A forest fire situation analysis method, device and medium based on satellite remote sensing

Abstract

The application discloses a forest fire situation analysis method and device based on satellite remote sensing and a medium, and belongs to the technical field of remote sensing application. The method comprises the following steps: collecting multispectral satellite remote sensing image data of a region to be researched, and preprocessing the multispectral satellite remote sensing image data to obtain a multispectral satellite orthographic image data set; in the case that a fire occurs in the region to be researched, performing false color band synthesis on the multispectral satellite orthographic image data set to obtain a false color image, and extracting a burned pixel from the false color image to determine a forest fire line contour; calculating an area in the forest fire line contour based on Arcgis software, and calculating a difference value of a vegetation normalized index before and after a disaster based on ENVI software to evaluate a fire intensity. The application realizes the identification of a forest fire through an efficient and accurate method, and timely risk evaluation after the occurrence of the fire.

Description

Technical Field

[0001] This application relates to the field of remote sensing application technology, and in particular to a method, equipment and medium for forest fire situation analysis based on satellite remote sensing. Background Technology

[0002] In recent years, frequent forest fires have caused enormous damage to the environment, ecology, and socio-economic conditions. How to efficiently and accurately identify forest fires and conduct timely risk assessments after a fire occurs in order to take appropriate disaster relief measures has become an urgent problem that needs to be solved to protect the ecological environment and the lives and property of the people. Summary of the Invention

[0003] This application provides a method, device, and medium for forest fire situation analysis based on satellite remote sensing, in order to solve the following technical problems: how to identify forest fires efficiently and accurately, and to conduct timely risk assessments after a fire occurs.

[0004] In a first aspect, embodiments of this application provide a method for forest fire situation analysis based on satellite remote sensing. The method includes: acquiring multispectral satellite remote sensing image data of the area to be studied, and preprocessing the multispectral satellite remote sensing image data to obtain a multispectral satellite orthophoto dataset; in the event of a fire in the area to be studied, performing false-color band synthesis on the multispectral satellite orthophoto dataset to obtain a false-color image, and extracting burned pixels from the false-color image to determine the forest fire fireline outline; calculating the area within the forest fire fireline outline using ArcGIS software, and calculating the difference in vegetation normalization index before and after the fire using ENVI software to assess fire intensity.

[0005] In one implementation of this application, after obtaining the multispectral satellite orthophoto dataset, the method further includes: when it is necessary to conduct forest fire pre-disaster warning monitoring in the area to be studied, performing band operations on the multispectral satellite orthophoto dataset to invert and obtain warning data, and classifying the vegetation cover of the multispectral satellite orthophoto dataset; wherein, the warning data includes: normalized index and temperature-vegetation drought index; inputting the warning data and vegetation cover classification into a preset forest fire warning model to determine the comprehensive fire risk forecast index; and determining the forest fire risk level based on the comprehensive fire risk forecast index.

[0006] In one implementation of this application, the multispectral satellite remote sensing image data includes: Sentinel-2 data, MODIS data, and Landsat-8 data; the multispectral satellite remote sensing image data is preprocessed to obtain a multispectral satellite orthophoto dataset, specifically including: performing image correction, image fusion, coordinate transformation, and image registration on the multispectral satellite remote sensing image data based on ENVI software to obtain a registered multispectral satellite orthophoto dataset.

[0007] In one implementation of this application, the outline of a forest fire line is determined by extracting burned pixels from a false-color image. Specifically, this includes: extracting burned pixels from the false-color image using a high-temperature fire point comprehensive threshold discrimination method to determine a number of burned pixels; determining any one of the burned pixels as a seed burned pixel point; and determining the outline of the forest fire line based on the seed burned pixel point using a preset region growing method.

[0008] In one implementation of this application, the forest fire line outline is determined based on the seed burned pixel point using a preset region growing method. Specifically, this includes: determining the pixel difference between the burned pixels in the neighborhood of the seed burned pixel point and the seed burned pixel point; determining the burned pixel as the forest fire line outline pixel point when the pixel difference is greater than a preset pixel threshold; and traversing the forest fire line outline pixel points to determine the forest fire line outline.

