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Open AccessArticle

The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results

Hydroinformatics Lab, DICA, Politecnico di Milano, Como Campus, via Valleggio 11, Como 22100, Italy
National Geomatics Center of China, 28 Lianhuachi West Road, Haidian District, Beijing 100830, China
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Remote Sens. 2015, 7(4), 4191-4212;
Received: 26 November 2014 / Revised: 11 March 2015 / Accepted: 23 March 2015 / Published: 8 April 2015
As result of the “Global Land Cover Mapping at Finer Resolution” project led by National Geomatics Center of China (NGCC), one of the first global land cover datasets at 30-meters resolution (GlobeLand30) has been produced for the years 2000 and 2010. The first comprehensive accuracy assessment at a national level of these data (excluding some comparisons in China) has been performed on the Italian area by means of a benchmarking with the more detailed land cover datasets available for some Italian regions. The accuracy evaluation was based on the cell-by-cell comparison between Italian maps and the GlobeLand30 in order to obtain the confusion matrix and its derived statistics (overall accuracy, allocation and quantity disagreements, user and producer accuracy), which help to understand the classification quality. This paper illustrates the adopted methodology and procedures for assessing GlobeLand30 and reports the obtained statistics. The analysis has been performed in eight regions across Italy and shows very good results: the comparison of the datasets according to the first level of Corine Land Cover nomenclature highlights overall accuracy values generally higher than 80%. View Full-Text
Keywords: land cover; GlobeLand30; accuracy assessment; Italy land cover; GlobeLand30; accuracy assessment; Italy
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MDPI and ACS Style

Brovelli, M.A.; Molinari, M.E.; Hussein, E.; Chen, J.; Li, R. The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results. Remote Sens. 2015, 7, 4191-4212.

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