GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery
In: Earth System Science Data, Vol 13, Pp 2753-2776 (2021, 2021
Online
academicJournal
Zugriff:
Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m land-cover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the CCI_LC (Climate Change Initiative Global Land Cover) land-cover and MCD43A4 NBAR products (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, a local adaptive random forest model was built for each 5 ∘ × 5 ∘ geographical tile by using the multi-temporal Landsat spectral and texture features and the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLC_FCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLC_FCS30-2015 achieved an overall accuracy of 82.5 % and a kappa coefficient of 0.784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71.4 % and kappa coefficient of 0.686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68.7 % and kappa coefficient of 0.662 for the ...
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GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery
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Autor/in / Beteiligte Person: | Zhang, X. ; Liu, L. ; Chen, X. ; Gao, Y. ; Xie, S. ; Mi, J. |
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Zeitschrift: | Earth System Science Data, Vol 13, Pp 2753-2776 (2021, 2021 |
Veröffentlichung: | Copernicus Publications, 2021 |
Medientyp: | academicJournal |
ISSN: | 1866-3508 (print) ; 1866-3516 (print) |
DOI: | 10.5194/essd-13-2753-2021 |
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