TITLE:
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
AUTHORS:
Naijia Zhang, Ryutaro Tateishi
KEYWORDS:
Global Land Cover; GLCNMO; Training Data; Accuracy
JOURNAL NAME:
Advances in Remote Sensing,
Vol.2 No.4,
December
26,
2013
ABSTRACT:
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.