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Area frames for land cover estimation: Improving the European LUCAS survey Javier Gallego

Area frames for land cover estimation: Improving the European LUCAS survey Javier Gallego Jacques Delincé. Sampling units are parts of a cartographic representation of a territory. Areal segments Regular shape (e.g.: square segments in MAST, Spain)

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Area frames for land cover estimation: Improving the European LUCAS survey Javier Gallego

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

  2. 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. Area Frames: reminder JRC – Ispra

  3. 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 JRC – Ispra

  4. 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 JRC – Ispra

  5. 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 JRC – Ispra

  6. Ratio of variances EU15. Largest land cover types JRC – Ispra

  7. 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? JRC – Ispra

  8. 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. JRC – Ispra

  9. 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. Stratification efficiency (1) JRC – Ispra

  10. 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. JRC – Ispra

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

  12. 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 JRC – Ispra

  13. 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 JRC – Ispra

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