Abstract
Accurately characterizing land surface changes with Earth Observation requires geo-located ground truth. In the European Union (EU), a tri-annual surveyed sample of land cover and land use has been collected since 2006 under the Land Use/Cover Area frame Survey (LUCAS). A total of 1351293 observations at 651780 unique locations for 106 variables along with 5.4 million photos were collected during five LUCAS surveys. Until now, these data have never been harmonised into one database, limiting full exploitation of the information. This paper describes the LUCAS point sampling/surveying methodology, including collection of standard variables such as land cover, environmental parameters, and full resolution landscape and point photos, and then describes the harmonisation process. The resulting harmonised database is the most comprehensive in-situ dataset on land cover and use in the EU. The database is valuable for geo-spatial and statistical analysis of land use and land cover change. Furthermore, its potential to provide multi-temporal in-situ data will be enhanced by recent computational advances such as deep learning.
Highlights
Background & SummaryAccurate, timely, and representative in-situ observations across large areas have always been needed to report statistics on land use, land cover, and the environment
This paper describes the Land Use/Cover Area frame Survey (LUCAS) point sampling/surveying methodology, including collection of standard variables such as land cover, environmental parameters, and full resolution landscape and point photos, and describes the harmonisation process
Since Eurostat has carried out LUCAS surveys every three years with the survey design ever evolving, the LUCAS core component
Summary
Representative in-situ observations across large areas have always been needed to report statistics on land use, land cover, and the environment. LUCAS land cover and land use survey data have been used to derive statistical estimates[2], to describe land cover/use diversity at regional level[12], and its sampling frame was used as a basis for various applications including assessing the availability of crowd-sourced photos potentially relevant for crop monitoring across the EU13. While the inconsistencies could be due to the enumerators’ subjectivity in interpretation of the legends and the legend itself, it is related to the complexity of the field survey: large number of surveyors (>700), complex documentation for the enumerators (>400 pages combining all the documents), translated to 20 languages These drawbacks hinder the further use of the LUCAS data by the scientific community as a whole and in particular by users who are active in emerging fields of big data analytics, data fusion, and computer vision. We have gone through an extensive process of cleaning by semantic and topological harmonisation, along with connecting the originally disjoint LUCAS datasets in one consolidated database with hard-coded links to the full-resolution photos, openly accessible along with this paper
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
