CN116839733A - Himaware-9-based variable time energy threshold fire remote sensing monitoring method - Google Patents
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- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
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Abstract
The invention discloses a variable time energy threshold fire remote sensing monitoring method based on Himawaii-9, and aims to mainly solve the problem that a remote sensing fire monitoring important data source Himawaii-8 is retired, develop a small-scale fire monitoring method of Himawaii-9 data aiming at a succession satellite Himawaii-9 satellite, evaluate the precision of the small-scale fire monitoring method in small-scale fire monitoring application, and expand the remote sensing fire monitoring method. The method mainly comprises the following steps: preprocessing (projection, calibration, atmosphere correction, cloud judgment and the like) based on Himawaii-9 satellite data, construction of a conventional background lighting Wen Shixu change function, determination of a conventional background lighting Wen Shixu change rate threshold, calculation of a satellite monitoring target pixel brightness temperature change rate, fire judgment and accuracy verification. The method verifies the monitoring capability of the new Himaware-9 data on small-scale fire, and expands the data source and method for remote sensing fire monitoring.
Description
Technical Field
The invention relates to the technical field of remote sensing fire monitoring, in particular to a variable time energy threshold fire remote sensing identification monitoring method based on Himawari-9 satellite data.
Background
With the continuous aggravation of global climate warming trend, natural conditions and human activities are more likely to cause the occurrence of fires such as forest and grasslands around the world, thereby threatening the safety of human lives and property and further aggravating global climate warming. Therefore, early monitoring and minimum monitoring of forest and grassland fires which develop rapidly and cause serious losses, especially in areas such as the original forests which are difficult to observe on the ground, provide earliest information for extinguishing fires, and are necessary ways for minimizing the influence degree of the fires. Satellite remote sensing, especially a stationary meteorological satellite, has become an important means for global fire business monitoring by virtue of the advantages of large range, strong behavior, high frequency and low cost. The sunflower satellite No. 8 (Himaware-8, hereinafter referred to as H8) is an international advanced stationary weather satellite which is transmitted by Japan weather service in 2014, 10 months and 7 days, and is formally started in 2015, 7 months and 7 days. The carried AHI imager (AdvancedHimawari Imager) comprises 16 channels from visible light to infrared, the spatial resolution of 3 channels of visible light is 500m, the spatial resolution of 13 channels of near infrared and infrared is 1km and 2km respectively, and the full disc observation can be completed every 10 min. H8 is widely applied to fire monitoring business by virtue of high space-time resolution and high data quality, but H8 is converted into standby operation in 12 months 13 of 2022, and data is not issued any more. Satellite No. 9 sunflower (Himaware-9, hereinafter referred to as H9) was launched on day 11 and day 2 of 2016, and day 12 and day 13 of 2022 began to take over from H8 for observation. H9 parameters are consistent with H8 and are planned to run to 2029, so in the next 6 years, H9 will become one of the important fire monitoring service stars worldwide.
The basic principle of remote sensing fire identification mainly depends on two conditions of heat radiation enhancement caused by temperature rise and difference of different heat infrared channel growth amplitudes. The main method of remote sensing fire judgment at the present stage is a context grammar, and is mainly based on single-time remote sensing data to calculate background lighting and high-temperature suspicious pixels in a window to monitor the fire, but the context grammar judgment threshold is higher, and small-scale fire at the initial stage of the fire is difficult to judge in time. The method is characterized in that the observation angle of a static weather satellite is comprehensively considered to be fixed, the front and back time positioning of the same-name pixels on an image is almost unchanged, the bright temperature of an infrared channel in the static weather satellite is only under the condition of solar radiation, the bright temperature of the pixels within 10min is different to be very small, the bright temperature of the adjacent time is used as the bright temperature of the background, when the bright temperature change rate of a certain time exceeds a threshold value, open fire is considered to occur, namely, the difference of the bright temperature of the pixels in the observation time is detected through the remote sensing data of the upper time and the lower time to monitor fire. The time-varying energy threshold method has a judgment threshold far lower than that of the context method, and is favorable for timely finding judgment in early fire.
