Show Summary Details

Page of

PRINTED FROM the OXFORD RESEARCH ENCYCLOPEDIA, NATURAL HAZARD SCIENCE ( (c) Oxford University Press USA, 2016. All Rights Reserved. Personal use only; commercial use is strictly prohibited. Please see applicable Privacy Policy and Legal Notice (for details see Privacy Policy).

date: 24 June 2017

Remote Sensing and Modeling of Natural Hazards

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Natural Hazard Science. Please check back later for the full article.

Remotely sensed data for the observation and analysis of natural hazards is becoming increasingly commonplace and accessible. Furthermore, the accuracy and coverage of such data is rapidly improving. In parallel with this growth are ongoing developments in computational methods to store, process, and analyze this data for a variety of geospatial needs. One such use of this geospatial data is for input and calibration for the modelling of natural hazards, such as the spread of wildfires, flooding, tidal inundation, and landslides. Computational models for natural hazards show increasing real world applicability, and it is only recently that the full potential of using remotely sensed data in these models is being understood and investigated. Some examples of geospatial data required for natural hazard modelling include:

• Elevation models derived from Radio Detection and Ranging (RADAR) and Light Detection and Ranging (LIDAR) techniques for flooding, landslide, and wildfire spread models.

• Accurate vertical datum calculations from geodetic measurements for flooding and tidal inundation models.

• Multi-spectral imaging techniques to provide land cover information for fuel types in wildfire models or roughness maps for flood inundation studies.

Accurate modelling of such natural hazards allows a qualitative and quantitative estimate of risks associated with such events. With increasing spatial and temporal resolution, there is an opportunity to investigate further value-added usage of remotely sensed data in the disaster-modelling context. Improving spatial data resolution allows greater fidelity in models allowing, for example, the impact of fires or flooding on individual households to be determined. Improvement of temporal data allows the incorporation into models of short- and long-term trends, such as the changing conditions through a fire season or the changing depth and meander of a water channel.