Working Group Land Cover and Land Use Statistics 13.03.2018 LUCAS 2018: Sample design Working Group Land Cover and Land Use Statistics 13.03.2018

Preparatory activities Grassland Protocol & Species Updated stratification of Master File DMT Sample Q2 2016 Q4

Requirements All EU coverage Field - Office PI Accessibility (revised) Propensity to change LC New elements Comparability in time Solid estimation at lowest regional.

Reachability / π [change] Data Treatment List “Cleaning” LC Model Indexes Reachability / π [change]

The model to estimate Land Cover Where P(Land Cover) is the probability to observe the Land Cover With this model we were able to add 16 new variables, representing the possibility that in each point can be observed the estimated LC. Note: it is possible to have more than one LC estimated.

Index of reachability Variables used: absolute difference in elevation [point and nearest road] distance to nearest point on a road angle to nearest point in a road Surveyors comments on accessibility Note The points: Previously (EXANTEPI=TRUE); Stratification variable; With difficult access comment, … were automatically associated with 1 (no access)

Probability of changes P(change) The model was estimated considered a dependent variable equals to 0 for all the points observed in at least 2 LUCAS surveys; this variables assumes the value 1 if: the land cover in LUCAS 2015 was the same as the one observed in LUCAS 2012; the land cover in LUCAS 2015 was the same as observed in LUCAS 2009 (and the point was not observed in 2012); the land cover in LUCAS 2012 was the same as observed in LUCAS 2009 (and the point was not observed in 2015);

Sample design 1/2 16 Target variables [TV] For each Master point π [presence of TV] Stratification [nuts2, CLC12, elevation, STR 18] CENSUS points [soil + grassland]

Sample design 2/2 Optimisation and allocation of the units in the strata – iterative Calculation of expected cv [mean of 100 sample replications] Sample selection and attribution to field or office

Elevation classes

Stratification 2018

CV mean: an example Country Roofed built-up areas Cereals Root, non permanent industrial crops, dry pulses, etc. Permanent crop Shrubland without tree cover AT -0,03 -0,09 -0,11 -0,01 BE 0,05 -0,07 0,00 0,15 BG 0,11 0,02 0,01 CZ -0,06 -0,08 DE 0,06 -0,10 DK 0,26 EE -0,02 -0,04 -0,05 EL -0,13 -0,20 ES FI 0,36 0,24 FR -0,12 HR 0,07 0,08 HU 0,18 IE 0,03 IT -0,18 LT LU LV NL PL PT RO 0,12 0,09 SE SI SK UK 0,04 0,20

Remarks Sampling errors and the generated sample size are given under the condition that the statistical model is adequate. Sample size at level of country has been fixed with the same procedure in 2015 and 2018 surveys, Allocation in strata: proportionally in 2015 optimisation algorithm in 2018. Selection : systematic in 2015 SRS in 2018 In both surveys sample size is not sufficient to guarantee the desired precision at territorial level NUTS2.

The longitudinal structure survey occasions count % 1 177658 52,6% 2 60858 18,0% 3 22154 6,6% 4 77209 22,9% total 337879 100,0% sample   longitudinal 2015 2018 count yes 119229 35,2% 35,3% no 218650 219145 total 2018 337879 total 2015 338374

Differences 2015 and 2018 surveys Elegibility and use of PI points Stratification of the sample Sample size and the allocation of the sampling units

New perspective In 2018 survey all points suitable for selection First selection, then “mode” of data collection From eligibility in List to modality PI where: impossible, dangerous, too expensive or low probability to change No Master partition For each point a reachability and propensity to change indexes After sample selection, to assign the points as PI ex ante or “in field”, given the constraint of PI quotas in each country fixed by the contracts. Probability to change calculated for land cover not for land use

Sample stratification In 2015: NUTS2 and STR05 combination; Number of strata is fixed ex ante In 2018: Strata through iterative optimization algorithm from cartesian product of STR18, CLC and ELEV To aggregation minimizing the coefficient of variations of the 16 target variables with respect to the desired sampling errors, in each NUTS2 . not fixed combination it depends on the most correlated combinations of stratification with target variables; the stratification “criteria” vary according to the specificity of the NUTS2/country

Actual stratification (a) Hypothetical stratification (b) ratio :   strata number in country Actual stratification (a) Hypothetical stratification (b) ratio : (a)/(b) AT 558 70 8,0 BE 400 82 4,9 BG 586 48 12,2 CZ 526 63 8,3 DE 1930 294 6,6 DK 406 40 10,2 EE 147 8 18,4 EL 1374 104 13,2 ES 2172 128 17,0 FI 372 39 9,5 FR 1920 176 10,9 HR 236 16 14,8 HU 568 55 10,3 IE 251 15,7 IT 1967 167 11,8 LT 189 23,6 LU 6,9 LV 171 21,4 NL 418 89 4,7 PL 1279 10,0 PT 725 18,1 RO 841 64 13,1 SE 657 62 10,6 SI 152 15 10,1 SK 284 32 8,9 UK 1776 6,3 total 19962 2060 9,7 number of strata according to the 2018 actual stratification and the hypothetical one obtained using the 2015 criteria

Land cover 2018 survey- desired accuracy at NUTS2 LC Relative error A10 ROOFED BUILT-UP AREAS 15% A20 ARTIFICIAL NON-BUILT UP AREAS B10 CEREALS B2-B5 ROOT, NON-PERMANENT INDUSTRIAL CROPS, DRY PULSES, VEGETABLES AND FLOWERS, FODDER CROPS 20% B7-B8 PERMANENT CROP C10 BROADLEAVED 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/SHRUB COVER E30 SPONTANEOUSLY RE-VEGETATED SURFACES F00 BARE LAND AND LICHENS/MOSS G00 WATER AREAS H00 WETLANDS land cover class relative error A 0,15 B B1 B2 0,25 B3 B5 B7 C C1 0,2 C2 C3 D E 0,075 F G

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