학술논문

Remote Sensing Contributions to Prediction and Risk Assessment of Natural Disasters Caused by Large-Scale Rift Valley Fever Outbreaks
Document Type
Periodical
Source
Proceedings of the IEEE Proc. IEEE Proceedings of the IEEE. 100(10):2824-2834 Oct, 2012
Subject
General Topics for Engineers
Engineering Profession
Aerospace
Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Geoscience
Nuclear Engineering
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Power, Energy and Industry Applications
Communication, Networking and Broadcast Technologies
Photonics and Electrooptics
Diseases
Remote sensing
Disaster management
Vegetation mapping
Risk management
Meteorology
Predictive models
Arthropod-borne virus
climate variability
El Niño/Southern Oscillation (ENSO)
normalized difference vegetation index
predictive model
Rift Valley fever virus (RVFV)
risk management and mitigation
Language
ISSN
0018-9219
1558-2256
Abstract
Remotely sensed vegetation measurements for the last 30 years combined with other climate data sets such as rainfall and sea surface temperatures have come to play an important role in the study of the ecology of arthropod-borne diseases. We show that epidemics and epizootics of previously unpredictable Rift Valley fever (RVF) are directly influenced by large-scale flooding associated with the El Niño/Southern Oscillation (ENSO). This flooding affects the ecology of disease transmitting arthropod vectors through vegetation development and other bioclimatic factors. This information is now utilized to monitor, model, and map areas of potential RVF outbreaks and is used as an early warning system for risk reduction of outbreaks to human and animal health, trade, and associated economic impacts. The continuation of such satellite measurements is critical to anticipating, preventing, and managing disease epidemics and epizootics and other climate-related disasters.