High-Resolution Land Use Land Cover Dataset for Meteorological Modelling—Part 1: ECOCLIMAP-SG+ an Agreement-Based Dataset

Geoffrey Bessardon (), Thomas Rieutord, Emily Gleeson (), Bolli Pálmason and Sandro Oswald
Additional contact information
Geoffrey Bessardon: Met Éireann, 65/67 Glasnevin Hill, D09 Y921 Dublin, Ireland
Thomas Rieutord: Met Éireann, 65/67 Glasnevin Hill, D09 Y921 Dublin, Ireland
Emily Gleeson: Met Éireann, 65/67 Glasnevin Hill, D09 Y921 Dublin, Ireland
Bolli Pálmason: Veðurstofa Íslands, Bústaðavegi 7–9, 105 Reykjavík, Iceland
Sandro Oswald: GeoSphere Austria, Hohe Warte 38, 1190 Vienna, Austria

Land, 2024, vol. 13, issue 11, 1-29

Abstract: ECOCLIMAP-SG+ is a new 60 m land use land cover dataset, which covers a continental domain and represents the 33 labels of the original ECOCLIMAP-SG dataset. ECOCLIMAP-SG is used in HARMONIE-AROME, the numerical weather prediction model used operationally by Met Éireann and other national meteorological services. ECOCLIMAP-SG+ was created using an agreement-based method to combine information from many maps to overcome variations in semantic and geographical coverage, resolutions, formats, accuracy, and representative periods. In addition to ECOCLIMAP-SG+, the process generates an agreement score map, which estimates the uncertainty of the land cover labels in ECOCLIMAP-SG+ at each location in the domain. This work presents the first evaluation of ECOCLIMAP-SG and ECOCLIMAP-SG+ against the following trusted land cover maps: LUCAS 2022, the Irish National Land Cover 2018 dataset, and an Icelandic version of ECOCLIMAP-SG. Using a set of primary labels, ECOCLIMAP-SG+ outperforms ECOCLIMAP-SG regarding the F1-score against LUCAS 2022 over Europe and the Irish national land cover 2018 dataset. Similarly, it outperforms ECOCLIMAP-SG against the Icelandic version of ECOCLIMAP-SG for most of the represented secondary labels. The score map shows that the quality ECOCLIMAP-SG+ is hetereogeneous. It could be improved once new maps become available, but we do not control when they will be available. Therefore, the second part of this publication series aims at improving the map using machine learning.

Keywords: land cover land use; meteorology; uncertainty quantification (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/11/1811/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/11/1811/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:11:p:1811-:d:1512406

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().