Disaggregation of CAPRI results
Renate Köble
Adrian Leip
Outline
1. Introduction
2. Existing approaches
3. Tentative solution of the problems
4. Overview on available data sets
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Corine Landcover 2000
LUCAS survey
7. First example
WP8 - CAPRI-GIS link
“Link of CAPRI to GIS covering soil, climatic and land use maps”
Distribution of agricultural activities on the land
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Correspondence between geo-coded data and geo-referenced data from
land cover maps
Distribution of secondary agricultural parameters
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livestock densities, feed composition
animal wastes (grazing / stabling / application)?
mineral fertilizer
other parameters?
Re-mapping for the calculation of indicators
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grid size / thematic maps
Allocation of land use changes in the reference year
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net / gross changes ?
use of additional information
Studies to built on ...
(1)
Trees species map (Renate)
Calculation of Agricultural Nitrogen Quantity for EU River
basins (JM Terres, JRC, 2000)
→
based on a methodology developed in a regional study
(Loire and Elbe catchment) 2000
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used by Declan to disaggregate NUTS 2 NewCronos data
to NUTS 3 level for the DNDC model
Use of a correspondence table Corine / FSS
CORINE
NUTS 2
allocation
matrix
NUTS 3
(or gridcell)
VARESE PROVINCE
Land to be redistributed (FSS 2000 - arable land (D except rice))
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→
Lombardia
Varese
641 640 ha arable land
6 440 ha arable land
Land available (Corine Land Cover 90)
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class 211: non-irrigated arable land
class 242: complex cultivation pattern
class 243: land principally occupied by agriculture
Lombardia
Varese
211
242
Corine Land Cover Area [1000 ha]
856
36
13
0
243
94
12
x distribution factor for potentially available land:
Lombardia
Varese
• non-irrigated arable land
0.95
• complex cultivation pattern
0.80
• land principally occupied by agriculture
0.60
Land available for FSS class arable land
813
22
56
12
0
7
2.2% of land potentially available in Lombardia is
located in the province of Varese
891
19
Disaggregation
FSS2000 data
Varese
Como
Lecco
Sondrio
Milano
Bergamo
Brescia
Pavia
Lodi
Cremona
Mantova
2.2%
1.5%
1.0%
1.5%
11.0%
7.6%
17.3%
13.3%
7.1%
16.4%
21.2%
13805
9745
6566
9902
70337
48449
110789
85456
45827
104922
135842
Varese
Como
Lecco
Sondrio
Milano
Bergamo
Brescia
Pavia
Lodi
Cremona
Mantova
1.0%
1.1%
0.5%
0.2%
8.9%
6.1%
18.3%
14.1%
7.2%
18.9%
23.8%
6440
7020
3220
1470
56870
38830
117190
90630
46510
120980
152480
Lombardia
100%
641640
Lombardia
100%
641640
→
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Total deviation: 45 000 ha (7% of total arable land distributed)
“Transfer” of arable land from the south to the north
total potential area: 891 kha
LOMBARDIA
Non-irrigated land
Permanently irrigated land
Complex cultivation pattern
Land principally
occupied by agriculture
with sign. areas
of natural veg.
Natural grassland
Pastures
Agro-forstry
Fruit trees and
berry plantations
Rice Fields
Vineyards
Forests
Water
Urban
Problems?
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Assumption of equal land use of land classes throughout
Europe
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Non-matching between statistical and land cover data
Interpretation errors and interpretation differences
Time lag between Satellite images and Statistical census
• Corine 1990 has is based on images between 1985-1993
• FSS 2000 represents the situation in 2000
Studies to built on ...
(2)
Spatial redistribution of statistical data from the Farm Structure
Survey (GIM report)
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Match absolute values of land in both data sets
regrouping of CLC and FSS to aggregate classes
Matrix of land cover classes potentially available
Classes are filled-up successively
Coefficients are optimized
Aj 

i
c ij  CLC i
Aj:
area of Farm Structure category j
CLCi: area of Corine class i
cij:
coefficient for redistribution of
Corine class i to FSS category j
GIM approach
SOIL MAP,
DEM
NUTS 2
allocation
algorithm
CORINE
NUTS 3
(or gridcell)
Ranking of differences FSS-CLC
→
for all classes except rough grazing
successive determination of coefficients
• Determination of the fraction of temporary pasture to be distributed
to permanent pasture:
• when CLC overestimates arable land together with an
underestimation of pasture, the possibility to allocate part of
(211+212) to (231) up to a max. of 25% is evaluated
• Distribution of complex classes
Redistribution of complex classes
recalculation of FSS and CLC by using coefficients
y
GROUP or CLASS
FSS
original CLC class
additional CLC classes
coeff.
