LUCAS 2006 J. Gallego, MARS AGRI4CAST

Sampling scheme Adaptation of the Italian AGRIT First phase: Systematic sampling of unclustered points (single stage) A master or first phase sample (pre-sample): One point every 2x2 km Stratification by quick photo-interpretation Stratified sub-sampling

Some sample details First phase: ~990,000 points photo-interpreted in EU23* EU23 = EU25 – Cyprus and Malta Second phase: ~265,000 points subsampled for ground visit Actual survey: 11 countries (organizational and economic constraints): ~ 169,000 points visited Excluded: >1200 m altitude and most islands Common projection (Lambert Azimuthal Equal Area)

Area covered

Two-phase systematic sample Subsampling tuned to minimise spatial auto- correlation Final sample

Stratification by photo-interpretation Strata 1 Arable land 2 Permanent Crops 3 Grassland 4 Wooded areas and shrubland 5 Bare land, rare vegetation 6 Artificial Land 7 Water Made in still used. To be renewed Heterogeneous imagery. In many cases it could not be used for field documents. Likely source of location errors. ~80% aerial ortho-photo, ~20% Image 2000 (Landsat ETM+, Panchro+Multispectral) Subsampling with different rates for each stratum 40-50% in agricultural strata, ~10% in the rest (some adaptations per country)

Stratification accuracy The agreement is not excellent. Weighted Kappa = 0.66 Bias for some classes (e.g.: Arable) if photo-interpretation is used for estimation

Stratified pre-sample

Second phase sample Square sampling blocks (9x9 points in this case) A replicate is the set of points in the same relative position in the sampling blocks Replicates are numbered at random, but maximising the distance between the first replicates

Second phase sampling

Subsampling different number of replicates per stratum Arable, permanent crops and grassland: 40replicates/81 Other strata: 8 replicates/81

In-situ data collection The improvement of location accuracy with GPS has been a fundamental reason to move from segments to points. Still the image is necessary for the field work and should be prioritary if disagreeement But the image should be the one used for stratification Ground survey Parameters Land cover Land use Transect, etc.

Landscape pictures from each point: 4 landscape pictures, Point location Crop detail

Variance estimation Usual variance estimator for two-phase random sampling (incomplete stratification) The estimated variance of Y in stratum h can be written This estimator is strongly biased for systematic sampling The bias is reduced with a local estimator of the variance of Y:

Accuracy of results for major crops

Sampling efficiency Sources of improvement: Using points instead of segments or clusters Systematic sampling instead of random (post-) stratification Unequal sampling rate per stratum. Relative efficiency between different point approaches Relative efficiency between Clustered and non- clustered sampling (non-stratified, systematic)

Impact of the exclusion of certain areas from the final sample in LUCAS 2006 Bias due to the exclusion of points > 1200 m (1 km resolution DTM) Indications by extrapolation Arable land: < 0.2% Permanent crops: ~0.2% Permanent grass: ~ 2% Bias due to the exclusion of islands (Balearic, Canary and minor islands) Arable land: ~ 0.1% Permanent crops: ~ 1%