A refined forest fire risk assessment method integrating multi-source data

Abstract

The present invention discloses a refined forest fire risk assessment method integrating multi-source data, including: determining the key influencing factors causing forest fire risks; conducting in-depth diagnostic analysis and clustering analysis on the key influencing factors to obtain 12 forest fire risk influencing factors; calculating the weight of each forest fire risk influencing factor; determining the calculation principle according to the correlation between each forest fire risk influencing factor to establish a refined forest risk assessment model, and at the same time determining the extraction forecast scale; referring to the preset forest and grassland emergency plan, the preset forest fire risk level forecast and response work management method, and the preset forest fire classification response procedure, analyzing the historical fire data within the preset years through the refined forest risk assessment model to divide the risk levels of refined forest fire risks. This method realizes the automatic release of forecast level information, and the fire risk forecast results meet the refined requirements, with the accuracy of the forecast, and can provide reference for forest fire prevention work.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing science and technology, and particularly to a refined forest fire risk assessment method integrating multi-source data. Background Art

[0002] In recent years, climate warming and increasing extreme weather have led to a global high-incidence period of forest fires. Forest fires occur frequently around the world. Doing a good job in forest fire prevention and extinguishment is of great significance for ensuring national security, forest area security and personal property security.

[0003] Forest fire risk assessment, that is, the possibility of a forest fire occurring in a certain area at a certain time, is an important basis in forest fire management work. Accurate prediction and forecasting of forest fire risk levels can provide scientific support for forest fire prevention work. At present, forest fire risk assessment in China is mainly carried out through meteorological conditions. Niu Ruoyun et al. summarized several common forest fire risk meteorological models, which have been widely used in forest fire risk meteorological services across the country. However, simply judging forest fire risk through meteorological conditions ignores factors such as terrain, dryness and wetness of combustibles, type characteristics of combustibles and human activities, and cannot predict and analyze the occurrence behavior of forest fires.

[0004] In view of the above situation, various methods have been proposed in related technologies. For example, Cao Shanshan et al. established a small-scale forest fire risk assessment model with the Jiulong Mountain in Beijing as the research area, and Lin Zhiqiang et al. established a method for calculating the forest fire risk level in Tibet based on GIS and RS. However, these methods cannot conduct refined and more accurate analysis of forest fire risk. Summary of the Invention

[0005] The present invention solves the technical problem of how to construct a refined forest fire risk assessment model that can determine the weight values of influencing factors and realize the automatic release of forecast level information.

[0006] To this end, the purpose of the present invention is to propose a refined forest fire risk assessment method integrating multi-source data. The fire risk forecast result obtained by this method meets the refined requirements and at the same time has the accuracy of the forecast.

[0007] To achieve the above object, an embodiment of the present invention provides a refined forest fire risk assessment method that integrates multi-source data, including the following steps: Step S1, determine the key influencing factors causing forest fire risks, where the key influencing factors include meteorological factors, combustible factors, terrain factors, and social factors; Step S2, conduct in-depth diagnostic analysis and clustering analysis on the key influencing factors to obtain 12 forest fire risk influencing factors; Step S3, calculate the weights of each forest fire risk influencing factor; Step S4, determine the calculation principle based on the correlation between each forest fire risk influencing factor to establish a refined forest risk assessment model. At the same time, determine the forecasting scale for extracting the refined forest risk assessment model based on multi-source basic data; Step S5, refer to the preset forest and grassland emergency plan, the preset forest fire risk level forecasting and response work management method, and the preset forest fire classification response procedure, and analyze the historical fire data within a preset number of years through the refined forest risk assessment model to divide the risk levels of refined forest fire risks.

[0008] The refined forest fire risk assessment method that integrates multi-source data in the embodiment of the present invention analyzes the causes of forest fires, selects 4 major categories of forest fire risk factors, including 12 forest fire risk influencing factors, determines the influencing factors of the model and the weight values of each influencing factor through methods such as diagnostic analysis, principal component analysis, and expert scoring, constructs a refined forest fire risk assessment model, and scientifically classifies the forecasting results to achieve the automated release of forecasting level information. Among them, the fire risk forecasting results meet the refined requirements and have forecasting accuracy, which can provide reference for forest fire prevention work and is of great significance for fulfilling the functions of fire prevention and extinguishment, protecting forest resources, and the safety of people's lives and property.

