A method for identifying forest and grassland fire hazards based on remote sensing images

Abstract

The present application relates to the field of image data processing technology, and specifically to a method for identifying forest and grassland fire hazards based on remote sensing images. Remote sensing image acquisition is performed using multi-source remote sensing image data. By scientifically segmenting the overall environment, the target area is divided into multiple specific parts, and the dryness and fuel index of a single area are accurately evaluated. Taking into account the connectivity of the overall ecological environment, the degree of fluctuation of the combustion conditions in each area is analyzed to clarify the degree of fire hazard and combustible conditions. The thermal radiation monitoring data is analyzed to calculate the fire hazard induction coefficient of each area. Combined with the degree of fire hazard conditions and the induction coefficient of a single area, its fire hazard degree is comprehensively evaluated. Since the fire hazard degree coefficient is evaluated by comprehensively considering parameters such as dryness index, fuel index, degree of fluctuation of combustion conditions, degree of combustible conditions, and fire hazard induction coefficient, the accuracy of regional fire hazard identification can be significantly improved.

Description

Technical Field

[0001] The present application belongs to the field of image data processing technology, and specifically relates to a method for identifying forest and grassland fire hazards based on remote sensing images. Background Art

[0002] With global climate change and increased human activities, the frequent occurrence of forest and grassland fires has become an important environmental and safety issue. In recent years, the development of remote sensing technology has provided a new way to identify and monitor fire hazards. By acquiring high-resolution remote sensing images through satellites or drones, large areas of forests and grasslands can be monitored in real time and continuously. By using advanced image processing and machine learning methods to extract relevant features from remote sensing images and identify fire hazards, it is expected to significantly improve the accuracy and timeliness of fire warnings and provide strong support for disaster prevention and mitigation work.

[0003] However, accurate fire warning assessment of areas with potential fire hazards is still challenging. This is mainly because the assessment of fire hazards involves the consideration of multiple complex factors, including vegetation type, humidity, terrain, and climate conditions, and the changes in these factors are significantly dynamic and uncertain, which increases the difficulty of identifying fire hazards.

[0004] Therefore, there is an urgent need for a forest and grassland fire hazard identification method based on remote sensing images. Summary of the invention

[0005] In order to solve the above problems, the present application embodiment provides a method for identifying forest and grassland fire hazards based on remote sensing images, the method comprising:

[0006] The collected remote sensing images are regionally fused and segmented according to preset sizes to obtain multiple fire hazard areas to be analyzed;

[0007] Obtaining the regional dryness index of the fire hazard area to be analyzed;

[0008] Obtaining a regional fuel index for the area to be analyzed for fire hazards;

[0009] According to the regional dryness index and the regional fuel index, obtaining the degree of fluctuation of regional combustion conditions in the fire hazard to be analyzed area;

[0010] According to the regional dryness index, the regional fuel index and the regional combustion condition fluctuation degree, obtaining the regional combustible condition degree of the fire hazard to be analyzed area;

[0011] Obtaining a regional fire risk induction coefficient of the fire hazard to be analyzed area according to historical thermal radiation data of the fire hazard to be analyzed area;

[0012] According to the degree of flammable conditions in the area and the fire risk induction coefficient in the area, the regional fire hazard degree coefficient of the area to be analyzed for fire hazards is obtained.

[0013] Optionally, obtaining the regional dryness index of the fire hazard to be analyzed area includes:

[0014] Obtaining a normalized vegetation coefficient of the fire hazard area to be analyzed;

[0015] Obtain the average time required for the soil thermal radiation in the fire hazard to be analyzed area to change by one degree;

[0016] The regional dryness index of the area to be analyzed for fire hazard is obtained according to the normalized vegetation coefficient and the mean time.

[0017] Optionally, obtaining the regional fuel index of the fire hazard to be analyzed area includes:

[0018] Obtain the area of ​​the fire hazard to be analyzed region;

[0019] Obtaining the number of types and proportion of vegetation in the fire hazard to be analyzed area, as well as the combustion efficiency of the vegetation;

[0020] Obtaining the area ratio of the dry vegetation area in the fire hazard to be analyzed area, wherein the dry vegetation area is an area where the normalized vegetation coefficient is less than a set threshold;

[0021] The regional fuel index of the area to be analyzed for fire hazards is obtained based on the area of ​​the area, the number of vegetation types, the proportion of vegetation, the combustion efficiency of the vegetation, and the regional proportion of the dry vegetation area.

[0022] Optionally, obtaining the degree of fluctuation of regional combustion conditions in the area to be analyzed for fire hazard includes:

[0023] According to the difference degree of the regional dryness index between the fire hazard to be analyzed area and the surrounding area, obtaining the regional dryness index difference coefficient of the fire hazard to be analyzed area;

[0024] Obtaining a regional fuel index difference coefficient of the fire hazard to be analyzed area according to the difference degree of the regional fuel index between the fire hazard to be analyzed area and the surrounding area;

[0025] According to the regional dryness index difference coefficient and the regional fuel index difference coefficient, the degree of fluctuation of the regional combustion conditions of the fire hazard to be analyzed area is obtained.

[0026] Optionally, obtaining the regional dryness index difference coefficient of the fire hazard to be analyzed area includes:

[0027] Obtain the number of areas surrounding the fire hazard area to be analyzed;

[0028] Obtaining the dryness index gradients of the fire hazard to-be-analyzed area and the surrounding area, and the dryness gradient mean of the dryness index gradients of all areas;

[0029] Get the overall dryness index for all areas;

[0030] The regional dryness index difference coefficient of the fire hazard area to be analyzed is obtained according to the number of areas, the dryness index gradient, the dryness gradient mean and the overall dryness index.

