Monday, March 31, 2014

Activity #8: Campus Microclimates



Introduction

In Activity #6, we created the geodatabase, domains, and feature class that we would be using for Activity #8. In this activity, groups of two were assigned different parts of campus and were tasked with collecting various weather related data. After the data was collected, we were to combine the data from all the groups, analyse it, and make several maps showing the different microclimates all over campus. The original goal was for each group to collect 50 points, each containing the predetermined information we decided on during Activity #6. Due to time constraints, equipment problems, and cold weather, most groups were not able to gather the full 50 points. We utilized what would could.

Study Area
Figure 1 - Study area for the different groups. My group's
study area is in the red box and our classroom is in the
purple box.

The first thing we had to do was divide up the parts of campus that each group was responsible for. We did this by simply allowing groups to pick, so first-come-first-serve. My partner and I did not speak up very quickly, and so ended up with a study area relatively far away from the building we had class in. Figure 1 shows the different groups and where their study area was located. Zach Howard and I were group 4, and our study area is withing the red rectangle. The purple rectangle is Phillips Hall, where our class meets every Monday.
Figure 2 - Our group's study area - field and parking lot
west of Haas Fine Arts Center








Figure 2 shows a close-up of our study area. This area lies to the west of the Haas Fine Arts Center. Our area included a grass (mostly snow covered at the time) field and a parking lot that had Water Street to the north and the bike trail and Chippewa River to the south.







Below are some pictures from our study area:


Figure 3 - Open field west of Haas Fine Arts Center, looking north.

Figure 4 - West side of Haas Fine Arts Center, looking northeast
from approximately same position as in figure 3.

Figure 5 - Parking lot to the west of field, from approximately
same position as figures 3 and 4.

Figure 6 - Parking lot south of the field, camera facing
southeast.

Figure 7 - Parking lot west of field, camera facing northwest.



Methods

Exporting Geodatabase


Figure 8 - Feature class and background raster in ArcMap
Before we could start gathering data, we needed to get the geodatabase, complete with domains and feature classes, prepped and onto the GPS device. Before exporting to the device, it's important to get the map ready so that it can easily be used in the field. We already had domains and a feature class created from Activity #6, but we also wanted a background image. Using an aerial image of campus as a base map, we brought everything into an ArcMap file (Figure 8).




Figure 9 - Image extent used when exporting to GPS device
Since our group would only be using our study area from this image, I zoomed to the field by Haas Fine Arts and made sure that when I exported the data to the GPS device, it would only be our study area, as opposed to the full image (Figure 9). At this point, it would be a good idea to double check that the domains and feature classes are properly set up before exporting (especially in this case since it has been a few weeks since I created them). We forgot this step, and thus had to manually fix the attribute table after we finished collecting data (more on this later). ALWAYS DOUBLE CHECK!



Figure 10 - Extensions menu where we select
ArcPad Data Manager


In order to export the map, a couple of things must be done within ArcMap. Under the Customize menu on the main tool bar, click on Extensions. Extensions are "add-ons" to ArcMap that usually cost more to have access to. The ArcPad Data Manager is one such extension that we will need access to. In this menu, we can check the boxes next  to any extensions we may need (Figure 10). Luckily, UWEC has access to all of them. Once we have access to ArcPad Data Manager options, we can add the toolbar. Once again under the Customize menu, select Toolbars and then ArcPad Data Manager (Figure 11) and the toolbar shown in figure 12 will pop up.









Figure 11 - How to add the ArcPad Data Manager toolbar

Figure 12 - The ArcPad Data Manager toolbar



Clicking the first icon on the left (with the arrow point to the right) opens the menu for choosing what data we want to export. The list automatically brings up the data that is on the current map. Where it says Action, click and choose Checkout all Geodatabase layers. The window should look like it does in figure 13. The next window is where we specify output options. I set the folder to export the data to (a previously created folder within my class folder on the network) and named the folder that would store the data micro_haasta_howardza (micro + both of our usernames) (Figure 14). Lastly, in deployment options (Figure 15), I made sure the box next to Create the ArcPad data on this computer now was checked. 


