1

Space Applications InstituteJoint Research CentreEuropean Commission21020 Ispra (VA), Italyhttp://www.gvm.sai.jrc.it

Unit nameGlobal Vegetation Monitoring

Potential of SPOT 4-VEGETATION Data for Mapping the Forest

Cover of Madagascar and Upper Guinea

Philippe Mayaux, Valéry Gond and Etienne Bartholomé

Global Vegetation Monitoring

Objectives of the study

The objectives of this study are to demonstrate the possibility of

updating the forest-cover maps in a near-real time manner using VEGETATION data.

to check the main advantages of VEGETATION for forest mapping at regional scale (geometry, data access, reflectance value)

to test several techniques for reducing the noise in the S-10 products (clouds, missing data, patchy aspect)

Global Vegetation Monitoring

Context: the TREES Project Baseline inventory of dense moist forests

based on AVHRR data of 1992-1993

update with ATSR and VEGETATION data Madagascar was missing in the first round West Africa was not up-to-date

Global Vegetation Monitoring

Forests of Madagascar

Dense dry forests with burns

Deciduous Thicket

Grasslands and gallery-forests

Dense moist forestwith agriculture

Secondary complex and dense forest

VEGETATION colour composite (R,G,B = SWIR, NIR, R) of June 1999 and Digital Elevation Model

Global Vegetation Monitoring

Data and methods

SPOT-4 VEGETATION data S-10 products October 1998 to September 1999

Data preparation monthly composition reduce noise (haze and clouds, patchy, sensor) minimum SWIR

Data classification unsupervised classification of 36 channels (12

months x 3 channels: R, NIR, SWIR) visual labelling

Global Vegetation Monitoring

Monthly compositing

June 1st - 10th

June 11th - 20th

June 21th - 30th

• Noise reduction• Elimination of remaining clouds • Elimination of missing data

Minimum SWIR

Global Vegetation Monitoring

Temporal profiles

0.05

0.15

0.25

0.35

Sep-98 Dec-98 Mar-99 Jun-99

Months

Dense Humid Forest Dense Dry Forest

Mangroves Secondary complex

Woodlands Savanna

0.05

0.15

0.25

0.35

Sep-98 Dec-98 Mar-99 Jun-99

10-day periods

SWIR

Re

fle

cta

nc

e

Dense Humid Forest Dense Dry Forest

Mangroves Secondary complex

Woodlands Savanna

Short Wave Infrared channel: monthly compositing

Global Vegetation Monitoring

Seasonal activity

November January March

May July September

Global Vegetation Monitoring

Data classificationUnsupervised classification

spectral

Labellingspectral, spatial

temporal, ancillary

6 classes

30 clusters

36 channels(R, NIR, SWIR)

Global Vegetation Monitoring

Forest cover map of

Madagascar

Dense humid forest

Secondary complex

Dense dry forest

Mangrove

Savannah

Swamp

Global Vegetation Monitoring

Map ValidationPixel-based comparison with 3 Landsat TM classifications

(interpreted by local experts)

Landsat TM (158-70) VEGETATION

Overall accuracy of the Forest class: 86 %

Global Vegetation Monitoring

Forest mapping in West Africa

Forest classes Evergreen forest (2 classes of density) Secondary complex Mangrove Non forest

Short period with cloud-free images

No well-marked topography

Global Vegetation Monitoring

Data and methods

SPOT-4 VEGETATION data S-1 products February 2000

Data preparation selection of cloud-free images (by eco-region

and viewing angle) channels R, NIR, SWIR

Data classification unsupervised clustering (20) and visual labelling

of the single-date selected images mosaic of the single-date classifications

Global Vegetation Monitoring

Spatial mosaic of 3 imagesFebruary 2000

VEGETATION colour composite (R,G,B = SWIR, NIR, R)

Global Vegetation Monitoring

Forest cover map of West Africa

Evergreen forest (dense)

Evergreen forest (less dense)

Secondary complex

Mangrove

Non forest

Water bodies

Global Vegetation Monitoring

Forest blocks in Ghana

Global Vegetation Monitoring

Conclusions

Capacity of SPOT-4 VEGETATION data to

update the forest-cover maps in a rapid

manner.

S-10 adapted to seasonal forests (dry

forests in Madagascar), S-1 adapted to

evergreen forests

Poor mapping of savannahs