WP 8 CAPRI GIS Link

CAPRIDynaSpat, Bonn 10.3.2003

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


LAND COVER/LAND USE MAPSdetermine mainly the “spatial resolution” of the disaggregation

LAND USE/COVER AREA FRAME STATISTICAL SURVEYcan 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

SPATIAL DATA SETS


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 1990


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 LAND COVER/LAND USE 2000


CORINE CLASSIFICATIONLevel1 Level2 Level3Artificial surfaces Urban fabric Continuous urban fabric

Discontinuous urban fabricIndustrial, com m ercial and transport units Industrial or com m ercial units

Road and rail netw orks and associated landPort areasAirports

Mine, dum p and construction sites Mineral extraction sitesDum p sitesConstruction sites

Artificial, non-agricultural vegetated areas Green urban areasSport and le isure facilities

Agricultural areas Arable land Non-irrigated arable land

Perm anently irrigated land

Rice fieldsPerm anent crops Vineyards

Fruit trees and berry plantationsOlive groves

Pastures Pastures

Heterogeneous agricultural areas Annual crops associated w ith perm anent cropsCom plex cultivation patternsLand principally occupied by agriculture, w ith significant areas of natural vegetation

Agro-forestry areasForest and sem i natural areas Forests Broad-leaved forest

Coniferous forest

Mixed forest

Scrub and/or herbaceous vegetation associations Natural grasslands

Moors and heathlandSclerophyllous vegetationTransitional w oodland-shrub

Open spaces w ith little or no vegetation Beaches, dunes, sands

Bare rocksSparsely vegetated areasBurnt areasGlaciers and perpetual snow

Wetlands Inland w etlands Inland m arshes

Peat bogsMaritim e w etlands Salt m arshes

SalinesIntertidal flats

Water bodies Inland w aters Water courses

Water bodiesMarine w aters Coastal lagoons

EstuariesSea and ocean


CORINE LC IN THE BONN AREA

CAPRI DYNASPATKICK-OFF MEETING


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

PELCOM LAND COVER/LAND USE


Level1 Level2 Level3Artificial surfaces Urban fabric Continuous urban fabric

Discontinuous urban fabricIndustrial, com m ercial and transport units Industrial or com m ercial units

Road and rail netw orks and associated landPort areasAirports

Mine, dum p and construction sites Mineral extraction sitesDum p sitesConstruction sites

Artificial, non-agricultural vegetated areas Green urban areasSport and leisure facilities

Agricultural areas Arable land Non-irrigated arable land

Perm anently irrigated land

Rice fieldsPerm anent crops Vineyards

Fruit trees and berry plantationsOlive groves

Pastures Pastures

Heterogeneous agricultural areas Annual crops associated w ith perm anent cropsCom plex cultivation patternsLand principally occupied by agriculture, w ith significant areas of natural vegetation

Agro-forestry areasForest and sem i natural areas Forests Broad-leaved forest

Coniferous forest

Mixed forest

Scrub and/or herbaceous vegetation associations Natural grasslands

Moors and heathlandSclerophyllous vegetationTransitional w oodland-shrub

Open spaces w ith little or no vegetation Beaches, dunes, sands

Bare rocksSparsely vegetated areasBurnt areasGlaciers and perpetual snow

Wetlands Inland w etlands Inland m arshes

Peat bogsMaritim e w etlands Salt m arshes

SalinesIntertidal flats

Water bodies Inland w aters Water courses

Water bodiesMarine w aters Coastal lagoons

EstuariesSea and ocean

Urban areas

Rainfed arable land

Irrigated arable land

Perm anent crops

Grassland

Deciduous forestConiferous forest

Mixed forest

Shrubland

Barren land

Perm anent Ice&Snow

Wetlands

Inland w aters

Sea

CORINE/PELCOM LC CLASSIFICATIONCORINE PELCOM


CORINE AND PELCOM LAND COVER

Rhein-Sieg-Kreis

PasturesComplex cultivation patternLand princip. occ. by agric. & sign areas of nat. veg.Not irrigated arable landForestUrban area

Grassland


LAND COVER DATA SETS AVAILABLE FOR THE CAPRI-DynaSpat AREA


COMPARISON OF CLC90 AND FARM STRUCTURE SURVEY DATA

Kayadjanian et al. (2001)

CLC90 11 agricultural classes, FSS 42 classes

RECLASSIFIED STATISTICS

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


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).