Figure 13 - Get data for ArcPad window



Figure 14 - Output options for data



Figure 15 - Last step in ArcPad data creation 


Figure 16 - Two copies of ArcPad data, in case a backup is needed


It may take a few minutes to create the data. Once it is complete, the data will be in the folder specified. Before moving ahead, copy-and-past this folder so that there is backup data in the event of any problems (Figure 16).

Figure 17 - Timble Juno
3 series





For this exercise, we are using the Trimble Juno GPS device (Figure 17). The device connects using a simple USB chord. Once it is connected, all that we need to do is cut-and-past one of the ArcPad data folders (in my case, micro_haasta_howardza) into the GPS device's storage using Windows Explorer.





Gathering Field Data

Once the geodatabase was on the Trimble, we were ready to head outside and collect the data. We brought several devices and tools (Figure 18) to help with this process. Here's the list:

Figure 18 - Equipment Zach and I used to collect data

1.) Trimble Juno GPS
2.) Kestrel 3000 weather meter
3.) Folding measuring stick
4.) Bandanna
5.) Orienteering compass









Once outside, it did actually take us a while before we were able to start collecting data. Neither of us were very familiar with using the Trimble Juno, and it took some time to get it working properly. After getting help from both Professor Hupy and Martin Goettl (the geography department's GeoSpatial Technology Facilitator), we found out that TerraSync (Trimble's GPS software that is also on the device) was also open. Only one program can be acquiring satellites on the device at a time, which is why ArcPad wasn't working.

When we got to our study area, we starting collecting points. For each point that was collected, we entered the following data:

Time
Temperature (degrees F)
Wind direction (azimuth)
Wind direction (cardinal)
Wind speed (mph)
Dewpoint (degrees F)
Relative humidity
Snow depth (inches)

When creating the domains, I had set the Group domain as being coded values. I either forgot to enter the codes, or they didn't save. Either way, I wasn't able to enter our group number until after we exported the collected data back to the computer. This wasn't that difficult, since '4' was the entry for every point, but had it been any other field, it would have been another setback. This is why you should always double check your domains before sending the geodatabase to your GPS device. This will avoid problems in the field.

Temperature, wind speed, dewpoint, and relative humidity could all be found using the Kestrel. All that was necessary was to scroll through the different features using the arrow buttons. The measuring stick was used to measure snow depth. Many of our points didn't have snow, as about half of our study area was parking lot, and much of the winter snow had melted by this point. The bandanna was used to find the wind direction azimuth. We could hold up the bandanna by a corner and hold the compass under it to get a general idea of wind direction. For the time, I simply used the time shown on my cell phone.

One of us would take measurements using the trimble, measuring stick, Kestrel, and compass/bandanna, while the other person plotted GPS points and recorded the information called out by the other. Zach and I switched off roles about every 10 points. This was partially so we could each get practice doing both, but also because our hands would get very cold doing the same thing for too long. In the end, we were only able to collect 27 points since class ended at 6:00 P.M.

Checking-in Data from Trimble Device

When we got back to the class room, we had to export our newly collected data from the GPS device to the computer. This was pretty simple. The Trimble was plugged back into the computer using the USB chord. Using Windows Explorer again, I navigated to the folder I created before going out into the field (micro_haasta_howardza, now with our fresh data) and cut-and-pasted it back into my personal class folder.

In ArcMap, we simply did the opposite of what we did when sending data to the Trimble. On the ArcPad Data Manager toolbar (again, make sure the ArcPad Data Manager extension is turned on), click the Get data from ArcPad button (Figure 19). Make sure you have the correct folder selected and click Check In.


Figure 19 - Check in data from ArcPad


In order to create maps and analyse the data, the shapefiles created by each group needed to be combined into one shapefile. One of my classmates was nice enough to do this by merging all seven of the separate shapefiles into one, and providing the result to the rest of the class to use.

