Journal of Forest and Environmental Science

Institute of Forest Science, kangwon National University (강원대학교 산림과학연구소)
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Detection of Trees with Pine Wilt Disease Using Object-based Classification Method

  • Park, Jeongmook (Department of Forest Management, College of Forest and Environmental Sciences, Kangwon National University) ;
  • Sim, Woodam (Department of Forest Management, College of Forest and Environmental Sciences, Kangwon National University) ;
  • Lee, Jungsoo (Department of Forest Management, College of Forest and Environmental Sciences, Kangwon National University)
  • Received : 2016.08.02
  • Accepted : 2016.11.07
  • Published : 2016.11.30
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Abstract

In this study, regions infected by pine wilt disease were extracted by using object-based classification method (OB-infected region), and the characteristics of special distribution about OB-infected region were figured out. Scale 24, Shape 0.1, Color 0.9, Compactness 0.5, and Smoothness 0.5 was selected as the objected-based, optimal weighted value of OB-infected region classification. The total accuracy of classification was high with 99% and Kappa coefficient was also high with 0.97. The area of OB-infected region was approximately 90 ha, 16% of the total area. The OB-infected region in Age class V and VI was intensively distributed with 97% of the total. Also, The OB-infected region in Middle and Large DBH class was intensively distributed with 99% of the total. In terms of the topographic characteristics of OB-infected region, the damages occurred approximately 86% below the altitude of 200 m, and occurred 91% with a slope less than 10 degree. The damage occurred a lot in low hilly mountain and undulating slope. In addition, the accessibility to road and residential area from OB-infected region was less than 300 m in large part. Overall, it was figured out that artificial effect is stronger than natural effect with regard to the spread of pine wilt disease.

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References

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