Microclimate Survey

Introduction

For this exercise we surveyed an area on the UWEC campus and created a map of microclimates.  First, we took the feature class that we had created previously and uploaded it to a GPS.  We took the GPS out into the field and, along with other tools, surveyed our study area.  We downloaded the data from the GPS into the geodatabase and combined our results with the results of the other teams’ surveys.  We then were able to make a map of microclimates on campus.

Study Area

Our study area was a portion of the UWEC campus.  The campus is divided between a lower and upper portion by a hill.  Upper campus is composed primarily of residence halls for students, while lower campus features predominantly academic buildings.  Groups were distributed so as to cover as much ground as possible. Our group's area was west of the Haas Fine Arts building, and consisted of a field and parking lot.

 The UWEC campus.  In the top portion of this image, separated from the rest of campus by the Chippewa River, is the Haas Fine Arts building and our study area.

Our portion of the study area.  This image is out of date; the brown areas are now paved parking lots and obviously the field is, at this time of year, covered with snow.

The field west of the Haas Fine Arts building.  This area had snow cover.

The parking lot west of the field west of the Haas Fine Arts building.  This portion of the area did not have any snow cover.

Methods

The class was divided up into two person teams.  Each team chose an area to canvas and then set about prepping for the survey.  First, the GPS needed to be loaded with the feature class.  Since both group members had previously set up their own feature class, each team decided which person’s would be used.  Once this was decided on, the GPS was hooked up to the computer through a USB cord and the feature class was uploaded using the Add to ArcPad feature in Arc. 

Then it was time to head out to the field.  Travis and I tested our GPS unit just after getting outside to make sure that we had satellites.  We were having some trouble with it, and had to go back inside twice to make sure that we had it working properly.  The last thing we wanted was to walk all the way over to our site only then to find out that we couldn’t collect anything.  Once we were assured of our GPS’s readiness, we headed to our site: across a walking bridge to the Haas Fine Arts building.  West of the building is a large open field covered with snow and further west is a parking lot.  We decided to start taking points along the building first, and then more or less take points along the outline of our area.  At each point, which we chose visually as well as by looking on the GPS unit to make sure it was a good distance from other points, we stopped and took measurements.  These measurements were entered onto the GPS with a stylus on the touch screen.  First, you tap on an icon that is a pencil and then on a purple circle.  This captures a point.  Then, a screen will come up with the feature class attributes and a blank space next to each one to record data.  At this point, the second person takes the measurements and calls them out for the GPS user to record. 

Utilizing the GPS in the field.

Utilizing the Kestrel in the field.

Using a Kestrel, we looked up the temperature, wind speed, relative humidity, and dew point.  Unfortunately, the Kestrel never seemed to settle on any reading.  For example, at any point we took, the temperature would swing anywhere between nine degrees in either direction, and was constantly going up or down.  This made it hard to know what the actual temperature was at each point.  Also, wind speed was somewhat difficult to gauge, as it was not constant; the wind was gusting, so one minute it would read 0 mph and the next minute it go up to 5 mph before coming back down.  This meant that for any reading we took, we had to basically watch the Kestrel until the reading seemed to stabilize for a few seconds and then record that.  We also measured wind direction and snow depth.  For wind direction we used a compass and handkerchief.  We held the handkerchief up in the air and let the wind push it.  We then brought the compass nearby and estimated the direction in degrees from north that the wind was blowing from.  Snow depth was measured by sticking a measuring stick straight down into the snow and reading the measurement.

Our full tool set: the handkerchief with compass, the GPS, the collapsible measuring stick and the Kestrel.

Once we had collected as many data points as we could within our time limit, we headed back and downloaded our data from the GPS.  We copied and pasted our data into the class geodatabase.  This allowed for all teams’ data to be used in one map. 

Results

The collected data can now be used to make a map.  This is done by taking all teams’ data and importing it into a single ArcMap document.  Maps can then be made which display any selected attribute. 

Discussion

Having two people teams made this go much smoother than if it had to be done as one person.  It could have been done with one person, but that would have required a lot of hand switching to handle all of the equipment, and would also have taken a lot longer to collect data.  Having one person to take the measurements and a second to record them is definitely more efficient.

The Kestrel seemed to be very inaccurate.  It is hard to be confident in the readings you have collected when it seems that you had to more or less guess on what reading was accurate.

All teams were required to make a Notes attribute for their feature class, but hardly anyone entered anything into this category, our team included.  There were several instances where data points were extreme outliers and have no notes clarifying why this could be; for instance, while the majority of temperatures recorded were around 32 degrees, one point recorded 81 degrees, but there are no notes explaining the reason for this high reading.  

Overall, the surveying was a good experience.  It was cold and windy but not unbearable.  The data we collected can be used to make several interesting maps of campus climate zones.