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Module 5: Coastal Flooding

 For the first part of this module, I compared before and after pictures of the New Jersey coast, pre and post Hurricane Sandy. I created DEM's from lidar data to compare the before and after. Below, you can see the magnitude of erosion and accretion on the Jersey coast due to the Hurrican Sandy in 2012. 


In the second portion of this lab, I created a storm surge impact model for Collier County, Fl. This was a very educational assignment and I learned a lot. We not only created a storm surge model for the coast of Florida, but we also did so utilizing both Lidar and USGS DEM data to compare accuracy. I first began by converting the Lidar layer to meters, and then reclassified both to only include data where the elevation is under 1 meter. I used the region group tool to clump together into one large area the land along the coast, and isolated it (via select by attributes) to only analysis this single large connected area. Lastly, converted that large area for both Lidar and USGS to a polygon. Here is where the analysis got very tricky and I struggled greatly. 

From here, I had to spatially join the buildings layer with the Lidar/USGS polygon respectively. I accomplished this task simply, and further was able to select by attributes only the areas where the join_count equaled one [where the building was found to be impacted by the flood] to create a new layer. What flabergasted me, was how do I determine the errors of Omission and Comission? How can I compare two different layers (Lidar/USGS)? 

I tried many tools [Overlay, Intersect, Select by location] but I keep getting errors or fails. After connecting with a peer and compairng our thought processes, I realized I needed to do a spatial join of the Lidar/USGS layers that contained all buildings. I spatially joined the impacted LIDAR to the impacted USGS layer. This resulted in a join_count column with 1 where both overlapped and 0 where they did not overlap. I was able to use select by attributes in the table to determine the top half of the formula for error of omission. I spatially joined the LIDAR layer to the USGS layer and did the same thing to determine the error of commission.

Below is my final map.


I do not think the assumptions we made in this analysis are accurate. First, we excluded low-lying areas that are that were not connected to the large polygon touching water. These are likely to get flooded as well and excluding them can cause an error of omission. I think we should also have taken into account the urban planning of the area for water runoff, percent of permeable surfaces to impermeable surfaces, the efficiency of collecting water runoff, nearby large bodies of water, and land topography. Lastly, we assumed the land was at a uniform height, which is very unlikely, and I believe that is what caused our high errors of commission. 



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