktawu Mountain and the Narat Mountain, which are northeast-southwest oriented; in the middle, there are the Wusun Mountain and the Awula Mountain, which are across, forming the landform outline of "three mountains and two valleys", with a total area of ​​56,400 square kilometers. The Yili River Valley is located in the middle latitude of the continent, surrounded by mountains on all sides, showing a distinct temperate continental arid climate. For the northwest region of my country, it has the most abundant precipitation, less evaporation, sufficient sunlight and less wind and sand. The Yili River Valley has an average annual temperature of 10.4℃, 2870 hours of sunshine per year, and an annual precipitation of 417.6 mm (600 mm in mountainous areas), making it the wettest area in Xinjiang.

[0036] This paper uses the medium-resolution remote sensing image data and meteorological data of the Ili River Valley in the past 20 years to analyze and study the trend of the change of snow cover area in the Ili River Valley and its relationship with meteorological elements. It aims to provide timely forest and grassland fire risk monitoring services to relevant departments and users, improve the ability of fire prevention and disaster relief, and reduce the harm caused by forest and grassland fires.

[0037] Figure 1 A flow chart of a method for fire monitoring and identification in the Yili River Valley based on the FY-4B meteorological satellite is provided in an embodiment of the present invention, as shown in FIG. Figure 1 As shown, the method comprises at least the following steps:

[0038] Step S110 , downloading the FY-4BAGRI L1 data set from the Fengyun satellite remote sensing data service website, including band images and radiometric calibration files corresponding to each band.

[0039] Here, meteorological satellites are satellites specially used to monitor and study the earth's atmosphere and climate change. They mainly observe the earth's atmosphere and surface through various sensors they carry. The satellite remote sensing data of the embodiment of the present invention selects the FY-4B L1 data set, which has 14 channels and 15-minute intervals, and can obtain data such as surface temperature, vegetation coverage, atmospheric composition and cloud information. The AGRI (Advanced Geostationary Radiation Imager) instrument carried by the FY-4B satellite provides earth observation data with multiple resolutions, including 250m, 500m, 1km, 2km and 4km. These data can be used for various applications such as weather monitoring, vegetation analysis, and disaster assessment.

[0040] like Figure 2 The figure shows the full disk image of the FY4-B Geostationary Radiation Imager (AGRI). It can be seen from the disk image that the coverage area with the Yili administrative region as the boundary is still relatively small. In the subsequent processing, the present invention cuts the disk image in the data processing and only performs fine processing on the disk area, such as Figure 3 As shown, the local 5°x3.5° data (the area indicated by the blue arrow) can be clipped out as the research scope.

[0041] Step S120, obtaining the attribute information of the FY-4B L1 data set, performing radiometric calibration, and calculating the longitude and latitude data according to the row and column numbers for geometric correction.

[0042] Here, radiation calibration is to convert the count value output by the satellite-borne scanning radiometer into the absolute value of the physical quantity to be detected. For visible light, it is generally converted into grayscale and albedo, and for infrared and water vapor channels, it is converted into grayscale level, radiation energy and absolute temperature.

[0043] The geometric correction operation is used to ensure the accurate geographic location of the image data. After completing the radiometric calibration and geometric correction, a data set containing longitude and latitude information is generated to prepare for further analysis and application. The nominal projection of the geostationary satellite is converted into the projection of the vector data through the longitude and latitude lookup table correction method. That is, the row and column numbers of the image data are converted into longitude and latitude. The steps are as follows: FY-4B data row and column number and longitude and latitude lookup table, according to the conversion relationship given by the National Satellite Meteorological Center, the longitude and latitude of the data row and column number are converted to each other to obtain the longitude and latitude information. Converting the row and column numbers of FY-4B L1 data into longitude and latitude is very important for geographic positioning and data visualization.

[0044] Step S130 , performing band analysis on the processed L1 data set and calculating the standard deviation in different bands, determining the mid-infrared band that has a strong reaction to the fire point, and constructing a candidate combination band in combination with the thermal infrared band.

[0045] Here, the spectral characteristics of the fire point samples in 14 bands are analyzed to determine the reflectivity or brightness temperature characteristics of the fire point in different bands. The standard deviation and value range of each band are analyzed to determine the band that is most sensitive to the fire point. For example, the HMIF (B7) band has a strong response to the fire point in the mid-infrared region.

[0046] By consulting a large amount of information and analyzing the actual band combinations of multiple fire point data, we can observe the separability of ground objects between bands and the spectral characteristics of fire points, and obtain candidate combination bands.

[0047] Step S140, by calculating the band combination OIF index, eliminating the band combination with the correlation coefficient between two bands exceeding 0.9 in the candidate combination, and obtaining the best combination band.

