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Detection of trees with Pine Wilt Disease in Chuncheon, South Korea using artificial intelligence technique by drone images

첫 페이지 보기
  • 발행기관
    강원대학교 산림과학연구소 바로가기
  • 간행물
    강원대학교 산림과학연구소 학술대회 바로가기
  • 통권
    2019 International Symposium of Institute of Forest Science (2019.09)바로가기
  • 페이지
    pp.104-104
  • 저자
    Chang-Wook Lee
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A450350

원문정보

초록

영어
Pine wilt disease (PWD) has recently caused substantial pine tree losses in South Korea. PWD is considered a severe problem due to the importance of pine trees to Korean people, so this problem must be handled appropriately. Previously, we examined the history of PWD and found that it had already spread to some regions of South Korea; these became our study area. Early detection of PWD is required. We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD. Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees. To differentiate healthy pine trees from those with PWD, we produced a land cover (LC) map from drone images collected from the villages of Anbi and Wonchang by classifying them using several methods, i.e., a pixel-based and object-based image analysis (OBIA). Furthermore, compared the accuracy of two types of global positioning system (GPS) data, collected using drone and hand-held devices, for identifying the locations of trees with PWD. We then divided the drone images into six LC classes for each study area and found that the object-based classification was more accurate than the pixel-based classification at classifying trees with PWD. In terms of the GPS data, we used two type of hand-held GPS device. GPS device 1 is corrected, while the GPS device 2 is uncorrected device. The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang. However, in Anbi, we obtained better results from GPS device 2 than from GPS device 1. In Anbi, the error in the data from GPS device 1 was 7.08 meters, while that of the GPS device 2 data was 0.14 meters. In conclusion, object-based classification is superior to the pixel-based classification, even both method can distinguish between healthy trees and those with PWD based on LC data. On the other hand, there were some differences between the hand-held and drone GPS datasets from both areas.

키워드

Pine wilt disease Drone remote sensing Artificial neural network Support vector machine Object-based image analysis OBIA Global positioning system

저자

  • Chang-Wook Lee [ Division of Science Education College of Education, Kangwon National University, Republic of Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    강원대학교 산림과학연구소 [Institute of Forest Science Kangwon National University]
  • 설립연도
    1975
  • 분야
    농수해양>임학
  • 소개
    강원대학교부설산림과학연구소(이하 “연구소”라 한다)는 산림에 관한 제반 학술적 연구를 통하여 산림자원의 효용을 밝히고 임업 및 임산업의 발전에 기여함을 목적으로 한다.

간행물

  • 간행물명
    강원대학교 산림과학연구소 학술대회
  • 간기
    부정기
  • 수록기간
    2017~2024
  • 십진분류
    KDC 526 DDC 634

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