A Study on Health Diagnosis of Zelkova serrata (Thunb.) Makino Using Hyperspectral Technique

Background and objective: Health diagnosis is essential for large, old trees that are designated and protected by the Act on the Preservation and Utilization of Natural Heritage and the Forest Protection Act. However, in the field, the overall health of trees is diagnosed by measuring the photosynthetic efficiency (PE) of some leaves. Therefore, this study aimed to examine the possibility of using hyper-spectral technology to replace the PE measurement as an approach for diagnosing the health of Zelkova serrata (Thunb.) Makino, which has a high rate of natural monument designation among South Korea’s natural heritage.Methods: The PE and hyper-spectral values were acquired from Zelkova leaves, and a Monte Carlo simulation was performed based on the spectral bands used to calculate vegetation indices (VI) in previous studies to select bands that have a high correlation with the PE. The explanatory power of these values on tree health was verified through a regression analysis of the VI and PE calculated from the selected bands, and the results of the study are as follows.Results: First, a variance analysis of the PE and VI of leaves found that there were differences depending on direction, and that the hyper-spectral values of leaves collected from the west were the most effective in measuring tree health. Second, it was found that it was effective to use 520 and 570 nm in the green region, 684 nm in the red region, 860 and 890 nm in the NIR region, 500 nm in the carotenoid region, and 531 nm in the xanthophyll region to derive the VI that replace PE metrics from the hyper-spectral values of leaves collected from the west. Third, PRI showed a very high effect with an explanatory power of 73.2% for the maximum quantum yield (Fv/Fm) regression equation. In other words, it was found that PRI can be used as an index derived from hyperspectral values to diagnose and record the healthy parts of Zelkova tree.Conclusion: By expanding the scope of this study to include Pinus densiflora and Ginkgo biloba, it is expected that if the utility and field applicability of this approach for diagnosing the health of large old trees are generally verified, it can be used for regular surveys of such trees designated as natural monuments in the future. Furthermore, it is expected that it can be expanded to diagnose park trees, street trees and the like, in addition to large, old trees.