[0048] Here, the OIF index between bands is calculated to evaluate the optimality of the band combination and select a more appropriate band combination for fire point identification. Combining the OIF index and the correlation analysis between bands, the optimal band combination is screened out, such as (B7, B8, B12); (B7, B10, B13); (B7, B12, B13).

[0049] Step S150, combining the optimal combination band and the brightness temperature map of the band sensitive to the surface temperature to realize the task of identifying the fire point and the burned area.

[0050] Here, the optimal band combination and the surface temperature sensitive band selected are used to identify the fire point and the burned area. The basis for identification is mainly the abnormal warming caused by the fire point in the 3.7um channel of the mid-infrared band, which causes a significant contrast with the surrounding pixels. Different objects (such as vegetation and barley soil) have certain differences in temperature in this channel. There are often obvious temperature differences at the edge of woodland and grassland. Finally, the identification results are verified, which may include comparative analysis with the ground truth to ensure the identification accuracy.

[0051] In the embodiment of the present invention, first, the FY-4B AGRI L1 data saved in HDF format can be downloaded from the Fengyun satellite remote sensing data service website, and pre-processing operations such as obtaining data set attributes, performing radiation calibration on the data set, and calculating longitude and latitude data according to row and column numbers are performed; then, the band characteristics of the fire point samples, the correlation coefficient between bands, and the band combination OIF index are calculated for the 14 bands of the FY-4B remote sensing data, and the separability of the objects between the bands and the spectral characteristics of the fire points are observed, and the optimal band combination for distinguishing forest fires is screened out; finally, the brightness temperature map of the bands sensitive to the surface temperature is combined to realize the identification task of the fire point and the burned area. Thus, an automatic identification method for the forest and grassland fire points in the Ili River Valley is established, which can improve the ability of fire prevention and disaster relief and reduce the harm caused by forest and grassland fires.

[0052] In some possible embodiments, the method further includes: calculating NDVI, NDWI and CDI and setting corresponding thresholds to respectively establish mask areas for vegetation, water bodies and clouds to remove non-target pixels and achieve ground object classification.

[0053] Here, the basis for fire point identification is mainly the abnormal temperature increase caused by the fire point in the 3.7um channel of the mid-infrared band, which causes a significant contrast with the surrounding pixels. Different landforms (such as vegetation, bare soil, woodland, and grassland edges) often have obvious temperature differences. In fire point identification, these factors can be easily eliminated and the fire point can be accurately identified.

[0054] Normalized Difference Vegetation Index (NDVI): NDVI is an indicator for assessing vegetation coverage and health. High values ​​generally indicate high vegetation coverage, and the threshold may be between 0.3 and 0.5. The calculation formula is as follows: Among them, p2 and p3 correspond to the reflectivity of FYB B2 and B3 bands respectively.

[0055] Normalized Difference Water Index (NDWI): NDWI is used to identify water bodies. Positive values ​​usually indicate water bodies, and the threshold may be between 0 and 0.5. Positive values ​​may indicate cloud cover, and the threshold may be between 0 and 0.3. The calculation formula is as follows: p4 and p3 correspond to the reflectivity of FY4B B4 and B3 bands respectively.

[0056] Cloud Detection Index (CDI): CDI is an index used for cloud detection. Different studies may use different CDI formulas. The CDI calculation formula is: Where T 12 , T9, and p2 correspond to the brightness temperature of the FY-4B B12 band, the brightness temperature of the B9 band, and the reflectivity of the B2 band, respectively.

[0057] It is worth noting that these thresholds may vary by region, season, and specific application and need to be adjusted based on actual conditions.

[0058] In some possible embodiments, the obtaining of attribute information of the FY-4B L1 dataset, performing radiation calibration, and calculating longitude and latitude data according to row and column numbers for geometric correction include: using the HDFVIEW tool to read the original HDF format AGRI L1 data and record the required attribute information; using the ENVI software to open the FY-4B remote sensing image data, identifying the image data layer and calibration table in the L1 dataset, and using the calibration table to convert the digital quantization value in the image data layer into a physically meaningful albedo or brightness temperature; downloading the GEO dataset corresponding to the L1 dataset from the official website, which includes the row and column numbers of the disk; calculating the longitude and latitude corresponding to each position according to the row and column numbers to generate a longitude and latitude dataset; and using the ENVI software to perform geometric correction on the longitude and latitude dataset by constructing a geographic positioning file GLT.

[0059] Here, the downloaded raw data can be read through HDFVIEW provided by the Fengyun satellite remote sensing data service website. After recording the required attribute information, ENVI can be used to open the FY-4B remote sensing image to prepare for the next step of radiation calibration. The recorded attribute information includes sensor information, timestamp, spatial resolution, etc.

