1 JRC – Ispra

Area frames for land cover estimation: Improving the European

LUCAS survey

Javier GallegoJacques Delincé

2 JRC – Ispra

Area Frames: reminder

• Sampling units are parts of a cartographic representation of a territory. – Areal segments

• Regular shape (e.g.: square segments in MAST, Spain) • Physical boundaries: roads, rivers…(e.g.: USDA)

– Transects: Straight lines of a certain length.• Often used in environmental studies (estimation of

species abundance)

– Points. In practice they are “small” pieces of land.

3 JRC – Ispra

The sample design of LUCAS 2001-2003 (Land Use/Cover Area-frame Survey)

• Non-stratified systematic sample: clusters (PSUs) every 18 km.

• Each cluster: 10 points (SSUs) + 1 transect

4 JRC – Ispra

LUCAS two-stage variance

• Question: How much can we reduce the variance by increasing the sample in the 1500x900m PSU?– 70% to 90% of the

variance is between PSUs.

– Precise mapping of the whole PSU only reduces 10 to 30% of the variance

5 JRC – Ispra

Effect of the number of SSUs per PSU

• What would happen if we keep only one SSU instead of 10 in each PSU?

• How larger would be the variance?

iyc = proportion of land cover c in PSU i

= 0-1 variable for land cover c in PSU i – SSU k iy kc,

cV = variance for land cover c using the whole PSUs

kcV , = variance for land cover c using only SSU k

kcc VaverageV ,

c

cV

V = equivalent number of points of a PSU

6 JRC – Ispra

Ratio of variances EU15. Largest land cover types

Area *1000 ha Variance ratio

Coniferous forest 54591 2.70

Perm grass 36747 3.12

Blvd forest 28942 2.27

Mixed forest 20717 3.29

Shrub no tree 17189 1.80

Perm grass+trees 14221 2.82

Common wheat 13224 4.31

Barley 11063 4.67

Wetland 10744 2.63

Temp. pastures 10373 3.13

7 JRC – Ispra

Equivalent number of points of a PSU

• A PSU with 10 points is equivalent to approx. 3-4 unclustered points. – Are 3-4 unclustered points more expensive or

cheaper to visit than the 10 points of a LUCAS PSU?

• Recent experiences in Italy and Greece indicate that 3-4 unclustered points are cheaper.

• An additional question:– Is stratification more efficient when applied to

unclustered points?

8 JRC – Ispra

Stratification

• A reason for non-stratified sampling: – We are looking at all the land cover types, not

only agriculture.

• Reasons for stratified sampling – Arable land must be visited every year. Other

land cover types can be visited every 5 years– The precision requirements for annual crops

are more restrictive than for other land cover types.

9 JRC – Ispra

Stratification efficiency (1)

• Simulation on LUCAS 2001 data. – 9800 LUCAS PSUs are seen as first-phase sample– 4 strata by “simulated photointerpretation”:

• Arable land, permanent crops, pastures, non agrigultural. • Photointerpretation simulated by adding noise to ground

data.

– Stratification by PSUs: each PSU is attributed to the stratum corresponding to the most frequent class in photo-interpretation.

– Stratification by unclustered points: • only one point per PSU is kept. • The photo-interpreted class determines directly the stratum.

10 JRC – Ispra

Stratification efficiency (2)

• Simulation with different photo-interpretation accuracy levels: – Perfect photo-interpretation (=ground observation)– Photo-interpretation with errors estimated from the

2004 experience in Greece.

11 JRC – Ispra

Stratification efficiency (3)

• Stratification efficiency computed comparing the estimated variances with a modified Matern estimator.

12 JRC – Ispra

Conclusions (1)

• For most land cover types, 70%-90% of the variance comes the variability between PSUs– Small improvement by increasing the number of

points in the PSU or mapping the whole PSU.

• Regarding the variance, the current 10 points of a PSU are equivalent to 3-4 unclustered points– Experiences in Italy and Greece suggest that the cost

of 3-4 unclustered points is cheaper to visit than the current cluster of 10 points

13 JRC – Ispra

Conclusions (2)

• Given the priorities of the EU, a possible yearly LUCAS survey should focus on annual crops. – Stratification recommended

• Stratification by photo-interpretation of a large pre-sample of points on ortho-photographs gives better efficiency than previously tested approaches in Europe (2-4).

• Stratification of unclustered points is expected to give an additional reduction of variance with a factor between 1.1 and 1.5