In this exercise we are adding up all the skills we have worked with over the 2014 Fall Semester GIS II class. The Goal of this lab was to use multiple geoprocessing tools to bring together multiple raster files in order to figure out a suitable area to have a frac-sand mine be built in the Lower Trempealeau County. In this lab we will be building a suitability model, using layers for bedrock layers, land type usages, proximity to railroads, the slope of the land, as well as, the water table level. Using those five layers we will create a suitability model which will overlay with the risk assessment model that includes closeness to streams, prime farmland, residential areas, wildlife areas, and visibility from some of the highest peaks in Trempealeau County.
Methods
To start the lab we first had to know what type of sand formations to look for in the bedrock. Research done throughout the semester shows that the Jordan and Wonewoc formations are the best formations to use for mining purposes.
The next step was to use this information to create a ranked raster file to show which places would have the best bedrock to mine and which would not be the primary choices.
Above in the bright green color is the ideal mining locations which include the Wonewoc and the Jordan formations. The areas in gray are what would not be considered the most ideal locations to mine. Converting this file into a more pleasing image would result in changing the gray to a red or black and keep the green part green, but that will all be taken care of at the end of the first seven objectives when they all get added together.
The second step was to import the National Land Cover Data set which we downloaded and used back in Exercise 5. In this section we wanted to select areas of land which would be suitable to drill on. Suitable areas to mine on include planted and cultivated lands, or forest areas. Those areas received the best ranking, while areas of developed land and water areas/marshlands received the worst rankings.
The green areas on this image show those which rank the best in areas of suitability, while black is the worst areas to conduct a sand mine operation, and blue are areas in between the best and worst.
The third step was to find the proximity to the rail depots. We are using the rail depots because we want to see where they can get on the rail line, not just some random point.
Just like the first two images, this one also is ranked.The closer you are to the rail depot the better ranking you would receive. Using a three category Euclidean Distance we were able to split the county up into three separate areas.
The next step of this project was to find a relatively flat slop area, mines do not want to be found in places of high slope, so a good slope would be one closer to a flat surface.
The image above shows what the area, using a Digital Elevation Model, the green areas are the much more flatter areas, than blue are the medium slope, and finally the red are the steeper slop areas. For this part it was important to remember that we have to have all the units the same. In the above image all units were changed to show meters.
Objective 5 required using ground water data. We used a generalized water table elevation map to make our jobs easier. After downloading, we had to import the .e00 file. Since we were unable to use the actual contour lines in the project, we converted it into a raster and added it to the list of ranked rasters which we will use in the next steps.
The image above is a screen shot from the website, Wisconsin Geological Survey, the green lines represent the individual contour lines of the ground water table.
Objective six involves using the map algebra tool to add up all the above mentioned areas to find the most suitable areas to construct a new frac sand mine.
After adding all the categories together we came up with a map like the one above. On the map black is the worst ranked area, followed by blue, then green, then red, then white is the best ranked areas.
In order to make the map look more appealing, I used the block statistics tool to get rid of the salt and pepper look. On the map below, green is the most ideal spot, and the closer to red the worse it gets.
We had to also find areas where we deemed at the beginning where we would not want to build regardless of how good the land is.
This map uses the same legend as the map above it, but the black areas are the areas where we will not be wanting to consider doing work due to what was mentioned toward the start of this blog post.
After completing the suitability model, we then moved on to creating a risk model.
Objective 8 we began to use more of the raster tools which we did not use very much in the first part. This step involved importing the river and stream flows for the lower section of the county. Since we had to pick certain streams, I chose all the streams which had a stream grade greater than or equal to 5. This way I would not have the entire county selected as being in too close proximity to a stream.
This map shows in red the close proximity to the streams, not wanting to build there this received the worst ranking, and then the yellow was the next worst, and green was the best ranking.
Step nine was to select areas where we were not building on prime farmland, this included prime farmland that is sometimes flooded.
The image above shows the proximity to the prime farmland, the red is either the farmland itself, or the closest area. Then like the previous maps, yellow is the third ranked, and green is the second best. White areas rank higher than green, making them the best areas.
For objective 10, we had to run the same tools to find the residential and populated areas. Since we do not want to build a mine there they will get the worst rankings. Much like the previous maps, we will create this one by selecting the areas of interest, and then running the euclidean distance tool. And lastly reclassify the data into three categories. The three categories make it set so it will be best, medium, and worst ranked.
Just like the previous maps, red is the worst, closest proximity to residential, or populated areas. Then yellow is the second closest, and lastly green is the best, furthest area.
The next task will find the impact to the schools. This was included in objective 10, as populated areas, but for the sake of this lab, I found the school data and had to separate the operational schools from the historical schools.
The last step I had was to do something of my choice. I chose to figure out how close into proximity the wild life areas were.
Again just like the other maps, the red is the worst area, the yellow is second best and the green is the best when it comes to proximity to the wild life areas.
After calculating the risk assessment for each of the five categories, just like in the previous half of this lab we will use the map algebra tool in order to find the areas of most risk.
After running the tool we get the near finished product pictured below.
The green areas are the best, down to the dark red being the worst. There are several areas on the map where a frac sand mine would be acceptable with little risk. Those involve the northeast corner of the county, as well as in the middle of the county there are several locations which would be fitting.
It is important to take not that this data may not be used to calculate an exact location for a frac mine because it only takes into account of this counties data. The northeastern section may not be as risk free as the map shows because it may have other implications from the surrounding counties.
Taking the risk assessment and combining it with the suitability map we created we can find the least issued based area.
Again, the areas of red are the worst, up to green being the best.
The last step of this project was to use the visibility tool from prime recreational areas. I chose to use the highest eight peaks in the county to use as my visual sites. Again using the Digital Elevation Model and the visibility tool we were able to see what areas were visible, and overlapped it onto the above map.
The most suitable and risk free areas, with the least amount of visibility are located in the middle as well as the northeastern corner of the county.
The last part of this lab was to create a flow chart data flow model of all the steps we did. All the steps mentioned above are included in the model, as well as many other steps which were not mentioned in the methods portion.
Lastly, mentioned in the reading regarding the rankings, the table shows the rankings again.
Python Portion
The second part of the lab was to create a Python Script to show which areas of the previously mentioned five risk assessment areas would have a heavier impact. The area I chose to have the highest impact was streams.
And then using this Pyscript we were able to come up with a slightly different map then from above, note this map was not combined with the sustainability map, it is solely a stream weighted risk assessment map.
Conclusion
Overall the data we used to make the map can show where the best areas are with the most suitability to the environment, and the least amount of risk. As stated in the blog this data should not be used alone to figure out where to build a mine. There are far more areas to consider such as road usages, proximity to other mines, cost, and you will have to use other county's data to get a more full looking and accurate map.
Sources
http://wgnhs.uwex.edu/maps-data/gis-data/
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