Data Gathering, Interoperability and Projections

Introduction

This is the first step in our analysis of sand mines in Wisconsin.  For this portion of the assignment I will be gathering and preparing data for sand mine analysis.  To accomplish this task, I first will become proficient downloading data from a multitude of sources across the Internet.  Secondly, I will become efficient at importing and compiling the data into a well organized geodatabase.  The final step will have me projecting data from different sources into the same projection and clipping the data to my area of analysis.

Trempealeau County, Wisconsin (Fig. 1) is the the main focus area for our analysis.  All of the data I will download and prepare will be for Trempealeau County.

(Fig. 1) Trempealeau County is located in the west central portion of Wisconsin (Map Source)

Methods

The initial objective to my assignment was to download data from the following 5 different state and federal agencies:

• US Department of Transportation (USDT).
• Railway Network Data

• US Geological Survey (USGS)
• National Land Cover Data 2011
• National Elevation Dataset

• US Department of Agriculture (USDA)
• Cropland Cover Data 

• Trempealeau County Land Records
• Entire Geodatabase from Trempealeau County

• Natural Resources Conservation Service (NRCS) sub-organization of USDA
• SSURGO Web Soil Survey Data

I navigated to the appropriate websites using links provided to me by my professor (see sources below).  The focus of this study is with in Trempealeau County, so when possible I downloaded only the data for the county.  Those images or feature classes downloaded will be clipped or extracted by mask (raster images) to the Trempealeau County boundary.

Each data set with the exception of the USDA was downloaded directly from the website.  The USDA data was sent to me in a email attachment.  All of the data sets were delivered in a .zip file format.  From the .zip files I extracted the actual data and placed the data in organized individual folders.

Steps in preparation for each dataset

USDT

From the USDT I downloaded the polyline, Railway Network 2015 data  for the entire United States.  After I downloaded the entire geodatabase from Trempealeau County, I clipped the railroad network to only those rail lines which fell within Trempealeau county.

USGS

From the USGS I downloaded the following 3 different datasets: National Land Cover Database 2011 (NCL),  National Elevation Dataset (2 different coverage areas).  Using the box/point tool within the National Map Viewer I made a box which enclosed all of Trempealeau County.  After setting the box as the area of interest (AOI) I selected each data set separately and downloaded them to my computer.

USDA

From the USDA I downloaded the Cropland Land Cover data set for the state of Wisconsin.  As stated before the data has to be processed and emailed to you for downloading.

Treampealeau County Land Records

From Treamplealeau County I downloaded the entire geodatabase from their land records divsion.

NRCS

From the NRCS I downloaded the SSURGO web soil survey database for Trempealeau County.  The SSURGO data is a very comprehesive dataset which takes a little more work to properly display need information.  I will not be going through the step by step tutuorial, just understand it is more than just a drop in shapefile/feature class with all the information.

Organization & Transformations

Before I organized everything into one nice neat package I had to combine my Digital Elevation Models (DEM) which I downloaded from the USGS.  The southern tip of Trempealeau County was in one image and the majority of the county was in another.  Using the Mosaic to New Raster tool within ArcMap I combined these two images to form one new raster image.

The next step in the process is to create a location to store all of our data.  For this instance I will be using the Trempealeau County Database as my location.  The database is very well organized already and will serve as a great base to build from.

However, I cannot just throw all of my data in the geodatabase.  All of the data I have retrieved is in varying projections and coverage areas.  I want to focus all of this data to the extent of Trempealeau County.  To assure accurate results in my analysis I want to have all of the data in the same projection.

After analyzing the Trempealeau County database I found they were using, NAD 1983 HARN WISCRS Trempealeau County_Feet. (NHWTCF)  This projection is the most accurate when analyzing data across Trempealeau county.

