M4 - Flood Analysis

 This week involves coastal flood analysis. Both of the below maps look at damage from severe storms or storm surge. The first of which looks directly at pre and post Hurricane Sandy elevation data from 2012 in New Jersey. That analysis utilizes change detection to show where damage areas are, where debris accumulation or shoreline accretion is taking place. The second map is of Naples Florida and is solely based on if there was a 1 meter storm surge what properties would be impacted. The crux is that two different elevation models are being compared to take the analysis a step further. All of this is helping to better understand coastal flood assessments, and how elevation models can be used to delineate coastal flood zones. Numerous raster analysis and modifications were undertaken to process the various LiDAR and DEM data. Followed by attribute table manipulation to determine some accuracy statistics between the two mentioned elevation models which will be discussed more below. 

The map above is essentially a hot and cold heat map, where hot is areas of high negative change. This means for example, a location where a building previously stood which is now gone. The opposite of this is the blue areas which indicate a positive change in that location. This for example could indicate areas where debris has accumulated, or sand has piled up. The information on the map also discusses some of how it came to be, but in simple terms it is the combining of a before raster with a post raster, specifically isolating the elevation change.

Now onto the storm surge map. 

 

The map above looks at a comparison of USGS DEM derived from traditional photogrammetry against one using a higher-resolution LiDAR dataset. Each dataset was transformed to only show areas where it predicts a 1 meter surge impact. The LiDAR layer is over the USGS layer, but both have areas where the other is not a factor. Because of the scale of the Naples and Marcos Island scene I wanted to provide a better look at how the two data layers are overlapping or not, so I provided an equally sized inset of Naples. There you can see representative examples of each of the impact types. From those buildings not impacted, to those only represented on one DEM dataset, to those represented on both datasets. In this case the most true representation of buildings impacted would be those that are Red for "both" and those that are blue, for LiDAR only. The red and blue together would be the most likely to be impacted. The orange USGS only, would likely not be impacted as its dataset was more coarse when analyzed. 

These are some excellent tools to determine flooded areas from elevation data, and impacted facilities. Thank you. 

v/r

Brandon