Method and system for lightning-caused fire risk zoning in forest areas

Abstract

The scheme relates to a forest lightning stroke fire risk risk region division method and system. The method comprises the following steps: acquiring forest environment data and forest meteorological data; determining a reference meteorological station, correcting the forest meteorological data to obtain target meteorological data, and calculating a forest fire risk meteorological index; calculating a forest lightning stroke disaster-causing factor index; calculating a disaster-bearing body vulnerability index according to the to-be-divided risk region; and according to the forest fire risk meteorological index, the forest lightning stroke disaster-causing factor index and the disaster-bearing body vulnerability index, the to-be-divided risk region is divided into various risk region grades by using a natural breakpoint method in ArcGIS. Through comprehensive consideration of the influences of environment, meteorology, lightning and the like on forest fire risk, various indexes are calculated, so that the risk region division is more targeted; due to the normalization processing of various indexes, high-precision risk region division can be realized.

Description

Methods and Systems for Zoning Forest Area Lightning Fire Risk Technical Field

[0001] This invention relates to the field of forest fire prevention technology, and in particular to a method and system for zoning forest areas for lightning-induced fire hazards. Background Technology

[0002] Forest fires can be categorized into two types based on their source: those caused by human factors and those caused by lightning strikes. In recent years, public awareness of forest fire prevention and the government's firefighting capabilities have increased, leading to a decrease in the number and severity of forest fires. However, the proportion of forest fires caused by lightning strikes is on the rise. Therefore, conducting risk zoning for lightning-induced fires in forest areas is extremely important. Currently, there is limited research on lightning-induced fire risk zoning in forest areas. Existing methods primarily employ natural disaster risk zoning approaches, selecting evaluation indicators, performing overlay analysis on these indicators, estimating the lightning disaster risk index, and finally classifying and grading the zones to complete the lightning disaster risk zoning.

[0003] However, traditional methods of risk zone classification suffer from low specificity and insufficient precision. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, a method and system for zoning forest lightning fire risk is provided, which can improve the pertinence and accuracy of risk area zoning.

[0005] A method for zoning forest fire risk areas caused by lightning strikes, the method comprising:

[0006] Obtain forest environmental data and forest meteorological data for the risk areas to be classified;

[0007] A reference meteorological station is determined, and the meteorological data of the forest area is corrected based on the reference meteorological station to obtain the target meteorological data. The forest fire risk meteorological index is then calculated based on the target meteorological data.

[0008] Determine lightning location data, and calculate the ground flash density and intensity in the forest area based on the lightning location data to obtain the forest area lightning disaster factor index;

[0009] Based on the risk areas to be classified, vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation indices are determined. The vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation indices are normalized to calculate the vulnerability index of the disaster-bearing body.

[0010] Based on the forest fire weather index, the forest lightning disaster factor index, and the vulnerability index of the disaster-bearing body, the natural breakpoint method in ArcGIS is used to divide the risk area to be classified into various risk zoning levels.

[0011] In one embodiment, the step of correcting the forest area meteorological data based on the reference meteorological station to obtain the target meteorological data includes:

[0012] Identify the regional meteorological stations for the risk areas to be delineated; the forest area meteorological data is collected by the regional meteorological stations, and the forest area meteorological data is meteorological data within the target time period;

[0013] The meteorological data of the forest area are corrected based on the meteorological data of the reference meteorological station to obtain the target meteorological data.

[0014] In one embodiment, the method further includes:

[0015] The spatial distribution pattern of climate elements was determined based on the meteorological data of the forest area.

[0016] Based on the spatial distribution pattern of the climate elements, the meteorological data of the forest area are corrected using a linear or nonlinear model to obtain a correction index.

[0017] The meteorological stations in the region were gridded to obtain the processed spatial distribution data of climate.

[0018] In one embodiment, calculating the forest fire weather index based on the target meteorological data includes:

[0019] A meteorological factor dataset is calculated based on the target meteorological data;

[0020] The forest fire risk meteorological index is calculated based on the meteorological factor dataset, the correction index, and the climate spatial distribution data.

[0021] In one embodiment, determining lightning location data and calculating the ground flash density and intensity in the forest area based on the lightning location data to obtain the forest area lightning disaster factor index includes:

[0022] Obtain lightning disaster risk reference data, and determine lightning location data based on the lightning disaster risk reference data;

[0023] Based on the lightning location data, the lightning density and lightning intensity of the risk area to be divided are calculated, and the data are gridded and normalized respectively with the terrain data of the risk area to be divided to obtain the processed data.

[0024] Based on the processed data, a gridded forest area lightning disaster factor index is calculated.

[0025] In one embodiment, determining the vulnerability characteristic factor, exposure characteristic factor, and disaster prevention and mitigation index based on the risk area to be classified includes:

[0026] Based on the risk areas to be classified, the topographic impact index, soil moisture impact index, and water flame retardancy index are calculated, and a vulnerability model is created to select vulnerability characteristic factors.

[0027] Calculate the forest flammability index and the population economic exposure index, and create an exposure model to select exposure characteristic factors;

[0028] Calculate the disaster relief accessibility index and the medical convenience index, and create a disaster prevention and mitigation model to calculate the disaster prevention and mitigation index.

