Abstract
ABSTRACTIn the past decades, global land cover datasets have been produced but also been criticized for their low accuracies, which have been affecting the applications of these datasets. Producing a new global dataset requires a tremendous amount of efforts; however, it is also possible to improve the accuracy of global land cover mapping by fusing the existing datasets. A decision-fuse method was developed based on fuzzy logic to quantify the consistencies and uncertainties of the existing datasets and then aggregated to provide the most certain estimation. The method was applied to produce a 1-km global land cover map (SYNLCover) by integrating five global land cover datasets and three global datasets of tree cover and croplands. Efforts were carried out to assess the quality: 1) inter-comparison of the datasets revealed that the SYNLCover dataset had higher consistency than these input global land cover datasets, suggesting that the data fusion method reduced the disagreement among the input datasets; 2) quality assessment using the human-interpreted reference dataset reported the highest accuracy in the fused SYNLCover dataset, which had an overall accuracy of 71.1%, in contrast to the overall accuracy between 48.6% and 68.9% for the other global land cover datasets.
Highlights
Land cover represents the understanding of complex interactions between human activities and the environment (Running, 2008)
The Global Land Cover Characterization (GLCC) and University of Maryland land cover product (UMD) land cover datasets were produced using satellite data in the early 1990s and GlobCover was produced for circa-2005, which are less than a decade away from 2000
We proposed an integration method to produce a global land cover dataset with improved accuracy by synthesizing multi-source global land cover data products using fuzzy logic method
Summary
Land cover represents the understanding of complex interactions between human activities and the environment (Running, 2008). BAI widely used in a variety of applications (DeFries, Field, Fung, Collatz, & Bounoua, 1999a; Giri, 2005; Quaife et al, 2008; Ramankutty, Foley, Norman, & McSweeney, 2002; Verburg, Neumann, & Nol, 2011; You, Wood, & Wood-Sichra, 2009) Their validation efforts revealed considerable errors and inconsistencies in the land cover maps at the global or continental scales, and further inter-comparison exposed significant disagreements among the maps in the forest and cropland domains (Bai, 2010; DeFries, Hansen, Townshend, Janetos, & Loveland, 2000; Fritz & See, 2008, 2005; Fritz, See, & Rembold, 2010; Giri, 2005; Herold, Mayaux, Woodcock, Baccini, & Schmullius, 2008; Kaptué Tchuenté, Roujean, & De Jong, 2011; Latifovic & Olthof, 2004; Mccallum, Obersteiner, Nilsson, & Shvidenko, 2006; Neumann, Herold, Hartley, & Schmullius, 2007; Quaife et al, 2008; Ran, Li, & Lu, 2010; See & Steffen, 2006; Wu et al, 2008). The errors and disagreements within the maps make it difficult for users to select the proper map for their research, and the uncertainties in the maps will be further transferred and exaggerated to the downstream applications
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