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© BUSHFIRE CRC LTD 2007
PROGRAM A
LINKING FIELD OBSERVATIONS WITH REMOTE SENSING TO DETERMINE GRASSLAND FIRE HAZARD
Stuart AndersonEnsis Forest Biosecurity and Protection, Scion, Christchurch, New Zealand
Elizabeth BothaEnsis Forest Biosecurity and Protection, CSIRO, Canberra
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Outline
1. Grassland curing2. Project overview3. Research methods4. Field data collection5. Remote sensing
2
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Grassland curing
• Degree of curing – the proportion of dead material in a grassland fuel complex (%)
(Source: Garvey and Millie 2001)
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Why is curing important?• Input into fire behaviour models and fire danger
rating systems in Australia and New Zealand• Grassfires significant in both countries• Prescribed fire also very important• Often poorly assessed
3
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Current curing assessment
VisualPoor correlation between visuals and actual curing
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Curing assessment: VisualVisual vs destructive - 2005-2007
% curing (destructive measurement)0 20 40 60 80 100
% c
urin
g (v
isua
l est
imat
e)
0
20
40
60
80
100
ACTWANZ
4
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Visual• Poor correlation between visuals and actual curing
Destructive• Time-consuming• Difficult
Remote sensing• Cloudiness• Pixel size• Non-uniform fuels• Discontinuous cover/patchiness• Outdated technology
Current curing assessment
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Aim:
This project will develop better methods to assess the current and predicted levels of curing in grasslands as an input into fire danger rating systems and fire behaviour models
5
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Research approaches
1. Remote sensing2. Curing modelling3. Field sampling
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Field data collection
• Field data is ESSENTIAL• Collected across grasslands of Australia
and New Zealand• A major task, but very important• Destructive sampling most reliable –
cost, effort, time• Alternative approach – modified Levy
Rod
6
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Field Sites
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Data collection: Levy rod method
• Total number of live and dead grass touches against a thin steel rod
• Record at intervals along fixed transects
• Less subjectivity
7
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Field sampling: WA (06/07)
Western Australia sites (Levy vs destructive curing estimates)
% curing (destructive measurement)0 20 40 60 80 100
% c
urin
g (L
evy
estim
ate)
0
20
40
60
80
100
y = 0.58x + 32R2 = 0.59, p < 0.0001n =39, RMSE = 10.22
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Field sampling: WA (06/07)Parry Lagoons
(Levy vs destructive curing estimates)
% curing (destructive measurement)0 20 40 60 80 100
% c
urin
g (L
evy
estim
ate)
0
20
40
60
80
100
R2 = 0.26, p = 0.1132
Silent Grove - Sandstone(Levy vs destructive curing estim ates)
% curing (destructive m easurem ent)0 20 40 60 80 100
% c
urin
g (L
evy
estim
ate)
0
20
40
60
80
100
R2 = 0.49, p = 0.1219
M t H art Sandstone (Levy vs destructive curing estim ates)
% curing (destructive m easurem ent)0 20 40 60 80 100
% c
urin
g (L
evy
estim
ate)
0
20
40
60
80
100
R2 = 0.86, p = 0.0235
Silent Grove - Black Soil (Levy vs destructive curing estimates)
% curing (destructive measurement)0 20 40 60 80 100
% c
urin
g (L
evy
estim
ate)
0
20
40
60
80
100
R2 = 0.94, p = 0.0066
Lorna Glen(Levy vs destructive curing estimates)
% curing (destructive measurement)0 20 40 60 80 100
% c
urin
g (L
evy
estim
ate)
0
20
40
60
80
100
R2 = 0.00, p = 0.9073
8
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Field sampling: NZ (06/07)
New Zealand sites (Levy vs destructive curing estimates)
% curing (destructive measurement)0 20 40 60 80 100
% c
urin
g (L
evy
estim
ate)
0
20
40
60
80
100
y = 0.90x + 5R2 = 0.67, p = 0.0021n = 10, RMSE = 7.69
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Field Data Collection
1. Levy Rod Assessments
2. Fuel Moisture Content
3. Destructive Sampling (selected sites)
Support from end-user agencies is appreciated
9
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Remote Sensing: Background
Wavelength0.5 1.0 1.5 2.0 2.5
Ref
lect
ance
0.0
0.2
0.4
0.6
0.8
1.0
green vegetationdead vegetationbare soil
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Satellite data
