The GISCO task force “Remote Sensing for Statistics” Javier.Gallego@ec.europa.eu

Some tasks for the group Increased availability of (satellite) images Copernicus Available RS layers Priority layers for the near future Specifications Validation of RS layers Using RS layers for statistics. Fitness for purpose SDG indicators Etc….. A bit biased……….

Remote sensing and area estimation: An old love story starting with agricultural statistics (1972- ?????) Or several possible love stories Sometimes a love-hate story Biblio: Catullus “odi et amo” (poems of love and hate), Rome, ca. 55 BC.

Other biblio to consider Experience available in many fields Agricultural statistics Forest …. “An expert is somebody who has made all possible mistakes in a specific field” Niels Bohr

One possible story: I will stand at your side every day of my life and will provide everything you need. Do not worry. I am here. = I will provide accurate area estimates and you will not need to collect sample data (or very little). But such intense love often finishes in a violent divorce. (The value added by remote sensing is proportional to the effort on sample data collection) Example: The EC-MARS ActivityB: rapid crop area change estimations:

Another possible story: Let us be friends and collaborate. = Observations on a sample provide reference data remote sensing give a general view on a larger area. Less romantic, but more practical Example: USDA-NASS Long-lasting, happy relationship

Naïf area estimator from classified images Pixel counting Measuring polygons No sampling error Bias = Commission Error – Omission error No image classification algorithm guarantees compensation

Bias of pixel counting: estimation and correction Some rules Probability sampling (no purposive or quota sampling) No spatial correlation between validation or bias-correction sample and data used to train the classification algorithm. Main techniques to correct the bias Regression estimator Calibration (not exactly the usual calibration estimator in statistics) Small area estimators Synergy between classified images and reference sample

Initial topic for the GISCO task force: Artificial area Exhaustive maps (Remote sensing) CORINE Land Cover Copernicus High Resolution Imperviousness Layer European Settlement Map Maps of selected areas (Remote sensing) Urban Atlas Reference data on a sample LUCAS field survey data LUCAS grid photo-interpreted for stratification Copernicus imperviousness sample for validation. Differences on Legend Scale-Resolution Thematic accuracy Location accuracy

Copernicus Imperviousness Layer Automatic classification from satellite images (10-30 m resolution) Product resolution: 20 m Fuzzy (sub-pixel): for each pixel the % of impervious land is estimated. 2006-2009-2012 More restrictive concept than “artificial” in CORINE, Urban Atlas or LUCAS. Tracks, construction sites?

Reference data Photo-interpretation on aerial ortho-photos Stratified sample of 22500 Units of 1 ha 25 points in per sampling unit

Copernicus imperviousness layer Direct area estimator (pixel counting) from classified image 2012 (EEA-39) 𝐴 = 𝑖 𝑚 𝑖 =117,500 km2 = 2.01%

Unweighted confusion matrix Map Imper-vious Other Total Omission error Ref. Impervious 4717 1252 5969 21.0% 1717 14810 16527 6434 16062 22496 Commission error 26.7% This would suggest to correct the area estimation as Pixel counting – Commission error + Omission error Leading to ≈ 110.800 km2

Weighted confusion matrix Map Imper-vious Other Total Omission error Ref. Impervious 92.4 59.0 151.3 39.0% 27.0 5657.3 5684.3 119.4 5716.2 5835.6 Commission error 22.6% Overall accuracy=98.5% Omission error >>> Commission error This would suggest to correct the area estimation as Pixel counting – Commission error + Omission error Leading to ≈ 149,500 km2

Extrapolation from reference data 𝑨 = 𝑖 𝒘 𝒊 𝒓 𝒊 = 151,300 km2 (CV=2.2%) 2.56% of the territory Regression estimator in each stratum 𝑅 𝑟𝑒𝑔 =𝐷 𝑟 +𝑏 𝑀 − 𝑚 = 150,500 km2 (CV=1.98%) Modest relative efficiency of Regression estimator ≈ 1.23 Main potential source of improvement: using Copernicus layer for stratification of the validation sample Potential efficiency with optimized sampling allocation ≈ 5.7

Area estimates for the largest reporting areas Naïf from image (km2) Extrapolated from reference data (km2) CV Regression estimator (km2) CV regression Turkey 6340 8543 13.3% 8289 France 15554 21525 6.4% 21922 5.2% Spain 8382 10945 8.1% 10941 7.5% Sweden 2151 5132 15.0% 5097 14.8% Germany 18343 17216 4.7% 17359 4.3% Finland 2003 2542 14.5% 2535 13.9% Norway 887 1778 20.9% 1814 16.8% Poland 7830 8540 9.6% 8392 9.3% Bias of pixel counting changes from country to country.

Artificial area: Copernicus imperviousness vs. LUCAS Copernicus regres km2 CV LUCAS 2012 Difference Copern France 21922 5.2% 26809 1.4% 22% Spain 10941 7.5% 14604 1.8% 33% Sweden 5097 14.8% 5915 4.8% 16% Germany 17359 4.3% 22168 1.9% 28% Finland 2535 13.9% 4491 5.4% 77% Poland 8392 9.3% 9167 3.4% 9% LUCAS: smaller CVs and higher estimates Main reason for differences: Wider concept of “artificial” in LUCAS

Overlaying CLC 2012 with LUCAS 1st phase sample CLC group Points artif N points % Artif Artif *1000 km2 CV Urban dense 1037 1377 75.3 4.2 1.54 Urban discontinuous 14287 36941 38.7 57.5 0.66 Other artificial 4633 13988 33.1 18.8 1.12 Agriculture 16022 491039 3.3 64.3 0.78 Forest &nat. veg 5378 480544 1.1 21.7 1.36 Bare land & water 365 66974 0.5 1.5 5.22 Total 41722 1090863 3.8 168.0 0.48 Post-stratified   0.43 Not a measure of CLC accuracy (scale effect) Rather a fine-scale profile of CLC classes CLC has a modest efficiency if used for post-stratification of LUCAS grid Much better efficiency if sampling rates per CLC class were optimized. Artificial land covers 3.3% CLC agricultural classes, but 38% of the overall artificial land

Increase in artificial area Ongoing assessments on changes 2006-2012. Low correlation between direct estimates from CLC changes 2006-2012 and Urban Area changes 2006-2012 Low correlation between changes from Copernicus imperviousness HRL and the relative validation sample. Simultaneous photo-interpretation under revision. Stratification and sample allocation to review LUCAS ??????