Comparative assessment of thematic accuracy of GLC maps for specific applications using existing reference data |
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| Institution: | 1. Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, Wageningen, 6708 PB, The Netherlands;2. GOFC-GOLD Land Cover Project Office, Droevendaalsesteeg 3, Wageningen, 6708 PB, The Netherlands;3. Rijkswaterstaat, Ministry of Infrastructure and the Environment, Utrecht, 3500 GE, The Netherlands;1. Department of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry (ESF), NY 13210, USA;2. C-CORE and Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada;3. The Canada Centre for Mapping and Earth Observation, Ottawa K1S 5K2, Canada;1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;1. Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;2. Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720-3114, USA;3. Joint Center for Global Change Studies, Beijing 100875, China;4. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;5. United States Geological Survey, Reston, VA 20192, USA;6. Ministry of Education Key Laboratory for Geospatial Technology for the Middle and Lower Yellow River Regions, College of Environment and Planning, Henan University, Kaifeng 475004, China;7. Google, Inc., 1600 Amphitheatre Pkwy., Mountain View, CA 94043, USA |
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| Abstract: | Inputs to various applications and models, current global land cover (GLC) maps are based on different data sources and methods. Therefore, comparing GLC maps is challenging. Statistical comparison of GLC maps is further complicated by the lack of a reference dataset that is suitable for validating multiple maps. This study utilizes the existing Globcover-2005 reference dataset to compare thematic accuracies of three GLC maps for the year 2005 (Globcover, LC-CCI and MODIS). We translated and reinterpreted the LCCS (land cover classification system) classifier information of the reference dataset into the different map legends. The three maps were evaluated for a variety of applications, i.e., general circulation models, dynamic global vegetation models, agriculture assessments, carbon estimation and biodiversity assessments, using weighted accuracy assessment. Based on the impact of land cover confusions on the overall weighted accuracy of the GLC maps, we identified map improvement priorities. Overall accuracies were 70.8 ± 1.4%, 71.4 ± 1.3%, and 61.3 ± 1.5% for LC-CCI, MODIS, and Globcover, respectively. Weighted accuracy assessments produced increased overall accuracies (80–93%) since not all class confusion errors are important for specific applications. As a common denominator for all applications, the classes mixed trees, shrubs, grasses, and cropland were identified as improvement priorities. The results demonstrate the necessity of accounting for dissimilarities in the importance of map classification errors for different user application. To determine the fitness of use of GLC maps, accuracy of GLC maps should be assessed per application; there is no single-figure accuracy estimate expressing map fitness for all purposes. |
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| Keywords: | Global land cover Accuracy assessment Comparison Reference dataset User applications Weighted accuracy assessment |
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