Therefore, aiming at the problems, the invention discloses a time-varying energy threshold fire remote sensing monitoring method based on Himawai-9 satellite data, verifies the accuracy of the Himawai-9 satellite data in small-scale fire monitoring, is suitable for future monitoring requirements of small-scale fires, and provides a new technical method for multi-source remote sensing fire monitoring.
Disclosure of Invention
The invention aims to mainly solve the problems that an important satellite Himawaii-8 for fire remote sensing monitoring is retired, small-scale fire monitoring capability of a satellite Himawaii-9 is required to be evaluated and the like, and provides a variable-time energy threshold high-sensitivity fire remote sensing identification monitoring method based on new stationary meteorological satellite Himawaii-9 data, so that a new technical method is provided for expanding a fire monitoring data source and for multi-source remote sensing fire monitoring.
The invention combines Himaware-9 static meteorological satellite channel characteristics on the basis of Himaware-8 fire judgment algorithm, improves the algorithm, and provides a Himaware-9 time-varying energy threshold method fire automatic monitoring method based on conventional background brightness Wen Shixu change characteristics, conventional background brightness Wen Shixu change rate threshold and satellite monitoring target pixel brightness temperature change rate.
A high-sensitivity fire remote sensing identification monitoring method based on Himawaii-9 satellite data time-varying energy threshold comprises the following steps:
(1) Data preprocessing: himawai-9 data projection conversion, radiation correction, atmospheric correction, etc.;
(2) Clear sky pixel marking: himawai-9 data is used for judging pixels such as clouds, water bodies, desert areas and the like;
(3) Constructing a conventional background lighting Wen Shixu change function;
(4) A conventional background lighting Wen Shixu rate of change threshold;
(5) Calculating the brightness temperature change rate of the satellite monitoring target pixel;
(6) Fire point judgment and identification;
(7) And (5) verifying precision.
As a further improvement of the invention, in the step (1), the reflectivity of the near infrared of the visible light and the bright temperature value of the thermal infrared radiation are corrected by utilizing the zenith angle and azimuth angle of the satellite and the sun for radiometric calibration; performing atmospheric correction on the thermal infrared channel data by using a 6S radiation transmission model; and project the original Himawari-9 full disk data.
As a further improvement of the invention, in the step (2), cloud area pixels, water body pixels, desert area pixels and the like have high reflection characteristics, and misjudgment of fire point monitoring is easy to be caused, so that cloud detection is performed by utilizing the high reflection (daytime) characteristic of visible light in the cloud area and the temperature characteristic of a thermal infrared channel; extracting the water body by utilizing the characteristic of the water body in a near infrared band; marking the pixels of the desert area by using land utilization type data; so as to obtain clear sky partial pixels for judging suspected fires.
Further, the cloud area pixel marking method is as shown in formula (1):
R VIS ＞R VIS_TCTH and T is FIR ＜T FIR_TCTH (1)
Wherein R is VIS : visible light channel reflectivity (%) corresponding to the 1 st, 2 nd, 3 rd channels of the H9/AHI; r is R VIS_TCTH : visible light channel reflectivity cloud zone identification threshold (%), T FIR : far infrared channel brightness temperature (K), corresponding to channel 14 of H9/AHI; t (T) FIR_TCTH : far infrared channel brightness temperature cloud zone identification threshold (K); r is R VIS_TC The reference value is 20%; t (T) FIR_TCTH The reference value is 270K.
Further, the water body pixel marking method is as shown in formula (2):
R NIR ＜R NIR_TWTH and (R) NIR ﹣R VIS )＜0 (2)
Wherein R is NIR : near infrared channel reflectance (%) corresponding to channel 4 of H9/AHI; r is R NIR_TWTH : near infrared channel reflectivity water body identification threshold (%), R NIR_TWTH The reference value is 10%.
Further, if the land use type of the pixel is a desert area, the pixel is marked as a pixel of the desert area.
