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.

Arcpad Data Collection I

Introduction

The objective this week in lab in to learn how to take the geodatabase that we created last week  (See here for details on geodatabse construction) and import this into a handheld GPS unit called the Trimble Juno.  The handheld GPS is important because it allows us to easily record the geographic position at the points where we are collecting data.

Figure 1. Picture of handheld GPS unit, Trimble Juno

The larger purpose for all of this is to map out a microclimate of parts of the UW Eau Claire campus.  I go into more detail next week as to what microclimates are, but the basic definition is a subset of a larger area where the climate is different.  Some examples of microclimate maps taken of the Eau Claire Area can be found on various blogs from the uwec geography 341 class, one interesting post of microclimate maps I found here.
 

This week we focused on learning how to use the equipment, and what procedures were needed to collect the data.  The types of data we will be collecting can be found listed in my previous blog post  The larger project will occur next week, and will involve the collection of about 100 survey points per person.

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 2. Eau Claire Campus, shown on Google maps

 

Methods

The Trimble Juno uses ArcPad, which is similar but a more simplified version of ArcMap.  The geodatabase we produced needed to be altered to put get into ArcPad.  This was done by the following steps.

1.  Open the file in ArcGIS with feature class from the geodatabase on the map.
2.  Go to the extension ArcPad Data Manager
3.  Go through the steps, and make a folder with the mxd of the feature class with a basemap.
4.  Create a copy of this folder.

After this, the Trimble Juno device was connected to the computer, and the folder was moved to the SD card of the device.  In this way it was possible to access the map from Trimble Juno and use it to make edits in the feature class.

I had several problems with this part in the lab, and it took me very long to get through it.  The largest problem I had was figuring out how to get a basemap to appear on the Trimble Juno.  With help, I eventually got the basemap on the device, but I had less time in class to work on practicing data collection.

The next part of the excessive was to go outside and practice collecting climate data.   To do this, we selected a survey point and turned the GPS on the device.  Once there was a strong signal, we could collect information at that location and edit the feature class in ArcPad to store the information.  The data we collected included,

Time of data collection

Location of data collection points

Wind Speed

Wind Chill

Surface Temperature

Temperature 2 meters above the surface

Relative Humidity

Dew Point

Ground Cover

We did not take measurements of wind direction this week, but we plan on doing this next week when we have a compass.  The time of data collection was done using the time listed on the Trimble Juno device, and the ground cover was evaluated by using complex biological remote sensing devices (our eyes).

All of these measurements, except for time of data collection, ground cover, and geographic location was taken using a kestrel meter. 

Figure 3.  Example of a Kestrel Meter

After measurements were collected in the field, this data was imported back into ArcGIS.  Unfortunately, due to complications with my Trimble Juno device, I was unable to collect more than 2 data points. 

Results

I was able to import the data back into ArcGIS but I was unable to perform any kind of interpolation due to my lack of data points.  Because of the lack of data, I do not have any meaningful results in terms of produced maps.

Figure 4.  Screenshot of collected data points on a basemap

Discussion

I had several problems in lab this week, which had an impact in what information I collected, and what I was able to do with this.  At first I was not able to get a background image to appear on my Trimble Juno. This was solved by taking an image from the geospatial folder.  I then had troubles putting the new folder that had the image on the device because the folder had the same name.  Renaming the folder solved the problem and allowed me to get the feature class with a basemap on the device.

The other problem that I had was with the Trimble Juno device itself.  The location that I clicked on the screen did not match with the response of the device.  I would try to type in letters and numbers when collecting data, but I couldn't figure out the right place on the screen to get it.  For this reason, even one data point took an extremely long time to get.  I told professor Hupy what happened and then sent an email to him with the number of the device so that the problem could be looked at.

Conclusion

I had much more trouble going through this lab then I have had in previous activities.  It was easy to get frustrated when it seemed like I couldn't get anything to work properly.  I think that it did serve an important lesson though, as sometimes equipment can break down and can have an impact on whatever research you are working on.  I am glad that we did not start with the more official data collection, because I do not feel like I would have been able to keep up.  The exercise helped with preparing for next week, and I think it served as a good way to troubleshoot potential problems. 
 

Microclimate Geodatabase Construction

Background

A microclimate is a local atmospheric zone where the climate differs from the surrounding area.  It can essentially be thought of as a subset of the climate of a larger area, but more specific to the terrain features and properties of the smaller area.  Microclimate research can be useful in many fields such as biological research and urban planning.  I will go into greater detail about Microclimates in future blogposts.  Data that is useful to collect when doing microclimate research includes the following.

• Time of data collection
• Location of data collection points
• Wind Speed
• Wind Direction
• Wind Chill
• Surface Temperature
• Temperature 2 meters above the surface
• Relative Humidity
• Dew Point
• Ground Cover

As with all data collection, it is also useful to take additional notes that may not fit into the categories that you had decided upon before starting research.

