Evidence map›Paper›PMID 33067440›Full record

ArticleScientific data2020

Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union.

Raphaël d'Andrimont, Momchil Yordanov, Laura Martinez-Sanchez, Beatrice Eiselt, Alessandra Palmieri, Paolo Dominici, Javier Gallego, Hannes Isaak Reuter, Christian Joebges, Guido Lemoine and 1 more

Abstract read
In one paragraph

Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Raphaël d'AndrimontEuropean Commission Joint Research Centre (JRC), Ispra, Italy. raphael.dandrimont@ec.europa.eu.ORCID http://orcid.org/0000-0002-7326-7684
Momchil YordanovEuropean Commission Joint Research Centre (JRC), Ispra, Italy.
Laura Martinez-SanchezEuropean Commission Joint Research Centre (JRC), Ispra, Italy.ORCID https://orcid.org/0000-0002-1354-1792
Beatrice EiseltEuropean Commission, Eurostat (ESTAT), Luxembourg, Luxembourg.
Alessandra PalmieriEuropean Commission, Eurostat (ESTAT), Luxembourg, Luxembourg.
Paolo DominiciEuropean Commission, Eurostat (ESTAT), Luxembourg, Luxembourg.
Javier GallegoEuropean Commission Joint Research Centre (JRC), Ispra, Italy.
Hannes Isaak ReuterEuropean Commission, Eurostat (ESTAT), Luxembourg, Luxembourg.ORCID https://orcid.org/0000-0001-6336-7801
Christian JoebgesGOPA Luxembourg, Luxembourg, Luxembourg.
Guido LemoineEuropean Commission Joint Research Centre (JRC), Ispra, Italy.ORCID https://orcid.org/0000-0002-2734-4538
Marijn van der VeldeEuropean Commission Joint Research Centre (JRC), Ispra, Italy. marijn.van-der-velde@ec.europa.eu.ORCID http://orcid.org/0000-0002-9103-7081

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID33067440
PMCPMC7567823

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.