[0009] In addition, the refined forest fire risk assessment method that integrates multi-source data according to the above embodiment of the present invention may further have the following additional technical features:

[0010] Further, in an embodiment of the present invention, in Step S1, through the historical fire data within a preset number of years, diagnose and analyze the historical fire data through the diagnostic analysis method to determine the key influencing factors causing forest fires and classify the key influencing factors.

[0011] Further, in an embodiment of the present invention, the 12 forest fire risk influencing factors are the daily maximum temperature influencing factor, the daily minimum relative humidity influencing factor, the daily maximum wind speed influencing factor, the consecutive non-precipitation days influencing factor, the forest type influencing factor, the forest canopy density influencing factor, the forest drought degree influencing factor, the altitude influencing factor, the slope influencing factor, the slope aspect influencing factor, the distribution of forest area residents influencing factor, and the distribution of forest area roads influencing factor.

[0012] Further, in an embodiment of the present invention, the weight value of the forest drought degree influencing factor is represented by the temperature vegetation drought index (TVDI) retrieved from the MODIS satellite data synthesized every eight days, and the calculation formula is:

[0013]

[0014] where T smax and T smin are the maximum and minimum values of the surface temperature when the vegetation normalized difference index (NDVI) is equal to a certain specific value, respectively, and T s is the surface temperature of any pixel.

[0015] Further, in an embodiment of the present invention, the weight value of the forest canopy density influencing factor is represented by the canopy density retrieved from the images of the Jilin-1 spectral satellite in August or September, and the calculation formula is:

[0016]

[0017] where CD is the canopy density, NDVI is the vegetation normalized difference index, NDVI min is the minimum vegetation normalized difference index, and NDVI max is the minimum vegetation normalized difference index.

[0018] Further, in an embodiment of the present invention, the distances from roads and residential areas to the forest are obtained by interpreting the high-resolution satellite remote sensing images of Jilin-1, and the weight values of the influencing factors of the distribution of residential areas in the forest area and the influencing factors of the distribution of roads in the forest area are determined by the expert scoring method.

[0019] Further, in an embodiment of the present invention, the terrain data of the preset key area is obtained by three-dimensional reconstruction of the DSM data of the Jilin-1 video satellite, and the terrain data of other areas except the preset key area is obtained by the publicly available 12.5-meter terrain data.

[0020] Further, in an embodiment of the present invention, based on the terrain data, the weight values of the elevation influencing factor, the slope influencing factor, and the aspect influencing factor are determined by the expert scoring method respectively.

[0021] Further, in an embodiment of the present invention, the refined forest fire risk assessment model is:

[0022] FRID = 0.48*A + 0.35*B + 0.15*C + 0.1*D

[0023] where A is the meteorological factor, B is the vegetation factor, C is the social factor, and D is the terrain factor.

[0024] Further, in an embodiment of the present invention, the forecasting scale is a forest fire risk level forecast with a spatial resolution of 1 km, automatically released daily for the next 24 hours, 48 hours, 72 hours, and the next week.

[0025] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0027] Figure 1 is a flowchart of a refined forest fire risk assessment method that integrates multi-source data according to an embodiment of the present invention;

[0028] Figure 2 is a warning execution diagram of a refined forest fire risk assessment method that integrates multi-source data according to an embodiment of the present invention;

[0029] Figure 3 is a schematic diagram of the forest fire risk level division in Beijing on May 10, 2021 according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0031] The refined forest fire risk assessment method that integrates multi-source data according to an embodiment of the present invention will be described below with reference to the accompanying drawings.

[0032] Figure 1 is a flowchart of a refined forest fire risk assessment method that integrates multi-source data according to an embodiment of the present invention.

[0033] Figure 2 is a warning execution diagram of a refined forest fire risk assessment method that integrates multi-source data according to an embodiment of the present invention.

