Method and system for monitoring fire points during dawn and dusk using polar-orbiting meteorological satellites

Abstract

The present invention discloses a method and system for monitoring fire points during the dawn and dusk periods by a polar-orbiting meteorological satellite, which is based on a polar-orbiting meteorological satellite. The method comprises the following steps: obtaining polar-orbiting meteorological satellite data during the dawn and dusk periods, and performing data preprocessing to generate grid data for a monitoring area. Based on the grid data for the monitoring area, target pixels in the grid data for the monitoring area are determined. Based on the target pixels, fire point pixels are generated. Based on the fire point pixels, the area of an open fire zone is generated. Based on the area of an open fire zone, a fire monitoring result is output. Thus, the method for monitoring fire points during the dawn and dusk periods by a polar-orbiting meteorological satellite of the present invention can detect fire points during the dawn and dusk periods with high timeliness and accuracy, and output fire point monitoring results, thereby meeting the monitoring needs during periods of high incidence of fire points.

Description

Technical Field

[0001] The present invention relates to the technical field of fire monitoring during the dawn and dusk periods, and in particular to a method and system for monitoring fire points during the dawn and dusk periods on a polar-orbiting meteorological satellite. Background Art

[0002] Fengyun-3E (FY-3E satellite) is the world's first commercial dawn-dusk orbit polar-orbiting meteorological satellite independently developed by my country. It was launched from the Jiuquan Satellite Launch Center on July 5, 2021, and officially put into use in March 2022. The FY-3E satellite observes the Earth's surface twice a day, with the evening transit time between 16:00 and 19:00 local time. It is equipped with the newly developed medium-resolution spectral imager (low-light type) MERSI-LL, with a total of 7 observation channels, including 1 low-light channel and 6 thermal infrared channels. The spatial resolution of two infrared split window channels is 250 meters, and the remaining channels are 1000 meters.

[0003] The principle of satellite fire monitoring is primarily based on Wien's displacement law, which states that the blackbody temperature T and the peak radiation wavelength λmax are inversely proportional; that is, the higher the temperature, the smaller the peak radiation wavelength. The peak wavelength of surface radiation at room temperature (approximately 300K) is in the FY-3D / MERSI-II far-infrared channel. Forest fire temperatures typically range from 500 to 1200K, and their peak thermal radiation wavelength is close to the channel's mid-infrared wavelength range. When a fire appears within an observation pixel, the high temperature of the small sub-region within the pixel (a large area with a resolution of 1km will not be entirely exposed to fire at the same time) causes the emissivity increase in the mid-infrared channel to be significantly higher than that in the far-infrared channel. This results in different increases in the weighted average emissivity and brightness temperature of each channel in that pixel. This difference can be used to analyze and extract fire information.

[0004] Through simulation calculations, it can be seen that when the fire point temperature rises, the brightness temperature increment of the mixed pixel in the mid-infrared channel will increase rapidly. Even if it is assumed that the fire point area only occupies 0.1% of the pixel area, its brightness temperature increment reaches about 10K when the fire point is 500K, and at 900K, the brightness temperature increment reaches about 44K. Although the brightness temperature increment of the far-infrared channel also increases with the increase of the fire point temperature, it is significantly lower than the amplitude of the mid-infrared channel.

[0005] In addition, when the fire area increases, the brightness temperature increment of the mixed pixel in the mid-infrared channel will also increase rapidly. Even if it is assumed that the fire area only occupies 0.01% of the pixel area, the brightness temperature increment reaches about 12K when the fire point is 900K. Although the brightness temperature increment of the far-infrared channel also increases with the increase of the fire area, it is significantly lower than the amplitude of the mid-infrared channel increment.

[0006] Existing monitoring methods have the following defects:

[0007] 1. Not considering high solar zenith angle observation conditions

[0008] Existing polar-orbiting satellites like the FY-3D typically pass through in the morning or afternoon, with a low solar zenith angle and good observation conditions. However, the FY-3E is a polar-orbiting meteorological satellite that orbits during the dawn and dusk periods, with a solar zenith angle close to 90°. This high zenith angle makes it difficult to identify underlying surface types such as clouds, thus affecting the accuracy of fire point identification. However, current monitoring methods do not take into account observation conditions at high solar zenith angles.

