Introduction:
During a two week time span, the GIS 2 class with the geography department at the University of Wisconsin - Eau Claire, was given the task of evaluating sand mining data within the study area of Trempealeau County, Wisconsin (Figure 1.A).
In general, sand mining in Western Wisconsin has drastically increased due to technological advancements in hydro-frac mining. This is due to the large amounts of high quality of quartz sand that is found in that region. The purpose of this assignment then was to create a suitability assessment of Trempeauleau County for potential mining areas. Areas of consideration for overall suitability is access to railways, elevation, land cover use, soil types, and habitat types within the study area.
Methods:
The project in itself was split into two parts. For the first section of the project, multiple datasets were downloaded from multiple websites. This data was then imported into Arc Catalog where it was joined, merged and veiwed.
The first step in this process was to download data specifically for the area of interest. This included:
*Note: The number listed behind data corresponds with metadata section listed farther below.*
Results:
From the imported and edited data and raster sets, four maps to help assess sand mining suitability were created. For organizational purposes, the results for each map will be discussed under its specific figure heading.
*Note: Each map includes railway information and thus will be excluded from individual results and will be further analyzed in discussion section, listed down below.*
Figure 1.1:
This map is a depiction of the land cover elevation differences within Trempeauleau County. The elevation ranges from the highest recorded elevation (dark red) of 415 meters above sea level to the lowest elevation (dark green) of 194 meters. It can also be noted that the locations of the previous sand mines in this area vary widely in elevation, however the majority of the sand mines were found in mid elevation regions (light green to yellow areas).
Figure 1.2:
The next map in question looks at the type of land cover types that occur within the county. To simplify the findings, the three most reoccurring land types found within the mining areas are deciduous forest (green), hay pastures (yellow) and cultivated crop land (brown). In respect to developed areas, some of the mines did overlap developed open space, while others went around low-density developed land. However, there was no close proximity to high-density areas of development.
Figure 1.3:
This figure looks at the different soil types found in the area. From this data, three main types of soil was observed. Nordon, Seaton, and Hixton silt loam. These soils were all categorized as being near the surface of low sloping hills (slopes were all less than 20%) and the sandy material being mainly qlauconitic (quartz) sandstone.
Figure 1.4:
The final map delves deeper into the land cover types, specifically agricultural land types. When compared with the mine locations, the three main agricultural classifications were, deciduous forest (green), corn (yellow), and alfalfa (pink). However pasture land (light green) and soybeans (dark green) were also relatively prevalent.
Discussion and Conclusion:
Based on the results found from the mapping data, sand mining location and thus suitability is found in mid-elevation, slightly sloped, hill land. The mines, are located mostly in deciduous forests or cultivated crop landscapes that include but not limited to; corn, alfalfa, and soybeans. The soil itself is lightly to moderately eroded silt loam with quartz based sandstone found relatively near the surface (most rock formations were noted to be within four feet of the surface). Although some of the mines were located near the railways, there was enough that were located farther away to show no direct correlation with distance. To better understand transportation routes, future analysis of other roadways should be conducted.
Metadata:
*Note: All metadata can be found on listed website links. Exceptions are 5 and 6, which are then credited to their website listed below data.
1. http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/2015/polyline
2. https://www.sciencebase.gov/catalog/item/550c31ebe4b02e76d759d4cf
3. https://www.sciencebase.gov/catalog/item/5311fa6be4b0d490f4ab47ca
4. https://www.sciencebase.gov/catalog/item/5311fa5de4b0d490f4ab4626
5. Title: USDA-NASS Cropland Data Layer
Publication_Date: 9-26-2007
Title: TrempCo_MasterDATA.DBO.County_Boundary
Geospatial_Data_Presentation_Form: vector digital data
Online_Linkage:
Server=trempweb_sqlexpressSERVER; Service=sde:sqlserver:trempweb\sqlexpressSERVER; Database=TrempCo_MasterDATA; Version=dbo.DEFAULT
Credited to http://www.tremplocounty.com/tchome/landrecords/metadata_page.aspx?MDpage=/tchome/landrecords/Metadata/2010Metadata/countyboundary.htm
During a two week time span, the GIS 2 class with the geography department at the University of Wisconsin - Eau Claire, was given the task of evaluating sand mining data within the study area of Trempealeau County, Wisconsin (Figure 1.A).
 |
| Figure 1.A: Location of study area; Trempeauleau County, Wisconsin |
Methods:
The project in itself was split into two parts. For the first section of the project, multiple datasets were downloaded from multiple websites. This data was then imported into Arc Catalog where it was joined, merged and veiwed.
The first step in this process was to download data specifically for the area of interest. This included:
*Note: The number listed behind data corresponds with metadata section listed farther below.*
- Railway networks from the US Department of Transportation (1)
- A national land cover database from USGS National Map Viewer (2)
- County land elevation also from USGS National Map Viewer (3 and 4)
- A crop land data layer from the USDA Geospatial Data Gateway (5)
- Trempealeau County land records from the county's website. (6)
- And finally, SSURGO soil survey data from USDA NRCS Web Soil Survey (7)
Once data was downloaded, zipped files were sent directly to it's corresponding file within a pre-made personal geodatabase. From here some of the data needed to be further manipulated in order to create functional data. For instance since most of the data collected was in raster format, creating a 'pyramid' from them was necessary for display purposes. During this portion of the project, the railway system was also clipped to the area of interest. Another task with the data, pertained to the elevation data collected, which was split between two datasets. To fix this problem, the two rasters were joined using the mosaic tool on Arc Map. In doing so, one continuous raster was created. Yet on of the biggest tasks pertaining to the data was importing the SSURGO soil survey data. This was due to the fact that the template only provided a database schema, but not the actual data. To acquire this data, the downloaded file was opened up into Microsoft Access, the content was enabled and the component table was imported into the pre-made geodatabase that was previously mentioned. Once back in Arc Catalog, a relationship class was added using the "MUKEY" field listed within the attributes. The final task in part one of the assignment was to add all the corresponding data onto Arc Map for further editing and analysis.