LUCAS 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”.


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)

ORGANISATION OF THE LUCAS SURVEY

Sampling design: Primary sampling units in NL


LUCAS SURVEY CLASSIFICATIONBuildings w ith 1 to 3 floors Com m on w heat Broadleaved forest Bare land

Buildings w ith m ore than 3 floors Durum Wheat Coniferous forest Inland w ater bodies

Greenhouses Barley Mixed forest Inland running w ater

Non built-up area features Rye Other broadleaved w ooded area Coastal w ater bodies

Non built-up linear features Oats Other coniferous w ooded area Wetland

Maize Other m ixed w ooded area Glaciers, perm anent snow

Rice Poplars, eucalyptus

Other cereals Shrubland w ith sparse tree cover

Potatoes Shrubland w ithout tree cover

Sugar beet Perm anent grassland w ith sparse tree/shrub cover

Other root crops Perm anent grassland w ithout tree/shrub cover

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


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

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Non-irrigated arable land 70 81 100 43 100 100 67 100 7 2 3 3 1 5 7 3 1 4 8 19Pastures 28 19 57 33 74 37 35 15 13 30 24 59 73 67 43 5 13 44 55 25Complex cultivation patterns 3 2 3 5 8 6Land princip. occ. by agric., with sign. areas of nat. veg. 5 15 15 10 3 8 8 4 6 8 5 6Coniferous forest 3 32 14 24 4 9 2 8Mixed forest 3 20 17Natural grasslands 1 1 1 3Moors and heathland 6 21 3 1 24Transitional woodland-shrub 2 6 3 8 30 27 20 11 18 17 5 2 4 3Bare rocks 2Sparsely vegetated areasInland marshes 2 1 2Peat bogs 7 28 21 55 18 8 4 9 55 5 72 5 6Salt marshes 1Water bodies 77 1Estuaries 1 43Sea and ocean 9 1Continuous urban fabric 4Discontinuous urban fabric 1 1 4 3 14 16 11 25Industrial or commercial units 1 16 5 13Green urban areas 3Sport and leisure facilities 3 3 7

Nr. of LUCAS SSU (Total 2160) 69 16 1 7 1 2 3 2 1284 178 34 40 150 37 25 27 11 6 33 7 22 126 25 38 16

LUCASC

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Test for Ireland


SPATIALISATION OF STATISTICALLAND 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


NETHERLANDS: Changes in Agricultural areas 1990 - 2000

-1200 -1000 -800 -600 -400 -200 0 200

Agricultural areas (total)

Arable land

Permanent crops

Pastures

Heterogeneous agricultural areas

km2

NETHERLANDS: Agricultural areas converted into:

0 200 400 600 800 1000

Forest and semi natural areas

Artifical surfaces

Wetlands

Water bodies

km2

IRELAND: Changes in Agricultural areas 1990 - 2000

-2000 -1500 -1000 -500 0 500 1000 1500 2000

Agricultural areas (total)

Arable land

Permanent crops

Pastures

Heterogeneous agricultural areas

km2

IRELAND: Agricultural areas converted into:

0 50 100 150 200 250 300 350

Forest and semi natural areas

Artifical surfaces

Wetlands

Water bodies

km2

94

CHANGES IN AGRICULTURAL AREABASED ON CLC90 AND CLC2000


Amsterdam

Agricultural area to Artificial surfaces

Agricultural area to Wetlands

Agricultural area to Forest & seminatural areas

LAND COVER CHANGES IN NL


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)•…

WP 8 CAPRI GIS Link

Documents

Transcript of WP 8 CAPRI GIS Link