CN114282743A - Fine forest fire danger grade forecasting method for township administrative area - Google Patents
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Abstract
The invention discloses a fine forest fire danger grade forecasting method facing a township administrative area, which comprises the following steps: constructing a forest fire danger grade forecasting model; inverting forest fire risk factors slowly changing along with time in a forest fire risk grade forecasting model by a remote sensing inversion method to construct a satellite remote sensing product database; constructing a global weather forecast product database; preprocessing data by using a GIS data management tool; carrying out normalization processing on the data in the last step according to the fire risk factor weight calculation principle to obtain forest fire risk forecast data; performing grid data superposition on the forest fire risk forecast data in the step by using a GIS space analysis function; and processing the superposed forest fire prediction data according to fire grade division standards, calculating the fire grade of each village and town, and generating vector data. The method effectively solves the problem that the national existing forest fire weather forecast and each provincial and urban level forest fire forecast cannot meet the refined requirement.
Description
Technical Field
The invention relates to the technical field of remote sensing application technology (RS) and Geographic Information System (GIS), in particular to a method for finely forecasting forest fire danger grades facing rural government regions.
Background
The national forest fire danger meteorological rating is released in 2018, a national forest fire danger early warning system is preliminarily established, the fire danger rating can be forecasted in time, fire danger forecasting and early warning information is made and released, and scientific basis is provided for decision-making deployment of forest fire prevention and control work.
At the present stage, most forest fire prediction in China is macroscopic, regional large-scale prediction, and city-saving forest fire prediction mostly mainly focuses on issuing a forest fire grade in one city, and specific and fine prediction is not performed on rural and township administrative areas, so that social resources are wasted, and accurate defense deployment cannot be achieved.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the invention aims to provide a fine forest fire danger grade forecasting method facing a township administrative area, and the method can better realize the fine forest fire danger forecasting of the township administrative area.
In order to achieve the purpose, the embodiment of the invention provides a fine forest fire risk grade forecasting method facing a township administrative area, which comprises the following steps: step S1, constructing a forest fire danger grade forecasting model based on the national forest fire danger meteorological forecasting model; s2, inverting forest fire risk factors slowly changing along with time in the forest fire risk grade forecasting model by a remote sensing inversion method to construct a satellite remote sensing product database; step S3, acquiring 14-hour weather forecast data of 24 hours, 48 hours, 72 hours, 96 hours, 120 hours, 144 hours and 168 hours in the future of the world by using the global numerical forecast data website to construct a global weather forecast product database; step S4, utilizing a GIS data management tool to carry out data preprocessing on 12 forest fire hazard causing factors in the satellite remote sensing product database and the global weather forecast product database; step S5, according to the fire factor weight calculation principle in the forest fire level forecasting model, carrying out normalization processing on the 12 forest fire factors to obtain forest fire forecast data; step S6, performing grid data superposition on the forest fire prediction data in the step S6 by utilizing a GIS space analysis function; and step S7, processing the superposed forest fire prediction data according to fire grade division standards, calculating the fire grade of each village and town, and generating vector data.
According to the method for forecasting the refined forest fire danger grade facing the township administrative area, a remote sensing database and a meteorological forecast database are constructed by taking a forecast scale of the optimized forest fire danger as an entry point and taking a remote sensing means as a support, the problem that the national existing forest fire danger meteorological forecast and each provincial and urban forest fire forecast cannot meet the refined requirement is effectively solved through a data management tool and a space analysis function of a GIS according to an autonomously researched and developed forest fire early warning model, the problem that the refined forest fire danger grade special topic with the township administrative area as a minimum forecast unit is made, and a corresponding conclusion can be obtained.
In addition, the method for forecasting the fire level of the refined forest facing the township administrative area according to the embodiment of the invention may further have the following additional technical features:
further, in an embodiment of the present invention, in step S1, based on the national forest fire weather forecast model, forest region terrain factors, combustible type factors, combustible water content factors, combustible carrying capacity factors, and human activity factors are considered, forest fire grade forecast models of different administrative regions and different forest regions are established, weights of the weather condition factors, the forest region terrain factors, the combustible type factors, the combustible water content factors, the combustible carrying capacity factors, and the human activity factors are calculated by a principal component analysis method and an expert scoring method, and a weight calculation rule is determined.
Further, in an embodiment of the present invention, the time-varying forest fire risk factors in step S2 include: terrain data, forest type data, forest drought characteristic data, forest canopy density data, and human activity data.
