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

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.

The Use of VARI, GLI, and VIgreen Formulas in Detecting Vegetation In aerial Images

“…In another attempt, an experiment ten vegetation indices formula to detect pine wilt disease on trees. They found that Equations 1 and 2 can detect infected trees earlier than other vegetation formulas (Kim et al, 2018). However, they also found that Equation 3 has better modeling than the others (Yuliantika et al, 2016).…”

Research Progress on the Early Monitoring of Pine Wilt Disease Using Hyperspectral Techniques

“…For example, pine wilt disease was diagnosed using hyperspectral methods by Kim’s group. In their research, significant differences were observed in vegetation indices such as the normalized difference vegetation Index (NDVI), the green normalized difference vegetation index (GNDVI), the plant senescence reflectance index (PSRI), and the pigment specific normalized difference (PSND), beginning on 20 August 2012 [ 51 ]. Our group study the hyperspectral characteristic of healthy and withered masson pine, and Figure 2 shows the research results.…”

Molecular Cloning and Expression Analysis of the Endogenous Cellulase Gene MaCel1 in Monochamus alternatus

“…Similar to other xylophagous insects, M. alternatus mainly digests cellulose to obtain nutrients throughout the growth and development process, and this process requires the participation of cellulase for better digestion of the plant material [26,27]. Pine wilt disease has many characteristics, such as high mortality and fast onset, is widespread, and is called pine cancer by scholars [28][29][30]. PWD causes huge damage to the forestry economy [31,32].…”