Application of FengYun Meteorological Satellite in Global Wildfires Monitoring ZHENG Wei zhengw@cma.gov.cn National Satellite Meteorological Center ( NSMC ) China Meteorological Administration ( CMA )
Outline 1 Developing process 2 Method and validation 3 Global application 4 New research and future plan
Wildfires Wildfires in global forests, grassland and farmland are a major source of natural disturbance. Humans 火 Satellite remote sensing systems can monitor the regional and global wildfires in near-real time, and provide the timely fire information for emergency and resource management. Active fires Burned area impact Fire spreading Emissions Satellite Wildfire risk …
Developing process of wildfire monitoring RUSSIA CHINA AVHRR FY-1C Daxinganling forest fire on 6 May, 1987 Mongolia grassland fire on 6 May, 2000 In middle of 1980’s Steady development of FY In late 1999 Data source: foreign data Operational service of FY-1C Main data source: FY-1C
Developing process of wildfire monitoring 08:00-14:00 FY-2 In the wake of operational service of FY-2, continuous wildfire detection based on FY-2 was used; Comprehensive application of geostationary satellite and polar orbit satellite were gradually developed; In early 2000, wildfires monitoring serviced mainly in China and adjacent area. FY-2C Nenjiang forest fires in May of 2006 FY-1 FY-1D AVHRR FY-1D
Current wildfire monitoring capability FY-3B,C,D FY-4A FY-3 and FY-4 as the second generation of Chinese meteorological satellite , the spatial, High response time temporal and spectral resolution improved largely. Fire monitoring capability has been enhanced greatly. More accurate and timely fire High positioning accuracy products can be generated. High monitoring frequency Especially in global application, FY become the most important data in NSMC.
Outline 1 Developing process 2 Method and validation 3 Global application 4 New research and future plan
The method of wildfire detection Automatic wildfire detection - Contextual method 1) Core algorithm Mid-infrared channel is sensitive to fire temperature. The temperature difference between target pixel and background in mid-infrared and far-infrared are used. 2) Cloud contaminate Different cloud conditions （ cloud, thin cloud, tiny cloud, cloud edge ） . 3) Sun glint When the sun glint angle is less than 10 degrees, no fire detection. 4) Water body ， desert Find the fire position in real time! Water body and desert can be masked by land cover data. NSMC developed the automatic wildfire detection method 5) Suspected fire with higher accuracy, considering complex earth surface, different cloud conditions, and solar radiation disturbances.
The method of sub-pixel wildfire information evaluation 1) Dual channel evaluation for P,T. active fire Using mid-infrared and far-infrared channels to evaluate sub-pixel size and temperature of fire. background ( ) ( ) = + − N P T , P N * 1 P * N MIR MIRt MIRbg ( ) ( ) = + − N P T , P N * 1 P * N FIR FIRt FIRbg 2) Single channel evaluation( T is set 750K) Using single channel to evaluate sub-pixel size when the temperature(750k) of active fire is set. = − − P ( N N ) /( N N ) MIR MIRbg MIRt MIRbg In daily wildfire monitoring ， tens or even hundreds of fire pixels often are detected. If using pixel size as the area of active fire, it will be FRP (Fire Radiation Power) evaluation much larger than the actual size. = σ 4 The method to evaluate the sub-pixel size of active fire is developed, FRP S * T f which can provide more accurate information and also be used to calculate FRP.
Burned area estimation Reflectance of burned 1) Two images before and after fires trees and grass strongly decreased in visible and infrared channels. Burned pine tree 3) Multisource satellite data 2) Single image after fires NDVI - NDVI C = NDVI - NDVI Mix S Burned birch Burned tree stool G V S The spectrum features of burned area Fresh grass Support for wildfire loss assessment and emission estimation NDVI and near infrared channel data are utilized to discern burned area. Furthermore, the fusion method of FY and high spatial resolution satellite data is developed fully using the temporal and spatial resolution. Burned grass Dried grass
The method of wildfire detection Expert interpretation Bright red: active fire NSMC build fire detection team; Dark red: burned area Expert interpretation method was developed for major fire monitoring ; Green: vegetation According to expert experience, the influence of cloud, water, urban heat Dark blue: water body island and other factors can be eliminated. Gray: smoke or cloud Provide targeted decision-making service products Night multiple channels composite image FY-3 multiple channels composite image
Validation based on man-made fire experiment Thermal Imaging System instrument Live picture of man-made fire field The man-made fire field in Guang Xi Province ， China when satellite scans on the night Validate the accuracy of fire monitoring NSMC made a man-made fire field experiment coincident with satellite overpasses in 2005. The man-made fire field was in circular shape , and was laid over firewood, tree branches and trunks. Thermal imaging system instrument was used to measure the radiance and the temperature distribution in the field. The experiment indicated the methods of fire detection and sub-pixel size evaluation are effective and satisfied.