[0034] As Figure 1 and 2 shown, the refined forest fire risk assessment method includes the following steps:

[0035] In step S1, determine the key influencing factors that cause forest fire risk. Among them, the key influencing factors include meteorological factors, combustible factors, terrain factors, and social factors.

[0036] Further, in an embodiment of the present invention, in step S1, historical fire data over a preset number of years is used to perform diagnostic analysis on the historical fire data through a diagnostic analysis method, to determine the key influencing factors causing forest fires, and to classify the key influencing factors.

[0037] For example, consult the China Forestry Yearbook from 2015 to 2020, collect information on major forest fires across the country in the past 5 years, and perform diagnostic analysis on the historical fire data through the diagnostic analysis method in big data analysis to determine the key influencing factors causing forest fires and classify them. There are mainly 4 major categories of forest fire influencing factors: meteorological factors; combustible factors; topographic factors; social factors.

[0038] In step S2, in-depth diagnostic analysis and clustering analysis are performed on the key influencing factors to obtain 12 forest fire risk influencing factors.

[0039] Specifically, deeper diagnostic analysis and clustering analysis are performed on the 4 major categories of forest fire influencing factors obtained in the embodiment of the present invention to obtain 12 forest fire risk influencing factors. Among them, meteorological factors mainly include: (1) daily maximum temperature influencing factor, (2) daily minimum relative humidity influencing factor, (3) daily maximum wind speed influencing factor, (4) consecutive days without precipitation influencing factor; combustible factors mainly include: (1) forest type influencing factor, (2) forest canopy density influencing factor, (3) forest drought degree influencing factor; topographic factors mainly include: (1) altitude influencing factor, (2) slope influencing factor, (4) aspect influencing factor; social factors mainly include: (1) distribution of forest area residential areas influencing factor and (2) distribution of forest area roads influencing factor.

[0040] In step S3, the weight of each forest fire risk influencing factor is calculated.

[0041] Specifically, for the analysis of meteorological factors:

[0042] For example, meteorological conditions are important factors affecting natural forest fires. The meteorological conditions during fires in the fire prevention period in the past 5 years can be investigated, and finally 4 meteorological factors are selected, namely: daily maximum temperature, daily minimum relative humidity, consecutive days without precipitation, and maximum wind speed.

[0043] (1) Daily maximum temperature influencing factor

[0044] Air temperature can change the physical properties of combustibles. The higher the temperature, the faster the water content of combustibles evaporates, the lower the moisture content, and the greater the possibility of being ignited. Referring to the national standard "Forest Fire Danger Meteorological Grade GB / T36743-2018", those skilled in the art customize the weight value of the daily maximum temperature influencing factor as shown in Table 1 below.

[0045] Table 1 Look-up Table of Daily Maximum Temperature and Its Weight Value

[0046]

[0047] (2) Influence Factor of Daily Minimum Relative Humidity

[0048] Air humidity has a great influence on the moisture content of combustibles. When the air humidity is greater than the moisture content of the combustibles, the combustibles absorb moisture from the air. At this time, the higher the air humidity, the higher the moisture content of the combustibles, and the less likely they are to be ignited. Referring to the national standard "Forest Fire Danger Meteorological Grade GB / T 36743-2018", those skilled in the art customize the weight values of the influence factor of daily minimum relative humidity as shown in Table 2 below.

[0049] Table 2 Look-up Table of Minimum Relative Humidity and Its Weight Value

[0050]

[0051] (3) Influence Factor of Daily Maximum Wind Speed

[0052] Wind speed also has a great influence on the moisture content of combustibles. The higher the wind speed, the faster the moisture of the combustibles evaporates, and the easier it is to be ignited. At the same time, after the combustibles burn, the wind speed is also the most direct influence factor for the spread of forest fires. Referring to the national standard "Forest Fire Danger Meteorological Grade GB / T 36743-2018", those skilled in the art customize the weight values of the influence factor of daily maximum wind speed as shown in Table 3.

