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 25 条
  • [1] High-Resolution Land Use Land Cover Dataset for Meteorological Modelling-Part 2: ECOCLIMAP-SG-ML an Ensemble Land Cover Map
    Rieutord, Thomas
    Bessardon, Geoffrey
    Gleeson, Emily
    LAND, 2024, 13 (11)
  • [2] High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe
    Hoffmann, Peter
    Reinhart, Vanessa
    Rechid, Diana
    de Noblet-Ducoudre, Nathalie
    Davin, Edouard L.
    Asmus, Christina
    Bechtel, Benjamin
    Boehner, Juergen
    Katragkou, Eleni
    Luyssaert, Sebastiaan
    EARTH SYSTEM SCIENCE DATA, 2023, 15 (08) : 3819 - 3852
  • [3] High-resolution land use and land cover dataset for regional climate modelling: a plant functional type map for Europe 2015
    Reinhart, Vanessa
    Hoffmann, Peter
    Rechid, Diana
    Boehner, Juergen
    Bechtel, Benjamin
    EARTH SYSTEM SCIENCE DATA, 2022, 14 (04) : 1735 - 1794
  • [4] OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping
    Xia, Junshi
    Yokoya, Naoto
    Adriano, Bruno
    Broni-Bediako, Clifford
    2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2023, : 6243 - 6253
  • [5] The first high-resolution meteorological forcing dataset for land process studies over China
    He, Jie
    Yang, Kun
    Tang, Wenjun
    Lu, Hui
    Qin, Jun
    Chen, Yingying
    Li, Xin
    SCIENTIFIC DATA, 2020, 7 (01)
  • [6] The first high-resolution meteorological forcing dataset for land process studies over China
    Jie He
    Kun Yang
    Wenjun Tang
    Hui Lu
    Jun Qin
    Yingying Chen
    Xin Li
    Scientific Data, 7
  • [7] Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling
    Sheffield, Justin
    Goteti, Gopi
    Wood, Eric F.
    JOURNAL OF CLIMATE, 2006, 19 (13) : 3088 - 3111
  • [8] An advanced high resolution land use/land cover dataset for Iran (ILULC-2022) by focusing on agricultural areas based on remote sensing data
    Karimi, Neamat
    Sheshangosht, Sara
    Rashtbari, Maryam
    Torabi, Omid
    Sarbazvatan, Amirhossein
    Lari, Masoumeh
    Aminzadeh, Hossein
    Abolhoseini, Sina
    Eftekhari, Mortaza
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2025, 228
  • [9] Use of High-Resolution Land Cover Maps to Support the Maintenance of the NWI Geospatial Dataset: A Case Study in a Coastal New Orleans Region
    Zou, Zhenhua
    Huang, Chengquan
    Lang, Megan W.
    Du, Ling
    Mccarty, Greg
    Ingebritsen, Jeffrey C.
    Herold, Nate
    Griffin, Rusty
    Gong, Weishu
    Lu, Jiaming
    REMOTE SENSING, 2023, 15 (16)
  • [10] A high spatial resolution soil carbon and nitrogen dataset for the northern permafrost region based on circumpolar land cover upscaling
    Palmtag, Juri
    Obu, Jaroslav
    Kuhry, Peter
    Richter, Andreas
    Siewert, Matthias B.
    Weiss, Niels
    Westermann, Sebastian
    Hugelius, Gustaf
    EARTH SYSTEM SCIENCE DATA, 2022, 14 (09) : 4095 - 4110