[0031] Optionally, obtaining the regional fuel index difference coefficient of the fire hazard to be analyzed area includes:

[0032] Obtain the number of areas surrounding the fire hazard area to be analyzed;

[0033] Obtaining the fuel index gradients of the fire hazard to-be-analyzed area and the surrounding area, and the fuel gradient mean of the fuel index gradients of all areas;

[0034] Get the overall fuel index for all regions;

[0035] The regional fuel index difference coefficient of the fire hazard area to be analyzed is obtained according to the number of areas, the fuel index gradient, the fuel gradient mean and the overall fuel index.

[0036] Optionally, obtaining the degree of fluctuation of the regional combustion conditions of the fire hazard to be analyzed area includes:

[0037] According to the average of the regional dryness index difference coefficient and the regional fuel index difference coefficient, the degree of fluctuation of the regional combustion conditions of the fire hazard to be analyzed area is obtained.

[0038] Optionally, obtaining the degree of regional flammability conditions of the fire hazard to be analyzed area includes:

[0039] Obtaining the sum of the regional dryness index and the regional fuel index;

[0040] Obtaining a ratio of the sum value to the degree of fluctuation of the combustion conditions in the area;

[0041] The ratio is normalized to obtain the degree of flammability conditions in the area to be analyzed for fire hazards.

[0042] Optionally, obtaining the regional fire risk induction coefficient of the fire hazard to be analyzed area includes:

[0043] Obtain a first mean value of the difference between the current thermal radiation degree and the historical thermal radiation degree of the fire hazard to be analyzed area;

[0044] Obtaining a second mean value of the difference between the current thermal radiation level of the fire hazard to-be-analyzed area and the current thermal radiation level of the surrounding area;

[0045] The regional fire risk induction coefficient of the fire hazard to be analyzed area is obtained according to the first mean value and the second mean value.

[0046] Optionally, the obtaining of a regional fire hazard degree coefficient of the area to be analyzed for fire hazard includes:

[0047] The regional fire hazard degree coefficient of the area to be analyzed for the fire hazard is obtained according to the product of the degree of flammable conditions in the area and the fire risk induction coefficient of the area.

[0048] In summary, the embodiment of the present application provides a forest and grassland fire hazard identification method based on remote sensing images, the method comprising: regional fusion of the collected remote sensing images and regional segmentation according to a preset size to obtain multiple fire hazard areas to be analyzed; obtain the regional dryness index of the fire hazard area to be analyzed; obtain the regional fuel index of the fire hazard area to be analyzed; obtain the regional combustion condition fluctuation degree of the fire hazard area to be analyzed according to the regional dryness index and the regional fuel index; obtain the regional combustible condition degree of the fire hazard area to be analyzed according to the regional dryness index, the regional fuel index and the regional combustion condition fluctuation degree; obtain the regional fire risk induction coefficient of the fire hazard area to be analyzed according to the historical thermal radiation data of the fire hazard area to be analyzed; obtain the regional fire hazard degree coefficient of the fire hazard area to be analyzed according to the regional combustible condition degree and the regional fire risk induction coefficient. Since the dryness index, fuel index, combustion condition fluctuation degree, combustible condition degree, fire risk induction coefficient and other parameters are comprehensively considered to evaluate and calculate the regional fire hazard degree coefficient, the embodiment of the present application can significantly improve the accuracy of regional fire hazard identification and evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the implementation scheme of the present application, the drawings required for use in the implementation scheme will be briefly introduced below. It should be understood that the drawings only show certain implementation schemes of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on the drawings without paying creative work.

[0050] Figure 1 The present invention is a flowchart of a method for identifying forest and grassland fire hazards based on remote sensing images according to an exemplary embodiment.

[0051] Figure 2 The present invention is a flow chart showing a method for obtaining a regional dryness index of a fire hazard area to be analyzed according to an exemplary embodiment.

[0052] Figure 3 The present invention is a flow chart showing a method for obtaining a regional fuel index of a fire hazard to be analyzed area according to an exemplary embodiment.

[0053] Figure 4 The present invention is a flow chart showing a method for obtaining the degree of fluctuation of regional combustion conditions in a fire hazard to be analyzed area according to an exemplary embodiment.

[0054] Figure 5 The present invention is a flow chart showing a method for obtaining a regional dryness index difference coefficient of a fire hazard area to be analyzed according to an exemplary embodiment.

[0055] Figure 6 The present invention is a flow chart showing a method for obtaining a regional fuel index difference coefficient of a fire hazard area to be analyzed according to an exemplary embodiment.

[0056] Figure 7 The present invention is a flow chart showing another method for obtaining the degree of fluctuation of regional combustion conditions in a fire hazard to be analyzed area according to an exemplary embodiment.

[0057] Figure 8 The present invention is a flow chart showing a method for obtaining the degree of regional flammability conditions of an area to be analyzed for fire hazards according to an exemplary embodiment.

[0058] Fig. 9 The present invention is a flow chart showing a method for obtaining a regional fire risk induction coefficient of a fire hazard to be analyzed area according to an exemplary embodiment.

[0059] Fig.10 The present invention is a flow chart showing a method for obtaining a regional fire hazard degree coefficient of a fire hazard to be analyzed area according to an exemplary embodiment. DETAILED DESCRIPTION

[0060] In order to clearly illustrate the technical features of the present solution, the present application is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0061] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0062] It should be understood that the various steps described in the method implementation of the present application can be performed in different orders and / or performed in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0063] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0064] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0065] It should be noted that the modifications of "one" and "multiple" mentioned in this application are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise clearly indicated in the context, they should be understood as "one or more". In the description of this application, unless otherwise specified, "multiple" refers to two or more than two, and other quantifiers are similar; "at least one item (item)", "one (item) or multiple items (items)" or similar expressions refer to any combination of these items (items), including any combination of single items (items) or plural items (items). For example, at least one item (item) a can represent any number of a; for another example, one (item) or multiple items (items) of a, b and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple; "and / or" is a kind of association relationship that describes the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural.

