Disaggregation of CAPRI results

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Disaggregation of CAPRI results

• Renate Köble
• Adrian Leip

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Outline

• 1. Introduction
• 2. Existing approaches
• 3. Tentative solution of the problems
• 4. Overview on available data sets
• Corine Landcover 2000
• LUCAS survey
• 7. First example

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WP8 - CAPRI-GIS link

• Link of CAPRI to GIS covering soil, climatic and
  land use maps
• Distribution of agricultural activities on the
  land
• Correspondence between geo-coded data and
  geo-referenced data from land cover maps
• Distribution of secondary agricultural parameters
• livestock densities, feed composition
• animal wastes (grazing / stabling / application)?
• mineral fertilizer
• other parameters?
• Re-mapping for the calculation of indicators
• grid size / thematic maps
• Allocation of land use changes in the reference
  year
• net / gross changes ?
• use of additional information

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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
• used by Declan to disaggregate NUTS 2 NewCronos
  data to NUTS 3 level for the DNDC model

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Use of a correspondence table Corine / FSS
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VARESE PROVINCE
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• Land to be redistributed (FSS 2000 - arable land
  (D except rice))
• Lombardia 641 640 ha arable land
• Varese 6 440 ha arable land
• Land available (Corine Land Cover 90)
• class 211 non-irrigated arable land
• class 242 complex cultivation pattern
• class 243 land principally occupied by
  agriculture

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• x distribution factor for potentially available
  land
• non-irrigated arable land 0.95
• complex cultivation pattern 0.80
• land principally occupied by agriculture 0.60

2.2 of land potentially available in Lombardia
is located in the province of Varese
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• Disaggregation FSS2000 data
• 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

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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-forst
ry Fruit trees and berry plantations Rice
Fields Vineyards Forests Water Urban
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Problems?

• Assumption of equal land use of land classes
  throughout Europe
• 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

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Studies to built on ... (2)

• Spatial redistribution of statistical data from
  the Farm Structure Survey (GIM report)
• 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 area of Farm Structure category
j CLCi area of Corine class i cij coefficient
for redistribution of Corine class i to FSS
category j
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GIM approach
SOIL MAP,DEM
NUTS 2
CORINE
allocationalgorithm
NUTS 3 (or gridcell)
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Ranking of differences FSS-CLC

• for all classes except rough grazing
• 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 (211212) to (231) up to a max.
  of 25 is evaluated
• Distribution of complex classes

successive determination of coefficients
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Redistribution of complex classes
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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 lt class 2.3.1.
• up to a maximum value of a that FSSarable ?
  CLCarable
• up to a maximum value of a that FSSarable ?
  CLCarable

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Parameters used for tuning

• 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 lt3, 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

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Problems

• Assuming pure classes
• also pure classes have a proportion of other land
  uses
• Assumptions on coefficients and sequence
  arbitrary
• methodology will have to be re-checked
• Geo-referencing with soil map and digital
  elevation model
• probabilities for aggregated classes only
• Time difference between data sets
• buy synchrony with statistical consistency

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Solution

• Use of updated data
• 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
• clustering of LUCAS points
• co-location problems
• derive matching function between Corine and LUCAS
  land cover classes
• Use of physical parameters
• determine relationships between the occurrence of
  
• disaggregated arable classes and
• soil (texture, chemistry), climate (temperature,
  precipitation, vegetation period), elevation
  (absolute elevation, slope)

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Co-location problem
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Proposed approach
SOIL MAP,DEM, climate
LUCAS
NUTS 2
CORINE
allocationmapalgorithm
GRID
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What is the target grid / map ?
1.3106 km2 x 1 min 2.5 years

• Nested approach for DNDC?

• Other maps according to the results of WP9
• Catchments ?
• homogeneous units ?

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CAPRI Definition of parameters to be disaggregated
WP8 Allocation of land use into the land cover map
CAPRI-indicators disaggregated (grid)
CAPRI-indicators disaggregated (map)
WP9 WP10 Definition of target scale
and target map
WP9 Landscape assessment indicators
WP10 Simulation of nutrient cycling