Field Activity 8: Gathering Data Using ArcPad and Development of Microclimate Maps

Introduction:

This weeks field activity involved using a previously constructed geodatabase prepared for ArcPad (see Field Activity 6) in order to gather data as a class and develop a microclimate map of the UWEC campus.  Seven groups were created and each group was assigned an area to survey using a kestrel weather detector (Figure 1) and a Trimple Juno 3D GPS with ArcPad installed (Figure 2).

This is one of the Kestrel devices that was used in order to gather weather data.  The information that was gathered using this device included:  relative humidity, dew point, temperature, and wind speed.  (Figure 1)

This is one of the Trimble GPS devices that was used to gather the point data for the microclimate maps.  The Trimble had ArcPad installed and each group had to upload their own created geodatabases (which were created in Field Activity 6) to the Trimble.  From here, data could be recorded at set positions.

The challenge with this activity would prove to not be the data gathering itself but to actually be figuring out how to combine each group's data into one feature class for analysis and displaying on a microclimate map.  This is because not every group used the same names for gathered data and some data was recorded differently.  For example, some groups had an attribute titled "temperature", while others simple had "temp".  Another example is that some groups recorded wind direction as "North", while others abbreviated and simply had an "N" to represent North.  This challenge would need to be overcome once the data was all gathered completely.

Study Area:  

The campus of the University of Wisconsin-Eau Claire (Figure 3) would be the study area for the microclimate map.  The class was split into seven groups and was assigned areas throughout the campus to gather data.  On this particular day, it had been snowing rather heavily for several hours and the temperature had been steady at around 32 degrees Fahrenheit, just warm enough to prevent groups from freezing their fingers off while collecting data.  As data collection began the snow was continuing to fall.  However, after about a half an hour of point collection and recording data, the snowfall ceased and the weather seemed to change slightly as the sun came out.

The Eau Claire campus is split into three main zones, there's lower campus, upper campus on top of a large hill made up of Putnam Park and the area across the bridge.  The groups spread out throughout these three areas to gather data.  It will be interesting to see whether the hill or the river have any effect on the microclimate maps.

This is a satellite image of the University of Wisconsin-Eau Claire campus which is the setting for the microclimate maps designed in this week's activity.  The west portion of campus in this image (to the left) is on top of a hill while the eastern portion makes up lower campus.  There is also a part of campus across the river that can be seen at the top of the satellite image.  This satellite image was used as the basemap for the microclimate maps. (Figure 3)

Methods:

Preparation and Data Gathering:

The very first step in preparing to create the microclimate map was insuring that the geodatabase, basemap, and feature class created in Field Activity 6 were checked out to the ArcPad on the Trimble GPS (Figure 2).  This involved first preparing the data in ArcMap and checking it out to a separate folder (Figure 4).  From here the Trimble was plugged into the computer and the data was copied over into the Trimble.

This is a look inside the folder of the data that has been checked out of ArcGIS to be used in ArcPad.  The data features the created geodatabase, a basemap of the study area, and a feature class ready to be edited in ArcPad.  This whole folder was copied into the Trimble GPS. (Figure 4)

The information that was to be gathered was:  temperature, relative humidity, dew point, wind speed, wind direction, time collected, and snow depth.  In order to get this data, tools that were needed included a meterstick, a kestrel (Figure 1), and a compass.

When outside, gathering the data was as simple as having one of the two group members plot the point with the Trimble, and record the data while the other group member held the kestrel and listed off the recorded numbers.  This was an easy, yet monotonous process as many data points had to be gathered in order to try and get an accurate representation.  However, if too much time was taken it could be possible that the weather would change, this actually occurred as it stopped snowing part way through data collection.  Some groups even saw dew point drop throughout the process.  

When around 50 points had been gathered, each group headed in to upload their data.  It was at this point that the instructor Joe Hupy informed the class that they would need to put all of the data in one folder and from here figure out how to get every groups separately labeled and classified data together as one feature class, which proved to be no small order.

Data Merging:

At this point all the data had been gathered and uploaded to a class geodatabase from each group (Figure 5).  However, all of this data was at that point, useless.  This is because every group had classified their feature class differently.  The seven separate feature classes (Figure 6) would need to be combined into one.  This proved to be difficult due to each group had labeled or recorded their attributes differently (Figure 7).  None of the groups had done it right or wrong, they had just all done it their own way.  In order for the feature classes to be combined into one for analysis and map creation, the attributes would first have to be joined together.

