Copyright Eurostat 2011 LUCAS 2009 Survey. copyright Eurostat 2011 Coverage 2008/2009 Member...

copyright Eurostat 2011

LUCAS 2009 Survey


Coverage 2008/2009Member States Sampling size

Member States Sampling size

Austria 4,969 Latvia 3,827

Belgium 1,808 Lithuania 3,864

Bulgaria * 8,100 Luxembourg 153

Czech Republic 4,674 Malta ** 80

Denmark 2,554 Netherlands 2,461

Estonia 2,680 Poland 18,530

Finland 19,946 Portugal 5,426

France 32,417 Romania * 20,600

Germany 21,157 Slovakia 2,895

Greece 7,819 Slovenia 1,201

Hungary 5,513 Spain 29,917

Ireland 4,165 Sweden 26,665

Italy 17,851 United Kingdom 14,508

Total 263,780

*Survey took place in 2008

** Survey financed with national funds


Informative contentParameters:•Land cover (1 & 2)•Land use (1 & 2)•Land Cover percentage•Area size•Height of trees•Width of features•Transect (250 m to east) with Land Cover and linear features•Land management (grazing)•Water management

WestEast

North

South

SOIL SAMPLE


LUCAS 2008/2009: Nomenclature

Fully hierarchical (3 levels)

Land cover and land use clearly distinguished

Comparable with other main international nomenclatures (e.g. FFS agriculture, FAO, forestry)

– Some new parameters introduced

– Forest types have been introduced for forestry areas, in line with the EUNIS classification on forests (http://eunis.eea.europa.eu/about.jsp)

http://eunis.eea.europa.eu/about.jsp

http://eunis.eea.europa.eu/about.jsp


A10 Built-up areas

A20 Artificial non built-up areas

B10 Cereals (+ triticale)

B20 Root crops

B30 Non permanent industrial crops

B40 Dry pulses, vegetables and flowers

B50 Fodder crops

B70 Fruit trees & berries

B80 Other Permanent Crops

C10 Broadleaved and evergreen woodland

C20 Coniferous woodland

C30 Mixed woodland

D10 Shrubland with sparse tree cover

D20 Shrubland without tree cover

E10 Grassland with sparse tree/shrub cover

E20 Grassland without tree cover

E30 Spontaneous vegetation

F00 Bare Land

G10 Inland water bodies

G20 Inland running water

G30 Coastal water bodies

G50 Glacier, permanent snow

H10 Inland wetlands

H20 Coastal wetlands

Nomenclature LUCAS 2008/09: Land Cover


U110 Agriculture ( + Kitchen garden + Fallow land)

U120 Forestry

U130 Fishing

U140 Mining, Quarrying

U150 Hunting

U210 Energy production

U220 Industry & Manufacturing

U310 Transport, communication, …

U320 Water & waste treatment

U330 Construction

U340 Commerce, Finance, Business

U350 Community Services

U360 Recreation, Leisure, Sport

U370 Residential

U400 Unused

Nomenclature LUCAS 2008/09: Land Use


Sampling strategy: sampling design

1 100 000 points

LAND COVER classes1 ARABLE LAND2 PERMANENT CROPS 3 GRASSLAND4 WOODED AREAS AND SHRUBLAND5 BARE LAND, RARE VEGET.6 ARTIFICIAL LAND7 WATER

First phase sample for stratification: orthophoto interpretation

2km squared grid

Ground survey

Parameters•Land cover•Land use•pictures•etc.

Sample of around 260,000 pts

Second phase sample: in-situ data collection


Aims : obtaining a reasonable balance among the strata (the

agricultural strata were over-represented in the previous rounds of the survey) since the focus of the survey changed from a merely agricultural to a broadly agro-environmental one;

providing reliable estimates at a geographical level more detailed than the EU one for the most relevant land cover classes in the various countries;

gathering longitudinal data on land cover and land use to monitor their changes (for the countries involved in the 2006 round).

Sampling design :


Sampling design: expected errors at NUTS2 level

LAND COVER CLASSES UPPER BOUND EXP. ERRORS

Cereals 15%

Root crops, Vegetables, floricolture, ornamental plants 25%

Fibre and oleaginous crops, non permanent industrial crops 25%

Fodder and temporary grassland 25%

Permanent crops and nursery 25%

Grassland 7.5%

Broadleaved woodland 20%

Coniferous woodland 20%

Mixed woodland 20%

Shrubland 20%

Bare land 20%

Artificial areas 15%

Water 20%


Selection of points: criteria– LUCAS 2006 sample points are included as

much as possible (panel approach)– Points above 1000m and very difficult to access

are excluded– Strategy to reduce autocorrelation among points

Sampling drawing:


Points selection strategy: recourse to replicates

9 23 66 44 68 10 48 16 42 76 23 66 44 68 10 48 16 42 76

8 71 12 69 25 51 29 60 37 7 71 12 69 25 51 29 60 37 7

7 20 79 18 72 78 3 31 63 70 20 79 18 72 78 3 31 63 70

6 33 11 80 11 59 32 38 9 64 33 1 80 11 59 32 38 9 64

5 28 40 26 49 55 17 53 50 77 28 40 26 49 55 17 53 50 77

4 45 27 41 67 6 65 15 73 5 45 27 41 67 6 65 15 73 5

3 35 39 13 36 62 21 57 24 47 35 39 13 36 62 21 57 24 47

2 8 58 74 46 14 75 2 56 34 8 58 74 46 14 75 2 56 34

1 43 30 4 54 61 19 81 22 52 43 30 4 54 61 19 81 22 52

9 23 66 44 68 10 48 16 42 76 23 66 44 68 10 48 16 42 76

8 71 12 69 25 51 29 60 37 7 71 12 69 25 51 29 60 37 7

7 20 79 18 72 78 3 31 63 70 20 79 18 72 78 3 31 63 70

6 33 11 80 11 59 32 38 9 64 33 11 80 11 59 32 38 9 64

5 28 40 26 49 55 17 53 50 77 28 40 26 49 55 17 53 50 77

4 45 27 41 67 6 65 15 73 5 45 27 41 67 6 65 15 73 5

3 35 39 13 36 62 21 57 24 47 35 39 13 36 62 21 57 24 47

2 8 58 74 46 14 75 2 56 34 8 58 74 46 14 75 2 56 34

1 43 30 4 54 61 19 81 22 52 43 30 4 54 61 19 81 22 52

Row in block

1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9

Column in block →

To reduce the autocorrelation within and between strata:

1. GRID (2km*2km) is divided into squared blocks of 81 points each

2. Pts coordinates are translated in relative position in the block

3. numbering of the replicates is done under the constraint that the distance with the previous one is maximized

4. Replicates are then selected successively in each stratum (starting with replicate 1) until the required sample size by domain is reached. From the replicate with the highest number, points are randomly selected


Quality assurance - Actions put in place:

Different actors/levels of control

Several training steps

Continuous monitoring

Independent data quality check

Standardisation and computerisation of the main phases of data management


External quality check: main results

Main conclusion of the external quality check:

the overall quality of the data is very good since only 4.5% had serious measurement errors;

Main sources of error:• Mistakes in classification of the transect• Mistakes in attribution of land cover and land use;• photos were not always taken in a proper way.

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