Data Gathering and Quality Assessment

- Introduction -

To analysis quartz sand mining in Wisconsin, six datasets will be downloaded from websites, geospatial gateways and online map viewers. This data includes: transportation (railways), landcover, a digital elevation model (DEM), cropland, a soil survey, and a Trempealeau county geodatabase containing various land record information for the county. Each dataset originates from a different source leading to a difference in projections, data accuracy, and metadata documentation. In this post, an overview of the data collection process is presented along with a quality assessment of the associated data.

- Methods -

Obtaining the data:

• Land records for Trempealeau County were downloaded from the Trempealeau County Land Records Department's website as a geodatabase. This geodatabase was renamed TMP.gdb and will be used as the primary location for all data gathered for this analysis. As such, each of the following datasets will need to be projected in the same projection used in the TMP.gdb and clipped to the Trempealeau County boundary.

• An updated railroad polyline shapefile was downloaded from the United States Department of Transportation Bureau of Transportation Statistics website.

• A landcover raster and a 1/3 arc Second DEM in ArcGrid format was downloaded from the National Map Viewer.

• A cropland raster was downloaded from the USDA geospatial data gateway.

• SSURGO data was downloaded from the USDA NRCS Web Soil Survey application as a polygon shape file.

Each shapefile was imported into the TMP.gdb and given the proper projection and clipped by the Trempealeau County boundary feature class. Each raster was projected, clipped(extract by mask), and imported into the TMP.gdb via a python script. The exact python script and discussion of python use can be viewed in my Python Page by clicking here or the Python tab at the top of the page.

- Results -


Map 1: Comparison of the data downloaded for the sand mining analysis

- Discussion -

Data accuracy assessment:

To assess the accuracy of the data, eight different measures of accuracy were collected from the metadata files associated with each data set. These attributes were recorded in Table 1 and include: source, scale, effective resolution, minimum mapping unit (MMU), planimetric coordinate accuracy, lineage, temporal accuracy, and attribute accuracy.


Table 1: Accuracy comparison of the data downloaded and imported into the TMP.gdb


The data originator was designated the source. Data sourced from accredited organizations is preferred because a certain level of quality is implied (though it is still important to check). For lineage, only the sources were recorded for sack of table space, though the processes also need to be considered. For temporal accuracy, either the range of data collection or the last update was recorded. For attribute accuracy, only a broad statement found in the metadata was recorded, not the entirety of the text. For some data sets, certain measures of accuracy were missing such as effective resolution, scale, planimetric coordinate accuracy, and minimum mapping unit. In these instances Table 2, Table 3, and Table 4 were consulted in an attempt to infer some of the values. In some cases the values could not be found or inferred and the value of N/A was recorded.



Table 2: Relationship between scale, MMU, and effective resolution for raster data. (Nagi)
http://blogs.esri.com/esri/arcgis/2010/12/12/on-map-scale-and-raster-resolution/

When attempting to infer values, the definitions of scale, effective resolution, MMU, and planimetric coordinate accuracy and their relationships needed to be understood. Scale is the relationship between map distance and real world distance. The scales recorded in Table 1 are representative fractions. Effective resolution is commonly denoted as the square root of the area of one pixel of raster data (30x30m pixel size equals an area of 900m^2 making the effective resolution 30m). MMU is the size of the smallest depictable feature for raster data and the size that features will retain their geometry for vector data(e.g. polygon to line or point, or whether polygons will merge or remain separate). The MMU of raster data is commonly 4 pixels. Planimetric coordinate accuracy refers to the root-mean-square (RMS) error corresponding to how well features are positioned and represented in relation to the real world.

Table 3: Relationship between planimetric accuracy(ft) and scale. (ASPRS)
http://www.asprs.org/a/society/committees/standards/1990_jul_1068-1070.pdf



Table 4: Relationship between planimetric accuracy(m) and scale. (ASPRS)
http://www.asprs.org/a/society/committees/standards/1990_jul_1068-1070.pdf


- Sources -

American Society of Photogrammetry and Remote Sensing (ASPRS). ASPRS Accuracy Standards for Large-scale Maps (1990). Accessed via web: http://www.asprs.org/a/society/committees/standards/1990_jul_1068-1070.pdf

Nagi, Rajinder. On Map scale and raster resolution (2010).  ESRI blog. Accessed via web: http://blogs.esri.com/esri/arcgis/2010/12/12/on-map-scale-and-raster-resolution/

Trempealeau County Land Records. Geodatabase. Accessed via web: http://www.tremplocounty.com/landrecords/

