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DATA REDUCTION and ENHANCEMENT of GLOBAL COMPOSITES of SPOT-VEGETATION (VGT) Herman Eerens, Else Swinnen, Yves Verheijen Vlaamse Instelling voor Technologisch.

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Presentation on theme: "DATA REDUCTION and ENHANCEMENT of GLOBAL COMPOSITES of SPOT-VEGETATION (VGT) Herman Eerens, Else Swinnen, Yves Verheijen Vlaamse Instelling voor Technologisch."— Presentation transcript:

1 DATA REDUCTION and ENHANCEMENT of GLOBAL COMPOSITES of SPOT-VEGETATION (VGT) Herman Eerens, Else Swinnen, Yves Verheijen Vlaamse Instelling voor Technologisch Onderzoek (Vito - Belgium) Frank Canters Vrije Universiteit Brussel (VUB - Belgium) Acknowledgements: Belgian Science Policy Office (Funding) JRC-SAI (Full year cycle of global VGT-S10)

2 MVC-Composites: - still affected by clouds, bidirectional effects, measurement errors - best visible / removable in longitudinal analysis (time series) - cleaning procedures: MVC-month, BISE, Verhoef,... NOAA-AVHRR: 365 x S1 SPOT-VEGETATION: 36 x S DecJan Feb Mar Apr May JuneJuly Aug Sep Oct Nov Amazonas Nile Delta Sahel Sahara

3 Extraction of Phenological Variables: - Simple: Annual mean / min / max / amplitude of NDVI - Complex: start / end / length of green season(s) - Often better inputs for classification - Only feasible through longitudinal analysis (time series) t 1 t 2 t Max = 0.7 NDVI 2 = Min + 0.8(Max – Min) = 0.6 NDVI 1 = Min + 0.2(Max – Min) = 0.3 Min = 0.2 Decades Monthly Mean NDVI

4 Phenological Variables: Examples from the “Global Land 1km AVHRR Data Set” April March 1993, Interrupted Goode Homolosine Mean Annual NDVI SI = [Range - Mean]/ [Range + Mean] Seasonality Index SI

5 Phenological Variables: Examples from the “Global Land 1km AVHRR Data Set” April March 1993, Interrupted Goode Homolosine None (desert/ice) Intermediate Entire year Length of Green Season January June December No Growing Season Start of Green Season

6 Phenological Variables: Examples from the “Global Land 1km AVHRR Data Set” April March 1993, Interrupted Goode Homolosine <0.1 <0.25 >0.5 <0.5 > 0.55 < 0.17 < 0.35 < 0.45 < 0.55 mean range Simple Biome Classification Bivariate Level Slice: - Annual Mean NDVI - Annual NDVI Range <0.1 <0.25 >0.5 <0.5 > 0.55 < 0.17 < 0.35 < 0.45 < 0.55 mean range

7 LONGITUDINAL TIME SERIES ANALYSIS REQUIRED It adds a new dimension to the results of the transversal analysis (per decade / day) At a given moment, áll information of a full year (36 x S10, 360 x S1) must be available simultaneously Band BPPCONTENTS VGT-SYNTHESIS BLUE Reflectance 2 2REDReflectance 3 2NIRReflectance 4 2SWIRReflectance 5 1NDVI 6 1Zenith angle of sun 7 1Zenith angle of sensor 8 1Azimuth angle of sun 9 1Azimuthangle of sensor 10 1Status map: errors in 4 bands, land, cloud, snow/ice 11 2Time grid:minutes between pixel registration and start of synthesis (LOG-file) Total16 GLOBAL VGT-SYNTHESIS  600 Mb pixels x 16 byte/pix  Mb  10 Gb FULL YEAR CYCLE S10: 360 GbS1: 3.6 Tb TOO MUCH DATA  CLASSICAL SOLUTIONS: Temporal selection: select limited number of composites Spectral selection: only NDVI, … Spatial selection: - Extract/analyse specific study areas - Work on degraded images

8 1. Radiometric Compression: 16  6 bytes (37.5%) - Eliminate BLUE and NIR - Rescale reflectances from 16 to 8 bit (0-250) RED/SWIR:R = 0%…62.5% in steps of 0.25% NIR:R = 0%…83.3% in steps of 0.33% - Values : special flags (saturation, error,...) - Status_out : Cloud + Snow/ice + Day_in_decade - Combine 2 zenith angles in 1 byte (steps of 5°) - Combine 2 azimuths in 1 byte (relative azimuth:0-180°)  Output = 6 Byte-layers 2. Eliminate all the Water Pixels (25%) - 134,134,736 land pixels left  Spatial context lost! - Results stored in Pseudo-Images (PI)  IDL/ENVI-images (Ncol=5000, Nrec=26827) - All spectral (per-pixel) operations still feasible (via IDL/ENVI): time series analysis, classification (!), Improved Land/Sea-Mask - VGT-mask: 5-10 sea pixels along coast (too much) - Boreal regions in winter: confused with sea (status map) 4. Conversion to Equal-Area Projection (IGH): - In: Plane carré of VGT (worst projection) at (1/112°)² - Out: Interrupted Goode Homolosine (IGH) at 1x1km²  REDUCTION: ± 10% without LOSS of DATA !  FULL YEAR CYCLE: S10: 36 Gb S1: 360Gb DEDICATED SCHEME FOR DATA REDUCTION

