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Fires in (Northern) Eurasia
Ivan Csiszar (UMd)Tatiana Loboda (UMd)
with contribution from
Evgeny Loupian (Moscow)Dmitry Ershov (Moscow)
Anatoly Sukhinin (Krasnoyarsk)Sergey Tashchilin (Irkutsk)
Vladimir Belov (Tomsk)
and others
PROJECTS• Intercalibration of fire products from AVHRR and
MODIS in Northern Eurasia– NASA NIP, 3 years, 3rd year starting– historical fire record and data continuity
• products from additional sensors can be included (ATSR, SPOT/VGT) – “from missions to measurements”
• Biomass burning observations in Northern Eurasia– NASA LCLUC, 2 years, 2nd year starting– current and future observations– research and networking – “collaborative links”
RELEVANCE TO NEESPI SCIENCE PLAN
• Chapter 2: Scientific questions and motivation– 2.3: Goals and deliverables: “An integrated
observational knowledge data base for environmental studies in Northern Eurasia that includes validated remote sensing products”
• Chapter 4: Remote Sensing– 4.1: Remote Sensing of Terrestrial Ecosystems
• 4.1.4 Forest and rangeland management• Chapter 6: Data and Information Technology• Chapter 8: Research Strategy
– 8.3: Five suggested research directions• 8.3.B: Monitoring
GOFC/GOLD-Fire Rationale• Multiple sources of fire information exist• Spatial and temporal coverage of datasets varies• Conflicting data are reported• Information is often complementary• Little information on data quality• Ground – and air-based data from operational
management agencies are often inadequate for research
• Skepticism in management community about satellite-based products
• Interdependence between stakeholders not fully recognized and utilized
Satellite-based fire observations in Northern Eurasia
• Historically, AVHRR-based active fire detection
• Burned area maps– aggregated AVHRR fire pixels– selective explicit burn scar mapping
• Use of MODIS emerging• SPOT VGT burned area mapping
(GBA2000 and ongoing efforts)• ATSR-based global datasets
Dataset Active fire Burn scar Temporal extent Spatial extent
Sukachev Forest Institute
(Krasnoyarsk)
+ selected scars
1996-present Eastern Russia
Institute of Solar and Terrestrial
Physics (Irkutsk)
+ 1997-present Eastern Russia
Space Research Institute
(Moscow)
+ 1995-present Entire Russia
Center for Forest Ecology and Productivity
(Moscow)
+ 2002-present Eastern Siberia
Insitute of Atmospheric
Optics (Tomsk)
+ 1998-present Central Russia
University of Tokyo
+ 1984-1999 Russian Far East
Major AVHRR-based fire datasets
Total fire map for 2002 season from AVHRR
A. Sukhinin, Sukachev Forest Institute, Krasnoyarsk
Satellite-based burned area product
In-situ fire data from operational management agencies
• Leshozes– Most detailed information– Reasonably accurate perimeter maps– Mostly (only) on paper
• Regional Avialesookhrana airbases– Daily information– Digital or digitized data on burned areas, number of fires
• National Avialesookhrana database– End of season– Least detailed
• Historical in-situ data record goes back to at least 50 years
Burned area maps from Avialesookhrana (red circles) and Sukachev ForestInstitute (blue clusters) for the 2001 burning season. Only data overAvialesookhrana protected area are shown.
Avialesookhrana vs. AVHRR
Year Agency Reports based onGround and Aerial Observations
Satellite Derived Data (NOAA AVHRR)Based on Fire Counts and Derived Area
Burned
Number of Fires
Reported
Total Area Burned
(ha)
Forest AreaBurned
(ha)
Number of Fire Events investigated
Total Area Burned
(ha)
Forest AreaBurned
(ha)
2002 35,000 1,834,000 1,200,000 10,355 11,766,795 n.a.
2003 28,000 2,654,000 2,074,000 16,112 17,406,900 14, 474, 656
Comparison of wildland fire data for the Russian Federation: Agency reports vs. satellite-generated data.