As a further improvement of the invention, in the step (3), in order to judge whether the brightness temperature change of the background pixel at any moment in the clear sky condition belongs to an abnormal condition, the conventional time sequence change characteristic of the background brightness temperature needs to be analyzed to construct a time sequence change function of the background brightness temperature. The bright temperature difference of the middle infrared channel of the stationary meteorological satellite Himaware-9 which is conventionally adjacent for 10min is not generally less than 0.5K, but can be influenced by time and place to fluctuate. By analyzing the change characteristics of the bright Wen Shixu of the infrared pixels in the conventional forest area pixels, the change amplitude of the bright temperature of the time when the middle infrared channels are adjacent has a certain daily change rule, wherein the heating amplitude from the early morning to the morning is larger, the maximum amplitude exceeds 1K, the change amplitude from the midday to the evening is smaller, and the maximum amplitude is smaller than 0.5K. Through analyzing the infrared bright temperature change data analysis in the conventional clear air under-cushion surface, the bright temperature change of the under-cushion surface and the solar angle change can be simplified into 3 time periods: (1) a daytime temperature rise period, (2) a daytime temperature reduction period, and (3) a nighttime temperature reduction period.
As a further improvement of the present invention, in the step (3), a conventional background lighting Wen Shixu change function construction is performed. It can be obtained by combining the above special authentication and the ground surface heating basic principle that the radiant energy is positively correlated with the solar altitude, so that the relationship between the ground surface background brightness temperature and the solar altitude can be described as formula (3) and is described as:
wherein T is instantaneous bright temperature; t (T) max Is the highest bright temperature of the day; t (T) min Is the lowest sunlight temperature; t (T) 1 Is the bright temperature when the solar altitude drops to 0 deg..
As a further improvement of the invention, in the step (3), the difference of the brightness temperature difference value is converted into the brightness temperature change rate in order to reduce the difference caused by different time and different areas by considering the difference of the brightness temperature change absolute values in different seasons and single days, as shown in the formula (4).
ΔΛ＝ΔT/Δu (4)
Where ΔΛ is the bright temperature change rate, Δt is the bright temperature difference of the front and rear times, and Δu is the time difference of the front and rear observations.
As a further improvement of the present invention, in the step (4), a bright Wen Shixu change rate threshold is constructed. Using equations (3) - (4), the rate of change of light and temperature of the pixel at any time can be estimated. According to the characteristics of different latitudes and different types of the underlying surface, the temperature difference of the daily temperature change is less than 60K, and a brightness temperature change rate curve conforming to the ideal daytime state is established. Dividing the daytime into 6 intervals through a height angle, and selecting the maximum assumed threshold value of each stage as follows: and (3) heating: the solar altitude is 0-30 degrees, and the bright temperature increase change rate is 0.35%; the solar altitude is 30-60 degrees, and the bright temperature increase change rate is 0.3%; the solar altitude is 60-90 degrees, and the change rate of the bright temperature increase is 0.2 percent. And (3) a cooling stage: the solar altitude is between 90 and 60 degrees, and the brightness temperature increase change rate is 0; the solar altitude is 60-30 degrees, and the brightness temperature increase change rate is 0.1%; the solar altitude is 30-0 deg. and the change rate of bright temperature is 0.2%. Comprehensive, the maximum change rate in the heating stage is less than 0.4%, and the maximum change rate in the cooling stage is less than 0.3%.
As a further improvement of the invention, in the step (5), the brightness temperature change rate of the satellite monitoring target pixel is calculated. And (3) calculating the brightness temperature change rate of the pixel of the object to be monitored by using the formulas (3) - (4).
As a further improvement of the present invention, in the step (6), the fire is identified. When the time sequence change rate of the pixel meets the following conditions, determining the fire point, wherein the formula is as follows:
ΔΛ>ΔΛbg (5)
wherein, deltaΛ is the brightness temperature change rate of the target pixel; ΔΛbg is a background change rate threshold, and the parameter is calculated by a bright temperature change rate curve function of a 60K bright temperature difference hypothesis condition.
As a further improvement of the invention, in the step (7), the accuracy verification refers to the comparison verification of the ignition point monitored by Himawari-9 data and the ignition point fed back by the simultaneous on-site checking, and the accuracy of the ignition point monitoring by the Himawari-9 variable time energy threshold method is calculated. The accuracy verification method is to verify the alarm accuracy by comparing the number of alarms based on the fire information data monitored by the Himaware-9 time-varying energy threshold method at the same time and the fire information data fed back by the field inspection.