9 Scan per Pixel LON LAT NORMAL IMAGES - Normal format - Land and water PSEUDO-IMAGES - Only land - All in IGH-Projection - Sorted by Region-ID PI_ID PI_X PI_Y PI_LON PI_LAT STEP 1b Inverse IGH-projection IGH-X IGH-Y STEP 1a Extract Country-ID Raster 0=sea SPOT4-VGT Decadal Synthesis - 11 HDF image layers - 16 bytes/pixel - LOG-File (geo-referencing) Pixel Lon/Lat Col/Rec in HDF Convert PI_RED PI_NIR PI_SWIR PI_THETA PI_AZIM PI_MASK 11 Input-Values 6 Output-Values Read HDF's Filter Output Output Pseudo-Images - 6 per decadal synthesis - 6 bytes/pixel - Repeat for 36 syntheses/year  36 x 6 = 216 pseudo-images - All in PI-format - Time series analysis - Elimination of clouds - Extraction of phenological variables STEP 2 Transversal Reduction STEP 3 Longitudinal Analysis - Normal Image Format - IGH-Projection - Limited number of final Images - Limited disk space STEP 4 Reconversion

10 Example of IDL/ENVI-FORMATTED PSEUDO-IMAGES Normal Images Pseudo-Images A B C NORMAL IMAGES ALand-See Mask (Inter. Goode Homolosine, 1x1km²) BGTOPO30-DEM (Geographic Lon/Lat) CVGT-S10, Dec.3 of May 1998, NIR (Geogr. Lon/Lat) PSEUDO-IMAGES (only land pixels) 1Longitude of pixel centre (float) 2Latitude of pixel centre (float) 3Altitude (from B - Short Integer) 4NIR of Dec.3 of May 1998 (from C - rescaled to Byte)

11 Example of IDL/ENVI-FORMATTED PSEUDO-IMAGES 1. PI_XY IN: Land/Sea mask in Master System (IGH) OUT:2 master-PI’s with IGH-X/Y of pixel centres 2. PI_IGH IN: 2 master-PI’s with IGH-X/Y of pixel centres OUT:2 master-PI’s with Lon/Lat of pixel centres 3. PI_EXTR IN: Any image in IGH or Lon/Lat (+ master-PI’s) OUT:PI-version of that image (Byte / Short Int / Float) 4. PI_VGT IN: VGT-S10/S1 + master-PI’s OUT:6 Byte PI-images 5. PI_BACK IN: Any previously created PI (+ master-PI’s) OUT:Corresponding normal image in IGH Option: selection of output window 6. PI_REDU IN: Set of all VGT-PI’s (+ master-PI’s) OUT:Corresponding set of normal images, IGH, degraded resolution (33x33km²), systematic selection 7. CLEAN IN: Set of 36 VGT-S10 images (normal or PI) OUT:Cleaned NDVI-profiles 8. PHENO IN: Set of 36 VGT-S10 images (normal or PI) OUT:Cleaned NDVI-profiles

12 SET of DEGRADED IMAGES NORMAL IMAGES - 36 decades x 6 = 216 images - Global but degraded (33km x 33km) - N pix = 1213x423 =  Total: 216 x 0.5 = 110 Mb - Systematic pixel selection  original signatures - Excellent data set to test performance of new procedures on global scale RED NIR SWIR End of June 1988

13 CONCLUSIONS 1. One Possible Pathway for Global Classification - Transverse reduction of all VGT-images  PI’s - Also extract external information: DEM, …  additional classification variable Regions  for post-processing (LC-statistics) Existing classifications  Training / Validation - Longitudinal analysis on PI-images: Cleaning, elimination of bidirectional effects, addition of phenological variables, improved VI’s,… - Classification in PI-form - Reconversion to normal image 2. Preliminary data enhancement via data reduction seems indispensible 3. To be integrated in CTIV (?)  Optional delivery of data in PI-form (better than ZIP)  More users get access to global data 4. Lots of improvements possible: other geo-systems, other output formats (now only ENVI + IDRISI), streamlining of software,… 5. High radiometric resolution redundant


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