Frac Sand Suitability Part II

The goal of this Project was to study the transportation routes from frac sand from the mines to a railroad terminal.  These transportation routes could result in significant impacts on local roads.  A network analysis was performed to determine the distance travelled on local roads and the cost of the impacts for each county in Wisconsin.

Data
The addresses of frac sand facilities were used in the network analysis.  I, along with three other class members, located these addresses through geocoding as well as aerial imagery.  Each class member created a feature class of their located mines;these feature classes were then merged into one feature class in ArcGIS.  X and Y coordinates could not be found for two mines.  These mines had to be removed in order to correctly perform the network analysis.  A railroad terminal dataset was provided by our professor, Christina Hupy.  This dataset represents locations within United States for transportation terminals such as bus stations, train stations, marine terminals, and other significant transportation nodes.  Street and county data was supplied by ESRI.  Each of these datasets were crucial to the success of the network analysis.

For this analysis, it was assumed that trucks travelled between each frac sand location and rail terminal 50 times (one-way) per year (100 trips total).  It was also assumed that the cost per truck mile was 2.2 cents.

Methods
A network analysis can be performed using dialog boxes in ArcMap, but this process can be tedious and time consuming.  To perform this network analysis, a model was built in ModelBuilder, an ArcGIS application.  ModelBuilder allows the user to input feature classes and tools into a model.  When all aspects of the model have been added and defined, the model can be performed.  The use of a model presents many benefits.  The process is performed in a single step, yet is editable in order to fix any issues or to change the output of the model.

In order to produce the desired results of this project, the "Closest Facility" network analysis tool was used.  Closest Facility network analysis created a route between a frac sand location and the nearest terminal.  It was used because it most closely resembles the interaction between these features in the real-world.  The model below represents the steps taken to perform the network analysis (Figure 1).

Figure 1- Workflow created in ModelBuilder to perform network analysis

Before the distance travelled per county could be calculated, the SUM Shape Length values had to be converted from meters to miles.  This was done through the field calculator tool using the equation "SUM Shape Length / 5,280."  Once the feature lengths were in miles, the field calculator was used again to calculate the cost per county using the equation "Miles * 100 * .022."

Results
The model resulted not only in a network analysis of the frac sand transportation routes, but also the cost and travel distance per county.  These results are displayed below in Figure 2.

Figure 2-Final results of Network Analysis

Conclusion
The counties that exhibited the most distance travelled were Chippewa, Eau Claire, La Crosse and Trempealeau.  These counties exhibited the highest cost as well.  It can be concluded through these findings that as the travel distances increase between the frac sand facilities and rail terminals, the cost also increases.  Although other factors can contribute to deteriorated road conditions, it can be implied through the results of this analysis that there is a significant relationship between the frac sand transportation, road deterioration and cost per county.

Discussion
The calculations of the network analysis outputs were equally as important as the network analysis. The results of the calculations and the network analysis can be used separately to provide information on the impact of frac sand mining in Wisconsin, but the combination of the results exponentially increase the value of the research.

The real-world replication of network analysis make this tool invaluable to geospatial technology.  It is a tool that not only replicated real-world networks, but also allows the users to define parameters to expand the capabilities of these results.  Many types of network analysis are available and can be used to produce different results.  It is important to know the ramifications of the type of network analysis used; if not used correctly, results may be flawed or inaccurate.

Frac Sand Suitability Part I

Before a suitability/risk model can be built, base data must be acquired.  This data includes environmental, transportation and demographic features.  A variety of agencies were used to import this data into a geodatabase in ArcGIS.

• The National Atlas provided a line feature class for railroads in Wisconsin.  Frac sand processes often use railways to transport silica sand from the mine to processing plants and oil wells.  This feature class was downloaded from National Atlas data and imported into the geodatabase.

• The United States Geologic Survey (USGS) provides a wealth of geographic data.  For the purposes of this model, elevation and land cover data was acquired from this agency.  USGS provided land cover data from 2006 in raster format.  The MRLC provided a description of the data and a legend for the raster codes.  A National Elevation Dataset was also in raster format.  The DEMs (Digital Elevation Models) had to be shared as two separate tiles because of the large storage capacities of the files.  In ArcGIS, the “Mosaic to New Raster” tool was used to merge the two tiles.  Both sets of data were imported as raster datasets in the geodatabase.

