1. 1 Work carried out by SCOT and KUL presented at VEGETATION 2000 Conference with the support of CNES and contribution of JRC and VTT

2. 2 mAssessment of the capabilities of VEGETATION for mapping forest at regional scale mPreparation of a European forest map prototype mwith simple methods applied to standard VGT products (10 days composites S10)  Partners  JRC : methodology as developed for FMERS study  KUL : alternative approach for stratification  SCOT : processing  VTT : clustering method

3. 3 mData : 21 composites S10 from April 1999 to October 1999 mOther data : CORINE LC, JRC/FIRS stratification of European forest ecosystems mPan-European study area

4. 4 mQuality of VEGETATION S10 data Decade #2 October 1999 Status map Clouds in dark blue Ice in light blue Bad SWIR sensors in red  However  problems of SWIR sensors  cloud mask to be improved  remaining directional effects  High visual quality

5. 5 S10 decade #2 October 1999 NIR/SWIR/R

6. 6 S10 decade #2 October 1999 NIR/SWIR/R

7. 7 m Mapping procedure  Stratification  utilisation of FIRS strata  alternative using landscape criteria  Spectral clustering  Selection of the most appropriate S10 for forest types discrimination and generation of a multi-spectral monthly composite  Stratified clustering of the monthly composite  Multi-temporal analysis of NDVI forest types CORINE Land Cover

8. 8 Two alternative location of cluster means in three ecosystem regions. A - means similarly distributed - no likely need for pre-clustering stratification. B - cluster means on the average different in different regions - a likely need for pre- clustering stratification.

9. 9 FIRS stratification superimposed over VGT S10 # 2 October 1999

10. 10 FIRS main regions after regrouping of main strata

11. 11 m Stratification alternative using landscape criteria  fragmentation patterns, e.g. Shannon index...  proportion of main land cover categories Proportion of forest within 5x5 Km bloc from CORINE LC 0 % < 10 % 11-40 % 41-80 % > 81 %

12. 12 m Generation of a monthly composite  remaining clouds in all S10 products  selection of three S10 composites  D3 August 1999  D1 September 1999  D2 September 1999  averaging procedure with elimination of bad pixels in the SWIR band and remaining cloudy pixels expected to minimise directional effects

13. 13 m Generation of a monthly composite 10 days composite #3 August 1999 monthly composite (#3 Aug / # 1 & 2 September 1999)

14. 14 Monthly composite by averaging three decades D3/Aug, D1 and 2/Sept 1999

15. 15 m Unsupervised clustering within each stratum into 50 classes  algorithm developed by VTT (Finland) and tested in the framework of FMERS for European forest mapping with IRS-WiFS data  based on homogeneous signatures within 2x2 pixels  automatic sort of the clusters according to their approximate biomass values

16. 16 Likely location of the target classes in the spectral range (FMERS/VTT)

17. 17 m Unsupervised clustering within each stratum into 50 classes coniferous mixed Broad - leaved coniferous mixed Broad - leaved 15 20 confusion Discrimination with SWIR band

18. 18

19. 19 m contribution of the SWIR band

20. 20 m NDVI temporal profiles  definition of temporal indicators  distinction of three “seasons” for better discrimination of NDVI profiles related to forest types April-MayJune-AugSept-Oct  application to the 21 S10 composites

21. 21 Colour composite NDVI three seasons

22. 22 m Temporal profiles of main forest cover types

23. 23 m Temporal profiles of other land cover types

24. 24 m Temporal profiles of CORINE classes Quality of CORINE classes ? Variability of vegetation phenology within broad land cover classes

25. 25 Prototype of a European Forest Map derived from VEGETATION data Coniferous forest Broad-leaved forest Mixed forest Evergreen vegetation

26. 26  VEGETATION S10 products of very good quality : geometry, radiometry  but some improvements are still needed : correction of directional effects, cloud masking…  the spectral clustering approach is leading to promising results with simple methods and a few S10 products  dramatic potential of NDVI temporal profiles for discriminating vegetation types : combination with spectral clustering ?  need to improve and develop new standard products on NDVI profiles, e.g. phenology, …  products on land cover changes