In this week's lab exercise we performed the same activity as we did last week however in this exercise we actually collected our microclimate data. As pairs went to various zones of UW Eau Claire campus to take a total of 50-100 microclimate data points. Using the Kestrel units provided for us we were able to collect temperature at the surface and 2m, dew point, relative humidity, windchill, and wind speed. The more points where we collected this data the more accurate of an overall microclimate dataset we would have for the study area. For this reason it will be the best use of our time and resources to divide up the UW Eau Claire campus.
Method
First each of the 7 groups of two were assigned on of the predetermined zones which would be our areas of focus within the study area. Once they were assigned we repeated the process of deploying the basemap and geodatabase to our Trimble Juno GPS units to take into the field with us to collect our data. One thing that is different about this week's procedure compared to last was the geodatabases we used. Since we wanted to collect microclimate data from different areas of the campus and compare it all we needed to have the same geodatabase to use in order to keep our data collection consistent. After deploying the data to the GPS units we headed outside to start our collection process.
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| This image shows the 7 zones that the UW Eau Claire was divided into for data collection outlined in red. The yellow points were each spot where microclimate data was collected by the members of the Field Methods class. |
On the day we went out into the field it was a beautiful, windy, partly cloudy, around 55 degree day. We took data points about every 15 to 25 feet to cover as much area as we could while not collecting overlapping points. Once we all finished collecting our 50 points within our zone we grouped the data together in order to compile a map showing the microclimate data of the entire study area. After everyone uploaded their feature classes into the class geodatabase it was imperative to combine the feature classes into one since until this is done the data is useless since every group classified theirs differently. Then after everything was combined we were able to construct various maps to show the microclimate data we collected and how it differed throughout the campus.
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| This map shows the distribution of relative humidity within the UW Eau Claire campus study area. |
Discussion
The hands on experience of going out into the field to collect the microclimate data proved to be a very interesting learning experience. It was a useful exercise to practice gathering data using the Trimble GPS units, ArcPad and the kestrel units. At first I thought that using the equiptment would be easy however I needed to review how to gather points and other functions of the units. By the end of the activity though I felt very confident using the units.
While organizing the class data it became more clear just how important it is to coordinate with everyone prior to going into the field. Not only was it so useful to use the same geodatabase for collecting the data but also determining the best way to break up the study area so that we could cover the greatest amount of area in the time constraint of class. This pre-planning was crucial to the success of this project. Also, having a week of practice prior to data collection in the field was really nice since it gave everyone a chance to become more familiar with the equipment and procedure.
Conclusion
Even though this was only a class field activity to collect microclimate data to be used in a UW Eau Claire campus map, it was a great deal of work. Not only was it a complicated process to prepare the geodatabase that could be used in the field effectively, utilize ArcPad to collect the data, making sure to properly use and read the other instruments in the field and downloading the data to prepare it in ArcMap.
It was really nice to have the whole class work together on this project, especially when technical problems arose we could all work together to solve it. Team work is important in any line of work but collaborating with peers in this case was great practice. Working together to develop a project and divide up the workload to collect data is something I can see being a real world example so I really appreciated the experience.
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