1. 1 Space Applications Institute Joint Research Centre European Commission 21020 Ispra (VA), Italy http://www.gvm.sai.jrc.it Global Vegetation Monitoring Unit name Potential of SPOT 4-VEGETATION Data for Mapping the Forest Cover of Madagascar and Upper Guinea Philippe Mayaux, Valéry Gond and Etienne Bartholomé

2. Global Vegetation Monitoring Objectives of the study The objectives of this study are  to demonstrate the possibility of updating the forest-cover maps in a near-real time manner using VEGETATION data.  to check the main advantages of VEGETATION for forest mapping at regional scale (geometry, data access, reflectance value)  to test several techniques for reducing the noise in the S-10 products (clouds, missing data, patchy aspect)

3. Global Vegetation Monitoring Context: the TREES Project Baseline inventory of dense moist forests  based on AVHRR data of 1992-1993  update with ATSR and VEGETATION data Madagascar was missing in the first round West Africa was not up-to-date

4. Global Vegetation Monitoring Forests of Madagascar Dense dry forests with burns Deciduous Thicket Grasslands and gallery-forests Dense moist forest with agriculture Secondary complex and dense forest VEGETATION colour composite (R,G,B = SWIR, NIR, R) of June 1999 and Digital Elevation Model

5. Global Vegetation Monitoring Data and methods SPOT-4 VEGETATION data  S-10 products  October 1998 to September 1999 Data preparation  monthly composition  reduce noise (haze and clouds, patchy, sensor)  minimum SWIR Data classification  unsupervised classification of 36 channels (12 months x 3 channels: R, NIR, SWIR)  visual labelling

6. Global Vegetation Monitoring Monthly compositing June 1 st - 10 th June 11 th - 20 th June 21 th - 30 th Noise reduction Elimination of remaining clouds Elimination of missing data Minimum SWIR

7. Global Vegetation Monitoring Temporal profiles Short Wave Infrared channel: monthly compositing

8. Global Vegetation Monitoring Seasonal activity NovemberJanuaryMarch MayJuly September

9. Global Vegetation Monitoring Data classification Unsupervised classification spectral Labelling spectral, spatial temporal, ancillary 6 classes 30 clusters 36 channels (R, NIR, SWIR)

10. Global Vegetation Monitoring Forest cover map of Madagascar Dense humid forest Secondary complex Dense dry forest Mangrove Savannah Swamp

11. Global Vegetation Monitoring Map Validation Pixel-based comparison with 3 Landsat TM classifications (interpreted by local experts) Landsat TM (158-70) VEGETATION Overall accuracy of the Forest class: 86 %

12. Global Vegetation Monitoring Forest mapping in West Africa Forest classes  Evergreen forest (2 classes of density)  Secondary complex  Mangrove  Non forest Short period with cloud-free images No well-marked topography

13. Global Vegetation Monitoring Data and methods SPOT-4 VEGETATION data  S-1 products  February 2000 Data preparation  selection of cloud-free images (by eco-region and viewing angle)  channels R, NIR, SWIR Data classification  unsupervised clustering (20) and visual labelling of the single-date selected images  mosaic of the single-date classifications

14. Global Vegetation Monitoring Spatial mosaic of 3 images February 2000 VEGETATION colour composite (R,G,B = SWIR, NIR, R)

15. Global Vegetation Monitoring Forest cover map of West Africa Evergreen forest (dense) Evergreen forest (less dense) Secondary complex Mangrove Non forest Water bodies

16. Global Vegetation Monitoring Forest blocks in Ghana

17. Global Vegetation Monitoring Conclusions Capacity of SPOT-4 VEGETATION data to update the forest-cover maps in a rapid manner. S-10 adapted to seasonal forests (dry forests in Madagascar), S-1 adapted to evergreen forests Poor mapping of savannahs