Figure 2-Land Cover Data
(Source: USGS)

Figure 3-Elevation Data
(Source: USGS)

• Cropland data was acquired through the United States Department of Agriculture (USDA) Geospatial Data Gateway.  This information will be used to determine land cover in the suitability/risk model.

Figure 1- Cropland Data
(Source: USDA)

• The National Resources Conservation Service (NRCS) provided soil data for this model through the Soil Survey Geographic Database (SSURGO).  This data was imported to the geodatabase as a feature class, the component table for soil data also had to be imported.  These features were joined through a simple relationship class using the primary key “MUKEY."

Figure 4- Soil & Railroad Data
(Source: NRCS, National Atlas)

Coordinate systems varied between each dataset.  In order for accurate representation and analysis of the data, a common coordinate system had to be set.  The datum of each dataset was inspected and a coordinate system was chosen based on two factors: common datum and area of interest.  The datum of each dataset was NAD 1983 and the area of interest was Western Wisconsin.  The NAD 1983 UTM Zone 15N coordinate system was chosen because it fit within the datum of all data and it was the best fit for the area of interest.

Frac Sand Suitability Overview

Wisconsin’s rich glacial history has provided abundant sandstone deposits upon its geologic landscape.  This glacial history has designed an expansive array of Jordan, Wonewoc and Mt. Simon sandstone formations containing high-quality silica frac sand.  Although sand mining has occurred in the state for hundreds of years, the practice of hydrofracking has rapidly increased the demand for sand mining in recent years.  Hydrofracking pumps a combination of water, frac sand and chemicals under high pressure into underground oil or natural gas wells to open natural fractures.  This allows natural gas or crude oil to be more easily extracted from a well.  It is important to note that no oil or gas wells exist in Wisconsin, but the state’s abundant frac sand resources are sought to meet the demands of the oil industry.

Western Wisconsin has become a hotbed for frac sand mining.  According to the Wisconsin Department of Natural Resources, “Wisconsin has approximately 60 mining operation and 30 processing facilities operating or under construction” and as of January, 2012 “20 new mining operation proposals” (WDNR, Silica Sand Mining in Wisconsin, P. 3).  The extraction of frac sand can potentially cause an array of issues.  Mining may cause dust particles and pollutants to be emitted into the air; the extent of emissions is considered minor, but is a concern for air quality purposes.  Frac sand facilities encounter water resources at a variety of levels.  Frac sand facilities may be located near rivers or streams; it is possible for run-off from these facilities to reach bodies of water and cause contamination.  Contamination can include increased siltation or erosion. (DNR, P. 24)  Water resource impacts can extend into loss of habitat and ecology, especially in regards to Wisconsin’s fisheries.  Transportation infrastructure can also be impacted by the processes of frac sand mining.  The sand must be transported for the extraction site to the processing site.  The amount of weight asserted onto existing roadways coinciding with increased traffic may cause road deterioration.  These factors can cause a decrease of property values in areas near frac sand facilities.  The potential implications of sand mining, while numerous, are highly contested.

The use of a geospatial information system (GIS) can be used to assess these possible implications through a suitability/risk model.  A geospatial information system is used to represent real-world features in a spatial manner.  Spatial information can be used to analyze geographic patterns and determine relationships between features.    A suitability/risk model will use geographic data pertaining to frac sand mining to determine spatial frequencies, analyze network associations and consider the relationships between environmental impacts and frac sand mining.  This model will focus on Western Wisconsin, specifically Trempealeau County.

Sources:

Silica Sand Mining in Wisconsin

Wisconsin Department of Resources, 2012

Balloon Mapping

Balloon Mapping I

INTRODUCTION

Our Geospatial Field Methods class will be launching two balloons for mapping purposes.  The first balloon launch will be a mapping balloon to map the newly reconstructed campus mall at the University of Wisconsin-Eau Claire.  The second launch will be a high altitude balloon that will capture aerial imagery of the area surrounding the launch point.  In order to have successful launches, we worked together as a class to construct the balloon rigs and to plan the future steps of the launch.

METHODS

Figure 1-Reviewing
Reference Materials

Our class reviewed reference materials provided by our instructor before planning the execution of our balloon launch (Figure 1).  This allowed the class to gain a better idea of what exactly we would be doing and what equipment would be needed.  The instructor had supplied the equipment needed for both balloon rigs as well as a scale.

