Geo Leah

In this part of the lab, we analyzed the most suitable corridor for black bears to travel between two parts of Coronado National Forest. The criteria given were an elevation raster, a land cover raster, and a roads shapefile. I used Euclidean Distance to create a raster of each cell’s distance from the nearest road. I reclassified this raster, along with the elevation and land cover rasters, on a suitability scale of 1-10 according to the specifications in the lab instructions. I then used Weighted Overlay (land cover: 60%, elevation: 20%, and road distance: 20%) to combine these three rasters. Using Raster Calculator, I created a cost surface by subtracting the weighted suitability raster from 10. I used Cost Distance to create two cost distance rasters from the two national park areas, which I then combined using the Corridor tool to create a raster of costs for corridors between them. I then adjusted the classifications and symbology for the corridor raster until I found a corridor ...

In this part of the lab, we analyzed the study area for its suitability for property development using two different weighting strategies. I reclassified each of the five raster layers (land cover, soils, slope, river, and roads) with a suitability rating from 1 to 5, with 5 being the most suitable for property development. The most suitable areas were meadow or grassland, soil class 0, had a slope less than 2 degrees, were more than 1000 feet from a river, and less than 1320 feet from a road. Then, using the Weighted Overlay tool, I combined the five reclassified rasters into one raster with a suitability rating for each cell. A cell with a suitability value of 5 is the most suitable for development in all five categories. First I created a raster that weighted each layer equally, then I created one with the weights seen below. Neither raster had any cells with a suitability value of 1. The raster with equal weights had very similar areas for ratings 3 and 4, while the alternative ras...

For this week's lab, we analyzed imagery from before and after Hurricane Sandy in New Jersey to assess the level of damage in an urban area. We first mapped the path of the storm as it progressed through different levels of storm/hurricane. We created a citizen damage assessment survey that could be used to crowdsource information, photos, and locations of damage from the hurricane. After creating domains for a new point feature class, we added points to this class by comparing the before and after imagery to assess the level of hurricane damage for each structure in a small study area. This part was tricky because the shadows in the post-storm imagery sometimes made it difficult to see debris or damage. It was easy to tell when a structure was completely destroyed, but I often wasn’t sure whether a structure had minor damage or not. On-the-ground photos or other damage assessment information, such as the citizen survey we created, would have been helpful in identifying damage that...