Method validation based on field investigation and experiment of wildfire In recent years, NSMC have hold many experiments for validation of wildfire product accuracy. In May 2007, fire intensity evaluating investigation in Heilongjiang using the helicopter. In September 2007, active fire area evaluating investigation in Heilongjiang using the UAV. 2007 In May 2013, grassland burned area field spectral measurement in Inner Mongolia. In June 2014, farmland burned area investigation in Henan Province. In July 2015, fuel load measurement and investigation in the northeast forest area. In May 2018, farmland straw active fire field monitoring experiment. In July 2019, background temperature field measuring experiment in Heilongjiang. 2013 2019 2015 2018 2014
Comparison between expert interpretation and automatic wildfire monitoring results Parts of South America Central and Southern Africa, 17:00 on June 14, 2018 12:15 on June 13, 2018 Validate the accuracy of FY-3 automatic detection at global scale Typical regions were selected and compared the automatic fire points with the expert interpretation ones which are thought as the truth value. The comparing results show the accuracy of the FY-3 automatic fire monitoring algorithm is acceptable. Northeast China Russian Far East 04:15 on June 2, 2018 17:40 on May 29, 2018
Outline 1 Developing process 2 Method and validation 3 Global application 4 New research and future plan
Operational flowchart of wildfire monitoring in NSMC Meteorological Satellite Data Real Time Receiving ( FY-3, FY-4, … ) Data processing And Products Generating Carbon Fire Monitoring Fire Thematic Fire Distribution Fire Information Burned Area Analyzing Fire Spread Emission Image Map and Statistics List Evaluation Report Estimation Estimation ... Internet, Fax, Hard copy ... Ministry of Emergency China Meteorological Provincial Meteorological Office International Users Management of China Administration (CMA) ...
Daily global wildfire product of FY-3D (On 21 August,2019) Daily FY-3 global fire products with 1 km spatial resolution; Product contents: fire location, sub-pixel size , intensity and FRP
Monthly global wildfire accumulation density product using FY-3D (In August,2019) Density
Wildfire in Arctic circle monitoring Russia Finland Arctic Arctic Circle Circle In the summer of 2018, continuous extreme high temperature weather hit the northern hemisphere, wildfires burned into the Arctic circle. FY-3 fire distribution map showed that in July 2018, wildfires in the Arctic Circle of Eurasia increased Fire distribution map of the Arctic circle monitored by significantly compared with the same period in 2017. FY-3 meteorological satellites (July 2018 VS July 2017)
FY-3D Wildfire dynamic monitoring map of California, USA Camp Wildfire Camp Wildfire Woolsey Wildfire 9 Nov. ， 2018 10 Nov. ， 2018
FY-3D Wildfire statistic analysis of California, USA 2018 （ to 11.5 ） 2017 2016 2015 2014 Wildfire statistic analysis of California Based on the long-time FY fire information dataset, wildfire frequency maps showed that wildfires in California are widely distributed. In the five years from 2014 to 2018, the number of fire pixels in 2016 was the largest.
Burned area estimation based on FY data FY-3A monitored burned area of forest fire in the northeast of China 5 May , 2009 Burned area FY-3A monitored burned area of forest fire continuously from 28 April to 5 May, 2009.
Wildfire evaluation by combining burned area and NDVI Grassland fires Burned area of 2015 overlaying the vegetation index map of 2014 20 March , 2015 to 20 April , 2015 based on FY-3C Russia Russia Mongolia China China Mongolia NDVI
2019-11-16
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