GIS and Natural Disasters

This story will exemplify how GIS can aid in natural disaster preparation, analysis, and post damage assessment.

Richard Schmidt

Mantoloking, NJ - Mapping Coastal Change

This map depicts the Mantoloking, New Jersey coastline after Hurricane Sandy. The areas depicted in red are those that suffered extensive erosion and destruction while the blue areas represent areas of build-up likely associated with building debris or sand accretion.

Collier County, FL - Storm Surge Modeling

This map shows an analysis of building risk using 1-meter storm surge models in Collier County, Florida. Two DEMs were initially used: one from the USGS and another derived from LiDAR. These were used to create two 1-meter storm surge models, which were then used to determine the number of buildings that would be affected by a 1-meter storm surge.

Hurricane Sandy - Mapping Storm Paths

Using GIS to map storm paths is essential, as it allows for the creation of a timeline that provides information about when a storm was at its weakest and strongest points. This historical data can be used to better understand future storms and improve public safety, enhance emergency response, identify where critical infrastructure might be needed in future planning, and provide support for recovery aid and reconstruction after a storm.

Ortley Beach, NJ - Damage Assessment

These images demonstrate how GIS can effectively be utilized as a tool for conducting post-disaster damage assessments. This type of assessment provides crucial information for rescue efforts, recovery, and future redevelopment plans.

Mantoloking, NJ - Mapping Coastal Change

This map depicts the Mantoloking, New Jersey coastline after Hurricane Sandy. The areas depicted in red are those that suffered extensive erosion and destruction while the blue areas represent areas of build-up likely associated with building debris or sand accretion.

Elevation models and LiDAR data are valuable tools for creating products that aid in post-disaster analysis. These products can be used by policymakers and first responders to identify the areas that require the most attention.

Collier County, FL - Storm Surge Modeling

This map shows an analysis of building risk using 1-meter storm surge models in Collier County, Florida. Two DEMs were initially used: one from the USGS and another derived from LiDAR. These were used to create two 1-meter storm surge models, which were then used to determine the number of buildings that would be affected by a 1-meter storm surge.

Models like these can be used to help predict the extent of natural disasters and help citizens in disaster preparation. Many local government entities provide custom web mapping applications that contain flood zone data for their citizens.

Hurricane Sandy - Mapping Storm Paths

Using GIS to map storm paths is essential, as it allows for the creation of a timeline that provides information about when a storm was at its weakest and strongest points. This historical data can be used to better understand future storms and improve public safety, enhance emergency response, identify where critical infrastructure might be needed in future planning, and provide support for recovery aid and reconstruction after a storm.

All this data and information can be used to help mitigate the future impacts of natural disasters which ultimately helps protect lives and property.

Ortley Beach, NJ - Damage Assessment

These images demonstrate how GIS can effectively be utilized as a tool for conducting post-disaster damage assessments. This type of assessment provides crucial information for rescue efforts, recovery, and future redevelopment plans.

In this assessment, point data was created for structures within a study area and then classified according to storm damage level. A pre-storm coastline was then established along with 100m, 200m, and 300m buffer zones that were used to identify possible patterns between location and the extent of storm damage. The analysis above found that 75% (6 of 8) of the structures within 100 meters of the pre-storm coastline were completely destroyed. The level of destruction was notably lower between 100m and 300m. If this type of analysis is conducted accurately, it could potentially be extrapolated to nearby areas, which would significantly decrease time spent on conducting damage assessments.