Remote Sens. 2014, 6(12), 12070-12093; doi:10.3390/rs61212070
Global Land Cover Mapping: A Review and Uncertainty Analysis
1
Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA
2
State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, No. 19, XinJieKouWai St., HaiDian District, Beijing 100875, China
3
US Geological Survey, 2255 N. Gemini Drive, Flagstaff, AZ 86001, USA
4
Department of Forest and Wildlife Ecology, University of Wisconsin, 1710 University Ave., Room 285, Madison, WI 53726, USA
â€
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 10 September 2014 / Revised: 6 November 2014 / Accepted: 24 November 2014 / Published: 3 December 2014
Abstract
Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of these global land cover projects including evaluating the sources of error and uncertainty is prudent and enlightening. Therefore, this paper describes our work in which we compared, summarized and conducted an uncertainty analysis of the four global land cover mapping projects using an error budget approach. The results showed that the classification scheme and the validation methodology had the highest error contribution and implementation priority. A comparison of the classification schemes showed that there are many inconsistencies between the definitions of the map classes. This is especially true for the mixed type classes for which thresholds vary for the attributes/discriminators used in the classification process. Examination of these four global mapping projects provided quite a few important lessons for the future global mapping projects including the need for clear and uniform definitions of the classification scheme and an efficient, practical, and valid design of the accuracy assessment. View Full-TextKeywords:
global land cover; uncertainty analysis; error budget; classification scheme; accuracy assessment
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Congalton, R.G.; Gu, J.; Yadav, K.; Thenkabail, P.; Ozdogan, M. Global Land Cover Mapping: A Review and Uncertainty Analysis. Remote Sens. 2014, 6, 12070-12093.
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