Geospatial knowledge-based verification and improvement of GlobeLand30

Assuring the quality of land-cover data is one of the major challenges for large- area mapping projects. Although the use of geospatial knowledge and ancillary data in improving land-cover classification has been studied since the early 1980s, mature methods and efficient supporting tools are still lacking. This paper presents a geospatial knowledge-based verification and improvement approach for global land cover (GLC) mapping at 30-m resolution. A set of verification rules is derived from three types of land cover and its change knowledge (natural, cultural and temporal constraints). A group of web-based supporting tools is developed to facilitate the integration of and access to large amounts of ancillary data and to support online data manipulation and analysis as well as collaborative verification workflows. With this approach, two 30-m GLC datasets (GlobeLand-2000 and GlobeLand-2010) were verified and modified. The results indicate that the data quality of GlobeLand30 has been largely improved.