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Distribution Characteristics Analysis of Pine Wilt Disease Using Time Series Hyperspectral Aerial Imagery
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  • Journal title : Korean Journal of Remote Sensing
  • Volume 31, Issue 5,  2015, pp.385-394
  • Publisher : The Korean Society of Remote Sensing
  • DOI : 10.7780/kjrs.2015.31.5.3
 Title & Authors
Distribution Characteristics Analysis of Pine Wilt Disease Using Time Series Hyperspectral Aerial Imagery
Kim, So-Ra; Kim, Eun-Sook; Nam, Youngwoo; Choi, Won Il; Kim, Cheol-Min;
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Pine wilt disease has greatly damaged pine forests not only in East Asia including South Korea and China, but also in European region. The damage caused by pine wood nematode (Bursaphelenchus xylophilus) is expressed in bundles within stands and rapidly spreading, however, present field survey methods have limitations to detecting damaged trees at regional level. This study extracted the damaged trees by pine wilt disease using time series hyperspectral aerial photographs, and analyzed their distribution characteristics. Hyperspectral aerial photographs of 1 meter spatial resolution were obtained in June, September, and October. Damaged trees by pine wilt disease were extracted using Normalized Difference Vegetation Index (NDVI) and Vegetation Index green (VIgreen) of the September photograph. Among extracted damaged trees, dead trees with leaves and without leaves were classified, and the spectral reflectance values from the photographs obtained in June, September, and October were compared to extract new outbreaks in September and October. Based on the time series dispersion of extracted damaged trees, nearest neighbor analysis was conducted to analyze distribution characteristics of the damaged trees within the region where hyperspectral aerial photographs were acquired. As a result, 2,262 damaged trees were extracted in the study area, and 604 dead trees (dead trees in last year) with leaves in relation to the damaged time and 300 and 101 newly damaged trees in September and October were classified. The result of nearest neighbor analysis using the data shows that aggregated distribution was the dominant pattern both previous and current year in the study area. Also, 80% of the damaged trees in current year were found within 60 m of dead trees in previous year.
distribution characteristics analysis;hyperspectral aerial photograph;pine wilt disease;time series;nearest neighbor distance;
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