GLC mapping in semi-arid regions: a case study in West Africa Jean-François Pekel and Pierre...

GLC mapping in semi-arid regions:

a case study in West Africa

Jean-François Pekel and Pierre DefournyDepartment 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

ObjectiveObjective : : interpretation method design interpretation method design forfor

the semi-arid regionthe semi-arid region

Data setData set : : 30 S1 images30 S1 images S10 images from 03/99 to S10 images from 03/99 to

12/0012/00

Study areaStudy area


Initial statements for the method designInitial statements for the method design

•S10 products quality assessmentS10 products quality assessment : :

S10 NDVI data are S10 NDVI data are temporally consistenttemporally consistentthanks to the BRDF reduction effects and thanks to the BRDF reduction effects and the low impact of clouds in the MVC compositethe low impact of clouds in the MVC composite

•S1 products quality assessment :S1 products quality assessment :

S1 multispectral data are S1 multispectral data are spatially consistentspatially consistent

• Regional seasonality allows to assume that theRegional seasonality allows to assume that the beginning of the dry season provides the mostbeginning of the dry season provides the most spatially constrasted imagespatially constrasted image

Overall approachOverall approach

Unsupervised classificationsUnsupervised classifications (ISODATA) (ISODATA) based onbased on

+ the + the best cloud free S1best cloud free S1 image(s) image(s) 21/10/99 complemented by 22/10/9921/10/99 complemented by 22/10/99

+ + 5 phenological variables5 phenological variables either computed either computed - - by pixelby pixel

- - by classeby classe

3 methods3 methods• S1S1 interactive classes merging and labeling interactive classes merging and labeling

• S1 + pixel-based phenological variablesS1 + pixel-based phenological variables interactive classes merging and labeling interactive classes merging and labeling

• S1 S1 classe-based phenological variablesclasse-based phenological variables interactive classes merging and labeling interactive classes merging and labeling

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87

0

0,1

0,2

0,3

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87


5 phenological variables from S10 NDVI time series5 phenological variables from S10 NDVI time series

Time

NDVI

NDVI min = average of 5 lowest NDVI valuesNDVI min = average of 5 lowest NDVI values

NDVI max = average of 3 highest NDVI valuesNDVI max = average of 3 highest NDVI values

NDVI range = NDVI max - NDVI min NDVI range = NDVI max - NDVI min

Veg. Duration = Veg. Duration = Date Veg. End - Date Veg. Start Date Veg. End - Date Veg. Start

Start Date = first date > Start Date = first date > NDVI min + 0.33 NDVI rangeNDVI min + 0.33 NDVI range End Date = last date > End Date = last date > NDVI min + 0.33 NDVI rangeNDVI min + 0.33 NDVI range

Best S1 unsupervised classificationBest S1 unsupervised classification• interactive merging into meaningful interactive merging into meaningful classesclasses• classes labelingclasses labeling


R, PIR, MIR

( 21/10/1999)

ISODATAMerging

Labeling

Maps and high resolution images

LC map 16 classes

30 classes


Best S1 classification Best S1 classification 16 classes LC map 16 classes LC map

S1 classes merged automaticallyS1 classes merged automatically • according to per class average phenological according to per class average phenological variables variables computed on the year-long S10 time seriescomputed on the year-long S10 time series


70

NDVI

Decades

Class n°

Decade

NDVI

30 classes

30 masks

LC map 16 classes

4 ph.var.

Cl.nbr Min Range Dur.End

ISODATA

ISODATA

Labeling

Phenological variables

Mean profile per class


S1 classes automatically merged S1 classes automatically merged 16 classes LC map 16 classes LC map


NDVI MinNDVI MaxNDVI Range

Step 1

Step 2

NDVI MinNDVI Range

If… Then

Maps and high resolution images

S1+NDVI Min+NDVI range unsup. classificationS1+NDVI Min+NDVI range unsup. classification• interactive merging into meaningful classesinteractive merging into meaningful classes• classes labelingclasses labeling

LC map 16 classes

ISODATA Merging

Labeling

30 classes

3 highest NDVINDVI max


S1 + ph. variables classificationS1 + ph. variables classification 16 16 classesclasses LC map LC map


S1 + ph. variables classificationS1 + ph. variables classification

Best S1 classificationBest S1 classificationS1 classes automatically mergedS1 classes automatically merged

Best results Best results (expert, computing)(expert, computing)

More robust resultsMore robust results(computing) (computing) water !water !

Most efficient resultsMost efficient results(expert)(expert)


Tree densityTree density

Crop areaCrop area

LCCS use - definition of 16 classes fromLCCS use - definition of 16 classes from steppe to open forest ! ! !steppe to open forest ! ! !


Evolution temporelle de l'activité photosynthétique

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0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

0,55

0,6

0,65

0,7

0,75

0,8

Temps

« steppe arbustive »

Time

NDVI Temporal evolution of NDVI Temporal evolution of NDVI

Complementary information to classe name/codeComplementary information to classe name/code average NDVI profile per classeaverage NDVI profile per classe


3 additionnal issues :3 additionnal issues :

• year to year variationyear to year variation

• agriculture density retrievalagriculture density retrieval

• method use for very large areamethod use for very large area

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Sensitivity of LC map to year to year Sensitivity of LC map to year to year variationvariation

Time

NDVI

0,00

0,10

0,20

0,30

0,40

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87

Culture

Grassland

Shrubland

NDVI

Time


Detection feasibility of the agriculture Detection feasibility of the agriculture densitydensity


Procedure to apply the method to very large areaProcedure to apply the method to very large area Indpt unsup. classification on moving windowIndpt unsup. classification on moving window


Procedure to apply the method to very large areaProcedure to apply the method to very large area Biomes stratification based on NDVI rangeBiomes stratification based on NDVI range


Perspectives for GLC2000 data setPerspectives for GLC2000 data set

great information contentgreat information content

information extraction quite feasibleinformation extraction quite feasible

New (?) issues because of the information quality :New (?) issues because of the information quality :

classes classes mergingmerging becomes a more becomes a more difficultdifficult exercise exercise

classes classes labelinglabeling becomes a becomes a tufftuff exercise exercise

need for labeling assistance through actual need for labeling assistance through actual matchingmatching of classification output and reference informationof classification output and reference information

need for 1-km² ‘ field ’ observation methodneed for 1-km² ‘ field ’ observation method

GLC mapping in semi-arid regions: a case study in West Africa Jean-François Pekel and Pierre...

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