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Test of forest classification over Bavaria (Germany) using a SPOT-VGT pixel mosaic

This study aims to create a cloud-free image of Europe by compositing SPOT-VGT data and evaluate the Maximum NDVI/Minimum Red method in classifying forest cover types in Bavaria, Germany. Pre-processing techniques like cloud cover removal and masking were applied to generate accurate composites for classification analysis.

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Test of forest classification over Bavaria (Germany) using a SPOT-VGT pixel mosaic

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

  2. Objectives • 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

  3. Databases • 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)

  4. Pre-processing: Compositing MASKING • 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

  5. Pre-processing: Compositing MASKING • 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

  6. Pre-processing: Compositing MASKING • 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

  7. Pre-processing: Compositing MASKING • 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

  8. s h s d hmin=2km hmax=12km Cloud shadow elimination • 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

  9. Pre-processing: Compositing MASKING • 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

  10. Pre-processing: Compositing MASKING • 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

  11. Pre-processing: Compositing MASKING • 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

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

  13. Resulting mask Image: 26th of August 2000

  14. Resulting mask Image: 26th of August 2000

  15. Resulting mask Image: 26th of August 2000

  16. Resulting mask Image: 26th of August 2000

  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. MaN Classic Raw image (August 26th 2000) MaNMiR Composite result

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

  21. 300 km 200 km 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 Corine classification Resolution : 100 m

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

  23. Classification • 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

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

  25. Classification Results Corine classification SPOT-VGT composite

  26. Coniferous forest Broad leaved forest Mixed forest Classification Results Corine classification SPOT-VGT composite

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

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

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

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

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

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

  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

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