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Proceedings Paper

Use hyperspectral remote sensing technique to monitoring pine wood nomatode disease preliminary
Author(s): Lin Qin; Xianghong Wang; Jing Jiang; Xianchang Yang; Daiyan Ke; Hongqun Li; Dingyi Wang
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Paper Abstract

The pine wilt disease is a devastating disease of pine trees. In China, the first discoveries of the pine wilt disease on 1982 at Dr. Sun Yat-sen's Mausoleum in Nanjing. It occurred an area of 77000 hm2 in 2005, More than 1540000 pine trees deaths in the year. Many districts of Chongqing in Three Gorges Reservoir have different degrees of pine wilt disease occurrence. It is a serious threat to the ecological environment of the reservoir area. Use unmanned airship to carry high spectrum remote sensing monitoring technology to develop the study on pine wood nematode disease early diagnosis and early warning and forecasting in this study. The hyper spectral data and the digital orthophoto map data of Fuling District Yongsheng Forestry had been achieved In September 2015. Using digital image processing technology to deal with the digital orthophoto map, the number of disease tree and its distribution is automatic identified. Hyper spectral remote sensing data is processed by the spectrum comparison algorithm, and the number and distribution of disease pine trees are also obtained. Two results are compared, the distribution area of disease pine trees are basically the same, indicating that using low air remote sensing technology to monitor the pine wood nematode distribution is successful. From the results we can see that the hyper spectral data analysis results more accurate and less affected by environmental factors than digital orthophoto map analysis results, and more environment variable can be extracted, so the hyper spectral data study is future development direction.

Paper Details

Date Published: 25 October 2016
PDF: 5 pages
Proc. SPIE 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology, 101561L (25 October 2016); doi: 10.1117/12.2247214
Show Author Affiliations
Lin Qin, Yangtze Normal Univ. (China)
Xianghong Wang, Yangtze Normal Univ. (China)
Jing Jiang, Yangtze Normal Univ. (China)
Xianchang Yang, Yangtze Normal Univ. (China)
Daiyan Ke, Yangtze Normal Univ. (China)
Hongqun Li, Yangtze Normal Univ. (China)
Dingyi Wang, Yangtze Normal Univ. (China)
Xi'an Jiatong Univ. (China)
Univ. of New Brunswick (Canada)

Published in SPIE Proceedings Vol. 10156:
Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology

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