[0066] Although the operations or steps are described in a specific order in the drawings in the embodiments of the present application, it should not be understood that it is required to perform these operations or steps in the specific order shown or in a serial order, or to perform all the operations or steps shown to obtain the desired results. In the embodiments of the present application, these operations or steps can be performed in series; these operations or steps can also be performed in parallel; or some of these operations or steps can be performed.

[0067] At the same time, it is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of the corresponding laws and regulations and relevant provisions. The present application is described below in conjunction with specific embodiments.

[0068] Figure 1 FIG. 1 is a flowchart of a method for identifying forest and grassland fire hazards based on remote sensing images according to an exemplary embodiment. Figure 1 As shown, the embodiment of the present application provides a method for identifying forest and grassland fire hazards based on remote sensing images, and the method may include the following steps:

[0069] In step S10, the collected remote sensing images are regionally fused and regionally segmented according to preset sizes to obtain multiple fire hazard areas to be analyzed.

[0070] In this step, the collected remote sensing images are regionally fused and regionally segmented according to a preset size to obtain multiple fire hazard areas to be analyzed. For example, in order to more accurately analyze the degree of relevant fire hazards in forest or grassland areas, multiple types of remote sensing images can be collected, including multispectral images, hyperspectral images, thermal infrared images, and radar images. The collected related images are regionally fused, and the collected images are collectively corrected using ground control points (GCP) and high-precision DEM data to ensure spatial alignment of images from different sources.

[0071] After that, the region is divided into blocks, that is, the entire study area is evenly divided according to the preset block size to form multiple unit modules of the same area. Specifically, the image can be divided into a series of grids, the size of each grid matches the set block size, and then the image data in each grid is extracted one by one to ensure that each unit module contains complete surface information. In this way, large-scale remote sensing images are converted into multiple independent small areas, which facilitates more efficient data processing and analysis.

[0072] In step S20, the regional dryness index of the fire hazard area to be analyzed is obtained.

[0073] In this step, the regional dryness index of the area to be analyzed for fire hazards is obtained. For example, the normalized vegetation coefficient of the area to be analyzed for fire hazards can be obtained first, and then the mean time required for the soil thermal radiation of the area to be analyzed to change by one degree can be obtained, and then the regional dryness index of the area to be analyzed for fire hazards can be obtained based on the normalized vegetation coefficient and the mean time.

[0074] In step S30, the regional fuel index of the fire hazard area to be analyzed is obtained.

[0075] In this step, the regional fuel index of the area to be analyzed for fire hazards is obtained. Exemplarily, the regional area of ​​the area to be analyzed for fire hazards can be obtained first, and then the number of vegetation types, the proportion of vegetation, and the combustion efficiency of vegetation in the area to be analyzed for fire hazards can be obtained, and then the regional proportion of dry vegetation areas in the area to be analyzed for fire hazards can be obtained, where the dry vegetation area is an area where the normalized vegetation coefficient is less than a set threshold, and finally, based on the regional area, the number of vegetation types, the proportion of vegetation, the combustion efficiency of vegetation, and the regional proportion of dry vegetation areas, the regional fuel index of the area to be analyzed for fire hazards can be obtained.

[0076] In step S40, the degree of fluctuation of regional combustion conditions of the fire hazard to be analyzed area is obtained according to the regional dryness index and the regional fuel index.

[0077] In this step, the degree of fluctuation of the regional combustion conditions of the fire hazard to be analyzed area is obtained according to the regional dryness index and the regional fuel index. Exemplarily, the regional dryness index difference coefficient of the fire hazard to be analyzed area can be obtained according to the degree of difference in the regional dryness index between the fire hazard to be analyzed area and the surrounding area, and then the regional fuel index difference coefficient of the fire hazard to be analyzed area can be obtained according to the degree of difference in the regional fuel index between the fire hazard to be analyzed area and the surrounding area, and then the regional fuel index difference coefficient of the fire hazard to be analyzed area can be obtained according to the regional dryness index difference coefficient and the regional fuel index difference coefficient.

[0078] In step S50, the degree of regional flammability conditions of the fire hazard to be analyzed area is obtained according to the regional dryness index, the regional fuel index and the degree of fluctuation of the regional combustion conditions.

[0079] In this step, the degree of regional combustible conditions in the area to be analyzed for fire hazards is obtained based on the regional dryness index, the regional fuel index, and the degree of fluctuation of the regional combustion conditions. For example, the sum of the regional dryness index and the regional fuel index can be obtained first, and then the ratio of the sum to the degree of fluctuation of the regional combustion conditions can be obtained, and then the ratio can be normalized to obtain the degree of regional combustible conditions in the area to be analyzed for fire hazards.

[0080] In step S60, the regional fire risk induction coefficient of the fire hazard to be analyzed area is obtained based on the historical thermal radiation data of the fire hazard to be analyzed area.

[0081] In this step, the regional fire risk induction coefficient of the fire hazard to be analyzed area is obtained based on the historical thermal radiation data of the fire hazard to be analyzed area. Exemplarily, the first mean value of the difference between the current thermal radiation degree of the fire hazard to be analyzed area and the historical thermal radiation degree can be obtained first, and then the second mean value of the difference between the current thermal radiation degree of the fire hazard to be analyzed area and the current thermal radiation degree of the surrounding area can be obtained, and then the regional fire risk induction coefficient of the fire hazard to be analyzed area can be obtained based on the first mean value and the second mean value.

