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Detection of Trees with Pine Wilt Disease Using Object-based Classification Method

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초록

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.

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.

참고문헌 (29)

  1. Baatz M, "eCognition Professional: User Guide 4" (2004), Definiens-Imaging
  2. Blaschke T, "Object based image analysis for remote sensing", ISPRS J Photogramm Remote Sens, 65, pp.2~16 (2010)
  3. 최진호, "Google Earth를 활용한 포항 송도해수욕장의 해안선 변화 감시(2003-2010)", 환경영향평가, 20(3), pp.257~267 (2011), 한국환경영향평가학회
  4. 함보영, "항공사진을 이용한 훼손 산지 탐지 연구", 한국지형공간정보학회지, 21(3), pp.11~17 (2013), 한국지형공간정보학회[DOI]
  5. Kim CS, "Site characteristics in the incident areas of pine wood nematode", J Korean For Soc, pp.128~129 (2006)
  6. 김동수, "소나무재선충의 매개충인 솔수염하늘소 성충의 우화 생태", 한국응용곤충학회지, 42(4), pp.307~313 (2003), 한국응용곤충학회
  7. 김의명, "라이다 자료와 정사영상을 이용한 산림지역의 수목영역추출", 한국지형공간정보학회지, 21(2), pp.25~32 (2013), 한국지형공간정보학회[DOI]
  8. 김응남, "지상형 원격탐사기술을 이용한 소나무 재선충 피해조사", 한국지역지리학회지, 14(1), pp.84~92 (2008), 한국지역지리학회
  9. 염종민, "변화지역 탐지를 위한 시계열 KOMPSAT-2 다중분광 영상의 MAD 기반 상대복사 보정에 관한 연구", 한국지리정보학회지, 15(3), pp.66~80 (2012), 한국지리정보학회
  10. 김중범, "IKONOS 위성영상과 GPS를 이용한 소나무 재선충 피해지역 추출에 관한 연구", 한국임학회지, 92(4), pp.362~355 (2003), 한국임학회
  11. Kim JB, "Analysis of topography spectral characteristics for extracting pine wilt disease using IKONOS satellite image", J Korean Assoc Geogr Inf Stud (2002)
  12. 김준범, "GIS와 GPS를 이용한 소나무재선충병 피해지 항공정밀예찰 기법 개발", 한국지리정보학회지, 13(1), pp.28~34 (2010), 한국지리정보학회
  13. 김민호, "WorldView-2 위성영상의 방사 해상도와 영상분할에 관한 연구", 국토지리학회지, 47(1), pp.37~46 (2013), 국토지리학회
  14. 김소라, "소나무재선충병 감염목의 분광반사 특성 구명", 한국임학회지, 102(4), pp.499~505 (2013), 한국임학회[DOI]
  15. Kim WK, "A study on correction of satellite imagery and accuracy enhancement of land-cover classification for GIS application", Res Geogr, 19, pp.48~61 (1992)
  16. Pine wilt disease of ecological characteristics research, "Korea Forest Research Institute", KFRI, 167~193 (2009)
  17. Resources characteristics and assessment by bajor species in the southern region, "Korea Forest Research Institute.", KFRI, 7~25 (2014)
  18. Prevention pine wilt disease policy agenda, "Korea Rural Economic Institute", KREI, 2~5 (2014)
  19. Ku CY, "The scale characteristics of satellite imagery with spatial resolution: focus on the landcover c1assification of coastal wetland", J Geogr, 40, pp.1~165 (2000)
  20. 이현직, "디지털항공사진영상을 이용한 객체기반 토지피복분류", 한국지형공간정보학회지, 19(1), pp.105~113 (2011), 한국지형공간정보학회
  21. 이정빈, "객체 기반 영상 분류에서 최적 가중치 선정과 정확도 분석 연구", 대한원격탐사학회지, 23(6), pp.521~528 (2007), 대한원격탐사학회
  22. 이정빈, "지상용 초분광 카메라를 이용한 소나무재선충병 감염목 분광 특성 분석", 대한원격탐사학회지, 30(5), pp.665~675 (2014), 대한원격탐사학회[DOI]
  23. 이승호, "Detection of The Pine Trees Damaged by Pine Wilt Disease using High Resolution Satellite and Airborne Optical Imagery", 대한원격탐사학회지, 23(5), pp.409~420 (2007), 대한원격탐사학회
  24. 이상현, "RapidEye 위성영상의 시계열 NDVI 및 객체기반 분류를 이용한 북한 재령군의 논벼 재배지역 추출 기법 연구", 한국농공학회논문집, 56(3), pp.55~64 (2014), 한국농공학회
  25. Mun IS, "Expression pattern and control method of pine wilt disease", Tree Health, 19, pp.26~34 (2014)
  26. 나현섭, "객체기반 분류기법을 이용한 토지피복 특성분석 - 강원도 인제군의 DMZ지역 일원을 대상으로 -", 한국지리정보학회지, 17(2), pp.121~135 (2014), 한국지리정보학회[DOI]
  27. Robert CWJ, "Object-based classification vs. pixel-based classification: comparative importance of multi-resolution imagery", The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-4, pp.C7 (2010), ~
  28. 손민호, "공간통계학적 방법에 의한 소나무 재선충 피해의 자연적 확산유형분석", 한국임학회지, 95(3), pp.240~249 (2006), 한국임학회
  29. Yun SL, "Vegetation change at the damage area of pine wilt disease", J Agric Life Sci, 40, pp.5~12 (2006)

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