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 (22.14%) and total Dry open dipterocarps forests (rich, medium and poor) area are 88,435 ha (76.54%), and another object is 1,531.86 ha (1.33%). The detailed land cover map with the 15-m resolution provided and is useful for forest management in the study area. The results of the assessment accuracy of the land cover mapping showed that 88.37% of overall accuracy, 89.35% of producer accuracy, and 90.60% of user’s accuracy. References Busch J. and Engelmann J., 2015. The Future of Forests: Emissions from Tropical Deforestation with and without a Carbon Price, 2016-2050. CGD Working Paper 411. Washington, DC: Center for Global Development. 42p. http://www.cgdev.org/publication/future-forests. D’Annunzio R., Lindquist E., MacDicken K. G., 2014. Global forest land-use change from 1990 to 2010: an update to a global remote sensing survey of forests. Food and Agriculture Organization of the United Nations. Report from FAO and European Commission Joint Research Centre, 6p. Dinh D.N., 2016. Automated Classification of Land Cover Using Landsat 8 Oli Surface Reflectance Product and Spectral Pattern Analysis Concept-Case Study in Hanoi, Vietnam. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41. FAO, 2015. Global Forest resources assessment 2015. How are the world’s forests changing? FAO, Rome 2015, 56p. Firoozynejad M., Torahi A.A., 2017. Evaluation of IRS1D-LISS-III and Landsat 8-OLI Images for Mapping in Maroon Riparian Forest. Iran. J Geogr Nat Disast, 7(198), 2167-0587. Gómez C., White J.C., Wulder M.A., 2016. Optical remotely sensed time series data for land cover classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 55-72. Jia K., Wei X., Gu X., Yao Y., Xie X., Li B., 2014. Land cover classification using Landsat 8 operational land imager data in Beijing, China. Geocarto International, 29(8), 941-951. Keenan R.J., Reams G.A., Achard F., De Freitas J.V., Grainger A., Lindquist E., 2015. Dynamics of global forest area: results from the FAO global forest resources assessment 2015. Forest Ecology and Management, 352, 9-20. Laben C.A., Brower B.V., 2000. U.S. Patent No.6, 011, 875. Washington, DC: U.S. Patent and Trademark Office. Li C., Wang J., Wang L., Hu L., Gong P., 2014. Comparison of classification algorithms and training sample sizes in urban land classification with Landsat thematic mapper imagery. Remote Sensing, 6(2), 964-983. Luong N.V., Tateishi R., Hoan N.T., Tu T.T., 2015. Forest Change and Its Effect on Biomass in Yok Don National Park in Central Highlands of Vietnam Using Ground Data and Geospatial Techniques. Advances in Remote Sensing, 4(02), 108. Ministry of Agriculture and Rural Development (MARD), 2009. Circular 34/TT-BNN issued by (MARD) of Vietnamese government. Quy định tiêu chí xác định và phân loại rừng (in Vietnamese), translated by Luong. N.V-Regulation on criteria for identifying and classifying forest (in English), 5, 3. NASA, 2017. https://landsat.gsfc.nasa.gov/operational-land-imager-oli/. Nguyen Ngoc Thach, Le Thi Khanh Hoa, V.S., Canh P.X., Hong P.V., 2014. Using NDVI differencing for mapping of depterocarp forest in Savanket province, Lao PDR. ACRS 20-08-2014. Nguyen Nghia Thin et al., 2008. Update Information of Flora and Plant’s Resources of Yok Don National Park, Daclak Province in 2008. Report. Nguyen Xuan Canh; Quynh H.Q., Anh. L.T, 2009. Report on Conservation Planning and Sustainable Development of Yok Don National Park in 2010-2020. Ministry of Agriculture and Rural Development of Vietnam: Hanoi, Vietnam. Patenaude G., Milne R., Dawson T.P., 2005. Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol. Environmental Science & Policy, 8(2), 161-178. Phung Ngoc Lan et al., 2006. Natural forest ecosystems Viet Nam. Ministry of Agriculture and Rural Development of Vietnam (MARD). Technical report, 95p. Ridder R.M., 2007. Global forest resources assessment 2010. options and recommendations for a global remote sensing survey of forests. FAO For. Resour. Assess. Programme Work. Pap, 141. Roy D.P. et al., 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote sensing of Environment, 145, 154-172. Stibig HJ Achard F., Fritz S., 2004. A new forest cover map of continental Southeast Asia derived from SPOT- VEGETATION satellite imagery, Appl. Veg. Sci., 7, 153-162. Tateishi R., Hoan N.T., Kobayashi T., Alsaaideh B., Tana G., Phong D.X., 2014. Production of Global Land Cover Data-GLCNMO2008. Journal of Geography and Geology, 6(3), 99. Thai Van Trung, 1998. Chapter IV. In The Tropical Forest Ecosystem in Vietnam, Science and Technics Publishing House, Hanoi, 291p. Turner W., Spector S., Gardiner N., Fladeland M., Sterling E., Steininger M., 2003. Remote sensing for biodiversity science and conservation. Trends in ecology & evolution, 18(6), 306-314. UNESCO Paris, 1973. International...