[0009] 2. No thermal infrared edge correction

[0010] According to the principles of satellite remote sensing fire monitoring, accurate acquisition of brightness temperature data in the mid- and far-infrared channels is crucial for fire identification. Affected by the Earth's curvature and atmospheric attenuation, infrared channel data vary depending on the satellite's viewing angle. The FY-3E operates at high solar zenith angles during the dawn-dusk period. The longer the atmospheric optical path required for the radiation from the measured pixel to reach the onboard detector, the greater the atmospheric attenuation. Therefore, to accurately obtain brightness temperature data in the mid- and far-infrared channels for the measured pixels, thermal infrared limb correction (TIR) is required for the raw data, but current monitoring methods do not implement this.

[0011] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the Invention

[0012] The purpose of the present invention is to provide a method and system for monitoring fire points during the dawn and dusk periods of polar-orbiting meteorological satellites. The method and system are based on the observation mode unique to the dawn and dusk periods, can detect fire points during the dawn and dusk periods with high timeliness and accuracy, and output fire point monitoring results to meet the monitoring needs during the high-incidence periods of fire points.

[0013] To achieve the above objectives, in a first aspect, the present invention provides a method for monitoring fire points during the dawn and dusk periods using a polar-orbiting meteorological satellite. The method, based on a polar-orbiting meteorological satellite, comprises: acquiring polar-orbiting meteorological satellite data during the dawn and dusk periods, performing data preprocessing, and generating grid data for a monitoring area. Based on the grid data for the monitoring area, target pixels within the grid data for the monitoring area are determined. Based on the target pixels, fire point pixels are generated. Based on the fire point pixels, the area of an open fire zone is generated. Based on the area of the open fire zone, fire monitoring results are output.

[0014] In one embodiment of the present invention, obtaining polar-orbiting meteorological satellite data during the dawn and dusk periods and performing data preprocessing to generate monitoring area grid data specifically includes obtaining polar-orbiting meteorological satellite Earth observation data during the dawn and dusk periods and performing data preprocessing to generate monitoring area grid data. Preprocessing the polar-orbiting meteorological satellite Earth observation data during the dawn and dusk periods includes data projection.

[0015] In one embodiment of the present invention, determining a target pixel in the monitoring area grid data based on the monitoring area grid data specifically includes: performing thermal infrared edge correction on the monitoring area grid data; extracting cloud pixel information based on the monitoring area grid data after the thermal infrared edge correction; identifying the cloud pixel information using a first identification strategy to obtain a cloud pixel; and removing the cloud pixel to determine the target pixel in the monitoring area grid data.

[0016] In one embodiment of the present invention, generating a fire point pixel based on a target pixel specifically involves: determining suspected high-temperature fire point pixels in the vicinity of the target pixel, and calculating the mean and standard deviation of the background brightness temperature in the vicinity of the target pixel to generate a thermal anomaly pixel. The thermal anomaly pixel is identified using a second identification strategy to generate a first candidate fire point pixel. The cloud pixel is supplementally identified using a cloud area fire point supplementary identification strategy to generate a second candidate fire point pixel. The first candidate fire point pixel and the second candidate fire point pixel are combined to generate a fire point pixel.

[0017] In one embodiment of the present invention, the thermal infrared edge correction is performed based on the monitoring area grid data by the following formula:

[0018] T=T b +ΔT

[0019]

[0020]

[0021]

[0022] Where, T is the corrected brightness temperature, T b is the brightness temperature, ΔT is the observation angle of different satellites The brightness temperature correction value is , C1, C2 are Planck constants, Re is the radius of the Earth, H is the satellite altitude, v is the channel wave number, E is the radiance observed by the satellite, and Ln is the natural logarithm. is the satellite observation angle.

[0023] In one embodiment of the present invention, the second identification strategy is specifically:

[0024] T 3.8 >340K

[0025] or

[0026] T 3.8 ≥T 3.8_bg +a(P v ,P c ,θ sz )×δT 3.8_bg &T 3.8_10.8≥T 3.8_10.8bg +a(P v ,P c ,θ sz )×δT 3.8_10.8_bg

[0027] Among them, a(P v ,P c ,θ sz )=(1.2×sinθ sz +1)×(1+P v )×(1+P c ) 2 ;

[0028] Where, T 3.8 is the mid-infrared brightness temperature, T 3.8_bg is the effective background brightness temperature in the mid-infrared, δT 3.8_bg is the standard deviation of the effective background brightness temperature in the mid-infrared, T 3.8_10.8 is the pixel brightness temperature difference between mid-infrared and far-infrared, T 3.8_10.8bg is the effective background brightness temperature difference between mid-infrared and far-infrared, δT 3.8_10.8_bg is the standard deviation of the pixel brightness temperature difference between the mid-infrared and far-infrared effective backgrounds, a(P v ,P c ,θ sz ) is corrected to a coefficient function, P v is the proportion of non-vegetation pixels in the region, P c is the proportion of cloud pixels in the region, θ sz is the solar zenith angle.