[0009] In one implementation of this application, band operations are performed on the multispectral satellite orthophoto dataset, as expressed by the following formula:

[0010] NDVI=(ρ nir -ρ red ) / (ρ nir +ρ red )

[0011] TVDI = (Ts - TS) min ) / (TS max -TS min )

[0012] Where NDVI is the normalization exponent, ρ nir ρ is the reflectance in the near-infrared band. red 1. Red band reflectance; TVDI is the temperature-vegetation drought index; Ts is the surface temperature value of any pixel; TS max TS represents the highest surface temperature corresponding to a given NDVI value. min This represents the lowest surface temperature corresponding to a given NDVI value.

[0013] In one implementation of this application, the forest fire early warning model is represented by the following formula:

[0014] F=(NDVI+1-TVDI+0.1*Landuse) / 3

[0015] Where F is the fire risk composite forecast index, and Landuse is the vegetation cover classification; NDVI and TVDI are 0- and 1-based characteristic quantities; Landuse is assigned a value according to the vegetation cover type: when the vegetation cover type is coniferous forest, Landuse = 1; when the vegetation cover type is shrub forest, Landuse = 2; when the vegetation cover type is broad-leaved forest, Landuse = 3; and when the vegetation cover type is other, Landuse = 4.

[0016] In one implementation of this application, a false-color band synthesis is performed on a multispectral satellite orthophoto dataset to obtain a false-color image. Specifically, this includes: synthesizing the mid-infrared band and the thermal infrared band in the multispectral satellite orthophoto dataset to obtain a false-color image.

[0017] Secondly, embodiments of this application also provide a forest fire situation analysis device based on satellite remote sensing, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: acquire multispectral satellite remote sensing image data of the area to be studied, and preprocess the multispectral satellite remote sensing image data to obtain a multispectral satellite orthophoto dataset; in the event of a fire in the area to be studied, perform false-color band synthesis on the multispectral satellite orthophoto dataset to obtain a false-color image, and extract burned pixels from the false-color image to determine the forest fire fireline outline; calculate the area within the forest fire fireline outline using ArcGIS software, and calculate the difference in vegetation normalization index before and after the disaster using ENVI software to assess the fire intensity.

[0018] Thirdly, this application also provides a non-volatile computer storage medium for forest fire situation analysis based on satellite remote sensing, storing computer-executable instructions. The computer-executable instructions are configured to: acquire multispectral satellite remote sensing image data of the area under study, and preprocess the multispectral satellite remote sensing image data to obtain a multispectral satellite orthophoto dataset; in the event of a fire in the area under study, synthesize the multispectral satellite orthophoto dataset using false-color bands to obtain a false-color image, and extract burned pixels from the false-color image to determine the forest fire fireline outline; calculate the area within the forest fire fireline outline using ArcGIS software, and calculate the difference in vegetation normalization index before and after the fire using ENVI software to assess fire intensity.

[0019] This application provides a method, equipment, and medium for forest fire situation analysis based on satellite remote sensing. It proposes a comprehensive analysis method for the entire process of forest fire situation before and after the fire, directly expressing pre-fire warnings, the area of ​​fire spread during the fire, and the degree of risk after the fire. A fire warning model is constructed based on multiple remote sensing indicators such as NDVI and TVDI, and risk levels are determined by combining actual experience data, improving the accuracy of fire warnings. In the process of fire contour extraction, a high-temperature fire point comprehensive threshold discrimination method based on multi-band false color synthesis is combined with a seed point-based regional growth contour extraction method, resulting in fast and accurate fire contour extraction. The burned area is calculated, and the combustion intensity level within the burned forest area is determined, along with the area proportion corresponding to different levels. Relevant information about forest fires is extracted qualitatively and quantitatively, providing valuable scientific evidence for decision-makers and relevant personnel to better understand the post-fire impact. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 A flowchart illustrating a forest fire situation analysis method based on satellite remote sensing, provided for embodiments of this application;

[0022] Figure 2 This is a schematic diagram of the internal structure of a forest fire situation analysis device based on satellite remote sensing, provided as an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] This application provides a method, device, and medium for forest fire situation analysis based on satellite remote sensing, in order to solve the following technical problems: how to identify forest fires efficiently and accurately, and to conduct timely risk assessments after a fire occurs.

