what if? prospects based on corilis
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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What if? prospectsbased on Corilis
Alex Oulton, Manuel Winograd
Ronan Uhel & Jean-Louis Weber
Land Use Interface Workshop EEA, Copenhagen, 1-2 December , 2008
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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
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From Corine land cover to Corilis
Ref.: EEA 2006, Land accounts for Europe 1990-2000
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CLC Urban areas and N2000 sites
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Processing urban areas in a grid…
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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)
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Urban “temperature” or “radiation” over N2000 (habitats) sites
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Note that not all the “temperature” is coming from large cities (here, agglomerations of pop>50 000 hab are in purple)
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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
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CORILIS map of artificial land cover 2000
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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
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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
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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
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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
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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
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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
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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
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Highest potential of conversion to cropland [2]
RED: within transition areas dominated by arable land
10 40
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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
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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
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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
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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
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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
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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
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Highest potential of conversion to cropland [7]
And Natura2000 sites: distribution
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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”
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Risks of soil erosion:
The PESERA map by JRC
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Highest potential of conversion to cropland [9]
And soil erosion risks (PESERA)
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Highest potential of conversion to cropland [10]
NUTS2/3 prone to conversion
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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