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Agricultural Policy – The Dynamic and Spatial Dimension
(CAPRI-DynaSpat)
WP 8 CAPRI GIS Link
Relevant spatial datasets for the disaggregation
of CAPRI-DynaSpat parameters
Description - Use - Constraints
Renate Köble and Adrian Leip
SPATIAL DATA SETS
LAND COVER/LAND USE MAPS
 determine mainly the “spatial resolution” of the disaggregation
LAND USE/COVER AREA FRAME STATISTICAL SURVEY
 can be used to create a decision matrix how to allocate the statistical agricultural
activity data and model outputs to the land cover classes
SPATIAL DATA ON ELEVATION, BIOGEOGRAPHICAL REGIONS
AND SOIL
 deliver additional information to allocate statistical agricultural activity data and
model outputs especially for complex land cover classes
CORINE LAND COVER/LAND USE 1990
 CORINE (Coordination of Information on the Environment) land cover
mapping program was proposed in 1985 by the EU Commission to satisfy the
need of precise and easy accessible information on land cover in Europe
 CLC describes land cover (and partly land use) according to a nomenclature
of 44 classes organised hierarchically in 3 levels
 Elaborated based on the visual interpretation of satellite images and ancillary
data (aerial photographs, topographic maps etc.)
 Acquisition period of satellite images 1985 to 1995
 Smallest surface mapped: 25 ha. Scale of the output product 1:100 000
 The 100 m2 grid data set is available for the CAPRI-DynaSpat area of interest
except Sweden
 For Switzerland a national land cover map is available with classes
corresponding to Level II of the CORINE classification system
CORINE LAND COVER/LAND USE 2000
 An update of the CORINE Land cover database for the year 2000 is under
processing
 The update will be more time consistent (satellite images from 2000 +/-1year)
 Improvement of the geometric accuracy
 CORINE LC90 will be revised (land cover classes and geometry will be
reviewed)
 Maps with land cover changes from “1990” to 2000 will be produced
 Currently data is available for Ireland, Netherlands, Latvia, Luxembourg and
Malta
 Data for Lithuania, Poland, Spain, Sweden, Italy might be available before
summer
 the aim is to finish 80% of EU25 (+ Bulgaria, Croatia, Romania) by the end
of 2004
CORINE CLASSIFICATION
Level1
Level2
Level3
Artificial surfaces
Urban fabric
Continuous urban fabric
Industrial, com m ercial and transport units
Discontinuous urban fabric
Industrial or com m ercial units
Mine, dum p and construction sites
Artificial, non-agricultural vegetated areas
Agricultural areas
Arable land
Road and rail netw orks and associated land
Port areas
Airports
Mineral extraction sites
Dum p sites
Construction sites
Green urban areas
Sport and leisure facilities
Non-irrigated arable land
Perm anently irrigated land
Perm anent crops
Rice fields
Vineyards
Pastures
Fruit trees and berry plantations
Olive groves
Pastures
Heterogeneous agricultural areas
Forest and sem i natural areas Forests
Annual crops associated w ith perm anent crops
Com plex cultivation patterns
Land principally occupied by agriculture, w ith
significant areas of natural vegetation
Agro-forestry areas
Broad-leaved forest
Coniferous forest
Mixed forest
Scrub and/or herbaceous vegetation associations
Natural grasslands
Open spaces w ith little or no vegetation
Moors and heathland
Sclerophyllous vegetation
Transitional w oodland-shrub
Beaches, dunes, sands
Bare rocks
Sparsely vegetated areas
Burnt areas
Glaciers and perpetual snow
Wetlands
Inland w etlands
Maritim e w etlands
Water bodies
Inland w aters
Marine w aters
Inland m arshes
Peat bogs
Salt m arshes
Salines
Intertidal flats
Water courses
Water bodies
Coastal lagoons
Estuaries
Sea and ocean
CORINE LC IN THE BONN AREA
CAPRI DYNASPAT
KICK-OFF MEETING
PELCOM LAND COVER/LAND USE
 The Pan-European Land Cover Monitoring (PELCOM) project was carried
out 1996-99. Funded as a shared cost action within FP4.