[0053] Table 3 Look-up Table of Maximum Wind Speed and Its Weight Value

[0054]

[0055] (4) Influence Factor of Consecutive Days without Precipitation

[0056] Consecutive days without precipitation have an impact on both the occurrence and behavior of forest fires. A higher number of consecutive days without precipitation will lead to a decrease in the moisture content of the soil, further affecting the moisture content of the combustibles. Referring to the national standard "Forest Fire Danger Meteorological Grade GB / T 36743-2018", those skilled in the art customize the weight values of the influence factor of consecutive days without precipitation as shown in Table 4.

[0057] Table 4 Look-up Table of Consecutive Days without Precipitation and Its Weight

[0058]

[0059] Then analyze the combustible factors

[0060] The characteristics of combustibles themselves are also important factors determining the occurrence of forest fires. The influence of combustible factors on forest fire risk is mainly reflected in three aspects: the type of combustibles themselves, the internal characteristics (water content) of combustibles, and the external characteristics (canopy density) of combustibles.

[0061] (1) Forest type influence factor

[0062] Based on the high-resolution, multi-spectral remote sensing images of "Jilin-1", forest combustibles are automatically classified according to the different combustible grades of different forest types, mainly including coniferous forests, broad-leaved forests, coniferous and broad-leaved mixed forests, and shrubs. Referring to the regulations on the flammability of different forest types in national forestry standards, those skilled in the art customize the weight values of forest type influence factors, as shown in Table 5.

[0063] Table 5 Forest types and their weight look-up table

[0064]

[0065]

[0066] (2) Forest canopy density influence factor

[0067] Forest canopy density refers to the ratio of the total projected area of the tree crowns in the forest on the ground under direct sunlight to the total area of this forest land, and is often used to characterize the sparse and dense degree of the forest. In China, forests in August - September of each year are usually selected to calculate the canopy density. In this paper, the images of the Jilin-1 spectral satellite in August are used for the inversion of the canopy density. The weight value representing the forest canopy density influence factor is updated once a year, and its calculation formula is as follows:

[0068]

[0069] Among them, CD is the canopy density, NDVI is the normalized difference vegetation index, NDVI min is the minimum normalized difference vegetation index, NDVI max is the minimum normalized difference vegetation index.

[0070] (3) Forest drought degree influence factor

[0071] The forest drought characteristic, which characterizes the combustible state of combustibles, is the main internal characteristic of combustibles in forest fire risk, and is generally represented by the relative drought degree in a specific area during a certain period. In this invention, the temperature vegetation drought index TVDI obtained by inverting the MODIS satellite data synthesized every 8 days is used to represent the weight value of the forest drought degree influence factor, and the calculation formula is as follows:

[0072]

[0073] In the formula, T smax and Tsmin are the maximum and minimum values of the land surface temperature when the vegetation normalized difference index (NDVI) is equal to a certain specific value, T s is the land surface temperature of any pixel. For T smax and T smin perform linear regression simultaneously, and the result of the regression equation is:

[0074] T smin = a1 + b1NDVI

[0075] T smax = a2 + b2NDVI

[0076] where a1, a2, b1, and b2 are the intercepts and slopes of the dry and wet edge equations, respectively.

[0077] Furthermore, analyze the social factors

[0078] The social factors are mainly determined by the distances from human settlements and roads. Therefore, use the distances from the forest area to the road and to the residential area to analyze the influence degree of human factors on forest fire risk. The roads and residential areas are interpreted from the remote sensing images of Jilin-1 high-resolution satellites, and the weight values of each influencing factor in the social factors are determined by the method of expert scoring, as shown in Table 6 below.

[0079] Table 6 Social factors and their weight values

[0080]

[0081] Finally, analyze the terrain factors

[0082] The terrain factors are also important factors affecting the occurrence of forest fires. Investigate relevant standards and the terrain factors during forest fire seasons in recent years. Finally, it is found that 3 terrain factors may have a great impact on forest fire risk, namely altitude, slope, and aspect.

[0083] The terrain data of key areas are mainly obtained from the DSM data reconstructed by the Jilin-1 video satellite in three dimensions, and the data of other areas are obtained from the publicly available 12.5-meter terrain data.

