The classification of forest tree species using satellite imagery in Mongolia

Dolgorsuren Sanjjav, Chi Farn Chen, Shou Hao Chiang

Research output: Contribution to conferencePaperpeer-review

Abstract

Forest is a very important ecosystem and natural resource for living things. Based on forest inventories, government is able to make decisions to converse, improve and manage forests in a sustainable way. Field work for forestry investigation is difficult and time consuming, because it needs intensive physical labor and costs, especially surveying in a widely and remotely mountainous area. A reliable forest inventory can give us more accurate and timely information to develop new and efficient approaches of solving problems in a forest area. The remote sensing technology have been recently used for forest investigation for large scale. To produce an informative forest inventory, forest attributes, including tree species and ages, are necessarily investigated. This research focuses on the classification of forest tree species in Erdenebulgan sum, Huwsgul province, Mongolia, using satellite imagery. The study area covers a forest area of 4230.1km2 and located in a high mountain region in northern Mongolia. Landsat 7 satellite imagery in July, 2011 were used in this study. Supervised classification, support vector machine (SVM), and 30×30 m digital elevation model (DEM), are applied to tree species classification. Result shows that main six different tree species were classified using Landsat imagery. Result of classification map compared with ground truth forest tree species map and overall accuracy was 66.2%.

Original languageEnglish
StatePublished - 2014
Event35th Asian Conference on Remote Sensing 2014: Sensing for Reintegration of Societies, ACRS 2014 - Nay Pyi Taw, Myanmar
Duration: 27 Oct 201431 Oct 2014

Conference

Conference35th Asian Conference on Remote Sensing 2014: Sensing for Reintegration of Societies, ACRS 2014
Country/TerritoryMyanmar
CityNay Pyi Taw
Period27/10/1431/10/14

Keywords

  • Forest inventory
  • Landsat image
  • Terrain variables
  • Tree species classification

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