Applications of Chinese FY series meteorological satellites in boreal forest fire management

AFSC Remote Sensing Workshop

Fairbanks, Alaska, USA. April 5, 2017

Chinese Academy of Forestry, Beijing, China

Center for Forest Disturbance Science, USFS, GA, USA

Fengjun Zhao

1

◆ China boreal forest and fire situation

◆ Satellite application in fire management

⚫ FY (FengYun) series

⚫ HJ (HuanJing)series

⚫ GF (GaoFen) series

◆ Potential application to fuel heat properties

◆Conclusions

Outline

2

Boreal forest in China◆Location: ⚫ 50º10’~53º33′N⚫ 84,600km2

⚫Helongjiang province, and Inner Mongolia Autonomous Region◆Mainly coniferous forest:⚫ Larch ( Larix gmelinii),⚫Mongolian scotch pine (Pinus sylvestris var. mongolica)⚫ Pinus pumila (shrub species)⚫Fir and spruce

Daxing’anMountain

3

Landscape scene and fire situation

This region contributes to 50% burned area in China.

4

1987 catastrophic fire in Northeast

May 6th, 1987 ;27 days; 3,325,000 acres burned; 213 people dead; 58,800 fire fighters

NOAA/AVHRR

5

Mission Purpose Abbr.DongFangHong Telecommunications and broadcasting

satellitesDFH Series

BeiDou Navigation and positioning satellites BD Series

ShenZhou Experimental spacecraft SZ Series

HaiYang Marine Satellites HY Series

ZiYuan Earth resource satellites ZY series

FengYun Meteorological satellites FY series

HuanJing Environment and disaster monitoring andforecasting

HJ series

GaoFen High resolution Chinese Earth observationsatellite

GF Series

China satellites

AVHRR/NOAA, MODIS/Terra and Aqua, USA6

FY meteorological satellites series

7

Polar System Geostationary System

FY

|

1A

1B

1C

1D

FY

|

3A

3B

3C

…

3F

FY

|

2A

2B

2C

2D

2E

Second Generation

First Generation

Second Generation

First Generation

FY

|

4A

4B

4C

…

4FNOAA/AVHRR, Terra and Aqua/MODIS, polar system

7

Launched time and Service period

⚫NOAA/AVHRR, 1978.⚫Terra(MODIS), Dec, 18th, 1999.⚫Aqua(MODIS), Mar, 4th,2002.

8

No. Products No. Products No. Products

1 Raw Image 10 Rainfall estimation 19 SST

2 Normalized Image 11 Precipitation 20 Snow cover

3 Projected Image 12 AMV 21 Sea ice monitor

4 Mosaic Image 13 Typhoon location 22 Fire spots (24times/day;5km)

5 Cloud classification 14 Upper troposphere humidity

23 Water bodies

6 Total cloud amount 15 Cloud water profile 24 Soil humidity

7 Sand storm detection

16 OLR 25 ISCCP dataset

8 Heavy fog monitor 17 TBB

9 Precipitation index 18 Solar irradiance

FY-2 Operational products

9

FY 2C Boreal forest fire monitoring image

Daxing’an Mountain, May, 2006

10

No. Products No. Products No. Products

1 Clear Sky Masks 10Downward Long-wave Radiation: Surface

19 Rainfall Rate

2 Cloud Top Height 11Upward Long-wave Radiation: TOA

20 Convective Initiation

3 Cloud Top Temperature 12Upward Long-wave Radiation: Surface

21Tropopause FoldingTurbulence Prediction

4 Cloud Top Pressure 13Reflected Shortwave Radiation: TOA

22 Sea Surface Temperature

5 Cloud Optical Depth 14Downward Shortwave Radiation: Surface

23Fire/Hot Spot (5 minutes; 2km )

6 Cloud Liquid Water 15Legacy Vertical Moisture Profile

24 Land Surface Temperature

7Cloud Particle Size Distribution

16 Ozone Profile & Total 25 Land Surface Emissivity

8Aerosol Detection (including Smoke and Dust)

17 Derived Motion Winds 26 Snow cover

9 Aerosol Optical Depth 18 Lightning Detection 27 Space and Solar products

FY-4 Anticipated products

Similar to the USA's GOES-13/15 3-axis stabilized satellites. 11

Satellites Instrument Primary use

FY 1C/1DMVISR (Multichannel Visible and IR Scanning Radiometer)

Cloud, ice and snow, vegetation, SST, water, heat source, night cloud, water vapor, ocean color, soil humidity, etc.

