Evaluating different compositing methods using SPOT-VGT S1 data for land cover mapping the dry season in continental Southeast Asia Hans Jurgen StibigSarah Mubareka 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 Objectives 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 ( pixels) 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 ( pixels) Inconveniences in the data set : 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 4 Masking unusable pixels no yes no yes no yes no yes no Mask=4 Mask=6 Cloud shadow angle Mask=7 Mask=8 Usable pixels SWIR strip masks Mask=3 Mask=2 Dilation =0 and =180 (20) =0 and =0 (20) pThrs>45 Mask=5 Mask=3 S1 Jan & Feb Dilation Mask=1 B any = 0 Blue >720 SWIR >32 0 SWIR band bitmap Viewing and solar angles yes (Fillol 1999, Simpson1998) (Lissens, 2000) February 24, 2000 image Cloud shadow SWIR defect Dilation Usable pixels VZ > 45 No data Hot spot Specular Cloud 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 ( pixels) 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 Pixel composites Single criteria Double criteria Pixel composites MaNMiRED MaxDVI MaxNDVI MaxNDWI S10Dry S10Wet MaxNDDI MaNMiVZ Pixel composites - visual interpretations MaxDVI (S1) [MaxDVI=(NIR-red)] MaxNDVI (S1) [NDVI=(NIR-red)/(NIR+red)] Pixel composites - visual interpretations MaxNDVI MinRED (S1) MaxNDVI MinVZA (S1) Pixel composites - visual interpretations MaxNDWI (S1) [NDWI=(NIR-SWIR)/(NIR+SWIR)] MaxNDDI (S1) [NDDI=(SWIR-NIR)/(SWIR+NIR)] Pixel composites - visual interpretations S10Wet [S10Wet=Minimum SWIR][S10Dry=Minimum NIR if pixel is not green for S10Wet] S10Dry Pixel composites - visual interpretations 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 ( pixels) De Wasseige et al. Sensitivity to atmosphere: reflectance in blue channel Sensitivity Analysis: heterogeneous test sites Mosaic is inconsistently sensitive in the blue channel MaxDVI most affected MaxNDVI composites least affected S10 composites moderately affected ex. Zone 8 ex. Zone 2 Sensitivity to vegetation: reflectance in NIR channel Sensitivity Analysis: heterogeneous test sites S10Dry underestimates green vegetation maxNDVI composites tend to overestimate green vegetation cover ex. Zone 1 ex. Zone 3 ex. Zone 6 ex. Zone 2 Texture Variance Sensitivity Analysis: heterogeneous test sites Mosaic can be used as control (least speckle) The composite with the least speckle is MaNMiRED S10 composites are mostly sensitive over dry zones View zenith angle distribution of pixels for S1 composites Sensitivity Analysis: heterogeneous test sites No composite consistently selects near-nadir pixels (except MaNMiVZ) - regardless of land cover type 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 ( pixels) Study site source Mekong River Commission 1997 forest cover map ( based on TM classification ) Selecting training sites Mosaic Mixed Evergreen Deciduous agriculture grassland bamboo regrowth Continuous forest cover Mosaic of forest cover evergreen deciduous mixed high density medium/low density high density deciduous mixed evergreen Wood & shrubland Bamboo Grass Mosaic of cropping cropping area 30% Agriculture Rock Forest Non-forest Land cover classes most confused Homogeneous test sites MaxNDVI MinRED (S1)S10DryMosaic (S1)MaxNDDI (S1) Homogeneous test sites In order to detect which classes are not clouded over in the maxNDDI composite, we compare reflectance values for the NIR bands. IF NIR maxNDDI > NIR MaNMiRED, then class is retained for classification with maxNDDI Isolating clear classes in maxNDDI Agriculture Deciduous - mosaic Grassland Deciduous - continuous Viewing angle differences for these classes Homogeneous test sites Conclusions MaxNDVI MinRED (S1)S10DryMosaic (S1)MaxNDDI (S1) rivers & lakes dry vegetationPossible evergreen vs mixed forest Base classification 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 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 Objectives 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 Land cover mapping High within class variance for composite max NDDI (ex zone 8): Max NDDI adjustments High within class variance for composite max NDDI (ex zone 8): Max NDDI adjustments 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 Conclusions Appendix Database for each pixel composite Day Month Solar zenith angle Solar azimuth angle View azimuth angle View zenith angle MaN Study site source MaNMiREDMaNMiVZ EVERGREEN High density Med/low density Mosaic Wd & shrb