[0082] In step S70, the regional fire hazard degree coefficient of the area to be analyzed for fire hazards is obtained according to the degree of flammable conditions in the area and the regional fire risk induction coefficient.

[0083] In this step, the regional fire hazard degree coefficient of the area to be analyzed for fire hazards is obtained according to the degree of regional combustible conditions and the regional fire hazard induction coefficient. Exemplarily, the regional fire hazard degree coefficient of the area to be analyzed for fire hazards can be obtained according to the product of the degree of regional combustible conditions and the regional fire hazard induction coefficient.

[0084] In summary, the embodiment of the present application provides a forest and grassland fire hazard identification method based on remote sensing images, the method comprising: regional fusion of the collected remote sensing images and regional segmentation according to a preset size to obtain multiple fire hazard areas to be analyzed; obtain the regional dryness index of the fire hazard area to be analyzed; obtain the regional fuel index of the fire hazard area to be analyzed; obtain the regional combustion condition fluctuation degree of the fire hazard area to be analyzed according to the regional dryness index and the regional fuel index; obtain the regional combustible condition degree of the fire hazard area to be analyzed according to the regional dryness index, the regional fuel index and the regional combustion condition fluctuation degree; obtain the regional fire risk induction coefficient of the fire hazard area to be analyzed according to the historical thermal radiation data of the fire hazard area to be analyzed; obtain the regional fire hazard degree coefficient of the fire hazard area to be analyzed according to the regional combustible condition degree and the regional fire risk induction coefficient. Since the dryness index, fuel index, combustion condition fluctuation degree, combustible condition degree, fire risk induction coefficient and other parameters are comprehensively considered to evaluate and calculate the regional fire hazard degree coefficient, the embodiment of the present application can significantly improve the accuracy of regional fire hazard identification and evaluation.

[0085] Figure 2 FIG. 1 is a flow chart showing a method for obtaining a regional dryness index of a fire hazard to be analyzed region according to an exemplary embodiment. Figure 2 As shown, the step of obtaining the regional dryness index of the fire hazard to be analyzed area may include the following steps:

[0086] In step S201, the normalized vegetation coefficient of the fire hazard area to be analyzed is obtained.

[0087] In this step, the normalized vegetation coefficient DNVI of the area to be analyzed for fire hazards is obtained. Exemplarily, the data of visible light and near-infrared bands of the area to be analyzed for fire hazards can be collected through multispectral imaging technology, and the normalized vegetation coefficient DNVI can be obtained by analyzing the reflectivity of the near-infrared band and the reflectivity of the red light band of the area. The specific calculation method is a prior art and will not be repeated here. When DNVI is close to 1, it means that the vegetation growth in the area is good and the relative dryness of the area is low; when NDVI is close to 0 or is a negative number, it means that the vegetation in the area is sparse or the soil is dry.

[0088] In step S202, the average time required for the soil thermal radiation of the fire hazard to be analyzed area to change by one degree is obtained.

[0089] In this step, the average time required for the soil thermal radiation in the fire hazard analysis area to change by one degree is obtained. in, Δf is the time required for the soil thermal radiation of the fire hazard analysis area i to change by one degree in the vth measurement, i,v represents the vth event of soil thermal radiation change of one degree in the fire hazard area i to be analyzed, v represents the vth measurement, MD i is the total number of measurements of the fire hazard area i to be analyzed. For example, the temperature variation of the ground surface is detected by thermal infrared imaging technology. When the area is relatively dry, the temperature variation of the soil is large. When the area is relatively moist, the temperature variation of the soil is relatively weak due to the high moisture content. Calculate the average time required for the soil thermal radiation in the area to change by one degree The shorter the time is, the greater the temperature change is and the less water content in the regional soil is.

[0090] In step S203, the regional dryness index of the fire hazard to be analyzed area is obtained according to the normalized vegetation coefficient and the mean time.

[0091] In this step, the normalized vegetation coefficient DNVI of the fire hazard area i to be analyzed is i and mean time Get the regional dryness index KI of the fire hazard area i to be analyzed i For example, the regional dryness index KI of the fire hazard to be analyzed region i is i It can be obtained by the following formula:

[0092]

[0093] Among them, norm is the normalization processing.

[0094] Figure 3 FIG. 1 is a flow chart showing a method for obtaining a regional fuel index of a fire hazard to be analyzed region according to an exemplary embodiment. Figure 3 As shown, obtaining the regional fuel index of the fire hazard to be analyzed area may include the following steps:

[0095] In step S301, the area of ​​the fire hazard to be analyzed region is obtained.

[0096] In this step, the area MS of the fire hazard area i to be analyzed is obtained. i .

[0097] In step S302, the number of types and proportion of vegetation in the fire hazard to be analyzed area, as well as the combustion efficiency of the vegetation are obtained.

[0098] In this step, the number of vegetation types MR in the fire hazard analysis area i is obtained. i , vegetation proportion KI i,r And the burning efficiency of vegetation RX i,r For example, different vegetation types will show unique reflection or absorption characteristics in the spectral dimension, and they can be classified accordingly to obtain the number of vegetation types MR in the fire hazard to be analyzed area i i The vegetation proportion KI can be obtained by analyzing the reflection intensity of the corresponding vegetation spectrum band. i,r , K.I. i,r Represents the proportion of the rth vegetation in the i-th area. The combustion calorific value, combustion speed and combustion efficiency of different vegetation can be obtained through relevant experimental data, and then the combustion efficiency RX of the corresponding vegetation can be obtained by multiplying the three. i,r , RX i,r represents the burning efficiency of the rth vegetation in the ith area.

