Name
Abstract | ||
|---|---|---|
This paper presents a rigorous validation of five widely used global land cover products, i.e., GLCC (Global Land Cover Characterization), UMd (University of Maryland land cover product), GLC2000 (Global Land Cover 2000 project data), MODIS LC (Moderate Resolution Imaging Spectro-radiometer Land Cover product) and GlobCover (GLOBCOVER land cover product), and a national land cover map GLCD-2005 (Geodata Land Cover Dataset for year 2005) against an independent reference data set over China. The land cover reference data sets in three epochs (1990, 2000, and 2005) were collected on a web-based prototype system using a sampling-based labeling approach. Results show that, in China, the highest overall accuracy is observed in GLCD-2005 (72.3%), followed by MODIS LC (68.9%), GLC2000 (65.2%), GlobCover (57.7%) and GLCC (57.2%), while UMd has the lowest accuracy (48.6%); all of the products performed best in representing "Trees" and "Others", well with "Grassland" and "Cropland", but problematic with "Water" and "Urban" across China in general. Moreover, in respect of GLCD-2005, there are significant accuracy differences across seven geographical locations of China, ranging from 46.3% in the Southwest, 77.5% in the South, 79.2% in the Northwest, 80.8% in the North, 81.8% in the Northeast, 82.6% in the Central, to 89.0% in the East. This study indicates that a regionally focused land cover map would in fact be more accurate than extracting the same region from a globally produced map. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3390/rs70810589 | REMOTE SENSING |
Keywords | Field | DocType |
reference data,stratified sampling | Reference data (financial markets),Data set,China,Remote sensing,Visual interpretation,Grassland,Stratified sampling,Sampling (statistics),Geology,Land cover | Journal |
Volume | Issue | Citations |
7 | 8 | 1 |
PageRank | References | Authors |
0.36 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| yan bai | 1 | 56 | 7.60 |
| Min Feng | 2 | 204 | 20.90 |
| hao jiang | 3 | 59 | 17.96 |
| Juanle Wang | 4 | 16 | 11.47 |
| yingzhen liu | 5 | 1 | 0.36 |