[0029] In a second aspect, the present invention provides a polar-orbiting meteorological satellite fire monitoring system for the dawn and dusk periods. The system is based on a polar-orbiting meteorological satellite and includes: an acquisition and generation module, a determination module, a fire pixel generation module, an open fire area generation module, and an output module. The acquisition and generation module is used to acquire polar-orbiting meteorological satellite data for the dawn and dusk periods, and perform data preprocessing to generate monitoring area grid data. The determination module is used to determine target pixels in the monitoring area grid data based on the monitoring area grid data. The fire pixel generation module is used to generate fire pixel based on the target pixel. The open fire area generation module is used to generate the open fire area based on the fire pixel. And the output module is used to output fire monitoring results based on the open fire area. The acquisition and generation module is communicatively connected to the determination module, the determination module is communicatively connected to the fire pixel generation module, the fire pixel generation module is communicatively connected to the open fire area generation module, and the open fire area generation module is communicatively connected to the output module.

[0030] In a third aspect, the present invention provides a polar-orbiting meteorological satellite dawn and dusk period fire point monitoring computing device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by one or more processors, the one or more processors implement the polar-orbiting meteorological satellite dawn and dusk period fire point monitoring method as described above.

[0031] In a fourth aspect, the present invention provides a computer-readable storage medium storing a program, which, when executed by a processor, implements the above-mentioned method for monitoring fire points during the dawn and dusk periods of polar-orbiting meteorological satellites.

[0032] Compared with the existing technology, the polar-orbiting meteorological satellite dawn and dusk period fire point monitoring method and system according to the present invention is based on the observation mode unique to the dawn and dusk period, can detect fire points in the dawn and dusk period with high timeliness and accuracy, and output the fire point monitoring results to meet the monitoring needs during the high-incidence period of fire points. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The figure is a flow chart of a method for monitoring fire points during the dawn and dusk periods using a polar-orbiting meteorological satellite according to one embodiment of the present invention.

[0034] Figure 2 The figure is a logical flow diagram of a method for monitoring fire points during the dawn and dusk periods of a polar-orbiting meteorological satellite according to one embodiment of the present invention.

[0035] Figure 3 4 is a schematic diagram of a process for determining a target pixel according to an embodiment of the present invention.

[0036] Figure 4 It is a schematic diagram of the process of generating fire point pixels according to one embodiment of the present invention.

[0037] Figure 5 Schematic diagram of a fire monitoring system for a polar-orbiting meteorological satellite during the dawn and dusk periods according to one embodiment of the present invention.

[0038] Figure 6 FIG. 1 is a schematic diagram of Qingdao fire monitoring according to an embodiment of the present invention.

[0039] Figure 7 Schematic diagram of Liaoning fire monitoring according to one embodiment of the present invention.

[0040] Figure 8 It is a structural diagram of a fire point monitoring and calculation device for a polar-orbiting meteorological satellite during the dawn and dusk periods according to one embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following is a further detailed description of the embodiments of the present invention in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the present invention and are not intended to limit the embodiments of the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions of the embodiments of the present invention, rather than all structures.

[0042] To facilitate understanding, the main implementation concepts of the embodiments of the present invention are first briefly described.

[0043] Most fires are closely related to human activities, so there are certain temporal patterns. The transit times of existing polar-orbiting satellites are mainly concentrated between 9:00 and 11:00 and 13:00 and 15:00 local time, and there are obvious gaps in fire monitoring between 16:00 and 19:00. Especially in China, statistics on the time of wildfire occurrence in China from 2019 to 2021 found that the number of wildfires occurring in the evening (16:00-19:00) was second only to the afternoon. FY-3E is a dawn-dusk orbit satellite, and its observation time falls within this gap. Therefore, how to use FY-3E satellite data to detect fire points in the dawn-dusk period with high timeliness and accuracy based on the unique observation mode of the dawn-dusk period to meet the monitoring needs during the high-incidence period of fire points is a technical problem that needs to be solved urgently.

[0044] In order to solve the defects in the prior art, the inventors of the present invention, through creative work, have obtained the present invention's method and system for monitoring fire points during the dawn and dusk periods on a polar-orbiting meteorological satellite.

[0045] The present invention utilizes FY-3E dawn-dusk satellite data and includes six technical steps to respectively realize functions such as thermal infrared limb correction, cloud identification, background suspected fire point pixel judgment, background brightness temperature calculation, heat source point extraction and cloud area fire point supplementary identification.