[0084] (1) Altitude influence factor

[0085] Altitude affects the composition of vegetation, the humidity of combustibles, and the atmospheric humidity; in mountainous areas, as the altitude increases, the temperature decreases, the humidity increases, and the possibility of forest fires decreases. According to the method of expert scoring, determine the weight value of the altitude influence factor, as shown in Table 7.

[0086] Table 7 Altitude and its weight lookup table

[0087]

[0088] (2) Slope influence factor

[0089] The influence of slope on forest fire occurrence and spread is high in the middle and low on both sides. As the slope increases, surface runoff speeds up, combustibles on the ground are prone to drying, and the fire risk level is high. However, when the slope reaches above the steep slope, the distribution of forest trees decreases and the forest fire risk level drops. Slope also has a great influence on heat transfer. For an uphill fire, the intensity of convective heat and radiant heat received by the combustibles increases, exacerbating the fire spread speed. According to the method of expert scoring, the weight value of the slope influence factor is determined as shown in Table 8.

[0090] Table 8 Slope and its weight lookup table

[0091]

[0092] (3) Aspect influence factor

[0093] Aspect directly affects the amount of solar radiation received by the ground, causing temperature differences on different aspects. Generally, the south slope receives more solar radiation than the north slope, the air is drier, and it is more likely to cause fires. According to the method of expert scoring, the weight value of the aspect influence factor is determined as shown in Table 9.

[0094] Table 9 Aspect and its weight lookup table

[0095]

[0096] In step S4, according to the correlation between each forest fire risk influence factor, the calculation principle is determined to establish a refined forest risk assessment model. At the same time, based on multi-source basic data, the prediction scale for extracting the refined forest risk assessment model is determined.

[0097] Specifically, the embodiments of the present invention perform principal component analysis on 4 types of forest fire influence factors, obtain the weight value of each influence factor, and determine the calculation rule of the model based on the correlation between each influence factor. Among them, for each factor in meteorological factors, terrain factors and social factors, the cumulative calculation principle is adopted, and for each factor in combustible factors, the multiplicative calculation principle is adopted. The weights of the 4 major types of factors are dynamically adjusted during major holidays, activities, etc. According to the above calculation principle and weight analysis, the refined forest fire risk assessment model is:

[0098] FRID = 0.48*A + 0.35*B + 0.15*C + 0.1*D

[0099] Among them, A is the meteorological factor, B is the vegetation factor, C is the social factor, and D is the terrain factor.

[0100] Furthermore, the entire refined forest fire risk assessment model extracts forest fire risk factors based on multi-source basic data. Since the spatial resolutions of different data sources are different and the refined requirements need to be met, the forecast scale of the grid-level forest fire risk forecast results is set to a spatial resolution of 1 km, and the forest fire risk level forecasts for the next 24 hours, 48 hours, 72 hours, and the next week are automatically released daily.

[0101] In step S5, referring to the preset forest and grassland emergency plan, the preset forest fire risk level forecast and response work management method, and the preset forest fire classification response procedure, the historical fire data within the preset years is analyzed through the refined forest risk assessment model to divide the risk levels of refined forest fire risks.

[0102] Specifically, referring to the "National Forest and Grassland Fire Emergency Plan", the "National Forest Fire Risk Level Forecast and Response Work Management Method", and the "National Forest Fire Classification Response Procedure", through the refined forest fire risk assessment model, the forest fire risks at the locations of forest fires in the past 5 years are analyzed, and the grade division of the model results is customized as shown in Table 10 below.

[0103] Table 10 Refined Forest Fire Risk Level Division Table

[0104]

[0105]

[0106] As Figure 3 shown, taking Beijing as a demonstration area, the forest fire risk level on May 10, 2021 in Beijing is forecasted, analyzed and displayed to verify a refined forest fire risk assessment method that integrates multi-source data proposed in an embodiment of the present invention. It can be seen that the refined forest fire risk assessment method that integrates multi-source data proposed in an embodiment of the present invention selects 4 major categories of forest fire risk factors, including 12 forest fire risk influencing factors, by analyzing the causes of forest fires. Through methods such as diagnostic analysis, principal component analysis, and expert scoring, the influencing factors of the model and the weight values of each influencing factor are determined, a refined forest fire risk assessment model is constructed, and a scientific grade division is made for the forecast results, realizing the automatic release of forecast grade information. Among them, the fire risk forecast results meet the refined requirements and at the same time have the accuracy of the forecast, which can provide a reference for forest fire prevention work and is of great significance for fulfilling the functions of fire prevention and extinguishment, protecting forest resources, and the safety of people's lives and property.

