GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method

Abstract ± 0.27  %) for the basic classification system (10 major land-cover types) and 73.04 % (± 0.30  %) for the LCCS (Land Cover Classification System) level-1 validation system (17 LCCS land-cover types). Meanwhile, two third-party time-series datasets used for validation from the United States and Europe Union are also collected for analyzing accuracy variations, and the results show that GLC_FCS30D offers significant stability in terms of variation across the accuracy time series and achieves mean accuracies of 79.50 % (± 0.50  %) and 81.91 % (± 0.09  %) over the two regions. Lastly, we draw conclusions about the global land-cover-change information from the GLC_FCS30D dataset; namely, that forest and cropland variations have dominated global land-cover change over past 37 years, the net loss of forests reached about 2.5 million km2, and the net gain in cropland area is approximately 1.3 million km2. Therefore, the novel dataset GLC_FCS30D is an accurate land-cover-dynamics time-series monitoring product that benefits from its diverse classification system, high spatial resolution, and long time span (1985–2022); thus, it will effectively support global climate change research and promote sustainable development analysis. The GLC_FCS30D dataset is available via 10.5281/zenodo.8239305 (Liu et al., 2023).

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method ; volume:16 ; number:3 ; year:2024 ; pages:1353-1381 ; extent:29 ; Earth system science data ; 16, Heft 3 (2024), 1353-1381 (gesamt 29)

Klassifikation und Themen

• Klassifikation: Natürliche Ressourcen, Energie und Umwelt

Beteiligte, Orts- und Zeitangaben

• Urheber: Zhang, Xiao ; Zhao, Tingting ; Xu, Hong ; Liu, Wendi ; Wang, Jinqing ; Chen, Xidong ; Liu, Liangyun

Weitere Informationen

• DOI: 10.5194/essd-16-1353-2024
• URN: urn:nbn:de:101:1-2024032208090731855534
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 14.08.2025, 10:48 MESZ