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


Objective

Development of indicators to quantify spatio-temporal variation of fire impacts

– Fire Severity (FS):“Percentage of the biomass per pixel that

is burned“


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


Fires

Multitemp

• Indication of fire frequency by area

• Very large fires exist, indicating a possible exaggeration


Fires byvegetation

type

Multitemp

Large areas in (i) forest and woodland, (ii) thicket and bushland, (iii) shrubland and fynbos, (iv) unimproved grassland, and (v) cultivated commercial dryland


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

i

N

iif *'

1

1

:Constraint

error residual the

endmembers ofnumber the

endmember theoffraction the

bandin endmember of ereflectanc the :where

1

N

ii

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i

iλ

f

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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)


VFi =vegetation content

pixel



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


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

Multitemp



Procedure

Endmember selection

Fraction images



Procedure

% Vegetation Component3 decades before fire

Multitemp


∆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


∆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


Analysis

Look at a fire as a complete entity:– Analysis of mean(FSi)j


j = fire id

Look at spatial variability for every fire:– Analysis of FSi



Spectral Mixture Analyis(fire.id)

Change curves of fractions for every fire scar

(Example 1)

Multitemp


Change curves of fractions for every fire scar

(Example 2)

Multitemp

Spectral Mixture

Analyis(fire.id)


∆Vegetation Index(fire.id)

Change curves of ∆VIfor every fireScar(Example 1)

Multitemp


∆Vegetation Index(fire.id)

Changecurvesof ∆VI for everyfire scar(Example 2)

Multitemp

SMA(fire.id)

and

∆VI(fire.id)

(Example 1)

SMA(fire.id)

and

∆VI(fire.id)

(Example 2)


∆VI

SMA Spatial

variability of every fire

Dark soil Vegetation Light soil

∆VI Spatial

variability of every fire


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


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


Conclusion

Two techniques to quantify spatio-temporal variation of the impact of fire were presented

Additional validation is necessary


Acknowledgements

Funding provided by the Belgium Science Policy Office (BELSPO) as part of the GLOVEG project

Jan Verbesselt for scientific inputs

[email protected]

Laboratory of Geomatics KU Leuven

Vital Decosterstraat 102, 3000 Leuven Belgium

Development of indicators of fire severity based on time series of SPOT VGT data Stefaan Lhermitte,...

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