01. The use of Landsat 8 imagery for mapping natural forest states in Dak Lak province

Khôi Dương Đăng


Assessment of forest’s carbon storage level requires up-to-date information on the area of ​​natural forest states. The purpose of this study is to use the Landsat 8 satellite images to create the map of natural forest states in Dak Lak province, which provides the baseline data for estimating forest carbon storage in the province. The supervised classification algorithm in the Image Analysis of the ArcGIS 10.3 software was applied to classify natural forest states in the province. The classification results showed that the Landsat 8 image was suitable for distinguishing natural forest states. Poor deciduous broadleaf forests occupied the largest proportion (15.5%) of deciduous broadleaf forests, followed by very poor deciduous broadleaf forests (8.09%), and medium deciduous broadleaf forests (5.81%). Rich evergreen broadleaf forests, medium evergreen broadleaf forests and poor evergreen broadleaf forests accounted for 14.40%, 13.74% and 9.48% of the evergreen broadleaf forests, respectively. Rubber forest area occupied a small area (5.08%). In terms of overall accuracy, using Landsat 8 images can produce the map of natural forest states by approximately 90% with Kappa coefficient of 0.8 which is acceptable for estimating carbon storage of natural forest states.

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[1]. Sở Nông nghiệp và Phát triển nông thôn tỉnh Đắk Lắk (2020). Bản đồ hiện trạng rừng năm 2020.
[2]. Asim Banskota; Nilam Kayastha; Michael J. Falkowski; Michael A. Wulder; Robert E. Froese & Joanne C. White (2014). Forest Monitoring Using Landsat Time Series Data: A Review. Canadian Journal of Remote Sensing, 40:5, 362 - 384, DOI: 10.1080/07038992.2014.987376.
[3]. Brown, S., (1996). Present and potential roles of forests in the global
climate change debate. Unasylva, Vol. 185: pp. 3 - 9.
[4]. Anderson, J.R.; Hardy, E.E.; Roach, J.T.; Witmer, R.E., (1976). A land use and land cover classification system for use with remote sensor data. Government Printing Office: Washington, DC, USA.
[5]. Congalton, R.G., (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Enviroment. 37:35 - 46.
[6]. Congalton, R.G., (2001). Accuracy assessment and validation of remotely sensed and other spatial information. International Journal of Wildland Fire 10: 321 - 328. doi:10.1071/WF01031.
[7]. Congalton, R.G; K. Green., (2009). Assessing the accuracy of remotely sensed data: principles and practices. 2nd ed. Boca Raton, FL: CRC Press.
[8]. IPCC., (2006). IPCC Guidelines for national greenhouse gas inventories. Prepared by the Natinal Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T., Tanabe K., (eds). Published: IGES, Japan.
[9]. Thomlinson, J.R; Bolstad, P.V; Cohen, W.B., (1999). Coordinating methodologies for scaling landcover classifications from site-specific to global: steps toward validating global map products. Remote Sensing of Environment, 70, 16 - 28.
[10]. USGS., (2019). Landsat 8 data users handbook. The United State Geological Survey (USGS).


Khôi Dương Đăng
ddkhoi@hunre.edu.vn (Primary Contact)
Dương Đăng, K. (2021). 01. The use of Landsat 8 imagery for mapping natural forest states in Dak Lak province. Science Journal of Natural Resources and Environment, (38), 3–10. Retrieved from https://tapchikhtnmt.hunre.edu.vn/index.php/tapchikhtnmt/article/view/357

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