[0029] In one embodiment, the step of dividing the risk area to be classified into various risk zoning levels using the natural breakpoint method in ArcGIS, based on the forest fire weather index, the forest lightning disaster factor index, and the vulnerability index of the disaster-bearing body, includes:

[0030] The forest fire weather index, the forest lightning disaster factor index, and the disaster-bearing body vulnerability index are normalized, and the gridded disaster-bearing body vulnerability index is calculated.

[0031] Based on the vulnerability index of the disaster-bearing body, the forest fire weather index, and the forest lightning disaster-causing factor index, a forest lightning fire risk zoning model is constructed, and the forest lightning fire risk assessment index is calculated.

[0032] Based on the forest area lightning fire risk assessment index, the natural breakpoint method in ArcGIS is used to divide the risk area to be classified into various risk zoning levels.

[0033] A forest area lightning fire risk zoning system, the system comprising:

[0034] The data acquisition module is used to acquire forest environmental data and forest meteorological data for the risk areas to be classified.

[0035] The forest fire weather index acquisition module is used to determine the reference weather station, correct the forest meteorological data according to the reference weather station, obtain the target meteorological data, and calculate the forest fire weather index according to the target meteorological data.

[0036] The forest area lightning disaster factor index acquisition module is used to determine lightning location data and calculate the ground flash density and intensity of the forest area based on the lightning location data to obtain the forest area lightning disaster factor index.

[0037] The vulnerability index acquisition module is used to determine vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation index based on the risk area to be divided, and to normalize the vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation index to calculate the vulnerability index of the disaster-bearing body.

[0038] The region division module is used to divide the risk area to be divided into various risk zoning levels based on the forest fire weather index, the forest lightning disaster factor index, and the vulnerability index of the disaster-bearing body, using the natural breakpoint method in ArcGIS.

[0039] The aforementioned method and system for zoning forest fire risk by lightning strikes involves: acquiring forest environmental data and meteorological data of the area to be classified; determining reference meteorological stations; correcting the forest meteorological data based on the reference meteorological stations to obtain target meteorological data; calculating the forest fire risk meteorological index based on the target meteorological data; determining lightning location data; calculating the ground lightning density and intensity of the forest area based on the lightning location data to obtain the forest lightning disaster-causing factor index; determining vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation indices based on the area to be classified; normalizing the vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation indices to calculate the vulnerability index of the disaster-bearing body; and using the natural breakpoint method in ArcGIS to divide the area to be classified into various risk zoning levels based on the forest fire risk meteorological index, the forest lightning disaster-causing factor index, and the vulnerability index of the disaster-bearing body. By comprehensively considering the impact of environment, meteorology, lightning and other factors on forest fire risk, various indices are calculated, making risk area delineation more targeted; and because the indices are normalized, highly precise risk zoning can be achieved. Attached Figure Description

[0040] Figure 1 is an application environment diagram of the forest area lightning fire risk zoning method in one embodiment;

[0041] Figure 2 is a flowchart illustrating a method for zoning forest fire risk areas by lightning strikes in one embodiment;

[0042] Figure 3 is a data diagram of a forest area lightning fire risk zoning model in one embodiment;

[0043] Figure 4 is a schematic diagram of the lightning disaster risk zoning in the Luzhou forest area during the fire prevention period.

[0044] Figure 5 is a schematic diagram of the average forest fire weather index during the fire prevention period in the Luzhou forest area.

[0045] Figure 6 is a schematic diagram showing the distribution of vulnerability of lightning-prone fire-bearing bodies in the forest area of ​​Luzhou City.

[0046] Figure 7 is a schematic diagram showing the distribution of exposure of lightning-prone fire-prone bodies in the forest area of ​​Luzhou.

[0047] Figure 8 is a schematic diagram showing the distribution of disaster prevention and mitigation capabilities of lightning-fire-prone bodies in the Luzhou forest area.

[0048] Figure 9 is a zoning map of the vulnerability of lightning-prone forest bodies in the Luzhou forest area.

[0049] Figure 10 is a risk zoning map of lightning-induced fire hazards in the forest area of ​​Luzhou during the fire prevention period.

[0050] Figure 11 is a structural block diagram of a forest area lightning fire risk zoning system in one embodiment. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] The forest lightning fire risk zoning method provided in this application embodiment can be applied to the application environment shown in Figure 1. As shown in Figure 1, the application environment includes computer equipment 110. Computer equipment 110 can acquire forest environmental data and forest meteorological data for the risk area to be classified; computer equipment 110 can determine reference meteorological stations, correct the forest meteorological data based on the reference meteorological stations to obtain target meteorological data, and calculate the forest fire weather index based on the target meteorological data; computer equipment 110 can determine lightning location data, calculate the ground lightning density and intensity of the forest area based on the lightning location data, and obtain the forest lightning disaster factor index; computer equipment 110 can determine vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation index based on the risk area to be classified, normalize the vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation index, and calculate the disaster-bearing body vulnerability index; computer equipment 110 can divide the risk area to be classified into various risk zoning levels using the natural breakpoint method in ArcGIS, based on the forest fire weather index, forest lightning disaster factor index, and disaster-bearing body vulnerability index. The computer equipment 110 can be, but is not limited to, various personal computers, laptops, smartphones, robots, unmanned aerial vehicles, tablets, etc.

[0053] In one embodiment, as shown in Figure 2, a method for zoning forest fire risk areas caused by lightning strikes is provided, comprising the following steps:

[0054] Step 202: Obtain forest environmental data and forest meteorological data for the risk areas to be classified.

