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

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

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

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

5. 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.

6. 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)

7. 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);

8. 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]

9. Sample design /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

10. Elevation classes

11. Stratification

12. 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

13. 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.

14. 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

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

16. 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

17. 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

18. 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

20. 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

21. Thank you for your attention!