[0025] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0026] Figure 1A flowchart illustrating a forest fire situation analysis method based on satellite remote sensing, provided as an embodiment of this application. Figure 1 As shown in the figure, the forest fire situation analysis method based on satellite remote sensing provided in this application embodiment specifically includes the following steps:

[0027] Step 101: Collect multispectral satellite remote sensing image data of the area to be studied, and preprocess the multispectral satellite remote sensing image data to obtain a multispectral satellite orthophoto dataset.

[0028] First, it should be noted that the multispectral satellite remote sensing image data in this application includes: Sentinel-2 data, MODIS data, Landsat-8 data, etc.

[0029] In one embodiment of this application, when monitoring or detecting a certain area under study, multispectral satellite remote sensing image data of that area are acquired in real time.

[0030] Furthermore, the multispectral satellite remote sensing image data is preprocessed to obtain a multispectral satellite orthophoto dataset.

[0031] Specifically, based on ENVI software, image correction, image fusion, coordinate transformation, and image registration are performed on multispectral satellite remote sensing image data to obtain a registered multispectral satellite orthophoto dataset.

[0032] In one embodiment of this application, after obtaining a multispectral satellite orthophoto dataset, if it is necessary to conduct forest fire pre-disaster early warning monitoring in the area to be studied, band operations are performed on the multispectral satellite orthophoto dataset to invert and obtain early warning data, and vegetation cover classification is performed on the multispectral satellite orthophoto dataset; wherein, the early warning data includes: normalized index, temperature-vegetation drought index; the early warning data and vegetation cover classification are input into a preset forest fire early warning model to determine the comprehensive fire risk forecast index; based on the comprehensive fire risk forecast index, the forest fire risk level is determined.

[0033] In one embodiment of this application, band operations are performed on a multispectral satellite orthophoto dataset, as expressed by the following formula:

[0034] NDVI=(ρ nir -ρ red ) / (ρ nir +ρ red )

[0035] TVDI = (Ts - TS) min ) / (TS max -TS min )

[0036] Where NDVI is the normalization exponent, ρnir ρ is the reflectance in the near-infrared band. red 1. Red band reflectance; TVDI is the temperature-vegetation drought index; Ts is the surface temperature value of any pixel; TS max TS represents the highest surface temperature corresponding to a given NDVI value. min This represents the lowest surface temperature corresponding to a given NDVI value.

[0037] In one embodiment of this application, the forest fire early warning model is represented by the following formula:

[0038] F=(NDVI+1-TVDI+0.1*Landuse) / 3

[0039] Where F is the fire risk composite forecast index, and Landuse is the vegetation cover classification; NDVI and TVDI are 0- and 1-based characteristic quantities; Landuse is assigned a value according to the vegetation cover type: when the vegetation cover type is coniferous forest, Landuse = 1; when the vegetation cover type is shrub forest, Landuse = 2; when the vegetation cover type is broad-leaved forest, Landuse = 3; and when the vegetation cover type is other, Landuse = 4.

[0040] It should be noted that, based on historical experience and relevant standards, the forest fire risk level can be determined by the comprehensive fire risk forecast index F (0≤F≤1): when F≥0.50, it indicates no danger; 0.40≤F<0.50 indicates a low-risk area; 0.30≤F<0.40 indicates a moderate-risk area; when F<0.30, it indicates that the plant water content and soil moisture are extremely low, the forest fire incidence rate is extremely high, and it is a high-risk area.

[0041] Step 102: In the event of a fire in the area to be studied, perform false color band synthesis on the multispectral satellite orthophoto dataset to obtain a false color image, and extract burned pixels from the false color image to determine the outline of the forest fire line.

[0042] In one embodiment of this application, after obtaining a multispectral satellite orthophoto dataset, in the event of a fire in the area to be studied, the multispectral satellite orthophoto dataset is first subjected to false color band synthesis.

