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THE AFRICAN SIDE OF THE MEDITERRANEAN BASIN : Vegetation cycles from SPOT4/VGT imagery

THE AFRICAN SIDE OF THE MEDITERRANEAN BASIN : Vegetation cycles from SPOT4/VGT imagery. Silvio Griguolo Istituto Universitario di Architettura Department of Planning VENEZIA (Italy). 3 rd INCOSUSW Meeting - Rabat, Morocco, April 2002. Satellite images convey the information :.

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THE AFRICAN SIDE OF THE MEDITERRANEAN BASIN : Vegetation cycles from SPOT4/VGT imagery

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  1. THE AFRICAN SIDE OF THE MEDITERRANEAN BASIN : Vegetation cycles from SPOT4/VGT imagery Silvio Griguolo Istituto Universitario di Architettura Department of Planning VENEZIA (Italy) 3rd INCOSUSW Meeting - Rabat, Morocco, April 2002

  2. Satellite images convey the information: to zone vast regions according to the length, intensity and shape of the vegetation cycle, creating eco-climatic maps; to monitor the cropping season, comparing current dynamics with an expected level, creating maps that show at-risk areas to create land cover classificationraster maps. Maps can be imported into a GIS and compared/integrated with raster or vector information derived from other sources

  3. International Co-operation Projects aimed at classifying the land cover at the continental or global scale: CORINE (EC, high resolution, visual interpretation, Europe) IGBP, NOAA-AVHRR, 1.1 km, automatic, global PELCOM (Pan-Europe Land Cover Monitoring) (EC-funded, NOAA-AVHRR, 1.1 km, automatic, Europe GLC2000 (Global Land Cover) - in progress Uses the new SPOT/VEGETATION images for year 2000 (Voluntary partecipation, SPOT4/VGT, 1 km, global).

  4. CORINE land cover database

  5. Details of the IGBP global land cover database

  6. PELCOMLand Cover Map- Pixel: 1.1 km

  7.  NOAA-AVHRR images (used for both IGBP and PELCOM) are not enough geometrically accurate. They do not guarantee good results when the clustering is done on multitemporal images.  Besides, everybody can set up a receiving station for NOAA satellites and process the images received. Therefore, images of very different quality can be encountered. A good standard is not guaranteed.  Next slide: images received from the MARS Archive for the PELCOM Project. Eventually, images processed by DLR-Berlin were used.

  8. The new VEGETATION images are much better! • They are captured, processed for calibration and atmospheric correction and distributed by three co-operating European Centres in Sweden, Belgium and France that guarantee a high homogeneous quality They are suitable for multitemporal processing …and they can be freely downloaded!

  9. 1998: SPOT4 is launched. It carries onboard the sensor VEGETATION1.  Images were initially distributed for a cost  Since short, with the support of the EC, all images older than 4-5 months can be freely downloaded from http://free.vito.vgt.be  Four bands, plus NDVI.  Daily synthesis (S1), 10-day synthesis (S10) May 2002: SPOT5 will be launched, with VEGETATION 2

  10. VEGETATION radiometric bands B0 0.43-0.47 μ Blue B2 0.61-0.68 μ Red B3 0.78-0.89 μ Near InfraRed MIR 1.58-1.75 μ Short Wave InfraRed NDVI(Normalised Difference Vegetation Index) computed pixel-by-pixel according to the following definition: where NIR and R represent the reflectances on the near-InfraRed and Red channels respectively.

  11. The NDVI is a good indicator of the amount of healthy vegetal biomass on the ground. -1 <= NDVI <= +1 Positive NDVI values: presence of vegetation (the higher the value, the more dense and vital the vegetation) Values close to zero: bare soil negative values: water

  12. Image of the whole planet (17,000 lines of 40,000 pixels ca.)  Global images or single continents are available from the distribution site

  13. Maximum Value Composites (MVC)  For the S10 product (ten days composites): Band by band and pixel by pixel, the value associated with the highest NDVI is chosen

  14. Improving the image quality Repartition of the 3648 ground control points

  15. Centralized reception & production systems ensuring standardized products • Daily and 10 days Synthesis with NDVI products directly interpretable, already calibrated & atmospherically corrected for direct comparison with field data • Direct integration to GIS in ‘Plate Carrée’ projection or user defined • Products with high geometric precision and very low distortions  multispectral < 200 m, multidate < 500 m