[0099] In step S303, the area ratio of the dry vegetation area in the fire hazard to be analyzed area is obtained, and the dry vegetation area is an area whose normalized vegetation coefficient is less than a set threshold.

[0100] In this step, the area ratio of dry vegetation area in the fire hazard area i to be analyzed is obtained. The dry vegetation area is the normalized difference vegetation index DNVI i Areas that are smaller than a set threshold (e.g. 0.3). i<0.3 means the normalized vegetation coefficient is less than 0.3. It represents the proportion of areas with normalized vegetation coefficient less than 0.3, that is, the proportion of dry vegetation areas in the fire hazard area i to be analyzed.

[0101] In step S304, the regional fuel index of the area to be analyzed for fire hazards is obtained based on the area, the number of vegetation types, the vegetation ratio, the combustion efficiency of the vegetation, and the regional ratio of the dry vegetation area.

[0102] In this step, according to the area MS i , the number of vegetation species MR i , vegetation proportion KI i,r , the burning efficiency of vegetation RX i,r and the percentage of dry vegetation areas Get the regional fuel index RL of the fire hazard area i to be analyzed i For example, the regional fuel index RL of the fire hazard to be analyzed area i i It can be obtained by the following formula:

[0103]

[0104] Among them, RL i represents the regional fuel index of the fire hazard area i to be analyzed, MR i The number of vegetation types in the fire hazard area i to be analyzed, MS i represents the area of ​​fire hazard area i to be analyzed, KI i,r represents the proportion of the rth vegetation in the fire hazard to be analyzed area i, RX i,r represents the combustion efficiency of the rth vegetation in the fire hazard analysis area i, It indicates the area proportion of dry vegetation area within the fire hazard to be analyzed area i.

[0105] For the spontaneous combustion of forests or grasslands, in addition to the necessary dryness conditions, the types and quantities of combustible fuels in the area are also crucial. Therefore, hyperspectral imaging technology can be used to finely distinguish the vegetation types in the area, and determine its combustion coefficient based on the evaluation of the number of each type of vegetation in the area and the corresponding vegetation combustion data provided in relevant academic literature. In response to this situation, the combustion energy per unit area is first calculated based on the distribution ratio of each type of vegetation and its combustion coefficient under the current environmental conditions. Subsequently, the threshold method is used to segment the dead vegetation based on the vegetation coefficient in the image metadata collected in the area, and then the proportion of dead vegetation in the current area is obtained, and the regional fuel index RL is calculated accordingly. i .

[0106] By analyzing the proportion and burning efficiency of various types of vegetation in area i, the total burning efficiency of all vegetation in area i is obtained. Divide by the area to get the combustion efficiency intensity per unit area Then, the regional vegetation coefficient is delineated according to the threshold method to obtain the regional proportion of dry vegetation area in region i. Finally, the fuel index RL per unit area of ​​the region i can be obtained: i .

[0107] Figure 4 FIG. 1 is a flow chart showing a method for obtaining the degree of fluctuation of regional combustion conditions in a fire hazard to be analyzed area according to an exemplary embodiment. Figure 4 As shown, obtaining the degree of fluctuation of regional combustion conditions in the fire hazard to be analyzed area may include the following steps:

[0108] In step S401, the regional dryness index difference coefficient of the fire hazard area to be analyzed is obtained according to the difference degree of the regional dryness index between the fire hazard area to be analyzed and the surrounding area.

[0109] In this step, according to the difference degree of the regional dryness index between the fire hazard area to be analyzed and the surrounding area, the regional dryness index difference coefficient of the fire hazard area to be analyzed is obtained. Exemplarily, the number of areas surrounding the fire hazard area to be analyzed can be obtained first, and then the dryness index gradient of the fire hazard area to be analyzed and the surrounding area, and the dryness gradient mean of the dryness index gradient of all areas can be obtained, and then the overall dryness index of all areas can be obtained, and finally, according to the number of areas, the dryness index gradient, the dryness gradient mean and the overall dryness index, the regional dryness index difference coefficient of the fire hazard area to be analyzed can be obtained.

[0110] In step S402, the regional fuel index difference coefficient of the fire hazard area to be analyzed is obtained according to the difference degree of the regional fuel index between the fire hazard area to be analyzed and the surrounding area.

[0111] In this step, the regional fuel index difference coefficient of the fire hazard area to be analyzed is obtained according to the difference degree of the regional fuel index between the fire hazard area to be analyzed and the surrounding areas. Exemplarily, the number of areas surrounding the fire hazard area to be analyzed can be obtained first, and then the fuel index gradient of the fire hazard area to be analyzed and the surrounding areas, and the fuel gradient mean of the fuel index gradient of all areas can be obtained, and then the overall fuel index of all areas can be obtained, and finally the regional fuel index difference coefficient of the fire hazard area to be analyzed can be obtained according to the number of areas, the fuel index gradient, the fuel gradient mean and the overall fuel index.

[0112] In step S403, the degree of fluctuation of the regional combustion conditions of the fire hazard to be analyzed area is obtained according to the regional dryness index difference coefficient and the regional fuel index difference coefficient.

[0113] In this step, the degree of fluctuation of the regional combustion conditions in the area to be analyzed for fire hazards is obtained according to the regional dryness index difference coefficient and the regional fuel index difference coefficient. For example, the degree of fluctuation of the regional combustion conditions in the area to be analyzed for fire hazards can be obtained according to the mean of the regional dryness index difference coefficient and the regional fuel index difference coefficient.

