GIS 5100 Module 4 - Coastal Flooding

This module of Applications in GIS was the most time-intensive, complex, and in-depth assignment yet. The lab was broken up into three separate analyses, each looking at the effects of coastal flooding. The first part of the lab, compared two Digital Elevation Models and illustrated their differences via a raster output. The first DEM was a LiDAR dataset that was obtained pre- Hurricane Sandy and the second was a LiDAR dataset that was obtained post- Hurricane Sandy. This portion of the lab was fairly straightforward; it was essentially taking the values of the post-storm dataset and subtracting the values of the pre-storm dataset. The output was the raster displayed in the map below. Some noteworthy points are as follows:

• The red areas are negative changes in elevation. It is apparent that these are mostly found along the shoreline and are representative of beach overwash, beach erosion, and buildings that were demolished during the storm.
• The lighter shades of the raster output represent areas that had less [or no] elevation changes due to Hurricane Sandy.
• The blue areas are positive changes in elevation. This is most likely due to water buildup, sand buildup, and debris buildup. 
• The red strip on the westerly border was excluded from the visual analysis due to its location along the border; it would not make sense for the elevation to gradually build up with an abruptly defined edge of negative elevation change.

The second portion of the lab was also very straightforward. For this analysis, we were given a dataset of New Jersey and tasked with calculating the percentage of Cape May County that was affected [flooded] by the two-meter storm surge that occurred during the 2012 hurricane. To accomplish this, we reclassified the data into two classifications: elevations that were less than or equal to two meters and all other elevations. After the data was reclassified, the raster was converted into a vector-based polygon, the area of the polygon was calculated, and the percentage of affected land was obtained by clipping the dataset to the Cape May County feature class and then solving the following equation:

Area Affected = [Total Area ≤ 2 meters] / [Total Area of Cape May County]

The third, and final, analysis of this assignment was a culmination of all steps executed throughout the first two portions. The dataset was a study area that is located in Collier County, Florida [southwest region of the state]. For this analysis, we were provided with a LiDAR dataset and a United State Geological Survey DEM, or Digital Elevation Model. For this particular scenario, the objective was to see how much land would be affected by each DEM for a one-meter storm surge. This was obtained by following the steps of the second portion of the lab for each DEM, the one from USGS and the LiDAR. After these affected areas were created, quantitative analyses could be done to determine the extent of damaged buildings within the study area. The map is displayed below. 

Although I am not entirely sure what caused all the confusion within this portion of the lab, I do know that stepping away, focusing on something else, and taking a few deep breaths were the most effective remedies. On a few of the functions utilized throughout this lab, simply shutting down the software, reopening it, and rerunning the tool was the solution to fixing incorrectly derived outputs. At other times, taking a few steps back in the process, slowing down, and reworking the lab was the solution to fixing other incorrectly derived outputs. I am confidently pleased with the deliverables of this lab, but there are some tools I want to become more accustomed to working with: spatial joins and table joins. As many roads often lead to the same destination, knowing the correct tools to use can make the workflow more streamlined, thereby increasing efficiency.