Abstract
Bursaphelenchus xylophilus, the pine wood nematode (PWN) which causes pine wilt disease, is currently a serious problem in East Asia, including in Japan, Korea, and China. This paper investigates the hyperspectral analysis of pine wilt disease to determine the optimal detection indices for measuring changes in the spectral reflectance characteristics and leaf reflectance in the Pinus thunbergii (black pine) forest on Geoje Island, South Korea. In the present study, we collected the leaf reflectance spectra of pine trees infected with pine wilt disease using a hyperspectrometer. We used 10 existing vegetation indices (based on hyperspectral data) and introduced the green-red spectral area index (GRSAI). We made comparisons between non-infected and infected trees over time. A t-test was then performed to find the most appropriate index for detecting pine wilt disease-infected pine trees. Our main result is that, in most of the infected trees, the reflectance changed in the red and mid-infrared wavelengths within two months after pine wilt infection. The vegetation atmospherically resistant index (VARI), vegetation index green (VIgreen), normalized wilt index (NWI), and GRSAI indices detected pine wilt disease infection faster than the other indices used. Importantly, the GRSAI results showed less variability than the results of the other indices. This optimal index for detecting pine wilt disease is generated by combining red and green wavelength bands. These results are expected to be useful in the early detection of pine wilt disease-infected trees.
Highlights
Pine wilt disease, caused by Bursaphelenchus xylophilus, the pine wood nematode (PWN), is one of the most damaging emerging pest problems in forests around the world [1,2,3,4,5]
The main purpose of this paper is to investigate the hyperspectral analysis of pine wilt disease-infected trees to determine the optimal detection indices via changes in spectral reflectance characteristics and leaf reflectance
This study investigated changes in leaf reflectance spectra along with the hyperspectral analysis of pine wilt disease to determine the optimal detection indices
Summary
Pine wilt disease, caused by Bursaphelenchus xylophilus, the pine wood nematode (PWN), is one of the most damaging emerging pest problems in forests around the world [1,2,3,4,5]. Forests 2018, 9, 115 controlling the spread of pine wilt disease [7,10]; early detection of infected trees is of paramount concern [11]. Controlling the spread of the disease involves early detection of infected trees based on frequent monitoring, exterminating PWN before its emergence to keep the disease contained, and taking preventative measures such as prohibiting the removal of wood from affected areas. Finding infected trees through a field study is not easy, since the areas thought to be damaged are quite large and the topographical characteristics of forests makes direct access to all necessary areas difficult [9,11]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
