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.

The spatial distribution of urban land use is an important issue in urban geography and urban planning, and the research on the spatial patterns of urban land use could help us to understand the status of urban systems. We reviewed the research progress on urban spatial structure and urban land use patterns, and we improved the existing methods of enrichment factors from two aspects to analyze the neighborhood patterns of urban land use. The first was to adopt vector grids instead of raster grids to decrease errors, and the second was to use fastigiated grids and Manhattan distance to measure the neighborhood. We used the proposed method to analyze six types of urban land uses in Shenzhen in 2015 and identified three neighborhood patterns that revealed the variation of enrichment degrees between different types of urban land uses with respect to distance. Pattern I showed that the enrichment degree between the same urban land use type was high over short distances and became low over increasing distances, indicating the attraction of the same type of urban land use within short distances and the decrease of attraction over long distances. The value of the enrichment degree between different types of land uses was negative over short distances in both Pattern II and Pattern III. In Pattern II the enrichment degree gradually increased to 0 when the distance increased, demonstrating the repulsion between different types of land uses over short distances and the decrease of repulsion over long distances. In Pattern III the enrichment degree increased quickly to a positive value, then decreased gradually to 0, which revealed repulsion between different types of land uses over short distances, and the interaction became attraction over long distance. Compared with the method based on raster grids, the proposed method based on fastigiated vector grids can reduce the error of neighborhood patterns of urban land uses.