Microclimate

Introduction

The field activity this week included using ArcCollector, which was downloaded onto our phones as an app.  The class was broken up into partners and set out into the different zones around campus to collect climate data including temperature, dew point, wind speed, and wind direction.  These can all be entered into a smartphone to instantly upload into ArcCollector.  The final part is to turn the data collected into a continuous surface map using the different types of data collected. The purpose of using ArcCollector is to generate a map that each student can download after each group uploads their data points.  This eliminates the need to share it on a share folder or have each student greater a different geodatabase with different domains which could make things more complicated than need be.

Study Area

• 9 November 2016 Sunny 50 degrees F.
• University of Wisconsin - Eau Claire Campus (Upper and Lower)
• Figure 1 Shows an aerial view of the study area. 

Figure 1: Area of Interest with the stations taken by each group.

Methods

Each of the 8 or so groups set off to their designated zone on campus to focus the most efforts in their area to ensure that most of the map has some kind of data points.  Each group was given a climate surveying device that calculated wind speed, temperature, and dew point, and to use ArcCollector on the app running on their Iphone or Android device.  Out class had the luxury of having a geodatabase already set up making the data entry easy when out in the field.  Since it was already all set up and each group was connected to it, all that needed to be done was to collect the information within the zones and come back to upload the data, therefore collecting all of the data was quick and painless.

Most groups seemed to be taking points about every 25 feet to get the outside of their zone before heading into the middle of their areas before making their way back inside to upload the data.  Then the data can be downloaded after everyone uploads theirs to create a set of feature classes that can be used by each individual.

Now that the data is downloaded and set up the fun process of interpolation is the next step.  Since there is not a pattern of points and not every point can be taken the choice for interpolation would be IDW (Inverse Distance Weighted).  This was chosen because this method weighs the points based on the distance from each other.  The farther away the next point the less weight that that single point will have.  This created Figure 2,3 and 4.

Results

There are most certainty some patterns that can be seen within the images created. The first patterns that are noticeable are with the wind speed (Figure 2).  Along the ridge or scarp, which separates upper campus and lower campus, there is a noticeable decrease of wind speed due to the scarp where the points are directly adjacent to.  Another pattern noticeable with the wind speed is around the river.  This is due to the river being a relatively open are allowing more wind to flow through that area.  Therefore, the area surrounding the river creates a zone of higher wind speeds.  Another area is the terrace, upper campus, which also has higher wind speeds because there are less trees within the area to slow it down.

Figure 2: Continuous map showing Wind Speed.  This map is also showing the wind direction taken at each station to show more patterns depending on the location. 

The temperature map (Figure 3) shows the pattern of having warmer temperature surrounding areas with buildings, compared to the areas that are heavily vegetated being cooler overall.  This could be due to the cement sidewalks absorbing the sun's rays, and emitting it to create a overall warmer climate. The vegetated areas are cooler because less of the sun's energy can reach the ground to emit its energy back into the surrounding atmosphere.

Figure 3: This is the Temperature version of the microclimate.  

The dew point (Figure 4) is lower around the Chippewa River and higher around the buildings on campus.  This is expected because there is going to be a higher temperature around the buildings on campus making the dew point higher because temperature directly influences the dew point.

Figure 4:  This is a map of the dew point around the UWEC campus.  

Conclusions

ArcCollector is a very powerful app for collecting data in the field.  Using some kind of smartphone can really add a lot to the power when collecting data in the field with its ability to use Bluetooth to a GPS that displays the necessary information on the smartphone.  Therefore, using ArcCollector on a smartphone is very similar, just without the Bluetooth technology which could easily be substituted for a smartphone's GPS.  Using ArcCollector sped up the data sharing with the class much faster than having to share around an excel file or any other way of getting everyone to enter the data making this an invaluable tool for group work like this when accessing data.  This can be adventurous in the field while working as a professional if there are multiple people working on a project, they could just update the data every day when they would come back from the field.  Being able to collaborate with all of the class made this sharing of data a lot faster and accurate than any of the other labs.  This lab was very important in the understand of creating a geodatabase and creating domains before entering the field.