Further, in an embodiment of the present invention, the process of inverting the terrain data in step S2 is: according to the method, the altitude data of a required area is obtained through three-dimensional reconstruction of DSM data and other area data through Jilin I video star, the data of the terrain with 12.5 meters are obtained through inversion, gradient data and slope direction data are calculated according to the altitude data, the spatial resolution is 12.5 meters, the local area is 3 meters, and the updating frequency is 1 time/year.
Further, in an embodiment of the present invention, the process of inverting the forest type data in step S2 is: according to basic geographic national condition monitoring content indexes, by means of a Jilin I high-resolution multispectral remote sensing image as auxiliary data, under the condition that forest investigation pattern spot types do not exist, by means of a human-computer interaction semi-automatic forest extraction algorithm, forest vegetation types of different provinces and cities are automatically classified, coniferous forests, broad-leaved mixed forests and shrubs are obtained, the spatial resolution is 5 meters, and the updating frequency is 1 time/year.
Further, in an embodiment of the present invention, the process of inverting the forest drought characteristic data in step S2 is: selecting MODIS satellite data synthesized every 8 days, and utilizing ground temperature data and earth surface reflectivity data to construct temperature vegetation drought index inversion to obtain forest drought distribution conditions of different provinces and cities and different regions, wherein the obtained resolution is 1 km, and the updating frequency is 1 time/8 days.
Further, in an embodiment of the present invention, the process of inverting the forest canopy density data in step S2 is: and (3) selecting 8-month MODIS images and Jilin' I spectrum star images to carry out inversion of the canopy intensity, and obtaining that the spatial resolution is 1 kilometer and the updating frequency is 1 time/year.
Further, in an embodiment of the present invention, the process of inverting the human activity data in step S2 is: the method comprises the steps of extracting roads and residential areas around a forest area by utilizing a Jilin I high-resolution satellite remote sensing image through an automatic classification and visual interpretation method, taking the roads and the residential areas as human activity data, wherein the spatial resolution is 0.75 m, and the updating frequency is 1 time/year.
Further, in an embodiment of the present invention, after the forest fire prediction data is superimposed through the GIS spatial analysis function in step S6, normalization processing and forest area masking processing are performed again on the superimposed data.
Further, in an embodiment of the present invention, the step S7 specifically includes: according to the vector boundary of the villages and towns, cutting the forest fire prediction data after the forest area is masked, and calculating the forest fire grade average value in the range of the forest area in each village and towns; and assigning values to the forest fire grades in the forest region range in each village and town according to the forest fire grade division standard, and generating village-grade forest fire grade forecast vector data of 24 hours, 48 hours, 72 hours and a week in the future.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a forest fire risk level forecasting method for township administrative areas according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a detailed implementation of a flow chart of a method for forecasting fire ratings of refined forests in rural administrative areas according to an embodiment of the present invention;
FIG. 3 is a diagram of a refined forest fire rating forecast in Beijing City, 5 months and 10 days in 2021 according to an embodiment of the present invention;
FIG. 4 is a diagram of a refined forest fire risk rating forecast from Hangzhou city, 5/10/2021 according to an embodiment of the present invention;
FIG. 5 is a graph showing the trend of the forecast of the forest fire weather level in Beijing City, 5.5.10.2021 and a part of the area according to an embodiment of the present invention;
FIG. 6 is a graph showing the trend of the forecast of the forest fire weather image level in Hangzhou city of 10/5/2021 and part of the area according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The detailed forest fire risk grade forecasting method for township administrative areas provided by the embodiment of the invention is described below with reference to the attached drawings.
Fig. 1 is a flowchart of a forest fire risk level forecasting method for township administrative districts according to an embodiment of the present invention.
As shown in fig. 1, the method for forecasting the fire risk level of the refined forest facing to the township administrative area comprises the following steps:
in step S1, a forest fire rating prediction model is constructed based on the national forest fire weather prediction model.
Further, in an embodiment of the present invention, step S1 is based on the national forest fire weather forecast model, and takes into account weather condition factors, forest region terrain factors, combustible type factors, combustible water content factors, combustible loading capacity factors, and human activity factors, to establish forest fire grade forecast models for different administrative regions and different forest regions, and calculates the weights of the weather condition factors, forest region terrain factors, combustible type factors, combustible water content factors, combustible loading capacity factors, and human activity factors by a principal component analysis method and an expert scoring method, and determines the weight calculation rule at the same time.