[0046] Example 1

[0047] like Figures 1 to 4 As shown, Example 1 provides a method for monitoring fire points during the dawn and dusk periods using a polar-orbiting meteorological satellite. The method includes:

[0048] Step S1: Acquire polar-orbiting meteorological satellite data during the dawn and dusk periods, perform data preprocessing, and generate monitoring area grid data.

[0049] Specifically, the earth observation data from polar-orbiting meteorological satellites during the dawn and dusk periods are acquired and pre-processed to generate grid data of the monitoring area. The pre-processing of the earth observation data from polar-orbiting meteorological satellites during the dawn and dusk periods includes data projection.

[0050] The grid data of the monitoring area can be obtained by projecting the polar-orbiting meteorological satellite data during the dawn and dusk periods.

[0051] Step S2, based on the monitoring area grid data, determines the target pixel in the monitoring area grid data. Step S2 is specifically as follows:

[0052] Step S201: Perform thermal infrared edge correction based on the monitoring area grid data.

[0053] Specifically, based on existing monitoring, this invention improves fire point identification based on the characteristics of satellite observations during the dawn and dusk periods. This mainly involves cloud information extraction in the absence of visible light and infrared limb correction under high solar zenith angle observation conditions. Fire point identification mainly includes the following parts:

[0054] 1. Thermal infrared edge correction

[0055] The most critical parameter in the fire identification algorithm is the mid-infrared brightness temperature. Thermal infrared channel monitoring data is related to the satellite's observation angle. The longer the detection path, the greater the atmospheric attenuation. The FY-3E observations of the sun are during the dawn-dusk period, and the oblique solar radiation significantly affects the accurate acquisition of brightness temperature information. Therefore, thermal infrared limb correction is required. This correction is performed based on the grid data of the monitoring area using the following formula:

[0056] T=T b +ΔT

[0057]

[0058]

[0059]

[0060] Where, T is the corrected brightness temperature, T b is the brightness temperature, ΔT is the observation angle of different satellites The brightness temperature correction value is , C1, C2 are Planck constants, Re is the radius of the Earth, H is the satellite altitude, v is the channel wave number, E is the radiance observed by the satellite, and Ln is the natural logarithm. is the satellite observation angle, θ is the intermediate parameter; where, It is a fitted function, and the calculated result is a number close to 1. After subtracting 1, it is a relatively small amount, which can ensure that ΔT will not be a very large number.

[0061] Step S202: Extract cloud pixel information based on the thermal infrared edge-corrected monitoring area grid data. Step S203: Identify the cloud pixel information using a first identification strategy to obtain cloud pixels. Step S204: Remove the cloud pixels and identify the target pixels in the monitoring area grid data.

[0062] 2. Cloud pixel identification

[0063] Specifically, cloud cover is complex and variable, and has the greatest impact on the algorithm. On the one hand, the presence of clouds can easily obscure ground fire information, attenuating the energy entering the sensor and leading to missed fire detections. On the other hand, specular reflections from clouds can lead to misidentification of fire points. Given that the FY-3E / MERSI-LL observes during the dawn and dusk periods, lacking effective visible light channel information, it is necessary to fully utilize mid-infrared and far-infrared brightness temperatures to extract cloud pixel information. The identification method is shown in Table 1. A pixel is identified as a cloud pixel if any of these conditions are met.

[0064] Table 1

[0065]

[0066]

[0067] In Table 1, T 3.8 、T 10.8 and T 11.8 They are the brightness temperatures of the mid-infrared channel, far-infrared channel and far-infrared split window channel respectively.

[0068] By removing the cloud pixels from the grid data of the monitoring area, the target pixels in the grid data of the monitoring area can be determined.

[0069] Step S3, generating a fire point pixel based on the target pixel. Step S3 is specifically as follows: Step S301, judging the target pixel's adjacent area as a suspected high-temperature fire point pixel, and calculating the mean and standard deviation of the background brightness temperature in the target pixel's adjacent area to generate a thermal anomaly pixel. Step S302, using the second identification strategy to identify the thermal anomaly pixel, generating a first undetermined fire point pixel. Step S303, using the cloud area fire point supplementary identification strategy to supplement the cloud pixel, generating a second undetermined fire point pixel. Step S304, combining the first undetermined fire point pixel and the second undetermined fire point pixel to generate a fire point pixel.