[0114] Forests and grasslands are highly connected ecological systems, and the internal vegetation types and energy flow cannot be ignored. Therefore, the pre-combustion conditions of a region not only depend on its own characteristics, but are also deeply affected by the surrounding areas and even the overall ecological environment.

[0115] In particular, when a region has high combustion conditions, but its surrounding environment has a significant gradient difference, this difference may weaken the combustion conditions in the region. Therefore, it is necessary to further analyze the gradient differences between the region and the surrounding environment to assess the degree of volatility of the combustion conditions in the region.

[0116] Figure 5 FIG. 1 is a flow chart showing a method for obtaining a regional dryness index difference coefficient of a fire hazard to be analyzed region according to an exemplary embodiment. Figure 5 As shown, obtaining the regional dryness index difference coefficient of the fire hazard to be analyzed area may include the following steps:

[0117] In step S4011, the number of areas surrounding the fire hazard area to be analyzed is obtained.

[0118] In this step, the number of areas MI surrounding the fire hazard area i to be analyzed is obtained. i .

[0119] In step S4012, the dryness index gradients of the fire hazard to-be-analyzed area and the surrounding area, and the dryness gradient mean of the dryness index gradients of all areas are obtained.

[0120] In this step, the dryness index gradient of the fire hazard area i to be analyzed and the surrounding area m is obtained. and the mean aridity gradient of the aridity index gradient for all regions in, Indicates the dryness gradient between the ith region and the adjacent mth region; Represents the mean of the aridity index gradients for all current regions.

[0121] In step S4013, the overall dryness index of all regions is obtained.

[0122] In this step, the overall dryness index KI of all current areas is obtained.

[0123] In step S4014, the regional dryness index difference coefficient of the fire hazard to be analyzed area is obtained according to the number of areas, the dryness index gradient, the dryness gradient mean and the overall dryness index.

[0124] In this step, according to the number of regions MI i , dryness index gradient Mean dryness gradient As well as the overall dryness index KI, obtain the regional dryness index difference coefficient of the fire hazard area i to be analyzed For example, the regional dryness index difference coefficient of the fire hazard to be analyzed area i is It can be obtained by the following formula:

[0125]

[0126] in, is the regional dryness index difference coefficient of the fire hazard area i to be analyzed, MI i is the number of areas surrounding the fire hazard area i to be analyzed, is the dryness index gradient between the fire hazard area i and the surrounding area m, is the mean aridity gradient of the aridity index gradient of all current regions, KI i is the dryness index of the ith region, KI is the overall dryness index of all current regions, and norm is the normalization process.

[0127] It indicates the difference in dryness index between the current area i and the surrounding area m. The larger the ratio is, the greater the difference in dryness between the current area i and the surrounding area m.

[0128] Indicates the difference in dryness between region i and the overall ecological environment.

[0129] Figure 6 FIG. 1 is a flow chart showing a method for obtaining a regional fuel index difference coefficient of a fire hazard to be analyzed region according to an exemplary embodiment. Figure 6 As shown, the obtaining of the regional fuel index difference coefficient of the fire hazard to be analyzed area may include the following steps:

[0130] In step S4021, the number of areas surrounding the fire hazard area to be analyzed is obtained.

[0131] In this step, the number of areas MI surrounding the fire hazard area i to be analyzed is obtained. i .

[0132] In step S4022, the fuel index gradients of the fire hazard area to be analyzed and the surrounding area, as well as the fuel gradient mean of the fuel index gradients of all areas are obtained.

[0133] In this step, the fuel index gradient of the fire hazard area i to be analyzed and the surrounding area m is obtained. and the fuel gradient mean of the fuel index gradients of all regions

[0134] In step S4023, the overall fuel index of all areas is obtained.

[0135] In this step, the overall fuel index RL of all current regions is obtained.

[0136] In step S4024, the regional fuel index difference coefficient of the fire hazard area to be analyzed is obtained based on the number of areas, the fuel index gradient, the fuel gradient mean and the overall fuel index.

[0137] In this step, according to the number of regions MI i , fuel index gradient Fuel gradient mean As well as the overall fuel index RL, obtain the regional fuel index difference coefficient of the fire hazard area i to be analyzed For example, the regional fuel index difference coefficient of the fire hazard to be analyzed area i is It can be obtained by the following formula:

[0138]

[0139] in, is the regional fuel index difference coefficient of the fire hazard area i to be analyzed, MI i is the number of areas surrounding the fire hazard area i to be analyzed, is the fuel index gradient between the fire hazard area i and the surrounding area m, is the fuel gradient mean of the fuel index gradient of all current regions, RL i is the fuel index of the ith region, RL is the overall fuel index of all current regions, and norm is the normalization process.

[0140] It indicates the difference in fuel index between the current area i and the surrounding area m. The larger the ratio is, the greater the difference in fuel index between the current area i and the surrounding area m.

[0141] Indicates the difference degree of fuel index between region i and the overall ecological environment.

[0142] Figure 7 FIG. 1 is a flow chart showing another method for obtaining the degree of fluctuation of regional combustion conditions in a fire hazard to be analyzed area according to an exemplary embodiment. Figure 7 As shown, the obtaining of the degree of fluctuation of the regional combustion conditions of the fire hazard to be analyzed area may include the following steps:

[0143] In step S4031, the degree of fluctuation of the regional combustion conditions of the fire hazard to be analyzed area is obtained according to the average of the regional dryness index difference coefficient and the regional fuel index difference coefficient.

