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What if? prospects based on Corilis. Alex Oulton, Manuel Winograd Ronan Uhel & Jean-Louis Weber. Land Use Interface Workshop EEA, Copenhagen, 1-2 December , 2008. What if? prospects based on Corilis. Dialogue on prospects based on common representations; versatile tool; incremental

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What if prospects based on corilis

What if? prospectsbased on Corilis

Alex Oulton,

Manuel Winograd

Ronan Uhel

& Jean-Louis Weber

Land Use Interface Workshop

EEA, Copenhagen, 1-2 December , 2008


What if prospects based on corilis1
What if? prospects based on Corilis

  • Dialogue on prospects based on common representations; versatile tool; incremental

  • Highlight (check, map, quantify) consequences of various assumptions  ideally defined with users

  • No real scenario, 3 to 5 assumptions at a time, maximum

  • Shows what it doesn’t deliver as well as what it delivers  formulation of variants, requirement for adjustments

  • Use of Corilis (smoothed Corine, fuzzy sets) properties:

    • Potentials in a neighbourhood  no need of complex topological analysis (no need to tell which pasture will be converted…)

    • Additive layers  simple calculations possible


From corine land cover to corilis
From Corine land cover to Corilis

Ref.: EEA 2006, Land accounts for Europe 1990-2000




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)‏



Note that not all the “temperature” is coming from large cities (here, agglomerations of pop>50 000 hab are in purple)‏


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


Corilis map of artificial land cover 2000
CORILIS map of artificial land cover 2000 computed. Here, MEAN value per site, radius of 5 km


10 computed. Here, MEAN value per site, radius of 5 km

100

What if? prospect: when urban sprawl takes place in the neighbouring countryside…

Baseline Data: Corilis / Urban Temperature 2000, scale of 0-100 // Average increase 2000-2010: 5%, even over Europe

Prospect 1: a constant of 5 points is added up to Corilis values > 5 (below 5 corresponds to remote countryside)

Urban temperature 2000

Urban temperature 2010 – prospect 1


What if prospect when urban sprawl takes place in the countryside

10 computed. Here, MEAN value per site, radius of 5 km

100

What if? Prospect: when urban sprawl takes place in the countryside

Corilis 2000

+3 points

+5 points

+10 points


What if prospect when urban sprawl takes place in the countryside1

10 computed. Here, MEAN value per site, radius of 5 km

100

What if? Prospect: when urban sprawl takes place in the countryside


Areas prone to agriculture intensification driven by the agro fuel demand

b computed. Here, MEAN value per site, radius of 5 km

a

Areas prone to agriculture intensification driven by the agro-fuel demand

Assessment based on Corilis, the computation in a regular grid of CLC values in and in the neighbourhood of each cell (in the application: radius of 5km)

Broad pattern intensive agriculture

Pasture and agriculture mosaics


What if prospect where conversion to broad pattern intensive agriculture may take place

-100 computed. Here, MEAN value per site, radius of 5 km

+100

What if? prospect: where conversion to broad pattern intensive agriculture may take place?

  • Analysis of Corilis values of classes 2a and 2b

    • 2a = broad pattern intensive agriculture (clc21, 22 + 241)

    • 2b = pastures and mosaics (clc231, 242, 243 & 244)

  • Each cell of the grid is given a value of:

    Ι(2a-2b)Ι *(2a+2b)

    Positive values (more broad pattern intensive agriculture) are brown, negative values (more pasture and mosaics) are green, yellow meaning transition areas

  • Assumption 1: 2a+2b = UAA is constant (e.g. no deforestation)

     Map of change in overall potential: the share of 2a within 2a-2b increases of 5, 10, 20 and 50%

  • Assumption 2: change may take place only when polarity < 80% AND when UAA > 20%

    Map of areas prone to conversion according to the demand for arable land


-100 computed. Here, MEAN value per site, radius of 5 km

+100

X

X

X

Highest potential of conversion to cropland

[1]

Landscape polarity: pixels in dark GREEN

and dark BROWN are NOT prone to more change, as well as pixels in light YELLOW (urban, forests, lakes…)


-100 computed. Here, MEAN value per site, radius of 5 km

+100

Effect of agriculture intensification over landscape polarity


10 computed. Here, MEAN value per site, radius of 5 km

40

Highest potential of conversion to cropland

[2]

RED: within transition areas dominated

by arable land


40 computed. Here, MEAN value per site, radius of 5 km

10

Highest potential of conversion to cropland

[3]

BLUE: within transition areas dominated by

pasture & mosaics


40 computed. Here, MEAN value per site, radius of 5 km

10

10

40

Highest potential of conversion to cropland

[4]

As of 2000


40 computed. Here, MEAN value per site, radius of 5 km

10

10

40

Highest potential of conversion to cropland

[4]

As of 2000 + 5% increase of arable land


40 computed. Here, MEAN value per site, radius of 5 km

10

10

40

Highest potential of conversion to cropland

[4]

As of 2000 + 10% increase of arable land


40 computed. Here, MEAN value per site, radius of 5 km

10

10

40

Highest potential of conversion to cropland

[5]

As of 2000 + 20% increase of arable land


40 computed. Here, MEAN value per site, radius of 5 km

10

10

40

Highest potential of conversion to cropland

[6]

As of 2000 + 50% increase of arable land


Highest potential of conversion to cropland computed. Here, MEAN value per site, radius of 5 km

[7]

And Natura2000 sites: distribution


Highest potential of conversion to cropland computed. Here, MEAN value per site, radius of 5 km

[8]

And Natura2000 sites: a first indicator

PCZ = “Prone to Conversion Zones”


Risks of soil erosion: computed. Here, MEAN value per site, radius of 5 km

The PESERA map by JRC


Highest potential of conversion to cropland computed. Here, MEAN value per site, radius of 5 km

[9]

And soil erosion risks (PESERA)


Highest potential of conversion to cropland computed. Here, MEAN value per site, radius of 5 km

[10]

NUTS2/3 prone to conversion


Next: computed. Here, MEAN value per site, radius of 5 km

  • Validate assumptions; differentiation according to countries, regions (e.g. important conversion of pasture is taking place in Ireland…)

  • Test new assumptions (taking into account roads, farming practices…), new scenarios

  • Work on change coefficients

  • Cross-check methodology and results with other models; integrate?

  • Prepare an interactive tool for users dialogue


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