[0055] The areas to be classified as risk zones refer to areas where risk levels need to be determined. Forest environmental data and forest meteorological data can be used to represent the data required for carrying out forest lightning and fire risk zoning. Forest environmental data can include lightning location data, elevation, water system, soil resistivity, soil moisture, forest distribution, tree age, tree species, population, GDP, hospitals, roads, etc. Forest meteorological data can be daily meteorological data such as precipitation, relative humidity, wind speed, and temperature from national meteorological stations and regional automatic weather stations since their establishment.

[0056] Users can input forest area environmental data and forest area meteorological data into computer devices.

[0057] Step 204: Determine the reference weather station, correct the forest area meteorological data based on the reference weather station, obtain the target meteorological data, and calculate the forest fire weather index based on the target meteorological data.

[0058] The reference meteorological station can be the national meteorological station. Computer equipment can correct and extend meteorological observation data from regional automatic weather stations with short observation periods and large errors, and compile and obtain complete and reliable meteorological data series from each station over the past 30 years.

[0059] Specifically, the computer equipment can use the national meteorological station as the base station to extend and correct meteorological data such as temperature, precipitation, and wind observed by automatic weather stations in the surrounding area, i.e., correction stations, for nearly 30 years.

[0060] Step 206: Determine the lightning location data, and calculate the ground lightning density and intensity in the forest area based on the lightning location data to obtain the forest area lightning disaster factor index.

[0061] Users can input the "Technical Guidelines for Lightning Disaster Risk Zoning" into the computer device. The computing device can determine the lightning location data based on the "Technical Guidelines for Lightning Disaster Risk Zoning", thereby calculating the ground flash density and ground flash intensity in the forest area, and further calculating the lightning disaster factor index in the forest area.

[0062] Step 208: Determine the vulnerability characteristic factor, exposure characteristic factor, and disaster prevention and mitigation index based on the risk areas to be classified. Normalize the vulnerability characteristic factor, exposure characteristic factor, and disaster prevention and mitigation index to calculate the vulnerability index of the disaster-bearing body.

[0063] Computer equipment can locate corresponding forest area environmental data based on the risk areas to be classified, thereby determining vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation indices. Among them, vulnerability characteristic factors can be soil moisture, water system, altitude, slope, aspect, etc.; exposure characteristic factors can be forest land, tree age, tree species, population, GDP, etc.; and disaster prevention and mitigation indices can be two indices: disaster relief accessibility and medical convenience.

[0064] Step 210: Based on the forest fire weather index, forest lightning disaster factor index, and disaster-bearing body vulnerability index, the natural breakpoint method in ArcGIS is used to divide the risk area to be classified into various risk zoning levels.

[0065] Computer equipment can calculate the forest area lightning fire risk assessment index based on the forest area fire weather index, forest area lightning disaster factor index, and disaster-bearing body vulnerability index. Then, based on the forest area lightning fire risk assessment index, the natural breakpoint method in ArcGIS is used to divide it into five risk zoning levels: low, medium, medium, high, and high.

[0066] In this embodiment, the computer equipment acquires forest environmental data and forest meteorological data for the risk area to be classified; determines a reference meteorological station, corrects the forest meteorological data based on the reference meteorological station to obtain target meteorological data, and calculates the forest fire weather index based on the target meteorological data; determines lightning location data, and calculates the ground lightning density and intensity of the forest area based on the lightning location data to obtain the forest lightning disaster factor index; determines vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation indices based on the risk area to be classified, normalizes the vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation indices, and calculates the disaster-bearing body vulnerability index; based on the forest fire weather index, forest lightning disaster factor index, and disaster-bearing body vulnerability index, the natural breakpoint method in ArcGIS is used to divide the risk area to be classified into various risk zoning levels. By comprehensively considering the impact of environment, meteorology, lightning, etc. on forest fire risk, various indices are calculated, making the risk area classification more targeted; due to the normalization of various indices, highly precise risk zoning can be achieved.

[0067] In one embodiment, a method for zoning forest lightning fire risk may further include a process of correcting meteorological data. The specific process includes: determining the regional meteorological station for the risk area to be delineated; the forest meteorological data is collected by the regional meteorological station and is meteorological data within the target time period; and correcting the forest meteorological data based on the meteorological data of the reference meteorological station to obtain the target meteorological data.

[0068] When making corrections, the extended correction formula can be Y. N =Y n +(R×Q Y / Q X )×(X N -X n ), where X n Y n X represents the average values ​​of the basic station and the correction station over a parallel observation period of n years, respectively. N YN Q represents the average value of N years of observation data from the base station and the correction station, respectively. X Q Y , where are the standard deviations of the years in the parallel observation period of n years for the base station and the correction station, respectively, and R is the correlation coefficient of the observation data of the base station and the correction station in the n-year period.

[0069] In one embodiment, a method for zoning forest lightning and fire risk may further include a gridded data processing process, which specifically includes: determining the spatial distribution pattern of climate elements based on forest meteorological data; correcting the forest meteorological data using a linear or nonlinear model based on the spatial distribution pattern of climate elements to obtain a correction index; and performing gridded processing on regional meteorological stations to obtain processed climate spatial distribution data.