[0060] The FY-4B L1 dataset mainly provides two types of datasets, including band images and radiation calibration files corresponding to each band. The first part is the image data layer "NOMChannelX", where X represents 14 channel numbers, which store the image data of 14 channels, namely digital quantization values ​​(DN), which have no physical meaning. The second part is the calibration table "CALChannelX", which is used to process digital quantization values ​​into albedo or brightness temperature. The effective value range of the visible light / near infrared band is [0,1.5], and the DN value is processed into albedo. The effective range of the infrared band is [100,500], and the DN value is processed into brightness temperature. The calibration table stores the albedo or brightness temperature data corresponding to each valid DN value according to the value range of the DN value, forming a one-to-one lookup table, and the index of the table is the DN value.

[0061] The FY-4B data at level L1 is full disk data in HDF format, and there is no longitude and latitude information in the data set. The longitude and latitude data corresponding to each location need to be downloaded separately. The data for this study are all 2KM GEO data sets downloaded from the official website at the 2KM level, which include the row and column number data sets of the disk, where the no-value area is -1 / the value area is the corresponding row and column number, and then the corresponding longitude and latitude are calculated from the row and column number according to the formula to generate the longitude and latitude data set, and then ENVI is used to construct the GLT file for geometric correction.

[0062] The above preprocessing process ensures that the FY-4B L1 dataset can be correctly processed and analyzed, providing high-quality remote sensing data for research in meteorology, environmental monitoring, agriculture and other fields.

[0063] In some possible embodiments, the digital quantization value conversion is performed using the following radiation calibration formula:

[0064] L λ =Gain·DN λ +Offset;

[0065] Where L λ , Gain, and Offset are the converted physical quantity radiance, gain coefficient, and offset, respectively. λ is the initial pixel digital value; the gain coefficient and the offset are determined according to the calibration table provided by the L1 data set.

[0066] Here, the specific implementation process is as follows: read the pixel values ​​of the image data layers of multiple bands; calibrate the data using a calibration table; and convert the original digital signal into actual physical quantities such as reflectivity.

[0067] In some possible embodiments, the calculation formula of the inter-band correlation coefficient is as follows:

[0068]

[0069] The band combination OIF index is calculated by the following formula:

[0070]

[0071] in, is the covariance of two bands i and j; δ ii is the standard deviation of band i; δ jj is the standard deviation of band j; n is the number of band combinations.

[0072] In some possible embodiments, the mid-infrared band that reacts strongly to fire points is the B7 band, the candidate combination bands are {B7, B8, B12}, {B7, B10, B13}, {B7, B12, B13}; and the optimal combination band is {B7, B8, B12}.

[0073] Here, through the data statistics between each band, it can be seen that the single-band standard deviation of the fire point is the largest in the mid-infrared band HMIF (B7) band, followed by the mid-infrared band LMIF (B8) band. The numerical range of the mid-infrared band HMIF (B7) band is the largest, followed by the mid-infrared band LMIF (B8) band. The temperature range is the largest in the HMIF (B7) band. Referring to the functions of each band of the FY4B remote sensing data provided by the official, it can be determined that the HMIF (B7) band reacts strongly to the fire point.

[0074] By consulting a large amount of information and analyzing the actual band combinations of multiple fire point data, we can observe the separability of ground objects between bands and the spectral characteristics of fire points, further screen the optimal band combination, and combine the OIF index to exclude the band combinations with correlation coefficients between two bands exceeding 0.9 in the candidate combinations. Then, we select the most suitable band combination for comparison, including {B7, B8, B12}, {B7, B10, B13}, and {B7, B12, B13}. It can be seen that {B7, B8, B12} has the best recognition effect. Figure 4 Part (a) is a fire point monitoring map synthesized by the mid-infrared high albedo band, infrared band 12 and 13 channels, part (b) is a fire point monitoring map synthesized by the mid-infrared high albedo band, infrared band 10 and 13 channels, and part (c) is a fire point monitoring map synthesized by the mid-infrared high albedo band, low albedo band and far infrared band.

[0075] In some possible embodiments, the combination of the optimal combination band and the brightness temperature map of the band sensitive to the surface temperature realizes the task of identifying the fire point and the burned area, including: refining the classification of the fire points according to the brightness temperature data of the optimal combination band and the mid-infrared band to obtain a fire point vector map layer; superimposing the fire point vector map layer on the true color composite image to intuitively judge the recognition effect.

