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Processing methodology for full exploitation of daily VEGETATION data

Processing methodology for full exploitation of daily VEGETATION data. C. Vancutsem, P. Defourny and P. Bogaert Environmetry and Geomatics (ENGE) Department of Environmental Sciences and Land Use Planning UCL Université Catholique de Louvain BELGIUM.

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Processing methodology for full exploitation of daily VEGETATION data

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  1. Processing methodology for full exploitation of daily VEGETATION data C. Vancutsem, P. Defourny and P. Bogaert Environmetry and Geomatics (ENGE) Department of Environmental Sciences and Land Use Planning UCL Université Catholique de Louvain BELGIUM Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 18 & 22 march 2002

  2. Objective : Develop an operational compositing strategy to produce spatially and temporally consistent images over large areas Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 18 & 22 march 2002

  3. State of issue Compositing criteria: BRDF correction: • Selection of specific angle configurations and atmospheric conditions • Use only 10% of the information (ten-days compositing) • Requiresstable land cover time series with low cloud frequency • Requires the on-line use of a large archive of daily data Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 18 & 22 march 2002

  4. Mean compositing strategy • Robust and simple compositing • The most stable parameter of a distribution • Use of all the available information The mean NDVI already suggested with AVHRR simulations (Meyer et al., 1995) Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 18 & 22 march 2002

  5. å å å å å xi xi xi xi xi 0 0 0 0 Mean Blue Mean Blue Mean Blue Blue Blue n n n n n If If If clear clear clear value value value . . . . 7 7 7 7 å å å å xi xi xi xi 0 0 0 Mean Red Mean Red Red Red n n n n If If clear clear value value . . . 7 7 7 Mean Mean NDVI= NDVI= ( ( Mean Nir Mean Nir – – Mean Red Mean Red ) ) å å å å å xi xi xi xi xi Mean Nir Mean Nir Mean Nir 0 0 0 0 ( ( Mean Mean Nir Nir + + Mean Red Mean Red ) ) Nir Nir n n n n n If If If clear clear clear value value value . . . . 7 7 7 7 å å å å å xi xi xi xi xi Mir Mir Mean Mean Mean Mir Mir Mir 0 0 0 0 n n n n n If If If clear clear clear value value value . . . . 7 7 7 7 Period of Period of compositing compositing P P Mean Compositing Strategy Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  6. Prerequisites of the methodology 1) Good superposition of the daily images Multitemporal location error < 500m (http://vegetation.cnes.fr/userguide/userguide.htm) Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 18 & 22 march 2002

  7. Prerequisites of the methodology SWIR Blue > 2.48 (Cherlet et al., 2001) 2) Efficient cloud screening Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 18 & 22 march 2002

  8. Prerequisites of the methodology 3) VZA 5-days cycles Compensation between backward and forward angles Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 18 & 22 march 2002

  9. Spatial consistency Best Best cloud cloud free image free image MVC NDVI MEAN composite MEAN composite Mali, first decade of november 99, (Red, Nir, Mir) Best Best cloud cloud free image free image MVC NDVI MEAN composite MEAN composite Nigeria, first decade of november 99, (Red, Nir, Mir) Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  10. Spatial consistency Mean composite Max NDVI composite First decade of november 99, RCA-Tchad (Red, Nir, Mir) Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  11. Spatial consistency Mean composite Max NDVI composite West Africa , first decade of november 99 (Mir, Nir, Red) Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  12. Spatial consistency Mean composite West Africa , first decade of november 99 (Mir, Nir, Red) Max NDVI composite Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  13. Spatial consistency Mean composite West Africa , first decade of november 99 (Mir, Nir, Red) Max NDVI composite Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  14. Spatial consistency NDVI channel MVC Mean Best daily image NIR channel Mali , first decade of november 99 Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 18 & 22 march 2002

  15. Temporal consistency NDVI channel MVC Mean Tanzania, year 2000 Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  16. Temporal consistency MVC Mean Observations number Tanzania, year 2000 Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  17. Reduction of daily variations RED 6.3% 1.58% NIR 8.3% 2.43% Tanzania , year 2000 Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  18. Conclusions • Robust and operational approach • Large spatial and temporal consistency of the results • Use all the available information • Low sensibility to BRDF effects • Accessibility for all users • Flexible methodology Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  19. Two applications Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  20. For more information : VANCUTSEM, C., BOGAERT, P., DEFOURNY, P., 2002, Mean compositing strategy as an operational temporal synthesis for high temporalresolution, IJRS in press. Contact : Vancutsem@enge.ucl.ac.be Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  21. Rush Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 18 & 22 march 2002

  22. Temporal consistency vancutsc: For each pixel, selection of the signal providing the maximum NDVI value amongst the last 10-day acquisitions At the end of 2001 the processing chain will be improved as the cloud screening The compositing strategy is not validated for spectral bands !!! Mir channel MVC Mean Tanzania, year 2000 Department of Environmental Sciences and Land Use Planning - UCLGLC 2000, 18 & 22 march 2002

  23. vancutsc: For each pixel, selection of the signal providing the maximum NDVI value amongst the last 10-day acquisitions At the end of 2001 the processing chain will be improved as the cloud screening The compositing strategy is not validated for spectral bands !!! Speckle effect MVC NDVI composite Mean composite Nigeria , first decade of november 99 Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 18 & 22 march 2002

  24. vancutsc: The current compositing technique for VEGETATION data (VGT-S10 product) shows radiometric artefacts in the reflective bands that may cause a significant noise for subsequent retrievals of surface parameters. The performances of various compositing strategies are assessed as well for the reflective bands as for the NDVI composites. Dedicated indicators and statistical analysis are computed to provide quantitative results by zone and by band. Theartefacts we can see on S1 products are more visible on S10 products Frequency of compositing Sliding window Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 18 & 22 march 2002

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