Introduction:
Lab
5’s goal is to familiarize myself with raster analysis while developing a faux
model for terrain suitability and environmental risk of sand mining in
Trempealeau County, WI. I will use Elevation, Land Use-Land Cover, Slope, Water
Table, and Railroad rasters to determine optimal locations for a sand mine in
the county; then DNR hydro, prime farmland, county schools, recreational, and
existing mine data will be used to determine locations within the county where
a silica sand mine is not desirable. These two indices will then be compared to
find the optimal locations for construction of a new mine.
Part I
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| Fig 1: Suitability Index |
Once
the elevation range for these layers was determined, a ranking of the county’s
territory was developed by reclassifying the original DEM into three regions
delineated by elevational proximity to the aforementioned elevation ranges
correlating with the Jordan and Wowenoc formations.
With
the DEM, the slope tool was also used to find %rise/%run in the county. From
this data, Trempealeau was also divided into three regions based on the
suitability of an area’s slope for construction (smaller slope=better).
The
land use raster that was downloaded for Lab 3 was also used in the terrain
analysis for this Lab: the county was reclassified based on the suitability of
each area for constructing a mine. For example, an open field or barren land
would be ranked highly whereas a forest would rank low because of the
differential in ease of construction between the two parcels.
The
last factor considered in land suitability for the sand mines was the water
table level. Silica sand mines use water in the process of extracting sand,
therefore a higher water table (closer to the ground surface) merited a higher
suitability coefficient. The exact
cutoffs for these factors were determined by examining the histograms of the
final rasters produced, that values can be found in the table appended to the
end of the methods section.
Finally, Raster Calculator was run to add the three
coefficients together, piecing together the final Trempealeau Silica Sand
Mining Suitability Index. Five coefficients ranked 1 to 3 means a range of 5 to
15. Areas of urbanization and open water
were also removed from the index, because it would be entirely impractical to
construct a mine in those locations if not impossible. The final mapped index can be found in the
results section below.
Part II
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| Fig 2: Unsuitability Index |
The
second portion of this exercise is to find areas in which a silica mine would
be unsuitable for its surroundings, in particular the environment and local
residents (Data Flow model in fig 2). This begins with an examination of potential impact of a silica sand
mine on Trempealeau streams. To determine the risk of impact, a Euclidean
Distance tool was used to find the distance of each cell within Trempealeau
County from the nearest stream. From this distance, the county was divided into
three regions of contamination risk determined by that distance. After viewing
the stream-distance data that was generated, the region was ranked into three
areas representing proximity to major streams.
This method is similar to the method for part I, however where in part I
areas of high desirability were ranked highly, the opposite is true here and
areas most suitable are ranked at a lowly one.
Schools,
residential zones, and existing mines are similar to streams in that proximity
to them is undesirable, and as such they both used a similar method to divide
the county into zones of proximity. The difference with these two features was
that they weren’t discrete features in the data provided by Trempealeau County;
however they could be derived from the parcel and zoning data that was provided. The
features were first selected out, then had the Euclidean Distance tool applied
to them before finally being reclassified. Also, because of the intense noise
produced by mining operations, it has been determined that 640 meters is the minimum
distance that they should be located from hoses or schools, therefore those
regions were excluded from the final analysis by using the Boolean “And” in
raster calculator.
Agriculture
is integral to the economy of Wisconsin, and Trempealeau County is absolutely
an example of this. As such, in the Trempealeau County geodatabase that had
been downloaded is data for “prime farmland,” agricultural land which is
important not just to the local farmers but to the state as a whole.
The
last coefficient to be determined was the effect of potential mines on recreational tourism in the area, in particular on the hiking trails. The factors for both models are given in Fig 3 below.
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| Fig 3: Suitability Indices |
Sources:
Trempealeau County Geodatabase, downloaded from the official Trempealeau County Land Records Division http://www.tremplocounty.com/landrecords/.
Results:
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| Fig 4: Suitability Index |
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| Fig 5: Unsuitailibty Index |
The results of the analysis (indices 4 and 5) speak
for themselves. Urban areas are clearly
to be avoided in developing a new sand mine, especially particular to this was
the exclusion of 640 meters around residential and developed land in the
Unsuitability Index, with large swathes cut out around the relatively quite small
urban areas in the county. For
comparison, I have included a model without the exclusion of these areas as to
view the (un)suitability of the area excluded by this single factor (fig 6). These indices could be subtracted from one another in order to derive a general suitability model for Sand Mining in the county.
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