[0070] Computer equipment can perform spatial fine-scale gridding of meteorological data, and can determine the spatial distribution pattern of climate elements based on forest meteorological data. The specific formula is: S=f(λ,φ,Z,g,m), where S is the actual value of a certain climate element in a certain place, λ is longitude, φ is latitude, Z is altitude, g is other macro-geographical environmental factors other than geographical location (longitude and latitude), mainly macro-topography (large-scale topography), and m is other micro-topographic factors other than altitude, mainly local small-scale topography.

[0071] The computer equipment can determine the spatial distribution pattern of climate elements based on the actual distribution patterns of various meteorological elements (especially the variation patterns with altitude). Linear models are used for elements such as station average temperature, accumulated temperature, date of occurrence of boundary temperature and number of consecutive days, while nonlinear models are used for elements such as station extreme temperature, humidity, precipitation and wind speed. The station is also gridded (provincial grids have a spatial resolution of 1km, and city and county grids have a spatial resolution of 100m).

[0072] In one embodiment, a method for zoning forest fire risk due to lightning strikes may further include a process for calculating a forest fire weather index. The specific process includes: calculating a meteorological factor dataset based on target meteorological data; and calculating the forest fire weather index based on the meteorological factor dataset, correction index, and climate spatial distribution data.

[0073] Computer equipment can process and generate a dataset of meteorological factors, including daily maximum temperature, daily maximum wind speed, daily minimum relative humidity, and consecutive days without precipitation, based on target meteorological data, i.e., daily meteorological observation data from various stations over the past 30 years. Then, the computer equipment can statistically obtain gridded and normalized spatial distributions of daily maximum wind speed, daily maximum temperature, daily minimum relative humidity, and consecutive days without precipitation from the meteorological factor dataset, correction indices, and climate spatial distribution data. Based on the processed data, the calculation formula is used: C r×(I1(Fj)+I2(Tm)+I3(Un)+I4(Nr)) is used to calculate the gridded forest fire weather index. Where C r The precipitation correction index is obtained based on local climate conditions and geomorphological experiments; Fj, Tm, Un, and Nr represent the daily maximum wind speed, daily maximum temperature, daily minimum relative humidity, and number of consecutive days without precipitation, respectively; I1, I2, I3, and I4 represent the corresponding fire risk indices. Specifically, the values ​​of I1, I2, I3, and I4 are based on the following table of fire weather factors and corresponding fire risk indices:

[0074]

[0075] In one embodiment, a method for zoning forest lightning fire risk may further include a process for establishing a forest lightning disaster-causing factor index. The specific process includes: acquiring lightning disaster risk reference data and determining lightning location data based on the lightning disaster risk reference data; calculating the lightning density and lightning intensity of the risk area to be classified based on the lightning location data, and performing gridding and normalization processing on the topographic data of the risk area to be classified, respectively, to obtain processed data; and calculating the gridded forest lightning disaster-causing factor index based on the processed data using a weighted average.

[0076] Computer equipment can acquire lightning disaster risk reference data, namely the "Technical Guidelines for Lightning Disaster Risk Zoning," to determine lightning location data. Then, based on the lightning location data, the computer equipment can calculate the ground flash density and intensity in the forest area, and perform gridding and normalization processing on this data along with topographic relief, altitude, and soil resistivity. The normalization calculation formula can be: D ij =0.5 + 0.5 × (A) ij -min i ) / (max i -min i ), where D ij A represents the normalized value of the i-th indicator at station (grid) j; ij Min represents the i-th index value at station (grid) j; i ,max i These are the minimum and maximum values ​​among the i-th index values, respectively.

[0077] Next, the computer equipment can use the processed lightning strike density, lightning current intensity, terrain undulation, altitude, and soil resistivity to generate a gridded forest area lightning strike causative factor index through weighted aggregation. The weighting weights are obtained using the analytic hierarchy process (AHP). The calculation method for the forest area lightning strike causative factor index is as follows: R H =(L d ×wd+L n×wn)×(S c ×ws+E h ×we+T r ×wt), where R H L represents the disaster risk index. d L represents the ground flash density, wd represents the ground flash density weight; n S represents the lightning current intensity, wn represents the weight of the lightning current intensity; c E represents soil resistivity, ws represents the weight of soil resistivity; h Indicates altitude, we represents altitude weight; T r wt represents the terrain undulation, and wt represents the terrain undulation weight.

[0078] In one embodiment, a method for zoning forest lightning fire risk may further include the process of determining vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation indices. Specifically, this process includes: calculating the topographic impact index, soil moisture impact index, and water flame retardancy index based on the risk area to be classified, and creating a vulnerability model to select vulnerability characteristic factors; calculating the forest flammability index and population economic exposure index, and creating an exposure model to select exposure characteristic factors; calculating the disaster relief accessibility index and medical convenience index, and creating a disaster prevention and mitigation model to calculate the disaster prevention and mitigation index.

[0079] Computer equipment can select soil moisture, water system, altitude, slope, and aspect as vulnerability characteristic factors. The vulnerability model is constructed by gridding and normalizing three indices—topographic influence, soil moisture influence, and water body flame retardancy—and then using a weighted comprehensive evaluation method. Specifically, the calculation method is: V f =W t ×D t +W sm ×D sm +W wb ×D wb The weighting coefficients in the formula are obtained using an expert scoring method; where V f Indicates the vulnerability of the disaster-bearing body; D t The influence of topography is represented by the weighted average of normalized elevation, slope, and aspect, W. t The weighting of terrain influence; D sm W indicates soil moisture. sm The weighting of soil moisture influence; D wb Indicates the flame retardancy of water bodies, i.e., the distribution of water systems, W wb Weighting for flame retardancy in water bodies.

