Goals
and Objectives:
There are two main objectives for
this lab. The first is to analyze data for several factors which affect sand
mining and develop a suitability map based on those factors. The second is to
analyze data associated with the risks of sand mining and create map which
indicates the levels of risk in any particular area based on those variables.
The study area for these projects was Trempealeau County, Wisconsin (figure 1).
 |
| Fig.1. The location of Trempealeau County, Wisconsin, USA. The west-central portion of Wisconsin has become populated with sand mining operations in recent years. |
Data
sets and Sources:
The data used in these analyses were obtained from several
sources. Elevation data were obtained from the United States Geological Survey
(USGS) National Map Viewer website, http://nationalmap.gov/viewers.html. (national elevation dataset 1/3 arc
second). From the USGS website we also obtained data on land cover, both data
sets were downloaded for the area of Trempealeau County, Wisconsin. We used the
legend located at http://www.mrlc.gov/nlcd01_leg.php to aid in the interpretation of the
land cover raster. Data for water table elevation were gathered from http://wisconsingeologicalsurvey.org/gis.htm. Railroad data including the location
of railroad terminals were gathered from the National Atlas website at http://www.nationalatlas.gov/. State
and county data were obtained from ESRI online, and other data unique to Trempealeau
County were obtained from the Trempealeau County geodatabase located in the
college system. Data for geologic formations was gathered from wcwis_bg_map on
the university system.
Methods:
Suitability
model:
I began by starting a new file geodatabase to work in and save all
the generated data in. Then opened a new .mxd and saved in to my file. Within
the .mxd I set the environments, the workspace and the default workspace to the
new geodatabase and in raster analysis the cell size to 30 (the land cover data
has this cell size and it is the largest of the data sets) and the mask to a
layer containing a polygon of Trempealeau County (TC). We also had to turn on
spatial analyst in the extensions in order to use many of the tools. All
feature classes were projected the same, NAD 83 UTM Zone 15N prior to any analysis.
When possible the feature classes were clipped or extracted down to the TC
layer before analysis to minimize the size of the data set. The railroad terminal
data set had to be handled at the state level because there were no terminals
located within TC. All reclasses were performed in model builder giving us the
opportunity to go back and make changes if needed, some of our ranking criteria
may not work the best the first time through (figure 2).
 |
| Fig.2. This model was used to reclass each variable and combine the layers using raster calculator. The suitability model used raster calculator twice; the first to create a suitability raster and a second to exclude populated and wetland areas. |
The first of our suitability criteria was elevation. We know that
the most suitable sands are the Jordan and Wonowoc formations. On the
wcwis_bg_map these formations are colored red and gold respectively. To obtain
the elevation ranges of each of these formations we layered the wcwis_bg_map
over a seamless USGS topographic basemap. Using the swipe tool we could
identify the approximate elevation ranges of each formation. The basemap
elevation was in feet so I had to convert the elevations to meters. The Jordan
formation ranges from 228.6 to 274.32 meters above sea level and the Wonowoc
formation ranges from 320 to 365.76 meters above sea level. Using the TC DEM we
ran the contour tool with the contours set at 10 meter intervals simplifying
the data set. Then examining the wcwis_bg_map map it was easily recognizable
that the Jordan formation covered a much larger area than the Wonowoc so this
information was used with elevation information for the sand formations to rank
the elevation for suitability. The elevation range containing the Jordan
formation was ranked highest, the range containing the Wonowoc formation was
intermediate and the rest of the elevation ranges were ranked the lowest. The
raster was reclassed as such (figure 3).
The second of our variables was land cover. The land cover raster
showed a range of cover types from open to forest to urban areas. This image
can be interpreted using the legend from http://www.mrlc.gov/nlcd01_leg.php. To
rank these areas I took into account the investment of time and money it would
take to clear the land for mining. Heavily forested areas would be the least
cost effective areas while open land would be the most cost effective areas to
build a sand mining operation. The raster was reclassed using the values from
the above legend with all open areas ranked the highest, brush areas ranked
intermediate and forested areas ranked the lowest (figure 2).
We also used this data to create a raster that would show all
areas of the county that were entirely impractical to mining. These areas would
include wetlands and urban areas. I performed a second reclass using the land
cover data set. This time I did not rank the data I simply gave all areas not
feasible for mining a zero value and all areas, even costly, a value of one.