CLC
coeff.
CLC
coeff
CLC
1 arable land without rice fields
D + G05 - I05A - I05B
- D07 - aD18
b1
211 + 212
c1
242
d1
243
2 rice fields
D07
b2
213
c2
242
d2
243
3 vineyards
G04
b3
221
c3
242
d3
243
G01 + G02
b4
222
c4
242
d4
243
5 olive groves
G03
b5
223
c5
242
d5
243
6 permanent pasture
F01 + Ad18
b6
231
c6
242
d6
243
7 rough grazing
F02
d7
243
4
fruit trees and
berry plantations
b7 or f7 231 or 321
8
combined crops;
permanent and annual
I05B + E
b8
241
c8
242
d8
243
9
combined crops;
annual and forestry
I05A
b9
244
c9
242
d9
243
Interaction of permanent pasture and rough grazing if
FSS (pasture) is bigger than CLC (pasture)
→
the coefficient “a” for allocating part of temporary pasture (D18) to
permanent pasture (F01) will be evaluated
•
•
•
if F01 < class 2.3.1.
up to a maximum value of “a” that FSSarable  CLCarable
up to a maximum value of “a” that FSSarable  CLCarable
recalculation of FSS and CLC by using coefficients
y
GROUP or CLASS
FSS
6 permanent pasture
F01 + aD18
7 rough grazing
F02
original CLC class
coeff.
b6
additional CLC classes
CLC
231
b7 or f7 231 or 321
c6(242) + d6(243) + e6(211+212) +
f6(321) + g6(322) + h6(323) +
i6(333)
d7(243) + g7(322) + h7(323) +
i7(333)
Parameters used for tuning
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→
class 2.4.2. (complex cultivation pattern)
• a minimum of 25% must be used for “pasture”
class 2.4.3. (Land principally occupied by agr. with sign.
areas of natural vegetation)
→
• a maximum of 75% can be used to match agricultural land.
• the share of natural vegetation can be used to match rough grazing
Maximum tolerable error at district-level
• the matching of FSS and CLC can be stopped if the error is <3%,
when the addition of new classes makes the distribution too
uncertain
→
class 2.1.1.
• a maximum of 25% can be used to match pasture land
Problems
Assuming pure classes
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also pure classes have a proportion of other land uses
Assumptions on coefficients and sequence arbitrary
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methodology will have to be re-checked
Geo-referencing with soil map and digital elevation model
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probabilities for aggregated classes only
Time difference between data sets
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“buy” synchrony with statistical consistency
“Solution”
Use of updated data
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“Corrected” Corine 1990 landcover map
Corine 2000 land cover map as it becomes available
Use of LUCAS to determine probabilities of a certain land use in a land cover
class
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clustering of LUCAS points
co-location problems
derive matching function between Corine and LUCAS land cover
classes
Use of physical parameters
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determine relationships between the occurrence of
•
•
disaggregated arable classes and
soil (texture, chemistry), climate (temperature, precipitation, vegetation period),
elevation (absolute elevation, slope)
Co-location problem
Gallego, 2003
Proposed approach
NUTS 2
LUCAS
SOIL MAP,
DEM,
climate
allocation
map+algorithm
CORINE
GRID
What is the target grid / map ?
Nested approach for DNDC?
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→
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5 or 10 km grid
landscape assessment:
complexity of landscape
2.5 or 1 km grid
Other maps according to the results of WP9
→Catchments ?
→homogeneous units ?
1.3·106 km2
x
1 min
=
2.5 years
CAPRI
Definition of parameters
to be disaggregated
WP8
Allocation of land use
into the land cover map
WP9
CAPRI-indicators
CAPRI-indicators
disaggregated (grid)
disaggregated (map)
WP10
Definition of target scale
and target map
WP10
WP9
Simulation of nutrient
cycling
Landscape assessment
indicators