Results

Figure 20 shows temperature data for my group's study area only. Warmer temperatures are on the north end of the parking lot, and are cooler to the south. This could be because of more shade closer to the bike path. There was more shaded areas from trees in that area, where as there was more exposure to sunlight closer to Water St. The black asphault could have been radiating more heat, as compared to the field that was also in the open. The ground was snow covered in those areas.

Figure 20 - Map showing distribution of temperatures for Group 4 study area
(using IDW interpolation method).
Figure 21 shows the temperature data for the entire class. There is one very high temperature in the middle of campus, which is why I used Natural Breaks classification for this map instead of a equal interval like I did in figure 20. This way, the outlier did effect the data.
Figure 21 - Map showing distribution of temperatures for campus using
data from all groups. Created using Nearest Neighbor interpolation method
and Natural Breaks (Jenks) classification.

Figure 22 shows the wind data. Each arrow points in the direction that the wind is blowing to and is color-coded to represent wind speed. The day that we collected data was not very windy in general, though there were some gusts.



Figure 22 - Map showing wind speed and direction for class data.
Figure 23 is a TIN (Triangulated Irregular Network) that shows snow depth around campus.

Figure 23 - Map of TIN showing snow depth around campus.

Discussion

The snow had started to melt before we collected the data, so that is why areas of higher snow depth seem concentrated. The areas where snow depth is highest is in the middle of campus. I would guess that this is simply from snow being plowed off of the sidewalks and pilling up, as opposed to actual snow accumulation depth. The other area where snow depth is deepest is down by the river, which makes sense.

The wind map in figure 22 and the temperature map in figure 21 seem to show that areas with higher wind tended to have lower temperatures. On upper campus where there was generally stronger winds (probably because it is out of the valley) had lower temperatures than much of campus. The area behind and around the residence halls on lower campus had very low winds (significant cover from buildings), and had higher temperatures (in general). Our study area (by Haas Fine Arts) had higher winds AND higher temperatures. This could be because our location was exposed more to the sun (and wind) than other areas on campus, and had parking lot asphalt reflecting the energy.

The one very high temperature in the middle of campus (81.8) was puzzling. There could have been ventilation from the nearby building, or simply operator error. Also, several points from one of the groups displayed on the equator. This could have been because of proximity to buildings and subsequent lose of satellite connection. But those errors did have to be removed so that data analysis could be done properly.

Conclusion

It was interesting to see just how different some of these variables are from one place on campus to another. It would have been better (though colder) to do it on a day when there was more wind. Since most wind speeds were below 4 mph, the differences shown on the map are not very conclusive. We also had to work together for this project; not just in our groups, but as a class to consolidate the data in a usable manner. I thought this went well, with people sharing the information with the rest of the class when they could. I general, I liked this assignment. I just wish spring would come already so I didn't have to be distracted by frozen fingers during every field exercise.

Thursday, March 13, 2014

Activity #7: UAS Field Day





Class on March 10th was the first break in the record cold winter since the semester started, so Professor Hupy made a last minute decision to get outside. Instead of a project or assignment, we took advantage of the beautiful day (temps into the 50s) by meeting at the Eau Claire Sports Center to test out different forms of UASs. We saw two different rotary copters, a kite, and a rocket; all with cameras attached. Max, who is a physics student at UWEC, was assisting Professor Hupy with flying and calibrating his copter as well as demonstrating his own copter that he is building for a senior project. The kite was interesting because I have never seen a 'real' kite (by that I mean one that wasn't $2 from K-mart with cartoons on it) before and wasn't aware they could support the weight of a camera. This would be relatively cheap, and a form of UAS that I could afford to do on my own. The rocket was fun to watch, despite not working properly. Professor Hupy taped two key-chain cameras (around $10, light weight, and take video) to the rocket that faced down, so that they would take video of the flight. Unfortunately, one of the rocket's engines was put in upside down. The rocket did not achieve much height, and the parachute did not launch (probably because not enough gas filled the tube from only one engine igniting). Either way, we got to see some UASs in action as well as experiment with video from a rocket and spend the day outside. Here are some pictures of the day.

