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 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

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

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>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

Legend

l_111hd_c1

5km.MEAN Value

0 - 2

3 - 6

7 - 12

13 - 22

23 - 62

Border

CORILIS map of artificial land cover 2000

Legend

C1a_pl

us10

VALUE

10 - 12

12.0000

0001 -

1616.0

000000

1 - 21

21.0000

0001 -

2727.0

000000

1 - 33

33.0000

0001 -

3838.0

000000

1 - 43

43.0000

0001 -

4848.0

000000

1 - 53

53.0000

0001 -

5858.0

000000

1 - 63

63.0000

0001 -

6868.0

000000

1 - 73

73.0000

0001 -

7878.0

000000

1 - 83

83.0000

0001 -

8888.0

000000

1 - 93

93.0000

0001 -

9898.0

000000

1 - 103

103.000

0001 -

110

10 100

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

Legend

C1a_pl

us10

VALUE

10 - 12

12.0000

0001 -

1616.0

000000

1 - 21

21.0000

0001 -

2727.0

000000

1 - 33

33.0000

0001 -

3838.0

000000

1 - 43

43.0000

0001 -

4848.0

000000

1 - 53

53.0000

0001 -

5858.0

000000

1 - 63

63.0000

0001 -

6868.0

000000

1 - 73

73.0000

0001 -

7878.0

000000

1 - 83

83.0000

0001 -

8888.0

000000

1 - 93

93.0000

0001 -

9898.0

000000

1 - 103

103.000

0001 -

110

10 100

+3 points+5 points

+10 points

Corilis 2000

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

Legend

C1a_pl

us10

VALUE

10 - 12

12.0000

0001 -

1616.0

000000

1 - 21

21.0000

0001 -

2727.0

000000

1 - 33

33.0000

0001 -

3838.0

000000

1 - 43

43.0000

0001 -

4848.0

000000

1 - 53

53.0000

0001 -

5858.0

000000

1 - 63

63.0000

0001 -

6868.0

000000

1 - 73

73.0000

0001 -

7878.0

000000

1 - 83

83.0000

0001 -

8888.0

000000

1 - 93

93.0000

0001 -

9898.0

000000

1 - 103

103.000

0001 -

110

10 100

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

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

ba

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?

• 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

Legend

Std_We

ighted_

Produc

t_VAL

UE-10,

000 - -7

,315-7,3

14 - -5,4

45-5,4

44 - -3,9

27-3,9

26 - -2,6

79-2,6

78 - -1,6

79-1,6

78 - -88

4-883

- -264

-263 - 45

1452

- 1,428

1,429 - 2

,5412,54

2 - 3,763

3,764 - 5

,0635,06

4 - 6,468

6,469 - 7

,9217,92

2 - 10,00

0cou

ntries

sea-100 +100

Highest potential of conversion to cropland [1]

Landscape polarity: pixels in dark GREENand dark BROWN are NOT prone to more change, as well as pixels in light YELLOW (urban, forests,

lakes…)

Legend

Std_We

ighted_

Produc

t_VAL

UE-10,

000 - -7

,315-7,3

14 - -5,4

45-5,4

44 - -3,9

27-3,9

26 - -2,6

79-2,6

78 - -1,6

79-1,6

78 - -88

4-883

- -264

-263 - 45

1452

- 1,428

1,429 - 2

,5412,54

2 - 3,763

3,764 - 5

,0635,06

4 - 6,468

6,469 - 7

,9217,92

2 - 10,00

0cou

ntries

sea

-100 +100X XX

Legend

Std_We

ighted_

Produc

t_VAL

UE-10,

000 - -7

,315-7,3

14 - -5,4

45-5,4

44 - -3,9

27-3,9

26 - -2,6

79-2,6

78 - -1,6

79-1,6

78 - -88

4-883

- -264

-263 - 45

1452

- 1,428

1,429 - 2

,5412,54

2 - 3,763

3,764 - 5

,0635,06

4 - 6,468

6,469 - 7

,9217,92

2 - 10,00

0cou

ntries

sea

-100 +100

Effect of agriculture intensification over landscape polarity

Highest potential of conversion to cropland [2]

RED: within transition areas dominated by arable land

10 40

Highest potential of conversion to cropland [3]

BLUE: within transition areas dominated by pasture & mosaics

LegendPotential_PM_conversion_polygonsGRIDCODE

-2673 - -2365-2364 - -2072-2071 - -1804-1803 - -1547-1546 - -1287-1286 - -1044-1043 - -816-815 - -608-607 - -427-426 - -265

10 40

Highest potential of conversion to cropland [4]

As of 2000

10 40

LegendPotential_PM_conversion_polygonsGRIDCODE

-2673 - -2365-2364 - -2072-2071 - -1804-1803 - -1547-1546 - -1287-1286 - -1044-1043 - -816-815 - -608-607 - -427-426 - -265

10 40

10 40

LegendPotential_PM_conversion_polygonsGRIDCODE

-2673 - -2365-2364 - -2072-2071 - -1804-1803 - -1547-1546 - -1287-1286 - -1044-1043 - -816-815 - -608-607 - -427-426 - -265

10 40

Highest potential of conversion to cropland [4]

As of 2000 + 5% increase of arable land

10 40

LegendPotential_PM_conversion_polygonsGRIDCODE

-2673 - -2365-2364 - -2072-2071 - -1804-1803 - -1547-1546 - -1287-1286 - -1044-1043 - -816-815 - -608-607 - -427-426 - -265

10 40

Highest potential of conversion to cropland [4]

As of 2000 + 10% increase of arable land

10 40

LegendPotential_PM_conversion_polygonsGRIDCODE

-2673 - -2365-2364 - -2072-2071 - -1804-1803 - -1547-1546 - -1287-1286 - -1044-1043 - -816-815 - -608-607 - -427-426 - -265

10 40

Highest potential of conversion to cropland [5]

As of 2000 + 20% increase of arable land

10 40

LegendPotential_PM_conversion_polygonsGRIDCODE

-2673 - -2365-2364 - -2072-2071 - -1804-1803 - -1547-1546 - -1287-1286 - -1044-1043 - -816-815 - -608-607 - -427-426 - -265

10 40

Highest potential of conversion to cropland [6]

As of 2000 + 50% increase of arable land

Highest potential of conversion to cropland [7]

And Natura2000 sites: distribution

Highest potential of conversion to cropland [8]

And Natura2000 sites: a first indicator

Legend

Number_PCZ_Per_N2000_Site

Sum_Count

0

1

2

3

4 - 5

6 - 8

9 - 11

12 - 14

15 - 17

18 - 20

21 - 25

26 - 30

30+Legend

Number_PCZ_Per_N2000_Site

Sum_Count

0

1

2

3

4 - 5

6 - 8

9 - 11

12 - 14

15 - 17

18 - 20

21 - 25

26 - 30

30+

PCZ = “Prone to Conversion Zones”

Risks of soil erosion:

The PESERA map by JRC

Highest potential of conversion to cropland [9]

And soil erosion risks (PESERA)

Highest potential of conversion to cropland [10]

NUTS2/3 prone to conversion

Next:

• 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