glc 2000 ‘final results’ workshop (jrc-ispra, 24 ~ 26 march, 2003) glc 2000 ‘final results’...

GLC 2000 ‘Final Results’ Workshop(JRC-Ispra, 24 ~ 26 March, 2003)

GLC 2000 ‘Final Results’ Workshop(JRC-Ispra, 24 ~ 26 March, 2003)

LAND COVER MAP OF FRANCE USING S1/S10 SPOT/VEGETATION DATA

LAND COVER MAP OF FRANCE USING S1/S10 SPOT/VEGETATION DATA

Jean-Louis CHAMPEAUXKyung-Soo HAN

SPOT4-VEGETATION1 DATASPOT4-VEGETATION1 DATA

S1 Daily Synthesis Products– 4 Spectral Channels (b0, b2, b3, SWIR)

– Angular Information (SZA, VZA, SAA, VAA)

Period– 1 January ~ 31 December, 2000

– 351 Daily Synthesis Sets (15 missing days)

Zone

5.50 W ~ 9.91 E

51.50 N ~ 40.01 N

METHODOLOGY SYNTHESISMETHODOLOGY SYNTHESIS

Spot4-VGT1S1 Daily Synthesis

Cloud Mask

SWIR BlindSuppression

BRDFCorrection

ReflectanceNormalization

Principal ComponentsTransformation

K-mean ClusteringAlgorithm

Map of Unnamed Classes

CORINE Land Cover

ConfusionMatrix

New LandCover MapNew LandCover Map

AngularInformation

Image Treatment CLOUD MASKCLOUD MASK

New Tuned Thresholdsfor Surface Reflectance

b0 220

SWIR 180

CLOUDY CLEARSKY

Yes

Yes

No

No

b0, 23/08/2000

b0, 5/02/2000

SWIR BLIND SUPPRESSION-case1SWIR BLIND SUPPRESSION-case1

Image Treatment

Core(defect)

dilatation

Core

–by threshold

Dilatation– Ps(i-1,j) and Pe(i+1,j) Ps(i,j): start pixel of core on a line Pe(i,j): end pixel of core on a line

SWIR BLIND SUPPRESSION-case2SWIR BLIND SUPPRESSION-case2

Image Treatment

(SWIR-b2)2

= (SWIR-b2)2

=

Threshold = M’ + (M 3.0)M’ : Mean of M values for a lineM : Standard dev of M values for a line

SEMI-EMPIRICAL BRDF MODELSEMI-EMPIRICAL BRDF MODEL

Image Treatment

s, v, = k0 + k1 f1s, v, + k2 f2s, v, , Roujean et al., 1992

N measured

N: number of clear days during a compositing period

k0: bidirectional reflectance, s = v = 0

Inversio

n

of

system

k0, k1, k2

Computati

on of

normalized

reflectanc

e

NORMALIZATION OF NORMALIZATION OF

Image Treatment

Method (Duchemin & Maisongrande, 2002):norm,i= model(s=moyen, v=0) + mesured,i model,i(s, v,)

S1-Syn.(RGB)19 June 2002R: SWIRG: b3B: b2

Norm. (RGB)19 June 2002R: SWIRG: b3B: b2

Day of Year1 10 20 30 40

Composite 1

Composite 2

Composite 3::

Average all norm values for clear day

- 31-day Screening- > 4 Observations

n

iinormN

n 1)(

1

N

NORMALIZATION OF NORMALIZATION OF

Image Treatment

norm(i): norm of day i

N: composite value by N norm

N: number of clear days for the compositing period

EXAMPLES OF 10-day COMPOSITEEXAMPLES OF 10-day COMPOSITE

Image Treatment

1st 10-day 8th 10-day 12th 10-day

17th 10-day 34th 10-day23 10-day

23th 10-day ( August)

TIME SERIES PROFILE TIME SERIES PROFILE

Image Treatment

AREA OPTIMIZATIONAREA OPTIMIZATION

Memory limitations to run the algorithm over the whole areaMemory limitations to run the algorithm over the whole area

Reduction of running size

Step1.Select a zone through a climate map

Step2.Divide into two parts to avoid

mosaic problems

North part

South part

Classification

PRINCIPAL COMPONENTSPRINCIPAL COMPONENTS

Classification

North area465,942 pixels26 10-days

(78 input images)

36 10-day images for each channel (=117 variables)