This was used to exclude these areas from our final output, eliminating them
from future analysis (figure 3).
Our third criterion was a sites proximity to a rail terminal for
loading and transport of the mined sand. The closer a site is to a terminal the
higher the rank for the site. Using the locations of the rail terminals I ran
Euclidean distance to determine the distance to any terminal, this had to be
done statewide and then extracted down to the TC layer. I then reclassed the
distance to divide the county equally into three levels split between two
terminals (figure 3).
The fourth variable dealt with the slope of the land. It is much
easier to mine flat ground than steep. Using the TC DEM I ran the slope tool
set to give slope as a percent. After running slope I ran block statistics with
a 3 x 3 pattern to filter through the raster and reduce some of the peppering
in the image. The final step was to reclassify the image ranking the steeper
slope percentages the lowest and the lower slope percentages the highest (figure 3).
The final variable was availability of ground water. Water usage
is extremely high in sand mining operations so the more readily available water
is the more suitable the land will be. This data was downloaded as a coverage
(eOO file) zipped file. The files were unzipped then imported using the import
from eOO tool, then the data could be exported to the geodatabase for use.
These data were then projected to match the rest of the data. The data were in
feet above sea level; this was different from all of our other data. In the
attribute table I added a field and used field calculator to convert the values
to meters. Then in using the contour to raster tool, with the newly created
field, I converted the data into a raster dataset. The new problem was this
data was in meters above sea level, we wanted to know the depth to the water
table. By using raster calculator and subtracting the water elevation from the
TC DEM we get raster cell values that are the difference between the two, the
depth to the water table. This raster was then reclassed into three levels with
the deepest water table ranked the lowest and the shallowest water ranked the
highest (figure 3).
From these reclasses we obtained several individual rasters
showing the rankings for each. We need to combine them to create a single
suitability map. Using raster calculator we add each of the individual rasters
except the land use exclusion raster (figure 4). The output is a map showing a continuous
range of values from a low of five, the number of variables and the lowest
value, to fifteen, the number of variables and the highest value. The lowest
values on the map indicate those areas that through our analysis are the least
suitable for sand mining while those with the highest values are the most
suitable. Now we can take this one step further, some of the areas on the map
are either urban areas such as towns or cities or are wetlands. These areas are
not at all feasible to mine in. Using raster calculator we input the
suitability model and multiply it with the land use exclusion raster. The
multiplication function will take all of the areas we want to exclude and give
them zero values (0 times any number is zero), and will not change the other
values (1 times any number is that number). The zero values were then
symbolized to show they were not a part of the range of suitability (figure 5).
Suitablity model
|
||
Elev. range (MAMSL)
|
rank
|
|
184
|
228.6
|
1
|
228.6
|
274.32
|
3
|
274.32
|
320.04
|
1
|
320.04
|
365.76
|
2
|
365.76+
|
1
|
|
Land cover
|
rank
|
|
11
|
31
|
3
|
41
|
43
|
1
|
51
|
52
|
2
|
71
|
95
|
3
|
Land cover exclude
|
rank
|
|
31
|
82
|
1
|
11
|
24
|
0
|
90
|
95
|
0
|
RR proximity (m)
|
rank
|
|
20466.44
|
34302.48
|
3
|
34302.48
|
46161.94
|
2
|
46161.94
|
59237.75
|
1
|
%slope
|
rank
|
|
0
|
10
|
3
|
10
|
25
|
2
|
25+
|
1
|
|
Water depth (m)
|
rank
|
|
0
|
15
|
3
|
15
|
50
|
2
|
50+
|
1
|
|
Table.1. These are all of the variables
used in determining land suitability
for mining and the reclass rankings
within each variable.