0
2
4
6
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12
Khabaro
vsk/A
mur
Krasnoya
rsk/Irku
tsk
Transb
aikal
Total
Area
Bur
ned
(mill
ion
ha)
RFFS
AVHRR
1998
Comparison of Burned Area Estimates
Remote sensing
Fire researchGlobal change
research
Operational fire management
Interaction between communities
Policy making
Fire as a climate data record: requirements for long-term monitoring
• Spatial characteristics– coverage
• gaps, overlaps, topography effects – homogeneity
• potential dependence of product quality/detection capability on view angle
– need for algorithm corrections/adjustments
• potential inter-satellite discontinuities due to differences in sensing system specifications
– need for algorithm/product inter-calibration
• Temporal characteristics– homogeneity
• potential effects from algorithm changes• potential effects from sensor changes
– continuity • long-term time series• systems need to be operated by agencies with
mandate for long-term operational climate record production – most often operational agencies -operational R&D
• continuous product validation based on agreed-upon protocols
Fire as a climate data record: requirements for long-term monitoring
Product validation
• “yes/no” binary active fire products: detection limits / probabilities
• fires in Northern Eurasia are episodic– long-term a-priori planning difficult– short-term response possible
• coincident high resolution imagery is the only practically feasible option
• metrics need to be meaningful for users
2001 2002
20042003
MODIS active firesmaps.geog.umd.edu
Active fire validation using coincident high resolution observations
Terra/ASTER
collocated high resolution observations are used for product evaluation
BIRD
AM satellite constellation: the real “A-train”?
Passengers are getting off: BIRD is drifting away from Terra
Active fire validation: MODIS and ASTER
Spatial distribution of the 133 ASTER scenes from 2001-2003
Comparison with coincident high-resolution satellite observations
ASTER (30m) + 1km MODIS grid:MODIS detections in color
July 23 2002 03:18 UTC62.57N 125.72E (Siberia)
MODIS v4 detection:
Yellow gridcells: “fire”, high confidence
Blue gridcells: “fire” nominal confidence
Gridcell with vertical shading:“cloud”
Black gridcells:“clear land”
MODIS active fires
Aug 22 2002, near Yakutsk, Russiahttp://rapidfire.sci.gsfc.nasa.gov/gallery/?2002234-0822/Russia.A2002234.0330.1km.jpg
Comparison with coincident high-resolution satellite observations
Probabilities of detection as a function of ASTER fire pixels within MODIS pixel
: MODIS “fire”; :MODIS “no fire”
P(MNF|AF)
0
0.1
0.2
0.3
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0.5
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0.7
0.8
0.9
1
0 100 200 300 400 500 600 700
ASTER threshold
Prob
abili
ty
Pixel-based accuracy assessment curve with 95% exact confidence intervals: omission error rate
significant contribution by Jeff Morisette, Louis Giglio; LBA and EOS projects
NPOESS/VIIRS/LANDSAT active fire validation!
