Acta Geographica Sinica ›› 2018, Vol. 73 ›› Issue (11): 2223-2235.doi: 10.11821/dlxb201811013

• Land Use and Geographic Information • Previous Articles     Next Articles

Data fusion and accuracy evaluation of multi-source global land cover datasets

BAI Yan1,2(),FENG Min3   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
    3. Global Land Cover Facility, Department of Geographic Sciences, University of Maryland, College Park, MD 20742, USA
  • Received:2018-01-24 Online:2018-11-25 Published:2018-11-22
  • Supported by:
    Basic Resources Investigation of Science and Technology, No.2017FY100900; Young Talents Training Fund of State Key Laboratory of Resources and Environmental Information System of China, No.Y6V60220YZ; National Earth System Science Data Sharing Infrastructure, National Science & Technology Infrastructure of China, No.2005DKA32300


Accurate global and regional land cover classification datasets based on remote sensing are of fundamental importance in research on global changes, land surface process modeling, ecological progress, and regional sustainable development and so on. The overall objective of this study is to present a decision-fuse method that integrates existing multi-source land cover information into a 'best-estimate' dataset using fuzzy logic. Combined with another three global datasets, i.e., MODIS VCF (Vegetation Continuous Field), MODIS Cropland Probability, and AVHRR CFTC (Continuous Fields of Tree Cover), this method is applied to five global land cover datasets (GLCC, UMD, GLC2000, MODIS LC, and GlobCover) to generate a new 1-km global land cover product SYNLCover with desired legends, which are properly defined in terms of plant functional types. Pixel-based comparisons among these six global land cover datasets are performed, and results reveal that compared with five original global land cover datasets: (1) In terms of map-specific consistency, overall consistencies of both eight life forms and twelve objective legends of SYNLCover are the highest, accounting for about 65.6% and 59.4%, respectively; followed by the accuracy of MODIS LC, GLC2000, GLCC, and GlobCover in a descending order, and the lowest map-specific consistencies of life forms and objective legends are separately 48.9% and 42.6% in UMD. Besides, among all dataset pairs, SYNLCover agrees best with each original land cover dataset regarding the occurrences of life forms and leaf attributes. (2) In terms of class-specific consistency, it is suggested that SYNLCover gets the highest average class consistencies for all the five leaf attributes, as well as major life forms except Shrubland, among which the consistency for Others in SYNLCover is up to 67.73%. (3) For Trees, Grassland, Cropland, Water, Urban and built-up and Others, SYNLCover shows particular improved average class-consistencies by about 10% to 15% over the maximum consistency of original datasets, and the consistencies of five leaf attributes in SYNLCover also increases by about 10%. This study indicates a successful integration of multi-source land cover information into a new refined dataset with improved characteristics scientifically.

Key words: land cover, fuzzy logic, affinity scores, data integration, consistency assessment, multi-source information