 |
| Fig.3. These six variables were used to determine the suitability of an area for sand mining. Each of these areas has been reclassed to rank the data as low, medium or high suitability. The urban and wetland areas were reclassed as a binomial to either include or exclude the data. |
 |
| Fig.4. This is the result of adding all of the variable layers together except urban and wetlands. This analysis gives us a ranked output with values ranging from 5 the number of variables with reclass values of 1, to 15 the number of variables with reclass values of 3. With this output we can determine which ares are suitable for sand mining. |
 |
| Fig.5. This is the result of multiplying the map in figure 3 with the data layer urban and wetland which was reclassed with urban/wetland as 0 and all other areas as 1. The binomial values in the urban/wetland raster when multiplied with the previous raster either give us our original values (1x'sany_value=that_value); or our exclusionary data (0x'sany_value=0). This analysis gives us a ranked output with values ranging from 5 the number of variables with reclass values of 1, to 15 the number of variables with reclass values of 3 and an area of no data. With this output we can determine which ares are suitable for sand mining without being in a developed area or a wetland area. |
Risk
model:
For the risk model I again began by starting a new file
geodatabase to work in and save all the generated data in. Then opened a new
.mxd and saved in to my file. Within the .mxd I set the environments, the
workspace and the default workspace to the new geodatabase and in raster
analysis the cell size to 30 (the land cover data has this cell size and it is
the largest of the data sets) and the mask to a layer containing a polygon of
Trempealeau County (TC). We also had to turn on spatial analyst in the extensions
in order to use many of the tools. All feature classes were projected the same,
NAD 83 UTM Zone 15N prior to any analysis.
Most of the variable data in this section were obtained from the TC
database. The feature classes were clipped or extracted down to the TC layer
before analysis to minimize the size of the data set. All reclasses were
performed in model builder giving us the opportunity to go back and make
changes if needed, some of our ranking criteria may not work the best the first
time through (figure 6).
 |
| Fig.6. This model was used to reclass each risk variable and combine the layers using raster calculator. |
The first risk variable was to watersheds in TC. Pollutants
entering a stream from a mine located to close could be an ecological disaster.
We used a feature class from the TC database called DNR-Hyro_junction. In this
feature class there are data on all of the streams in TC including stream type
and flow. Through examination of the data I decided to use the
primaryflowinwaterperennial classification, this included major waterways that
flow year-round. I did not include others as it cluttered the map severely and
when we buffer the distance we include many of the lesser streams. I used
select by attribute to query out the streams needed and then in
selection-created layer from selected features, this layer was saved as layer
file then imported into the geodatabase as a feature class. This feature class
was run in Euclidean distance to give a range of distances from all points
along all of the streams. We used the new raster to reclass into our buffer
area, the areas closest to the streams receiving the highest value and those
furthest from the streams receiving the lowest values, the breaks were set at
2500 meters and 5000 meters (figure 7).
The second variable is prime farmland; this land is of
economic importance to the area and should be avoided if possible. We used the Prime_Farmland feature class
located in the Land feature dataset of the TC database. I selected both
classifications of All areas are prime farmland and Farmland of statewide
importance and followed the same procedure to make feature classes in the
geodatabase. I used the polygon to raster tool to create a raster image then
reclass to rank the data. This reclass I ranked the included farmland as a two
and all other land as a one. The farm land is important however it is also open
and using it might be less destructive than logging off a forested area (figure 7).
The third risk variable we examined dealt with populated
areas. There is great concern with noise and dust pollution from the mines affecting
the health of people in urban areas. This data was obtained from the college
network and is located in W:\geog\CHupy\geog491_s13\CAMRENSL\ex5\TrempWebDATA2-4-2013.gdb\Boundaries\Composite_Boundaries.
I used the census boundaries because I think they gave the clearest picture of
urban populations in the county. I queried out the cities and villages using
the MCD column and selecting F which corresponds to cities and villages not
townships which are rural areas. I again used the protocol to move the data
into the geodatabase where I ran Euclidean distance. The distances are
calculated from the perimeters of each of the urban areas. I ran a reclass to
rank the distances into three risk subsets with the closest distances ranking
the highest. The minimum distance used for this operation was 640 meters. This
distance has been determined the minimum for noise pollution and we have
adopted it for dust pollution also, the second break was set at 1600 meters (figure 7).
