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Land cover classification using maximum likelihood method (2000 and 2019) at Khandgait valley in Mongolia

IOP conference series. Earth and environmental science, 2019-11, Vol.381 (1), p.12054 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1755-1307 ;EISSN: 1755-1315 ;DOI: 10.1088/1755-1315/381/1/012054

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  • Title:
    Land cover classification using maximum likelihood method (2000 and 2019) at Khandgait valley in Mongolia
  • Author: Norovsuren, B ; Tseveen, B ; Batomunkuev, V ; Renchin, T ; Natsagdorj, E ; Yangiv, A ; Mart, Z
  • Subjects: Classification ; Deforestation ; Forest management ; Land cover ; Land use ; Landsat ; Landsat satellites ; Maximum likelihood method ; Remote sensing ; Satellite imagery ; Valleys
  • Is Part Of: IOP conference series. Earth and environmental science, 2019-11, Vol.381 (1), p.12054
  • Description: Promoting the recovery of forest management has been identified as a key priority by the Government of Mongolia. The objective of this paper is to define land cover classification and land cover change in Khandgait valley between 2000 and 2019. The study area is located in the North central part of Mongolia in Bulgan province. Landsat satellite images with 30m resolution were applied. For the validation, we used ground truth measurements. Maximum-likelihood method was applied in this study. The output map of land cover classification was analyzed and compared with the ground truth measurements. The results showed an overall accuracy of 86.5% and 89.0% for the 2000 and 2019 images, respectively. Land cover changes were quantitatively presented with the results of accuracy assessments between 2000 and 2019. In the future, we need to improve forest monitoring and analyze forest management using satellite images.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1755-1307
    EISSN: 1755-1315
    DOI: 10.1088/1755-1315/381/1/012054
  • Source: Open Access: IOP Publishing Free Content
    AUTh Library subscriptions: ProQuest Central
    IOPscience (Open Access)
    Alma/SFX Local Collection
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