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Evaluating different compositing methods using SPOT-VGT S1 data for

Evaluating different compositing methods using SPOT-VGT S1 data for land cover mapping the dry season in continental Southeast Asia. Sarah Mubareka. Hans Jurgen Stibig. Objectives.

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Evaluating different compositing methods using SPOT-VGT S1 data for

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  1. Evaluating different compositing methods using SPOT-VGT S1 data for land cover mapping the dry season in continental Southeast Asia Sarah Mubareka Hans Jurgen Stibig

  2. Objectives 1. To maximise the SPOT-VGT S1 data set potential in mapping land cover in continental Southeast Asia for the dry season (January & February 2000) 2. To compare S1 composites to S10 composites for the dry season vegetation mapping

  3. Methods 1. Masking “unusable” pixels 2. Generating cloud-free composites using pixel and sub-image compositing based on 1 and 2 criteria methods (vegetation index-based) 3. Evaluating composite sensitivity to atmosphere and vegetation; view angle preference; and texture variance using A. General heterogeneous test sites (50x50 pixels) B. Land-cover-specific test sites (180-600 pixels)

  4. Methods 1. Masking “unusable” pixels 2. Generating cloud-free composites using pixel and sub-image compositing based on 1 and 2 criteria methods (vegetation index-based) 3. Evaluating composite sensitivity to atmosphere and vegetation; view angle preference; and texture variance using A. General heterogeneous test sites (50x50 pixels) B. Land-cover-specific test sites (180-600 pixels)

  5. Inconveniences in the data set 1 2 3 1 4 1: The gap between orbit 0 and 1 of the same day resulting in one or two black bands within each scene 2: Defective SWIR detectors, resulting in a streak appearing in some scenes 3: Pixels buffering the error in (2) resembling pixels representing land cover 4: Cloud and cloud shadow

  6. Masking unusable pixels SWIR strip masks Viewing and solar angles S1 Jan & Feb SWIR band bitmap Δθ=0º and Δφ=0º (±20º) yes yes yes Bany=0 Mask=6 Mask=2 Mask=5 no Dilation Blue>720 SWIR>320 no yes Δθ=0º and Δφ=180º (±20º) Mask=1 yes Mask=7 Mask=3 no Dilation no Mask=3 pThrs>45 yes Mask=4 Cloud shadow angle no yes Usable pixels Mask=8 (Lissens, 2000) (Fillol 1999, Simpson1998)

  7. February 24, 2000 image

  8. Usable pixels Cloud Cloud shadow Dilation VZ > 45 No data SWIR defect Hot spot Specular

  9. Methods 1. Masking “unusable” pixels 2. Generating cloud-free composites using pixel and sub-image compositing based on 1 and 2 criteria methods (vegetation index-based) 3. Evaluating composite sensitivity to atmosphere and vegetation; view angle preference; and texture variance using A. General heterogeneous test sites (50x50 pixels) B. Land-cover-specific test sites (180-600 pixels)

  10. Sub-image composite METHOD (theoretically..): -Each image in the database is divided into a 12x12 grid -The least polluted sub-image is selected -Unsupervised classification per sub-image followed by fusion of classified sub- images GLITCHES -Visible seam, difficult to calibrate sub-images to reduce contrast -Not a completely cloud-free image

  11. Pixel composites Single criteria Double criteria

  12. Pixel composites MaxDVI MaNMiVZ MaNMiRED MaxNDVI MaxNDWI MaxNDDI S10Dry S10Wet

  13. Pixel composites - visual interpretations MaxDVI (S1) [MaxDVI=(NIR-red)]

  14. Pixel composites - visual interpretations MaxNDVI (S1) [NDVI=(NIR-red)/(NIR+red)]

  15. Pixel composites - visual interpretations MaxNDVI MinVZA (S1) MaxNDVI MinRED (S1)

  16. Pixel composites - visual interpretations MaxNDWI (S1) MaxNDDI (S1) [NDWI=(NIR-SWIR)/(NIR+SWIR)] [NDDI=(SWIR-NIR)/(SWIR+NIR)]

  17. Pixel composites - visual interpretations S10Wet S10Dry [S10Wet=Minimum SWIR] [S10Dry=Minimum NIR if pixel is not green for S10Wet]

  18. Methods 1. Masking “unusable” pixels 2. Generating cloud-free composites using pixel and sub-image compositing based on 1 and 2 criteria methods (vegetation index-based) 3. Evaluating composite sensitivity to atmosphere and vegetation; view angle preference; and texture variance using A. General heterogeneous test sites (50x50 pixels) B. Land-cover-specific test sites (180-600 pixels) De Wasseige et al.

