j-f. pekel and p. defourny department of environmental sciences and land use planning - geomatics...

J-F. Pekel and P. DefournyDepartment of Environmental Sciences

and Land Use Planning - GEOMATICS

UCL Université Catholique de Louvain BELGIUM

Supported by the SSTC (Services fédéraux des affaires Scientifiques, Techniques et culturelles)

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 18 - 22 march 2002

Mapping of the African Great Lakes Mapping of the African Great Lakes

region from daily VEGETATION dataregion from daily VEGETATION data


Main challenge for VGT time series data interpretation

How to use simultaneously :

• multispectral information

• temporal information

• full spatial resolution consistency


Window: 4°N-14°S / 25°E-35°E

Area: 2.257.920Km²

Study area


• Clouds and large gaps

06 may 20000708091011121314091516

(Blue, Red, NIR)

Particularities of the study area

• TopographyTopography




• Inversion of seasonality

6 mars 2000 (R, PIR, MIR)6 mars 2000 (R, PIR, MIR)3 july 2000 (R, PIR, MIR)3 july 2000 (R, PIR, MIR)

Spatial consistency ?



1st Strategy: Manual compositing

Compositing


2nd Strategy: Mean compositing

Annual

Compositing


2nd Strategy: Mean compositing

Feb-March-April July-August-September Annual

Compositing


Annual

mean

Classification

(50 classes)

Mean reflectance per class

Seasonal mean

(Feb-March-April)

Seasonal mean

(July-August-September)

RNIR

MIR

RNIR

MIR

Classification methodology


Mean reflectance

per class

Classification

(17 classes)

RNIR

MIR

RNIR

MIR

Classification


Classe 1: forêt dense humide semprevirente et semi-décidue

Classe 2: forêt dense humide semi-décidue

Classe 3: forêt ombrophile secondaire

Labelling


Labelling

1 Lacs et fleuves Perennial waterbodies2 Forêt galerie et formation marécageuse Gallery-forests and ,,,3 Forêt dense humide et forêt de basse montagne Dense moist forest and ,,,,4 Miombo (Forêt claire) Dense dry forest and Miombo woodland5 Mosaîque forêt claire - savane (Savane parc) Open forest-savannah mosaic6 Forêt de transition et forêt de montagne Mountain forest and ,,,,7 Miombo très clair (mosaîque forêt claire - savane) Very open forest-savannah mosaïc8 Mosaîque forêt dense - savane Dense forest- Savanna mosaic9 Savane herbeuse et complexe rural Grass-Savannah and rural complex

10 Savane herbeuse à arbustive Grassland and Shrub Savannah 11 Forêt secondaire (=forêt semi-décidue) Secondary forest12 Forêt de montagne Mountain forest13 Mosaîque forêt - savane et complexe rural Forest-Savannah-rural complex mosaic14 Formation buissonante et fourés Shrub savannah15 Forêt de bambou Bamboo forest16 Prairie flottante Swamp grasslands17 Plaine de lave : Forêt et brousaille sclérophyle Lava plain


Landsat TM(Nir, MIR, Green)

Classification result of VEGETATION

data

Validation: visual comparison



Classification result of VEGETATION data



Landsat TM(Ni r, MIR, Green)

Classification result of

VEGETATION data




Classification result of VEGETATION

data



Roads

Primary forest

Secondary forest

Validation: visual assesment


TREES map Classification result of VEGETATION data



TREES map

(2000)





• Automatic and operational methodology of mapping

• Use of all reflectance channels and not only the NDVI

• High local contrast between land cover types

• Mean compositing provides a large spatial consistency

• Methodology based on phenology

• Methodology most probably applicable to many areas and

various conditions

Conclusions

j-f. pekel and p. defourny department of environmental sciences and land use planning - geomatics...

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