Journal of the Korean Society of Environmental Restoration Technology (한국환경복원기술학회지)

The Korea Society of Environmental Restoration Technology (한국환경복원기술학회)
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Applicability of Supervised Classification for Subdividing Forested Areas Using SPOT-5 and KOMPSAT-2 Data

산림지역 분류를 위한 SPOT-5 및 KOMPSAT-2 영상의 감독분류 적용성

  • Choi, Jaeyong (Department of Environment & Forest Resources, Chungnam National University) ;
  • Lee, Sanghyuk (Department of Environment & Forest Resources, Chungnam National University) ;
  • Lee, Sol Ae (Department of Environment & Forest Resources, Chungnam National University) ;
  • Ji, Seung Yong (Department of Environment & Forest Resources, Chungnam National University) ;
  • Lee, Peter Sang-Hoon (Institute of Agricultural Science, Chungnam National University)
  • 최재용 (충남대학교 산림환경자원학과) ;
  • 이상혁 (충남대학교 산림환경자원학과) ;
  • 이솔애 (충남대학교 산림환경자원학과) ;
  • 지승용 (충남대학교 산림환경자원학과) ;
  • 이상훈 (충남대학교 농업과학연구소)
  • Received : 2015.03.18
  • Accepted : 2015.04.10
  • Published : 2015.04.30
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Abstract

In order to effectively manage forested areas in South Korea on a national scale, using remotely sensed data is considered most suitable. In this study, utilizing Land coverage maps and Forest type maps of national geographic information instead of collecting field data was tested for conducting supervised classification on SPOT-5 and KOMPSAT-2 imagery focusing on forested areas. Supervised classification were conducted in two ways: analysing a whole area around the study site and/or only forested areas around the study site, using Support Vector Machine. The overall accuracy for the classification on the whole area ranged from 54.9% to 68.9% with kappa coefficients of over 0.4, which meant the supervised classification was in general considered moderate because of sub-classifying forested areas into three categories (i.e. hardwood, conifer, mixed forests). Compared to this, the overall accuracy for forested areas were better for sub-classification of forested areas probably due to less distraction in the classification. To further improve the overall accuracy, it is needed to gain individual imagery rather than mosaic imagery to use more spetral bands and select more suitable conditions such as seasonal timing. It is also necessary to obtain precise and accurate training data for sub-classifying forested areas. This new approach can be considered as a basis of developing an excellent analysis manner for understanding and managing forest landscape.

Keywords

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