Specifically, the forest fire danger grade forecasting model independently constructed in the embodiment of the invention is based on a national forest fire danger weather forecasting model, comprehensively considers factors such as weather conditions, forest region terrain, combustible types, combustible water contents, combustible carrying capacity and human activities, establishes forest fire danger grade forecasting models aiming at different administrative regions and different forest regions, and determines the weight and the calculation rule of each factor in the model through methods such as principal component analysis and expert scoring. The forest fire danger of the forest fire occurrence place in 5 years can be analyzed by referring to a national forest fire danger grade forecasting and responding work management method and a national forest fire graded responding program, and the result of the model is scientifically forecasted and graded.
It should be noted that, because the breadth of our country is broad, the climate conditions, terrain conditions and forest region characteristics of different provinces and cities and different regions are different, the weighting factors and parameters in the forest fire risk level forecasting models of different provinces and cities need to be dynamically adjusted.
In step S2, the forest fire risk factors which change slowly with time in the forest fire risk level forecasting model are inverted through a remote sensing inversion method, so that a satellite remote sensing product database is constructed.
Further, in an embodiment of the present invention, the time-varying forest fire risk factor in step S2 includes: terrain data, forest type data, forest drought characteristic data, forest canopy density data, and human activity data.
Specifically, the forest fire danger level forecasting model constructed in the embodiment of the invention inverts forest fire danger factors which change slowly along with time in the model by a remote sensing means, and mainly comprises the following steps:
(1) topographic data
According to the embodiment of the invention, the elevation data of a required area is obtained by inverting the three-dimensional reconstruction DSM data of Jilin I video star and other area data through the public terrain data of 12.5 meters, the slope data and the slope direction data are calculated according to the elevation data, the spatial resolution is 12.5 meters, the local area is 3 meters, and the updating frequency is 1 time/year.
(2) Forest type data
Based on 'Jilin I' high resolution and multispectral remote sensing images, according to basic geographic national condition monitoring content indexes and different forest type combustible grades, the high resolution remote sensing images are used as auxiliary data, under the condition that forest investigation pattern types do not exist, automatic classification of forest vegetation types in different provinces and cities is carried out by means of a human-computer interaction semi-automatic forest extraction algorithm, the types mainly comprise coniferous forests, broad-leaved forests, coniferous forests, broad-leaved mixed forests and shrubs, the spatial resolution of forest type data can be 5 m, and the updating frequency is 1 time/year.
(3) Data on drought characteristics of forest
The embodiment of the invention selects MODIS satellite data synthesized every 8 days, constructs temperature vegetation drought index inversion by utilizing ground temperature data and surface reflectivity data to obtain forest drought distribution conditions of different cities and different areas, wherein the resolution ratio is 1 kilometer, and the updating frequency is 1 time/8 days.
(4) Forest canopy density data
The forest canopy density is the ratio of the total projected area of the crown on the ground under the direct sunlight to the total area of the forest land in the forest, and is often used for representing the sparseness and denseness degree of the forest. China usually selects forests of 8-9 months per year to calculate the canopy density, so the embodiment of the invention selects MODIS images of 8 months and Jilin spectral star images for inversion of the canopy density, the spatial resolution is 1 km, and the updating frequency is 1 time/year.
(5) Human activity data
The influence of human activities on forest fire danger is mainly determined by the conditions of distance of human living and distance of roads, so that the embodiment of the invention utilizes the Jilin I high-resolution satellite remote sensing image to extract the roads and residential areas around the forest area by an automatic classification and visual interpretation method, and uses the roads and the residential areas as human activity data, the spatial resolution is 0.75 m, and the updating frequency is 1 time/year.
In step S3, a global weather forecast product database is constructed by using the global numerical forecast data website to acquire 14-hour weather forecast data of 24 hours, 48 hours, 72 hours, 96 hours, 120 hours, 144 hours and 168 hours in the future of the world.
Specifically, according to the national weather forecast standard, the weather data at 14 hours per day is the optimal value of the data all day, so that the 14-hour weather forecast data at 24 hours, 48 hours, 72 hours, 96 hours, 120 hours, 144 hours and 168 hours in the future of the world are automatically acquired through the united states global numerical forecast data website, and are used as a refined forest fire risk forecast weather database, namely a global weather forecast product database, wherein the spatial resolution of the database is 25 kilometers, and the updating frequency is 1 time/day.
It should be noted that according to the meteorological factors required in the forest fire risk level forecast model, data such as air temperature, relative humidity, wind speed, precipitation amount, precipitation type and the like are extracted from the meteorological forecast database and used as basic meteorological data, so that subsequent data preprocessing is facilitated.