[0070] 3. Judgment of suspected background fire point pixels

[0071] Specifically, the target pixels are selected as the fire point identification pixels with a high temperature ratio of 20%. The suspected fire points are identified one by one for the high temperature pixels, and the suspected fire point pixels are excluded. The method for identifying suspected fire points is as follows:

[0072]

[0073] In the above formula, T 3.an is the average brightness temperature of all pixels in the monitoring area, is the standard deviation of effective background pixels in the monitoring area, T th is the background brightness temperature threshold, which is related to the uniformity of the underlying surface. The initial value can be set to 5K.

[0074] 4. Background temperature calculation

[0075] Calculate the background temperature information of the target pixel, that is, the average value of the neighboring pixels, including the average mid-infrared brightness temperature, the average far-infrared brightness temperature, the average difference between mid-infrared and far-infrared, the average visible light reflectance, etc., which is used as the basis for determining the fire point. The calculation method is as follows:

[0076] Select the monitorable pixels in the 7×7 neighborhood pixels around the target pixel and exclude the pixels adjacent to the detection pixel. If the number of monitorable pixels is less than 20% of the total number of neighborhood pixels, expand to 9×9, 11×11, and finally 19×19. If the conditions are still not met, abandon the monitoring of the pixel.

[0077] Calculate the average and standard deviation of the mid-infrared brightness temperature, the average and standard deviation of the far-infrared brightness temperature, the average and standard deviation of the difference between the mid-infrared and far-infrared brightness temperatures, and the average and standard deviation of the visible light channel for the monitorable pixels in the neighborhood.

[0078]

[0079]

[0080] In the above formula, c is the serial number, which is the mid-infrared channel, far-infrared channel and the difference between the mid-infrared and far-infrared channels respectively; n is the total number of monitorable pixels in the neighborhood; T ci are the mid-infrared brightness temperature, far-infrared brightness temperature, the difference between mid-infrared and far-infrared brightness temperatures, and visible light reflectance of the identified pixel; T c_bg are the average brightness temperatures of the surrounding background pixels in the mid-infrared, far-infrared, and the difference between the mid-infrared and far-infrared, δT c_bg They are the standard deviations of brightness temperature in the mid-infrared, far-infrared, and the difference between the mid-infrared and far-infrared. When the standard deviation is less than 2K, it is set to 2K. If the standard deviation is greater than 3.5K, it is set to 3.5K.

[0081] 5. Heat source extraction

[0082] Satellite heat source extraction relies primarily on the characteristics of mid-infrared thermal anomalies, which can be categorized into two types: absolute and relative conditions. A pixel meeting condition i) or ii) is identified as the first candidate hotspot. Because the FY-3E satellite operates in a twilight orbit, the zenith angle of solar observation is relatively large. Since visible light correction is not considered, the infrared channel identification criteria are relatively strict.

[0083] i)T 3.8 >340K

[0084] ii)T 3.8 ≥T 3.8_bg +a(P v,P c ,θ sz )×δT 3.8_bg &T 3.8_10.8 ≥T 3.8_10.8bg +a(P v ,P c ,θ sz )×δT 3.8_10.8_bg

[0085] Among them, a(P v ,P c ,θ sz )=(1.2×sinθ sz +1)×(1+P v )×(1+P c ) 2 ;

[0086] Where, T 3.8 is the mid-infrared brightness temperature, T 3.8_bg is the effective background brightness temperature in the mid-infrared, δT 3.8_bg is the standard deviation of the effective background brightness temperature in the mid-infrared, T 3.8_10.8 is the pixel brightness temperature difference between mid-infrared and far-infrared, T 3.8_10.8bg is the effective background brightness temperature difference between mid-infrared and far-infrared, δT 3.8_10.8_bg is the standard deviation of the pixel brightness temperature difference between the mid-infrared and far-infrared effective backgrounds, a(P v ,P c ,θ sz ) is corrected to a coefficient function, P v is the proportion of non-vegetation pixels in the region, P c is the proportion of cloud pixels in the region, θ sz is the solar zenith angle.

[0087] 6. Supplementary identification of fire points in cloud areas (supplementary identification of cloud pixels)

[0088] The main strategy for identifying fire points in cloud areas is to identify fire points under thin clouds. The steps are as follows:

[0089] (1) Calculate the average value of cloud pixels

[0090] When a cloud pixel meets the following conditions, calculate its cloud area average value.

[0091] T 3.8 ≥TC 3.8 th&T 3.8_10.8 ≥T 3.8_10.8 th

[0092] TC 3.8 th is the infrared brightness temperature threshold of cloud pixels, and the initial value is set to 325K; T 3.8_10.8th is the threshold of the difference between the mid-infrared and far-infrared brightness temperatures of cloud pixels, and its initial value is set to 30K.