[0080] Computer equipment can select forest land, tree age, tree species, population, GDP, etc., as exposure characteristic factors. The exposure model is constructed by gridding and normalizing two indices: forest flammability and population economic exposure, and then using a weighted comprehensive evaluation method. Specifically, the calculation method is as follows: V e =W wl ×D wl +W pe ×D pe The weighting coefficients in the formula are obtained using an expert scoring method; where V e Indicates exposure level; D wl Forest flammability is represented by a weighted average of normalized forest area, tree age, and tree species flammability. Tree species flammability is determined according to the "National Forest Fire Risk Zoning Standard" LY / T1063-2008. W wl Weighting for forest flammability; D pe W represents population economic exposure, obtained by normalizing the population and GDP weighted average. pe Weighted by population economic exposure.

[0081] The disaster prevention and mitigation model is constructed by gridding and normalizing two indices: disaster relief accessibility and medical convenience, and then using a weighted comprehensive evaluation method. The calculation method can be: V dp =W r ×D r +W h ×D h The weighting coefficients in the formula are obtained using an expert scoring method; where V dp Indicates the disaster prevention and mitigation index; D r To represent disaster relief accessibility, W is used as a factor representing the transportation network. r Weighting for disaster relief accessibility; D h To indicate accessibility to healthcare, W is a factor representing the density of hospital facilities. h Weighting for medical convenience.

[0082] Computer equipment can calculate the vulnerability index of disaster-bearing bodies. Specifically, it can calculate the vulnerability index based on three indices: gridded exposure, vulnerability, and disaster prevention and mitigation capabilities of the disaster-bearing bodies. After normalization, the index is calculated using the formula: V z =W e ×V e +W f ×V f +W dp ×(15-V dp The vulnerability index of the gridded disaster-bearing body is calculated, and the weight coefficients are obtained using the analytic hierarchy process (AHP); where V z V represents the vulnerability index of the disaster-bearing body; e W represents the level of exposure.e Exposure weight; V f Indicating vulnerability, W f V represents the vulnerability weight. dp W represents disaster prevention and mitigation capabilities. dp Weighting of disaster prevention and mitigation capabilities.

[0083] In one embodiment, a method for zoning forest area lightning fire risk may further include a process of classifying risk levels. The specific process includes: normalizing the forest fire weather index, the forest lightning disaster-causing factor index, and the vulnerability index of disaster-bearing bodies, and calculating a gridded vulnerability index of disaster-bearing bodies; constructing a forest area lightning fire risk zoning model based on the vulnerability index of disaster-bearing bodies, the forest fire weather index, and the forest lightning disaster-causing factor index, and calculating a forest area lightning fire risk assessment index; and using the natural breakpoint method in ArcGIS, dividing the risk area to be classified into various risk zoning levels based on the forest area lightning fire risk assessment index.

[0084] After acquiring the lightning strike hazard index, fire weather index, and vulnerability index of disaster-bearing bodies in the gridded forest area, the computer equipment can establish a forest area lightning fire risk zoning model as follows: R = (LDR) we ×(FMI) ws ×(RCI) wh Where R is the forest lightning fire risk index, used to represent the degree of forest lightning fire risk; the higher the value, the greater the disaster risk. LDR represents the normalized value of lightning-induced disaster risk. FMI represents the normalized value of the forest fire weather index. RCI represents the normalized value of the comprehensive assessment index of disaster-bearing bodies. we, ws, and wh are the weights of the corresponding evaluation factors, and the magnitude of the weights is determined by the analytic hierarchy process.

[0085] In this embodiment, as shown in Figure 4, the forest area lightning fire risk zoning model can be constructed using the vulnerability index of disaster-bearing bodies, the forest fire weather index, and the forest lightning disaster-causing factor index. The forest lightning disaster-causing factor index can include ground flash density, ground flash intensity, topographic relief, altitude, and soil conductivity, etc.; the forest fire weather index can include the number of consecutive days without precipitation, daily minimum relative humidity, daily maximum wind speed, and daily maximum temperature, etc.; the vulnerability index of disaster-bearing bodies can include the exposure degree of disaster-bearing bodies, the vulnerability of disaster-bearing bodies, and disaster prevention and mitigation capabilities. The exposure degree of disaster-bearing bodies can include soil moisture, water systems, and topographic influence; the vulnerability of disaster-bearing bodies can include forest area, tree age distribution, tree species distribution, population, and economy; and disaster prevention and mitigation capabilities can include hospitals, transportation, etc.

