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

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

3. classifying the land International Co-operation Projects aimed at classifying the land cover cover at the continental or global scale: CORINE  CORINE (EC, high resolution, visual interpretation, Europe) IGBP  IGBP, NOAA-AVHRR, 1.1 km, automatic, global PELCOM  PELCOM (Pan-Europe Land Cover Monitoring) (EC-funded, NOAA-AVHRR, 1.1 km, automatic, Europe GLC2000  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 MapPixel: 1.1 km PELCOM Land Cover Map - Pixel: 1.1 km

7.  For various reasons, the radiometric information alone is in reliable automatic classification general not sufficient for a reliable automatic classification. ancillary information  It is always necessary to use some ancillary information to solve dubious cases.  Here below, an example of some ancillary information used in the frame of the PELCOM Project

8. The 1 km Digital Terrain Model (raster) for SE Europe (1430x1700)

9. Ancillary information available to help the classification procedure classification procedure For each area, the distribution of each theme over elevation: For each area, the distribution of each theme over elevation:

10. supervised automatic  The quality of a partition obtained via a supervised automatic clustering clustering must be carefully checked. post-classification decisionrules  Usually, some suitable post-classification decision rules are devised, to re-assign some of the pixels to the classes. PELCOM did it differently  PELCOM did it differently: the ancillary information was used within the very assignment procedure, not in a post-classification different step.

11. Example of post-classification decision rules

12. Distribution of training pixels used for Italy in PELCOM. For each area of interest, a certain number of “pure” pixels (at least 87% belonging to the same theme in CORINE) have been selected, checked through a control procedure, and used to train the classifier. All other pixels are then assigned to the most similar group.

13. candidate training pixels ITN - Projection of candidate training pixels onto the first factor plane. For most themes the level of confusion is high.

14.  NOAA-AVHRR images (used for both IGBP and PELCOM) not enough geometrically accurate. are not enough geometrically accurate. They do not guarantee good results when the clustering is multitemporal images 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. is not guaranteed 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.

16. 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!

17. 1998: SPOT4 is launched 1998: SPOT4 is launched. VEGETATION1 It carries onboard the sensor VEGETATION 1.  Images were initially distributed for a cost  Since short, with the support of the EC, all images freely downloaded older than 4-5 months can be freely downloaded fromhttp://free.vito.vgt.be  Four bands, plus NDVI.  Daily synthesis (S1), 10-day synthesis (S10) May 2002: SPOT5 will be launched, with VEGETATION 2

18. VEGETATION radiometric bands B00.43-0.47 μBlue B20.61-0.68 μRed B30.78-0.89 μNear InfraRed MIR1.58-1.75 μShort Wave InfraRed NDVI 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.

19. NDVI 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

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

21. 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

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

23. 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 multispectral < 200 m, multidate < 500 m distortions  multispectral < 200 m, multidate < 500 m

24. 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

25. A SAMPLE ANALYSIS ON VEGETATION DATA  The North Africa Mediterranean Region was cropped from the series of 36 ten-day composite (S10) NDVI images. 1450 lines5300 pixels 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)

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

27.  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 some manual intervention is almost always necessary...

28. Redunsmoothedobserved  Red (irregular) curve: unsmoothed observed NDVI series for a pixel Blue  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.

29. The Clustering sequence  The input is a time series of NDVI images 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 |................  Dekads are highly correlated. Few Principal Components are sufficient to capture most of the spatial variability

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

31.  RGB Image obtained from the first three Principal Components (overall explained variance: 94.65 %)  the 2nd and 3rd Principal Components relate to the cycle shape

32. Classification in two steps 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.

33. The preliminary classification in 14 classes 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

34. 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.

35.  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

36. Final partition with 14 classesthe north-west region  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

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

39. Classes 1 and 8

40. Classes 6 5 9 4

41. Classes 3 10 2 7

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