After we reviewed, the class formed groups to concentrate on specific aspects of the launch:

                -construction of the Mapping rig

                -construction of the High Altitude rig

                -Parachute Testing

                -Payload weights of both rigs

                -Design of implementing continuous shot on the cameras

                -Implementation and testing of the tracking device (Figure 8)

                -Filling the balloons with helium and securing the balloon to the rig

My group concentrated on the payload weights of both rigs.  We had to measure each and every item so when it comes time to construct the rigs, we will know exactly how much the rigs weigh.  We weighed both balloons, the parachute, two carabineers, all three cameras and memory cards, every type of rubber band, small and large zip ties, rope, string, empty liter bottles, hand warmers, Styrofoam, a “minno thermo” container, and a yellow cord with a metric scale.

Figure 2- My group weighing items

We also took pictures of each item and gave that picture a detailed label that matched the payload spreadsheet.  These pictures were uploaded into the class folder so each student has access to the images.  This is important because there is a large number of items and everyone in the class needs to know which weight corresponds to which item.  The total payload for the High Altitude Mapping Rig was determined by the end of the class period and was added to the payload spreadsheet(Figure 9).

Figure 9- Weight Chart

While my group weighed the items, other groups tested and timed the parachute, constructed rigs for both balloon applications, determined the best camera for each launch and designed the implementation of the continuous shot (Figure 3) and tested the tracking device.

Figure 3- Implementing Continuous
Shot

Figure 4- Building the Mapping
Balloon Rig

DISCUSSION

Each group made sure to document their progress and outcomes during this activity.  In the end, the documentation will allow the entire class to build both the Balloon Mapping rig as well as the High Altitude Mapping rig.  The documentation provides a framework of what has been done and what still needs to be done before we can initiate the launches.  At the end of class time, one mapping rig was constructed, "The Hindenburg" (Figure 5).

The process of preparing for the launch of both of the balloon rigs was somewhat chaotic because there were so many aspects that needed to be considered.  By breaking up into groups, students could apply their strengths to their category.  This helped because people who were better at construction worked to build the rigs (Figure 7) or people who were better at organizing the data concentrated on the payload weights, ect.

Figure 5- "The Hindenburg"

Figure 6- Balloon Mapping Rig

Figure 7- Construction of the Rigs

Figure 8- Testing the Tracking Device

The class had to work together to share the equipment because each group needed every item throughout the class period.  We also worked together to communicate to other groups what we had accomplished and what still needed to be done.  At the end of the class period, all of the groups should have recapped what was accomplished so everyone was on the same page.

CONCLUSION

As stated earlier, this is just the first step in our class’ balloon launching.  The data and designs we came up with will be improved upon and then implemented into the balloon launches.  It was a team building activity for each group and for the class as a whole.  This will help in the future steps of this process because a strong team will be needed to overcome the possible obstacles for this project.

The next steps are to design how we will fill the balloons with helium and then connects the balloons to each rig.  Once this is determined we will be able to move onto the fun part-the launch!

Balloon Mapping II

Introduction
Our class has created and implemented a balloon mapping rig in the previous weeks.  This is an innovative and cost-effective way to collect aerial imagery.  In order to use the aerial imagery, the images collected by the balloon must be georeferenced and mosaiced.  This report concentrates on the process of both mosaicing and georeferencing images from our balloon mapping.

Methods
Two techniques were used to mosaic the aerial imagery.  For both techniques the images were uploaded to a desktop after the balloon was grounded.  The techniques used were very different; the first technique was using a website called mapknitter, the second technique was georeferencing through ArcMap.

Mapknitter is a website that provides tools necessary to "knit" together aerial images to create a map.  A Google Imagery base layer was offered by the website.  This base layer could be used as a reference for the placement and scale of the imported images.  The images had to be uploaded to the site one at a time.  Once an image was uploaded it could be scaled and moved so it could be located in the correct position.  This was done repeatedly with numerous images until the images were sufficiently placed.  Once this was complete, the map had to be exported so it was visible to all users of the mapknitter site. The map I created on mapknitter is seen in Figure 1.

