Dalton Inman

For the second part of this module, we were tasked to identify a least cost wildlife corridor between two sections of the Coronado National Forest. The goal was to identify potential movement routes for black bears by combining environmental criteria into a suitability model, converting that model into a travel cost surface, and then using corridor analysis to locate low cost connection areas between the parks.  I did this by creating three suitability rasters on a scale of 1 - 10 using the reclassify tool. These were the distance to roads, elevation, and land cover, with values assigned according to their relative importance for black bear habitat. I then used the Weighted Overlay tool to combine these layers, assigning weights of land cover 60%, elevation 20%, and distance to roads 20% creating a suitability map. I then inverted the suitability raster (10 – suitability) to create a cost surface where highly suitable areas became low cost travel zones. I calculated the cost d...

For this module, we were tasked with identifying areas that were the most suitable for future development using a suitability analysis method. We used a weighted overlay to assign relative ratings to each pixel in the study area based on five different criteria: land cover, soil, slope, distance to roads, and distance to rivers.  Each one was reclassified on a 1 to 5 suitability scale, where 5 indicated the best conditions for development. I created two separate suitability maps with different weighting strategies, with the first map having all five criteria equal (each at 20%), while the second map prioritized slope (40%) more heavily, to simulate the increased cost and difficulty of construction on steeper terrain.  Areas numbered as highly suitable (value 5) had characteristics like flat slopes, dry soils, and a far distance from water. When slope was weighted more heavily however, steeper areas were less suitable even if they had other favoring attributes.  Analysis's...

For this module, we were tasked with assessing structural damage caused by 2012 Hurricane Sandy along the New Jersey coastline using aerial imagery provided to us. The goal was to simulate a FEMA style damage assessment by manually digitizing structures and determining damage severity, and even creating a practice survey. The pre-storm and post-storm imagery was relied on heavily to inspect structures in a defined study area within Ocean County, NJ. A Structure Damage feature class was and was used to digitize each structure and classified its level of damage using FEMA coded domains (e.i. Structural Damage, Wind Damage, Inundation, etc.). Parcels were sometimes divided into multiple units (such as duplexes), which meant placing multiple points on a single building footprint to reflect the actual number of properties which can actually be seen from the image below. To analyze the severity of damage to proximity from the coast, I created a new Coastline line feature class and digitized ...