Global land cover products have been created for global environmental studies by several institutions and organizations. The Global Mapping Project coordinated by the International Steering Committee for Global Mapping (ISCGM) has been periodically producing global land cover datasets asone of the eight basic global datasets. It has produced a new fifteen-second (approximately 500 m resolution at the equator) global land cover dataset – GLCNMO2013 (or GLCNMO version 3). This paper describes the method of producing GLCNMO2013. GLCNMO2013 has 20 land cover classes, and they were mapped by improved methods from GLCNMO version 2. In GLCNMO2013, five classes,which are urban, mangrove, wetland, snow/ice, and waterwere independently classified. The remaining 15 classes were divided into 4 groups and mapped individually by supervised classification. 2006 polygons of training data collected for GLCNMO2008 were used for supervised classification. In addition, about 3000 polygons of new training data were collected globally using Google Earth, MODIS Normalized Difference Vegetation Index (NDVI) seasonal change patterns, existing regional land cover maps, and existing four global land cover products. The primary data of this product were Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013. GLCNMO2013 was validated at 1006 sampled points. The overall accuracy of GLCNMO2013 was 74.8%, and the overall accuracy for eight aggregated classes was 90.2%. The accuracy of the GLCNMO2013 was not improved compared with the GLCNMO2008 at heterogeneous land covers. It is necessary to prepare the training data for mosaic classes and heterogeneous land covers for improving the accuracy.

Global tree-cover percentage is an important parameter used to understand the global environment. However, the available global percentage tree-cover products are few. Producing a new global-scale data set facilitates comparison analysis among maps. Our study was undertaken to map tree-cover percentage on a global scale with better accuracy than previous studies. In this study, we estimated the tree-cover percentage on a global scale at a pixel size of 500 m using a modified supervised regression tree algorithm from the Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2008. Training data were derived from high-resolution images displayed in Google Earth and created by linear mixture simulation. The estimation model was modified to fit the reference data, which were randomly collected from the global area. The estimation result was validated at 1106 random sample points. The root mean square error (RMSE) between estimated and observed tree cover was 13.8%. The produced map was also compared with existing global data sets. The RMSE of our result was better than that of two existing global percentage tree-cover data sets (about 1.8% and 9.2% lower than Vegetation Continuous Fields MOD44B data set and Global Map – Percent Tree Cover data set, respectively). In our result and all other data sets, the accuracy was lower for forests than for other areas. When the produced map was compared with the Vegetation Continuous Fields MOD44B data set, the low agreement between them was concentrated in closed forests of Colombia, India, and Indonesia, and closed to open forests of Central Africa.