Figure 1: Aerial Imagery map created with MapKnitter (mapknitter.org)

The second technique was georeferencing with ArcMap.  Because there was such a plethora of images collected by the balloon, the class divided sections of the campus into groups to lighten the workload for all students.

I started a new map document then loaded data that would be beneficial to the process of georeferencing.  The data included a polygon feature class for my groups section and a CAD polygon feature class of the buildings on campus (Figure 2).  I used an imagery basemap provided by ESRI in the beginning of the process as a reference.

Figure 2- Group Section & Buildings feature classes

After this data was added the process of georeferencing could begin.  This process if not very complicated, but it is time consuming and must be done carefully.  Below are the steps necessary for georeferencing.

1.) Turn on the georeferencing toolbox (Figure 3)

Figure 4: ArcGIS Desktop 10.1 Georeferencing toolbar

2.) Click the "Control Points Tool"

3.) Zoom to the current image

4.) Click a point on the current image that can be easily referenced to the buildings feature class

5.) Zoom to the Group Section layer

4.) Click on the area that matches the point previously selected on the current image

The image will move to a new location using the georeferenced point; do this repeatedly for each image until the image is placed in the correct area.  This process is repeated for each image until an area is accurately represented on the map.

Zooming between layers is helpful because the aerial imagery is not spatially referenced and is located very far from the needed area.

The georeference control points can be edited using the "Control Points Table" (Figure 5).  The control points are labeled by a number and when clicked on, the control point will be highlighted on the map.  Editing mainly consists of deleting control points if it distorts the image or the image's location on the map.

Figure 5: Control Points Table

Discussion

Georeferencing in ArcMap 10.1 resulted in a better outcome than Mapknitter.  The control points 

Conclusion

Figure 6: Final mosaiced raster of the aerial images

Field Navigation

INTRODUCTION
Navigation in the field is incredibly important in field methods.  Accuracy in the field is dependent on the type of navigational resources available and can be skewed with the simplest miscalculation.  In the previous weeks, our class used different type of navigational resources to navigate through a newly acquired property for UW-Eau Claire, The Priory.  Maps, compasses and GPS units were used as navigational resources.  Students were put into groups of three and given a specific course to navigate.  Each group had to find waypoints in order to finish their course.  When the GPS units were used groups plotted points at the waypoints and a tracklog was used to note the course.  After we navigated the course in the field we used ArcGIS to import the GPS data and to create maps of our routes.

Each activity built on our knowledge of field collection.  The activities also introduced the students to other ways of navigating.  It was important for us to experience working in the field because we learned to overcome challenges that were presented by the weather, terrain and technology.

2.)This week we used the navigation maps from the previous exercise and applied our pace count to find waypoints at The Priory.  This straightforward exercise presented challenges due to the weather, terrain and lack of navigation technology.

3.) This week, we expanded on the navigation exercises of the previous weeks.  A GPS unit was used to navigate to waypoints without the use of a map or compass.  Students were provided a list of Lat/Long points by the professor for each waypoint.  Students activated the tracklog feature of the GPS unit in order to track their route throughout the activity.

4.)You have the class period to complete all 15 points from all three courses. The first group finished wins.  Make sure you use the punch on your cards at each flagged location.

METHODS

For the first weeks exercise only a map and compass were used to navigate The Priory.  Each student had to calculate their pace count before going in the field.  A pace count takes into account how many steps a person takes within a given distance.  This information allows a person to know how far they have traveled without the use of a GPS unit.  The distance for our pace count was 100 meters.  To determine my pace count, I walked at a normal pace counting every pace (every other step) for a pre-measured distance of 100 meters.  I repeated this process three times and took the average of the count-70 paces.  Knowing my individual pace count helped me to account for the distance I travel while navigating at the priory.

Two navigation maps were created for the first week's exercise.  The first map was an overview of the area (Figure 1), while the second was more precise and include topography (Figure 2).  Our professor supplied data included CAD drawings, aerial imagery and polygon feature classes.  Topographic data was also provided by the USGS.  The data was projected to NAD 1983 UTM Zone 15 North so a UTM grid could be applied to the maps.  A polygon feature class of the boundary was supplied by our professor as well as a point feature class of the waypoint locations.  This data helped us to reference our location in relation to the waypoints.

