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A platform for research: civil engineering, architecture and urbanism
Discriminant Analysis of the Damage Degree Caused by Pine Shoot Beetle to Yunnan Pine Using UAV-Based Hyperspectral Images
Due to the increased frequency and intensity of forest damage caused by diseases and pests, effective methods are needed to accurately monitor the damage degree. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is an effective technique for forest health surveying and monitoring. In this study, a framework is proposed for identifying the severity of damage caused by Tomicus spp. (the pine shoot beetle, PSB) to Yunnan pine (Pinus yunnanensis Franch) using UAV-based hyperspectral images. Four sample plots were set up in Shilin, Yunnan Province, China. A total of 80 trees were investigated, and their hyperspectral data were recorded. The spectral data were subjected to a one-way ANOVA. Two sensitive bands and one sensitive parameter were selected using Pearson correlation analysis and stepwise discriminant analysis to establish a diagnostic model of the damage degree. A discriminant rule was established to identify the degree of damage based on the median value between different degrees of damage. The diagnostic model with R690 and R798 as variables had the highest accuracy (R2 = 0.854, RMSE = 0.427), and the test accuracy of the discriminant rule was 87.50%. The results are important for forest damage caused by the PSB.
Discriminant Analysis of the Damage Degree Caused by Pine Shoot Beetle to Yunnan Pine Using UAV-Based Hyperspectral Images
Due to the increased frequency and intensity of forest damage caused by diseases and pests, effective methods are needed to accurately monitor the damage degree. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is an effective technique for forest health surveying and monitoring. In this study, a framework is proposed for identifying the severity of damage caused by Tomicus spp. (the pine shoot beetle, PSB) to Yunnan pine (Pinus yunnanensis Franch) using UAV-based hyperspectral images. Four sample plots were set up in Shilin, Yunnan Province, China. A total of 80 trees were investigated, and their hyperspectral data were recorded. The spectral data were subjected to a one-way ANOVA. Two sensitive bands and one sensitive parameter were selected using Pearson correlation analysis and stepwise discriminant analysis to establish a diagnostic model of the damage degree. A discriminant rule was established to identify the degree of damage based on the median value between different degrees of damage. The diagnostic model with R690 and R798 as variables had the highest accuracy (R2 = 0.854, RMSE = 0.427), and the test accuracy of the discriminant rule was 87.50%. The results are important for forest damage caused by the PSB.
Discriminant Analysis of the Damage Degree Caused by Pine Shoot Beetle to Yunnan Pine Using UAV-Based Hyperspectral Images
Mengying Liu (author) / Zhonghe Zhang (author) / Xuelian Liu (author) / Jun Yao (author) / Ting Du (author) / Yunqiang Ma (author) / Lei Shi (author)
2020
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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