Module 4: Coastal Flooding and Storm Surge Impact Modeling

This week applied ArcGIS Pro towards analyzing hurricane impacts to buildings using LIDAR and USGS DEM raster data. Part one of the assignment included building data and USGS .las data collected pre and post hurricane Sandy in New Jersey. By utilizing the RASTER CALCULATOR tool to subtract the pre-hurricane data from the post hurricane data, I was able to create a raster dataset that shows areas with negative and positive changes in elevation. 

As shown in the map above, red areas are areas that experiences a loss in elevation. Specifically the red areas in the center of the map can be assumed to be buildings/structures demolished during the storm. The blue areas are most likely areas were sediment was deposited and resulted in a gain in elevation. One other thing to note is the change in elevation running parallel to the shoreline, shown as a linear orange area. This is most likely a loss of elevation due to the erosion of the sand dunes during the storm. The black building outlines were most likely created after the storm, as it does not include the buildings that were destroyed from the hurricane. This type of analysis shows details likely used by insurance companies to determine areas that are the most at risk of hurricane impacts. 

Part 2 of the module created a model using two types of raster DEM datasets, USGS and LIDAR. The LIDAR dataset has a horizontal resolution of 25 ft, while the USGS dataset is much coarser, with a horizontal resolution of 100 ft. Therefore, it was assumed that the LIDAR dataset was more accurate in this exercise. A buildings dataset with Building Type attribute info was used to compare. The SELECT BY LOCATION tool and creating two new fields within the buildings attribute table, one field for USGS and one field for LIDAR. The SELECT BY LOCATION tool was used to select all the buildings that intersect the USGS flood zone and give those buildings a value of 1. Then by switching the selection to select all values that are outside the flood zone, I could then give those buildings a value of 0. Therefore 1 = within and 0 = not within. Then the same procedure was used to select all the buildings that intersect the LIDAR flood zone. By using these Boolean values, we could then create a query expression to select all buildings that are within the LIDAR only, the USGS only, both LIDAR and USGS, and buildings that are outside both flood zones. The map and table shown below indicates the building type and whether the impacted building was located within the LIDAR predicted flood zone (1 meter assumed storm surge) or the USGS predicted flood zone (1 meter assumed storm surge). 

 The errors of omission and commission percentages were calculated using the equations below. 

Error of omission (%) = # buildings impacted based on Lidar DEM AND NOT impacted based on

USGS DEM / total # buildings impacted based on Lidar DEM *100%

Error of commission (%) = # buildings impacted based on USGS DEM AND NOT impacted based on

Lidar DEM) / total # all buildings impacted based on Lidar DEM *100%

By calculating the error of omission and commission, we could determine the accuracy of each model. In this model, the LIDAR dem model is more accurate, which is seen in the difference in the predicted flood zone between the LIDAR and USGS DEM derived model. This reiterates the importance of using high resolution data for analysis.