Figure 1 - Professor Hupy's Multicopter


Figure 2 - Carload full of goodies.

Figure 3 - Max's Multicopter

                                       

Figure 4 - A big kite.

Figure 5 - Professor Hupy attaching the camera to the kite.

Figure 6 - Kite in flight.

Figure 7 - Professor Hupy building a bomb...
I mean rocket. A science rocket.

Figure 8 - It's for science, honest.

Figure 9 - Rocket on launch pad.

Figure 10 - Professor Hupy taping the cameras
to the science rocket.

Video - Launch of science rocket.

Sunday, March 9, 2014

Activity #6: Microclimate Geodatabase Construction

Introduction

Later in the semester, we will be collecting data from around UWEC in order to make a microclimate map of campus. To do this, ten groups of two will gather GPS data from different locations on campus. The activity we are engaged in today, however, involves getting ready for and planning organizational methods for that data collection. 

When collecting data in the field, it is always wise to decide in advance what type of data we will need for the project. Skipping this step can lead to more time needed out in the field because of poor planning, mistakes on data entry, and loss of data.

In the case of using ArcGIS, this planning stage involves creating a geodatabase and setting rules on what types of data are allowed to be used, as well as the ranges that that information can fall in. These rules are referred to as domains, and are very important in maintaining data integrity (especially when there is a great deal of data being collected). These domains are then applied to feature class fields and exported to the GPS devices that will be used in data collection. For example, if technicians were going out into the field to collect wind speeds at certain points, the feature class that the data is applied to could contain a domain that restricts the information entered to numbers between 0 and 60 (if using mph). Without the domain, a technician might accidentally enter 90 instead of 9. This would be a huge error in data, but caused by a simple human mistake. Domains ensure that errors are kept to a minimum, makes data collection in the field quicker, and forces GIS personnel to think about data entry before going into the field.

 For our purposes, our class brainstormed what kinds of data would be useful for analyzing microclimates. Air temperature, wind speed and direction, relative humidity, and dew point were all early ideas. We also included Group (our groups are numbered 1-10) and Notes (general notes to leave for points taken, such as 'standing by a heating vent' to explain data that doesn't seem to fit) to fields we would be using. During this discussion, several ideas cropped up that would have been skipped had we just went out into the field to collect general data. Snow Depth and Time of Day were added to our list. Time is important, as some groups might collect data in early morning, when the weather is coldest, and others might collect in the afternoon, when it is warmest. Snow depth may reveal interesting patterns that have gone unnoticed.

Tutorial

Figure 1 - Creating a new File Geodatabase






In ArcCatalog (or in catalog within ArcMap), right click on the folder you want your geodatabase to be, select New, then click File Geodatabase (Figure 1). 











Figure 2 - Domain tab with no domains entered yet





The new geodatabase will appear in that folder simple titled New File Geodatabase.gdb. To rename it, right click the geodatabase and choose rename.

Now we will set up the domains for the geodatabase. Right click the geodatabase again and select Properties. The properties dialog box contains two tabs: General and Domains. Clicking on the Domains tab will bring up a window that looks like figure 2.





Figure 3 - Domain name and description
Now we can enter new domains. Under Domain Name, enter a name that avoids using spaces. Something short will be fine, as you can type out an explanation under the Description field. For example, a domain for relative humidity might have RH under the domain name and Relative humidity % for the description (Figure 3).


Figure 4 - Field Type and Do
 Next you'll have to decide the Field Type and Domain Type. This depends on what the domain will be used for. For the the relative humidity example, we would set the field type to Short Integer. This is used when the data being input must be a non-decimal number. Long Integer is used for integers that are very large (or very negative), and won't be used during this activity. Since relative humidity is represented as a percentage, we set the domain type a Range between 0 and 100 (Figure 4). When collecting data in the field, a number outside of that range will not be able to be entered, increasing data integrity.


