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.
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页数:29
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