In step S4, data preprocessing is performed on the 12 forest fire hazard causing factors in the satellite remote sensing product database and the global weather forecast product database by using the GIS data management tool.
Specifically, in the satellite remote sensing product database established in step S2 and the global weather forecast product database established in step S3, the spatial resolution of each forest fire risk influence factor data is different, so that in order to facilitate calculation of a subsequent model, data in the databases needs to be preprocessed, and resampling of the data and cutting of the data are mainly included.
The forest fire danger level forecasting model extracts forest fire danger causing factors based on multi-source basic data, and due to the fact that spatial resolutions of different data sources are different and meanwhile the requirement for refinement of the county and town level forecasting scale needs to be met, data of grid-level forest fire danger causing factors in a database are resampled to be 100 meters in a unified scale.
The data in the remote sensing product database and the data in the weather forecast product database are global data, and meanwhile, the forest fire danger level forecast models of different provinces and cities need to be dynamically adjusted, so that the data need to be cut out of the different provinces and cities, the specific analysis of the forest fire danger levels of specific areas is achieved, the precision of the forest fire danger forecast levels is improved, and the model operation time is prolonged.
In step S5, the normalization processing is performed on the 12 kinds of forest fire risk causing factors according to the fire risk factor weight calculation principle in the forest fire risk grade prediction model, so as to obtain forest fire risk prediction data.
Namely, according to the fire factor weight calculation principle in the forest fire risk grade forecasting model, 12 forest fire risk causing factors required by the model are normalized, all data are normalized between 0 and 1, and forest fire risk forecasting data are obtained.
In step S6, grid data superposition is performed on the forest fire prediction data in step S6 by using a GIS space analysis function.
Specifically, according to the calculation principle and the calculation formula of the forest fire danger level forecasting model, after forest fire danger forecasting data are superposed through a GIS space analysis function, normalization processing and forest area mask processing are carried out on the superposed data again.
The calculation formula is as follows:
FRID＝0.4*A+0.35*B+0.15*C+0.1*D
wherein FRID is a meteorological index of forest fire, A is a meteorological factor, B is a vegetation factor, C is a social factor, and D is a terrain factor.
In step S7, the superimposed forest fire prediction data is processed according to the fire rating classification standard, the fire rating of each town is calculated, and vector data is generated.
Specifically, according to the vector boundary of the villages and towns, cutting forest fire prediction data after the forest zones are masked, and calculating the forest fire grade average value in the range of the forest zones in each village and towns; and assigning values to the forest fire grades in the forest region range in each village and town according to the forest fire grade division standard (shown in the following table 1), and generating village grade forest fire grade forecast vector data of 24 hours, 48 hours, 72 hours and a week in the future.
TABLE 1 forest fire hazard grading Standard
Taking Beijing and Hangzhou demonstration areas as examples, as shown in FIG. 2, by the method for forecasting forest fire disaster grades based on the refinement of forest fire disaster grades in rural administrative areas provided by the embodiment of the invention, the forest fire disaster grades in 5-10 months in 2021 are subject to thematic map making, analysis and display, the results are shown in FIGS. 3-4 and are compared with the forest fire disaster weather grade forecasts issued by the country, as shown in FIGS. 5-6, the forecasting results finally obtained by the embodiment of the invention are completely consistent with the trend of the national forest fire disaster weather forecasts, which indicates that the embodiment of the invention can better realize the forest fire disaster forecast in rural administrative areas.
According to the method for forecasting the refined forest fire danger grade facing the township administrative area, provided by the embodiment of the invention, the remote sensing database and the meteorological forecast database are constructed by taking the forecast scale of the optimized forest fire danger as an entry point and taking a remote sensing means as a support, and the problem that the national existing forest fire danger meteorological forecast and each provincial and urban forest fire danger forecast cannot meet the refined demand is effectively solved according to the independently researched and developed forest fire danger early warning model through the data management tool and the space analysis function of the GIS, so that the preparation of the refined forest fire danger grade special topic taking the township administrative area as the minimum forecast unit is realized, and the corresponding conclusion can be obtained.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A fine forest fire risk grade forecasting method for a township administrative area is characterized by comprising the following steps:
step S1, constructing a forest fire risk grade forecasting model based on the national forest fire risk meteorological forecasting model;
s2, inverting the forest fire risk factors slowly changing along with time in the forest fire risk grade forecasting model by a remote sensing inversion method to construct a satellite remote sensing product database;
step S3, acquiring 14-hour weather forecast data of 24 hours, 48 hours, 72 hours, 96 hours, 120 hours, 144 hours and 168 hours in the future of the world by using the global numerical forecast data website to construct a global weather forecast product database;
step S4, utilizing a GIS data management tool to carry out data preprocessing on 12 forest fire hazard causing factors in the satellite remote sensing product database and the global weather forecast product database;
step S5, according to the fire factor weight calculation principle in the forest fire level forecasting model, carrying out normalization processing on the 12 forest fire factors to obtain forest fire forecast data;
step S6, performing raster data superposition on the forest fire prediction data in the step S6 by utilizing a GIS space analysis function;
and step S7, processing the superposed forest fire prediction data according to the fire grade division standard, calculating the fire grade of each village and town, and generating vector data.
