Spatial Disaggregation of Land Cover and Cropping Information: Current Results and Further steps

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1 CAPRI CAPRI Spatial Disaggregation of Land Cover and Cropping Information: Current Results and Further steps Renate Koeble, Adrian Leip (Joint Research Centre) Markus Kempen (Universitaet Bonn) JRC-AL Agroscope/FAT Taennikon,

2 OUTLINE STATE OF THE ART: Methodological Approach Current Results / Error Assessment STEPS TOWARDS THE FINAL DISAGGREGATED CROP MAPS LINKED PROJECTS / DATA POLICY INSEA NITROEUROPE IP INSTITUTIONAL (disaggregation of forest area/species/biomass) REPORTS/PUBLICATIONS

3 WHY DO WE NEED TO DISAGGREGATE AGRICULTURAL STATISTICS IN SPACE?

4 WHY: (1) REDUCING UNCERTAINTY SCOPE: deriving environmental indicators from statistics and modelling results Problem: Sub-regional differences in soil type, land cover, climate and other factors are important in modulating the effect of the anthropogenic activities Assessment of the soil nutrient balance Large uncertainties are introduced be averaging of environmental parameters over administrative regions

5 simulation using minimum and maximum values for e.g. Soil Organic Carbon results in large errors for mean N 2 O emissions!!! agricultural soil N20-N emissions t N y = x R 2 = IPCC (Nfert*0.0125) IPCC low 1,000 Linear (Mean N2O-N Gg N due to n fert) 10,000 Mineral N Fertiliser application t N minus the fraction volatised NH3 and NOx Mean N2O-N Gg N due to n fert IPCC high Range Range of of N2O N2O emissions emissions simulated simulated with with minimum minimum and and maximum maximum SOC SOC values, values, ITALY, ITALY, NUTS NUTS 3 100,000

6 WHY: (2) Facilitating interpretation Facilitating interpretation for environmental policies Less-favored areas Rural development Nitrate directive National ceilings directive GHG emissions Indirect emissions Assessment of farm practices in their spatial context IRENA indicators

7 N surplus calculation using NUTS 3 census data (Farm Structure Survey 2000) N surplus calculation using NUTS 3 census data (Farm Structure Survey 2000) and an overlay overlay between the administrative areas, river basins and CORINE Land Cover Terres et al., 2002: Calculation of Agricultural Nitrogen Quantity for EU River Basins, JRC - EUR EN

8 WHAT DO WE NEED FOR SPATIAL DISAGGREGATION

9 REQUIREMENTS factors causing differences in crop growing patterns social factors (traditions, policies, ) economic factors (markets, industry, infrastructure, ) natural factors (climate conditions, soil quality, orography) spatial information available for Europe b b b b b soil map digital elevation map climate map land cover map land cover surveys

10 CORINE LAND COVER High spatial resolution (100 by 100m) Low detail of agricultural land use classes (11) Non-irrigated arable land land land cover cover classes classes arable land Permanently irrigated arable land Rice fields Vineyards Fruit trees and berry plantations permanent crop Olive grows Pastures Annual crops associated with perm. crops Complex cultivation patterns Land principally occupied by agriculture with significant areas of natural vegetation Agro-forestry areas pastures heterogeneous agricultural areas

11 The CORINE LAND COVER MAP: Elaborated based on the visual interpretation of satellite images and ancillary data (aerial photographs, topographic maps etc.) Smallest surface mapped: 25 ha. Scale of the output product 1: Disc. urban fabric Coniferous forest Arable land REAL WORLD REAL WORLD CORINE LAND COVER MAP Source: FROM LAND COVER TO LANDSCAPE DIVERSITY IN THE EUROPEAN UNION U (2000): modified.

12 Non-irrigated arable land Pastures REGION X Complex cultivation patterns Land princ. occupied by agri with sign areas of nat.veget. DISTRICT Y 95% 60% 80% Coniferous forest Artificial areas Land potentially available for wheat production: 80% 95% Region: ha District: ha 60% Result: percentage of wheat grown in region X (known) that is grown within district Y 20% of land potentially available for wheat production is in the district. with ha wheat production in the region, the methods estimates a production of ha in the district!

13 LUCAS statistical frame survey on land cover and land use Main land cover/use survey raster: 18 km by 18 km with 10 subsampling Units Phase 1: field survey at ~ 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

14 The LUCAS survey data stratifying the data by: biogeographic regions (reflecting differences in natural conditions) and country (reflecting cultural differences)

15 The CORINE LAND COVER MAP: Analysis of Gallego (2002) comparing high resolution georeferenced aerial photos with CORINE data showed spatial inconsistencies. This might lead to co-location errors when overlaying with precisely georeferenced LUCAS Points. Solution: Eliminating LUCAS points close to the border of a CORINE Land Cover class. focus on improved georeferencing for the CORINE Land Cover Map 2000 CORINE: URBAN AREA LUCAS points not considered for the analysis AERIAL PHOTOGRAPH: URBAN AREA Source: Gallego (2002): Fine scale profile of CORINE Land Cover classes with LUCAS data. In: Building Agro Environmental Indicators. EUR Report EN

16 REGIONAL LAND COVER UNITS Non-irrigated arable land continental alpine Pastures Complex cultivation patterns Land princ. occupied by agri with sign areas of nat.veget. Coniferous forest Artificial areas A regional land cover unit (RLCU) is characterised by a Corine Land Cover class within a region (NUTS 2) and within a biogeographical zone Biogeographical Biogeographicalzones: continental continental continental continental south south alpine alpine alpine alpineboreal alpine alpine pyrennees pyrennees atlantic atlantic atlantic atlanticnorth north mediterranean mediterranean boreal boreal

17 MAP BASIS FOR THE ESTIMATION FROM TOPOGRAPHY, SOIL AND CLIMATE INFORMATION

18 MAP BASIS FOR THE ESTIMATION FROM TOPOGRAPHY, SOIL AND CLIMATE INFORMATION

19 MAP BASIS FOR THE ESTIMATION FROM TOPOGRAPHY, SOIL AND CLIMATE INFORMATION

20 HOMOGENEOUS SPATIAL MAPPING UNITS SLOPE SLOPE (5 (5 classes): classes): 0º 1º 2º, 3º 4º 7º 8º Regional Regional Landcover Landcover Units Units * * Regional Regional Soil Soil Topogr.Units Topogr.Units (selected Corine Land Cover classes) = = HOMOGENEOUS HOMOGENEOUS SPATIAL SPATIAL MAPPING MAPPING UNITS UNITS (HSMUs) (HSMUs) SAND SAND (4 (4 classes): classes): 0% >0 25% >25 50% >50 100% 12

21 HOMOGENEOUS SPATIAL MAPPING UNITS ADVANTAGES: Land Cover map is maintained Cropping pattern within a unit should be uniform Highest flexibility for future re-mapping Administrative boundaries are maintained Computing resources allocated to areas with high complexity Easy-to-handle by econometric method

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