This was a very involved yet incredibly fun lab.
This week, amongst a deluge of analysis questions which needed to be answer in our lab report, we were additionally required to create two maps.
The first map we were tasked with creating was for a property developer looking to purchase land to build a development before it either became a conservation area or a route for a pipeline. We were tasked with creating a suitability analysis to convey which areas would be best suitable to build upon.
The main tool that was used throughout this project was the reclassify tool. With this, we could take a raster image and asccribe new, simple suitability rating values to every pixel value within the image. For the provided image of Land Cover, we reclassified those into varying degrees of suitability. We were also provided a feature class with a littany of soil-type polygons, which we converted into a raster image and then preformed a reclassifcation on. Next, we were given specific parameters for what would consitute ideal elevation slope. We converted the provided digital elevation model into a slope grid and then reclassified it to the desired suitability rating.
With the polyline shapefiles provided for rivers and roads, we used the Euclidean Distance tool to create a raster of distance from the lines representing rivers and roads and then reclassified those to the appropriate suitability rating.
Using the weighted overlay tool we then combined the rasters giving each variable mentioned above equal representation in terms of statistical weight and combined the rasters into one analysis.
We then adjusted the percent of statistical weight (amounts are explained on the map) in order to compare how our results changed as we statistically weighted our results towards slope, soils, and land cover.
In order to create the corridor analysis we first needed to create a suitablility analysis to identify which which pixel values represented the best suitability for black bear habitat to encourage thei crossing between parks.
We reclassified the digital elevation model into suitable altitudes, the distance from the roads to a safe and unprovoking distance, and the land cover was reclassified to identify the most suitable environments for black bears. We then combined these into a weighted overlay statistically weighing our results towards land cover.
We then had to develop a cost distance anlysis from each park so we would have two cost distances to combine in our corridor to evalurate which path would represent the least combined amount of suitability pixels to represent the most forrtuitous path.
To create the value raster for our cost analysis, we took our weighted overlay and reclassified the values to have the most suitable pixels (each representing 30 square meters) with the lowest value (therefore the lowest cost). Once reclassified, each feature layer representing each park exlcusively was plugged into a cost distance tool and combined with the cost raster. This gave us two cost distance rasters which we combined in the corridor tool and our result presented us with an area shaded green (representing the lowest values) connecting the two parks.
Since each pixel represented the combination of all neighboring pixels tracing back to our respective park polygons, using the explore tool we were able to isolate those values and then stratify them into 3 sperate classes representing the 'cheapest' cost corridor, the second 'cheapest,' and the third 'cheapest.'
Though these values were not monetary, I use the title cheap to represent the lowest combined values therefore representing the most suitable.
Once defined, they were presented into a map layout shown below.
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