GIS5100 Module 4: Hazards - Coastal Flooding

    Module 4 in Applications in GIS focused on coastal flooding. For this week’s lab, we became familiar with procedures to assess coastal flooding and perform analysis on the differences between LiDAR and USGS data in application.

First, we focused on the area of Mantoloking, New Jersey and how Hurricane Sandy (2012) impacted the area before and after the Category 1 storm event. We used data from NOAA for this.

We used a Spatial ETL Tool to translate the .laz files that were downloaded from NOAA for the pre- and post- Sandy event into .las files. This helped us to start creating Digital Elevation Models (DEMs) of the before and after event data. Once we converted the .las files into TINs, we then used the TIN to Raster tool and used the Raster Calculator to determine the difference in erosion/accretion before and after Hurricane Sandy.

Mantoloking, New Jersey coastal impact from Hurricane Sandy (2012)

The red indicates erosion, while the blue indicates accretion or buildup. We can infer from the data at this point that Hurricane Sandy was damaging to the study area.

   

    For the next portion of this lab, we needed to determine the effect of storm surge in Florida by comparing LiDAR DEMs and USGS DEMs with the amount of Florida buildings in the Collier County, Florida area. We assumed that storm surge was 1 meter. We also wanted to see the differences in data and results from both DEM types – see map below to see the outcome of this lab goal.

LiDAR DEM and USGS DEM data modeling indicating storm surge in Collier County, Florida


There was a lot of different tools and methods used in this portion – I first ensured that both provided DEMs were in the same units (meters) by converting the LiDAR DEM into meters from feet using the Raster Calculator tool. I then used Reclassify to adjust the data to create a new raster that would show elevation of 1 meter or less on both DEM LiDAR (meters) and USGS DEM. I used the Extract By Attributes tool to determine the values of the area that was the largest mass connecting to land. I then used the Raster to Polygon tool to convert the output rasters we received from the Extract by Attributes tool for both the USGS and LIDAR data. I did not simplify or create multiparts in doing so. I used the Spatial Join tool to spatially join the layers for each DEM to the Florida Buildings layer. I further spatially joined those output USGS and LiDAR layers together. This allowed to Select By Attributes for specific buildings that were present in both/either USGS and LiDAR layers and used the data to calculate error of omission (%), which are the buildings impacted based on LiDAR data but not USGS data, and error of commission (%), which are the buildings impacted based on USGS data but not LiDAR data. In this lab, we consider the LiDAR to be the “true” data.

The assumptions we made in this portion of the lab are not very realistic – employing a uniform height of storm surge at 1 meter and excluding the low-lying disconnected areas prevents more accuracy of the results. To make a more realistic analysis if the storm surge, we should consider factors such as the permeability vs. impermeability of the area, topography, and locations of storm drains and/or sewers.

 

 

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