Theoretical active fire detection envelopes: MODIS, nadir view
MODIS active fire product; from radiative transfer simulation L. Giglio
Burned area validation: Landsat/ETM+
•coverage
•clouds
•smoke
->
difficulty to implementtwo-scene burned area validation protocol
y = 1.2407x + 157.85R2 = 0.9454
0
5,000
10,000
15,000
20,000
25,000
0 5,000 10,000 15,000 20,000 25,000
Landsat
AV
HR
R(M
) co
mb
o a
ll
y = 1.3729x + 88.575R2 = 0.9832
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000
Landsat
AV
HR
R (K
) af
y = 0.9136x + 135.35R2 = 0.9921
0
100,000
200,000
300,000
400,000
500,000
600,000
0 100,000 200,000 300,000 400,000 500,000 600,000
Landsat
MO
DIS
af
y = 0.8662x - 71.588R2 = 0.9807
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
0 20,000 40,000 60,000 80,000 100,000 120,000 140,000
LandsatM
OD
IS b
a
SRI AVHRR AF+BASFI AVHRR AF
MODIS AF MODIS BA
y = 1.0181x - 57.264R2 = 0.9971
0
50,000
100,000
150,000
200,000
250,000
0 50,000 100,000 150,000 200,000 250,000
Landsat ba
AVHR
R ba
(SFI
)
SFI AVHRR BA
050,000
100,000150,000200,000250,000300,000350,000400,000450,000500,000
1200
13_0
7200
2
1200
14_0
7200
2
1220
16_0
6160
2
1220
16_0
8030
2
1220
16_0
8190
2
1220
17_0
6160
2
1220
17_0
8190
2
1250
16_0
7230
2
1250
17_0
7070
2
1250
17_0
7230
2
1290
18_0
7190
2
1310
15_0
7170
2
1350
18_0
7290
2
Landsat pathrow_date
Are
a bu
rned
(ha)
Landsat baAVHRR (K) afMODIS afMODIS ba
2002Inventory burned area validation
SFI: Sukachev Forest InstituteSRI: Space Research Institute
MODIS BA: experimental algorithm (courtesy L. Giglio)
AF: Active Fires BA: Burned Areas
(explicit mapping)(calibration regression)indications that these are
somewhat variable
Product Commission OmissionProducer Accuracy
User Accuracy
Overall Accuracy Kappa
AVHRR (Krasnoyarsk) AF 61.62 45.38 54.62 38.38 98.58 0.43
AVHRR (Moscow) AF NOAA 12 67.33 80.43 19.57 32.67 97.85 0.22
AVHRR (Moscow) BA NOAA 12 56.76 76.45 23.55 43.24 97.94 0.26
AVHRR (Moscow) Combo NOAA 12 59.17 57.86 42.14 40.83 97.84 0.38
AVHRR (Moscow) AF NOAA 14 63.85 79.48 20.52 36.15 74.43 0.26
AVHRR (Moscow) BA NOAA 14 47.14 59.04 40.96 52.86 99.60 0.46
AVHRR (Moscow) Combo NOAA 14 49.02 46.25 53.75 50.98 99.59 0.52
AVHRR (Moscow) Combo AF 54.27 54.80 45.20 45.73 99.55 0.45
AVHRR (Moscow) Combo BA 51.45 56.73 43.27 48.55 99.58 0.45
AVHRR (Moscow) Combo All 55.94 36.06 63.94 44.06 99.52 0.52
MODIS AF 49.77 53.47 46.53 50.23 98.86 0.46
MODIS BA 27.09 49.40 50.60 72.91 99.34 0.56
Geospatial burned area validation
contribution by S. Trigg, J. Hewson and N. French
Julia
n D
ate
Fire detections
Projections of fire detections on the respective axes
Fire spread reconstruction
Fire spread rate (km/hr)
Uses of fire spread rate
• characterization of spatial and temporal fire dynamics
• understanding burned area mapping errors from active fires
ETM+SFI/AVHRR AF
MODIS AF MODIS BA (Giglio)
00.05
0.10.15
0.2
0.250.3
0.35
0.40.45
0 21 23 33 46 48 56 69 71 80 94 95 103
105
151
153
166
168
176
178
191
hours of burning
aver
age
fire
spre
ad ra
te (k