  19. Sensitivity Analysis: heterogeneous test sites ex. Zone 8 ex. Zone 2 Sensitivity to atmosphere: reflectance in blue channel • Mosaic is inconsistently sensitive in the blue channel • MaxDVI most affected • MaxNDVI composites least affected • S10 composites moderately affected

  20. Sensitivity Analysis: heterogeneous test sites ex. Zone 1 ex. Zone 3 Sensitivity to vegetation: reflectance in NIR channel • S10Dry underestimates green vegetation • maxNDVI composites tend to overestimate green vegetation cover

  21. Sensitivity Analysis: heterogeneous test sites ex. Zone 6 ex. Zone 2 Texture Variance • Mosaic can be used as control (least speckle) • The composite with the least speckle is MaNMiRED • S10 composites are mostly sensitive over dry zones

  22. Sensitivity Analysis: heterogeneous test sites View zenith angle distribution of pixels for S1 composites No composite consistently selects near-nadir pixels (except MaNMiVZ) - regardless of land cover type

  23. Methods 1. Masking “unusable” pixels 2. Generating cloud-free composites using pixel and sub-image compositing based on 1 and 2 criteria methods (vegetation index-based) 3. Evaluating composite sensitivity to atmosphere and vegetation; view angle preference; and texture variance using A. General heterogeneous test sites (50x50 pixels) B. Land-cover-specific test sites (180-600 pixels)

  24. Study site source Mekong River Commission 1997 forest cover map (based on TM classification)

  25. Deciduous grassland agriculture Mosaic Evergreen bamboo Mixed Selecting training sites

  26. Land cover classes most confused high density evergreen medium/low density Continuous forest cover deciduous mixed high density Forest regrowth evergreen Mosaic of forest cover deciduous mixed Wood & shrubland evergreen Rock Agriculture Non-forest Bamboo Grass cropping area >30% Mosaic of cropping cropping area <30%

  27. Homogeneous test sites

  28. MaxNDVI MinRED (S1) Mosaic (S1) MaxNDDI (S1) S10Dry

  29. Deciduous - mosaic Grassland Deciduous - continuous Agriculture Homogeneous test sites Isolating clear classes in maxNDDI In order to detect which classes are not clouded over in the maxNDDI composite, we compare reflectance values for the NIR bands. IF NIRmaxNDDI > NIRMaNMiRED, then class is retained for classification with maxNDDI

  30. Homogeneous test sites Viewing angle differences for these classes

  31. rivers & lakes Base classification MaxNDVI MinRED (S1) Mosaic (S1) Possible evergreen vs mixed forest dry vegetation MaxNDDI (S1) S10Dry Conclusions

  32. Objectives 1. To maximise the SPOT-VGT S1 data set potential in mapping land cover in continental Southeast Asia for the dry season (January & February 2000) 2. To compare S1 composites to S10 composites for the dry season vegetation mapping

  33. Objectives 1. To maximise the SPOT-VGT S1 data set potential in mapping land cover in continental Southeast Asia for the dry season (January & February 2000) 2. To compare S1 composites to S10 composites for the dry season vegetation mapping

  34. Though the S1 and S10 composites cannot be compared directly since too many parameters separating them exist (2 months of data vs 8; spilling over outside of dry season..), it can be said that 1. A more cloud-free image is obviously possible with the S10 composites (for filling holes of missing data?) 2. Since MaxNDVI criteria is used for generating the 10-day data set, it is difficult to assess the degree to which green vegetation is exaggerated and therefore may affect the borders between green vegetation and other land cover

  35. Land cover mapping

  36. Max NDDI adjustments High within class variance for composite max NDDI (ex zone 8):

  37. Max NDDI adjustments High within class variance for composite max NDDI (ex zone 8):

  38. Conclusions • Classification approach: By ecosystem • Classification method • hybrid unsupervised and supervised • integration of vegetation index channels • fusion of classifications : • 1/ Combination of MaNMiRED (used for most • classes), mosaic, MaxNDDI (by masking classes) • 2/ Classification of sub-images using S10 composites for filling cloud-contaminated zones • Areas for improvement • Masking parameters: hot spot/specular zones; cloud height estimation; automating SWIR sensor defect masking; cloud/haze thresholding • Bi-directional effects: normalisation of pixels to a common geometry

  39. Appendix

  40. Database for each pixel composite • Day • Month • Solar zenith angle • Solar azimuth angle • View azimuth angle • View zenith angle

  41. MaN MaNMiRED MaNMiVZ EVERGREEN High density Med/low density Mosaic Wd & shrb Study site source

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