Published on

Published in: Technology, Education
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide


  1. 1. A land cover map of Africa at 100m using a mosaic of ALOS PALSAR dual-polarization data - Preliminary developments - Alexandre Bouvet, Gianfranco De Grandi European Commission DG Joint Research Centre 21027, Ispra (VA), Italy e-mail: [email_address]
  2. 2. ALOS Kyoto & Carbon (K&C) Initiative CONTEXT Global coverage of the African continent with PALSAR observations at two polarizations (HH + HV), one date (mainly July-August 2007) Objectives of K&C: define, develop and validate thematic products used to meet the specific information requirements relating to the international environmental Conventions, Carbon Cycle Science and Conservation of the environment Systematic data acquisition strategy <ul><li>Forest/Non-forest map? </li></ul><ul><li>Land cover map? </li></ul>De Grandi et al., IEEE TGRS, in press
  3. 3. VCF (Vegetation Continuous Field) University of Maryland - NASA MODIS - 500m GLC2000 (Global Land Cover 2000) JRC SPOT VEGETATION - 1km GLOBCOVER ESA - JRC MERIS – 300m EXISTING LARGE-SCALE MAPS
  4. 4. VCF GLC2000 GLOBCOVER PALSAR HV <ul><li>derived from the mosaic </li></ul><ul><li>&quot;Naive&quot; approach: </li></ul><ul><li>threshold on HV only. </li></ul><ul><li>Refined analysis requires: </li></ul><ul><li>Dealing with topography </li></ul><ul><li>Detection of water </li></ul><ul><li>Use of ancillary information </li></ul>PRELIMINARY APPROACH
  5. 5. East North incident wave     lon lat INFLUENCE OF TOPOGRAPHY Local incidence angle: θ loc depends on: θ : the SAR incident angle φ : the viewing azimuth angle α : the absolute slope β : the slope orientation In the mosaic, the SAR geometry is almost constant: θ≈ 39˚ φ≈ 10.5˚ θ loc is fully described by the local topography ( α and β ). SAR geometry local topography
  6. 6. correction of effective scattering area correction of backscatter angular dependence (exact correction) (approximated correction – dependant on land cover type) INFLUENCE OF TOPOGRAPHY <ul><li>In most correction methods, the effect of topography on backscatter is supposed to depend only on θ loc : </li></ul><ul><li>The change in the effective scattering area varies with sin( θ loc ) </li></ul><ul><li>The change in the volume scattering varies with cos n ( θ loc ) (Castel et al. 2001) </li></ul>
  7. 7. Detail Not corrected TOPOGRAPHIC CORRECTION
  8. 8. INFLUENCE OF TOPOGRAPHY Tropical forest sin sin & cos
  9. 9. INFLUENCE OF TOPOGRAPHY Shrubland sin sin & cos
  10. 10. INFLUENCE OF TOPOGRAPHY Tropical forest facing perpendicular opposite sin sin & cos
  11. 11. INFLUENCE OF TOPOGRAPHY Shrubland facing perpendicular opposite sin sin & cos
  12. 12. INFLUENCE OF TOPOGRAPHY <ul><li>Summary: </li></ul><ul><li>The backscatter angular dependence is a function of the polarization and the vegetation type </li></ul><ul><li>Topographic effects are not described completely by θ loc . </li></ul><ul><li>An explicit expression of α and β is needed. </li></ul><ul><li>Conclusions: </li></ul><ul><li>No overall topographic correction before classification </li></ul><ul><li>(only correction of scattering area) </li></ul><ul><li> Topography must be taken into account in the training </li></ul><ul><li>(class signatures as a function of α and β ) </li></ul>
  13. 13. S01 E09 (Gabon) 1990 2000 <ul><li>Classification scheme: </li></ul><ul><li>5 ˚ x 5˚ tiles </li></ul><ul><li>In each tile: </li></ul><ul><li>HH and HV signatures (pdf) of each land cover class using coarser resolution maps (e.g. GlobCover) and SRTM: </li></ul><ul><li>S HH,c , S HV,c </li></ul>CLASSIFICATION
  14. 14. BACKSCATTER SIGNATURES Tropical forest
  16. 16. S01 E09 (Gabon) 1990 2000 <ul><li>Classification scheme: </li></ul><ul><li>5 ˚ x 5˚ tiles </li></ul><ul><li>In each tile: </li></ul><ul><li>HH and HV signatures (pdf) of each land cover class using coarser resolution maps (e.g. GlobCover) and SRTM: </li></ul><ul><li>S HH,c , S HV,c </li></ul><ul><li>Error E c for each class c: </li></ul><ul><li>E c =( σ 0 HH, α , β -S HH,c, α , β ) 2 + ( σ 0 HV, α , β -S HV,c, α , β ) 2 </li></ul><ul><li>- Select class that minimize E c </li></ul>CLASSIFICATION
  17. 17. WATER BODIES HH-HV-HH White: α =0° Grey: buffer 10 pix Classified Pros: false detection (bare soil/low vegetation detected as water) is reduced Cons: some water bodies are missed
  18. 18. VCF GLC Tile centered on: 32.5°S–27.5° E South Africa RESULTS VCF GLC2000 GLOBCOVER PALSAR
  19. 19. Tile centered on: 2.5°S – 17.5° E Border between the Congos Transition from tropical forest to savanna. VCF GLC GlobCover VCF GLC2000 GLOBCOVER PALSAR RESULTS
  20. 20. PALSAR GlobCover 2009 Central African tile Subset 1 RESULTS
  21. 21. Central African tile Subset 2 RESULTS PALSAR GlobCover
  22. 22. PALSAR GlobCover South African tile Subset 1 RESULTS PALSAR GlobCover
  23. 23. Reference data from the Global Forest Resources Assessment 2010 (FRA2010) Remote Sensing Survey (RSS) – FAO & JRC <ul><li>Systematic sampling every square degree </li></ul><ul><li>20km x 20km Landsat boxes (30m resolution ) </li></ul><ul><li>Automatic classification revised by national experts </li></ul>1990 2000 VALIDATION Tree cover Tree cover mosaic Shrub Other vegetation Water
  24. 24. 2°S – 16° E 1990 2000 VALIDATION
  25. 25. VALIDATION erosion Confusion matrices <ul><li>Land cover classification </li></ul><ul><li>4 classes: forest (dense + open), shrub , bare/grass , water </li></ul><ul><li>Forest/non-forest mapping </li></ul><ul><li>2 classes: forest (dense + open), non-forest (shrub, bare/grass, water) </li></ul>Pixel-based accuracies: Not eroded Eroded Land cover 72.4% 79.4% Forest/non-forest 88.3% 93.9%
  26. 26. <ul><li>Conclusion: </li></ul><ul><li>Parts of the mosaic have been transformed into land cover and forest/non-forest maps, using topographic descriptors derived from the SRTM DEM, and training from GlobCover </li></ul><ul><li>The produced maps are validated with a good accuracy </li></ul><ul><li>Perspectives: </li></ul><ul><li>Map the whole mosaic and check accuracy </li></ul><ul><li>Other years using archive data: 2007 to 2010 </li></ul><ul><li>Not only in Africa (downscale the 25m global mosaics to 100m to use SRTM) </li></ul><ul><li>From qualitative (land cover classes) to quantitative estimates of vegetation (biomass) </li></ul>CONCLUSION & PERSPECTIVES