[0043] Specifically, the mid-infrared band and thermal infrared band in the multispectral satellite orthophoto dataset are combined to obtain a false-color image.

[0044] Furthermore, by extracting burned pixels from the false-color images, the outline of the forest fire line can be determined.

[0045] Specifically, the high-temperature fire point comprehensive threshold discrimination method is used to extract burned pixels from false color images to identify several burned pixels; any one of the burned pixels is identified as a seed burned pixel point, and based on the seed burned pixel point, a preset region growing method is used to determine the outline of the forest fire line.

[0046] In one embodiment of this application, the forest fire line outline is determined based on the seed burned pixel point using a preset region growing method. Specifically, this includes: determining the pixel difference between the burned pixels in the neighborhood of the seed burned pixel point and the seed burned pixel point; determining the burned pixel as the forest fire line outline pixel point when the pixel difference is greater than a preset pixel threshold; and traversing the forest fire line outline pixel points to determine the forest fire line outline.

[0047] Step 103: Calculate the area within the forest fire line outline using ArcGIS software, and calculate the difference in vegetation normalization index before and after the disaster using ENVI software to assess the fire intensity.

[0048] In one embodiment of this application, after determining the fireline outline of a forest fire, the area within the fireline outline can be calculated using ArcGIS software. The difference in vegetation normalization index before and after the disaster can be calculated using ENVI software to assess fire intensity.

[0049] Furthermore, histogram statistics can be performed, and finally, based on the histogram distribution of NDVI change values ​​and the remote sensing color tone and texture characteristics of different intensities of the burned area, the burned area can be further subdivided into four combustion intensity levels: burning ember area, heavily burned area, lightly burned area, and unburned area.

[0050] In one embodiment of this application, when 0.2 ≤ ΔNDVI < 0.4 and sporadic orange-red dots appear on the true-color image, it is classified as an ember area; when ΔNDVI ≥ 0.4 and the image appears dark black or inky black with smooth texture, clearly distinguishable from surrounding vegetation and other ground features, it is classified as a heavily burned area; when 0.1 ≤ ΔNDVI < 0.2 and the image appears light green with a few gray-black specks, flocculent and rough, it is classified as a lightly burned area; when ΔNDVI < 0.1 and the image appears gray-green or green, often with a clear boundary abruptly changing from the burned area, it is classified as an unburned area. Finally, ArcGIS software is used to calculate the area percentage of each intensity level within the entire forest fire area.

[0051] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a forest fire situation analysis device based on satellite remote sensing, the structure of which is as follows: Figure 2 As shown.

[0052] Figure 2 This is a schematic diagram of the internal structure of a forest fire situation analysis device based on satellite remote sensing, provided as an embodiment of this application. Figure 2 As shown, the device includes:

[0053] At least one processor 201;

[0054] And a memory 202 that is communicatively connected to at least one processor;

[0055] The memory 202 stores instructions executable by at least one processor, which are executed by at least one processor 201 to enable at least one processor 201 to:

[0056] Multispectral satellite remote sensing image data of the area to be studied is collected and preprocessed to obtain a multispectral satellite orthophoto dataset.

[0057] In the event of a fire in the area under study, false color band synthesis is performed on the multispectral satellite orthophoto dataset to obtain false color images. Then, the fire line outline of the forest fire is determined by extracting burned pixels from the false color images.

[0058] The area within the forest fire line outline is calculated using ArcGIS software, and the difference in vegetation normalization index before and after the disaster is calculated using ENVI software, in order to assess the fire intensity.

[0059] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium for forest fire situation analysis based on satellite remote sensing, storing computer-executable instructions, wherein the computer-executable instructions are configured as follows:

[0060] Multispectral satellite remote sensing image data of the area to be studied is collected and preprocessed to obtain a multispectral satellite orthophoto dataset.

[0061] In the event of a fire in the area under study, false color band synthesis is performed on the multispectral satellite orthophoto dataset to obtain false color images. Then, the fire line outline of the forest fire is determined by extracting burned pixels from the false color images.

[0062] The area within the forest fire line outline is calculated using ArcGIS software, and the difference in vegetation normalization index before and after the disaster is calculated using ENVI software, in order to assess the fire intensity.

