Global Land Cover Mapping: A Review and Uncertainty Analysis

By Russ Congalton, Jianyu Gu, Kamini Yadav, Prasad S. Thenkabail, Mutlu Ozdogan. Remote Sensing, 2014

321 citations recorded by the source corpus.

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.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/1bddcf9d4a1542176db50c121d3e9877ff9dec74

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Jordi Inglada
2. J. Xiao
3. Xing Li

In Media Technology

1. Jordi Inglada
2. Shiyong Cui

Citations in the corpus

• A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data (2019, background): “Therefore, considering the large uncertainty of current land cover classifications [63] and the potential land cover changes during the study period, we predicted SIF only based on EVI, PAR, VPD, and air temperature.”
• Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series (2017, background): “These low accuracies make change detection analysis problematic and comparison between different LC products difficult [27].”