THE AFRICAN SIDE OF THE MEDITERRANEAN BASIN THE AFRICAN SIDE OF THE MEDITERRANEAN BASIN ::

Vegetation cycles fromVegetation cycles from SPOT4/VGT imagerySPOT4/VGT imagery

Silvio GriguoloIstituto Universitario di Architettura

Department of Planning

VENEZIA (Italy)

3rd INCOSUSW Meeting - Rabat, Morocco, April 2002

Satellite imagesSatellite images convey the information:

to zoneto zone vast regions according to the length, intensity and shape of the vegetation cycle, creating eco-climatic mapseco-climatic maps;

to monitorto monitor the cropping season, comparing current dynamics with an expected level, creating maps that show at-risk areasat-risk areas

to create to create land cover classificationland cover classification raster maps.raster maps.

Maps can be imported into a GIS and compared/integrated with raster or vector information derived from other sources

International Co-operation Projects aimed at classifying the landclassifying the land

covercover at the continental or global scale:

CORINECORINE (EC, high resolution, visual interpretation, Europe)

IGBPIGBP, 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).

CORINECORINE land cover database land cover database

Details of the IGBP global land cover databaseDetails of the IGBP global land cover database

PELCOMPELCOM Land Cover MapLand Cover Map - Pixel: 1.1 kmPixel: 1.1 km

NOAA-AVHRR images (used for both IGBP and PELCOM)

are not enough geometrically accurate.not enough geometrically accurate.

They do not guarantee good results when the clustering is

done on multitemporal imagesmultitemporal 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 guaranteedis not guaranteed.

Next slide: images received from the MARS Archive for the

PELCOM Project.

Eventually, images processed by DLR-Berlin were used.

The new VEGETATION images are much better!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!

and they can be freely downloaded!

1998: SPOT4 is launched1998: SPOT4 is launched.

It carries onboard the sensor VEGETATIONVEGETATION 11.

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 downloadedfreely downloaded from

http://free.vito.vgt.behttp://free.vito.vgt.be

Four bands, plus NDVI.

Daily synthesis (S1), 10-day synthesis (S10)

May 2002: SPOT5 will be launched, with VEGETATION 2

VEGETATION radiometric bandsVEGETATION 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

NDVINDVI (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.

NIR RNDVI

NIR R

The NDVINDVI 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

Image of the whole planet (17,000 lines of 40,000 pixels ca.)

Global images or single continents are available from the distribution site

Maximum Value Composites (MVC)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

Improving the image qualityRepartition of the 3648 ground control points

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 < multispectral < 200 m, multidate < 500 m500 m

GENERAL CONCLUSIONS on VEGETATION dataGENERAL 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

A SAMPLE ANALYSIS ON VEGETATION DATAA 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 lines1450 lines of 5300 pixels5300 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)

Example of the 5 km wide (5 pixels) halo along coastlines

• Sea and big lakes are masked (value = 0 in NDVI images)

• But along coastlines, a 5-km strip is not maskeda 5-km strip is not masked.

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 necessarysome manual intervention is almost always necessary...

RedRed (irregular) curve: unsmoothedunsmoothed observed observed NDVI series for a pixel

BlueBlue 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.

The Clustering sequenceThe 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

explains 83.68 % of the overall variance;

captures the contrast between vegetatedvegetated (lighter shades)

and generally aridarid pixels (darker shades).

The first Principal ComponentThe first Principal Component

Classification in two stepsClassification 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.

The preliminary classification in 14 classesThe 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

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.

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

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

Details of final partition: location of class 8location of class 8, with a double cycle

in blueblue: cycle of

a pixel in Morocco

in redred: average cycle of class 8

Classes 1 and 8

Classes 6 5 9 4

Classes 3 10 2 7

ConclusionsConclusions

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.

Local expert’s knowledge is always necessaryalways necessary for an

appropriate interpretation.

The map is a tool of synthesis intended as a help.

But it is a rich toola rich tool: the classified map gives access to detailed

information on local features.