There are multiple approaches to accomplishing the projection and clipping of the data.  Our class had to use Python scripting to write a geoprocessing script to achieve the desired end result.  For this portion we were only dealing with the raster images we have extracted from our data.  To preform these objectives I will be using the Project tool and Extract by Mask (Clip) tool with in ArcMap via PyScripter.  The benefit of using a script, is it allows us to run the same tool across the 3 raster images at once.  If I was to process this by hand in ArcMap I would have to run the Project tool and the Extract by Mask (Clip) 3 times individually.  Though it may not seem like a great time savings in this instance, if I had 100 images to project and clip, it would save me a ton of time.  The exact Python script can be seen via my Python tab at the top of my blog or by click here.

The final step in this exercise was to make a map of all three of the raster images from the previous steps. (Fig. 2)

(Fig. 2) Comparison of the raster images with locator map.

Data Accuracy

Data accuracy is important to the quality and accuracy of your analysis.  Evaluating data accuracy can be a challenge.  Information about the data accuracy should be stored in the Metadata file which is usually provided when you download the data.  However, as Dr. C. Hupy has stated there is no "data police" to assure the information is provided.  Even if the information is provided, the chances of it being in the proper format is even less likely.  Industry standards have started to improve this issue with Metadata but it will take time for all of the available data to catch up and revise or create proper Metadata.

For our assignment we were asked to look at 7 different aspects of data accuracy.

• Scale
• Effective resolution
• Minimum mapping unit
• Planimetric Coordinate Accuracy
• Lineage
• Temporal Accuracy
• Attribute accuracy

I examined the metadata for hours in addition to searching the websites the data was retrieved from for additional information.  In the end, I could not find many of the items I was requested to retrieve.  I created a table with the information I found to display the accuracy differences between datasets (Fig. 3).

(Fig. 3) Compilation of data accuracy components for downloaded data.

 
Conclusion

This exercise was a great introduction to the available data sources across different governmental agencies.  Being able to see the different formats the data is delivered in gives me a additional list of things to prepare for when starting a project.  Our introduction to Python adds another way to run geoprocessing tools within ArcMap while saving time.  The data accuracy examination is a great eye opener to understand that you really don't always know everything about your data.  I would not make a life or death decision based on this data due to lack of accuracy information available.


Sources

Cropland Cover. In United States Department of Agriculture. Retrieved October 14, 2015. http://datagateway.nrcs.usda.gov/

Trempealeau County Land Records. Retrieved October 14, 2015, from http://www.tremplocounty.com/tchome/landrecords/


United States Department of Transportation. Retrieved from http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html

United States Geological Survey. Retrieved October 12, 2015, from http://nationalmap.gov/about.html

Web Soil Survey. In United States Department of Agriculture. Retrieved October 14, 2015, from http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

Frac Sand Mining in Western Wisconsin: Project Overview

Introduction 

Sand mining is not new to Wisconsin, this type of mining has occurred in Wisconsin for over 100 years.  The sand mined in Wisconsin is used in a multitude of way such as, glass manufacturing, foundry molds, and golf course sand traps.  Due to recent surges in hydrofracking the state of Wisconsin has seen an increase in mining permit applications and proposals.

What is Hydrofracking?

Hydrofracking is a technique used by the oil and natural gas companies to extract resources thousands of feet below the earth's surface (Figure 1)  The technique involves using explosives to create cracks in the surrounding rocks.  Following the creation of the cracks, then frac sand, water, and various chemicals are pumped into the well to expand the cracks while also holding them open.  With the rocks cracked and held open it is easier to extract the product which was previously contained by the rock.  Though hydrofracking is not new, advances in the industry have allowed previously non-extractable gas and oil to be extracted.  The same advances have also made the process cost effective.

(Fig. 1)  Illustration of hydrofracking also referred to as hydraulic fracturing. (http://en.skifergas.dk/technical-guide/what-is-hydraulic-fracturing.aspx)

Frac sand is the product which the majority of the new mining companies are extracting.  Frac sand is silica sand, commonly called quartz.  Not all silica sands meet the standards to being used for hydrofracking.  The sand must be almost entirely quartz, very well rounded, uniform grain size, and have high compressive strength (Figure 2).  Large deposits of this type of sand are located in sandstone formations within the state of Wisconsin (Figure 3).

