Module 3: Coastal Flooding

 In this module, the geophysical effects of coastal flooding after a tropical storm were analyzed. LiDAR data was utilized to create a digital elevation model, and that DEM raster dataset was then used to measure erosion after the coastal flooding event. 


For Part A, I created a new Spatial ETL Tool and converted the provided data to the appropriate LAS data needed for the lab. After some initial analysis on the pre and post files, I converted the LAS data to TIN files, and then TIN files were converted to Raster. The Raster calculator was then used to subtract the presandy layer from the post sandy layer. The result was a raster that displays the elevation change produced from Hurricane Sandy, which could then be symbolized correctly to show how the Hurricane may have caused erosion and affected the study area. The following map displays the results from this analysis.


For Part B, Storm surge was assessed in New Jersey. The DEM Raster was reclassified into flooded cells, of 2 meters or less and no data. The resulting raster was then converted to a polygon. The percent of Cape May county could then be analyzed. In order to calculate the percent of Cape May County that may have been affected by the 2 - meter storm surge, I clipped the flood polygon to the County, then calculated the area of both the flood polygon within cape may county and the area of cape may county itself. 

Therefore, the percent of Cape May County that might have been affected by the 2m Storm surge is 9.5%


In Part C, I used the raster calculator to multiply the LiDAR elevation layer by 0.3048 to convert from feet to meters. I then ran the raster calculator to set the new layer <=1 to show which cells are flooded, or have an elevation of 1 meter or less. I then ran the raster calculator for the USGS layer which was already in Meters, so I set that to <= 1 as well. The region group tool was completed for both layers. In the LiDAR Region, the explore tool was used to find a value of 5255 in the large flooded coastal region. I then selected by attributes to keep only that region and converted that raster to a polygon. I repeated this process for the USGS region, with a value kept of 46. For the next step, I opened the Buildings attribute table and added two more fields, then performed a spatial join. Due to some troubles with joining the fields of data from the spatial join into one table, I opted to do a series of SQL queries. This involved selecting by both attributes and location to determine where each type of building was affected by one flooding zone versus the other, and vice versa. After determining the affected buildings, I was able to complete the table and create new layers to finalize the map product. The final map shows which buildings are affected by the USGS layer only, LiDAR layer only, and both. This map also includes a table showing values calculated for errors of commission and omission.



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