This is the view inside of the class geodatabase.  Each group had created their own feature class when they'd gathered data.  These feature classes alone have almost no use when trying to create a microclimate map of the entire campus.  Therefore, the feature classes needed to be merged into one feature class that encompassed them all (Figure 5)

When each group uploaded their data, the class was confronted with seven different feature classes.  This shows the view of the seven different feature classes on the Eau Claire campus without the basemap.  It's difficult to design a map that uses all of the feature classes equally when there are so many.  In order to take the next step the feature classes needed to be combined. (Figure 6) 

This is a comparison of the feature classes from Group One and Group Two.  As can be seen, the attributes are all different.  Group One used "windspeed" to represent the speed of the wind in miles per hour while Group Two used "wind_s".  These little differences can cause a big headache when trying to combine a group of feature classes into one and they appear throughout the seven groups' feature classes. (Figure 7)

The Merge tool combines multiple input datasets of the same datatype into a single, new dataset.  It can be used to combine point feature classes such as the seven that were present for the microclimate map.  This was the clear solution to the problem, however, it wasn't as easy as it would originally seem.  As the Merge tool combines the feature classes it allows the user to map the fields (Figure 8).  In other words, it allows the user to take input fields (attributes) from the input datasets and turn them into output fields (new attributes for the new feature class).  This was the key in combining the feature classes because if the different attribute names for the same attributes for each feature class could be determined, then they could be mapped together to be combined as one output using the format of one of the feature classes.  In other words, the values under "rel_humid" for Group Two could be combined with the values under "relative" for Group One (Figure 9).  The output would also have to be chosen, and it was deemed easiest to simply keep Group One's output titles so each feature class's attribute data would eventually end up being classified under the Group One field names.  In this way the final feature class ended up having the same field names as Group One's original feature class.  This can be done in different ways, and this way was simply the one chosen this time.

This image shows the merge tool with the inputs of Group One and Group Two's feature classes.  All seven of the separate feature classes could have been input in one step, yet this was deemed to be too messy and prone to mistakes.  Due to this, the merging process was done methodically; merging 1 with 2, then 3 with that merge, then 4 with that merge and so on.  This process was continued until all of the feature classes were merged into one. (Figure 8)

This is the field map area of the merge tool.  In this image Group One's attributes have been merged with Group Two's.  This is done by selecting inputs for the field maps.  These inputs are the sub-selections coming off of the main output text.  It was deemed easiest to keep the output attribute field names the same as Group One's throughout the whole process. (Figure 9)

The most difficult part of this process was avoiding making a simple mistake.  Each feature class's attribute tables had to be analyzed in order to determine which field names represented which attributes.  In order to avoid error, instead of inputting all of the feature classes at once to be merged, the feature classes were merged methodically one at a time (Figure 10).  Group One was merged with Group Two.  Then Group Three was merged with the resulting merge.  Then Group Four was merged with that.  In this way, there were only ever two inputs at a time; this may have taken longer but helped eliminate error that could be associated with dealing with an extremely busy field map menu.  In the end, the resulting feature class was a combination of all of the feature classes with all of the relevant attributes.  There was, however, still many null values where data hadn't been recorded by some groups but had been recorded by others such as wind azimuth.  These little errors would complicate slightly the designing of the microclimate maps.

These feature classes show that methodical process that was used to merge the original seven feature classes.  This process was used in order to avoid having to work with a very busy field map menu and avoid human error.  One and Two were combined, which was then combined with Three, which was then combined with Four and so on. (Figure 10)

Map Design:

With all of the feature classes finally merged into one, it was possible to begin map designing and analysis of the feature classes.  When trying to first create a raster using the points that were in the feature class something was apparently wrong.  Upon inspection of the points and thanks to collaboration among the class, it was discovered that some points were sitting somewhere on the Equator.  This was likely due to mechanical error and the points were promptly deleted.  From here, designing rasters using the points went much more smoothly.

The first analysis of the data showed that points near the water appeared to have a slightly higher dew point than those away from the river (Figure 11).  Finding the dew point is one way of measuring the moisture in the air and is represented as a temperature.  The nearer the dew point is to the actually temperature, the more moisture in the air.  It is important to consider that dew point temperature should never exceed the temperature of the aire.  A raster of the dew points was created using natural neighbors.  From here on out all of the rasters were created using this method.

An apparent pattern in this map is that the data gathered by the group near the river had higher dew point values than those on the rest of campus, save a small pocket near the nursing building. (Figure 11)

Another way to measure the moisture in the air is using relative humidity.  It is represented as a percentage of moisture in the air.  If dew point temperature equals actual temperature then relative humidity is 100%.  From here, a raster representing relative humidity was created (Figure 12).

