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Proceedings Paper

NDVI and RVI-based dry hot wind comparative monitoring research
Author(s): Ying Li; Huailiang Chen; Yu Zhang; Wensong Fang
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Paper Abstract

MERSI data are applied to generate NDVI and RVI change graphs for comparative monitoring of one large-area dry-hot wind disaster in the wheat-growing area of Henan province, analysis of the correlation of the NDVI variation and RVI variation arising from mild and severe dry-hot wind processes to the daily highest temperature, 14:00 ground wind speed of 10m and 14:00 humidity and establishment of a mono-factor and multi-factor regressive forecast model between vegetation index variation and disaster-causing atmospheric elements. The results show that the monitoring results of dry-hot wind disasters on the basis of two vegetation indexes highly conform to each other. In case of severe dry-hot wind process, the two vegetation index variations have a high relevance to the key meteorological elements, with R2 in the trinary linear regression model of meteorological elements being 0.706 and 0.708 respectively and the highest daily mean temperature passing the 0.05 significance level check. In case of mild dry-hot wind process, the two vegetation index variations have a very low relevance to the key meteorological elements and modeling is impossible and there is a high degree of difference between the variations of vegetation indexes of different stations, i.e. the lower the level of meteorological disaster is, the more telemetric data are needed to ensure the truthful disaster loss monitoring results and the more important field management measures as a defense against meteorological disasters.

Paper Details

Date Published: 22 October 2018
PDF: 8 pages
Proc. SPIE 10777, Land Surface and Cryosphere Remote Sensing IV, 107770Y (22 October 2018); doi: 10.1117/12.2324256
Show Author Affiliations
Ying Li, China Meteorological Administration (China)
Henan Institute of Meteorological Science (China)
Huailiang Chen, China Meteorological Administration (China)
Henan Provincial Meteorological Service (China)
Yu Zhang, Zhengzhou Univ. (China)
Wensong Fang, China Meteorological Administration (China)
Henan Institute of Meteorological Science (China)


Published in SPIE Proceedings Vol. 10777:
Land Surface and Cryosphere Remote Sensing IV
Mitchell Goldberg; Jing M. Chen; Reza Khanbilvardi, Editor(s)

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