The second part of this project was to create Python Script in order to project and clip all the raster data and then importing newly edited data into the previously mentioned database. For further information on scripting methods see Python Scripting blog post:
http://zahuroej.blogspot.com/2016/03/python-scripting.html
http://zahuroej.blogspot.com/2016/03/python-scripting.html
From the imported and edited data and raster sets, four maps to help assess sand mining suitability were created. For organizational purposes, the results for each map will be discussed under its specific figure heading.
*Note: Each map includes railway information and thus will be excluded from individual results and will be further analyzed in discussion section, listed down below.*
Figure 1.1:
This map is a depiction of the land cover elevation differences within Trempeauleau County. The elevation ranges from the highest recorded elevation (dark red) of 415 meters above sea level to the lowest elevation (dark green) of 194 meters. It can also be noted that the locations of the previous sand mines in this area vary widely in elevation, however the majority of the sand mines were found in mid elevation regions (light green to yellow areas).
Figure 1.2:
The next map in question looks at the type of land cover types that occur within the county. To simplify the findings, the three most reoccurring land types found within the mining areas are deciduous forest (green), hay pastures (yellow) and cultivated crop land (brown). In respect to developed areas, some of the mines did overlap developed open space, while others went around low-density developed land. However, there was no close proximity to high-density areas of development.
Figure 1.3:
This figure looks at the different soil types found in the area. From this data, three main types of soil was observed. Nordon, Seaton, and Hixton silt loam. These soils were all categorized as being near the surface of low sloping hills (slopes were all less than 20%) and the sandy material being mainly qlauconitic (quartz) sandstone.
Figure 1.4:
The final map delves deeper into the land cover types, specifically agricultural land types. When compared with the mine locations, the three main agricultural classifications were, deciduous forest (green), corn (yellow), and alfalfa (pink). However pasture land (light green) and soybeans (dark green) were also relatively prevalent.
Discussion and Conclusion:
Based on the results found from the mapping data, sand mining location and thus suitability is found in mid-elevation, slightly sloped, hill land. The mines, are located mostly in deciduous forests or cultivated crop landscapes that include but not limited to; corn, alfalfa, and soybeans. The soil itself is lightly to moderately eroded silt loam with quartz based sandstone found relatively near the surface (most rock formations were noted to be within four feet of the surface). Although some of the mines were located near the railways, there was enough that were located farther away to show no direct correlation with distance. To better understand transportation routes, future analysis of other roadways should be conducted.
Metadata:
*Note: All metadata can be found on listed website links. Exceptions are 5 and 6, which are then credited to their website listed below data.
1. http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/2015/polyline
2. https://www.sciencebase.gov/catalog/item/550c31ebe4b02e76d759d4cf
3. https://www.sciencebase.gov/catalog/item/5311fa6be4b0d490f4ab47ca
4. https://www.sciencebase.gov/catalog/item/5311fa5de4b0d490f4ab4626
5. Title: USDA-NASS Cropland Data Layer
Originator:
Department of Agriculture, National Agricultural Statistics Service (NASS),
Research and Development Division, Geospatial Information Branch, Spatial
Analysis Research Section (SARS)
Abstract:The USDA-NASS Cropland
Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover
data layer. The CDL Program began with one state in 1997 and expanded to cover
the entire Continental United States in 2008.
The
CDL has a ground resolution of 30 or 56 meters depending on the state and year.
For metadata files with detailed product and technical information or to
download a national seamless Cropland Data Layer (.img raster in Albers Conical
Equal Area projection), please visit: http://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php or the CropScape web portal at http://nassgeodata.gmu.edu/CropScape.
The
data layer is aggregated to a possible 85 standardized categories for display
purposes, with the emphasis being agricultural land cover. Most data layers
average about 10 to 20 categories out of the 85 possible categories.
The
purpose of the Cropland Data Layer Program is to use satellite imagery on an
annual basis to (1) provide supplemental acreage estimates for the state's
major commodities and (2) produce digital, crop specific, categorized
geo-referenced output products.
This
program represents a cooperative venture between three USDA Agencies
(headquarters units of NASS, the Foreign Agriculture Service, and the Farm
Service Agency) plus in-state agreements among the Agricultural Statistics
Service, the Department of Natural Resources and the Department of Agriculture.
Maps
are included for all available years when the product is ordered. However,
previewing the metadata or image in step two will only show the most recent
year. All states do not have all years in the maps delivered from the Gateway.
Use 'Status Maps' to see a map with labels of the years that are available.
Format: Raster GeoTIFF files.
A vector shapefile is also included for each state.
Spatial Reference Information:Universal Transverse Mercator (UTM), North American Datum of 1983
or World Geodetic System 1984.
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