This map shows the relative humidity of areas on campus.  It's interesting to not that the majority of the high humidity values are far from the river and are near the bottom of the campus hill along Putnam Park.  The weather was snowing at first and some drops were noticed in relative humidity over time as the sky cleared and the sun came out.  This cluster of high relative humidity may be due to these areas being close to the science building and being recorded earlier than the others points, before the weather began to change. (Figure 12)

The next step was to combine the dew point and relative humidity data into one map (Figure 13) as the two numbers both represent the amount of moisture in the air in different ways.

This map attempts to show the relationship between relative humidity and dew point as it pertains to the data gathered by the class.  It is however, rather difficult to see any distinct pattern in the map when it comes to the comparison of the two variables.  One slightly noticeable pattern is that the lower and higher dew points in the southern portion of the map have the same border as the lower and higher relative humidity values. (Figure 13)

Temperature is one of the main measurements people think of when they think "weather".  Therefore a temperature raster was created using the points (Figure 14).  This was then made into a map of the recorded temperatures throughout the campus.

This is a map of the recorded temperatures around campus.  One noticeable pattern is that the area to the west, which is on top of the large campus hill, appears to have been slightly cooler than the rest of campus.  Also a surprising result is that the area by the river was one of the warmer areas that day.  In the center of the campus is an extremely hot area which was the result of being near a vent of a building. (Figure 14).

Two other variables of the weather around campus which were recorded were wind speed and direction (Figure 15).  It appeared the overwhelming direction of the wind was from the West.  In order to create this map, wind azimuth was needed and the symbols were rotated accordingly.

This map represents the wind speed and direction on the campus at the recorded locations.  The large majority of the wind seems to be coming from the West and some from the North.  There are however interesting patterns around the building in which the wind appears to be whipping around them, changing up the wind direction. (Figure 15)

Snow depth is another variable that each group measured (Figure 16).  Something to keep in mind is that this value is extremely dependent on where the points were taken considering it was up to each group to decide what exact locations to record values.  This could skew the results from reality.

This is a map of the recorded snow depth in centimeters on the UWEC campus.  The areas down by the river appear to have deeper snow depth, while the areas around campus, except a few very deep areas, seem to be less deep.  This may be due to the clearing of snow from the campus by campus workers. (Figure 16)

Discussion:

The process of going out and designing these microclimate maps was educational in many ways.  Firstly, simply learning how to gather data using ArcPad and proper use of a kestrel are great lessons for the future.  Using a Trimble GPS may seem like a trivial matter, thought there are many things to know to use it well; an example of this is that the GPS should be help out away from the body when gathering points, if someone were to hunch over the GPS it might skew the points.

The most important lesson that was learned is that it's extremely important to coordinate beforehand.  When the groups gathered their data, they each chose areas to gather the data, but that's pretty much the extent of the cooperation which occurred.  The result of this is that despite the groups having gathered similar attributes, there was no common names for these attributes.  This led to more work in the long run, however, the class began cooperating well after this insuring that the data would have everything required such as wind azimuth, which some groups didn't end up recording.  Also, planning out a methodical plot of where to gather points before hand would have helped prevent groups from simply going out and gathering random points where they saw fit.  This may have helped improve the quality of the maps.  Sadly it's hard to pick up on patterns in many of the maps as there are no obvious ones.  One can't help but wonder that if the activity and data gathering had been done more methodically if the quality of the data and maps would have been improved.  

Also the note section of the data was most of the time neglected.  This is even after the instructor Professor Joe Hupy affirming that the notes section is one of the most, if not the most, important section.  Many groups said that they tried to record notes however something went wrong in the process and they were unable to type anything into the field.  It's possible that the notes section could have helped explain some anomalies in the data such as the random deep portions of snow (Figure 16) and the extremely hot areas (Figure 14).

One aspect that was noticed by some groups is that the change in the weather affected their numbers.  Particularly the day that this data was collected (March 24th) it was snowing rather heavily.  However, after 30 minutes to an hour outside, the wind picked up, the snow stopped, and the sun came out.  It may have been possible to see this transition in weather, however some groups failed to fill in the field regarding time the data was gathered.  Even though this can't be represented due to this, the quick and drastic change in the weather may have had effect on the data.

Conclusion:

Even the construction of something as simple as a microclimate map should not be taken lightly.  There are so many aspects to consider regarding preparing the geodatabase with the proper feature classes and attribute names, getting it onto a device such as a Trimble with ArcPad to collect the data, collecting the data using proper measurements and recording everything needed, then getting the data back onto the computer to be analyzed and represented using a GIS.  Even when all of these steps are planned ahead and done well, when working with a large group it's likely that something will have been missed or something needs to be done to insure that everyone is up to speed and everyone's data is normalized in some way.

This field activity not only greatly aided in the process of teaching the technical aspects of designing a microclimate map and using ArcPad, it also aided in teach how to work as a large group.  This being the first activity where the class really worked together as a whole, it was important to communicate to insure everything ran smoothly.  While the communication could have been better, especially beforehand, the activity turned out well in the end.