FY-3 VIRR (Visible and Infrared Radiometer)

Cloud, vegetation, snow and ice, SST, LST, water vapor, aerosol, ocean color, hot spot monitoring, etc.

FY-3MERSI (Medium Resolution Spectral Imager)

True color imagery, cloud, vegetation, snow and ice, ocean color, aerosol, rapid response products (fires, flooding, etc.)

FY 1C/1D,FY-3 operational products

12

Sensor parameters comparison

NameSpatial

resolution at nadir (km)

Swath width(km)

Spectral range (µm)

No of Channels/

bands

Global data availability

MVISR-FY-1C/D 1.1 2800 0.58~12.5 10 12 days

VIRR-FY3 1.1 2800 0.43~12.5 10Daily and

hourly

MERSI-FY3 0.25~1.0 2800 0.41 ~ 12.5 20Daily and

hourly

MODIS-NASA 0.25,0.5,1.0 2330 0.4~14.4 36 Daily

AVHRR/3-NOAA 1.1 3000 0.58~12.5 6 Hourly

13

FY-1D images for boreal forest fire monitoring

Fire monitoring image Hot spot map

Daxing’an Mountain , May, 28th , 2006

14

FY-3 images for fire monitoring and burned area mapping

15

Daxing’an Mountain, Oct, 25, 2005

Using FY-3A MERSI, 2009, 250m

FY 3 MERIS Vegetation/fuel type identification

16

Relative greenness monitoring

Using FY-3 MERIS 17

Parameters of HJ-1A/B satellites

Platform Payload Spectral range(µm)Spatial

resolution (m)Swath

width(km)Repeat

cycle (days)

HJ-1A

CCD0.43~0.52;0.52~0.600.63~0.69; 0.76~0.90

30 700 4

HSI0.45~0.95(110~128

Bands)100 50 4

HJ-1B

CCD0.43~0.52;0.52~0.600.63~0.69;0.76~0.90

30 700 4

IRS0.75~1.10;1.55~1.753.50~3.90;10.5~12.5

150300

720 4

18

The forest fire in Boundary of Inner

Mongolia autonomous region and

Heilongjiang province in June, 2010

Forest fire in Heilongjiang

Province in April, 2009

HJ images for boreal fire monitoring

19

GF -1 Parameters

2m/8m Camera 16m Camera

Spectral (µm) Pan: 0.45~0.90;Multi spectrum: 0.45~0.89

Multi spectrum: 0.45~0.89

Resolution(m) Pan:2mMulti spectrum: 8m

16m

Swath (km) 60 (2 camera combined) 800(4 camera combined)

Cycle4 day (with side-look)/42 day (without side-look)

4 day (without side-look)

Application Smoke, burned area mapping

20

GF -2 Parameters2m/8m Camera

Spectral Pan: 0.45~0.90µm; Multi spectrum: 0.45~0.89µm

Resolution Pan:1m; Multi spectrum: 4m

Swath 60 km(2 camera combined)

Cycle 5 day (with side-look)/69 day (without side-look)

Application Smoke, burned area mapping

GF -4 Parameters2m/8m Camera

Spectral VNIR Camera: 0.45~0.90µm; NWIR Camera: 3.5~4.1µm

Resolution VNIR(50m); NWIR(400m)

Swath 400km

Cycle 20s

Application Fire monitoring, smoke, burned area mapping

21Note: VNIR(Visible light Near Infrared); MWIR( Medium wave Infrared)

GF-1 images application in burned area mapping

Burned area mapping (a) Using GF-1 WFV image; (b) Using GF-1 PMS image.

22Note: WFV (Wide Field of View); PMS(Panchromatic and MultiSpectral)

AVHRR/NOAA application in China fire monitoring

Morning, May 6, 1987

Afternoon, May 6, 1987

Morning, May 8, 1987

Afternoon, May 20, 1987

23

MODIS/NASA application in China fire monitoring

May 28, 2006, Daxing’an Mountain June 3, 2006, Daxing’an Mountain

24

Note: The content of volatile-extracts is difficult to measure accurately. However, the fuels have high fatty-extracts always have high volatile-extracts at the same time, for example: conifers, eucalyptus.