MODIS MOD09 image producta) 7 spectral bandsb) 500x500m spatial-resolutionc) 8-day temporal resolution
Image source: http://www.ga.gov.au
Image source: http://terra.nasa.gov/Brochure/Sect_4-7.html
10
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Remote Sensing: What the satellite sees
Wavelength0.5 1.0 1.5 2.0 2.5
% R
efle
ctan
ce
0.0
0.5
1.0
Wavelength0.5 1.0 1.5 2.0 2.5
% R
efle
ctan
ce
0.0
0.5
1.0
Wavelength0.5 1.0 1.5 2.0 2.5
% R
efle
ctan
ce
0.0
0.5
1.0
Wavelength0.5 1.0 1.5 2.0 2.5
% R
efle
ctan
ce
0.0
0.5
1.0
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Remote Sensing
Wavelength0.5 1.0 1.5 2.0 2.5
Ref
lect
ance
0.0
0.2
0.4
0.6
0.8
1.0
green vegetationdead vegetationbare soil
MO
DIS
Ban
d 1
MO
DIS
Ban
d 2
MO
DIS
Ban
d 6
MO
DIS
Ban
d 7
1212
BBBBNDVI
+−
=
67
BBSWIRRatio =
11
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Spectral unmixing
NDVI0.0 0.2 0.4 0.6 0.8 1.0
B7/
B6
0.0
0.2
0.4
0.6
0.8
1.0 Bare soil
Dead vegetation Green vegetation
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
NO DATA (CLOUDS)100%PV
100%NPV
100%BS
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© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Parry Lagoons, WA(May 2006 - May 2007)
DateMay Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May
fraction non-photosynthetic vegetationfraction photosynthetic vegetationfraction bare soil
WA Spectral Unmixing
CLOUDS
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Results: WA (Kimberley)Silent Grive (black soil), WA
estimated fraction cured vegetation-1.0 -0.5 0.0 0.5 1.0 1.5
mea
sure
d fr
actio
n cu
red
vege
tatio
n
-1.0
-0.5
0.0
0.5
1.0
1.5Silent Grove (black soil), WA
(April - November 2006)
DateApr Jul Oct
est. fraction non-photosynthetic vegetationfraction cured grassest. fraction photosynthetic vegetationfraction green grass
y = 0.5 + 0.76xR2 = 0.97, p = 0.0004RMSE = 0.0415, n = 6
Silent Grove (black soil), WA
13
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Results: WA (Goldfields)
Lorna Glen, WA(June 2006 - June 2007)
DateJul Oct Jan Apr Jul
Lorna Glen
estimated fraction cured vegetation-1.0 -0.5 0.0 0.5 1.0 1.5
mea
sure
d fr
actio
n cu
red
vege
tatio
n
-1.0
-0.5
0.0
0.5
1.0
1.5
y = 0.62 + 0.15xR2 = 0.81, p = 0.0004RMSE = 0.0337, n = 10
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Results: QldRyan's Farm
estimated fraction cured vegetation-1.0 -0.5 0.0 0.5 1.0 1.5
mea
sure
d fr
actio
n cu
red
vege
tatio
n
-1.0
-0.5
0.0
0.5
1.0
1.5
Ryan's Farm, Qld(April - November 2006)
DateApr Jul Oct
est. fraction non-photosynthetic vegetationfraction cured grassest. fraction photosynthetic vegetationfraction green grass
y = 0.61 + 0.5xR2 = 0.79, p = 0.0004RMSE = 0.1159, n = 10
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© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Results: ACT/NSWBraidwood, NSW
(October 2006 - June 2007)
DateOct Jan Apr
Braidwood, NSW
estimated fraction cured vegetation-1.0 -0.5 0.0 0.5 1.0 1.5
mea
sure
d fr
actio
n cu
red
vege
tatio
n
-1.0
-0.5
0.0
0.5
1.0
1.5
y = 0.53 + 0.24xR2 = 0.33, p = 0.0396RMSE = 0.1872, n = 13
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Results: ACT/NSWMajura, ACT
(January 2006 - February 2007)
DateJan Apr Jul Oct Jan
Majura, ACT
estimated fraction cured vegetation-1.0 -0.5 0.0 0.5 1.0 1.5
mea
sure
d fr
actio
n cu
red
vege
tatio
n
-1.0
-0.5
0.0
0.5
1.0
1.5
y = 0.64 + 0.31xR2 = 0.68, p = 0.0066RMSE = 0.0659, n = 8
15
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Preliminary results
1. Spectral patterns of dead/live vegetation correlate with ground observations
2. No 1:1 relationship between unmixing results and ground data – model will have to be calibrated separately for each region
3. Best results in tropical savanna area where model was developed
4. Each region may require different end-members to optimize the spectral unmixing algorithm.
5. We need more ground observations to validate this method –especially of the grasslands in the southern part of Australia.
© BUSHFIRE CRC LTD 2007Program A : Linking field observations with remote sensing to determine grassland fire hazard
Acknowledgements
Wendy Anderson (UNSW@ADFA)Jennifer Hollis (DEC, WA)Ruth Gibbs (Ensis/CSIRO)Kelsy Gibos (formerly Ensis/Scion)Juan Pablo Guerschman (CSIRO Land and Water)Leigh Douglas (formerly Ensis/CSIRO)Peter Ellis (Ensis/CSIRO)Francis Hines and Adam Fountain (formerly UNSW@ADFA)End-user agencies:
Australia: Dept of Environment & Conservation (WA), Qld Fire & Rescue Service, ACT Government, private landownersNew Zealand: Dept of Conservation, National Rural Fire Authority, NZ Forest Owners Assn, Local Government NZ, NZ Defence Force, private landowners