Development of indicators of fire severity based on time series of SPOT VGT data Stefaan Lhermitte,...
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Transcript of Development of indicators of fire severity based on time series of SPOT VGT data Stefaan Lhermitte,...
Development of indicators of fire severity
based on time series of SPOT VGT data
Stefaan Lhermitte, Jan van Aardt, Pol Coppin
Department BiosystemsModeling, monitoring, and management of bioresponse
Geomatics group KU Leuven
Belgium
May 2005 Multitemp 2005
Outline Global burn datasets:
– GBA2000 (SPOT VGT data)– Globscar (AATSR)
Fire detection vs. quantification of impacts
General constants/biome
Essential– Global and regional carbon models– Understanding of vegetation recovery
May 2005 Multitemp 2005
Objective
Development of indicators to quantify spatio-temporal variation of fire impacts
– Fire Severity (FS):“Percentage of the biomass per pixel that
is burned“
May 2005 Multitemp 2005
Data
Study area: South Africa
Satellite Data: SPOT Vegetation S10 – Year 2000– 10-daily Maximum Value Composites (B, R, NIR, SWIR, NDVI)– 1x1 km²
Fires: GBA2000 Burnt Areas– Year 2000– Monthly detected fire scars (no exact date, only month)– 1x1 km²
May 2005 Multitemp 2005
Fires
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• Indication of fire frequency by area
• Very large fires exist, indicating a possible exaggeration
May 2005 Multitemp 2005
Fires byvegetation
type
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Large areas in (i) forest and woodland, (ii) thicket and bushland, (iii) shrubland and fynbos, (iv) unimproved grassland, and (v) cultivated commercial dryland
May 2005 Multitemp 2005
Techniques
Spectral mixture analysis (SMA):– Bare soil, charcoal, vegetationHypothesis:
FSi ~ ∆(vegetation fraction)i where i = fire
pixel
--> Absolute values Changes in vegetation indices (∆VI)
Hypothesis: FSi ~ ∆(vegetation index)i
where i = fire pixel
--> Relative values
Assumes that the reflectance spectrum can be deconvolved into a linear mixture of the spectra of endmembers (pure pixels)
Spectral Mixture Analyis
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May 2005 Multitemp 2005
Assumes that the reflectance spectrum can be deconvolved into a linear mixture of the spectra of endmembers (pure pixels)
Result: – relative abundance (fractions) of different endmembers
for every pixel– when only 1 vegetation endmember is chosen, the
fractions reflect an absolute measure of
– FSi be expressed by ∆(VFi)
Spectral Mixture Analyis
VFi =vegetation content
pixel
May 2005 Multitemp 2005
Spectral Mixture Analyis
ProcedureEndmember selection
– ‘Iterative Error Analysis’ (IEA)
(Neville et al., 1999)
• An automated selection procedure• Grouping of all burnt pixels
– 3 observations before fire– 3 observations afterwards
• Selection of desired endmembers• Assumption that endmember
spectra are time invariant• Only correct estimation for the
‘Forest and Woodland’ Type
May 2005 Multitemp 2005
Spectral Mixture Analyis Procedure
Typical reflectance of 3 endmembers: Vegetation, dark wet soil (or charcoal), and light or dry soil
Problem: IEA could only retrieve meaningfull endmembers for the Forest and Woodland landcover type
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May 2005 Multitemp 2005
Spectral Mixture Analyis
Procedure
% Vegetation Component3 decades before fire
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May 2005 Multitemp 2005
Spectral Mixture Analyis
Procedure
% Vegetation Component3 decades before fire
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May 2005 Multitemp 2005
∆Vegetation Index
Assumes that the FS can be expressed by ∆(VI)
VI:– no absolute measure of vegetation quantity– related to vegetation but have phenological
fluctuations– cannot be used for FS without normalization
vegetation contentpixel
May 2005 Multitemp 2005
∆Vegetation Index
Normalization:– use relative index (RI) to reduce phenological
influences
– reference areas: areas located adjacent or close to the burned sites, but not affected by the disturbance. They should have similar environmental conditions and vegetation
reference
fireVI VI
VIRI
May 2005 Multitemp 2005
Analysis
Look at a fire as a complete entity:– Analysis of mean(FSi)j
where i = fire pixel
j = fire id
Look at spatial variability for every fire:– Analysis of FSi
where i = fire pixel
May 2005 Multitemp 2005
Spectral Mixture Analyis(fire.id)
Change curves of fractions for every fire scar
(Example 1)
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May 2005 Multitemp 2005
Change curves of fractions for every fire scar
(Example 2)
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Spectral Mixture
Analyis(fire.id)
May 2005 Multitemp 2005
∆Vegetation Index(fire.id)
Change curves of ∆VIfor every fireScar(Example 1)
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May 2005 Multitemp 2005
∆Vegetation Index(fire.id)
Changecurvesof ∆VI for everyfire scar(Example 2)
Multitemp
May 2005 Multitemp 2005
Actual Fire Severity
FS can now be derived from change detection of the derived data sets– Change detection on the RI-images before and
after fire– Change detection on the fraction images of the
vegetation component before and after fire
E.g.: Image differencing was performed and the FS was calculated for both techniques
May 2005 Multitemp 2005
Validation
Fire records of Kruger National Park (KNP)– Validation of FS with field data containing burn severity– Statistical regression techniques to assess the
performance of both techniques and the resulting quantitative indicators of burning efficiency
– Results were unsatisfactory• Possible errors: endmembers, reference areas• KNP fire records are very subjective
Additional validation is necessary:– severity indices Landsat imagery
May 2005 Multitemp 2005
Conclusion
Two techniques to quantify spatio-temporal variation of the impact of fire were presented
Additional validation is necessary
May 2005 Multitemp 2005
Acknowledgements
Funding provided by the Belgium Science Policy Office (BELSPO) as part of the GLOVEG project
Jan Verbesselt for scientific inputs