[0093] (2) Determine whether the cloud pixel meets the fire point identification conditions

[0094] If T 3.8 ≥T 3.8_bg +TC fire th condition, and judge that the pixel is the second undetermined fire point pixel.

[0095] TC fire th is the cloud pixel fire point identification threshold, and its initial value is set to 20K.

[0096] At this time, the fire point pixel can be obtained by combining the first fire point pixel to be determined and the second fire point pixel to be determined.

[0097] Step S4: Generate the area of the open fire zone based on the fire point pixels.

[0098] Specifically, the sub-pixel fire point area, i.e., the area of the open fire area, is estimated using the single-channel sub-pixel fire point area estimation method. The estimation formula is shown in formula (1). When the middle infrared channel is not saturated, the middle infrared channel is used for estimation, as shown in formula (2). When the middle infrared channel is saturated, the far infrared channel is used for estimation, as shown in formula (3):

[0099] S f =P×S (1)

[0100] P=(L MIR_mix (T MIR_mix )-L MIR_bg (T MIR_bg )) / (L MIR (T f )-L MIR_bg (T MIR_bg )) (2)

[0101]

[0102] Where: Sf is the sub-pixel fire point area, P is the sub-pixel fire point area ratio, S is the pixel area. Tf is the sub-pixel fire point temperature, set to 750K. MIR_mix , L FIR_mix are the mixed pixel radiances of mid-infrared channel and far-infrared channel, L MIR_bg , L FIR_bg are the background pixel radiances of the mid-infrared channel and the far-infrared channel, T MIR_mix , T FIR_mix are the brightness temperatures of mid-infrared and far-infrared mixed pixels, T MIR_bg , T FIR_bg are the background pixel brightness temperatures of the mid-infrared and far-infrared channels, L MIR is the mid-infrared channel, LFIR For the far infrared channel.

[0103] Step S5: Output the fire monitoring result based on the area of the open fire zone.

[0104] In actual applications, the National Satellite Meteorological Center has used FY-3E dawn and dusk satellite data to conduct monitoring service tests for forest and grassland fires and straw burning incidents. The center has also conducted comparative analysis of the service performance with existing products and similar foreign products, demonstrating the significant effectiveness of FY-3E satellite data in both major fire incidents and routine fire operations. Using FY-3E / MERSI-LL data for routine fire operations in my country, individual tests were conducted in high-latitude areas (Jiagedaqi), low-latitude areas (Guangxi), and Russia, where fire conditions are more typical. Daily tests show that the FY-3E can effectively identify fire points during the dawn and dusk periods. Table 2 below lists the daily fire point monitoring data for the FY-3E:

[0105] Table 2

[0106]

[0107] In March and April 2022, numerous forest and grassland fires occurred in and around my country. The FY-3EL provided emergency support services for several major fires, particularly grassland fires. Using FY-3E data, we achieved precise location of the fires and identified key areas. This data was immediately provided to decision-makers, supporting fire weather support services. The service was highly effective and received high recognition from the local government of Xing'an League.

[0108] In addition, if Figures 6 and 7 As shown, corresponding disaster protection services were provided for the forest fires in Dali and Nujiang, Yunnan, Qingdao, Shandong, and Fushun, Liaoning. For detailed information, please see Tables 3, 4, and 5 below.

[0109] Table 3 Yunnan fire monitoring (March 15, 18:25)

[0110]

[0111] Table 4 Qingdao fire monitoring (April 19, 17:20)

[0112]

[0113] Table 5 Liaoning fire monitoring (April 23, 16:05)

[0114]

[0115] Example 2

[0116] like Figure 5As shown, Example 2 provides a polar-orbiting meteorological satellite fire monitoring system for the dawn and dusk periods. Based on the polar-orbiting meteorological satellite, the system includes: an acquisition and generation module, a determination module, a fire pixel generation module, an open fire area generation module, and an output module. The acquisition and generation module is used to acquire polar-orbiting meteorological satellite data for the dawn and dusk periods, and perform data preprocessing to generate monitoring area grid data. The determination module is used to determine the target pixels in the monitoring area grid data based on the monitoring area grid data. The fire pixel generation module is used to generate fire point pixels based on the target pixels. The open fire area generation module is used to generate the open fire area based on the fire pixel. And the output module is used to output the fire monitoring results based on the open fire area. Among them, the acquisition and generation module is communicatively connected to the determination module, the determination module is communicatively connected to the fire pixel generation module, the fire pixel generation module is communicatively connected to the open fire area generation module, and the open fire area generation module is communicatively connected to the output module.