Figure 5 - Text field type and Coded Values domain type
for coding cardinal direction
Another field type is Text, and is used for the WindDir_C domain. This is the domain for recording wind direction using cardinal directions instead of Azimuth. Obviously, integers will not help here. If we set the field type to Text and the domain type to Coded Values, however, we can make a code for each direction (Figure 5). This way, someone in the field won't have to type 'Northeast' if the wind is from the northeast, they simply have to type 'NE.' Coded vales will also be used for the Group domain. Instead of Group 1 or Group 2, we will used coded values text so that we only have to enter 1 or 2. A text domain does not have to be coded values, as in the case with the Notes section. This will use a text field, but no coded values will be entered.





Figure 6 - Temp domain using Float field type and
Range domain type.
The Temp domain will use the Float field type. This field type should be used for decimal data. Since the Temp domain will be used for both air temperature dew point data, we need to set the range accordingly. I set the range between -20 and 100 (F). I can only assume that data will not reach either of those limits, but it's good to be prepared.













Figure 7 shows all of the domains I created along with the field type, domain type, and range (if applicable) associated with them. Figure 8 is a table with the coded values for WindDir_C (wind direction using cardinal directions) and figure 9 is the coded values for Group. Figure 10 shows the dialog window with all of the domains created.


Figure 7 - Table showing my domains with field types, domain types, and range.

Figure 9 - Coded values for Group

Figure 8 - Coded values for
WindDir_C
Figure 10 - End product of domain creation
Figure 11 - New feature class window






Now it is time to create a feature class that will utilize these domains. In the catalog, right click your geodatabase and select New ---> Feature class. This opens the dialog box allowing you to name your feature class and select what kind of features you want (line, point, polygon, etc.). For this exercise, I named my feature class microclimate and went with point features (Figure 11).











Figure 12 - Choosing coordinate system for new
feature class





Next you will have to decide the coordinate system that will be applied to the new feature class. I chose NAD_1983_2011_UTM_Zone_15N since I will be dealing with data in Eau Claire (Figure 12). The next two screens (XY Tolerance and Configuration Keyword) I always leave at default settings.












Figure 13 - New feature class field list
The screen shown in figure 13 is where we enter the fields that will be in our new feature class. For me, it ended up very similar to my list of domains. The only real difference was that I used the Temp domain for both dew point and air temperature.

In this table, enter the different fields into the Field Name column. This includes temperature, dew point, snow depth, etc. The Data Type column should match the Field type column from when we were setting up the domain for that field (float, short integer, text, etc.). When you have the data type set, you can then choose the domain at the bottom using the pull down menu. All domains within the geodatabase that where set with that same data type will populate the menu (Figure 14). Figure 15 shows a completed field list.


Figure 14 - Setting data type and domain for each field
Figure 15 - Finished field list for new feature class


Once we click finish, the feature class is added to the geodatabase with no data. We can open up the attribute table for the shapefile by right clicking it and choosing Open Attribute Table. This shows all the fields we created (Figure 16). We are now ready to export the feature class to a GPS device for data collection in the field.


Figure 16 - Attribute table for new feature class

Conclusion 

I have learned how to create geodatabases and set up domains in other classes. What was helpful about this lab, though, was getting in the habit of planning ahead. Thinking thoroughly about the project is crucial before jumping into the data collection. It is always a good idea to know exactly what data you will be collecting so that you can set up domains to protect against error. You will still be able to add new things when in the field, but cutting down on the amount of information you add on the spur of the moment will greatly reduce the chances of error.




Monday, March 3, 2014

Activity #5: Creating a Field Navigation Map

Introduction

Later in the semester, when weather conditions are more hospitable, our class will be going to The Priory for an activity involving navigation by map and compass. For that exercise, we will be using navigation maps that we created during this current activity. Our professor provided us with several different data sets that we could use, though we didn't have to use them all. With this data, we were to create two maps: one using a Universal Transverse Mercator (UTM) projection, and one using a Geographic Coordinate System (GCS) using decimal degrees.