[0086] In one embodiment, a forest fire risk zoning method based on lightning strikes was used to zonify forest fire risks in Luzhou City, Sichuan Province. Luzhou City is mostly located in the mid-subtropical humid monsoon climate zone, characterized by mild temperatures and distinct seasons. From 2011 to 2019, Luzhou City experienced 709 forest fires. The method of this invention was used to conduct forest fire risk zoning during the fire prevention period (January-September) in Luzhou City, Sichuan Province. The specific fire risk zoning process is as follows:

[0087] (1) Obtaining the data required for conducting lightning fire risk zoning in forest areas. There are 211 national and regional meteorological stations in Luzhou City. Based on the completeness of the observation data, the observation duration, and the distribution of the stations, meteorological observation data from 55 of these stations were selected. Lightning data were obtained from cloud-to-ground lightning detector observations, including parameters such as the occurrence time of lightning strikes, latitude and longitude, lightning current polarity, peak intensity, and lightning current rise steepness, covering the period from 2010 to 2022. Other data included geographic information data of Luzhou area, soil resistivity data, socio-economic data, disaster information, disaster prevention and mitigation data, and forest area data.

[0088] (2) The data from more than 50 regional meteorological stations were corrected to the same time series length (1980-2022) as the observation data from national meteorological stations.

[0089] (3) Conduct spatial fine-grained gridding of meteorological data. Based on the situation in Luzhou, we selected three factors—longitude, latitude, and altitude—to construct a multivariate regression model for meteorological elements in Luzhou, and then performed spatial fine-grained interpolation of the meteorological data.

[0090] Spatial interpolation regression model of temperature in Luzhou area

[0091]

[0092] (4) Calculation of the lightning hazard index in forest areas. Based on the "Technical Guidelines for Lightning Disaster Risk Zoning" (QX / T405-2017), gridded data of ground flash density and ground flash intensity during the forest fire prevention period in Luzhou were collected. Soil resistivity, altitude, and topographic relief were gridded, and a weighted comprehensive index of lightning hazard factors in forest areas was obtained. The weights of each assessment factor were obtained using the analytic hierarchy process (AHP). The corresponding weights for ground flash density, ground flash intensity, topographic relief, altitude, and soil resistivity were calculated to be 0.3057, 0.3674, 0.1637, 0.0999, and 0.0632, respectively. Then, the natural breakpoint method was used to divide the area into five levels, resulting in the lightning hazard zoning map of Luzhou forest areas during the fire prevention period, as shown in Figure 4.

[0093] (5) Calculation of the forest fire weather index. Daily meteorological observation data from various stations were obtained, processed, and used to generate a dataset of meteorological factors including daily maximum temperature, daily maximum wind speed, daily minimum relative humidity, and number of consecutive days without precipitation over the past 40 years. Then, the gridded and normalized spatial distributions of daily maximum wind speed, daily maximum temperature, daily minimum relative humidity, and number of consecutive days without precipitation were statistically obtained. Based on the processed data, the gridded forest fire weather index for the Luzhou forest area during the fire prevention period was calculated, as shown in Figure 5.

[0094] (6) Assessing the vulnerability of disaster-bearing bodies. Soil moisture, water system, altitude, slope, and aspect were selected as vulnerability characteristic factors. Three indices were constructed: topographic influence, soil moisture influence, and water flame retardancy. After gridding and normalization, a weighted comprehensive analysis was performed to obtain the gridded vulnerability index of forest area lightning fire hazard-bearing bodies. In Luzhou area, the weights of topographic influence, soil moisture influence, and water flame retardancy were 0.4, 0.3, and 0.3, respectively. The natural breakpoint method was used to divide the vulnerability into 5 levels, resulting in the distribution of lightning fire hazard-bearing bodies in Luzhou forest area, as shown in Figure 6.

[0095] (7) Assessing the exposure of disaster-bearing bodies. Forest land, tree age, tree species, population, and GDP were selected as exposure characteristic factors to construct two indices: forest flammability and population economic exposure. After gridding and normalization, a weighted comprehensive analysis was used to obtain the gridded forest area lightning fire hazard exposure index. The weight coefficients were obtained using an expert scoring method, with forest flammability and population economic exposure corresponding to weights of 0.6 and 0.4, respectively. The natural breakpoint method was used to divide the index into 5 levels, resulting in the distribution of lightning fire hazard exposure in the forest areas of Luzhou City, as shown in Figure 7.

[0096] (8) Assessing Disaster Prevention and Mitigation Capabilities. The disaster prevention and mitigation model uses two indices—accessibility to disaster relief and accessibility to medical care—and after gridding and normalization, a weighted comprehensive analysis is used to obtain the gridded disaster prevention and mitigation capability index for forest areas susceptible to lightning-fire hazards. The weight coefficients in the formula are obtained using an expert scoring method. The weights for accessibility to disaster relief and accessibility to medical care are 0.6 and 0.4, respectively. The model is divided into 5 levels using the natural breakpoint method, resulting in the distribution of disaster prevention and mitigation capabilities for forest areas susceptible to lightning-fire hazards in Luzhou City, as shown in Figure 8.

[0097] (9) Calculate the vulnerability index of the disaster-bearing body. According to steps (6)-(8), the three indices of exposure, vulnerability and disaster prevention and mitigation capability of the gridded disaster-bearing body were obtained respectively. After normalization, the vulnerability index of the gridded disaster-bearing body was calculated. The weight coefficients of each in the formula were obtained by the analytic hierarchy process. The corresponding weights of vulnerability, exposure and disaster prevention and mitigation capability were 0.68, 0.20 and 0.12 respectively. The natural breakpoint method was used to divide it into 5 levels, and the vulnerability zoning map of the lightning fire disaster-bearing body in the forest area of ​​Luzhou City was obtained, as shown in Figure 9.