This week in lab we developed a geodatabse that we will use when we collect microclimate data in Eau Claire.  A well made geodatabse will help you keep your data organized, and will make data collection and mapping go smoother. Before we go out to collect climate data around our campus we made the domains based on what data we were going to collect and what we thought those values would be.  The domain in this sense is what range of values, and what kind of data we expect to collect and record in our various fields. 

Tutorial

2. In ArcCatalog, right click on the folder you want you geodatabse in and navigate to New>File Geodatabase

2. Rename the geodatabase by right clicking it, navigating to rename, and then typing Microclimate

3. Right click the Microclimate Geodatabase and navigate to properties

4.  The data that we plan to collect fall into these categories

Time of data collection

Location of data collection points

Wind Speed

Wind Direction

Wind Chill

Surface Temperature

Temperature 2 meters above the surface

Relative Humidity

Dew Point

Ground Cover

Temperature and wind chill measuremnts will have the same range so they can be grouped together.  This means you will have the following domains

• Time collected
• Wind Speed
• Wind Direction
• Temperature
• Relative Humidity
• Dew Point
• Ground Cover

Type these in under domain name, also add descriptions that say what units these observations will be recorded in.

5. For each domain you will need to edit the domain properties.  If the data you expect to collect is a number under 5 digets long select short interger, if it is a longer number select long interger, if it is data that will be in words select text.  You will select a minimum and maximam value for numeric domains based on what you expect to be the range when you are collecting data. For example, below we will be collecting data on windspeed and we expect the wind to be between 0-20 mph, we have the minimum and maximum values make a range where we expect are data to be.

6.  For ground cover, select text and coded values.  You will write in words that represent categories that you expect to see.  You can also give these categories descriptions so that you can keep observations consistent.  A good idea will to have one of the coded values be "other" so that you will always be able to record an observation even when it doesn't match any other categories you made.

7.  It will be a good idea to make an extra field called notes, for if you ever need to make an observation that is outside of the predetermined ranges or codes.

8.  When you are done making your domains and defining their properties, click OK and then right click on your geodatabase and navigate to New>Feature Class.  Name the new feature class Microclimate.  Make sure that it is a point feature class and then click next.

9.  Select a coordinate system based on the location of your microclimate

10. Type in all of the field names that correspond to the data you will collect under field names.  Select the data type that you would like to store each field as, and then go to field properties and select the domain that goes with the predicted range for that variable.  Then click finish and you will have made your feature class.

If you have any questions try using ArcHelp, as it often can give some explanations.

Unmanned Aerial System Mission Planning

Introduction

The objective of this weeks activity was to learn about and evaluate various UAS platforms. One part of the activity was to use  Real Flight 7.5 to test fly 4 different platforms. The next part of the activity was to look at different scenarios and give a recommendation and justification for what platform we think would work best for the specified project.  This part of the activity is of particular importance because this is a valuable skill to have. The goal of this activity, like our other activities is to gain skills that can be applied to real world situations.

Having an understanding of UAS is important because there are many applications of remote sensing that can be done using UAVs.  They are not without issues, legally and ethically speaking, but they are almost certainly going to continue to have a large impact on geospatial information systems. 

UAS platforms

UAS stands for Unmanned Aerial Systems.  Describing it as a system is more complete than just talking about UAVs- Unmanned Aerial Vehichles.  UAVs are part of a system, but they are not the only part.  There is also the software used to control the UAVs.  There is additional hardware involved which varies on what type of sensor you need to use.  Then there is also the software needed to compile and analyze the data collected by UAVs. 

In this activity we were largely focused on the role that UAVs played in Unmanned Aerial Systems.  UAVs are useful in that they can be cheaper than traditional aircraft, the can fly lower to the ground, and they can collect more detailed information because of this.  Costs of UAVs continue to decrease as time goes on.

UAVs can be divided into two general categories, fixed wing and multirotor.  There are trade offs in choosing one type over another, as both have properties that can be ideal for different situation.  A quick table of general comparisons between these two UAV types can be seen below.

Figure 1. Comparison of UAVproperties

Real Flight 7.5

Using a flight simulation is a great way to get some level of experience with flying UAV without the risk of destroying one.  The program Real Flight 7.5 offers a wide range of different UAVs and different airfields that can be used for practice.  Each of the airfields was unique, and gave you the opportunity to adjust conditions such as windspeed and direction, and visability.

Figure 2. Description of the Japenese Temple Airfield in Real Flight 7.5

There are also different obstacle courses and challanges that are offered within the program.  An example of one of these challanges that I tried was the baloon challange.  In this challange, the onbjective is to pop all of the baloons as quickly as possible.