Field Activty 7: Introduction to Unmanned Aerial Systems

Introduction:

This field activity involved getting outside and seeing unmanned aerial systems (UAS) in action.  The class was told to meet at the Eau Claire Outdoor Sports Center (Figure 1). This is a location with a large amount of open space perfect for launching a rotary-winged aircraft, a kite, and even a rocket.  The weather was also perfect for launching UAS as the wind wasn't too strong as to effect the rotary craft.  The temperature was approximately 50 Fahrenheit outside, a heat wave compared to previous weeks.

The Eau Claire Outdoor Sports Center, located at point A, has a wide open area perfect for launching unmanned aerial systems and is close enough to the UWEC campus for ease of access. (Figure 1)

UAS Demonstrations:

Rotary-Winged Aircraft:

The class was first treated to a rotary-wing craft demo from professor Joe Hupy and his three-wing copter (Figure 2) and Max, a physics student who has been working with Joe and his six-wing copter (Figure 3).  Both of these copters were constructed by Joe and Max respectively from ordered parts (Figure 4).

The closest craft is Joe's three-armed craft.  It has a flight time of approximately 15 minutes.  There is a digital camera below the craft that always points down and is set to take pictures in intervals.  As can be seen, each arm has two propellers mounted to them. (Figure 2) 

This is Max's six-winged craft.  It appears simpler than Joe's, though its payload capacity is higher due to the larger amount of arms.  The flight time is on average 15 minutes just like the three-armed craft. (Figure 3)

This close up of the three-armed craft shows the details that are put into it.  The front mounted camera can be seen on the left and the two propellers on the wings can also be seen. (Figure 4)

Max was able to fly both craft using a controller without any problems.  He demonstrated the six-armed craft's ability to hover in place and also to fly around at great speeds with high precision (Figure 5).  Max informed the class that if something were to happen with the remote control and its connection to the craft, the craft was programmed to automatically land where it originally took off.

Here Max is piloting his six-armed aircraft and demonstrating how precisely it can be controlled.  This demo lasted for about 10 minutes and consisted of Max showing the various capabilities of the aircraft including its maneuverability and its ability to hover in one place despite the wind.  This great stability helps the craft take clearer aerial images. (Figure 5)

Kite:

When many people think of UAS, they think of super expensive drones or copters, but kites and balloons are also a type of UAS that are extremely viable when the budget is tight.  The next demonstration involved using a kite to take images of the ground.  This is an extremely cheap option, and despite the kite being used in class being on the nicer side of kites, it usually costs less than $100 to purchase a kite that is serviceable for taking aerial images.  The kite can easily be assembled in the field (Figure 6) and launched (Figure 7) given there is a decent wind.  If no wind is present a balloon may be a better option for taking cheap aerial imagery.

Assembly of the craft is rather simple and only takes about five minutes maximum depending on the type of kite.  Here, Jacob and Blake assemble the kite as the class looks on. (Figure 6)

Here Tanner and Blake launch the kite.  The only trick of this process is insuring there is enough space to launch and knowing the wind direction. (Figure 7)

After launch, the kite should be let out about 100 feet before the camera is attached to the string line using a specially designed string set up which allows the weight of the camera to let it point down throughout the flight.  (Figure 8).  From here the kite is let out further and further (Figure 9).  The camera is preset to take picture over set intervals (Figures 10-12).

Here Joe is attaching the camera to the kite string.  The kite is already about 100 feet out and will be let out further to allow the camera to rise once it is properly attached.  This utilizes a simple digital camera set to take pictures at intervals. (Figure 8)

The kite is let further as the camera rises.  The camera has already started taking images and will continue throughout the duration of the flight as set. (Figure 9)

The camera rises as it takes pictures, here it hasn't risen enough to get a good view of the area.  The class can be seen eagerly looking on. (Figure 10)

The kite continues to rise taking the camera with it.  The kite easily supports the camera's weight due to the wind.  A larger area can be more readily seen as the camera rises. (Figure 11)

This is one of the higher images that was taken by the kite and camera setup.  The image is clear and is of a high quality despite the inexpensive kite and digital camera set up.  This lends to the fact that using UAS doesn't have to be an expensive venture all of the time. (Figure 12)

Rocket:

Professor Joe Hupy was extremely excited for this demonstration as he had never attempted to launch his rocket before.  Joe assembled the rocket on site, putting the engines in them and putting the parts together (Figure 13).  From here the rocket was set on a launcher and two video cameras were attached to it using tape (Figure 14).  These cameras were extremely small, approximately the size of a key chain.  The rocket had two engines inside and when it reached its peak it is designed to have a parachute open to slow the fall and allow for video to be taken of the area.  When launched the rocket appeared to not fly to maximum height and the parachute didn't set off, though luckily the rocket landed in a pile of snow.  Apparently, one of the engines had accidentally been put in backwards and hadn't fired.  This caused the rocket to not reach maximum height and also caused the pressure inside the rocket to not reach a great enough level to allow the parachute to fire.  More attempts at the launch will be made in the future.