2. A forest fire-risk grade forecasting method for rural areas according to claim 1, wherein step S1 is based on a national forest fire-risk weather forecasting model, and takes into account weather conditions, forest area terrain, combustible type, combustible water content, combustible loading and human activities, to build forest fire-risk grade forecasting models for different areas and different forest areas, and calculates the weather conditions, forest area terrain, combustible type, combustible water content, combustible loading and human activities by a principal component analysis method and an expert scoring method, and determines a weight calculation rule.
3. A method for fine forest fire rating forecast for township-oriented administrative areas according to claim 1, wherein said forest fire risk factors slowly changing with time in step S2 include: terrain data, forest type data, forest drought characteristic data, forest canopy density data, and human activity data.
4. A township-government-oriented fine forest fire-risk grade forecasting method according to claim 3, wherein the process of inverting the terrain data in step S2 is: according to the method, the altitude data of a required area is obtained through inversion of Jilin I video star three-dimensional reconstruction DSM data and other area data through public terrain data of 12.5 meters, gradient data and slope direction data are calculated according to the altitude data, the spatial resolution is 12.5 meters, the local area is 3 meters, and the updating frequency is 1 time/year.
5. A township-government-oriented fine forest fire-risk grade forecasting method according to claim 3, wherein the process of inverting the forest type data in the step S2 is as follows: according to basic geographic national condition monitoring content indexes, by means of a Jilin I high-resolution multispectral remote sensing image as auxiliary data, under the condition that no forestry investigation pattern spot type exists, by means of a human-computer interaction semi-automatic forestry extraction algorithm, forest vegetation types of different provinces and cities are automatically classified, coniferous forests, broad-leaved forests, coniferous mixed forests and shrubs are obtained, the spatial resolution is 5 m, and the updating frequency is 1 time/year.
6. A method for forest fire rate forecasting based on township political district refinement as claimed in claim 3, wherein the step S2 for inverting the forest drought characteristic data comprises: selecting MODIS satellite data synthesized every 8 days, and utilizing ground temperature data and earth surface reflectivity data to construct temperature vegetation drought index inversion to obtain forest drought distribution conditions of different provinces and cities and different regions, wherein the obtained resolution is 1 km, and the updating frequency is 1 time/8 days.
7. A method for forest fire rate forecasting based on refinement of rural areas according to claim 3, wherein the step S2 for inverting the forest canopy density data comprises: and (3) selecting 8-month MODIS images and Jilin' I spectrum star images to carry out inversion of the canopy intensity, and obtaining that the spatial resolution is 1 kilometer and the updating frequency is 1 time/year.
8. A township-oriented fine forest fire-risk grade forecasting method according to claim 3, wherein the process of inverting the human activity data in step S2 is as follows: the method comprises the steps of extracting roads and residential areas around a forest area by utilizing a Jilin I high-resolution satellite remote sensing image through an automatic classification and visual interpretation method, taking the roads and the residential areas as human activity data, wherein the spatial resolution is 0.75 m, and the updating frequency is 1 time/year.
9. The method for finely forecasting the forest fire disaster grade towards the township administrative area according to claim 1, wherein after the forest fire disaster forecast data are overlaid through a GIS space analysis function in the step S6, normalization processing and forest area masking processing are performed again on the overlaid data.
10. The method for forecasting the fire ratings of refined forests in township administrative areas according to claim 1, wherein the step S7 specifically comprises:
according to the vector boundary of the villages and towns, cutting the forest fire prediction data after the forest area is masked, and calculating the forest fire grade average value in the forest area range in each village and towns;
and assigning values to the forest fire grades in the forest region range in each village and town according to the forest fire grade division standard, and generating village-grade forest fire grade forecast vector data of 24 hours, 48 hours, 72 hours and a week in the future.
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