Articles | Volume 13, issue 3
https://doi.org/10.5194/essd-13-1119-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-1119-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
LUCAS Copernicus 2018: Earth-observation-relevant in situ data on land cover and use throughout the European Union
Raphaël d'Andrimont
CORRESPONDING AUTHOR
European Commission Joint Research Centre (JRC), Ispra, Italy
Astrid Verhegghen
European Commission Joint Research Centre (JRC), Ispra, Italy
Michele Meroni
European Commission Joint Research Centre (JRC), Ispra, Italy
Guido Lemoine
European Commission Joint Research Centre (JRC), Ispra, Italy
Peter Strobl
European Commission Joint Research Centre (JRC), Ispra, Italy
Beatrice Eiselt
European Commission, Eurostat (ESTAT), Luxembourg, Luxembourg
Momchil Yordanov
European Commission Joint Research Centre (JRC), Ispra, Italy
Laura Martinez-Sanchez
European Commission Joint Research Centre (JRC), Ispra, Italy
Marijn van der Velde
CORRESPONDING AUTHOR
European Commission Joint Research Centre (JRC), Ispra, Italy
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Cited
22 citations as recorded by crossref.
- Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10) Z. Venter & M. Sydenham 10.3390/rs13122301
- Lessons learned in developing reference data sets with the contribution of citizens: the Geo-Wiki experience L. See et al. 10.1088/1748-9326/ac6ad7
- Time-first approach for land cover mapping using big Earth observation data time-series in a data cube – a case study from the Lake Geneva region (Switzerland) G. Giuliani 10.1080/20964471.2024.2323241
- Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation L. Martinez-Sanchez et al. 10.1016/j.ecoinf.2022.101757
- Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data Y. Luo et al. 10.3390/rs14081809
- From parcel to continental scale – A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations R. d’Andrimont et al. 10.1016/j.rse.2021.112708
- Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using spatiotemporal machine learning C. Bonannella et al. 10.7717/peerj.13728
- A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat M. Witjes et al. 10.7717/peerj.13573
- Ha Long—Cam Pha Cities Evolution Analysis Utilizing Remote Sensing Data G. Nguyen et al. 10.3390/rs14051241
- Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data L. Kleinewillinghöfer et al. 10.3390/rs14194903
- Current state and challenges in producing large-scale land cover maps: review based on recent land cover products F. Gilić et al. 10.1080/10106049.2023.2242693
- Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series B. Ghassemi et al. 10.3390/land11091397
- Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation H. Li et al. 10.1016/j.rse.2023.113623
- Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data B. Ghassemi et al. 10.3390/rs14030541
- Rapid early-season maize mapping without crop labels N. You et al. 10.1016/j.rse.2023.113496
- Crop Identification Using Deep Learning on LUCAS Crop Cover Photos M. Yordanov et al. 10.3390/s23146298
- Seasonal Spatio-temporal Land Cover Dynamics in the Upper Brantas Watershed S. Beselly et al. 10.1088/1755-1315/930/1/012021
- Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union R. d’Andrimont et al. 10.1038/s41597-020-00675-z
- Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data B. Ghassemi et al. 10.3390/rs14030541
- Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App J. Laso Bayas et al. 10.3390/land9110446
- Global annual wetland dataset at 30 m with a fine classification system from 2000 to 2022 X. Zhang et al. 10.1038/s41597-024-03143-0
- Modelling Accessibility to Urban Green Areas Using Open Earth Observations Data: A Novel Approach to Support the Urban SDG in Four European Cities G. Giuliani et al. 10.3390/rs13030422
16 citations as recorded by crossref.
- Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10) Z. Venter & M. Sydenham 10.3390/rs13122301
- Lessons learned in developing reference data sets with the contribution of citizens: the Geo-Wiki experience L. See et al. 10.1088/1748-9326/ac6ad7
- Time-first approach for land cover mapping using big Earth observation data time-series in a data cube – a case study from the Lake Geneva region (Switzerland) G. Giuliani 10.1080/20964471.2024.2323241
- Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation L. Martinez-Sanchez et al. 10.1016/j.ecoinf.2022.101757
- Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data Y. Luo et al. 10.3390/rs14081809
- From parcel to continental scale – A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations R. d’Andrimont et al. 10.1016/j.rse.2021.112708
- Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using spatiotemporal machine learning C. Bonannella et al. 10.7717/peerj.13728
- A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat M. Witjes et al. 10.7717/peerj.13573
- Ha Long—Cam Pha Cities Evolution Analysis Utilizing Remote Sensing Data G. Nguyen et al. 10.3390/rs14051241
- Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data L. Kleinewillinghöfer et al. 10.3390/rs14194903
- Current state and challenges in producing large-scale land cover maps: review based on recent land cover products F. Gilić et al. 10.1080/10106049.2023.2242693
- Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series B. Ghassemi et al. 10.3390/land11091397
- Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation H. Li et al. 10.1016/j.rse.2023.113623
- Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data B. Ghassemi et al. 10.3390/rs14030541
- Rapid early-season maize mapping without crop labels N. You et al. 10.1016/j.rse.2023.113496
- Crop Identification Using Deep Learning on LUCAS Crop Cover Photos M. Yordanov et al. 10.3390/s23146298
6 citations as recorded by crossref.
- Seasonal Spatio-temporal Land Cover Dynamics in the Upper Brantas Watershed S. Beselly et al. 10.1088/1755-1315/930/1/012021
- Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union R. d’Andrimont et al. 10.1038/s41597-020-00675-z
- Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data B. Ghassemi et al. 10.3390/rs14030541
- Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App J. Laso Bayas et al. 10.3390/land9110446
- Global annual wetland dataset at 30 m with a fine classification system from 2000 to 2022 X. Zhang et al. 10.1038/s41597-024-03143-0
- Modelling Accessibility to Urban Green Areas Using Open Earth Observations Data: A Novel Approach to Support the Urban SDG in Four European Cities G. Giuliani et al. 10.3390/rs13030422
Latest update: 23 Apr 2024
Short summary
The Land Use/Cover Area frame Survey (LUCAS) is a regular in situ land cover and land use ground survey exercise that extends over the whole of the European Union. A new LUCAS module specifically tailored to Earth observation was introduced in 2018: the LUCAS Copernicus module. This paper summarizes the LUCAS Copernicus survey and provides the unique resulting data: 58 426 polygons with level-3 land cover (66 specific classes including crop type) and land use (38 classes).
The Land Use/Cover Area frame Survey (LUCAS) is a regular in situ land cover and land use ground...
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