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Remote Sensing / Interview GlobeLand30, the world’s first global land cover dataset at 30m resolution for the years 2000 and 2010, was recently released and donated by China to the United Nations. Organised into ten major land cover classes, this dataset will prove to be critical for climate change studies, environment monitoring, and several other areas of benefit, says Prof Chen Jun, President, National Geomatics Center of China & President, ISPRS GlobeLand30 is a reliable dataset for Sustainable Development these reasons, China has decided to develop a high resolution global land cover map. C hina recently launched GlobeLand30 — the global land cover map in 30m resolution. What prompted this project? Humans are facing unprecedented challenges, for which tracking and monitoring of global environmental changes are very important. While discussing about climate change or sustainable development, we realise that we do not have reliable and relevant information like maps and/or spatial distribution and change of land cover. Land cover, like topog- raphy, is an important fundamental data. So far, countries have been producing land cover data of their own territories. There are very few initiatives around the world to create land cover data at global scale, and the accuracy of this data is not good. Further, these datasets were created by different organisations from different countries, so consistency of data isn’t good for global change analysis, environment change studies, etc. For 42 / Geospatial World / February 2015 Is this solely a Chinese project? Which agencies and companies participated in this project? GlobeLand30 is a Chinese project spearheaded by the National Geomatics Center of the National Administration of Surveying, Mapping and Geoinformation (NASG). The datasets are organised into ten major land cover classes and provide essential high resolution land cover and change information for climate change studies, environment monitoring, resource management, sustainable development, and many other societal benefit areas. About 18 institutions/ universities were involved in this project but this project has been largely supported by international community. For instance, in the beginning of the project, we got support from ISPRS, GEO, and many other organisations. The University of Maryland gave us processed images at 30m resolution, and our primary data was from Landsat of USGS. When we finished, European scientists from Sweden, Greece and Italy validated a lot of data for us. It’s a team work, not only by the Chinese