Figure 1- Overview navigation map

Figure 2- Navigation Map with 2 and 5 foot contours

When we reached The Priory on our first day of navigation,our professor provided a list of X and Y coordinates of the waypoints (Figure 3).  We used these coordinates to plot the points on our navigation map. We then used a compass to note the angle of direction on our map.  This information would be used in the field to better navigate the waypoint courses.  We also measured the distance in meters from one waypoint to the next so we could use our pace count in the field to determine our distance.

Figure 3-X and Y coordinates of waypoints provided by Joseph Hupy

Once in the field, we started at point 1A.  We used our compasses to find the correct angle of direction.  We sent one person out about 150 feet and aligned their position to the necessary angle (Destination).  One person stayed behind to make sure the angle of direction was followed (Angler).  The other person walked while using their pace count to the person who was aligned with the angle of direction (Runner).  We kept track of how many paces it took for the runner to reach the destination so we could determine how much further we had to travel to reach our waypoint.  We broke up the distance between two waypoints so we could send out the destination person to an area where they were still visible to make sure we kept the correct angle of direction.  This process was repeated over and over to navigate through the course.  Once we reached a way points, we punched a course card given to us by our professor with the stamp at each waypoint.

During the second navigation activity students were allowed to use a GPS unit and map to navigate the course.  To begin we had to find the starting point of our route, the location was indicated by the list of lat/long points given to us by our professor.  At the starting point, we activated the tracklogs on the GPS units.  We made sure to activate only when we reached the starting point so only our course was tracked.  From the starting point, we used the lat/long feature of the GPS unit to navigate to the first way point (Figure 4).  We observed the increase and decrease of the lat/longs on the GPS unit to determine which direction to travel.  This was done for all six waypoints on our course.  After we had found each waypoint, we traveled back to the starting point to complete the course.  Upon reaching this point, we turned off the tracklog.

Figure 4-Navigating with the GPS unit

Figure 5- Zac & Phil located a waypoint

Using the DNR Garmin application, students uploaded their individual tracklogs onto a computer.  Through this program, the tracklog could be easily converted into a point shapefile.  The shapefile was then imported into the class geodatabase.  Once the data was imported, three maps were created.  One map showed the tracklogs for every student in the class (Figure 6).  Another map showed the tracklogs for my group (Figure 7) and another for my own tracklog (Figure 7).

Figure 6- Map of the tracklogs for each student in the class

Figure 7- Map of my group's tracklogs

Figure 8- Map of my individual tracklog

For the third activity, we used a GPS unit and map again to navigate to waypoints.  For this activity groups had to navigate to every waypoint, 15 in total.  We were also given paintball guns to add some excitement to the activity.    The same techniques were used as the previous week.  The same three maps were also created for this activity (Figure 9).

Figure 9-Week three maps: Class, Group and Individual Tracklogs

DISCUSSION

These activities taught me how different technologies can be used for navigation.  It is easy to assume the the highest techology is always best, but these activities showed me that it is possible to navigate accurately with simply a map and compass.

I also learned a lot about working together as a group through these activities.  Our group worked together very well, this helped our group to navigate efficiently.  One thing that did not work well for our group was using the pace count.  The pace count was difficult to use  because we measured our individual pace count on a flat surface with no obstacles before this exercise.  We found that our pace counts came up short for each waypoint in the field due to rough terrain and the amount of snow on the ground.

Technology does not always make navigating easier.  Although we were allowed to use a GPS unit to navigate in the last two weeks, it was not easier than the compass and map navigation.  It was somewhat difficult to determine the direction of travel using lat/long the the GPS and a group member had to be constantly watching the lat/long numbers to make sure we didn't stray off of our direction.  Even though it was more difficult to navigate with a GPS unit, it took less time to navigate using this technique.

The transformation from the GPS unit to a GIS shapefile caused the features to be somewhat skewed.  Waypoints did not fall exactly in the correct location and the tracklogs were somewhat inaccurate as well.  This is one deterrence that is unfortunate but can be fixed through editing in ArcGIS.

CONCLUSSION

All of the techniques that we used to navigate were important in their own way.  It is important to know not only how to use these technologies individually, but also how to use the technologies as a combination.  We used techniques that are not technologically advanced (map, compass) and technologies that were advanced (GPS units).  The activities showed the benefits and drawbacks of each technique.

