1. Development of indicators of fire severity based on time series of SPOT VGT data Stefaan Lhermitte, Jan van Aardt, Pol Coppin Department Biosystems Modeling, monitoring, and management of bioresponse Geomatics group KU Leuven Belgium

2. May 2005Multitemp 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

3. May 2005Multitemp 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“

4. May 2005Multitemp 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

5. May 2005Multitemp 2005 Fires Multitemp Indication of fire frequency by area Very large fires exist, indicating a possible exaggeration

6. May 2005Multitemp 2005 Fires by vegetation 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

7. May 2005Multitemp 2005 Techniques  Spectral mixture analysis (SMA): –Bare soil, charcoal, vegetation Hypothesis: FS i ~ ∆(vegetation fraction) i where i = fire pixel --> Absolute values  Changes in vegetation indices (∆VI) Hypothesis: FS i ~ ∆(vegetation index) i where i = fire pixel --> Relative values

8. Assumes that the reflectance spectrum can be deconvolved into a linear mixture of the spectra of endmembers (pure pixels) S pectral M ixture A nalyis

9. May 2005Multitemp 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 –FS i be expressed by ∆(VF i ) S pectral M ixture A nalyis VF i = vegetation content pixel

10. May 2005Multitemp 2005 S pectral M ixture A nalyis Procedure Endmember 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

11. May 2005Multitemp 2005 S pectral M ixture A nalyis 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

12. May 2005Multitemp 2005 S pectral M ixture A nalyis Procedure  Endmember selection  Fraction images

13. May 2005Multitemp 2005 S pectral M ixture A nalyis Procedure % Vegetation Component 3 decades before fire Multitemp

14. May 2005Multitemp 2005 S pectral M ixture A nalyis Procedure % Vegetation Component 3 decades before fire Multitemp

15. May 2005Multitemp 2005 ∆V egetation I ndex  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 content pixel

16. May 2005Multitemp 2005 ∆V egetation I ndex  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

17. May 2005Multitemp 2005 Analysis  Look at a fire as a complete entity: –Analysis of mean(FS i ) j where i = fire pixel j = fire id  Look at spatial variability for every fire: –Analysis of FS i where i = fire pixel

18. May 2005Multitemp 2005 S pectral M ixture A nalyis (fire.id)  Change curves of fractions for every fire scar (Example 1) Multitemp

19. May 2005Multitemp 2005  Change curves of fractions for every fire scar (Example 2) Multitemp S pectral M ixture A nalyis (fire.id)

20. May 2005Multitemp 2005 ∆V egetation I ndex (fire.id) Change curves of ∆ VI for every fire Scar (Example 1) Multitemp

21. May 2005Multitemp 2005 ∆V egetation I ndex (fire.id) Change curves of ∆VI for every fire scar (Example 2) Multitemp

22. SMA(fire.id) and ∆VI(fire.id) (Example 1)

23. SMA(fire.id) and ∆VI(fire.id) (Example 2)

24. May 2005Multitemp 2005 ∆VI SMA Spatial variability of every fire Dark soilVegetationLight soil

25. ∆VI Spatial variability of every fire

26. May 2005Multitemp 2005 Actual F ire S everity  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

27. May 2005Multitemp 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

28. May 2005Multitemp 2005 Conclusion  Two techniques to quantify spatio-temporal variation of the impact of fire were presented  Additional validation is necessary

29. May 2005Multitemp 2005 Acknowledgements  Funding provided by the Belgium Science Policy Office (BELSPO) as part of the GLOVEG project  Jan Verbesselt for scientific inputs

30. stefaan.lhermitte@biw.kuleuven.be stefaan.lhermitte@biw.kuleuven.be Laboratory of Geomatics KU Leuven Vital Decosterstraat 102, 3000 Leuven Belgium