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

被引:2
作者
Bessardon, Geoffrey [1 ]
Rieutord, Thomas [1 ]
Gleeson, Emily [1 ]
Palmason, Bolli [2 ]
Oswald, Sandro [3 ]
机构
[1] Met Eireann, 65-67 Glasnevin Hill, Dublin D09Y921, Ireland
[2] Vedurstofa Isl,Bustadavegi 7-9, IS-105 Reykjavik, Iceland
[3] GeoSphere Austria, Hohe Warte 38, A-1190 Vienna, Austria
关键词
land cover land use; meteorology; uncertainty quantification; SURFACE PARAMETERS; MAP; DATABASE; SUPPORT; PRODUCE; IMAGERY;
D O I
10.3390/land13111811
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 & Eacute;ireann 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.
引用
收藏
页数:29
相关论文
共 72 条
  • [1] Allen George H, 2018, Zenodo, DOI 10.5281/ZENODO.1297434
  • [2] [Anonymous], 2020, European Environmental Agency - EEA Glossary
  • [3] [Anonymous], 2018, Waste in Sweden
  • [4] Ballin M., 2022, New LUCAS 2022 Sample and Subsamples Design: Criticalities and Solutions, DOI [10.2785/957524, DOI 10.2785/957524]
  • [5] Present and future Koppen-Geiger climate classification maps at 1-km resolution
    Beck, Hylke E.
    Zimmermann, Niklaus E.
    McVicar, Tim R.
    Vergopolan, Noemi
    Berg, Alexis
    Wood, Eric F.
    [J]. SCIENTIFIC DATA, 2018, 5
  • [6] The HARMONIE-AROME Model Configuration in the ALADIN-HIRLAM NWP System
    Bengtsson, Lisa
    Andrae, Ulf
    Aspelien, Trygve
    Batrak, Yurii
    Calvo, Javier
    de Rooy, Wim
    Gleeson, Emily
    Hansen-Sass, Bent
    Homleid, Mariken
    Hortal, Mariano
    Ivarsson, Karl-Ivar
    Lenderink, Geert
    Niemelza, Sami
    Nielsen, Kristian Pagh
    Onvlee, Jeanette
    Rontu, Laura
    Samuelsson, Patrick
    Santos Munoz, Daniel
    Subias, Alvaro
    Tijm, Sander
    Toll, Velle
    Yang, Xiaohua
    Koltzow, Morten Odegaard
    [J]. MONTHLY WEATHER REVIEW, 2017, 145 (05) : 1919 - 1935
  • [7] Bessardon G., 2024, **DATA OBJECT**, DOI 10.5281/zenodo.10944693
  • [8] Bessardon G., 2019, Using the Best Available Physiography to Improve Weather Forecasts for Ireland
  • [9] Bocher E., 2021, J OPEN SOURCE SOFTW, V6, P3541, DOI DOI 10.21105/JOSS.03541
  • [10] Bruzzone L., 2024, **DATA OBJECT**, DOI 10.5285/f107a4ce186844bb8adf8cd1f2f6d552