Methods

Pace Count

Before we started making the maps, we found our 'pace count.' Pace count is the number of steps an individual takes within a set distance, in this case 100 meters. As a class, we went outside and marked 100 meters using a laser range finder. To find our pace count, we simply walked the 100 meters and counted every other step (I counted only when my left foot hit the ground). I did this twice and came up with a pace count of 66 the first time and 67 the second time.

Knowing our pace count will come in useful when we eventually do our map and compass navigation. While at The Priory, we will be able to measure distance on our navigation map, then be able to estimate how far we travel by counting our steps. When we found our pace count, we were walking on a flat sidewalk with no obstructions. This won't be the case when out in the woods, meaning our pace count will be higher. We'll have to take that into consideration while navigating.

Map Creation

To create the maps, the class was given access to many different data sets. It is important to avoid the temptation to put all the data on the map, however. Though it seems that more data would be better, in this case the map could be too cluttered. Since we are using these for navigation, we want to be able to see our grid and contour lines more than anything else.

For the first map, I chose to use a Wisconsin UTM projection with a grid system based on meters (each grid line at 50m). This would make navigation easier because my pace count is based on 100 m distance. I also wanted to keep this map more simple for navigation purposes. Figure 1 shows the 2 ft contour line data set, and Figure 2 shows the same data but with our navigation boundaries overlaid on top. The red box is the extent of where our points will be located, so all of our navigation will be concentrated there. Figure 3 shows the 5 ft contour lines. As you can see, this data is only available to the extent of the black box, which is a shapefile created by our professor to show a maximum extent of our study area.
Figure 1 - 2 ft contour line elevation data for Priory

Figure 2 - 2 ft contour line elevation data with navigation
boundary and point boundary boxes

Figure 3 - 5 ft contour lines within navigation boundary
For the second map, I used an unprojected coordinate system using the Geographic Coordinate System of decimal degrees (GCS NAD 1983 2011). The grid system than was also displayed in decimal degrees. I set the interval to .001 degrees on both the x and y axis. I also used a satellite image of the Priory (Figure 4) as well as a DEM (digital elevation model) (Figure 5) in this map, neither of which I used in the first map. This time I left out 2 ft contour lines and just used the 5 ft contour lines (Figure 3) to avoid the map getting too cluttered.
Figure 4 - Satellite image of part of Eau Claire. Black box
shows our study area.

Figure 5 - DEM of part of Western Wisconsin. Black box
shows our study area.

 Results

My first map, the one using a UTM projection, is shown in Figure 6. This map contains 2 ft and 5 ft contour lines. I labeled the 5 ft contour lines so there was some reference. Figure 7 is the second map I created that is using the Geographic Coordinate System in decimal degrees. As I mentioned earlier, I added satellite imagery as well as DEM data colorized showing higher elevations in green, and lower elevations in red.
Figure 6 - Navigation map of The Priory featuring 2 and 5 ft
contour lines using a UTM projection and grid system every
50 ft.

Figure 7 - Priory map featuring 5 ft contour lines using a GCS
coordinate system and a grid system in decimal degrees
every .001 degrees.

Discussion

The first map I made I think will work best for navigation. I used both 2 ft and 5 ft contour lines with no other data sets so that I could see detail in the terrain with out making the map too cluttered. Since this map uses a grid system in meters, it will be easier to use my pace count to judge distance. The second map will be more for reference. That map has more visual data, so that if we end up off course we can find a land mark and get our bearings. Since this one uses decimal degrees, it would be more difficult to actually navigate by because distance would not be as easy to calculate.

Conclusion

I am really looking forward to using our navigation maps in the field. Typically, this activity would be followed by the activity where we actually go to the Priory, but because there is still a great deal of snow on the ground and still very cold, we are putting that exercise off until later in the semester. As a geography student, I have made many maps. This is the first one that I will put to use and have to depend on, so I am eager to test it out.