Hyperspectral Analysis of Pine Wilt Disease to Determine an Optimal Detection Index

Authors

So-Ra Kim, Korea Forest Conservation Association
Woo-Kyun Lee, Korea University - Korea
Chul-Hee Lim, Korea University - Korea
Moonil Kim, Korea University - Korea
Menas Kafatos, Chapman UniversityFollow
Seung-Ho Lee, Neighbor System, Seoul
Sung-Soon Lee, Korea Institute of Geoscience and Mineral Resources

Document Type

Article

Publication Date

3-3-2018

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.

Comments

This article was originally published in Forests, volume 9, in 2018. DOI: 10.3390/f9030115

Recommended Citation

Kim, S.-R.; Lee, W.-K.; Lim, C.-H.; Kim, M.; Kafatos, M.C.; Lee, S.-H.; Lee, S.-S. Hyperspectral Analysis of Pine Wilt Disease to Determine an Optimal Detection Index. Forests 2018, 9, 115. doi: 10.3390/f9030115

Peer Reviewed

1

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This work is licensed under a Creative Commons Attribution 4.0 License.