摘要
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 1980 s, 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(Globe Land-2000 and Globe Land-2010) were verified and modified. The results indicate that the data quality of Globe Land30 has been largely improved.
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 veri- fication and improvement approach for global land cover （GLC） mapping at 30-m resolution. A set of verification rules is de- rived 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.
作者
ZHANG WeiWei
CHEN Jun
LIAO AnPing
HAN Gang
CHEN XueHong
CHEN LiJun
PENG Shu
WU Hao
ZHANG Jun
ZHANG WeiWei;CHEN Jun;LIAO AnPing;HAN Gang;CHEN XueHong;CHEN LiJun;PENG Shu;WU Hao;ZHANG Jun(National Geomatics Center of China, Beijing 100830, China;School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China;State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China)
基金
funded by the National Natural Science Foundation of China (Grant No. 41231172)
the Special Fund for Surveying, Mapping and Geoinformation Scientific Research in the Public Welfare (Grant No. 201512028)
National High-Tech R&D Program of China (Grant No. 2013AA122802)