Distance Azimuth Survey

INTRODUCTION
Survey technology has dramatically improved in the last few decades.  Surveyors have a variety of tools at their disposal that allow for increased accuracy and efficiency, but sometimes technologies can fail.  In this activity we used a range finder and compass to conduct a distance azimuth survey.  The devices we used can come in handy when other resources are not available.  Our study area consisted of a 1/4 hectare plot on the new campus mall at the University of Wisconsin-Eau Claire.

METHODS
OBJECTIVE I

The first objective of this activity was to familiarize ourselves with the tools.  I worked with a partner, Phil, throughout the entire project.  We went outside behind Phillips Hall at the University of Wisconsin-Eau Claire and used a laser range finder as well as a compass to collect distance and azimuth of trees in the area. The distance was taken in meters and the azimuth was taken in degrees.  We recorded the point data in a notebook.

After we had become familiar with both devices, we used Microsoft Excel to create a table of our point data.  The initial fields were simply "SD" (Slope Distance) and "AZ" (Azimuth).  We imported this table into a file geodatabase and added it to a blank ArcMap document.  Because we did not use a GPS unit to collect the X and Y coordinates of our origin point, we had to add an aerial basemap and locate our position.  When I located our origin location, I used editor to add an X and Y field in our table.

The next step was to use the "Bearing Distance to Line" tool in ArcToolbox in the Data Management-Features folder. This tool creates a new feature class with line features calculated by the X and Y locations, the bearing field (AZ) and the distance field (SD).  After completing that process, I used the "Feature Vertices to Points" tool located in the same folder as the previous tool.  This tool creates a point feature class from the vertices of the newly created line feature class.  These new points represented the location of the data points (trees) that we collected.  To my dismay, the tree data points were located miles from the actual site location.  After some troubleshooting, Phil and I realized that our origin coordinates were not specific enough for accurate representation.  To overcome this, we changed the Data Frame Properties in ArcMap so the Display Units were decimal degrees.  I then used the Identify tool to click on the origin points on the aerial image.  This gave me X and Y coordinates with 6 decimal places, which was specific enough to accurately represent our data points.  The slope distance and azimuth are represented by the blue lines and the trees are represented as the lighter blue circles in Figure 1.

Figure 1-Test data represented in ArcMap

OBJECTIVE 1
After we had become familiar with the range finder as well as functioning in ArcMap, Phil and I conducted another distance azimuth survey.  This time, we collected data points for the stone benches located in the new campus mall.  We used two origin points, the steps of the library and steps on the south side of Schofield Hall.  The benches were located in a somewhat large area with a south-western slope.  By using to origin points, we could ensure the accuracy of the slope distance and azimuth angle.

Again we used the range finder to collect the Slope Distance (SD) and Azimuth (AZ) for each bench.  When the range finder could no longer accurately measure the SD and AZ of the benches, we moved to the steps of Scholfield Hall.  While collected the data, we had to keep a close eye to make sure we did not skip benches or collected benches twice.  We wrote the SD and AZ measurements in a notebook while collected the data and then transferred this data into an Excel spreadsheet.

Because I had run into errors on our previous attempt to import the spreadsheet into ArcMap, I created ID, X and Y fields directly into the spreadsheet.  I opened ArcMap and used the Identify tool to locate the X and Y coordinates of our origin locations (Figure 2).  This data was then pasted into the spreadsheet in the corresponding fields.  The ID field was generated simply by assigning a number in numerical order to each data point (Figure 3).

Figure 2-Identify tool in ArcMap

Figure 3- Excel spreadsheet for
bench data points

Once the Excel table was normalized, the table was imported into ArcMap and the "Bearing Distance to Line" tool was implemented (Figure 4).  After the new feature class was created, the "Feature Vertices to Points" tool was used (Figure 5).  This created a point for each bench we had collected.

Figure 4- Bearing Distance to Line tool

Figure 5- Feature Vertices to Points tool

The following images show the transformations of the data from a blank aerial image to the bearing distance line data and finally to point data, the final representation of our data points, benches in the new campus mall.  The old Davies Center on campus was removed in the previous summer, but the aerial image does not show that.  In reality, the benches exist on an open courtyard.