[0144] In this step, the regional dryness index difference coefficient and regional fuel index difference coefficient The mean of the fire hazard to be analyzed area i is obtained to obtain the fluctuation degree of regional combustion conditions WJ i .Right now

[0145]

[0146] Figure 8 FIG. 1 is a flow chart showing a method for obtaining the degree of regional flammability conditions in a fire hazard to be analyzed area according to an exemplary embodiment. Figure 8 As shown, obtaining the degree of regional flammability conditions of the fire hazard to be analyzed area may include the following steps:

[0147] In step S501, the sum of the regional dryness index and the regional fuel index is obtained.

[0148] In this step, the regional dryness index KI is obtained. i With regional fuel index RL i The sum of KI i +RL i .

[0149] In step S502, the ratio of the sum value to the degree of fluctuation of the combustion conditions in the area is obtained.

[0150] In this step, the sum value KI is obtained i +RL i The degree of fluctuation of regional combustion conditions WJ i Ratio

[0151] In step S503, the ratio is normalized to obtain the degree of flammability conditions in the area to be analyzed for fire hazards.

[0152] In this step, the ratio Normalization processing is performed to obtain the degree of flammable conditions TJ of the fire hazard area i to be analyzed i For example, the degree of flammability of the fire hazard area i to be analyzed is TJ i It can be obtained by the following formula:

[0153]

[0154] Among them, norm is the normalization processing.

[0155] The higher the dryness index of area i, the larger the fuel index, and the lower the fluctuation of combustion conditions, the higher the degree of fire hazard and combustible conditions of area i.

[0156] Fig. 9 FIG. 1 is a flow chart showing a method for obtaining a regional fire risk induction coefficient of a fire hazard to be analyzed area according to an exemplary embodiment. Fig. 9 As shown, the acquisition of the regional fire risk induction coefficient of the fire hazard to be analyzed area may include the following steps:

[0157] In step S601, a first mean value of the difference between the current thermal radiation level and the historical thermal radiation level of the fire hazard to be analyzed area is obtained.

[0158] In this step, the first mean value of the difference between the current thermal radiation degree and the historical thermal radiation degree of the fire hazard area i to be analyzed is obtained. Among them, MY i represents the number of thermal radiation detections in the same historical period of the i-th region, w i Indicates the current thermal radiation level of area i, w i,p Indicates the thermal radiation detection value of the current region i in the pth detection history stage, │w i -w i,p │ represents the difference between the current thermal radiation level of region i and the thermal radiation level at the pth time in history.

[0159] It represents the cumulative average of the difference between the current thermal radiation level and the historical thermal radiation level of the fire hazard area i to be analyzed. The larger the result value is, the higher the thermal radiation of the corresponding area i is, and the greater the possibility of inducing fire.

[0160] In step S602, a second mean value of the difference between the current thermal radiation level of the fire hazard to-be-analyzed area and the current thermal radiation level of the surrounding area is obtained.

[0161] In this step, the second mean value of the difference between the current thermal radiation degree of the fire hazard to be analyzed area i and the current thermal radiation degree of the surrounding area m is obtained. Among them, MI i represents the number of adjacent regions around the ith region, w i Indicates the current thermal radiation level of area i, w i,m Indicates the degree of thermal radiation of the mth area around area i, │w i -w i,m │ represents the difference in thermal radiation between area i and surrounding area m.

[0162] It represents the cumulative average of the difference in thermal radiation between area i and surrounding area m. The larger the result is, the greater the thermal radiation of area i is compared with the surrounding areas, and the greater the possibility of fire in area i.

[0163] In step S603, the regional fire risk induction coefficient of the fire hazard to be analyzed area is obtained according to the first mean value and the second mean value.

[0164] In this step, according to the first mean and the second mean Get the regional fire risk induction coefficient YF of the fire hazard to be analyzed area i i For example, the regional fire risk induction coefficient YF of the fire hazard to be analyzed area i is i It can be obtained by the following formula:

[0165]

[0166] Among them, norm is the normalization processing.

[0167] Fig.10 FIG. 1 is a flow chart showing a method for obtaining a regional fire hazard degree coefficient of a fire hazard to be analyzed region according to an exemplary embodiment. Fig.10 As shown, the obtaining of the regional fire hazard degree coefficient of the fire hazard to be analyzed area may include the following steps:

[0168] In step S701, the regional fire hazard degree coefficient of the area to be analyzed for fire hazards is obtained according to the product of the degree of flammable conditions in the area and the fire risk induction coefficient of the area.

[0169] In this step, according to the degree of combustible conditions in the area, i and regional fire risk induction factor YFi The product of the fire hazard to be analyzed in area i is obtained to obtain the regional fire hazard degree coefficient P i For example, the regional fire hazard degree coefficient P of the fire hazard to be analyzed region i is i It can be obtained by the following formula:

[0170] P i =norm(YF i ×TJ i ) Formula 7

[0171] Among them, norm is the normalization processing.

[0172] The higher the fire hazard induction coefficient of area i, the higher the degree of fire flammable conditions, and the greater the fire hazard level of area i.

[0173] For areas with a high degree of fire hazard, we can strengthen remote sensing monitoring of the area and dispatch drones to the area to collect high-density thermal infrared radiation images. Once signs of fire are found, we can immediately take corresponding rescue measures to quickly and effectively control the source of the fire, ensure the safety of forests and grasslands, and reduce losses.

[0174] The present application also provides a computer-readable storage medium on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the forest and grassland fire hazard identification method based on remote sensing images provided in the present application are implemented.

[0175] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable electronic device, and the computer program has a code portion for executing the above-mentioned forest and grassland fire hazard identification method based on remote sensing images when executed by the programmable electronic device.

