Comparing Different Methods of Calculating Red-Edge and Blue-Edge Inflection Position from Hyperspectral Data to Early Detect Tree Disease

Pine wilt disease (PWD) is a destructive pine disease with a fast onset rate, high mortality rate, and high difficulty in prevention and control. Accurate and efficient monitoring is the foundation of disease prevention and control. This study aims to explore the potential of red-edge and blue-edge inflection positions from hyperspectral data in monitoring physiological changes and early detecting PWD. We obtained samples of Japanese pines and measured their needles hyperspectral data (wavelength range: 350-2500nm) and physiological parameter data, and obtained hyperspectral drone images of Chinese red pine (wavelength range: 400-1000nm). We used linear fitting algorithms to investigate the linear relationships between vegetation indices and physiological parameters and tested the sensitivity of vegetation indices for PWD early identification using linear discriminant analysis (LDA). The results showed that the indices of the blue-edge inflection position and the red-edge inflection position can reflect the changes in needle pigment content and moisture content, with the 4 point linear interpolation of the blue-edge point showing the best fit for water content. At the needle scale, linear interpolation indices of the blue-edge inflection position showed high accuracy in identifying both early and full-stage PWD. However, the accuracy of these indices decreases when using drone data. We concluded that the developed blue-edge inflection position vegetation indices can be used for PWD early identification. However, further optimization of band selection is needed to improve their application with drone data.