 The PELCOM land cover map distinguishes 14 land cover classes (4
agricultural classes)
 Classification is based on 1km resolution satellite images (NOAA AVHRR)
and ancillary data as e.g. topographic information, rivers/lakes/coastlines
 Acquisition period of satellite images 1997
 Covers Europe and parts of Russia and the Middle East
CORINE/PELCOM LC CLASSIFICATION
CORINE
Level1
Artificial surfaces
PELCOM
Level2
Urban fabric
Level3
Continuous urban fabric
Industrial, com m ercial and transport units
Discontinuous urban fabric
Industrial or com m ercial units
Road and rail netw orks and associated land
Port areas
Airports
Mineral extraction sites
Dum p sites
Construction sites
Green urban areas
Sport and leisure facilities
Non-irrigated arable land
Rainfed arable land
Perm anently irrigated land
Irrigated arable land
Perm anent crops
Rice fields
Vineyards
Perm anent crops
Pastures
Fruit trees and berry plantations
Olive groves
Pastures
Grassland
Annual crops associated w ith perm anent crops
Com plex cultivation patterns
Land principally occupied by agriculture, w ith
significant areas of natural vegetation
Agro-forestry areas
Broad-leaved forest
Coniferous forest
Deciduous forest
Coniferous forest
Mine, dum p and construction sites
Artificial, non-agricultural vegetated areas
Agricultural areas
Arable land
Heterogeneous agricultural areas
Forest and sem i natural areas Forests
Wetlands
Mixed forest
Mixed forest
Scrub and/or herbaceous vegetation associations
Natural grasslands
Shrubland
Open spaces w ith little or no vegetation
Moors and heathland
Sclerophyllous vegetation
Transitional w oodland-shrub
Beaches, dunes, sands
Barren land
Bare rocks
Sparsely vegetated areas
Burnt areas
Glaciers and perpetual snow
Perm anent Ice&Snow
Inland m arshes
Wetlands
Peat bogs
Salt m arshes
Salines
Intertidal flats
Water courses
Inland w aters
Inland w etlands
Maritim e w etlands
Water bodies
Urban areas
Inland w aters
Marine w aters
Water bodies
Coastal lagoons
Estuaries
Sea and ocean
Sea
CORINE AND PELCOM LAND COVER
Pastures
Complex cultivation pattern
Land princip. occ. by agric. & sign areas of
nat. veg.
Not irrigated arable land
Forest
Urban area
Rhein-Sieg-Kreis
Grassland
LAND COVER DATA SETS AVAILABLE
FOR THE CAPRI-DynaSpat AREA
COMPARISON OF
CLC90 AND FARM STRUCTURE SURVEY DATA
RECLASSIFIED STATISTICS
CLC90 11 agricultural classes, FSS 42 classes
Kayadjanian et al. (2001)
LANDCOVER MAP
THE POSSIBLE REASONS FOR THE DEVIATIONS
ARE MANYFOLD
Data is related to different time spans (FSS 1990, CLC 1985-95)
Per definition CLC omits areas <25 ha, therefore non irrigated arable land
may be included to some extend also in other CLC classes as e.g. “Complex
cultivation patterns with significant area of natural vegetation” or
“Grassland”
FSS classes can not be exactly regrouped in the CLC classes due to different
classification systems (e.g. within irrigated land)
Photo-interpretation inaccuracy for CLC
Errors in the FSS
LUCAS SURVEY
The Land Use/Cover Area Frame Statistical Survey (LUCAS) has been
launched by Eurostat and DG Agri* to:
 obtain harmonised data (unbiased estimates) at EU 15 level of the main
Land Use / Cover areas and changes.
 evaluate the strengths and weaknesses of a point area frame survey as one
of the pillars of the future Agriculture Statistical System (area frame means
that the observation units are territorial subdivisions instead of agricultural
holdings as in the Farm Structure Survey).
*Decision N°1445/2000/EC of the European parliament and of the Council of the 22.05.2000 “on the application of area-frame survey
and remote-sensing techniques to the agricultural statistics for 1999 to 2003”.
ORGANISATION OF THE LUCAS SURVEY
 Main land cover/use survey raster: 18 km by 18 km with 10 subsampling
Units
 Phase 1: field survey at ~100000 observation points in EU15 (spring)
 Phase 2: interview with ~5000 farmers to obtain additional technical or
environmental information (autumn)
 The first survey has been carried out in 2001 (UK 2002)
 57 land cover classes are separated including 34 agricultural classes
 High geometrical accuracy of the sampling locations (+/- 3m)
Sampling design:
Primary sampling units in NL
LUCAS SURVEY CLASSIFICATION
Buildings w ith 1 to 3 floors
Buildings w ith m ore than 3 floors
Greenhouses
Non built-up area features
Non built-up linear features
Com m on w heat
Durum Wheat
Barley
Rye
Oats
Maize
Rice
Other cereals
Potatoes
Sugar beet
Other root crops
Sunflow er
Rape seeds
Soya
Cotton
Other fibre and oleaginous crops
Tobacco
Other non perm anent industrial crops
Dry pulses
Tom atoes
Other fresh vegetables
Floriculture and ornam ental plants
Tem porary, artificial pastures
Fallow land
Apple fruit
Pear fruit
Cherry fruit
Nuts trees
Other fruit trees and berries