The technical scheme has the following advantages or beneficial effects: the high-sensitivity fire remote sensing identification monitoring method based on the Himawai-9 satellite data time-varying energy threshold is provided, the accuracy of the Himawai-9 satellite data in small-scale fire monitoring is verified, the data source of remote sensing fire monitoring is further expanded, and a novel method is provided for multi-source remote sensing fire monitoring.
Drawings
FIG. 1 is a schematic diagram of a fire remote sensing monitoring technology route based on Himawai-9 satellite data time-varying energy thresholding
FIG. 2 is a graph of Himaware-9 visible and mid IR channel results after target area pretreatment
FIG. 3 is a clear sky image element marking result diagram of target area Himaware-9 data
Detailed Description
The winter and spring fires in the south area of China are mostly small-scale fires, so 2023, 2, 15 and 18: the middle part of the Guangxi province 00 is used as a monitoring target area for fire judgment and monitoring, and the specific implementation of the invention is further described by referring to the accompanying drawings:
1 target area himaware-9 data preprocessing: himaware-9 data scaling process, atmospheric correction and cloud identification process.
1.1 Using the zenith and azimuth angles of the satellite and sun, the reflectance of visible and near infrared and the hot infrared radiation bright temperature values were corrected for radiometric calibration, corresponding to channels 1 (0.46 μm), 2 (0.51 μm), 3 (0.64 μm), 4 (0.86 μm), 5 (1.6 μm), 7 (3.9 μm), 14 (11.2 μm) and 15 (12.3 μm) in Himaware-9 satellite data.
1.2 atmospheric corrections were made to the thermal infrared channel data using the 6S radiation transmission model, corresponding to channels 7, 14 and 15 in Himaware-9 satellite data. The original himaware-9 data is full-disc data, and usable data in the range of the research area is obtained after pretreatment and atmospheric correction, as shown in fig. 2, which is the result of pretreatment of visible light (fig. 2 a) and a mid-infrared channel (fig. 2 b) in the range of the research area.
2 clear sky pixel marking: and judging pixels of a cloud area, a water body area, a desert area and the like.
2.1, distinguishing non-fire points and cloud areas to perform cloud detection by utilizing the high reflection characteristic of visible light in the daytime and the temperature characteristic of a thermal infrared channel in the cloud area, and performing cloud detection on the 1 st, 2 nd and 15 th channels in the corresponding Himawari-9 satellite data. The cloud detection results are shown in fig. 3, and it can be seen that 17:50 and 18: the cloud of 00 times is mainly distributed in the western region, and fire can be monitored in sunny and sunny parts of northeast.
2.2, carrying out water body identification by utilizing the characteristic of the water body in a near infrared band, wherein the corresponding channel 3 and channel 4 are in Himaware-9 satellite data. The water extraction results are shown in figure 3.
2.3 land utilization types in non-desert areas.
And 3, calculating the brightness temperature change rate of the target area. Based on the data of Himaware-9 before and after 2023, 2, 15, 17, 50 minutes and 18 hours, the brightness temperature change rate of the target area is calculated pixel by pixel according to the formulas (3) and (4).
4 a bright temperature time sequence change rate threshold value. According to the characteristics that the temperature difference of the day temperature change of different latitude and different underlying surface types is less than 60K, a bright temperature change rate curve conforming to the ideal state in the daytime is established, and when the temperature difference is 60K, the maximum change rate in the heating stage is less than 0.4%, and the maximum change rate in the cooling stage is less than 0.3%.
5, judging fire points. And (3) performing fire judgment based on a time sequence change rate fire threshold judgment condition, wherein 5 fire situations are finally monitored to occur in the middle 2023, 2 months, 15 days and 18 days of Guangxi, and the fire situations are small-scale fire points. The identification fire information is shown in table 1.
Table 1 fire point identification information
And 6, verifying the precision. According to the monitored fire information and the verification information fed back in the field, the Himaware-9 satellite data time-varying energy threshold fire remote sensing monitoring method is shown to be capable of accurately monitoring small-scale fires (table 1).