The rocket is being assembled by Joe, unfortunately one of the engines was put in wrong which caused failure during launch. (Figure 13)

Here the cameras are being attached to the rocket using simple tape.  These cameras are relatively inexpensive (less than 20 dollars) and are very light.  Two were mounted on the rocket in order to take video of the flight (Figure 14)

The rocket is ready to launch.  The launching station keeps it upright and fires the engines when the times comes.  The class's anxiousness was high at this point as everyone was eager to see the launch happen. (Figure 15)

The preparation and the launch can be seen here.

Conclusion:

This introduction to simple unmanned aerial systems helped greatly in showing what is involved during the whole process.  The class had previously researched these systems but had never gotten to be hands-on with them.  Thanks to the great weather, Max, and Joe this was possible and was a great experience.

Field Activity 6: Microclimate Geodatabase Construction for Deployment to ArcPad

Introduction:

This week the class was assigned the task of creating a geodatabase to be deployed to ArcPad to create a microclimate map.  The microclimate map itself will be a part of a future activity; this week the class is just to focus on creating the geodatabase.  One might think of the construction of a geodatabase as a trivial task.  Though a geodatabase can be constructed rather easily thanks to ArcCatalog's user-friendly layout, setting up a geodatabase properly for data collection is another matter entirely.  This whole activity is pre-planning for a future activity, the better this activity goes, the better the activity out in the field will go.  Part One will cover the importance of pre-planning and having a proper geodatabase, while Part Two will be a tutorial in how the microclimate geodatabase was actually instructed.


Part One:

Being properly prepared is one of the keys to good field work.  If someone goes out into the field without the correct tools, the data collection will likely go horribly wrong.  ArcPad (Figure 1) is a good tool to use when collecting data out in the field.  It can be used to capture, edit, and display geographic information quickly on the go.  Data can be checked in and out of a geodatabase from ArcPad itself.

This is an example of a tool with ArcPad installed.  A geodatabase can be uploaded into this device and used to survey and collect data about features that can be created on the fly.  It's best to have the device properly set up for the most efficient and effective data collection. (Figure 1)

In order to effectively use ArcPad to collect data, a proper geodatabase should be installed.  A geodatabase is a common data storage and management framework when using ArcGIS.  It is a data repository that allows easy access and management of GIS data.

When creating a geodatabase, one must consider every aspect that the project may entail.  This is where a large amount of pre-planning comes in.  When pre-planning for geodatabase creation, one of the key aspects to consider is the domain.  Domains are a set or range of valid attribute values that can be recorded for features collected or updated in a specific field.  They are essential to ensure that data entry is accurate and consistent.  A single domain can be used for multiple feature classes within a geodatabase as the domain is a property of the geodatabase which can be set.  There are several different domain field types which can be set (Figure 2).  These include:  short integer, long integer, float, double, text, and date.

These are some of the Field Type options for domains.  Text is also an option when choosing a field type.  Typically short integer and float are the more popular of the options.  These are the only two options, other than text, which will be used when creating the geodatabase for the microclimate map. (Figure 2)

If someone wishes to go out and collect temperature data of an several points around an area (which will be done for the microclimate map) they will likely set their domain as a float with a reasonable temperature range (0-100 degrees Fahrenheit).  Setting this domain and range will help prevent them from accidentally recording a value such as 200 Fahrenheit as it will not be possible when using ArcPad.  Getting all of the domains properly set with appropriate ranges can help save time when entering in values and will help the user avoid errors when recording data.  Also doing all of this beforehand can help save time when out in the field.


Part Two:

Steps to Complete Proper Geodatabase Construction for a Microclimate Map:
1. Pre-planning for preparation of the geodatabase
2. Construction of the file geodatabase
3. Creation of geodatabase domains based on pre-planning
4. Construction of a feature class to be used to collect data
5. Preparing project in ArcMap

Step One:

In preparation of the geodatabase the project itself must be considered.  A microclimate is a small area that is different from the area around it.  It may vary in temperature, humidity, or in other aspects.  Microclimates can be very small.  For example, a courtyard next to a building will likely be warmer and less windy than an open area just 100 meters away.  Microclimates may also be very large.  For example, an urban area typically doesn't experience the harsh cold that areas in the surrounding countryside do.

