Land Cover Project ESA Climate Change Initiative. Processing chain for land cover maps dedicated to climate modellers.

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1 Land Cover Project ESA Climate Change Initiative Processing chain for land cover maps dedicated to climate modellers land_cover_cci S. Bontemps 1, P. Defourny 1, V. Kalogirou 2, F.M. Seifert 2 and O. Arino 2 1 Earth and Life Institute UCLouvain, Belgium 2 European Space Agency, Italy

2 Land Cover for Climate Modeling ESA Climate Change Initiative (CCI) : significant contribution to the global monitoring effort required by UNFCCC and IPCC 6 months dedicated to users consultations Land Cover Data User Community Broad assessment of ESA GLOBCOVER Users 4,6 % (372/8000) Climate User Community Associated user survey 17,6% (15/85) Scientific literature review Key user surveys: MPI M, LSCE, MOHC Global users distribution

3 Users consultations Qualitative requirements Long term consistency of land cover and a dynamic component Consistency among the different model parameters is often more important than accuracy of individual datasets, Relationship between land cover classifiers with the climatically relevant surface parameters (variable importance of different LC classes) Providing information on natural versus anthropogenic vegetation Changes in land cover (human disturbance) Land cover products should provide flexibility to serve different scales and purposes both in terms of spatial and temporal resolution Quality of land cover products need to be transparent by using quality flags and controls

4 Revisited land cover concept 2 kinds of output products -> 300 m global land cover (state) maps consistent for 3 different epochs -> 4 global land cover condition products -> 7-day 300 m surface reflectance time series based on MERIS FR & RR

5 LC-CCI Products specification 300 m global land cover products for 3 different epochs LAND COVER CCI PRODUCT REFERENCE PERIOD INSTRUMENTS Land Cover SPOT- VEGETATION daily images Land Cover Envisat MERIS (FR&RR) daily images SPOT VEGETATION daily images Land Cover Envisat MERIS (FR&RR) daily images SPOT VEGETATION daily images

6 Input EO time series SPOT Vegetation 1 & 2 Global daily 1-km surface reflectance in 4 bands (blue, red, NIR, SWIR) Envisat MERIS Full Resolution Nearly global every 3-9 days 300-m reflectance in 15 bands (blue to NIR) Envisat MERIS Reduced Resolution Global every 3 days 1.2 km reflectance in 15 bands (blue to NIR)

7 LC CCI : Pre-processing Input: MERIS FR or RR ( ) to produce 7-day global composites of surface reflectance Scientific challenges: Geolocation issues and inter-sensor geometric compatibility Inter-sensor radiometric calibration Efficient cloud screening whithout ThIR Reliable land, water and snow detection Processing challenge: HUGE amount of data Fully automated chain

8 LC_CCI : Classification Key idea: capitalize on the GlobCover unsupervised classification chain and improve it through the addition of supervised classification, the better use of the time series temporal dimension, the development of a multiple-year strategy Principles: Regionally-tuned approach based on 22 equal-reasoning areas Use of several years of multispectral composites and of NDVI time series Combine supervised and unsupervised, spectral and temporal classification algorithms Ensure consistency between successive LC maps Typology defined using FAO LCCS to be in line with existing products and compatible with the PFTs

9 Classification strategy

10 Stratification layer 22 equal-reasoning regions from ecological and remote sensing point of view

11 Reference layer Used for training dataset in the supervised classification and for labelling in the unsupervised classification algorithms Based on existing LC data: GLC2000 global and regional maps GlobCover 2005 map Corine Land Cover maps (2006 and 2000) Land cover map of Canada National land cover database of United States Land cover database of Greater MesoAmerica North America Environment Dataset GLCN Senegal land cover map Land Cover Database of Burkina Faso Africover maps Burundi Democratic Rep. Of Congo Egypt Eritrea Kenya Rwanda Somalia Sudan Tanzania Uganda Central Africa forest types map Democratic Republic of Congo map Southern Africa Development (SADC) Countries land cover map National land cover map of China Land use map of Cambodia JCR irrigated and rainfed cropmasks Global mangrove altas MODIS map of global urban extent CCI-SAR Water Body dataset

12 Compositing From 7-day composites to seasonal / annual composites

13 Spectral classification made of supervised and unsupervised algos For each equalreasoning area Seasonal multispectral composites Reference LC layer Unsupervised classification Training dataset Machine learning classification Unlabelled spectrallyhomogeneous clusters Automated labelling Reference LC layer CLASSIF 1a (sup. spectral map) Fusion CLASSIF 1b (unsup. spectral map) CLASSIF 1 (spectral map)

14 Supervised classification for specific patterns Unsupervised Unsupervised MERIS annual composite MERIS annual composite Supervised Supervised

15 Temporal classification CLASSIF 1 (spectral map) VI 7-day time series Mask for specific areas Unsupervised classification Unlabelled temporallyhomogeneous clusters Reference LC layer Automated labelling CLASSIF 2 (temporal map)

16 Temporal classification

17 Multi-year perspective for the spectral classification 2 strategies, according to the equal reasoning-area Classification chain Classification chain 10 annual global land cover products

18 Multi-year perspective for the spectral classification Summary at the compositing level improves the imagery quality Annual Multi-annual

19 Multi-year perspective for the spectral classification Summary at the compositing level improves the imagery quality Annual Multi-annual

20 Multi-year perspective for the spectral classification Summary at the compositing level improves the imagery quality Annual Multi-annual

21 Multi-year perspective for the spectral classification Summary at the compositing level improves the imagery quality Annual Multi-annual When no necessary, strategy 2 with aggregation rules to interpret the multiple land cover maps

22 Multi-level classification strategy Temporal classification Multi-year approach Supervised classification Unsupervised classification

23 LC map using the composite

24 From 1 map to 3 consistent maps Fusion with LC maps from , 2009, 2010, 2011, epoch ( ) LC maps from , 2004, 2005, 2006, 2007 LC maps from , 1998, 1999, 2000, 2001, epoch ( ) INTERNAL DELIVERY END OF APRIL VALIDATED PRODUCTS PUBLICLY AVAILABLE IN AUTUMN epoch ( )

25 From 1 map to 3 consistent maps epoch

26 From 1 map to 3 consistent maps epoch 2010 epoch

27 Multi-level validation strategy Presentation of F. Achard on Tuesday

28 Thank you for your attention