1. 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 CHAMPEAUX Kyung-Soo HAN

2. SPOT4-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

3. METHODOLOGY SYNTHESIS Spot4-VGT1 S1 Daily Synthesis Cloud Mask SWIR Blind Suppression BRDF Correction Reflectance Normalization Principal Components Transformation K-mean Clustering Algorithm Map of Unnamed Classes CORINE Land Cover Confusion Matrix New Land Cover Map New Land Cover Map Angular Information

4. Image Treatment CLOUD MASK New Tuned Thresholds for Surface Reflectance b0  220 SWIR  180 CLOUDY CLEAR SKY Yes NoNo NoNo b0, 23/08/2000 b0, 5/02/2000

5. SWIR 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

6. SWIR BLIND SUPPRESSION-case2 Image Treatment (SWIR-b2) 2 =  (SWIR-b2) 2 =  Threshold = M’ + (  M  3.0) M’ : Mean of M values for a line  M : Standard dev of M values for a line

7. SEMI-EMPIRICAL BRDF MODEL Image Treatment  s,  v,  = k 0 + k 1  f 1  s,  v,  + k 2  f 2  s,  v, , Roujean et al., 1992 N measured  N: number of clear days during a compositing period k 0 : bidirectional reflectance,  s =  v = 0 Inversion of system k 0, k 1, k 2 Computation of normalized reflectance

8. 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 2002 R: SWIR G: b3 B: b2 Norm. (RGB) 19 June 2002 R: SWIR G: b3 B: b2

9. Day of Year 110203040 Composite 1 Composite 2 Composite 3 : Average all  norm values for clear day - 31-day Screening - > 4 Observations NN 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

10. EXAMPLES OF 10-day COMPOSITE Image Treatment 1st 10-day 8th 10-day12th 10-day 17th 10-day 34th 10-day23 10-day

11. 23th 10-day ( August)

12. TIME SERIES PROFILE Image Treatment

13. AREA OPTIMIZATION Memory 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

14. PRINCIPAL COMPONENTS Classification North area 465,942 pixels 26 10-days (78 input images) 36 10-day images for each channel (=117 variables) Select images by % of cloudy pixels over each area South area 517,314 pixels 24 10-days (72 input images) PRINCIPAL COMPONENTS ANALYSIS Selection of Components Components having 99% accumulated correlation North area 48 components South area 34 components

15. K-MEAN CLUSTERING Classification North area 48 components South area 34 components K-mean Clustering for 40 classes 1 st output (40 classes) 2 nd 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, sands CORINE MASK

16. Agglomeration of classes fitting the same landcover MOSAIC Classification North part Map North part Map South part Map South part Map Mosaic

17. MISSING MISSING PIXEL TREATMENT Classification Missing due to cloud Missing due to cloud & snow Missing due to cloud & snow K-mean clustering for missing pixels A B C With a clear image after the end of snow melting - Julian day 254

18. NEW URBAN DETERMINATION Classification A Supervised Classification for New Urban Area Class Toulouse New Urban Artificial Surfaces Mask from CORINE 1992 - Shrub Land - Sparsely Vegetated Area Artificial Surfaces Mask from CORINE 1992 Initial classificationAfter re- classification

19. FINAL RESULT Classification

20. VALIDATION Reference: CORINE EUROPE glc2000 MODIS PELCOM FRANCE glc2000 Confusion Matrix Comparison

21. CONFUSION 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 FRANCEMODISEUROPEPELCOM FRANCE * % (Accuracy  100)

22. CONFUSION MATRIX WITH CORINE Validation Reliability = (User’s accuracy) Correctly Detected Pixels Total Detected Pixels in the classified data FRANCE * : Grasslands & Forest+Pastures 72.3192.5180.8988.40Barren Land 50.4357.7240.2352.1748.68Grasslands 52.67-55.7458.94Permanent Crop 60.4241.0051.7447.26Arable Land + Perm. Crop 63.1448.9153.1548.60Arable Land 35.417.0820.6437.01Shrubland 67.1349.9560.7352.26Forest FRANCEMODISEUROPEPELCOM FRANCE * % (Reliability  100)

23. CONFUSION MATRIX WITH CORINE Validation Comparison Index (CI) = (Reliability  Accuracy) 0.5 FRANCE * : Grasslands & Forest+Pastures 0.700.340.680.38Barren Land 0.46 0.150.470.44Grasslands 0.450.090.390.46Permanent Crop 0.690.610.640.61Arable Land + Perm. Crop 0.710.630.650.62Arable Land 0.250.120.060.23Shrubland 0.720.490.670.61Forest FRANCEMODISEUROPEPELCOM FRANCE * 0 -1

24. Reliability CI CONFUSION MATRIX WITH CORINE Validation 11.5413.7915.4712.45Mixed Forest 46.0557.5942.1130.60Coniferous Forest 49.3417.6733.3834.50Broad-leved Forest FRANCEMODISEUROPEPELCOM 12.4034.6415.0621.86Mixed Forest 63.3925.6851.4541.52Coniferous Forest 51.3313.5242.4342.08Broad-leved Forest Accuracy 0.120.220.150.17Mixed Forest 0.540.390.470.36Coniferous Forest 0.500.160.38 Broad-leved Forest Forest…in detail

25. Overall Accuracy CONFUSION MATRIX WITH CORINE Validation

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