Paper
26 October 2013 Application of HJ-1A/B and ZY-3 remote sensing data for drought monitoring in Hubei Province China
He Huang, Yida Fan, Siquan Yang, Qi Wen, Donghua Pan, Chunbo Fan, Haixia He
Author Affiliations +
Proceedings Volume 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 892111 (2013) https://doi.org/10.1117/12.2031198
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
Drought is one kind of nature disasters in the world. It has characteristics of temporal-spatial inhomogeneity, wide affected areas and periodic happening. The economic loss and affected population caused by different droughts are the largest in all natural disasters. Remote sensing has the advantages of large coverage, frequent observation, repeatable observation, reliable information source and low cost. These advantages make remote sensing a vital contributor for drought disaster risk assessment and monitoring. In this paper, three drought monitoring models, such as Vegetation Condition Index (VCI), Temperature Vegetation Dryness Index (TVDI), and Water Supplying Vegetation Index (WSVI) had been selected to monitor the drought occurred from January 2012 to June 2012 in Hubei province, China. Two kinds of remote sensing data, including HJ-1A/B CCD/IRS and ZY-3, had been employed to assess the integrated risk of Hubei drought based on three drought monitoring models. The results shown that the risk of northwest regions and middle regions in Hubei province were higher than that in the other regions. The results also indicated that the extreme risk regions were located in Shiyan, Xiangyang, Suizhou and Jingmen.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
He Huang, Yida Fan, Siquan Yang, Qi Wen, Donghua Pan, Chunbo Fan, and Haixia He "Application of HJ-1A/B and ZY-3 remote sensing data for drought monitoring in Hubei Province China", Proc. SPIE 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 892111 (26 October 2013); https://doi.org/10.1117/12.2031198
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KEYWORDS
Remote sensing

Vegetation

Data modeling

CCD image sensors

Satellites

Near infrared

Agriculture

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