[0107] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0108] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0109] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A refined forest fire risk assessment method integrating multi-source data, characterized in that: The following steps are involved: Step S1, determining key influencing factors causing forest fire risk, wherein the key influencing factors include meteorological factors, combustible factors, terrain factors and social factors; Step S2, performing in-depth diagnostic analysis and cluster analysis on the key influencing factors to obtain 12 forest fire risk influencing factors; Step S3, calculating the weight of each forest fire risk influencing factor; Step S4, determining a calculation principle based on the correlation between each forest fire risk influencing factor to establish a refined forest risk assessment model, and at the same time, determining a forecast scale for extracting the refined forest risk assessment model based on multi-source basic data; Step S5, referring to the preset forest and grassland emergency plan, the preset forest fire risk level forecast and response management method, and the preset forest fire graded response procedure, the refined forest risk assessment model is used to analyze historical fire data within a preset period of time to classify the refined forest fire risk level; The 12 forest fire risk influencing factors include the daily maximum temperature influencing factor, the daily minimum relative humidity influencing factor, the daily maximum wind speed influencing factor, the continuous precipitation-free day influencing factor, the forest type influencing factor, the forest canopy density influencing factor, the forest drought degree influencing factor, the altitude influencing factor, the slope influencing factor, the slope aspect influencing factor, the forest area residential area distribution influencing factor and the forest area road distribution influencing factor; The temperature fuel drought index TVDI obtained by inverting the MODIS satellite data synthesized every eight days is used to represent the weight value of the factors affecting the forest drought degree. The calculation formula is: Among them, T smax and T smin are the maximum and minimum surface temperature when the Normalized Difference Vegetation Index (NDVI) is equal to a certain value, T s is the surface temperature of any pixel; The canopy density inversion obtained from the Jilin-1 spectral satellite imagery in August or September represents the weighted value of the forest canopy density influencing factor. The calculation formula is: Among them, CD is canopy density, NDVI is normalized vegetation index, min is the minimum normalized difference vegetation index, NDVI max is the minimum normalized vegetation index; The distances between roads and residential areas and forests are obtained by interpreting Jilin-1 high-resolution satellite remote sensing images, and the weight values of the influencing factors of residential distribution in forest areas and the influencing factors of road distribution in forest areas are determined by expert scoring method; The refined forest fire risk assessment model is: FRID=0.48*A+0.35*B+0.15*C+0.1*D Among them, A is the meteorological factor, B is the vegetation factor, C is the social factor, and D is the terrain factor.

2. The refined forest fire risk assessment method based on multi-source data integration according to claim 1 is characterized in that: In step S1, historical fire data of a preset number of years is used to perform diagnostic analysis on the historical fire data by a diagnostic analysis method to determine key influencing factors causing forest fires and classify the key influencing factors.

3. The refined forest fire risk assessment method integrating multi-source data according to claim 1 is characterized in that: The terrain data of the preset key areas were obtained through the three-dimensional reconstruction DSM data of the Jilin-1 video satellite, and the terrain data of other areas except the preset key areas were obtained through the public 12.5-meter terrain data.

4. The refined forest fire risk assessment method integrating multi-source data according to claim 3 is characterized in that: Based on the terrain data, the weight values of the altitude influencing factor, the slope influencing factor, and the aspect influencing factor are respectively determined by an expert scoring method.

5. The refined forest fire risk assessment method integrating multi-source data according to claim 1 is characterized in that: The forecast scale is a spatial resolution of 1 km, and the forest fire risk level forecast for the next 24 hours, 48 hours, 72 hours and the next week is automatically released once a day.

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