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https://doi.org/10.5194/essd-2020-182
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-182
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  01 Oct 2020

01 Oct 2020

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This preprint is currently under review for the journal ESSD.

GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery

Xiao Zhang1, Liangyun Liu1,2, Xidong Chen1,2, Yuan Gao1,3, Shuai Xie1,2, and Jun Mi1,2 Xiao Zhang et al.
  • 1State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China University of Chinese Academy of Sciences, Beijing 100049, China
  • 3College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China

Abstract. Over past decades, a lot of global land-cover products have been released, however, these is still lack of a global land-cover map with 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 MCD43A4 NBAR and CCI_LC land-cover products. Secondly, a local adaptive random forest model was built for each 5° × 5° geographical tile by using the multi-temporal Landsat spectral and textures features of 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 UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCI_LC, MCD12Q1, FROM_GLC and GlobeLand30) indicated that GLC_FCS30-2015 provides more spatial details than CCI_LC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROM_GLC-2015 and GlobeLand30-2010, and that GLC_FCS30-2015 achieved the best overall accuracy of 82.5% against FROM_GLC-2015 of 59.1 % and GlobeLand30-2010 of 75.9 %. Therefore, it is concluded that the GLC_FCS30-2015 product is the first global land-cover dataset that provides a fine classification system with high classification accuracy at 30 m. The GLC_FCS30-2015 global land-cover products generated in this paper is available at https://doi.org/10.5281/zenodo.3986871 (Liu et al., 2020).

Xiao Zhang et al.

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GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery Liangyun Liu, Xiao Zhang, Xidong Chen, Yuan Gao, and Jun Mi https://doi.org/10.5281/zenodo.3986872

Xiao Zhang et al.

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Short summary
Over past decades, a lot of global land-cover products have been released, however, these is still lack of a global land-cover map with fine classification system and spatial resolution simultaneously. In this study, a novel global 30-m landcover 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 on the Google Earth Engine computing platform.
Over past decades, a lot of global land-cover products have been released, however, these is...
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