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Radar image and data fusion for natural hazards characterisation

International Journal of Image and Data Fusion

By:
, , , , and
DOI: 10.1080/19479832.2010.499219

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Abstract

Fusion of synthetic aperture radar (SAR) images through interferometric, polarimetric and tomographic processing provides an all - weather imaging capability to characterise and monitor various natural hazards. This article outlines interferometric synthetic aperture radar (InSAR) processing and products and their utility for natural hazards characterisation, provides an overview of the techniques and applications related to fusion of SAR/InSAR images with optical and other images and highlights the emerging SAR fusion technologies. In addition to providing precise land - surface digital elevation maps, SAR - derived imaging products can map millimetre - scale elevation changes driven by volcanic, seismic and hydrogeologic processes, by landslides and wildfires and other natural hazards. With products derived from the fusion of SAR and other images, scientists can monitor the progress of flooding, estimate water storage changes in wetlands for improved hydrological modelling predictions and assessments of future flood impacts and map vegetation structure on a global scale and monitor its changes due to such processes as fire, volcanic eruption and deforestation. With the availability of SAR images in near real - time from multiple satellites in the near future, the fusion of SAR images with other images and data is playing an increasingly important role in understanding and forecasting natural hazards.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Radar image and data fusion for natural hazards characterisation
Series title:
International Journal of Image and Data Fusion
DOI:
10.1080/19479832.2010.499219
Volume
1
Issue:
3
Year Published:
2010
Language:
English
Publisher:
Taylor & Francis
Publisher location:
Philadelphia, PA
Contributing office(s):
Cascades Volcano Observatory
Description:
26 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
International Journal of Image and Data Fusion
First page:
217
Last page:
242