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ABSTRACT
Highly accurate and detailed information on land cover products is crucial in studying global climate change and sustainable development. GlobeLand30 is the first global 30 m resolution Land cover (LC) product based on remote sensing data developed by Chinese scientists. GlobeLand30 has 10 first-level classes of products. However, no second-level classification products have been released. This study presents an integration method based on fuzzy theory and combines three 30 m resolution LC products, namely, National Land Cover Database 2011 (NLCD 2011), Fine Resolution Observation and Monitoring of Global Land Cover Segmentation 2010 (FROM-GLC-Seg) and global forest cover data (treecover2010) products, using the European Environment Information and Observation Network Action Group on Land Monitoring in the European (EAGLE) system of semantic translation. The conterminous United States region is adopted as the research area, and the GlobeLand30 (2010) forest class is subdivided into coniferous, broadleaf and mixed forests. Three different weighted voting methods are applied. The difference is whether the user or local accuracy of the source product is considered. The result shows that the respective accuracies of broadleaf, coniferous, and mixed forests are approximately 65.8%, 57.0%, and 40.9% of the weighted voting method without considering any product’s accuracy; 79.3%, 65.9%, and 58.4% of the weighted voting method considering the user accuracy; and 79.9%, 69.9%, and 59.3% of the weighted voting method considering the local accuracy of each product, respectively. The proposed method for refining the GlobeLand30 first class forest can be applied to other classes and land cover products.