Figure 3. Baloon challange with a fixed wing UAV in Real Flight 7.5 

One of the objectives was to log 30 minutes of flight time on 4 different UAV platforms (2 fixed wing, and 2 multirotor).  Shown below are the UAVs that I chose, the descriptions given in Real Flight 7.5, as well as my personal experiences with them.



Figure 4.  Quadcopter 1260 in Real Flight 7.5

I thought that for the most part the Quadcopter was easy to take off and land.  It moved slower compared to the fixed wings that I tried, but was much easier to hover.  The viewpoints fixed position, and nose were both relatively easy to use.  The most difficulties I had was when I tried incresing the wind, which made it more difficult to move.

Figure 5.  Ultra Sport in Real Flight 7.5

I generally found the fixed wing UAVs harder to fly compared with the multirotor UAVs.  The Ultrasport was relatively easy to fly during take off, but I had great difficulty when trying to sucessfully land it.  The Ultrasport seemed to be less affected by incresed wind speeds, was faster than the Quadcopter, and unable to hover.  It was easiest to fly using the nose viewpoint, and hardest to fly when using fixed position.

Figure 6. L-39 streamer cut in Real Flight 7.5 

The L-39 streamer cut was similar to the Ultrasport, but I found it harder to fly.  I wasn't able to switch direction as easily which led to crashing more often.  This plane was faster, and also much more tolerant of heavy wind conditions.  The viewpoint that I found most useful for this was chase, although I belive this is one of the least realistic viewpoints, because it would be rare to have that in a real world situation.

Figure 7. Tricopter 900 in Real Fligth 7.5

The tricopter 900 was similar to the quadcopter, but I found it to be less stable, and less tolerant of varying wind conditions.  The fixed position viewpoint was the easiest to use, and I found the nose viewpoint to be extremely disorienting.

During the time I was flying these UAVs in the flight simulator, I kept a log to record how long each flight lasted, as well as any other details of the flight that I thought would be important to mention. 

Figure 8. Flight log part 1

Figure 9.  Flight log part 2

Scenarios

The next part of this activity was to consider 2 situations and determine if they could benefit from using some type of UAS platform and then if so making a reccomendation for what this platform could potentially be. 

Methods

For each of the scenarios I tried to start by thinking of what needed to be done, and what were the characteristics needed in both the sensors and the UAVs.  My next step was to look up information that would be specific or relevant to the scenario. For example, when trying to make a reccomendation for the pinapple plantation I looked up studies of monitoring pinapple health and saw if any of these could also be related to remote sensing and aerial images.  I found that when I looked for it, I could find examples that were extremely relevant to the scenarios, and these helped me for my reccomendations for each of them.

Discussion 

Recommendations

1)  A power line company spends lots of money on a helicopter company monitoring and fixing problems on their line. One of the biggest costs is the helicopter having to fly up to these things just to see if there is a problem with the tower. Another issue is the cost of just figuring how to get to the things from the closest airport.

Using an unmanned aerial system would most liklely be of great benifit to this company in terms of saving costs.  At one point in time helicopters were probably the best monitoring solution, but now there are cheaper alternatives that could be utilized. 

There are already power companies that are using UAVs for inspections with great success. Finnish Energy Industries used UAVs with advanced sensors as part of a research project conducted with the Electricity Research Pool and saw a 50% reduction in costs compared with their traditional aerial survey methods.

I would recommend using essentially the same system that they did because of its proven effectiveness.  A helicopter or a quadcopter UAV should give you the stability to get high quality photos.  A high resolution camera would be enough to show any major problems with the powerline, but I would also reccomend having a laser scanner in order to get a 3-D model of the power lines and their surrounding terrains.  Having this might allow for more preventative work to be done, identifying potential problem areas, and avoiding those problems altogether.

Source:Transmission and Distribution World Magazine

2)  A pineapple plantation has about 8000 acres, and they want you to give them an idea of where they have vegetation that is not healthy, as well as help them out with when might be a good time to harvest.

I think that what will give you the best information about this field will be to have take a normalized difference vegetation index.  Although an NDVI can be done with ordinary aerial photos and satellite images, the resolution would not be very high. 

8000 acres is a relatively large area, so using a helicopter or quadcopter UAV would probably not be as efficient as using some form of fixed wing UAV.  As for the sensor that you should use, a research study on pinapple plantations in Malaysia found that an optical sensor called Greenseeker prduced by the tech company trimble was an effective way to get NDVI measurements.

Sources:  Pinapple Study  Greenseeker info
 

Conclusions

The activity was useful in acheiving the objectives that were set out.  I learned more about, and was able to evaluate the usefulness of various unmanned aerial systems in different situations.  The Real Flight simulator was great for actually getting a sense of what it would feel like to fly them, and the scenarios given were a great opportunity to do some additional research and develop a reccomendation based on our current knowledge of UAS.