Oranges
Other citrus fruit
Olive groves
Vineyards
Nurseries
Perm anent industrial crops
Broadleaved forest
Coniferous forest
Mixed forest
Other broadleaved w ooded area
Other coniferous w ooded area
Other m ixed w ooded area
Poplars, eucalyptus
Shrubland w ith sparse tree cover
Shrubland w ithout tree cover
Perm anent grassland w ith sparse tree/shrub cover
Perm anent grassland w ithout tree/shrub cover
Bare land
Inland w ater bodies
Inland running w ater
Coastal w ater bodies
Wetland
Glaciers, perm anent snow
FINE SCALING CORINE LC CLASSES WITH
LUCAS DATA
Based on a study from J. Gallego (2002)
 Fine scaling in this case means estimating the proportion of other land
cover classes within a given CORINE class as e.g. “pastures”
 To examine the possibility of fine scaling the CLC classes J. Gallego
overlaid the CLC with the point observation of the LUCAS 2001
survey
 The operation produces a matrix with 56 columns (LUCAS land cover
classes) and 44 rows (CLC) that allows to analyse the composition of
other land cover classes within a specific CLC land cover class
FINE SCALING CLC 2000 WITH LUCAS DATA
CORINE
Nr. of LUCAS SSU (Total 2160)
69 16
7
74
2
5
1
2
7
1
6 21
6 3
2
3
8
2
28 21 55
1 5
13 30
5
3 8
32 14
3
1
1
30 27
1
18
8
7
24 59 73 67
8
24
20
4
4
9
Buildings with 1 to 3 floors
3
24
20 11 18 17
4
9
5
55
5
77
2
2
72
1
1
4
3
5
6
43
9
4
4
1
1
1 4 8 19
13 44 55 25
8
6
6 8 5 6
2
8
17
1
1
Non built-up linear features
5
Non built-up area features
43
3
Wetland
Bare land
Other mixed wooded area
Other coniferous wooded area
Other broadleaved wooded area
Mixed forest
Broadleaved forest
Coniferous forest
Shrubland without tree cover
Shrubland with sparse tree cover
2 3 3
37 35 15
3
15 15 10
3
Inland water bodies
70 81 100 43 100 100 67 100
28 19
57
33
3
Inland running water
Non-irrigated arable land
Pastures
Complex cultivation patterns
Land princip. occ. by agric., with sign. areas of nat. veg.
Coniferous forest
Mixed forest
Natural grasslands
Moors and heathland
Transitional woodland-shrub
Bare rocks
Sparsely vegetated areas
Inland marshes
Peat bogs
Salt marshes
Water bodies
Estuaries
Sea and ocean
Continuous urban fabric
Discontinuous urban fabric
Industrial or commercial units
Green urban areas
Sport and leisure facilities
Permanent grassland with sparse tree/shrub cover
Fallow land
Sugar beet
Potatoes
Oats
Maize
Dry pulses
Common wheat
Barley
Test for Ireland
Permanent grassland without tree/shrub cover
LUCAS
1
3 14
16 11 25
16 5 13
3
3
1
7
1
2
3
3
7
2 1284 178 34 40 150 37 25 27 11
6 33
7 22 126 25 38 16
SPATIALISATION OF STATISTICAL
LAND USE WITHIN ONE LAND COVER CLASS
 A study of the Geographical Information Management (G.I.M, 2002)
group showed to possible value of using topographical (elevation, slope)
and soil information to disaggregate CLC land cover classes with
complex patterns into single “classes” .
 Example: CLC class ‘complex cultivation patterns’ contains 30% arable
land, 40% pasture, 30% forest (based on CLC/LUCAS analysis).
Roughly speaken: arable land will be attributed to the best
growing/farming conditions -> good soils / low altitudes / flat terrain
 The G.I.M method will be reviewed
 Analysis if the assumptions can be improved by looking at relationships
between LUCAS data – soil – topography
CHANGES IN AGRICULTURAL AREA
BASED ON CLC90 AND CLC2000
NETHERLANDS: Changes in Agricultural areas 1990 - 2000
-1200 -1000
-800
-600
-400
-200
0
NETHERLANDS: Agricultural areas converted into:
200 km2
0
Agricultural areas (total)
Forest and semi natural areas
Arable land
Artifical surfaces
Permanent crops
Wetlands
200
400
600
800
1000 km2
Water bodies
Pastures
Heterogeneous agricultural areas
IRELAND: Changes in Agricultural areas 1990
94 - 2000
-2000 -1500 -1000 -500
Agricultural areas (total)
Arable land
0
IRELAND: Agricultural areas converted into:
0
500 1000 1500 2000 km2
Forest and semi natural areas
Artifical surfaces
Wetlands
Permanent crops
Water bodies
Pastures
Heterogeneous agricultural areas
50
100 150 200 250 300 350 km2
LAND COVER CHANGES IN NL
Agricultural area to Artificial surfaces
Amsterdam
Agricultural area to Wetlands
Agricultural area to Forest & seminatural areas
INFRASTRUCTURE FOR SPATIAL DATA IN EUROPE
(INSPIRE)
 With the INSPIRE initative, the European Commission intends to trigger
the creation of a European Spatial Data Infrastructure (ESDI)
 The ESDI has to be set up in a way that will allow public users at
European to local level to discover, access and acquire spatial data from a
wide range of sources for a wide range of applications
 INSPIRE expert groups has been set up for several topics e.g.
•Reference data and metadata
•Data policy and legal issues
•Architecture and standards (reference system, projections, European
reference grid system)
•…