WO2021194165A1

WO2021194165A1 - Forest fire risk forecast method using artificial satellite data

Info

Publication number: WO2021194165A1
Application number: PCT/KR2021/003374
Authority: WO
Inventors: 이주형
Original assignee: 고려대학교 산학협력단
Priority date: 2020-03-23
Filing date: 2021-03-18
Publication date: 2021-09-30
Also published as: KR20210118695A; KR102481827B1

Abstract

Disclosed is a forest fire risk forecast method using artificial satellite data. According to one aspect of the present invention, provided is a forest fire risk forecast method using artificial satellite data, which is implemented on a processor, the forest fire risk forecast method using artificial satellite data comprising the steps of: obtaining vegetation information by using data observed by a artificial satellite; calculating soil moisture by using the data observed by the artificial satellite; and determining a forest fire outbreak risk from the vegetation information and the soil moisture.

Description

How to predict forest fire risk using satellite data

The present invention relates to a method for predicting forest fire risk using satellite data. More specifically, it relates to a forest fire risk forecasting method using satellite data, which can predict the risk of a local forest fire that is not easily accessible to humans by using vegetation information and soil moisture obtained by observation from a satellite.

Large-scale wildfires spread by the wind in a dry state of vegetation and fallen leaves are over 1000 ha in size. . The characteristics of these large-scale forest disasters are not easy to grasp or approach with the human eye, so countermeasures or forecast management are essential.

The Korea Forest Service also uses the Daily Weather Index (DWI) model to forecast the risk of forest fires using weather information such as humidity and wind speed and clinical information such as forest distribution information.

In predicting the risk of forest fires, it is important to understand the combustibility of materials such as trees and fallen leaves accumulated in the mountains for many years that can be fueled. have.

In addition, there is a limit to predicting the risk of large-scale wildfires that are not easily accessible to humans.

An object of the present invention is to provide a method for predicting forest fire risk using satellite data, which can predict the risk of forest fires to areas that are not easily accessible by humans using vegetation information and soil moisture obtained by observation from satellites.

According to one aspect of the present invention, there is provided a forest fire risk forecasting method using satellite data implemented on a processor, the method comprising: obtaining vegetation information using data observed from the satellite; calculating soil moisture using the data observed from the satellite; A forest fire risk forecasting method using satellite data is provided, including the step of determining the risk of a forest fire from the vegetation information and the soil moisture.

The vegetation information may include a vegetation temperature (Tc) according to the following [Equation].

[ceremony]

here,

A passive microwave sensor may be mounted on the satellite, and in this case, the soil moisture may be calculated using a brightness temperature obtained through the passive microwave sensor.

An infrared sensor may be mounted on the satellite. In this case, the soil moisture may be calculated using a brightness temperature calculated through the infrared sensor.

The satellite may be equipped with an active microwave sensor, and in this case, the soil moisture may be calculated using backscattering coefficients obtained through the active microwave sensor.

Determining the risk of occurrence of the forest fire comprises: calculating a vegetation stress index (Canopy Stress Index, CSI) according to the following [Equation];

[ceremony]

It may include the step of determining the risk of wildfire occurrence according to the vegetation stress index (Canopy Stress Index, CSI) value.

In the step of determining the risk of occurrence of a forest fire according to the Canopy Stress Index (CSI) value, it may be determined that the risk of occurrence of a forest fire is high when the CSI value is 120K/% or more.

According to an embodiment of the present invention, vegetation information and soil moisture can be calculated using data observed from satellites to predict the risk of forest fires even in areas that are not easily accessible by humans.

1 is a flowchart of a forest fire risk forecasting method using satellite data according to an embodiment of the present invention.

Figure 2 is a block diagram of a forest fire risk forecasting apparatus for performing a forest fire risk forecasting method using satellite data according to an embodiment of the present invention.

3 is a view showing a forest fire stress index in a method for predicting forest fire risk using satellite data according to an embodiment of the present invention.

Since the present invention can apply various transformations and can have various embodiments, specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and it should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the present invention. In describing the present invention, if it is determined that a detailed description of a related known technology may obscure the gist of the present invention, the detailed description thereof will be omitted.

Hereinafter, the forest fire risk forecasting method using satellite data according to the present invention will be described in detail with reference to the accompanying drawings, and in the description with reference to the accompanying drawings, the same or corresponding components are given the same reference numbers, A redundant description thereof will be omitted.

1 is a flowchart of a forest fire risk forecasting method using satellite data according to an embodiment of the present invention. And, FIG. 2 is a block diagram of a forest fire risk forecasting apparatus for performing a forest fire risk forecasting method using satellite data according to an embodiment of the present invention.

A forest fire risk forecasting method using satellite data according to this embodiment is a forest fire risk forecasting method using satellite data implemented on a processor, the method comprising: obtaining vegetation information using data observed from the satellite; calculating soil moisture using the data observed from the satellite; and determining the risk of forest fire from the vegetation information and the soil moisture.