[0117] Example 3

[0118] like Figure 8 As shown, Example 3 provides a polar-orbiting meteorological satellite dawn and dusk period fire point monitoring computing device, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by one or more processors, the one or more processors implement the polar-orbiting meteorological satellite dawn and dusk period fire point monitoring method as described above.

[0119] Figure 8 It is a structural diagram of a fire point monitoring and calculation device for a polar-orbiting meteorological satellite during the dawn and dusk periods according to one embodiment of the present invention. Figure 8 The polar-orbiting meteorological satellite dawn and dusk period fire point monitoring computing device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0120] like Figure 8 As shown, the polar-orbiting meteorological satellite dawn and dusk fire monitoring computing device is implemented as a general-purpose computing device. Components of the polar-orbiting meteorological satellite dawn and dusk fire monitoring computing device may include, but are not limited to, one or more processors or processing units, memory, and a bus connecting various system components (including the memory and processing units).

[0121] The term "bus" refers to one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0122] The polar-orbiting meteorological satellite dawn and dusk period fire monitoring computing device typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the polar-orbiting meteorological satellite dawn and dusk period fire monitoring computing device, including volatile and non-volatile media, removable and non-removable media.

[0123] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory. The polar-orbiting meteorological satellite dawn and dusk period fire monitoring computing device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system may be used to read and write non-removable, non-volatile magnetic media ( Figure 8 Not shown, often called a "hard drive"). Although Figure 8 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus via one or more data medium interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0124] A program / utility having a set (at least one) of program modules, which may be stored, for example, in a memory, includes, but is not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. The program modules generally implement the functions and / or methods of the embodiments described herein.

[0125] The polar-orbiting meteorological satellite dawn and dusk fire monitoring computing device can also communicate with one or more external devices (such as keyboards, pointing devices, displays, etc.), and can also communicate with one or more devices that enable a user to interact with the polar-orbiting meteorological satellite dawn and dusk fire monitoring computing device, and / or communicate with any device that enables the polar-orbiting meteorological satellite dawn and dusk fire monitoring computing device to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through an input / output (I / O) interface. In addition, the polar-orbiting meteorological satellite dawn and dusk fire monitoring computing device can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter. Figure 8As shown, the network adapter communicates with other modules of the polar-orbiting meteorological satellite dawn and dusk fire monitoring computing device via a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the polar-orbiting meteorological satellite dawn and dusk fire monitoring computing device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0126] The processing unit executes various functional applications and data processing by running the programs stored in the memory, such as implementing the polar-orbiting meteorological satellite dawn and dusk fire point monitoring method provided in any embodiment of the present invention.

[0127] Example 4

[0128] Example 4 provides a computer-readable storage medium, in which a program is stored. When the program is executed by a processor, the method for monitoring fire points during the dawn and dusk periods of a polar-orbiting meteorological satellite is implemented.

[0129] In summary, the polar-orbiting meteorological satellite fire point monitoring method and system of the present invention, which targets the observation mode unique to the dawn and dusk periods, can detect fire points in the dawn and dusk periods with high timeliness and accuracy, and output fire point monitoring results to meet the monitoring needs during the high-incidence periods of fire points.

[0130] The foregoing descriptions of specific exemplary embodiments of the present invention are for purposes of illustration and description. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is apparent that many variations and modifications are possible in light of the foregoing teachings. The exemplary embodiments have been selected and described for the purpose of explaining the specific principles of the invention and their practical application, thereby enabling those skilled in the art to realize and utilize a variety of exemplary embodiments of the invention and various options and modifications. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A method for monitoring fire points during the dawn and dusk periods using a polar-orbiting meteorological satellite, based on a polar-orbiting meteorological satellite, characterized in that: The method comprises: Obtain polar-orbiting meteorological satellite data during the dawn and dusk periods, perform data preprocessing, and generate grid data for the monitoring area; Based on the monitoring area grid data, determining the target pixel in the monitoring area grid data; Based on the target pixel, generating a fire point pixel; Generate the area of the open fire zone based on the fire point pixels; Outputting fire monitoring results based on the area of the open fire zone; Wherein, based on the monitoring area grid data, the target pixel in the monitoring area grid data is determined to be: Performing thermal infrared limb correction based on the monitoring area grid data; Extracting cloud pixel information based on the grid data of the monitoring area after thermal infrared limb correction; Using a first identification strategy to identify the cloud pixel information to obtain a cloud pixel; Removing the cloud pixels and determining the target pixels in the grid data of the monitoring area; The generation of fire point pixels based on the target pixels is specifically as follows: Determine the suspected high-temperature fire point pixel in the vicinity of the target pixel, and calculate the mean and standard deviation of the background brightness temperature in the vicinity of the target pixel to generate a thermal anomaly point pixel; Using the second identification strategy to identify the thermal anomaly point pixel, generating a first undetermined fire point pixel; Supplementary identification of the cloud pixel is performed using a cloud area fire point supplementary identification strategy to generate a second undetermined fire point pixel; The first undetermined fire point pixel and the second undetermined fire point pixel are combined to generate a fire point pixel.