United States Department of Agriculture National Resource Conservation Service. Cropland raster. Accessed via web: http://datagateway.nrcs.usda.gov/

United States Department of Agriculture National Resource Conservation Service. Soil survey shapefile. Accessed via web: http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

United States Geological Survey, National Map Viewer. Landcover raster and DEM. Accessed via web: http://nationalmap.gov/viewer.html

United States Department of Transportation. Railway shapefile. Accessed via web: http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html





Hydraulic Fracturing, Quartz Sand and the Dairy State

- An overview of sand mining in the state of Wisconsin and hydraulic fracturing -

Figure 1: Illustration of hydraulic fracturing
http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

Also know as hydrofracking or fracing, hydraulic fracturing is a technique used for resource extraction from with rock formations. To obtain the desired resource, a well is drilled into the earth, explosives are detonated, then water, sand, and chemicals under high pressure are pumped down the well to expand and maintain the cracks formed by the explosives which allows for the resource to be removed (Figure 1).  This technique has been used for over 60 years but recently, with technological improvements in horizontal drilling, allowing for new areas of economical extraction, and a desire for new fuel deposits, an interest in hydraulic fracturing for oil and natural gas has increased dramatically. With an increase in hydraulic fracturing comes an increase in demand for sand, but not every kind of sand is appropriate for fracing purposes.



Figure 2: Wisconsin quartz sand
http://wcwrpc.org/frac-sand-factsheet.pdf

Sand for hydraulic fracturing goes by different names: quartz sand, silica sand, or frac sand. Regardless of the name, the sand is the same; it needs to be very well rounded, almost entirely quartz, and have uniform grain size (Figure 1). The largest deposits of this type of sand are found in Cambrian sandstone formations in Western Wisconsin (Figure 2, 3).

Figure 3: A snipping of a USGS Geologic map of the USA.
Areas in red are Cambrian quartz sandstone, where quartz sand is mined
http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf
 

Figure 4: Cambrian sandstone and mining site locations in Wisconsin
http://wcwrpc.org/frac-sand-factsheet.pdf

As seen in Figure 4, mining of quartz sand is primarily located in western Wisconsin ranging from Barron County to Monroe County. Special emphasis will be given to Trempealeau County in subsequent analyses and blog posts. Typically, a quartz sand mining operation will engage in seven stages: overburden removal, excavation, blasting, crushing, processing, transportation and reclamation.  Overburden is the undesired soil on top of the sought after sandstone formations. These soils and materials are removed with scrapers or excavators and dumped around the edges of the mining site creating berms that act as a noise, light, and visual barrier to the possible irritants of the mining operation for local residents. Excavation and blasting at times go hand-in-hand. During these stages the sand is removed either by straight excavation or by blasting heavily cemented sandstone. If blasting occurs, noise, vibrations, and airborne dust can become sources of contempt for local residents. After blasting, the chucks of sandstone are hauled by trucks to be crushed, either on site or elsewhere, until they become nothing more than grains. The small grains of sand are then processed in four steps: washing, drying, sorting, and storing. The is done to ensure the sand is pure and uniform in size. Once the sand is sorted it can be transported to hydraulic fracturing sites, mainly shale gas formations, around the country (Figure 5). Now that the sand has been mined, the reclamation process can be started, if it has not already. Each county has different rules to determine how the land should be treated to be considered reclaimed, though the general policy is to return the land to a proper condition for land use such as farming or residential building.

Figure 5: Areas in red depict shale gas formations
http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

Throughout the mining process, fossil fuels are burned in most, if not all, stages. Groundwater is used for washing and dust control. Noise is generated through explosive detonations and use of heavy machinery. Hauling of sand and materials via trucks cause degradation of infrastructure. The removal of tons of sand will permanently alter natural landscapes causing potential ecological issues. Some of these issues have been addressed by use of  Wisconsin Department of Natural Resources (WDNR) permits and regulations, electrical equipment and generators when applicable, and pollution modeling. Though measures have been taken to reduce negative impacts of an increase in mining operations, the results have yet to be seen.

The issues related to quartz sand mining in Wisconsin are spatial in nature and GIS will be used to analyze and interpret them. As the semester continues, additional blog posts will discuss these issues in more detail.

- Sources -

Wisconsin Department of Natural Resources. (2012). Silica Sand Mining in Wisconsin. Accessed via web: http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

Wisconsin Geological and Natural History Survey. (2012). Frac Sand in Wisconsin. Accessed via web: http://wcwrpc.org/frac-sand-factsheet.pdf