In general, the forest fire risk forecasting method determines the possibility of a wildfire using clinical information and weather information of a certain area. For example, it obtains clinical information such as a 1/25,000 scale numerical clinical map issued by the National Academy of Forest Sciences and weather information such as relative humidity, wind speed, and temperature from weather stations installed across the country, and predicts forest fire risk based on it. will do

However, in this forest fire risk forecast, information on fuel combustibility of vegetation fuel, such as wood or fallen leaves, burned in a forest fire is missing, so a practical forest fire risk forecast is not achieved.

In this embodiment, it is possible to provide a forest fire risk forecast in consideration of the combustibility of vegetation by using information on vegetation information and soil moisture that can be calculated through data observed from artificial satellites. In other words, it is possible to forecast the risk of forest fire to a wider area inaccessible to humans by indirectly acquiring information on the combustibility of vegetation on the surface using vegetation information and soil moisture containing moisture. .

Hereinafter, according to the flowchart of FIG. 1 , a method for predicting forest fire risk using satellite data will be described in detail.

First, the vegetation information is obtained by using the data observed from the satellite (S100). Vegetation information is information about a group of plants growing on the surface of the earth, and the vegetation information of a certain area is indirectly grasped using data observed from satellites.

Since vegetation covering the surface of the earth contains moisture for growth, it is possible to predict the risk of forest fires along with information on soil moisture, which will be described later, by understanding the degree of drying of the vegetation.

Vegetation information that can be calculated using data observed from satellites includes vegetation index and canopy temperature. have.

The vegetation index is an index for emphasizing the characteristic information of vegetation using signals in wavelength bands sensitive to vegetation and signals in wavelength bands that are not sensitive to vegetation observed from satellites. Normalized differential vegetation index (NDVI), emphasized vegetation index (enhanced vegetation index; EVI) and the like. For example, the normalized differential vegetation index (NDVI) has a high value because the reflectivity in the near-infrared region is high in the case of a bush where vegetation grows vigorously, whereas the soil has a high value. There is almost no difference between the two reflectivity values, so it has a value close to 0.

Vegetation temperature (canopy temperature) is a factor indirectly indicating the moisture level of vegetation, and means a temperature indicating the interaction between the atmosphere and vegetation.

In this embodiment, a method of predicting the risk of a forest fire using the vegetation temperature (Tc) as the vegetation information will be described.

The vegetation temperature (Tc) can be calculated by the following [Equation 1].

[Equation 1]

here,

In order to calculate the vegetation temperature (T _c), is required, such as a directional surface temperature (T _R), the soil surface temperature (Ts) in accordance with the angle of view (view angle).

The directional surface temperature (T _R ) according to the view angle is calculated by the above formula, where T _B (θ) is the directional brightness temperature measured by satellite at a specific angle of view θ,

ε(θ) is the directional emissivity measured at the angle of view θ, and represents the efficiency of energy release from the surface of an object during thermal radiation. T _{SKY It is a} hemispherical melting point in the sky, and 2.7K, the amount of blackbody radiation that fills the universe, is applied.

n in [Equation 1] can be calculated according to the above equation through the wavelength (λ) of the sensor mounted on the artificial satellite that observed the brightness and temperature and the Plank function. T _O is the brightness temperature input to the Plank function. For Soil Moisture Active Passive (SMAP) satellites, n = 1 can be applied in the _{21 cm wavelength condition, that is, the brightness temperature measured at λ = 210000 μm, T O = 190 ~ 315K.} .

And, f in [Equation 1] is the portion occupied by vegetation among the pixel data of the satellite, and is an LAI function. LAI is the leaf area index measured by satellite, and φ is the view zenith angle measured by the LAI. For vegetated areas, a clumping factor can be considered in the above LAI.

The canopy temperature calculated according to [Equation 1] is a factor representing the moisture level of vegetation, and the moisture content of the vegetation is indirectly calculated and can be used to predict the risk of a forest fire together with soil moisture.

Next, using the data observed from the satellite calculates soil moisture (soil moisture) (S200). Soil moisture refers to the amount of water contained in the soil, and it can be expressed as a percentage by expressing the volume of water among the total volume of soil, water, and air. Such soil moisture can be calculated on a global scale through satellites.

The satellite is equipped with a microwave sensor so that passive microwaves or active microwaves can be observed. When passive microwaves are observed, soil moisture can be calculated through brightness temperature. In the case of observing active microwaves, soil moisture can be calculated using backscattering coefficients.

On the other hand, an infrared sensor may be mounted on the satellite, and it is also possible to calculate a brightness temperature from a value observed through the infrared sensor, and to calculate soil moisture therefrom.

The soil moisture can indirectly confirm the moisture contained in the soil, and information on the combustibility of vegetation near the surface of the earth can be obtained together with the above-described vegetation information.