A microclimate map is a good way to help visualize the various microclimates of a given area.  When collecting data to design a microclimate map, various aspects must be taken into consideration.  Temperature, wind speed, wind direction, relative humidity, dew point, time and date, and even snow depth are all information of interest that will need to be gathered to help design a microclimate map for the University of Wisconsin-Eau Claire campus.  All of these data will need to be taken into consideration when designing the geodatabase.

Step Two:

Constructing the geodatabase is one of the easier steps of the whole process.  One way to do this is to open up ArcCatalog, choose a folder to insert the geodatabase, right click and select "new" "file geodatabase" (Figure 3).

This shows what the new geodatabase will look like in ArcCatalog.  From here the geodatabase is ready to have its properties set. (Figure 3)

For this project, the new geodatabase was named "mc_condontd.gdb".  This is the geodatabase in which all data will be collected, stored, analyzed, and displayed from for the microclimate map. (Figure 4)

Step Three:

When setting geodatabase domains, it is important to consider all aspects of data that may be collected; for the project as it is easier to prepare the domains initially than to go back once collection has begun.  In the case of this microclimate map, several domains were set based on the pre-planning in step one.  To set the domains, one simply needs to right click on the geodatabase and click on "properties".  From here clicking on the Domains tab will allow domains and ranges to be set (Figure 5).

This is the domains tab underneath the geodatabase properties.  From here, the domains and ranges can be set to the desired field types and values.  Multiple different domains can be set and domains can be used multiple times or not at all in the creation of feature classes which will be explained further on in this report. (Figure 5)

Looking at the data that will be collected will help determine what domains and ranges to be set.  Temperature in Fahrenheit will be one of the main aspects of the map.  Numerical data will be collected and the temperature will likely be greater than zero and less than one hundred.  Also, fractions will likely need to be collected as well.  Therefore the field type will be set at float with a range of 0-100.  Another piece of information that will be of interest for the microclimate map is dew point (Figure 6).  The dew point will be collected using decimals and will likely be no lower than -20 and no higher than 100; as can be seen in Figure 6, the domain and range have been set accordingly.

The domain and range are set for every different bit of information.  The way it is set depends on the conditions and what should be recorded.  For example, the notes domain is set to a text field type, this means that text can be entered when notes are entered in the field. (Figure 6)

Step Four:

Once proper domains have been created for all of the information that will be collected a feature class can be created.  Feature classes are collections of features with each having a spatial representation such as a point, line, or polygon (Figure 7) and having a common set of attribute columns.

This image shows the different ways a feature class can be represented.  In the case of the microclimate map only one feature will need to be created and it will be a point feature class. (Figure 7)

A feature class can be created in the geodatabase by navigating to the inside of the geodatabase, right clicking, and selecting create new feature class. From here point feature class should be selected and the proper projection should be selected (UTM zone 15 in this case).  After these two options are selected the option to create attribute field names will pop up (Figure 8).  A feature class can have many attributes, this is why only one feature class is being created to create the microclimate map.

This is the menu in which the attributes can be added to the feature class.  Once the attributes are added (ie Temperature), a domain that was previously created can be set.  This is why it is important to have the domains set before creating the feature class. (Figure 8)

Each attribute that was created was matched with the proper domain to help insure against mistakes being made in recording the data, and to help speed up the data recording process.  For example, the snow depth attribute field uses the snow depth domain (Figure 9).

Each attribute field was matched to a domain that was previously designed in the geodatabase.  Because snow depth is matched to the snow depth domain, the values that are recorded have to be between 0 and 36 as snow depth will be measure in inches. (Figure 9)

This is inside of the mc_condon geodatabase.  The mc_points is the created feature class that contains all of the relevant attributes with their domains.  The EC6 is a raster aerial image of Eau Claire that was imported from a separate folder and will be used for the final map. (Figure 10)

Step Five:

From here ArcMap can be opened and the EC6 raster and mc_points can be brought into the layers (Figure 11).  Saving this as a project for the future helps prepare the project to be put into ArcPad in future weeks when the microclimate map is created.

This shows the contents of the geodatabase put into ArcMap.  The mc_points obviously has no features as nothing has been gathered yet. (Figure 11)

Conclusion:

Creating a geodatabase can be an easy task.  However, this task needs to be performed well in order to ensure successful field work.  This is all a part of pre-planning for a project.  Domains can be put into a geodatabase to help combat human error and extrapolate the process of data collection.  This is best done before going out into the field.  In fact, it's best done before creating feature classes.  Many times only one feature class is required, as it can contain many different attributes and is more interesting to analyze the more information it has.  This also helps prevent needing to gather many different features for one location.  If everything has been planned out well before hand, data collection can go smoothly and be a simple process.