[0176] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for identifying forest and grassland fire hazards based on remote sensing images, characterized in that: The method comprises: The collected remote sensing images are regionally fused and segmented according to preset sizes to obtain multiple fire hazard areas to be analyzed; Obtaining the regional dryness index of the fire hazard area to be analyzed; Obtaining a regional fuel index for the area to be analyzed for fire hazards; According to the regional dryness index and the regional fuel index, obtaining the degree of fluctuation of regional combustion conditions in the fire hazard to be analyzed area; According to the regional dryness index, the regional fuel index and the regional combustion condition fluctuation degree, obtaining the regional combustible condition degree of the fire hazard to be analyzed area; Obtaining a regional fire risk induction coefficient of the fire hazard to be analyzed area according to historical thermal radiation data of the fire hazard to be analyzed area; According to the degree of combustible conditions in the area and the fire risk induction coefficient in the area, a regional fire hazard degree coefficient of the area to be analyzed for fire hazards is obtained; The step of obtaining the regional dryness index of the fire hazard to be analyzed area includes: Obtaining a normalized vegetation coefficient of the fire hazard area to be analyzed; Obtain the average time required for the soil thermal radiation in the fire hazard to be analyzed area to change by one degree; According to the normalized vegetation coefficient and the mean time, obtaining the regional dryness index of the fire hazard to be analyzed area; The step of obtaining the regional fuel index of the fire hazard to be analyzed area includes: Obtain the area of ​​the fire hazard to be analyzed region; Obtaining the number of types and proportion of vegetation in the fire hazard to be analyzed area, as well as the combustion efficiency of the vegetation; Obtaining the area ratio of the dry vegetation area in the fire hazard to be analyzed area, wherein the dry vegetation area is an area where the normalized vegetation coefficient is less than a set threshold; Obtaining the regional fuel index of the area to be analyzed for fire hazards according to the area, the number of types of vegetation, the proportion of vegetation, the combustion efficiency of vegetation, and the regional proportion of dry vegetation areas; The step of obtaining the degree of fluctuation of regional combustion conditions in the fire hazard to be analyzed area includes: Obtaining a regional dryness index difference coefficient of the fire hazard to be analyzed area according to the difference between the regional dryness index of the fire hazard to be analyzed area and the surrounding area; Obtaining a regional fuel index difference coefficient of the fire hazard to be analyzed area according to the difference degree of the regional fuel index between the fire hazard to be analyzed area and the surrounding area; According to the regional dryness index difference coefficient and the regional fuel index difference coefficient, the degree of fluctuation of the regional combustion conditions of the fire hazard to be analyzed area is obtained.

2. The method for identifying forest and grassland fire hazards based on remote sensing images according to claim 1, characterized in that: The step of obtaining the regional dryness index difference coefficient of the fire hazard to be analyzed area includes: Obtain the number of areas surrounding the fire hazard area to be analyzed; Obtaining the dryness index gradients of the fire hazard to-be-analyzed area and the surrounding area, and the dryness gradient mean of the dryness index gradients of all areas; Get the overall dryness index for all areas; The regional dryness index difference coefficient of the fire hazard area to be analyzed is obtained according to the number of areas, the dryness index gradient, the dryness gradient mean and the overall dryness index.

3. The method for identifying forest and grassland fire hazards based on remote sensing images according to claim 1, characterized in that: The step of obtaining the regional fuel index difference coefficient of the fire hazard to be analyzed area includes: Obtain the number of areas surrounding the fire hazard area to be analyzed; Obtaining the fuel index gradients of the fire hazard to-be-analyzed area and the surrounding area, and the fuel gradient mean of the fuel index gradients of all areas; Get the overall fuel index for all regions; The regional fuel index difference coefficient of the fire hazard area to be analyzed is obtained according to the number of areas, the fuel index gradient, the fuel gradient mean and the overall fuel index.

4. The method for identifying forest and grassland fire hazards based on remote sensing images according to claim 1, characterized in that: The step of obtaining the degree of fluctuation of the regional combustion conditions of the fire hazard to be analyzed area includes: According to the average of the regional dryness index difference coefficient and the regional fuel index difference coefficient, the degree of fluctuation of the regional combustion conditions of the fire hazard to be analyzed area is obtained.

5. The method for identifying forest and grassland fire hazards based on remote sensing images according to claim 1, characterized in that: The step of obtaining the degree of regional flammability conditions of the fire hazard to be analyzed area includes: Obtaining the sum of the regional dryness index and the regional fuel index; Obtaining a ratio of the sum value to the degree of fluctuation of the combustion conditions in the area; The ratio is normalized to obtain the degree of flammability conditions in the area to be analyzed for fire hazards.

6. The method for identifying forest and grassland fire hazards based on remote sensing images according to claim 1, characterized in that: The step of obtaining the regional fire risk induction coefficient of the fire hazard to be analyzed area includes: Obtain a first mean value of the difference between the current thermal radiation degree and the historical thermal radiation degree of the fire hazard to be analyzed area; Obtaining a second mean value of the difference between the current thermal radiation level of the fire hazard to-be-analyzed area and the current thermal radiation level of the surrounding area; The regional fire risk induction coefficient of the fire hazard to be analyzed area is obtained according to the first mean value and the second mean value.

7. The method for identifying forest and grassland fire hazards based on remote sensing images according to claim 1, characterized in that: The step of obtaining the regional fire hazard degree coefficient of the fire hazard to be analyzed area includes: The regional fire hazard degree coefficient of the area to be analyzed for the fire hazard is obtained according to the product of the degree of flammable conditions in the area and the fire risk induction coefficient of the area.

Images