ArcPad data collection Part II

Introduction

In last week's lab activity we learned how to deploy ArcPad, and had practice collecting data with the Juno GPS device. The objective of lab this week is to  use a standardized geodatabase to collect microclimate data as a class and produce microclimate maps of the UW Eau Claire campus.

A microclimate is the climate in a smaller, more specific subset of a larger area.  They generally explain temperature variablility whcih can result from elevation, exposure, ground cover, and a variety of other factors.  Microclimates can be of great interests to a variety of different fields including biology, environemtnal public health, construction, and landscaping.   The information that we are collecting includes

Location of data points

Wind Speed

Wind Chill

Surface Temperature

Temperature 2 meters above the surface

Relative Humidity

Dew Point

Ground Cover

These observations should give us a good idea of several aspects of the UW Eau Claire Microclimate.  Once we have collected this data we will make maps that convey the information we have found.  If there was a more specific purpose for the maps that we are going to produce, it could result in us taking different or additional observations.  Although we do not have a client for the maps that we are making today, it is still important to consider how these kind of maps could have changed based on what information people are looking for.

Study Area
The study area that we will be looking points for mapping the microclimate is the UW Eau Claire Campus located at 105 Garfield Avenue, Eau Claire, WI 54702.  The study area was chosen for convenience of access for our class, and will also allow us to compare our findings with previous classes that have done this project.

Figure 1. Location of study area shown by Google maps

To find more information about our study area, I visited weatherspark.com to find the average weather observations for the month of March in Eau Claire Wisconsin.  The UW Eau Claire campus is located within the city of Eau Claire, so an understanding of the average weather in the city of Eau Claire could be useful if the data we collect is to be analyzed further.  The following graphs from weatherspark.com show average weather recordings for March in Eau Claire Wisconsin.

Figure 2.  Average daily high and low temperatures for March in Eau Claire Wisconsin

 

Figure 3.  Probability of snow cover for March in Eau Claire Wisconsin

Figure 4. Average daily high and low dew points for March in Eau Claire Wisconsin

Figure 5. Average high and low relative humidities for March in Eau Claire Wisconsin

Figure 6. Average high, median, and minimum wind speeds for March in Eau Claire Wisconsin

The average daily high temperature for March 9th (the day of our data collection) was approximately 35°F.  The average daily low temperature for the same day was approximately 19°F.  The probability for finding snow on the ground on was approximately 46%.  The average high dew point was about 25°F and the average low dew point was about 11°F.  The average high relative humidity was about 88% and the average low relative humidity was about 53%.  The average mean wind speed was about 8mph.


Methods
The class worked in groups of two, and collected data from various points on campus using the kestral meter to make several of the observations that were recorded. The process of deploying the database and getting our Trimble Juno unit ready was the same as described in my previous blog post

The main differences between last week and this week, was that this week we were coordinating as an entire class to make sure that we got a good coverage of the campus.  To do this, polygons were drawn on our Eau Claire basemap, and groups were designated different areas.

Figure 7.  UW Eau Claire campus with labeled polygons that represent zones for data collection

I worked with a partener to collect observations in zone 3.  We switched between using the Trimble Juno and the Kestral meter, and we worked on getting as many points as we could in the alloted time.  When we were in the field we realized that we did not have a compass, so we did not collect any observations for wind direction and recorded values of 0 for this.

A concern that I had when taking measurements is that the value on the kestral meter seemed to be constantly changing.  I tried to wait for the variation to stop, but I would often end up taking an estimate of the values it was showing.  If we were to repeat the experiement, I would want to make sure that the class had a standard procedure for this.

Otherwise the data collection went smoothly, we collected data from slightly after 3:00 till 5:40 and then went inside and had our data uploaded to the combined data.  This combined data was then made avaialable for the class so that we could work on making our individual maps.

Before starting anything, I looked through the attribute table to see if there were any problems with the data.  When looking through the table I had a few concerns. The temperature at 2m for a large section was listed as null.  I checked to see if the notes field had any values for the temperature at 2 meters for these, but the notes did not have any information on this. This lead me to the decision not to use this value in the maps I make.


