1. H.D.Eva E.E. de Miranda C.M. Di Bella V.Gond O.Huber M.Sgrenzaroli S.Jones A.Coutinho A.Dorado M.Guimarães C.Elvidge F.Achard A.S.Belward E.Bartholomé A.Baraldi G.De Grandi P.Vogt S.Fritz A.Hartley A VEGETATION MAP OF SOUTH AMERICA MAPA DA VEGETAÇÃO DA AMÉRICA DO SUL MAPA DE LA VEGETACIÓN DE AMÉRICA DEL SUR

2. Contributing Institutions

3. Venezuela Southern Cone Amazon forest Regional Experts working on data Brazil

4. South America Map Production Multi-sensor approach -Humid forests detected using the ERS ATSR-2 -Flooded forests ecosystems detected using the JERS- 1 RADAR -Urban areas selected using the DMSP ‘night lights’ -Remaining land cover from SPOT VGT -Montane forests from G5 TOPO DEM (ammended)

5. Humid forests detected using the ERS ATSR-2 -Over 1000 images to create a mosaic based on highest surface temperature – “tropical dry season” -Unsupervised spectral clustering -Class labeling for humid forests and non-forests ATSR-2 1 km resolution: 500 km swath: Green / Red / NIR / SWIR and TIR channels

6. ATSR-2 view of Rondonia - R/G/B SWIR/NIR/Red

7. JERS-1 RADAR for flooded forests - Two JERS-1 Mosaics – high water and low water -Radar backscatter is increased by the the ‘double bounce’ off water and trees – high backscatter shows flooded forests -The difference between the two images shows up seasonally flooded areas

8. Land cover from the SPOT VGT data -Preparation of ‘seasonal’ mosaics from S10 data -‘Winter’ ‘Spring’ ‘Summer’ ‘Autumn’ – selected on lowest SWIR (thresholded) -Composited to the full year (Red NIR and SWIR) -Humid forests (ATSR) mask -Unsupervised classification 60 classes to remaining area -Class labeling -Extraction of particular areas from seasonal mosaics (e.g. removal of Snow)

9. Creation of seasonal mosaics from S10 product Jan-Mar Apr-Jun July-Sept Oct-Dec

10. Combining of VGT seasonal images Masking of evergreen forest (use of ATSR forest) Unsupervised clustering to 60 classes Class labeling and aggregation with seasonal profiles

11. Seasonal profiles extracted for each class

12. DMSP Night lights data for urban areas

13. DMSP night lights for urban areas -Extraction of DMSP night lights – highest 80% luminance -Extraction of SPOT VGT data for the equivalent areas -Unsupervised classification of VGT data into 10 classes -Visual examination of classes to remove ‘light cone’

14. Validation data TREES High resolution data set – 40 scenes spread across the humid forest domain Advantages – cost, availability, expert interpretation Disadvantages – a bias in selection and in classification scheme used (i.e. for forest change), data are from 1997, fragmented classes

15. Relationship between forest classes on the map and on the reference data

16. Forest class: 83% forest Mosaic 1 (agriculture and degraded forest): 39% forest 35 % agriculture 21 % mosaics (agriculture and other) Mosaic 2 (agriculture and other vegetation): 21% woodland/shrubland 25% grassland 29% agriculture Agriculture : 57% agriculture 18% mosaic (agriculture and other) A brake-down of map classes by reference classes

20. Conclusions Map is a result of a network of experts and institutions Final data product is available at the regional level and can be aggregated to different general levels Data have undergone a first level validation Data have been provided on the Web and downloaded by over 120 different institutions Thanks to regional experts and to GIS support