Our goal for this blog post is to use rasters to convert a
message that what mines would be both suitable and environmental to the county
of Trempealeau Wisconsin. We will be assessing the risks involved with mines in
proximity of schools, lakes, and other important features. The risks associated
with mining are great. Mining can cause a vast array of ecological and human
hardships in the form of water pollution, air pollution, and erosion. in order
to combat these three big three hardships we will be using the raster modeling
to look at the slope of the selected mining areas and of the water close to the
selected mining areas. When we know the areas that would be less optimal to set
a mine on we can then select an area that will minimalize the effects of mining
on the environment. This is called the risk model. We will use this model in
order to pick an area that offers the lowest amount of risk to the mining
operation as well as to the environment.
We will also have to look at the area that we are mining. We
will want an area that is both high in frac mine sand and in a fairly easily accessible
area to mine. The more excessive mining techniques that we have to utilize in
order to mine frac sand will increase the amount of cost to the mining
companies. We will want to select a mine that is easily accessible via rail
roads or roads in order to cut down on transportation costs. We will also have
to look at what kind of soil or land type that we are going to be mining in.
Rocky areas and heavily treed areas as well as wet lands are not appropriate
nor cost effective to mine so we will have to look at shrub land farrow land
and other grassland areas. Another focus will have to be looking at cropland to
mine since this is already developed land that is easily accessible. we will
also have to look at the proximity these areas are in with developed land where
people live as well as water sheds. This is called a Suitability model and we
will use it in order to select a good area to mine that will both be productive
and cost effective.
In order to start
out with this project will only be using part of Trempealeau county data
otherwise the vastness of the county will dwarf the area. To start the suitability
model I used the boundary function clipped with the Geology to create a smaller
version of the Trempealeau county data. This smaller version of the Trempealeau
county data will be easier to manage and will allow a faster area to materialize
for mining data. From this point we will have to select data that is needed for
mining. The two most desirable areas for geologic sand mining are the Jordan
and Wonewoc formations. In order to create a raster from these formations I
took the TMP Geology layer and created a raster with the "Feature class to
raster using table tool" The table that I used to create the raster was
the Geology unit field. This field holds the Jordan and Wonewoc features. When
the raster was created I used a re-class tool that I made in Model Builder to
create a ranking with Wonewoc formation as the top since it has the greatest
amount available, I Then used the Jordan as the second rank since there was
less and it was closer to water areas the final rank I assigned was for all the
data that we did not want on our model.
This is the selected Geological areas in raster format
I Selected Wonewoc as the top rank and Jordan as the 2nd choice
due to the lack of mining oppurtunities that Jordan offers.
due to the lack of mining oppurtunities that Jordan offers.
In order to use the geological raster in conjunction with
our other rasters we need to find out what kind of land cover we have on the
map and what is the best choice to choose for a sand mining location. In order
to do this we need to go to the NLCD website to download the NLCD (National
Land Cover Data) so we know exactly what is cover the Trempealeau County. The
things that we need to think about is how much effort will we have to do on the
land in order to extract the frac sands. For my top rank I chose Clover,
Wildflowers, Seed, Sod grass, and other pasture/hay NLCD cover, this will allow
for the easiest mining area and is more abundant than the second rank which is shrub
land and grasslands which is not owned by farmers or other private parties. For
the last rank I selected all farm land that is not a tree farm in order to
allow ease of mining. The reason why I selected wild grassland over farm land
was because it will allow ease of mining and it will not displace any farmers,
avoiding bad press and the already controversial issue of frac mining in Wisconsin.
I selected Pasture land as my top selection mainly because the amount that is
available in Wisconsin as well as the ease of mining. It however, may displace
farmers but you have to crack a few eggs in order to make an omelet. I believe
that this is a necessary risk that has to be taken in order to have the best mining
area possible.
This is the Raster for the selected areas of Land Cover based on
NLCD criteria and on effort needed to clear the land to mine.
This is area that is unsuitable to mine based on NlCD criteria
it is mostly water, wetlands, tree cover and rocky areas.
these are not optimal nor cost effective to mine.
it is mostly water, wetlands, tree cover and rocky areas.
these are not optimal nor cost effective to mine.
After I selected the Land cover that will be used to select an
area that frac sand mining is possible I had to find the distance between the
railroad loading stations and the distance between the optimal land that I had
to use. For this I used the Euclidean Distance tool on the rail depots as this
allows us to know the distance to the closest loading station. The problem that
I encountered was when I went to convert the feature point to a Euclidean
Distance it would result in errors. To combat this problem I went and took my
final rail loading mines merge from exercise 7, which contained the loading
stations for frac mines to rail stations, and converted it to a raster using
the tool Point to Raster. With this new raster I could use it as a source cell
for the Euclidean Distance tool to use. I set the Euclidean Tool to 5000
meters, anything in this area would be optimal for a mining operation, of
course the closer to the center the better. This is useful to know because the
less travel that the mining operations have to make to a rail station is money
that is saved that can be used for other more important things, this also
allows for a larger profit.
Selected Euclidean Distance of Rail Loading Terminals
After the Euclidian distance tool was ran I then had to find
out the suitable slope that will be used to have the optimal mining criteria.
Slope is important to note when it comes to mining as if you do not take into
account of the slope it could lead to ecological problems such as run off and
erosion. This can turn into a health hazard as the run off can come into
contact with a watershed or a river. This could turn into an ecological
disaster and is probably best to be avoided. To find the suitable land slope I
used the old Trempealeau county data geodatabase from exercise four and used
the DEM to calculate the slope. I had to change it from ft. to meters in order
to have the output show correct values and I used the filter tool to create a
mean average of the slope that is used in the Trempealeau county data. This is
set in a 3x3 cell size in order to create the average slope throughout
Trempealeau county. I then used this data to create a new layer based on the
average slopes that I thought would allow the easiest and most accessible areas
for mining. I chose the mean averages to reclassify in order to have the
easiest access for mining. I excluded the lowest points and the highest points
as the lowest would have run off into the mining areas that could hamper
operations, I excluded the highest points as it would be difficult to mine
those areas. I used the reclassify tool to exclude the two highest points and
two lowest points to accomplish the slope suitability areas that you can see
below.
Suitable Slope that can be mined
Water depth is an important criteria when it comes to sand
mining as although it may be in our best interest to try not to be close to
pollute water systems we still need water in order to lubricate the drill for
mining. I had to use a generalized water
table elevation map for Trempealeau county for a good location to mine which I
downloaded at wisconsingeologicalsurvey.org we then had to use this coverage
data and convert it to a raster. To do this I converted it from coverage data
to a raster using the topo to raster tool.
It takes topological data and converts it to a raster format we are
using it in this format as the contours data can be subjected to a raster
format. I had to use the large boundary surface in order to convert the
contours into a raster format. Once the contour data was converted into raster
I clipped the data with my Trempealeau County base layer. Once I had a suitable
raster I then used my Reclass tool based on areas that would allow a better
mining area to be found based on my criteria.
Watershed Suitablilty model
Finally once I have all these rasters it was time to create a sustainablility raster. To do this I combined all the rasters into one resulting index model. I then had to exclude all the data that I cannot use in our raster format. once we removed all the excluded lands we finally have our final product. A sustainability Raster for the best areas in Trempealea county to mine.
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