[0076] Here, we can see Figure 4 Part (c) has the best recognition effect on fire points and burned areas (the area indicated by the blue arrow). The fire points are classified and processed according to the corresponding bands (B7, B8, B12) and the brightness temperature data of bands sensitive to surface temperature such as B12 and B13. Figure 5 The geostationary satellite multi-channel fire monitoring map shown, Figure 5The white curve in the middle represents the administrative boundary of Yili Prefecture, the red area represents the fire point, the dark red area represents the burned area, the green area represents the grassland, the brown area represents the mountain range, and the blue area represents the cloud area. Finally, the fire point is compared with the true color composite satellite cloud map, such as Figure 6 The figure shows a true color synthetic image of the FY-4B fire point monitoring. It can be seen that the present invention realizes the effective use of the FY-4B remote sensing data to identify and analyze forest fires.

[0077] It should be noted that in the embodiment of the present invention, if the above-mentioned fire monitoring and identification method based on the FY-4B meteorological satellite Ili River Valley is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for an electronic device to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk. In this way, the embodiment of the present invention is not limited to any specific combination of hardware and software.

[0078] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0079] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0080] In the several embodiments provided by the present invention, it should be understood that the disclosed methods can be implemented in other ways. The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments. The features disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0081] The above is only an embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A fire monitoring and identification method for the Yili River Valley based on the FY-4B meteorological satellite, characterized in that: include: Download the FY-4B geostationary radiation imager AGRI L1 dataset from the Fengyun satellite remote sensing data service website, including band images and radiation calibration files corresponding to each band; Obtain the attribute information of the FY-4B L1 dataset, perform radiometric calibration, and calculate the longitude and latitude data based on the row and column numbers for geometric correction; The processed L1 data set was subjected to band analysis and the standard deviations in different bands were calculated to determine the mid-infrared bands that had a strong response to the fire point, and the candidate combination bands were constructed in combination with the thermal infrared bands; By calculating the OIF index of the band combination, the band combination with the correlation coefficient between the two bands exceeding 0.9 in the candidate combination is eliminated to obtain the best combination band; The task of identifying the fire point and the burned area is achieved by combining the optimal combination band and the brightness temperature map of the band sensitive to the surface temperature.

2. The fire point identification method according to claim 1, characterized in that: The method further comprises: By calculating the normalized difference vegetation index NDVI, the normalized difference water index NDWI and the cloud detection index CDI and setting the corresponding thresholds, mask areas of vegetation, water bodies and clouds are established respectively to remove non-target pixels and realize ground object classification.

3. The fire point identification method according to claim 1, characterized in that: The method of obtaining the attribute information of the FY-4B L1 dataset, performing radiometric calibration, and calculating the longitude and latitude data according to the row and column numbers for geometric correction includes: Use the HDFVIEW tool to read the AGRI L1 data in the original HDF format and record the required attribute information; Using ENVI software to open the FY-4B remote sensing image data, identifying the image data layer and calibration table in the L1 data set, and using the calibration table to convert the digital quantization value in the image data layer into albedo or brightness temperature with physical meaning; Download the GEO dataset corresponding to the L1 dataset from the official website, including the row and column numbers of the disks; Calculate the longitude and latitude corresponding to each position according to the row and column numbers to generate a longitude and latitude data set; The latitude and longitude datasets were geometrically corrected by constructing a geolocation file GLT using ENVI software.

4. The method for identifying a fire point according to claim 3, characterized in that: The digital quantization value conversion is performed using the following radiation calibration formula: L λ =Gain·DN λ +Offset; Where L λ , Gain, and Offset are the converted physical quantity radiance, gain coefficient, and offset, respectively. λ is the initial pixel digital value; the gain coefficient and the offset are determined according to the calibration table provided by the L1 data set.

5. The method for identifying a fire point according to any one of claims 1 to 4, characterized in that: The calculation formula of the inter-band correlation coefficient is as follows: The band combination OIF index is calculated by the following formula: Among them, R ij is the inter-band correlation coefficient of two bands i and j, is the covariance of two bands i and j; δ ii is the standard deviation of band i; δ jj is the standard deviation of band j; n is the number of band combinations.

6. The method for identifying a fire point according to any one of claims 1 to 4, characterized in that: The mid-infrared band that reacts strongly to fire points is the B7 band, the candidate combination bands are {B7, B8, B12}, {B7, B10, B13}, {B7, B12, B13}; and the best combination band is {B7, B8, B12}.

7. The fire point identification method according to any one of claims 1 to 4, characterized in that: The task of identifying the fire point and the burned area by combining the best combined band and the brightness temperature map of the band sensitive to the surface temperature includes: The fire point classification is refined according to the brightness temperature data of the optimal combination band and the mid-infrared band to obtain a fire point vector layer; The fire point vector map layer is superimposed on the true color composite image to visually judge the recognition effect.

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