Fuel components and heat properties

Component Content (%)Heat

Value (HV) (MJ/kg)

PyrolysisTemperature(℃) Note

Cellulose 38~50 16 220~320 CO, H2, CH4,etc.Hemi-

cellulose7~26 16 320~370

Lignin 23~34 25 200~500Fatty-Extracts

<15 35~40 180~240 Extracts-rich:conifers,eucalyptus,etc.

Volatile-Extracts

32 155~175

Mineral <1

25

Fatty-extracts from different species

Zhao, FJ, et al. Supercritical extracts of forest fuels in Great Xing’an Mountains. Journal of Forestry Research,2016,27(5):1143-1151

Instrument: Supercritical fluid CO2 extraction (SFE)

⚫Pressure: 40 Mpa

⚫Temperature:

45℃

⚫ Time: 80 min

⚫CO2 flow :

2.0 L/min.

⚫Sample size:

60 mesh

26

48 samples (12 coniferous twigs and needles, 30 hardwood tree and shrub leaves and twigs, 6 herbaceous species)

12 samples (6 needles, 6 conifers twigs)

6 needle samples

Extract – heating relationships

27

Cone Calorimeter Test

Time from start of test (s)

Schima mass loss rate

Pine mass loss rate

CO2 yield of Schima

CO2 yield of pine

0

0.03

0.06

0.09

0.12

0.15

50 100 150 200 250 300

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Sample mass loss rate

(g/s

)

Carbon dioxide yield

(kg

/kg

)

The experiments made us a deep understanding on the combustion

difference of the varied tree species.

28

Extract-rich species- coniferous tree species

Larch, Daxing’an Mountain Scotch pine, Daxing’an Mountain

Black spruce, Alaska Douglas fir, USA29

Extract-rich species- others

Pinus pumila, shrub, China Ledum palustre, shrub, China

Eucalyptus forest-Australia Rosemary Mediterranean coast 30

Vegetation classification of China (MODIS MCD12Q1: 2012)

Vegetation/fuel classification:⚫ Spectral information⚫ Phenology

Spectral information⚫Chlorophyll content⚫Water content

Extract contents have not been considered.

31

Oct 17th,2016 Nov 14th,2016

Extract-rich species: Fraser fir(Abies fraseri), Red spruce (Picea rubens), Pitch pine

(Pinus rigida), White pine (Pinus strobus), Virginia or scrub pine (Pinus virginiana)

2016 Southern Appalachian Wildfires: Fuel, Emissions, and Smoke Impacts

32

Satellite potential application to fuel heat properties

◆Boreal forest are mainly

composed by exacts rich tree

species, such as, fir, spruce, larch,

etc, and exacts rich shrub species,

such as, Ledum palustre, etc.

◆In the future, if the exacts

content of vegetation can be

detected and monitored by RS. It

will be very useful for fire

management in boreal forest!

Crowning fire in a black spruce stand during the lightning-ignited, 2004 Taylor Complex Wildfire in southeastern interior Alaska.

https://www.fs.fed.us/database/feis/fire_regimes/AK_black_spruce/all.html

High fire intensity and more emission, especially black carbon

33

Satellites development trends in China

⚫GF-5, GF-6,GF-7

⚫A number of geo-science satellites and

small satellites

FY-4A, Dec 11, 2016

By 2020, planned to

launch:

⚫FY 4B, FY-3F, FY-

4C, FY-RM2, FY-3G,

FY-4M

34

Conclusions

➢Chinese satellites have been important part of the fire

management. FY,HJ,GF series and other satellites did good job on

fire detection, monitoring, burned area mapping, etc.

➢However improvements are needed in, such as, spectrum

resolution and analysis technologies to increase the capacities of

detecting fuel extract contents and the impacts on heat release and

fire emission.

➢Conduct more comparisons between FY and other satellites,

such as Terra and Aqua, and produce more fire products from FY.

35

Acknowledgement ⚫Chinese Academy of Forestry / Institute of Forest Ecology, Environment and Protection; China State Forestry Administration ; Chinese Scholarship Council

⚫Alaska Fire Science Consortium⚫Center for Forest Disturbance Science, Forest Service; USDA Forest Service International Program

36