Next, the forest fire risk is determined from the vegetation information and soil moisture (S300). According to the method described above, vegetation information is obtained using data observed from satellites, and by standardizing it with the calculated value of soil moisture in the area, information on the combustibility of vegetation on the ground can be obtained and the risk of forest fire can be determined through this. .

In this embodiment, in order to quantitatively present the basis for judging the risk of such a forest fire, the vegetation stress index (CSI) according to the following [Equation 2] is calculated, and the method of judging the risk of forest fire according to this is calculated. present.

[Equation 2]

In [Equation 2], T _C is the vegetation temperature and can be calculated according to [Equation 1] using the data observed from the satellite, and the soil moisture [%] observed by the satellite is the above-mentioned brightness temperature or backscattering index using the is calculated

According to the study of the applicant, when the calculated Vegetation Stress Index (CSI) value is 120K/% or higher, it can be judged that the risk of forest fire is high, 80~120K/% is caution, 20~80K/% is medium, 20K Below /%, it can be judged that the risk of forest fire is low.

3 is a case in which the forest fire risk forecasting method using the satellite data is applied, and shows the vegetation stress index according to the fire area that has actually occurred.

For areas with a high risk forecast one week prior to the occurrence of the wildfire, such as the vegetation stress index indicated by the red line, the area actually damaged by the forest fire was large. After 4 days of the fire, the vegetation stress index was significantly reduced as shown in the blue line, and it was remarkably distinguished from the vegetation stress one week before the fire. .

2 is a block diagram of a forest fire risk forecasting apparatus for performing a forest fire risk forecasting method using satellite data according to an embodiment of the present invention. The forest fire risk forecasting method using satellite data may be implemented in a process including a computer .

2 , the database unit 12 , the calculation unit 14 , and the determination unit 16 are illustrated.

The database unit 12 may store data observed from satellites, vegetation information including vegetation temperature, soil moisture, and the like. The satellite may be equipped with a microwave sensor, and data observed therefrom may be stored in the database unit 12 . Of course, the database unit 12 may store data such as various geography, weather, vegetation information, and soil moisture in addition to the data observed by the microwave sensor.

The calculator 14 calculates and obtains vegetation information using data observed from the satellite, and calculates soil moisture (%) using the data observed from the satellite.

Vegetation temperature (canopy temperature), as a factor representing the moisture degree of vegetation, may be calculated by the calculator 14 according to [Equation 1].

In addition, the calculator 14, when the artificial satellite observes passive microwaves, can calculate soil moisture through the brightness temperature, and when observing active microwaves, backscattering coefficients Soil moisture can be calculated using

The determination unit 16 determines the risk of occurrence of a forest fire from the vegetation information and the vegetation information and the soil moisture calculated by the calculation unit 14 . In this embodiment, in order to quantitatively present the basis for the determination of the risk of forest fire occurrence, the determination unit 16 calculates the vegetation stress index (CSI) according to [Equation 2] above, and, accordingly, the risk of forest fire occurrence. provides a way to judge

Although the above has been described with reference to the embodiments of the present invention, those skilled in the art can variously modify the present invention within the scope without departing from the spirit and scope of the present invention described in the claims below. and can be changed.

Claims

As a forest fire risk forecasting method using satellite data implemented on a processor,

obtaining vegetation information using the data observed from the satellite;

calculating soil moisture using the data observed from the satellite;

A forest fire risk forecasting method using satellite data, comprising the step of determining the risk of a forest fire from the vegetation information and the soil moisture.
According to claim 1,

The vegetation information is

Forest fire risk forecasting method using satellite data, characterized in that it includes the vegetation temperature (Tc) according to the following [Equation].

[ceremony]

here,

here,

here,

here,
According to claim 1,

The satellite is equipped with a passive microwave sensor,

The soil moisture is

Wildfire risk forecasting method using satellite data, characterized in that calculated using the brightness temperature (brightness temperature) obtained through the passive microwave sensor.
According to claim 1,

The satellite is equipped with an infrared sensor,

The soil moisture is

Forest fire risk forecasting method using satellite data, characterized in that calculated using the brightness temperature (brightness temperature) obtained through the infrared sensor.
According to claim 1,

The satellite is equipped with an active microwave sensor,

The soil moisture is

Wildfire risk forecasting method using satellite data, characterized in that calculated using the backscattering coefficients (backscattering coefficients) obtained through the active microwave sensor.
3. The method of claim 2,

The step of determining the risk of occurrence of a forest fire,

Calculating a vegetation stress index (Canopy Stress Index, CSI) according to the following [Equation];

[ceremony]

A method of predicting forest fire risk using satellite data, comprising the step of determining the risk of forest fire occurrence according to the vegetation stress index (Canopy Stress Index, CSI) value.
7. The method of claim 6,

The step of determining the risk of wildfire occurrence according to the vegetation stress index (Canopy Stress Index, CSI) value,

When the CSI value is 120K/% or more, it is characterized in that it is determined that there is a high risk of forest fire.