GLC mapping in semi-arid regions: a case study in West Africa Jean-François Pekel and Pierre Defourny Department of Environmental Sciences and Land Use Planning - GEOMATICS UCL Université Catholique de Louvain BELGIUM In close collaboration with E. Bartholomé (JRC-SAI) Supported by the Joint Research Center European Commision

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 Objective : interpretation method design for the semi-arid region the semi-arid region Data set : 30 S1 images S10 images from 03/99 to 12/00 S10 images from 03/99 to 12/00 Study area

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 Initial statements for the method design Initial statements for the method design S10 products quality assessment :S10 products quality assessment : S10 NDVI data are temporally consistent thanks to the BRDF reduction effects and the low impact of clouds in the MVC composite S1 products quality assessment :S1 products quality assessment : S1 multispectral data are spatially consistent Regional seasonality allows to assume that the Regional seasonality allows to assume that the beginning of the dry season provides the most beginning of the dry season provides the most spatially constrasted image spatially constrasted image

Overall approach Unsupervised classifications (ISODATA) based on + the best cloud free S1 image(s) 21/10/99 complemented by 22/10/99 21/10/99 complemented by 22/10/ phenological variables either computed + 5 phenological variables either computed - by pixel - by pixel - by classe 3 methods S1  interactive classes merging and labeling S1  interactive classes merging and labeling S1 + pixel-based phenological variables S1 + pixel-based phenological variables  interactive classes merging and labeling  interactive classes merging and labeling S1  classe-based phenological variables S1  classe-based phenological variables  interactive classes merging and labeling  interactive classes merging and labeling

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars phenological variables from S10 NDVI time series Time NDVI  NDVI min = average of 5 lowest NDVI values NDVI max = average of 3 highest NDVI values  NDVI range = NDVI max - NDVI min  Veg. Duration = Date Veg. End - Date Veg. Start Start Date = first date > NDVI min NDVI range Start Date = first date > NDVI min NDVI range  End Date = last date > NDVI min NDVI range

Best S1 unsupervised classification interactive merging into meaningful classes interactive merging into meaningful classes classes labeling classes labeling Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 R, PIR, MIR ( 21/10/1999) ISODATA Merging Labeling Maps and high resolution images LC map 16 classes 30 classes

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 Best S1 classification 16 classes LC map

S1 classes merged automatically according to per class average phenological variables according to per class average phenological variables computed on the year-long S10 time series computed on the year-long S10 time series Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars NDVI Decades Class n°DecadeNDVI 30 classes 30 masks LC map 16 classes 4 ph.var. Cl.nbr Min Range Dur.End ISODATA Labeling Phenological variables Mean profile per class

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 S1 classes automatically merged 16 classes LC map

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 NDVI Min NDVI Max NDVI Range Step 1 Step 2 NDVI Min NDVI Range If… Then Maps and high resolution images S1+NDVI Min+NDVI range unsup. classification interactive merging into meaningful classes interactive merging into meaningful classes classes labeling classes labeling LC map 16 classes ISODATA Merging Labeling 30 classes 3 highest NDVI NDVI max

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 S1 + ph. variables classification 16 classes LC map

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 S1 + ph. variables classification Best S1 classification S1 classes automatically merged Best results (expert, computing) More robust results (computing) water ! Most efficient results (expert)

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 Tree density Crop area LCCS use - definition of 16 classes from steppe to open forest ! ! ! steppe to open forest ! ! !

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 « steppe arbustive » Time NDVI Temporal evolution of NDVI Complementary information to classe name/code  average NDVI profile per classe  average NDVI profile per classe

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars additionnal issues : year to year variation year to year variation agriculture density retrieval agriculture density retrieval method use for very large area method use for very large area

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 Sensitivity of LC map to year to year variation Time NDVI

Culture Grassland Shrubland NDVI Time Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 Detection feasibility of the agriculture density

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 Procedure to apply the method to very large area  Indpt unsup. classification on moving window

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 Procedure to apply the method to very large area  Biomes stratification based on NDVI range

Department of Environmental Sciences and Land Use Planning - UCL GLC 2000, 28 & 29 mars 2001 Perspectives for GLC2000 data set  great information content  information extraction quite feasible New (?) issues because of the information quality :  classes merging becomes a more difficult exercise  classes merging becomes a more difficult exercise  classes labeling becomes a tuff exercise  classes labeling becomes a tuff exercise  need for labeling assistance through actual matching of classification output and reference information of classification output and reference information need for 1-km² ‘ field ’ observation method need for 1-km² ‘ field ’ observation method