m/h
)
July 20, 2002
low spread ratehigh spread rate
1km2 squares around MODIS active fire detection locations
Understanding mapping errors: fire spread
T+95 T+103 T+105
AVHRR geolocation error simulation
150,000
160,000
170,000
180,000
190,000
200,000
210,000
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350
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850
1100
1350
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1850
2100
2350
2600
2850
3100
3350
3600
3850
random error boundaries (m)es
timat
ed a
rea
(ha)
G Error True area
AVHRR geolocation error simulation
4,000
4,200
4,400
4,600
4,800
5,000
5,200
5,400
100 350 600 850 1100 1350 1600 1850 2100 2350 2600 2850 3100 3350 3600 3850
random error boundaries (m)
Est
imat
ed a
rea
(ha)
Error
True area
Large scar
Small scarGrowth of total burned area estimate as a function of
geolocation error
The effect of AVHRR geolocation errors
MODIS
Monte Carlo simulation of AVHRR geolocation errors
Scar1 (true area = 3425.74ha)
y = 0.0063x + 0.9677R2 = 0.7886
0.00.20.40.60.81.01.21.41.61.82.0
100
350
600
850
1100
1350
1600
1850
2100
2350
2600
2850
3100
3350
3600
3850
error boundaries (m)
erro
r/tru
e ra
tio
Av spread rate = 0.032
Scar5 (true area = 23357.899ha)
y = 0.0038x + 0.9779R2 = 0.8549
0.00.20.40.60.81.01.21.41.61.82.0
100
350
600
850
1100
1350
1600
1850
2100
2350
2600
2850
3100
3350
3600
3850
error boundaries (m)
erro
r/tru
e ra
tio
Av spread rate = 0.065
Scar9 (true area = 66131.803ha)
y = 0.0022x + 0.9918R2 = 0.9407
0.00.20.40.60.81.01.21.41.61.82.0
100
350
600
850
1100
1350
1600
1850
2100
2350
2600
2850
3100
3350
3600
3850
error boundaries (m)
erro
r/tru
e ra
tio
Av spread rate = 0.115
y = -0.0419x + 0.0072R2 = 0.5171
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140
fire spread rate
ratio
slo
pe
Geolocation error and spread rate
Slope of the linear fit as function of average fire spread rate.
Relative area estimates
Burn severity and burning intensity
ETM+ burn severity index MODIS Fire Radiative Power
What happened here?
2002 WRS-2 125/017
(also see McRae et al poster)
1
23
45
6
7
10 km
1 100 10000 MW
Zoomed fragments of forest fire images at Baikal, obtained by MODIS and BIRD on 16 July 2003
Projection on the MIR band
BIRDMODIS
B. Zhukov (DLR, SRI) , D. Oertel (DLR), E. Lorenz (DLR), Ya. Ziman (SRI), I. Csiszar (UMd)
Northern Eurasian Regional Fire Network
• Part of GOFC/GOLD - NERIN
• Partnership between
– Providers of satellite-based fire data by Russian and international partners
– Operational providers of in-situ fire data
– Users of fire data
Global Observing Systems (G3OS)
Program activitiesTechnical panelsRegional activities
Terrestrial Carbon Observations (TCO)
Terrestrial Observation Panel for Climate (TOPC)Global Observation of Forest
and Land Cover Dynamics(GOFC/GOLD)
GLOBAL TERRESTRIAL OBSERVING SYSTEM
Scie
ntifi
c an
d Te
chno
logy
B
oard G
OFC
Sec
reta
riat
Implementation Teams, Activities and Projects
* Fire Monitoring and Mapping……..
* Cover Characteristics and Changes..
