1. 1 JRC – Ispra Area frames for land cover estimation: Improving the European LUCAS survey Javier Gallego Jacques Delincé

2. 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. 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. 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. 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? = proportion of land cover c in PSU i = 0-1 variable for land cover c in PSU i – SSU k = variance for land cover c using the whole PSUs = variance for land cover c using only SSU k = equivalent number of points of a PSU

6. 6 JRC – Ispra Ratio of variances EU15. Largest land cover types Area *1000 ha Variance ratio Coniferous forest545912.70 Perm grass367473.12 Blvd forest289422.27 Mixed forest207173.29 Shrub no tree171891.80 Perm grass+trees142212.82 Common wheat132244.31 Barley110634.67 Wetland107442.63 Temp. pastures103733.13

7. 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. 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. 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. 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. 11 JRC – Ispra Stratification efficiency (3) Stratification efficiency computed comparing the estimated variances with a modified Matern estimator.

12. 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. 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