1. Test of forest classification over Bavaria (Germany) using a SPOT-VGT pixel mosaic Erwann FILLOL, Pamela KENNEDY, Sten FOLVING

2. To create a cloud-free image of Europe by pixel compositing SPOT-VGT S1data To evaluate the efficiency of the Maximum NDVI / Minimum Red (MaNMiR) pixel compositing method in discriminating three types of forest cover: Evergreen, deciduous, and mixed through classic classification methods Objectives

3. SPOT-VGT S1 : 45 daily acquisitions for the months of July and August, 2000 for all of Europe CORINE Land Cover database (CLC) : 44 classes for 3 hierarchical levels (Artificial surfaces, Agricultural areas, Forests and semi-natural areas), obtained in part using Landsat TM imagery (resolution: 100 meters) Databases

4. Cloud cover (Lissens et al.) if  blue > 0.36 and  swir > 0.16  Cloud Dilation of cloud cover – 5kmx5km window Elimination of cloud shadow Scan angle limitation If  v > 45°  Ground resolution degradation SWIR detector defects If  swir > 0.75  SWIR defect Hot-Spot and Specular limitation Pre-processing: Compositing MASKING

5. Cloud cover (Lissens et al.) if  blue > 0.36 and  swir > 0.16  Cloud Dilation of cloud cover - 5x5 window Elimination of cloud shadow Scan angle limitation If  v > 45°  Ground resolution degradation SWIR detector defects If  swir > 0.75  SWIR defect Hot-Spot and Specular limitation Pre-processing: Compositing MASKING

6. Cloud cover (Lissens et al.) if  blue > 0.36 and  swir > 0.16  Cloud Dilation of cloud cover - 5x5 window Elimination of cloud shadow Scan angle limitation If  v > 45°  Ground resolution degradation SWIR detector defects If  swir > 0.75  SWIR defect Hot-Spot and Specular limitation Pre-processing: Compositing MASKING

7. Cloud cover (Lissens et al.) if  blue > 0.36 and  swir > 0.16  Cloud Dilation of cloud cover - 5x5 window Elimination of cloud shadow Scan angle limitation If  v > 45°  Ground resolution degradation SWIR detector defects If  swir > 0.75  SWIR defect Hot-Spot and Specular limitation Pre-processing: Compositing MASKING

8. Solar zenith and azimuth angles are known Cloud height minimum and maximum are estimated The distance [d=h/tan(90-  s )] and direction of the cloud shadow can be estimated Cloud shadow elimination h ss h min =2km h max =12km d ss

9. Cloud cover (Lissens et al.) if  blue > 0.36 and  swir > 0.16  Cloud Dilation of cloud cover - 5x5 window Elimination of cloud shadow Scan angle limitation If  v > 45°  Ground resolution degradation SWIR detector defects If  swir > 0.75  SWIR defect Hot-Spot and Specular limitation Pre-processing: Compositing MASKING

10. Cloud cover (Lissens et al.) if  blue > 0.36 and  swir > 0.16  Cloud Dilation of cloud cover - 5x5 window Elimination of cloud shadow Scan angle limitation If  v > 45°  Ground resolution degradation SWIR detector defects If  swir > 0.75  SWIR defect Hot-Spot and Specular limitation Pre-processing: Compositing MASKING

11. Cloud cover (Lissens et al.) if  blue > 0.36 and  swir > 0.16  Cloud Dilation of cloud cover - 5x5 window Elimination of cloud shadow Scan angle limitation If  v > 45°  Ground resolution degradation SWIR detector defects If  swir > 0.75  SWIR defect Hot-Spot and Specular limitation Pre-processing: Compositing MASKING

12.    90  180  hotspot specular d hs d spec dcrux hs dcrux spec To minimise directional effects, the acquisitions situated near the hot spot and specular zones (± 20°) are eliminated Hot-Spot and Specular limitation

13. Image: 26 th of August 2000 Resulting mask

14. Image: 26 th of August 2000 Resulting mask

15. Image: 26 th of August 2000 Resulting mask

16. Image: 26 th of August 2000 Resulting mask

17. Pre-processing: Compositing DOUBLE CRITERIA COMPOSITING Double criteria compositing : Maximum NDVI (MaN), to eliminate haze and unscreened pixels top 15% retained Minimum reflectance in the red channel (MiR), to limit atmospheric effects and enhance green vegetation D’Iorio and al., 1991

18. Composite result

19. Raw image (August 26 th 2000) MaNMiRMaN Classic

20. Test area : Bavaria (Germany) Corine classification Resolution : 100 m

21. Test area : Bavaria (Germany) Test site selection based on: little topographic effect 3 forest types present: coniferous, deciduous, mixed site is representative of temperate forests 200 km 300 km

22. Test area : Bavaria (Germany) Corine classification Resolution : 100 m

23. Maximum Like-lihood algorithm Training site selected over a homogeneous area (according to Corine classification) Using channels SWIR, NIR & Red 3 classes : Coniferous, deciduous, mixed forests Classification

24. Red reflectance NIR reflectance SWIR reflectance NIR reflectanceRed reflectance SWIR reflectance Broad leaved forest Coniferous forest Mixed forest Spectral separability

25. Corine classificationSPOT-VGT composite Classification Results

26. Broad leaved forest Coniferous forest Mixed forest CorineSPOT-VGT Coniferous 24 %20.2 % Broad- Leaved 4 %1.4 % Mixed 5 %3.0 % Non-Forest 67 %75.4 % Corine classificationSPOT-VGT composite

27. Dense forest zones most accurately classified Over estimation in sparse forest due to surrounding (pasture) Classification Results Broad leaved forest Coniferous forest Mixed forest Corine classificationSPOT-VGT composite

28. Classification Results Broad leaved forest Coniferous forest Mixed forest Corine classificationSPOT-VGT composite

29. Classification Results Broad leaved forest Coniferous forest Mixed forest Under estimation in sparse and fragmented forest. Surrounding : Non-irrigated arable land Corine classificationSPOT-VGT composite

30. Coniferous from SPOT-VGT Composition of actual land cover (based on CLC) classified as Coniferous according to SPOT-VGT

31. Composition of actual land cover (based on CLC) classified as Broad-Leaved according to SPOT-VGT Broad-Leaved from SPOT-VGT

32. Mixed from SPOT-VGT Composition of actual land cover (based on CLC) classified as Mixed Forest according to SPOT-VGT

33. Non-Forest from SPOT-VGT Composition of actual land cover (based on CLC) classified as Non-Forest according to SPOT-VGT

34. Classification sensitivity to sub-pixel forest density Low Medium High

35. Conclusions and discussion High quality composites are possible with Spot-VGT High potential in discriminating dense coniferous wood-land Must be careful with area estimation of forest cover in Europe, especially in fragmented and mixed forest Potential of combining medium resolution radiometer like IRS-WiFS (200m resolution) and low resolution SPOT-VGT