* Biophysical Parameters……………
Collaborations e.g. CEOS WGISS and WGCV/LPV
GLOBAL CLIMATE OBSERVING SYSTEM
AVHRR AVHRRMODIS
AVHRR MODIS
Tomsk node Irkutsk node Krasnoyarsknode
AVHRRMODIS
Federal level of the integrated network
Moscow node
MODIScenters UMd node
Tomsk Airbase Irkutsk Airbase Krasnoyarsk Airbase
Central AirbaseMoscow
National Ground fire data
MODIS data
Derived satellite data
Validation results
Derived fire satellite products
Regional Ground Fire products
Europe
Japan
NorthernEurasia
NorthAmerica
Northern Eurasian Fire Network structure
Primary network participants• Space Research Institute (Moscow)
– data integration, operational monitoring• Center for Forest Ecology and Productivity
(Moscow)– fire and disturbance mapping
• Institute for Atmospheric Optics (Tomsk)– radiative transfer, atmospheric correction
• Sukachev Forest Institute (Krasnoyarsk)– fire mapping, new sensors and technologies
• Institute for Solar and Terrestrial Physics (Irkutsk)– validation, high resolution sensors
Current national operational network in Russia
Example imagery
7/18/2002 (AVHRR) 10.15.2004 (MODIS)
Current network status and results• Organizational
– E. Loupian and A. Sukhinin on the GOFC/GOLD Fire Implementation Team (data systems and new sensors)
– network status is mature – regional champions taking over initiative– several products registered in NERIN metadata base – GOFC/GOLD-Fire Regional workshop: second day of Russian Remote
Sensing Symposium (November 16-18 2004)– Russia ratified the Kyoto protocol
• satellite-based fire products gaining official status• formal national satellite-based fire monitoring network in Russia
– opportunity, but also a challenge– further countries in Northern Eurasia being involved– umbrella research agreement between SRI and UMd
• Collaborative– joint multi-product database developed at SRI and accessible to network
participants (including UMd)– ongoing data and software exchange between UMd and SRI– ongoing data exchange between network participants in Russia– joint peer-reviewed publications on network status (published) and
product validation (in preparation)
GOFC/GOLD Regional Fire WorkshopMoscow, 17 November 2004
Future network priorities• Facilitate involvement of additional stakeholders
in Russia and in neighboring countries• Harmonize activities with NERIN and its land
cover component• Strengthen product validation based on
consensus protocols• Secure further funding
– national agencies within Russia and neighboring countries
– agencies in Europe, North America, Japan etc.– international programs: GOFC/GOLD, NEESPI etc.
• Further develop data system
Research plans for year 2005• Refine and complete multi-year AVHRR dataset
– at least back to 1996 • Validate forthcoming MODIS standard operational
burned area product (Roy et al.)• Validate locally developed / tuned active fire and burned
area products• Land cover type / vegetation continuous fields• Evaluate science quality of network output• Analyze burn severity and fire intensity• Finish papers on
– fire spread rate retrieval – multi-product burned are validation– fire characterization
PublicationsLoupian E.A., A.A Mazurov, E.V. Flitman, D.V. Ershov, G.N. Korovin, V.P. Novik, N.A. Abushenko,
D.A. Altyntsev, V.V. Koshelev, S.A. Tashchilin, A.V. Tatarnikov, I. Csiszar, A.I. Sukhinin, E.I. Ponomarev, S.V. Afonin, V.V. Belov, G.G. Matvienko and T. Loboda, Satellite monitoring of forest fires in Russia at federal and regional levels. Mitigation and Adaptation Strategies for Global Change, in press.
Sukhinin, A.I., N.H.F. French, E.S. Kasischke, J.H. Hewson, A.J. Soja, I. Csiszar, E.J. Hyer, T. Loboda, S.G. Conard, V.I. Romasko, E.A. Pavlichenko, S.I. Miskiv, and O.A. Slinkina, 2004: Satellite-based mapping of fires in Eastern Russia: new products for fire Management and carbon cycle studies. Remote Sensing of Environment, 93, 546-564.
Csiszar, I., T. Loboda and J. G. Goldammer, 2004: Contribution of GOFC/GOLD-Fire to fire monitoring in the Russian Federation. New Approaches to Forest Protection and Fire Management at an Ecosystem Level. World Bank and Alex Publishing, Moscow., 138-146. (in Russian, original English version forthcoming).
Goldammer, J.G., A. Sukhinin and I. Csiszar, The Current Fire Situation in the Russian Federation: Implications for Enhancing International and Regional Cooperation in the UN Framework and the Global Programs on Fire Monitoring and Assessment. New Approaches to Forest Protection and Fire Management at an Ecosystem Level. World Bank and Alex Publishing, Moscow., 26-65 (in Russian, original English version forthcoming).
Csiszar, I., J. Morisette and L. Giglio, Validation of active fire detection from moderate resolution satellite sensors: the MODIS example. In review.
