Production of Global Land Cover Data – GLCNMO2013

Toshiyuki Kobayashi, Ryutaro Tateishi, Bayan Alsaaideh, Ram Sharma, Takuma Wakaizumi, Daichi Miyamoto, Xiulian Bai, Bui Long, Gegentana Gegentana, Aikebaier Maitiniyazi, Destika Cahyana, Alifu Haireti, Yohei Morifuji, Gulijianati Abake, Rendy Pratama, Naijia Zhang, Zilaitigu Alifu, Tomohiro Shirahata, Lan Mi, Kotaro Iizuka, Aimaiti Yusupujiang, Fedri Rinawan, Richa Bhattarai and Dong Phong

Journal of Geography and Geology, 2017, vol. 9, issue 3, 1

Abstract: 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.

Date: 2017
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