Field Activity 5: Development of a Field Navigation Map and Orienteering Tutorial

Introduction:

This week's assignment involved preparing the class to navigate a point course set up in the field at the UWEC Priory.  Professor Hupy assigned the class the task of creating two navigation maps of the area around the Priory.  Also, a brief introduction into using distance-bearing navigation was a part of the in class portion of the activity.

In order to properly navigate the course set up at the Priory by UWEC's Joe Hupy and Al Wiberg, the class first was taught the basics of orienteering using a compass and a navigation map.  This method of navigation is known as distance-bearing.  It is a simple, effective method of using a navigation map, a compass, and a pace count to navigate an area.  Al Wiberg, a director of outdoor trips at the UWEC Environmental Adventure Center was a guest instructor and taught the class how to properly use this method.

The first step in distance-bearing navigation is knowing one's pace count.  A pace count is how many steps one takes in order to cover 100 meters.  This can be found by measuring out 100 meters on a flat surface and counting out paces to cover the distance.  If one takes a step with his/her right foot first he/she must always count on that foot, same goes with the left.  Essentially, this means that counting occurs every two steps.  The class performed this pace count test twice in order to find a good measure.  Pace counts of the class ranged from 60-70 depending on height and stride length.  However, when navigating in rough terrain, the class was told to add around 30 paces to their pace count.  Knowing one's pace count allows the ability to judge placement on a map from a certain distance away from a point.

The next step in learning distance-bearing navigation is knowing how to effectively use a compass (Figure 1) and navigation map to set a bearing.

This is a typical compass used for distance bearing navigation.  The labeled features that will be mentioned in this write up are as follows:  1: base plate with ruler for measuring scale, 2: rotating bezel, 3: rotating needle, 5: orienting arrow fixed on rotating bezel used to indicate north, 6: bearing line fixed on the base plate, 8: direction of travel arrow    
(Figure 1)

In order to know how to get from one point on a navigation map to another, the edge of the compass must be placed on a line between the two points with the direction of travel arrow facing toward the desired destination point.  From here the bezel should be rotated until the orienting arrow is facing north on the navigation map (direction of north should be clearly marked if it's not upward).  Once this is accomplished, the compass can be taken off the map and examined.  From here the compass should be held in front of the body parallel with the ground.  The person should then rotate until the rotating needle is pointing the same direction and within the orienting arrow, or as Al Wiberg put it "red in the shed."  If all of the steps were followed correctly, the bearing line should be marking the direction of the destination as long as the direction of travel arrow is still pointed toward the walking direction.  In order to maintain the bearing "red in the shed" must be maintained while walking.  Magnetic declination must be taken into account when performing this as the orienting arrow should be pointing towards true north, not magnetic north.  This adjustment needs to be made by rotating the bezel a certain amount of degrees depending on the declination between magnetic north and true north of the area being navigated.

In order to navigate from point to point on a navigation map, the pace count should be used in conjunction with the compass.  Al Wiberg explained that, typically, a team of three people is the most effective way to perform distance-bearing navigation.  Person one holds the compass and is in charge of determining bearing, the only job of this person is to direct person two to an object that is determined to be in line with the bearing.  Person two has the job of running up to the object in the bearing line marked by person one, this allows person one to avoid staring at the compass and walking at the same time.  Person three has the job of keeping pace count in order to know how much distance has been covered and just has to walk up to the point where person two is standing.  The approximate distance measured with pace count can be related back to the map to determine proximity to the desired destination.

All of this hard work and knowledge will be in vain at the Priory if the navigation map isn't readable or isn't set up well with the proper coordinates and grid.  The methods section covers the creation and explanation of navigation maps that will be used at the Priory in future weeks.


Methods:

It was required to create two navigation maps for the area surrounding the Eau Claire Priory.  One of the navigation maps was required to have a grid using Universal Transverse Mercator (UTM) (Figure 2), the other using decimal degrees of latitude and longitude.  UTM is a uses a two dimensional coordinate system to assign locations on the Earth's surface.  The Earth is divided into sixty zones, each using a six-degree band of longitude.  UTM utilizes a secant transverse Mercator projection in each separate zone.

This figure shows the various UTM zones of the contiguous United States.  Each zone contains six degrees of longitude which is shown at the top of the map.  Eau Claire, WI, the location of these navigation maps falls within zone 15. (Figure 2)

Several data was provided in a geodatabase that was useful for creating the navigation maps.  This data included an aerial image of Priory (Figure 3), a digital elevation model of the Eau Claire area (Figure 4), 2-foot contour elevation lines, the point boundaries of where the navigation will be occuring (Figure 3), and an actual USGS contour map.

