Name
Abstract | ||
|---|---|---|
30-m Global Land Cover (GLC) data products permit the detection of land cover changes at the scale of most human land activities, and are therefore used as fundamental information for sustainable development, environmental change studies, and many other societal benefit areas. In the past few years, increasing efforts have been devoted to the accuracy assessment of GlobeLand30 and other finer-resolution GLC data products. However, most of them were conducted either within a limited percentage of map sheets selected from a global scale or in some individual countries (areas), and there are still many areas where the uncertainty of 30-m resolution GLC data products remains to be validated and documented. In order to promote a comprehensive and collaborative validation of 30-m GLC data products, the GEO Global Land Cover Community Activity had organized a project from 2015 to 2017, to examine and explore its major problems, including the lack of international agreed validation guidelines and on-line tools for facilitating collaborative validation activities. With the joint effort of experts and users from 30 GEO member countries or participating organizations, a technical specification for 30-m GLC validation was developed based on the findings and experiences. An on-line validation tool, GLCVal, was developed by integrating land cover validation procedures with the service computing technologies. About 20 countries (regions) have completed the accuracy assessment of GlobeLand30 for their territories with the guidance of the technical specification and the support of GLCVal. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1080/10095020.2021.1894906 | GEO-SPATIAL INFORMATION SCIENCE |
Keywords | DocType | Volume |
Land cover, GlobeLand30, validation, accuracy Assessment, collaborative | Journal | 24 |
Issue | ISSN | Citations |
1 | 1009-5020 | 0 |
PageRank | References | Authors |
0.34 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Chen | 1 | 207 | 21.33 |
| Lijun Chen | 2 | 657 | 52.72 |
| Fei Chen | 3 | 0 | 0.34 |
| Yifang Ban | 4 | 0 | 0.34 |
| Songnian Li | 5 | 72 | 13.95 |
| Gang Han | 6 | 2 | 0.81 |
| Xiaohua Tong | 7 | 254 | 56.51 |
| Chuang Liu | 8 | 0 | 0.34 |
| Vanya Stamenova | 9 | 0 | 0.34 |
| Stefan Stamenov | 10 | 0 | 0.34 |