30-year changes of natural forests under human activities in the Indochina peninsula - case studies in Cambodia, Laos and Vietnam


  • Nguyen Dinh Duong Institute of Geography, Vietnam Academy of Science and Technology, Hanoi, Vietnam
  • Lai Vinh cam Institute of Geography, Vietnam Academy of Science and Technology, Hanoi, Vietnam




Natural forest, landsat, time series, simplified spectral patterns, automated classification, human activity, phenology


Natural forests are a basic component of the earth's ecology. It is essential for biodiversity, hydrological cycle regulation, and environmental protection. Natural forests are gradually degraded and reduced due to timber logging, conversion to cropland, production forests, commodity trees, and infrastructure development. Decreasing natural forests results in loss of valuable habitats, land degradation, soil erosion, and imbalance of water cycle on the regional scale. Thus, operational monitoring of natural forest cover change has been in the interest of scientists for a long time. Current forest mapping methods using remotely sensed data provide limited capability to separate natural forests and planted forests. Natural forest statistics are often generated using official forestry national reports that have different bias levels due to different methodologies applied in different countries in forest inventory. Over the last couple of decades, natural forests have been over-exploited for various reasons. This led to forest cover degradation and water regulation capability, which results in extreme floods and drought of a watershed in general. This situation demands an urgent need to develop a fast, reliable, and automated method for mapping natural forests. In this study, by applying a new method for mapping natural forests by Landsat time series, the authors succeeded in mapping changes of natural forests of Cambodia, Laos, and Vietnam from 1989 to 2018. As a focused study area, three provinces: Ratanakiri of Cambodia, Attapeu of Laos, and Kon Tum of Vietnam were selected. The study reveals that after 30 years, 51.3% of natural forests in Ratanakiri, 27.8% of natural forests in Attapeu, and 50% of natural forests in Kon Tum were lost. Classification results were validated using high spatial resolution imagery of Google Earth. The overall accuracy of 99.3% for the year 2018 was achieved.


Download data is not yet available.


Banskota A., Kayastha N., Falkowski M.J., Wulder M.A., Froese R.E., White J.C., Banskota A., Kayastha N., Falkowski M.J., Michael A., Froese R.E., White J.C., Monitoring F., Landsat U., Banskota A., Kayastha N., Falkowski M.J., Michael A., Froese R.E., White J.C., Forest C., Pacific S., Centre F., Canada N.R., 2014. Forest Monitoring Using Landsat Time Series Data : A Review Forest Monitoring Using Landsat Time Series Data : A Review, 8992, 362-384. https://doi.org/10.1080/07038992.2014.987376.

Charalambides C.A., 2002. Enumerative Combinatorics, in: Enumerative Combinatorics, 40-42.

Coppin P.R., Bauer M.E., 1996. Digital change detection in forest ecosystems with remote sensing imagery Digital Change Detection in Forest Ecosystems with Remote Sensing Imagery. Remote sensing reviews, 13, 207-2341. https://doi.org/10.1080/02757259609532305.

Dennis R.A., Meijaard E., Nasi R., Gustafsson L., 2008. Biodiversity Conservation in Southeast Asian Timber Concessions : a Critical Evaluation of Policy Mechanisms and Guidelines. Ecology and Society, 13.

Duong N.D., 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 and Spatial Information Sciences - ISPRS Archives, 41, 987991. https://doi.org/10.5194/isprsarchives-XLI-B8-987-2016.

Duong N.D., 2018. Decomposition of Landsat 8 OLI Images by Simplified Spectral Patterns for Land Cover Mapping. 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), 1-13.

Duong N.D., 2020. Automated classification of natural forests with Landsat time series uisng simplified spectral patterns, in: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci, XLIII-B3-2020, 983-988. https://doi.org/https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-983-2020.

Duong N.D., Hang L.M., Tuan T.A., Ouyang Z., 2017. Development of a spectral-pattern-analysis-based method for automated water body extraction using Landsat image data: A case study in central Vietnam and southern Laos. Limnology and Oceanography: Methods, 15, 945-959. https://doi.org/10.1002/lom3.10215.

FAO, 1995. FAO: Forest Resources Assessment 1990: Global Synthesis: FAO Forestry Paper 124.

FAO, 2005. State of the World’s Forests.

Hughes A.C., 2017. Understanding the drivers of S outheast A sian biodiversity loss. Ecosphere 8, e01624. 10.1002/ecs2.1624.

Stibig H., Stolle F., Dennis R., Feldkötter C., 2007. Forest Cover Change in Southeast Asia - The Regional Pattern -. Biogeosciences.

USGS, 2015. Landsat 8 (L8) Data Users Handbook. Earth Resources Observation and Science (EROS) Center.


USGS, 2019a. Landsat 8 (L8) Data Users Handbook. Book 8.


White C.J., Wulder M.A., Hobart G.W., Luther J.E., Hermosilla T., Griffiths P., Guindon L., 2014. Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science Pixel-Based Image Compositing for Large-Area Dense. Canadian Journal of Remote Sensing, 40, 192-212. https://doi.org/10.1080/07038992.2014.945827.



How to Cite

Nguyen Dinh, D., & Lai Vinh, C. (2021). 30-year changes of natural forests under human activities in the Indochina peninsula - case studies in Cambodia, Laos and Vietnam . VIETNAM JOURNAL OF EARTH SCIENCES, 43(3). https://doi.org/10.15625/2615-9783/16196