The results of the assessment accuracy of the land cover mapping showed that of overall accuracy, of producer accuracy, and of user’s accuracy. | Vietnam Journal of Earth Sciences, 39(4), 393-406, DOI: Vietnam Academy of Science and Technology (VAST) Vietnam Journal of Earth Sciences Land cover mapping in Yok Don National Park, Central Highlands of Viet Nam using Landsat 8 OLI images Nguyen Viet Luong1,2*, Ryutaro Tateishi2, Akihiko Kondoh2, Ngo Duc Anh4, Nguyen Thanh Hoan3, Luu The Anh3 1 Remote Sensing Application Department, Space Technology Institute, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet str., Cau Giay dist., Hanoi, Vietnam 2 Center for Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 2638522, Japan 3 Institute of Geography, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet str., Cau Giay dist., Hanoi, Vietnam 4 Vietnam National Space Center, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet str., Cau Giay dist., Hanoi, Vietnam Received 27 September 2016. Accepted 29 September 2017 ABSTRACT Over the past four decades, remote sensing has more useful and effective contributions in the classification, mapping of land cover, forest cover map. Out of these achievements, there are still limitations in the application, especially in the tropical region, because of the diversity and abundance of land cover objects, of course including tropical forests, where are the vegetation status varies due to the seasons of the year. In this study, we selected Landsat 8 satellite imagery from both dry and rainy seasons for the purpose of building detailed land cover maps of Yok Don National Park, Central Highlands of Vietnam where has two major forest types (a) deciduous broadleaf forest and (b) evergreen broadleaf forest. The land cover mapping was based on supervised classification approach. The results of forest cover area showed that total Evergreen broad-leaved forests (rich, medium and poor) area are 25,578 ha () and total Dry open dipterocarps forests (rich, medium
