Disaggregation of CAPRI results

JRC-AL – Bonn on 10.03.2004

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

→ 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

→ 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


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


Use of a correspondence table Corine / FSS

NUTS 2 NUTS 3(or gridcell)

CORINE

allocationmatrix


VARESE PROVINCE


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

211 242 243

Lombardia 856 36 94Varese 13 0 12

Corine Land Cover Area [1000 ha]


2.2% of land potentially available in Lombardia is located in the province of Varese

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

Lombardia 813 22 56 891Varese 12 0 7 19

Land available for FSS class arable land


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

Varese 1.0% 6440Como 1.1% 7020Lecco 0.5% 3220Sondrio 0.2% 1470Milano 8.9% 56870Bergamo 6.1% 38830Brescia 18.3% 117190Pavia 14.1% 90630Lodi 7.2% 46510Cremona 18.9% 120980Mantova 23.8% 152480

Lombardia 100% 641640

Varese 2.2% 13805Como 1.5% 9745Lecco 1.0% 6566Sondrio 1.5% 9902Milano 11.0% 70337Bergamo 7.6% 48449Brescia 17.3% 110789Pavia 13.3% 85456Lodi 7.1% 45827Cremona 16.4% 104922Mantova 21.2% 135842

Lombardia 100% 641640


Non-irrigated landPermanently irrigated landComplex cultivation patternLand principally occupied by agriculture with sign. areas of natural veg.Natural grasslandPasturesAgro-forstry Fruit trees and berry plantationsRice FieldsVineyardsForestsWaterUrban

LOMBARDIA


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


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

i

iijj CLCcA 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

NUTS 2

NUTS 3(or gridcell)

CORINEallocationalgorithm

SOIL MAP,DEM


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 (211+212) to (231) up to a max. of 25% is evaluated

• Distribution of complex classes

successive determination of coefficients


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

4 fruit trees and berry plantations 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 b7 or f7 231 or 321 d7 243

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

original CLC class additional CLC classesyrecalculation of FSS and CLC by using coefficients

FSSGROUP or CLASS

Redistribution of complex classes


coeff. CLC

6 permanent pasture F01 + aD18 b6 231

7 rough grazing F02 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)

y GROUP or CLASSrecalculation of FSS and CLC by using coefficients

FSSoriginal CLC class additional CLC classes

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


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 <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

→ 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


“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)


Co-location problem

Gallego, 2003


Proposed approach

NUTS 2

GRID

CORINEallocationmap+algorithm

SOIL MAP,DEM,climate

LUCAS


What is the target grid / map ?

Nested approach for DNDC?

→ landscape assessment: complexity of landscape

→ 5 or 10 km grid

→ 2.5 or 1 km grid

1.3·106 km2 x

1 min=

2.5 years

Other maps according to the results of WP9

→Catchments ?

→homogeneous units ?


WP8Allocation of land use into the land cover map

WP9 WP10Definition of target scale and target map

CAPRIDefinition of parameters to be disaggregated

CAPRI-indicatorsdisaggregated (grid)

CAPRI-indicatorsdisaggregated (map)

WP9Landscape assessment indicators

WP10Simulation of nutrient cycling

Disaggregation of CAPRI results

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