[0063] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0064] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0065] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0066] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0067] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0068] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0069] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0070] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0071] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0072] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0073] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for analyzing forest fire situation based on satellite remote sensing, characterized in that, The method includes: Multispectral satellite remote sensing image data of the area to be studied is collected, and the multispectral satellite remote sensing image data is preprocessed to obtain a multispectral satellite orthophoto dataset. In the event of a fire in the area under study, the multispectral satellite orthophoto dataset is synthesized using false color bands to obtain a false color image. The fire line outline of the forest fire is then determined by extracting burned pixels from the false color image. The area within the forest fire line outline is calculated using ArcGIS software, and the difference in vegetation normalization index before and after the disaster is calculated using ENVI software, in order to assess the fire intensity. After obtaining the multispectral satellite orthophoto dataset, the method further includes: In cases where forest fire early warning monitoring is required for the area under study, band operations are performed on the multispectral satellite orthophoto dataset to retrieve early warning data, and vegetation cover classification is performed on the multispectral satellite orthophoto dataset; wherein, the early warning data includes: normalized index and temperature-vegetation drought index; The early warning data and vegetation cover classification are input into a preset forest fire early warning model to determine the comprehensive fire risk forecast index. Based on the aforementioned comprehensive fire risk forecast index, the forest fire risk level is determined; Band operations are performed on the multispectral satellite orthophoto dataset, as expressed by the following formula: in, The normalized index, For near-infrared reflectivity, Reflectivity in the red light band; The temperature-vegetation drought index. For any pixel, the surface temperature value. The highest surface temperature corresponding to a given NDVI value. The lowest surface temperature corresponding to a given NDVI value; By extracting burned pixels from the false-color image, the outline of the forest fire line is determined, specifically including: The false color image is subjected to fire-damaged pixels by using a high-temperature fire point comprehensive threshold discrimination method to identify several fire-damaged pixels. Any one of the aforementioned burned pixels is identified as a seed burned pixel point, and based on the seed burned pixel point, the outline of the forest fire line is determined using a preset region growing method. Based on seed burned pixel points, the outline of the forest fire line is determined using a pre-defined region growing method, specifically including: Determine the pixel difference between the burned pixel in the neighborhood of the seed burned pixel and the seed burned pixel; If the pixel difference is greater than a preset pixel threshold, the burned pixel is determined to be a forest fire line outline pixel point; Traverse the pixel points of the forest fire fireline outline to determine the forest fire fireline outline. The forest fire early warning model is expressed by the following formula: in, The fire risk index is defined as Landuse, which is a vegetation cover classification. NDVI and TVDI are 0- and 1-based feature values. Landuse is assigned a value based on the vegetation cover type: Landuse=1 when the vegetation cover type is coniferous forest, Landuse=2 when the vegetation cover type is shrub forest, Landuse=3 when the vegetation cover type is broadleaf forest, and Landuse=4 when the vegetation cover type is other.

2. The forest fire situation analysis method based on satellite remote sensing according to claim 1, characterized in that, The multispectral satellite remote sensing image data includes: Sentinel-2 data, MODIS data, and Landsat-8 data; The multispectral satellite remote sensing image data is preprocessed to obtain a multispectral satellite orthophoto dataset, specifically including: Based on the ENVI software, image correction, image fusion, coordinate transformation, and image registration are performed on the multispectral satellite remote sensing image data to obtain a registered multispectral satellite orthophoto dataset.

3. The forest fire situation analysis method based on satellite remote sensing according to claim 1, characterized in that, The multispectral satellite orthophoto dataset is subjected to false color band synthesis to obtain a false color image, specifically including: The mid-infrared band and thermal infrared band in the multispectral satellite orthophoto dataset are combined to obtain a false-color image.

4. A forest fire situation analysis device based on satellite remote sensing, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform a forest fire situation analysis method based on satellite remote sensing as described in any one of claims 1-3.

5. A non-volatile computer storage medium for forest fire situation analysis based on satellite remote sensing, storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed, they implement a forest fire situation analysis method based on satellite remote sensing as described in any one of claims 1-3.

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