Select images by % of cloudy pixels over each area

South area517,314 pixels

24 10-days(72 input images)

PRINCIPAL COMPONENTS ANALYSIS

Selection of ComponentsComponents having 99% accumulated correlation

North area48 components

South area34 components

K-MEAN CLUSTERINGK-MEAN CLUSTERING

Classification

North area48 components

South area34 components

K-mean Clustering for 40 classes

1st output (40 classes)

2nd output (44 classes)- 40 classes from K-mean clustering- 4 classes from CORINE

CORINE Mask for misdetected pixels

- Artificial surfaces- Water bodies- Wetlands- Beachs, dunes, sandsCORINE MASK

Agglomeration of classes fitting

the same landcover

MOSAICMOSAIC

Classification

North partMap

North partMap

South partMap

South partMap

Mosaic

MISSING

MISSING PIXEL TREATMENTMISSING PIXEL TREATMENT

Classification

Missing due tocloud

Missing due to cloud & snow

Missing due to cloud & snow

K-mean clusteringfor missing pixels

A

B C

With a clear imageafter the end of snow melting

- Julian day 254

NEW URBAN DETERMINATIONNEW URBAN DETERMINATION

Classification

A Supervised Classification for New Urban Area Class

Toulouse Toulouse New Urban

Artificial Surfaces Mask from CORINE 1992

- Shrub Land- Sparsely Vegetated Area

Artificial Surfaces Mask from CORINE 1992

Initial classification

After re- classification

FINAL RESULTFINAL RESULT

Classification

VALIDATIONVALIDATION

Reference: CORINE

EUROPEglc2000

MODIS

PELCOM

FRANCEglc2000

Confusion Matrix

Com

pari

son

CONFUSION MATRIX WITH CORINECONFUSION MATRIX WITH CORINE

Validation

Accuracy =Correctly Detected Pixels

Total Detected Pixels from reference

FRANCE* : Grasslands & Forest+Pastures

66.8112.3056.5616.07Barren Land

43.4337.115.5642.7039.75Grasslands

39.03-27.7135.42Permanent Crop

80.7991.1880.7480.43Arable Land + Perm. Crop

80.2680.4880.5980.10Arable Land

17.4919.721.6914.74Shrubland

77.8247.1773.2370.77Forest

FRANCE

MODISEUROP

EPELCO

MFRANCE

*% (Accuracy 100)


Validation

Reliability =(User’s accuracy)

Correctly Detected Pixels

Total Detected Pixels in the classified data


72.3192.5180.8988.40Barren Land

50.4357.7240.2352.1748.68Grasslands

52.67-55.7458.94Permanent Crop


63.1448.9153.1548.60Arable Land

35.417.0820.6437.01Shrubland

67.1349.9560.7352.26Forest

FRANCE

MODISEUROP

EPELCO

MFRANCE

*% (Reliability 100)


Validation

Comparison Index (CI) = (Reliability Accuracy)0.5


0.700.340.680.38Barren Land

0.460.460.150.470.44Grasslands

0.450.090.390.46Permanent Crop


0.710.630.650.62Arable Land

0.250.120.060.23Shrubland

0.720.490.670.61Forest

FRANCE

MODISEUROP

EPELCO

MFRANCE

*0 -1

Reliability

CI


Validation

11.5413.7915.4712.45Mixed Forest

46.0557.5942.1130.60Coniferous Forest

49.3417.6733.3834.50Broad-leved Forest

FRANCE

MODISEUROP

EPELCO

M

12.4034.6415.0621.86Mixed Forest



Accuracy

0.120.220.150.17Mixed Forest



Forest…in

detail

Overall Accuracy

Total

%

0 10 20 30 40 50 60 70 80 90 100

%

0 10 20 30 40 50 60 70 80 90 100

FRANCEMODISEUROPEPELCOM

CI x 100

Accuracy

Reliability50.94

57.51

59.83

61.7664.96

70.00

0.560.61

0.650.45

50.44

45.00


Validation

Improvement of the classification due to the use of reflectances (B2,B3,SWIR) instead of NDVI

CONCLUSIONSCONCLUSIONS

Production of consistent normalized 10-day composites

Determination of new urban increase

Improvement of regional classification compared to the whole Europe (glc2000), PELCOM and MODIS products

glc 2000 ‘final results’ workshop (jrc-ispra, 24 ~ 26 march, 2003) glc 2000 ‘final results’...

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