  16. GENERAL CONCLUSIONS on VEGETATION data • The VEGETATION instrument offers a global monitoring capacity, thus the potential market is also global • Operational S10 products can be delivered almost everywhere in the world by Internet connection few hours after data production • Complete products (all bands, daily) are accessible only with well developed Internet connections

  17. A SAMPLE ANALYSIS ON VEGETATION DATA  The North Africa Mediterranean Region was cropped from the series of 36 ten-day composite (S10) NDVI images. The size of each cropped image was 1450 lines of 5300 pixels.  34 images (from January, dekad 2 to December, dekad 2) were used for clustering; The first and the last were used only to smooth the series.  The series represents the vegetation cycle of each pixel. Pixels were assigned to classes with similar cycles (height, length, shape)

  18. Sea and big lakes are masked (value = 0 in NDVI images) But along coastlines, a 5-km strip is not masked. Example of the 5 km wide (5 pixels) halo along coastlines

  19.  The strip left along the coastline creates problems with the MVC compositing.  The NDVI value of clouds is higher than that of water, so clouds are chosen instead of water by the compositing algorithm.  The coastline strips must be eliminated • either with an appropriate and precise water mask • or with a preliminary classification that issue some classes that can be identified with water • also a sea dilatation can be useful; • some manual intervention is almost always necessary...

  20.  Red (irregular) curve: unsmoothedobserved NDVI series for a pixel Blue curve: the same series after filling the negative peaks caused by partial cloudiness or mist, and after smoothing with a weighed 3-order moving average, using equal weights.

  21. The Clustering sequence  The input is a time series of NDVI images  Dekads are highly correlated. Few Principal Components are sufficient to capture most of the spatial variability TOTAL INERTIA = 34.000000 | | |EXPLAIND|CUMULATE| | # | EIGENVALUE| INERTIA| INERTIA| | | | (%) | (%) | |----|-----------|-----------------| | 1 |28.4502781 | 83.677 | 83.677 | | 2 | 2.4745416 | 7.278 | 90.955 | | 3 | 1.2551798 | 3.692 | 94.647 | | 4 | 0.7815209 | 2.299 | 96.946 | | 5 | 0.4864285 | 1.431 | 98.376 | | 6 | 0.2041538 | 0.600 | 98.977 | | 7 | 0.1141090 | 0.336 | 99.312 | | 8 | 0.1023029 | 0.301 | 99.613 | . . . . . . . . . . . . . . . .

  22. The first Principal Component  explains 83.68 % of the overall variance;  captures the contrast between vegetated (lighter shades) and generally arid pixels (darker shades).

  23. Classification in two steps:  A preliminary classification with 14 classes • produced two classes of mostly water pixels; • issued numerous arid classes;  Water pixels were masked, so as to eliminate the coastline strips. The eight most arid classes were aggregated into two and then masked, together with two semi-arid classes.  All pixels belonging to the remaining four classes (that had a significant vegetation cycle) were re-clustered into ten classes.  The 10 non-arid and the 4 arid classes were mosaiked, eventually resulting in a partition with 14 classes.

  24. The preliminary classification in 14 classes.  The number of arid pixels is very high  The non-hierarchical clustering procedure allocates for them many classes, capturing even small differences in the NDVI value, often due to type and colour of soil  So many arid classes are not interesting. The 8 most arid classes were aggregated into 2, and then masked

  25. The time profiles of the 14 classes in the initial partition. Only few of them show the existence of a cycle. The most vegetated class gathers pixels located on the Nile Delta and along the Mediterranean coast.

  26.  The provisional classification obtained by merging the 8 most arid classes into two.  The four more vegetated classes along the coast (in green shades) were submitted to a further more detailed classifications

  27. Final partition with 14 classes: the north-west region. Permanently arid pixels are included in four classes, while the other 10 classes capture differences in the cycles of vegetated pixels

  28. Details of final partition: location of class 8, with a double cycle in blue: cycle of a pixel in Morocco in red: average cycle of class 8

  29. Classes 1 and 8

  30. Classes 6 5 9 4

  31. Classes 3 10 2 7

  32. Conclusions  The image is too large. Too much variety. More classes would be necessary to capture differences.  The studyregion should be limited in extension, and not too internally dishomogeneous. A previous stratification appears necessary; each stratum should be classified separately.  Local expert’s knowledge is always necessary for an appropriate interpretation. The map is a tool of synthesis intended as a help.  But it is a rich tool: the classified map gives access to detailed information on local features.

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