1. Time series – spatial information ESPON workshop, 6 th May 2010 Oscar Gomez, EEA

2. My understanding of time series and GIS Traditional GIS does not consider time dimension  miss dynamics of some phenomena Time dimension is important in: – Administrative boundaries – Land cover/land use – Population – Hydrology – Vegetation/crops – Wildlife On the EEA side, this is secured in land cover with land cover changes layers

3. Example: hydro systems Our context: ECRINS hydro database Drainage doesn’t change with our time scale (*) Continental water dynamics (*) except in the case of canals

5. Spatial information and time Our context: CORINE Land Cover 1:100.000, EEA MSs + collaborating countries geographic extents, with exceptions Homogeneous across participating countries 1990, 2000, 2006, with exceptions 39 countries CLC2000-2006 LC snapshots + LC changes Many datasets derived  spatial disaggregation generally depends on CLC + something else PHARE  1975 – 1990 LC Bulgaria, Hungary, Romania, Slovakia, Czech Republic LACOAST  1975 – 1990 coastal LC: EU15 (except UK, LU)

6. Exchange of data

7. CORINE Land Cover Co-ownership EEA/Communities and Member States, data flow control They are modified only to harmonize borders Problem: time span  in 2010 we have 2006 data GLOBCorine (ESA): from GLOBCover (medium resolution), trained with CORINE  GlobCorine – Better geographic coverage – Nowcasting – Less classes – Less certainty about individual changes – 2006 done, 2009 before summer (ESA)

8. GlobCORINE Urban and associated areas Rainfed cropland Irrigated cropland Forest Heathland and sclerophyllous vegetation Grassland Sparsely vegetated area Vegetated low-lying areas on regularly flooded soil Bare areas Complex cropland Mosaic cropland / natural vegetation Mosaic of natural (herbaceous, shrub, tree) vegetation Water bodies Permanent snow and ice No data (burnt areas, clouds,…)

9. Land cover and HANTS(EVI 2006) Salting (Bosplaat, Terschelling) 0 0.2 0.4 0.6 0.8 1 171319 16-days-NDVI-composites NDVI original HANTS fitted Urban (Amsterdam) 0 0.2 0.4 0.6 0.8 1 171319 16-days-NDVI-composites NDVI original HANTS fitted Grassland (Friesland) 0 0.2 0.4 0.6 0.8 1 171319 16-days-NDVI-composites NDVI original HANTS fitted Deciduous forest (Harderbos, Flevopolder) 0 0.2 0.4 0.6 0.8 1 171319 16-days-NDVI-composites NDVI original HANTS fitted Drifting sand (Veluwe) 0 0.2 0.4 0.6 0.8 1 171319 16-days-NDVI-composites NDVI original HANTS fitted Pine forest (Veluwe) 0 0.2 0.4 0.6 0.8 1 171319 16-days-NDVI-composites NDVI original HANTS fitted Agriculture (Flevoland) 0 0.2 0.4 0.6 0.8 1 171319 16-days-NDVI-composites NDVI original HANTS fitted Grain cultivation (Dollard, Groningen) 0 0.2 0.4 0.6 0.8 1 171319 16-days-NDVI-composites NDVI original HANTS fitted Red; medium, Green; medium, Blue; high Red; medium, Green; high, Blue; medium Red; high, Green; low, Blue; low Red; low, Green; low, Blue; lowRed; high, Green; high, Blue; low Red; medium, Green; high, Blue; low Red; high, Green; low, Blue; medium Red; low, Green; medium, Blue; low Red = average NDVI Green = Annual Amplitude Blue = Six months Amplitude

11. Water quantity Run-off data No data flow established Based on the good willingness of MSs The data flow is being defined

12. Spatial dimension

13. Land cover – and derived Neighbourhood: yes, on analysis; for example, green landscape outside cities Spatial disaggregation: Not directly us (by now), mainly the JRC: – Population density – Agro-land use: AFOLU  crops, livestock Spatial aggregation: used constantly; OLAP cubes

14. Livestock density EU27

15. Water drainage (ECRINS) Interpolation of climate data (rainfall, temperature)  “spatial disaggregation” Spatial aggregation: to the catchment level

16. Time dimension

17. Water: climate data is always spatially interpolated data; extrapolation: IPCC scenarios, forecast Land cover: no interpolation; extrapolation: land use modelling (JRC: MOLAND, LUMOCAP) Need for time-dimensioned GIS layers: i.e. transport networks, protected areas,...  start_date, end_date fields!

18. Trends in the coast: 1975 to 2006 (30 years of changes) Artificialisation has a constant growth rate: 0.5% relative increase each year Water bodies were created in 1975-2000 Agriculture shows a constant decline Wetlands and forest and semi-natural decreased heavily (around 10%) in 1975-1990; it has slowed down

19. Trends 1990 – 2000 – 2006 (*) (*) 100% = status in 1990; the lines show the relative increase (trend) for the 2 periods, 1990-2000, 2000-2006 Urbanisation: same trend, above 0.5% yearly increase Forest and semi-natural are stable Wetlands don’t disappear as quickly as in the previous period; strong trend change (from 0.22% yearly loss to 0.06% yearly loss) Water bodies are created at a slower pace (0.19% yearly increase to 0.08%)