The above examples are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above examples, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principle of the present invention should be made in the equivalent manner, and the embodiments are included in the protection scope of the present invention.
Claims (5)
1. A time-varying energy threshold fire remote sensing monitoring method based on Himaware-9 data is characterized in that:
(1) Fire monitoring based on new generation stationary meteorological satellite Himaware-9 satellite data;
(2) Calibration of Himawaii-9 data, atmospheric correction, cloud identification processing and water body identification processing;
(3) Conventional background illumination Wen Shixu change function construction based on Himawai-9 satellite data;
(4) Calculating the brightness temperature change rate of a satellite monitoring target pixel based on Himawai-9 satellite data;
(5) Fire identification based on himawai-9 satellite data.
2. The remote sensing monitoring method for fire conditions based on himawai-9 satellite data according to claim 1, wherein the calibration, the atmosphere correction, the cloud identification process and the water body identification process of himawai-9 data comprise the following steps:
channels 1 (0.46 μm), 2 (0.51 μm), 3 (0.64 μm), 4 (0.86 μm), 5 (1.6 μm), 7 (3.9 μm), 14 (11.2 μm) and 15 (12.3 μm) in the himaware-9 satellite data for which the radiation calibration corresponds;
the 7 th, 14 th and 15 th channels in the Himaware-9 satellite data corresponding to the atmospheric correction;
and detecting the 1 st, 2 nd and 15 th channels in the corresponding Himaware-9 satellite data.
3. The remote sensing monitoring method of fire conditions based on himawai-9 satellite data according to claim 1, wherein the conventional background lighting Wen Shixu change function construction based on himawai-9 satellite data comprises the following steps:
radiant energy is positively correlated with solar altitude, so the relationship of ground background bright temperature to solar altitude can be described as equation (1) and as:
(1)
in the method, in the process of the invention,Tis instant bright temperature;T max is the highest bright temperature of the day;T min is the lowest sunlight temperature;T 1 the brightness temperature is the brightness temperature when the solar altitude angle is reduced to 0 degrees;
according to the characteristics of different latitudes and different types of under-pad surface, the temperature difference of the day temperature change is less than 60 and K, and a brightness temperature change rate curve conforming to the ideal state in the daytime is established. Dividing the daytime into 6 intervals through a height angle, and selecting the maximum assumed threshold value of each stage as follows: and (3) heating: the solar altitude is 0-30 degrees, and the bright temperature increase change rate is 0.35%; the solar altitude is 30-60 degrees, and the bright temperature increase change rate is 0.3%; the solar altitude is 60-90 degrees, and the change rate of the bright temperature increase is 0.2 percent. And (3) a cooling stage: the solar altitude is between 90 and 60 degrees, and the brightness temperature increase change rate is 0; the solar altitude is 60-30 degrees, and the brightness temperature increase change rate is 0.1%; the solar altitude is 30-0 deg. and the change rate of bright temperature is 0.2%. Comprehensive, the maximum change rate in the heating stage is less than 0.4%, and the maximum change rate in the cooling stage is less than 0.3%.
4. The remote sensing monitoring method for fire conditions based on himawai-9 satellite data according to claim 1, wherein the calculation for the change rate of brightness and temperature of the monitoring target pixel based on himawai-9 satellite data comprises the following steps:
considering that the absolute values of the bright temperature change in different seasons and single days are different, in order to reduce the difference caused by different time and different areas, the bright temperature difference value is converted into the bright temperature change rate, as shown in the formula (2):
（2）
where ΔΛ is the rate of change of the light temperature, ΔΛTIs the bright temperature difference delta of the front time and the back timeuIs the time difference between the front and back observation.
5. The remote sensing method for monitoring fire conditions based on himawai-9 satellite data according to claim 1, wherein the fire point identification based on himawai-9 satellite data comprises the following steps:
and judging fire points when the time sequence change rate of the pixels meets the formula (3):
ΔΛ >ΔΛbg （3）
in the formula deltaΛIs the brightness temperature change rate of the target pixel; deltaΛbgFor the background change rate threshold, the parameter is calculated by a bright temperature change rate curve function of 60K bright temperature difference assumption conditions.
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