(Fig. 2) Frac Sand with a penny for size correlation. (http://www.disclosurenewsonline.com/wp-content/uploads/2013/07/frac-sand.jpg)

(Fig. 3) Frac sand mines and sandstone formations located in Wisconsin. (http://wcwrpc.org/frac-sand-factsheet.pdf)

Sand Mining Process

Sand mining operations vary from mine to mine.  The following 6 step process illustrates a typical sand mine and sand processing plant.

1. Overburden removal/excavation-- The removal of topsoil and subsoil to expose the underlying sand.  Often the overburden is used on the perimeter of the site to create a berm.
2. Excavation--  Removal of the sand.  Typical mines use large excavating equipment such as excavators or front end loaders for sand removal.  Certain situations require blasting to release the sand from the geological formation.  The excavated materials is then stacked for storage or hauled to the processing plant.  Hauling is done via a semi-truck or trains.
3. Crushing--  The sand deposits which require blasting often require crushing to reduce the size of the particles.
4.  Processing--  The sand must go through additional steps to be used for hydrofracking.  The sand will be washed, dried, sorted, to achieve the desired uniformity.
5.  Transportation-- Through the entire process the sand is transported using a variety of methods.  The preferred method to haul sand is currently the railroad though in some areas dump trucks, gondola compartmentalized trucks, and barges are being used.
Reclamation--  After exhausting the supply of sand from the site the owner/permittee must reclaim the mine area.  There is variation on the requirements for mine reclamation from county to county.  The general rules for reclamation is no steep slopes, no vertical walls though some may be authorized with approval from the county.  After the grading is complete the surface must receive topsoil to allow plants to grow.  Once the topsoil is places, seeding and mulching can occur.

Issues with frac sand mining is Western Wisconsin

The list of issues associated with frac sand mining is quite long as described by the Wisconsin Department of Natural Resources (WIDNR).  I will highlight a few of the issues from the list I feel are important.  If you would like to read more about additional issues please refer to Silica Sand Mining (WIDNR).

During the entire mining process machinery which is burning fossil fuels are being used, thus adding pollutants to the air.  The machinery which is being driven on the public roadway is causing additional degradation to the roadways.  Dust particles are released throughout the entire process.  The sand is often watered in an attempt to reduce the dust.

(Fig. 4) Pin heads displaying air monitoring stations for particulate matter in Wisconsin. For more information see the following link. (http://dnr.wi.gov/topic/Mines/AQSandMap.html)

The processing of the sand requires a large amount of water.  Depending on the plant the average water use is expected to range from 420-2 million gallons per day.  Some processing plants have a closed-loop processing system and others have an open-loop system.  Acrylamide may be present in the wash water from the processing facility.  The US Enviornmental Protection Agency (EPA) set the the maximum allowable level of acrylamides at 0 in public drinking water.  This equates to the possibility of contaminating the ground water with the wash water from the processing facilities.

Class Overview

Frac sand mining is very controversial especially since the great increase in the number of mines across the state.  Many people rejoice with all of the job opportunities which become available when a new mine is created in their area.  The environmental effects, landscape changes, and risk for water, or air contamination are argued from the opposing side.

The main focus for our GIS II class will be the suitability and risk of mining within Trempeleau County and several other counties in Western Wisconsin. Our class will be downloading data from multiple federal and state agencies for analysis from which our suitability and risking modeling with be derived.  The progression of this work will be displayed in later blogs.

Sources

Industrial Sand Mining (n.d.). In Wisconsin Department of Natural Resources. Retrieved October 7,  2015, from http://dnr.wi.gov/topic/Mines/Sand.html