Figure 8.  Table shows a large section of the temperature at 2 meters devoid of information

 

 

The next field that I looked at was the notes field.  The notes often contained nothing, and sometimes contained random observation of the area, sometimes the notes gave very useful information that helped correct measurements that were taken.  People could write down a more accurate measurement if it was outside of the expected range.  I was then able to take this, and use it to edit the data.

Figure 9.  The 10th note show above an example of a note telling when one of the temperature measurements was outside of the range.

Figure 10.  Additional useful information given in the notes field

I did not expect anyone to have recorded wind directions, but one group apparently did, as I found some values listed in the attribute table.  I will make a smaller map that shows the wind directions for the area where data was collected for this.

Figure 11. Wind direction listed in the attribute table

 

 

The next step for me was to take several interpolations, and compare them to each other.  I could not make an interpolation out of the ground cover because it had unique values that we not numbers, so I instead just changed the colors of the data points where the ground cover was snow white and left the rest of the data points gray.  For the interpolations I kept the color scale the same so that I could more easily make comparisons between them.  All interpolations were made using the nearest neighbor method.

Figure 12. Variation and distribution of measurements in measuremen taken. 

I decided that  since the interpolations of relative humidity and dew point looked so simlar I would only make a map out of one of these.I also decided that I would not make a map out of the wind chill, because the coloration over the river makes me think the interpolation might be inaccurate.  I think that it might be more accurate because the wind chill could have varied more with gusts of wind.

Figure 13. Comparisons between the interpolations of Dew Point and Relative Humidity

Figure 14. Comparison between interpolation of Wind chill and interpolation of suface temperature

In the end I decided I would make the following maps

• A map of wind directions for the small area that had measurements for this
• A map that shows variation in surface temperature
• A map that shows variation in wind speed
• A map that shows variation in relative humidity

Figure 15. Map showing which data collection points had snow

Figure 16.  Map showing variation in temperature

Figure 17.  Map showing variation in wind speed

Figure 18.  Map showing variation in relative humidity

Figure 19.  Map showing wind direction at data collection points

Figure 20. Graphs comparing temperature, humidity, and wind speed averages.

For the map of snow cover I decided to show which of the data point locations had snow, and I showed these as white.  I decided that this might be better than using an interpolation because the snow was very scattered and inconsistent. 

For the map of wind direction, I changed the sybology of the data points to arrows, I selected the data points that had values for wind direction and I made the arrows point in the direction specified by the recorded measurements.

For all of the other maps I used the nearest neighbor method to make rasters.  The colors were chosen based on what I thought would make the most readily understandable (like temperature being shown on a blue to red spectrum).  I set the transparency at 20% so that it would be possible to see some of the features on the basemap. 

I also decided to make a few charts that compared the average measurements that our class took, with the average measurements for the city of Eau Claire for the same day (averaged over a span of years).


Discussion

Although the lab had originally intended for each group to collect about 100 data points, most groups were unable to collect this many in the alloted time.  In the end we still collected well over 300 data points, and I think that this gave us a relatively accurate representation of the UW Eau Claire campus. 

My main concerns about the data would be consistency.  The kestral meters would take time before stopping on one reading, and sometimes it would constintly be changing.  This might lead to some inaccuracy when recording values, but hopefully the number of data points would make up for this potential extra variability and error.

The other concerns I had about the data were addressed in the methods section of this report, but I will briefly restate what they were.  I checked the notes field, and when it had information about a value that was out of range, I went back and I fixed the value.  I decided not to use the temperature at 2 meter values for any of the maps because there was no data in this category for one of the zones. 

An interesting analysis might be to compare these with microclimate maps made in previous years, and look to see if there is a definitive pattern to the variation of measurements or if it is possible that some of it is just due to chance.


Conclusion

This lab was very useful, and taught a variety of skills that could be applicable in any projects.  Knowing how to use handheld GPS devices, how to collect climate data, and how to work with an entire classroom of people to do a survey are good skills to have for the future.  I think that the project turned out well, and that I was able to produce maps that conveyed meaningful information about the microclimate on the Eau Claire campus.