Author(s)

Naijia Zhang, Ryutaro Tateishi

Center of Environmental Remote Sensing (CEReS), Chiba University, Chiba, Japan.

It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLCNMO and GLOBCOVER), it has been identified that certain areas’ accuracy has dragged down the overall accuracy of these global land cover datasets. In this paper, those areas have been defined as the “unreliable area”. This study has recollected the training data from the “unreliable area” within the above four mentioned datasets and reclassified the “unreliable area” by using two supervised classifications. The final result has shown that compared with any existing datasets, a relatively higher accuracy has been able to achieve.

Global Land Cover; GLCNMO; Training Data; Accuracy

N. Zhang and R. Tateishi, "Integrated Use of Existing Global Land Cover Datasets for Producing a New Global Land Cover Dataset with a Higher Accuracy: A Case Study in Eurasia," Advances in Remote Sensing, Vol. 2 No. 4, 2013, pp. 365-372. doi: 10.4236/ars.2013.24039.