Author:
Dr. Hannes I. Reuter | European Commission, Eurostat (ESTAT), Luxembourg, Luxembourg | Luxembourg
Earth Observation (EO) is the measurement of physical, chemical and biological systems of our planet earth. It delivers proxies of measurable parameters which can be detected via a sensor (optical, radar, laser) influenced by a variety of parameters (e.g. clouds, atmosphere, solar winds). Earth observation includes airborne and satellite based measurements as well as in situ sensor data (e.g. air or water temperature) for the purpose of calibration.
Using EO data for statistical purposes can contribute to the survey methodology, the analytical possibilities, timeliness, spatial coverage and semantic harmonisation. Several statistical domains like tourism, transport (port activities, lorry traffic) and production estimates (e.g. oil storage, car, agriculture) do have the potential to use earth observation data for statistical data production.
The presentation will outline a limited set of usage of EO at Eurostat and the European Statistical Systems (ESS) - the national statistical data providers - at large.
Inside Eurostat provides the LUCAS survey micro-data on land cover and land use, as well as environmental information, serving as in-situ observation for EO, which has been used by Member States to produce classified Copernicus maps based on a subset and to validate the results (EEA, 2015). Additionally, for the reporting of the Sustainable Development Goals (SDG) EO derived information is used for a restricted set of parameters. Lastly, A variety of EO information from space and airborne sensors is provided centrally by GISCO to facilitate spatial analysis and visual interpretation.
Eurostat facilitates the usage of Earth Observation at ESS level in the National Statistical Systems with the ESSNET Big Data program, and with individual Member States through the GEOS grants. Examples presented include classical approaches like land use/land-cover mappings, determining urban sprawl, crop recognition and yield prediction systems up to advanced systems using Artificial Intelligence and Machine learning to detect solar cells on roofs to support knowledge on transformation of energy generation/SDG. In addition, some NSIs have managed relevant projects on their own budget to early test the opportunities and implication of using earth observation data on similar topics. To increase the internal knowledge specific sessions are integrated in the European Statistical Training Program (ESTP) course program.