Modelling with CORILIS Change in land cover patterns, landscape ecological potential & “temperatures” on N2000, river basins and UMZ Wire frame and examples (MWI & JLW – 19 Aug 2008)

Background (1) LEAC record land cover flows and stocks. They are the first step towards ecosystem accounting. Pressure on nature doesn’t come only from land use change but as much (or more) from existing land use/ land cover Potential pressure from existing land use/ land cover (stocks) happens in their neighbourhood and is proportional to the size of the source and to its proximity Note: several small sources can generate a big impact altogether, even though they could be neglected individually Neighbourhood analysis can inform on the “radiation”, “diffusion” or ”temperature” of a given source over designated areas CORILIS, is a Gaussian smoothing of Corine land cover classes [produced with 1km² grid at 5, 10 and 20 km of radius and with 1 ha grid at 1 km of radius]. For each scale, a fuzzy set is computed with values for each cells normalised at An important property of these sets is additivity; the aggregated total of the 44 Corilis layers has the same composition as CLC's.

Background (2) Indexes/Indicators of stocks and change in stocks can be computed from Corilis: –'temperature” of a class or group of classes on crisp objects like countries, regions, catchments, N2k sites, UMZ... [urban or agriculture “temperature” on N2k sites or forest or “temperature” on cities - UMZ] and their change –Green [background] Landscape Index [GLI] is a particular aggregate which groups [addition] Corilis in 2 classes 1/ urban and intensive agriculture, and 2/ less intensive land use and semi-natural landscape. GLI can be fine tuned on purpose by changing or weighting one or the other class of group. –the Landscape Ecological Potential [LEP] is a composite indicator of headline importance – composition: see slide –Dominant Landscape Types is another aggregation based on modelling the relative importance of Corilis layers – see LEAC report. –Other formulas can highlight transitions between competing classes (typically in Europe, intensive agriculture vs. pasture&mosaic agriculture): e.g ratios of differences of these 2 classes in a given cell

Project’s steps 1 st step: test of algorithms based on LEAC Use of fuzzy values alone: various combinations and scenarios (grid values) Combination fuzzy sets with crisp objects (N2K, UMZ, river sub-catchments, NUTS3, SES) and average values Candidate issues for modelling and indicators: urban sprawl in the countryside; urban green landscape neighbourhood; DLT of the coastal zone; landscape effects of biofuels/food new demand; withdrawal of farming & forest creation; change in urban and intensive agriculture temperature of N2K zones; change in LEP. Other:… Computation and analysis of indicators relevance: soundness, policy objective, communication Publication of an EEA working document (e-format) with description of results and methodologies 2nd step: integration to the LEAC toolkit - communication, training and DIY reporting Proposed solutions and first tests Integration into HyperAtlas (with ETCLUSI/HyperAtlas support) Integration into QuantumGIS LEAC tool – modifiable maps 3rd step: integration with other datasets and advanced modelling Recommendations for approaching advanced modelling with LEAC; opportunities and threats Priority points: definition and preliminary tests (according to available time – action to be continued in 2009) Integration with DEM: better location of intensification areas or/and withdrawal of farming/reforestation areas Integration with transport networks: shift from Gaussian to smoothing with realist distances Integration with green urban areas database (green urban index) Integration with soil data: e.g. impacts on soil erosion using the JRC/PESERA map Integration with Climate Change scenarios: e.g. droughts as limiting factors Integration/Calibration with medium resolution satellite images: urban texture/density; soil humidity Relation to other spatial models Next slides: illustration of reference data layers and possible use

From Corine land cover to CORILIS

CLC Urban areas and N2000 sites

Processing urban areas in a grid…

Smoothing CLC values, accounting for urban surface inside each cell + within a radius of 5 km (values of urban surface decreasing with the square of the distance to the centre of the grid cell)‏

Urban “temperature” or “radiation” over N2000 (habitats) sites

Note that not all the “temperature” is coming from large cities (here, agglomerations of pop> hab are in purple)‏

An index of urban “temperature” of N2000 sites can be computed. Here, MEAN value per site, radius of 5 km

Urban temperature & Urban land uptake

Just the indexes…

Index: Intensive agriculture in the neighbourhood 5 km – mean value

Index: Intensive agriculture in the neighbourhood of N2000 sites 5 km – mean value

Index: Forest in the neighbourhood of N2000 sites 5 km – mean value

Index: Forest in the neighbourhood of N2000 sites 5 km – mean value

CLC CORILIS GLIDLT CRITERIA DEPENDENT LAYERS Aggregation and/or Dominance STANDARD LAYERS

Green landscape (background) index & map (GLI)

Green Landscape (Background) Index & N2k

The making of LEP Corine land cover (derived from satellite images) Green Background Landscape Index (derived from CLC) Naturilis (derived from Natura2000 & CDDA) Effective Mesh Size (MEFF, derived from TeleAtlas and CLC) net Landscape Ecological Potential (nLEP) 2000, by 1km² grid cell nLEP 2000 by NUTS 2/3

DLT & Land accounting units Grids Administrative Units River basins Sea catchments Bio-geographical regions Coastal units Dominant Landscape Types as: Corilis values > mean+ std deviation (highlights urban)  NB: 1.other DLT formulas are possible, e.g. the majority value as in next slide 2.DLT are useful for interpreting land cover change: e.g. urban sprawl may take place against agriculture landscape or more natural DLTs… 3.Preliminary tests of DLT (std deviation) change have produced strange results…

CORILIS: Intensity of land cover in the neighbourhood of Regional Parks (DLT: majority rule) (Lacaze, 1998) Change in intensity can be monitored as well

Normalised index Forests / Mixed Agriculture (Lacaze, 1998) The ratio a/b should probably be weighted for reflecting the importance of the pair of classes in a given cell. E.g. as (a/b)*(a+b) or a better formula – a subject for research