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Land Cover Map of France Using S1/S10 Spot/Vegetation Data: Final Results Workshop (JRC-Ispra, 24-26 March 2003)

This paper presents the methodology and results of generating a land cover map of France using S1/S10 Spot/Vegetation data. The map covers the period from 1 January to 31 December 2000 and includes daily synthesis sets and angular information. The methodology includes synthesis, principal components transformation, cloud mask, K-mean clustering algorithm, SWIR blind suppression, angular information correction, and reflectance normalization.

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Land Cover Map of France Using S1/S10 Spot/Vegetation Data: Final Results Workshop (JRC-Ispra, 24-26 March 2003)

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  1. GLC 2000 ‘Final Results’ Workshop (JRC-Ispra, 24 ~ 26 March, 2003) 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 Principal Components Transformation Cloud Mask K-mean Clustering Algorithm SWIR Blind Suppression Map of Unnamed Classes Angular Information BRDF Correction Confusion Matrix New Land Cover Map Reflectance Normalization CORINE Land Cover

  4. b0, 23/08/2000 b0, 5/02/2000 Image Treatment CLOUD MASK New Tuned Thresholds for Surface Reflectance Yes b0  220 No No Yes SWIR  180 CLEAR SKY CLOUDY

  5. dilatation Core (defect) Image Treatment SWIR BLIND SUPPRESSION-case1 • 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. Image Treatment SWIR BLIND SUPPRESSION-case2 (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. Image Treatment SEMI-EMPIRICAL BRDF MODEL s, v,  = k0 + k1 f1s, v,  + k2 f2s, v, , Roujean et al., 1992 Computation of normalized reflectance Inversion of system N measured  k0, k1, k2 N: number of clear days during a compositing period k0: bidirectional reflectance, s = v= 0

  8. Image Treatment NORMALIZATION OF  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. Image Treatment NORMALIZATION OF  norm(i): norm of day i N: composite value by N norm N: number of clear days for the compositing period Average all normvalues for clear day N • 31-day Screening • > 4 Observations Composite 1 Composite 2 Composite 3 :: 1 10 20 30 40 Day of Year

  10. Image Treatment EXAMPLES OF 10-day COMPOSITE 1st 10-day 8th 10-day 12th 10-day 23 10-day 34th 10-day 17th 10-day

  11. 23th 10-day ( August)

  12. Image Treatment TIME SERIES PROFILE

  13. Memory limitations to run the algorithm over the whole area Classification AREA OPTIMIZATION Reduction of running size Step1. Select a zone through a climate map North part South part Step2. Divide into two parts to avoid mosaic problems

  14. Classification PRINCIPAL COMPONENTS 36 10-day images for each channel (=117 variables) Select images by % of cloudy pixels over each area North area 465,942 pixels 26 10-days (78 input images) 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. CORINE Mask for misdetected pixels • - Artificial surfaces • Water bodies • Wetlands • Beachs, dunes, sands CORINE MASK Classification K-MEAN CLUSTERING North area 48 components South area 34 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

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

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

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

  19. Classification FINAL RESULT

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

  21. Correctly Detected Pixels Total Detected Pixels from reference % (Accuracy  100) PELCOM EUROPE MODIS FRANCE FRANCE* Forest 70.77 73.23 47.17 77.82 Shrubland 14.74 1.69 19.72 17.49 Arable Land 80.10 80.59 80.48 80.26 Arable Land + Perm. Crop 80.43 80.74 91.18 80.79 Permanent Crop 35.42 27.71 - 39.03 Grasslands 39.75 42.70 5.56 37.11 43.43 Barren Land 16.07 56.56 12.30 66.81 Validation CONFUSION MATRIX WITH CORINE Accuracy = FRANCE* : Grasslands & Forest+Pastures

  22. Correctly Detected Pixels Total Detected Pixels in the classified data % (Reliability  100) PELCOM EUROPE MODIS FRANCE FRANCE* Forest 52.26 60.73 49.95 67.13 Shrubland 37.01 20.64 7.08 35.41 Arable Land 48.60 53.15 48.91 63.14 Arable Land + Perm. Crop 47.26 51.74 41.00 60.42 Permanent Crop 58.94 55.74 - 52.67 Grasslands 48.68 52.17 40.23 57.72 50.43 Barren Land 88.40 80.89 92.51 72.31 Validation CONFUSION MATRIX WITH CORINE Reliability = (User’s accuracy) FRANCE* : Grasslands & Forest+Pastures

  23. 0 -1 PELCOM EUROPE MODIS FRANCE FRANCE* Forest 0.61 0.67 0.49 0.72 Shrubland 0.23 0.06 0.12 0.25 Arable Land 0.62 0.65 0.63 0.71 Arable Land + Perm. Crop 0.61 0.64 0.61 0.69 Permanent Crop 0.46 0.39 0.09 0.45 Grasslands 0.44 0.47 0.15 0.46 0.46 Barren Land 0.38 0.68 0.34 0.70 Validation CONFUSION MATRIX WITH CORINE Comparison Index (CI) = (Reliability  Accuracy)0.5 FRANCE* : Grasslands & Forest+Pastures

  24. Forest…in detail PELCOM EUROPE MODIS FRANCE Broad-leved Forest 34.50 33.38 17.67 49.34 Coniferous Forest 30.60 42.11 57.59 46.05 Mixed Forest 12.45 15.47 13.79 11.54 Broad-leved Forest 42.08 42.43 13.52 51.33 Coniferous Forest 41.52 51.45 25.68 63.39 Mixed Forest 21.86 15.06 34.64 12.40 Accuracy Broad-leved Forest 0.38 0.38 0.16 0.50 Coniferous Forest 0.36 0.47 0.39 0.54 Mixed Forest 0.17 0.15 0.22 0.12 Validation CONFUSION MATRIX WITH CORINE Reliability CI

  25. Validation CONFUSION MATRIX WITH CORINE Overall Accuracy

  26. CONCLUSIONS Production of consistent normalized 10-day composites Improvement of the classification due to the use of reflectances (B2,B3,SWIR) instead of NDVI Determination of new urban increase Improvement of regional classification compared to the whole Europe (glc2000), PELCOM and MODIS products

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