This is an aerial image of the Priory outside of Eau Claire, WI.  The aerial image is courtesy of the United States Geological Survey.  The area within the blue polygon is the point boundary and all of the points which will be navigated fall within it.  This is just one component of the final navigation maps. (Figure 3)

This is a digital elevation model (DEM) of the area surrounding the Priory.  This DEM contains points of elevation for the area and was clipped to cover a small area to insure a difference in the elevation symbolism.  As can be seen, the area around the Priory has many different elevations ranging from low to high, the slopes should be considered when taking pace count.  From this DEM a three-meter contour line feature was generated. (Figure 4)

After examining the two-feet contour lines, it was determined that the close proximity of the lines made the map appear too busy.  Having a busy navigation map is very disadvantageous when performing distance-bearing navigation and marking points.  Due to this, three-meter contour lines were generated in ArcMap (Figure 5) using the "Surface Contour" tool in ArcGIS located under 3D Analyst.

This is the contour line feature that was created using the "Surface Contour" tool in ArcMap from the DEM in Figure 4.  These are 3-meter contour intervals.  It was determined that this was the best measurement for the intervals to be precise, yet keep the map from getting too busy.  The intervals have been labeled according to elevation and this is a crucial portion of the final navigation map allowing the user to more easily tell his/her location on the map based on comparing the slope of the terrain to the lines on the map.  (Figure 5)

It was decided to include the aerial image (Figure 3), the DEM (Figure 4), and the generated 3-m contour interval (Figure 5) for the final maps.  The other data such as the 2-ft contours and the actual USGS contour map were too busy to use for navigation purposes.  All of the data was properly projected into UTM and inserted into the maps.  Now all that remained was inserting the grids into the maps to insure proper navigation.  Using the layer properties in the layout view, a different grid was added to each map.  The first grid contained the aforementioned UTM coordinate system.  This was added to the map to create the first navigation map (Figure 6).  The second grid was made up of degrees of longitude and latitude, it was labeled according to decimal degrees as this is a more tangible representative that is easier to understand than degrees, minutes, seconds.  The second grid was transposed onto the second final navigation map (Figure 7).

This is one of the final maps created to aid in navigation of the course set up at the Priory.  This map contains an aerial image with a DEM transposed over it with transparency set high so as to view the images beneath.  It also contains the 3-m contour intervals and the point boundary.  This map uses the UTM coordinate system to aid in navigation.  The coordinate system is split into intervals of 50m as UTM uses meters as its primary measurement. (Figure 6)

This is one of the final maps created to aid in navigation of the course set up at the Priory.  This map contains an aerial image with a DEM transposed over it with transparency set high so as to view the images beneath.  It also contains the 3-m contour intervals and the point boundary.  What makes this map different from the first is that it utilizes latitude and longitude as its coordinate system.  The intervals are of one second in latitude and longitude but are labeled based on decimal degrees. (Figure 7)

Discussion:

These two maps (Figures 6-7) are similar but completely different in the way that they utilize different coordinate systems.  This may make navigating with one completely different from the other and it will be interesting to see the difference when actually navigating the point course at the Priory.  Both maps contain what should be best for navigating.  They contain aerial imagery for referencing particular points such as buildings and tree positions, a DEM to be able to tell elevation of an area and relate it to the features around the navigator, contour intervals to better tell slope of certain areas, and coordinate systems that will aid in establishing point locations and knowing distance.

It will be interesting to see which map's coordinate system will be better for navigating the course.  UTM uses meters so it is inherently easier to understand how far away objects are on the map; while the latitude and longitude may be more accurate in this area due to Eau Claire being on the edge of zone 15 of the UTM zones (Figure 2).

Performing orienteering using distance-bearing navigation is not a complicated endeavor.  It simply involves good tools such as a compass and navigation map, the ability to use said tools, and teamwork to have someone finding the bearing, someone marking the bearing, and someone keeping a pace count to tell difference.  The class is now eager to go out sometime within the semester and put their navigation maps to the test and see if the skills learned during this activity can be easily applied to the real world situation of navigating the point course.


Conclusion:

Distance-bearing navigation is a low tech way of navigating an area.  It involves simple measures such as keeping pace count to know how far one has traveled, a simple compass, and a clear navigation map.  However, just because this method involves simple tools does not mean that it is easy to do well.  Without proper knowledge, orienteering can go extremely poorly.  This activity has helped teach the class the proper techniques that will hopefully transition into the field in future weeks when the point course is navigated at the Priory.  Also, the navigation maps that were created will be put to the test and it the advantages and disadvantages of each map will be determined and explored more fully.  As of now, the class will have to wait until the meters thick snow melts before venturing to the course out at the Priory.