Applications in GIS - M4 Coastal Flooding

 For our fourth lab in this course we examined coastal flooding from tropical events utilizing digital elevation models. These models for sites in New Jersey and South Florida were provided, and we used spatial and three dimensional analysis to determine areas most impacted, initial damage, and make predictions for flooded areas. 

For the New Jersey site, we used LiDAR datasets from before and after the impact of Hurricane Sandy. A theme of this module was converting LiDAR data into rasters and polygons. Tools like LAS Datast to TIN, TIN to Raster changed the three dimensional dataset into a two dimensional raster with elevation values, and by using Raster Calculator between the pre-hurricane and post-hurricane raster layers I created a new raster that just displayed change. By selecting an appropriate color ramp, rasters like this can be used to highlight the differences in elevation between two datasets over a period of time. 

A raster with negative elevation change in red and positive elevation change in dark blue. Areas in red indicate where there used to be buildings and sand that no longer exist.

One of the last assignments in the New Jersey area was to take a raster with coastal area imagery and reclassify it with the Reclassify tool to display only the areas that would theoretically be affected by a two meter storm surge. To do this I reclassified with natural breaks and set an upper limit of 2, this meant the raster now showed only areas with an elevation between -37 and 2 meters. I used the Raster to Polygon tool to make a multipart polygon feature class from this shape, and calculated geometry with this shapes area and the area of a Cape May County Clip to determine the percentage of Cape May County would have been impacted by a 2 meter storm surge. 

While Florida and New Jersey sit at different latitudes, both states have a large amount of low, non-draining land, and are at risk for storm surge from squalls and hurricanes. Moving to a dataset from South Florida I used digital elevation models from LiDAR and USGS to perform a flood prediction analysis. 

The DEM raster files had to be saved to the local computer, and for easier comparison I converted the LiDAR dataset to meters instead of feet with the Times tool and a constant value of 0.3048. From there I used the Reclassify tool to create rasters with values that had an upper limit of 1, displaying the areas only with an elevation of 1 meter or less. This did return every area that was below 1 meter, which was unhelpful for displaying storm surge since comes in from the coast and doesn't reach unconnected lakes or low areas. I ran the Region Group tool for both, then Extract by Attributes for each layer to return rasters that represented just the flooded connected area as moving in from the coast. I ran Raster to Polygon and got a feature classes with flood predictions from USGS data and LiDAR data. 

Just looking at the basic polygons, it was clear they covered different amounts of land. 

To look at the number of structures affected by each layer combined the LiDAR and USGS data with a buildings footprint using Spatial Join, this added fields to the building layer based on whether or not that building was within the flood prediction polygon. After using Select By Attributes to tabulate the number of different building types intersected by the LiDAR and USGS-based flood predictions, it became clear that there were large differences between the number of buildings in these prediction maps. Because I already had a layer with buildings and both polygons, I changed the symbology of my map to display which structures were covered by each USGS and LiDAR flood prediction. 

Differences in properties covered by floodmaps based on the USGS and LiDAR datasets. The USGS dataset includes far more structures than the LiDAR dataset. 

We were encouraged to use overlays to calculate the number of omissions and commissions (additions) between the USGS and the LiDAR layer, and calculate a percent error given that the LiDAR was known to be more accurate. I feel confident I would be able to calculate this with overlays, however when I attempted this - even as it was written in the assignment - my ArcPro application ran for several hours each time without a result, and had to be force terminated. In order to get around this limitation, I populated new fields in the combined table using Calculate and If Else statements with buildings that were included by USGS but excluded by LiDAR, and vice versa. I then used Select By Attributes on these new fields to get the number of omissions and commissions, then Calculated the percentage error for each. 

Using the "long way" to calculate the number of omissions and commissions when my ArcPro started stalling on longer Python logic entries.

Overall, the LiDAR map is more conservative than the USGS map, indicating fewer areas at risk of a 1 meter storm surge.