GlobeLand30, donated to the United Nations by China in September 2014, is the first wall-to-wall 30 m global land cover (GLC) data product. GlobeLand30 is widely used by scientists and users around the world. This paper provides a review of the analysis and applications of GlobeLand30 based on its data-downloading statistics and published studies. An average accuracy of 80% for full classes or one single class is achieved by third-party researchers from more than 10 countries through sample-based validation or comparison with existing data. GlobeLand30 has users from more than 120 countries on five continents, and from all five Social Benefit Areas. The significance of GlobeLand30 is demonstrated by a number of published papers dealing with land-cover status and change analysis, cause-and-consequence analysis, and the environmental parameterization of Earth system models. Accordingly, scientific data sharing in the field of geosciences and Earth observation is promoted, and fine-resolution GLC mapping and applications worldwide are stimulated. The future development of GlobeLand30, including comprehensive validation, continuous updating, and monitoring of sustainable development goals, is also discussed.

As result of the “Global Land Cover Mapping at Finer Resolution” project led by National Geomatics Center of China (NGCC), one of the first global land cover datasets at 30-meters resolution (GlobeLand30) has been produced for the years 2000 and 2010. The first comprehensive accuracy assessment at a national level of these data (excluding some comparisons in China) has been performed on the Italian area by means of a benchmarking with the more detailed land cover datasets available for some Italian regions. The accuracy evaluation was based on the cell-by-cell comparison between Italian maps and the GlobeLand30 in order to obtain the confusion matrix and its derived statistics (overall accuracy, allocation and quantity disagreements, user and producer accuracy), which help to understand the classification quality. This paper illustrates the adopted methodology and procedures for assessing GlobeLand30 and reports the obtained statistics. The analysis has been performed in eight regions across Italy and shows very good results: the comparison of the datasets according to the first level of Corine Land Cover nomenclature highlights overall accuracy values generally higher than 80%.

A number of national, regional and global land cover classification systems have been developed to meet specific user requirements for land cover mapping exercises, independent of scale, nomenclature and quality. However, this variety of land-cover classification systems limits the compatibility and comparability of land cover data. Furthermore, the current lack of interoperability between different land cover datasets, often stemming from incompatible land cover classification systems, makes analysis of multi-source, heterogeneous land cover data for various applications a very difficult task. This paper provides a critical review of the harmonization of land cover classification systems, which facilitates the generation, use and analysis of land cover maps consistently. Harmonization of existing land cover classification systems is essential to improve their cross-comparison and validation for understanding landscape patterns and changes. The paper reviews major land cover classification standards according to different scales, summarizes studies on harmonizing land cover mapping, and discusses some research problems that need to be solved and some future research directions.

Selection of roads is an intractable generalization operation due to the difficulty in retaining the density difference and connectivity of a road network. This paper proposes a novel approach of selective omission for roads based on mesh density. The density of a road network and its local variations are calculated using meshes as units. Since maps at different scales usually reveal different densities, different density thresholds for road networks are determined on the basis of theoretical analysis and empirical study of mesh densities on maps at different scales. The selection process starts with the identification of the meshes that have a density beyond the threshold. The mesh with the largest density is first treated. Its bounding road segments are ordered according to their relative importance. The least important segment is eliminated. The remaining segments are then merged with the adjacent mesh, thus forming a new mesh. The selection procedure is repeated until none of the meshes has a density beyond the threshold. Such a process of eliminating road segments and merging meshes can ensure the road network connectivity. In this study, the meshes are classified depending on the types of road segment. For the different mesh types, their density thresholds are set to be different, which can be used as an indicator for the preservation of the density difference. This proposed approach considers topological, geometric and semantic properties of the road network. It was applied to two sets of road networks, and the results of selection are convincing. This methodology has now been adopted for the updating of 1:50,000 maps of China.