Figure 6- Blank aerial image of study area

Figure 7-Bearing distance lines from origins

Figure 8-Point data; Final data representation

DISCUSSION
Although these tools are beneficial when technology isn't available, there are still downfalls to this technique.  When hand-eye coordination is essential to the accuracy of a tool, human error can occur.  In this activity, we had to hold the laser range finder with one hand while aiming at the benches to record the SD and AZ.  The measurements were quite accurate, but some distortion will always accompany such methods.

Another error that can occur in this method is the duplication of information.  Below are images that seem to show this error, but the scale of the map had to be changed to show that the data points did not overlap (Figures 9, 10, 11, 12).

Figure 9-Image showing a zoomed out view
with overlapping data points

Figure 10-Image showing a close up of the
data with no overlapping data points

One must know how to overcome magnetic declination while taking a distance-azimuth survey.  Magnetic declination is the angle between true north and magnetic north which changes over time and differs on location.  Complex algorithms must be used to calculate the magnetic north because it is constantly changing.  It is necessary to know, calculate and use magnetic north while using a compass, otherwise angles could be very skewed.

CONCLUSION
This survey taught me the possibilities of somewhat simple data collectors like a compass of laser range finder.  Technology can fail in one way or another; knowing how to collect data with alternative resources is very important in the geospatial realm.  Distance-Azimuth surveys are also beneficial because sometimes extreme technology isn't needed to collect data and create an informational product.  The skills learned in this activity can be applied in a vast array of ways and can be applied to a variety of different formats.

Digital Elevation Survey I & II

Digital Elevation Survey I

Introduction

The purpose of this assignment was to create a surface terrain and survey this area without standard survey techniques.  In order to do so, we had to come up with a suitable coordinate system and survey technique that would account for our study area.  Geospatial and critical thinking were essential because only a tape measure, rope and a meter stick were available to conduct the survey.  Unfortunately, migraines don’t take class assignments into consideration and I wasn't able to attend the actual survey.

Methods

Because I was sick and unable to attend the survey, so I am using the data of my group members Phil and Tonya.  The first step in this assignment was to construct the terrain (Figures 1 and 2).  Our terrain included a ridge, hill depression, valley and plain.  This was done by hand in our study area which was planter box in the courtyard of Phillips.

Figure 2- Creating the terrain

Figure 1- Creating the terrain

The next step was to set up the coordinate system for the survey.  Before the physical collection of the survey, we had discussed what the best coordinate system would be.  We decided that the short end of the planter box became the X Axis and the long edge became the Y Axis.  When it came time to collect the X and Y measurements, my group members decided it would be best to use a mobile X Axis for accuracy.  A meter stick was taped to a larger stick for the mobile X axis.  Because the mobile X axis would be gliding on the outer wooden edges of the planter box, all elevations had to be lower than this height.  Phil and Tonya had to “shave” down some of the previously formed ridges and hills.  The Y axis also had a tape measure taped to the edge of the planter box.  This helped not only for an accurate Y measurement, but also an X measurement because the mobile X axis could be measured with the Y Axis so it was completely straight.  The origin of our coordinate system was very traditional at 0,0.

X and Y measurements were taken at 5 centimeter intervals where the surface had in increased amount of terrain features and 10 centimeter intervals where the surface was smoother.  As seen in image 3, once the measurements were taken for the length of the X axis, the mobile axis was shifted up to the Y axis.  The Z measurements were taken with a meter stick vertically placed along the terrain (Figure 4).  This was done for the entire study area.  The measurements were recorded onto an Excel spreadsheet.

Figure 3- Collecting X, Y and Z coordinates

Figure 4- Collecting X, Y and Z coordinates

Figure 4- Excel Spreadsheet

After the survey was conducted, the measurements were digitally imported into the Excel spreadsheet (Figure 4).  All Z measurements were in negative numbers, so 17 was added to each measurement.  The number 17 was added because the lowest Z measurement was -16.  This ensured all measurements were a positive number which allows for easier calculations.

Discussion

I am really disappointed that I was not able to help with the physical collection of our data.  Being absent from the most important part of an activity really shows how important this aspect of the assignment is.  My group members would not have struggled as much if I would have been there because it is a hands-on activity and as they say, “more hands make for lighter work.”  Also, it is hard to comprehend the data because I was not present in the collection.  This can be thought of in ways outside of just this assignment.  Data interpretation is essential in geography because it can manipulate the meaning of the data if it is misinterpreted.  It is essential in this activity to be present to really comprehend the importance of Geospatial thinking.  Without being physically present in the data collection, I missed out on the problems and wasn't able to suggest ideas to overcome these problems with the survey.

