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PRINTED FROM the OXFORD RESEARCH ENCYCLOPEDIA, NATURAL HAZARD SCIENCE (oxfordre.com/naturalhazardscience). (c) Oxford University Press USA, 2019. All Rights Reserved. Personal use only; commercial use is strictly prohibited (for details see Privacy Policy and Legal Notice).

date: 17 July 2019

Summary and Keywords

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 these data for a variety of geospatial needs. One such use of this geospatial data is for input and calibration for the modeling 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 modeling include:

• elevation models derived from 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

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

Accurate modeling of such natural hazards allows a qualitative and quantitative estimate of risks associated with such events. With increasing spatial and temporal resolution, there is also an opportunity to investigate further value-added usage of remotely sensed data in the disaster modeling 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. Improving temporal data allows short and long-term trends to be incorporated into models, such as the changing conditions through a fire season or the changing depth and meander of a water channel.

Keywords: remote sensing, computational modeling, model-data fusion, floods, wildfires, landslides

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