[0098] (10) Calculate the lightning fire risk assessment index for forest areas. Based on steps (4), (5), and (9), the lightning disaster risk index, fire weather index, and vulnerability index of the disaster-bearing body in the gridded forest area were obtained respectively. The lightning fire risk index of the gridded forest area was calculated. The corresponding weights of the lightning disaster risk, forest fire weather index, and vulnerability index of the disaster-bearing body were 0.54, 0.30, and 0.16, respectively. The natural breakpoint method was used to divide it into 5 levels, and the lightning fire risk zoning map of Luzhou forest area during the fire prevention period was obtained, as shown in Figure 10.

[0099] It should be understood that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart above may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0100] In one embodiment, as shown in Figure 11, a forest area lightning fire risk zoning system is provided, including: a data acquisition module 1110, a forest area fire weather index acquisition module 1120, a forest area lightning disaster factor index acquisition module 1130, a disaster-bearing body vulnerability index acquisition module 1140, and a region division module 1150, wherein:

[0101] Data acquisition module 1110 is used to acquire forest environmental data and forest meteorological data of the risk area to be classified;

[0102] The forest fire weather index acquisition module 1120 is used to determine the reference weather station, correct the forest meteorological data based on the reference weather station, obtain the target meteorological data, and calculate the forest fire weather index based on the target meteorological data.

[0103] The forest area lightning disaster factor index acquisition module 1130 is used to determine lightning location data and calculate the ground flash density and intensity in the forest area based on the lightning location data to obtain the forest area lightning disaster factor index.

[0104] The vulnerability index acquisition module 1140 is used to determine the vulnerability characteristic factor, exposure characteristic factor, and disaster prevention and mitigation index based on the risk area to be divided, and to normalize the vulnerability characteristic factor, exposure characteristic factor, and disaster prevention and mitigation index to calculate the vulnerability index of the disaster-bearing body.

[0105] The regional division module 1150 is used to divide the risk area to be divided into various risk zoning levels based on the forest fire weather index, forest lightning disaster factor index, and disaster-bearing body vulnerability index, using the natural breakpoint method in ArcGIS.

[0106] In one embodiment, the forest fire weather index acquisition module 1120 is also used to determine the regional meteorological station for the risk area to be classified; the forest meteorological data is collected by the regional meteorological station, and the forest meteorological data is the meteorological data within the target time period; the forest meteorological data is corrected according to the meteorological data of the reference meteorological station to obtain the target meteorological data.

[0107] In one embodiment, the forest fire weather index acquisition module 1120 is also used to determine the spatial distribution pattern of climate elements based on forest meteorological data; correct the forest meteorological data using a linear or nonlinear model based on the spatial distribution pattern of climate elements to obtain a correction index; and perform gridding processing on the regional meteorological stations to obtain the processed climate spatial distribution data.

[0108] In one embodiment, the forest fire weather index acquisition module 1120 is further used to calculate a meteorological factor dataset based on the target meteorological data; and to calculate the forest fire weather index based on the meteorological factor dataset, the correction index, and the climate spatial distribution data.

[0109] In one embodiment, the forest lightning disaster factor index acquisition module 1130 is also used to acquire lightning disaster risk reference data and determine lightning location data based on the lightning disaster risk reference data; based on the lightning location data, it calculates the ground flash density and ground flash intensity of the risk area to be divided, and performs gridding and normalization processing on the topographic data of the risk area to be divided, respectively, to obtain the processed data; based on the processed data, it calculates the gridded forest lightning disaster factor index by weighted calculation.

[0110] In one embodiment, the vulnerability index acquisition module 1140 is further configured to calculate the topographic impact index, soil moisture impact index, and water flame retardancy index based on the risk areas to be classified, and to create a vulnerability model to select vulnerability characteristic factors; calculate the forest flammability index and population economic exposure index, and to create an exposure model to select exposure characteristic factors; calculate the disaster relief accessibility index and medical convenience index, and to create a disaster prevention and mitigation model to calculate the disaster prevention and mitigation index.

[0111] In one embodiment, the region division module 1150 is further used to normalize the forest fire weather index, the forest lightning disaster factor index, and the disaster-bearing body vulnerability index, and to calculate the gridded disaster-bearing body vulnerability index; to construct a forest lightning fire risk zoning model based on the disaster-bearing body vulnerability index, the forest fire weather index, and the forest lightning disaster factor index, and to calculate the forest lightning fire risk assessment index; and to divide the risk area to be divided into various risk zoning levels using the natural breakpoint method in ArcGIS based on the forest lightning fire risk assessment index.