2. The method for monitoring fire points during the dawn and dusk periods of a polar-orbiting meteorological satellite according to claim 1, wherein: Acquiring the polar-orbiting meteorological satellite data during the dawn and dusk periods, and performing data preprocessing to generate the monitoring area grid data. Specifically, acquiring the polar-orbiting meteorological satellite earth observation data during the dawn and dusk periods, and performing data preprocessing to generate the monitoring area grid data; The preprocessing of the polar-orbiting meteorological satellite earth observation data during the dawn and dusk periods includes data projection.

3. The method for monitoring fire points during the dawn and dusk periods of a polar-orbiting meteorological satellite according to claim 1, wherein: Based on the grid data of the monitoring area, the thermal infrared edge correction is calculated using the following formula: ； ； ； ； Where T is the corrected brightness temperature, is the brightness temperature, Different satellite observation angles The brightness temperature correction value of , Re is the radius of the earth, H is the altitude of the satellite, is the channel wave number, E is the radiance observed by the satellite, Ln is the natural logarithm, is the satellite observation angle, is the fitted function, is the intermediate parameter.

4. The method for monitoring fire points during the dawn and dusk periods of a polar-orbiting meteorological satellite according to claim 1, wherein: The second identification strategy is specifically: T 3.8 > 340K； or & ； in, ; Where, T 3.8 is the mid-infrared brightness temperature, is the effective background brightness temperature in the mid-infrared, is the standard deviation of the effective background brightness temperature in the mid-infrared, is the pixel brightness temperature difference between mid-infrared and far-infrared, is the effective background brightness temperature difference between mid-infrared and far-infrared, is the standard deviation of the pixel brightness temperature difference between the mid-infrared and far-infrared effective backgrounds, The correction is a function of the coefficients, is the proportion of non-vegetation pixels in the area, is the proportion of cloud pixels in the region, is the solar zenith angle.

5. A polar-orbiting meteorological satellite fire point monitoring system during the dawn and dusk periods, based on a polar-orbiting meteorological satellite, characterized in that: include: The acquisition and generation module is used to obtain polar-orbiting meteorological satellite data during the dawn and dusk periods, and perform data preprocessing to generate grid data for the monitoring area; A determination module, configured to determine a target pixel in the monitoring area grid data based on the monitoring area grid data; A fire point pixel generation module, used for generating fire point pixels based on the target pixels; An open fire area generating module, for generating the open fire area based on the fire point pixels; as well as An output module, configured to output fire monitoring results based on the area of the open fire zone; The acquisition and generation module is in communication with the determination module, the determination module is in communication with the fire point pixel generation module, the fire point pixel generation module is in communication with the open fire area generation module, and the open fire area generation module is in communication with the output module; Wherein, based on the monitoring area grid data, the target pixel in the monitoring area grid data is determined to be: Performing thermal infrared limb correction based on the monitoring area grid data; Extracting cloud pixel information based on the grid data of the monitoring area after thermal infrared limb correction; Using a first identification strategy to identify the cloud pixel information to obtain a cloud pixel; Removing the cloud pixels and determining the target pixels in the grid data of the monitoring area; The generation of fire point pixels based on the target pixels is specifically as follows: Determine the suspected high-temperature fire point pixel in the vicinity of the target pixel, and calculate the mean and standard deviation of the background brightness temperature in the vicinity of the target pixel to generate a thermal anomaly point pixel; Using the second identification strategy to identify the thermal anomaly point pixel, generating a first undetermined fire point pixel; Supplementary identification of the cloud pixel is performed using a cloud area fire point supplementary identification strategy to generate a second undetermined fire point pixel; The first undetermined fire point pixel and the second undetermined fire point pixel are combined to generate a fire point pixel.

6. A polar-orbiting meteorological satellite fire point monitoring and calculation device during the dawn and dusk periods, characterized in that: include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, so that the one or more processors implement the polar-orbiting meteorological satellite dawn and dusk period fire point monitoring method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which, when executed by a processor, implements the polar-orbiting meteorological satellite dawn and dusk fire point monitoring method according to any one of claims 1 to 4.

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