Conclusion

The biggest thing I have taken away from this assignment is the great necessity of being physically present in data collection of our study or survey.  Without being present, you can miss important aspects to the data and you are not able to solve problems with group members.  I look forward to revisiting our study area and hopefully the weather will cooperate so our surface terrain is similar to the state we left it.

Digital Elevation Survey II

Introduction

This week, we revisited our "Digital Elevation Survey" activity.  The goal was to refine our survey area and tweak our methods for the best representation of our terrain.  Before we could refine our data, we imported our X and Y coordinates into ArcMap.  In ArcMap we used the IDW, Kriging, Natural Neighbor and Spline Raster Interpolation tools to visualize the terrain.  We also used ArcScene as a 3D interpretation.  Below is an image of the Spline technique in ArcScene (Figure 1).  This method was the best representation of our terrain.  The spline tool uses a 2D "minimum curvature technique" to interpolate the raster surface.  This tool differs from the other because the resulting surface passes through the input points exactly and creates a smoothing effect.

Figure 1-Spline Interpolation of the first digital elevation survey

Methods

Once we had visualized our survey, we came together as a group to discuss ways to better our research.  We decided to tie string at 10 mm intervals on the Y axis so our coordinates would be more precise (Figure 4).  The next step was to replicate the activity from the previous week.  Because it had snowed, we had to recreate our terrain (Figure 2).  After the terrain was recreated, we tied the string at 10 mm intervals on the Y axis (Figure 2 and 3).

Figure 2-Creating the terrain

Figure 3-Tying string at 10 mm intervals

Figure 4- 10 mm intervals on the Y Axis

  

After the string was tied, we began to collect our survey data in the same process as before.  For most of the survey, we took X, Y and Z coordinates at 5 mm intervals.  This would give us more data points and therefore a more precise digital elevation survey.  Like the previous time, we used a mobile X axis (Figure 5) to measure from X, but because we had string tied at every 10 mm on the Y axis, the measurements were more precise.  In areas where the terrain was flat, we took measurements every 10 mm (Figure 7).  We used a Microsoft Excel spreadsheet to record the data points.

Figure 5- Mobile X Axis

Figure 6-Laurel and Phil collecting data points

Figure 7- Meter stick with mobile X Axis to collect data

Once the entire planter box (112.5 cm by 224 cm) was surveyed, we converted the Excel spreadsheet into a digital copy.  We then imported this spreadsheet into ArcMap.  Again, we used IDW, Kriging, Natural Neighbors and Spline Raster Interpolation tools to visualize our data.  In the first attempt at this activity Spline Interpolation resulted in the best terrain model, this technique produced the best visual as well in the second attempt (Figure 8).

Figure 8- Spline Interpolation for the second survey

The second time we conducted the survey elevation features were more pronounced.  This was most likely because we took more data points and the data points were more precise.  The features were not vague representations the second time, they were replicated extremely similar to the real world features in our planter box.

Discussion
By revisiting our activity, we were able to use our previous data and outcomes to produce a better product the second time around.  We made two major changes between the first survey and the second.  We collected more data points and used string to collect more precise Y locations.  There is an obvious difference between the first Spline interpolation visual and the second.  The changes we made allowed for a more detailed representation of our digital elevation survey.  To make our survey even better, we could have used a measurement tool other than a meter stick.  A meter stick is rather wide, so our measurements would only account for the general elevation of a point.  If we used a thinner tool, the data points would be represented more accurately.  We were somewhat restrained from using this tool because we were instructed to use a meter stick.  Also, I didn't think of this until we were about halfway done collected the data for the second survey.

Conclusion
This activity really advanced my ability in the Geographic realm.  It is easy to sit at a computer and import XY coordinates and project them into a visual display, but it is harder to come up with your own data collection technique and import that into ArcMap to produce a visualization of our survey data.  This activity pushed me to think more critically about data collection and the importance it has on the desired outcome.  My group performed very well together.  We were able to account for each other's weaknesses and use our strengths to come up with the best possible survey.