[0112] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0113] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for zoning forest fire risk areas caused by lightning strikes, characterized in that, The method includes: acquiring forest environmental data and forest meteorological data for the risk area to be classified; determining a reference meteorological station, correcting the forest meteorological data based on the reference meteorological station to obtain target meteorological data, and calculating the forest fire risk meteorological index based on the target meteorological data; determining lightning location data, and calculating the ground lightning density and intensity of the forest area based on the lightning location data to obtain the forest lightning disaster factor index; determining vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation indices based on the risk area to be classified, including: calculating the topographic impact index, soil moisture impact index, and water flame retardancy index based on the risk area to be classified, and creating a vulnerability model to select vulnerability characteristic factors; calculating the forest flammability index and population economic exposure index, and creating an exposure model to select exposure characteristic factors; calculating the disaster relief accessibility index and medical convenience index, and creating a disaster prevention and mitigation model to calculate the disaster prevention and mitigation index; The vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation indices are normalized to calculate the vulnerability index of the disaster-bearing body. Based on the forest fire weather index, the forest lightning disaster-causing factor index, and the vulnerability index of the disaster-bearing body, the natural breakpoint method in ArcGIS is used to divide the area to be classified into various risk zoning levels. This includes: normalizing the forest fire weather index, the forest lightning disaster-causing factor index, and the vulnerability index of the disaster-bearing body, and calculating a gridded vulnerability index of the disaster-bearing body; constructing a forest lightning fire risk zoning model based on the vulnerability index of the disaster-bearing body, the forest fire weather index, and the forest lightning disaster-causing factor index, and calculating the forest lightning fire risk assessment index; and using the natural breakpoint method in ArcGIS, dividing the area to be classified into various risk zoning levels based on the forest lightning fire risk assessment index.

2. The method for zoning forest fire risk areas according to claim 1, characterized in that, The step of correcting the forest area meteorological data based on the reference meteorological station to obtain target meteorological data includes: determining the regional meteorological station for the risk area to be classified; the forest area meteorological data is collected by the regional meteorological station, and the forest area meteorological data is meteorological data within the target time period; and correcting the forest area meteorological data based on the meteorological data of the reference meteorological station to obtain target meteorological data.

3. The method for zoning forest fire risk areas according to claim 2, characterized in that, The method further includes: determining the spatial distribution pattern of climate elements based on the meteorological data of the forest area; correcting the meteorological data of the forest area using a linear or nonlinear model based on the spatial distribution pattern of climate elements to obtain a correction index; and performing gridding processing on the regional meteorological stations to obtain processed climate spatial distribution data.

4. The method for zoning forest fire risk areas according to claim 3, characterized in that, The step of calculating the forest fire risk meteorological index based on the target meteorological data includes: calculating a meteorological factor dataset based on the target meteorological data; and calculating the forest fire risk meteorological index based on the meteorological factor dataset, the correction index, and the climate spatial distribution data.

5. The method for zoning forest area lightning fire risk according to claim 1, characterized in that, The process of determining lightning location data and calculating the lightning density and intensity in the forest area based on the lightning location data to obtain the forest area lightning disaster factor index includes: acquiring lightning disaster risk reference data and determining lightning location data based on the lightning disaster risk reference data; calculating the lightning density and intensity of the risk area to be divided based on the lightning location data, and performing gridding and normalization processing on the topographic data of the risk area to be divided to obtain processed data; and calculating the gridded forest area lightning disaster factor index based on the processed data using weighted average.

6. A forest area lightning fire risk zoning system, characterized in that, The system includes: a data acquisition module for acquiring forest environmental data and forest meteorological data of the risk area to be classified; a forest fire weather index acquisition module for determining reference meteorological stations, correcting the forest meteorological data based on the reference meteorological stations to obtain target meteorological data, and calculating the forest fire weather index based on the target meteorological data; a forest lightning disaster factor index acquisition module for determining lightning location data, calculating the ground flash density and intensity of the forest area based on the lightning location data, and obtaining the forest lightning disaster factor index; and a vulnerability index acquisition module for determining vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation indices based on the risk area to be classified, including: calculating the topographic impact index, soil moisture impact index, and water flame retardancy index based on the risk area to be classified, creating a vulnerability model to select vulnerability characteristic factors; calculating the forest flammability index and population economic exposure index, creating an exposure model to select exposure characteristic factors; and calculating the disaster relief accessibility index and medical... The system includes a medical convenience index, a disaster prevention and mitigation model, and a disaster prevention and mitigation index. It normalizes the vulnerability characteristic factors, exposure characteristic factors, and disaster prevention and mitigation index to calculate the vulnerability index of the disaster-bearing body. A regional division module is used to divide the area to be classified into various risk zoning levels based on the forest fire weather index, the forest lightning disaster-causing factor index, and the vulnerability index of the disaster-bearing body, using the natural breakpoint method in ArcGIS. This includes: normalizing the forest fire weather index, the forest lightning disaster-causing factor index, and the vulnerability index of the disaster-bearing body, and calculating a gridded vulnerability index of the disaster-bearing body; constructing a forest lightning fire risk zoning model based on the vulnerability index of the disaster-bearing body, the forest fire weather index, and the forest lightning disaster-causing factor index, and calculating a forest lightning fire risk assessment index; and dividing the area to be classified into various risk zoning levels based on the forest lightning fire risk assessment index using the natural breakpoint method in ArcGIS.

7. The forest area lightning fire risk zoning system according to claim 6, characterized in that, The forest fire weather index acquisition module is also used to: determine the regional meteorological station for the risk area to be classified; the forest meteorological data is collected by the regional meteorological station, and the forest meteorological data is meteorological data within the target time period; and correct the forest meteorological data according to the meteorological data of the reference meteorological station to obtain the target meteorological data.

8. The forest area lightning fire risk zoning system according to claim 7, characterized in that, The forest fire weather index acquisition module is also used for: determining the spatial distribution pattern of climate elements based on the forest meteorological data; correcting the forest meteorological data using a linear or nonlinear model based on the spatial distribution pattern of climate elements to obtain a correction index; and performing gridding processing on the regional meteorological stations to obtain processed climate spatial distribution data.

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