Along with urban populated areas we were concerned with
schools; we dealt with this separately because some schools are not located
within populated areas. To gather data on the schools we used the parcels
feature class in the TC database. I queried out all properties owned by a
school using the statement, "LastName" = 'SCHOOL DIST' OR
"LastName" = 'SCHOOL DIST # 5' OR "LastName" = 'SCHOOL
DISTRICT' OR "LastName" = 'SCHOOL DISTRICT # 1' OR
"LastName" = 'SCHOOL DISTRICT #1' OR "LastName" ='SCHOOL
DISTRICT #2' OR "LastName" = 'SCHOOL DISTRICT OF ARCADIA' OR
"LastName" = 'SCHOOL DISTRICT OF BLAIR' OR "LastName" = 'SCHOOL
DISTRICT OF INDEPENDENCE'. Using this selection and the previous protocol to
import the data into my geodatabase for use I then ran Euclidean distance from
the edges of the properties and reclassed the raster using the same breaks as
populated areas (figure 7).
Prime recreational areas were our fifth concern. We do not
want to locate a mine in an area that is plainly visible to anyone visiting a
park our using any of a number of recreational trails in the county. We used
both the parks and the trails feature classes from the TC database and ran them
in viewshed. It was also not possible to run viewshed from polygon so for the
parks I first ran a polygon to feature point tool giving me a point coincident
with the center of each of the park polygons. When running viewshed from a
single point you get results for that point; however, running viewshed for
multiple points or lines as we did here we get a continuous set of values.
These values are rankings, the higher the value the more visible an area is
from any number of the points used in the viewshed analysis. When the rasters
were reclassed those with the highest values were given the highest scores
(greatest risk) (figure 7).
I included one last area that might be of concern, wildlife
areas. These areas are important not only to wild life but also for the county’s
economy by way of tourism, hunting and more. Using the wildlife areas feature
class I imported it into my geodatabase and ran Euclidean distance from the
perimeters of the areas; the new raster was then reclassified to show a buffer
around the areas with the closest areas being ranked as the highest risk (figure 7).
The last step to generate our risk assessment map was to use
raster calculator and add each of the layers. The result was a map showing a
continuous risk gradient from the lowest scores possible; seven, the lowest
risk, to the highest score possible and the highest risk, twenty (figure 8).
Risk assessment model
|
||
Streams buffer(m)
|
rank
|
|
0
|
2500
|
3
|
2500
|
5000
|
2
|
5000+
|
1
|
|
Resiential buffer(m)
|
rank
|
|
0
|
640
|
3
|
640
|
1600
|
2
|
1600+
|
1
|
|
School buffer(m)
|
rank
|
|
0
|
640
|
3
|
640
|
1600
|
2
|
1600+
|
1
|
|
Trails.view (VALUES)
|
rank
|
|
500
|
3936
|
3
|
250
|
500
|
2
|
0
|
250
|
1
|
Parks.view (VALUES)
|
rank
|
|
4
|
20
|
3
|
1
|
4
|
2
|
0
|
1
|
1
|
W.life areas buf.(m)
|
rank
|
|
0
|
800
|
3
|
800
|
1600
|
2
|
1600+
|
1
|
|
Farmland
|
rank
|
|
farmland
|
2
|
|
other
|
1
|
|
used in determining risk associated
with mining and the reclass rankings
within each variable.
 |
| Fig.7. These seven variables were used to determine the risk involved in an area for sand mining. Each of these areas has been reclassed to rank the data as low, medium or high risk. The prime farmland areas were reclassed as a medium risk while areas not were considered low. |
 |
| Fig.8. This is the result of adding all of the variable layers together. This analysis gives us a ranked output with values ranging from 7 (low risk, the number of variables with reclass values of 1) to 15 (high risk, the number of variables with reclass values of 3). With this output we can determine which ares are at a high risk from sand mining operations. |
Results and
Discussion:
The suitability model shows a large range of variability of suitability of the land in Trempealeau County. There are three areas on the map that stand out with high suitability; the northern most portion of the county, the north central portion of the county and the south central portion of the county. When you look at these areas on the risk model the northern most area of the county is also a high risk area, so this area is not a suitable area for sand mining. The north central region and the south central region of the county both have a reasonably low risk in conjunction with the high suitability. These areas may be candidates for mining operations. Both of these areas are also located reasonably close to railroad terminals which are the main transportation mean of shipping the sand out of the area.
 |
| Fig.9. Trempealeau County, WI, sand mining suitability map. |
|
| Fig.10. Trempealeau County, WI, sand mining risk map. |
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