Activity 7: ArcPad Data Collection II: Deployment of Standardized Geodatabase ArcMap Project to ArcPad for microclimate data collection and map series production

Introduction

This weeks field activity involved the use of the geodatabase we created last week for data collection part 1. We used this database to collect data as a class and create a microclimate map of the UW- Eau Claire campus. There were seven groups of two students, each equipped with a Kestrel (Figure 1) and Juno Trimble GPS unit with ArcPad on it  (Figure 2).


This Kestrel unit was used to collect data such as humidity, dew point, temperature and wind speed/chill. (Figure 1)

This is the GPS unit we used to collect our data point coordinates. (Figure 2)

Each group of 2 students was sent out with the goal of collecting as many points as possible in a 2 hour time period. All groups collected at least 50 points with 78 being the most collected. The key to making this activity work and being able to create the final map is bringing all those collected points together into one feature class.

 

Study Area

Our study are for the creation of this microclimate is the campus of the University of Wisconsin - Eau Claire. We split into seven groups of two and covered both upper and lower campus. Before we went out we created suggested collection zones and sent a group to each one. Those are the red shapes in the map below. (Figure 3) When we started collecting data it was in the mid 50's with a light wind out of the southwest. It was a nice day with some sun and clouds with increasing winds as the afternoon progressed. After a while the wind got a bit chilly.

The campus here at Eau Claire has 3 main chunks, upper campus, lower campus and across the bridge and river. We sent 1 group across the river, 2 to upper campus and 4 to the lower campus portion of campus. All these areas are unique and I wondered if the physical feature differences would effect the data we collected for the micro climate map.


Here you see the UWEC campus in a satellite image. You can see the zones we laid out as well as all the location where data was collected by the groups. (Figure 3)

Methods

There were two parts to this assignment. First comes the preparation and data gathering and later the data merging and map creation.

Part 1 Prep and Gathering

The first step in creating our microclimate map was making sure our geodatabase, background imagery, and point feature class we created in activity 6 were checked out to ArcPad on the Juno GPS unit. Once we new these were ready we made a copy of the folder containing them all and pasted it to the memory card of the Juno unit.
We then grabbed our Juno and Kestrel units and headed outside to collect our data categories of surface and 2 meter temperature, dew point, wind speed/chill and humidity. We were going to collect wind direction information as well however we did not have compasses and decided as a class to not take a direction reading for each point.
Once outside we began gathering the data. This is a simple task but it needs to be done properly. We had one group member read the Kestrel unit while the other partner entered the info into the ArcPad file on the Juno unit. This process can get very monotonous and attention to detail can be lost. This is the part of the activity where the most user error could occur. If you remember from a previous activity we set domains to limit entry errors which helped me catch mistakes on a few occasions. We had to collect as fast as possible to reduce the effect of changing weather conditions as the sun went down and the temperature began to drop, effecting our data. Every group collected at least 50 points and when we were all done we had approximately 400 data points collected in roughly 2 hours. After collection we all met back in the lab to combine our collected data points.

Part 2 Data Merging

The next step was to bring all the individual group's data points together into one big feature class. Before we went to collect our data we had taken one persons geodatabase from the previous week and given it to each group to use this week so that all of the data we collected would be in the same form and easy to combine. We had points collected from all over campus and did a pretty good job of covering the areas assigned to each group. Below is the group by group data collected. (Figure 4)

Figure 4



In to be useful all this data needs to be combined into one large file. In order to do this we used the Append tool in ArcMap. Basically all you do is select each of the groups of data points and bring them into the tool. (Figure 5)You hit ok and select a save location and it combines everything into one point file that we can use to create our microclimate maps. Figure 6 is the resulting feature class of using the Append tool. Now we can make our maps.


This is the Append tool menu. Here you add you feature classes to be joined together. (Figure 5)

Figure 6
 

 Part 2 Continued Map Design

Once we have all the feature classes combined into one we could begin our map creation and analysis. In order to do this a continuous raster surface was created. I used the Kriging Interpolation method to create all of my rasters, which we used in a previous activity. The first climate factor I mapped was dew point. Dew point is one way of measuring the amount of moisture in the air. The closer this value gets to the air temperature the more moisture there is in the air. Below is the raster of the dew point data collected. (Figure 7)


From this map we can see that the dew points across campus were fairly similar with the highest values being on lower campus by the hill and next to the river. (Figure 7)

The next map I created was for another way of measuring moisture content of the air. It is a humidity map. (Figure 8) Humidity is measured as a percentage the closer to 100% it gets the better chance there is of precipitation.


We see that there is a strong correlation between the humidity values in this map and the dew point numbers of figure 7. This makes sense because the higher the dew point the higher the humidity. (Figure 8)

Temperature is another important factor we were looking at in this activity. Temperature effects most of the other factors we looked at so it is important to take into consideration when looking at microclimates. This first temperature map is of surface temperature.(Figure 9) The second map is of temperature at a height of 2 meters. (Figure 10) The last map shows the temperature with wind chill factored in. (Figure 11)


We can see there were definitely cool spots on campus when we were collecting our data. The areas in purple and blue are mostly concentrated on a north facing slope that is covered in trees, still had snow on the ground and was in the shade. It makes sense that this area is cooler than the areas of oranges and reds which are more open spaces. (Figure 9)

Looking at the 2 meter temperature map we notice that pretty much across the board the areas have increased slightly in temperature. The biggest difference is in the hill region. Getting the Kestrel up away from the snow pack can have an impact on the temperature and it obviously did the day this data was collected.(Figure 10)

Wind chill wasn't too much of a factor when we collected our data because it was in the mid to upper 50's. On days when the air temp is around freezing or lower wind chill has a huge impact on temperature. Wind chill is a calculation of the air temperature and the wind speed and is only applicable in a certain range of air temperatures.   11)

Wind is another important factor in climate. It was definitely a factor when we were collecting data. The wind was predominantly coming out of the west southwest. If we would have had compasses we would have used wind azimuth to collect wind direction data. This first map is just showing the wind speeds across campus.(Figure 12) The second shows the wind speed and the wind direction in arrows. (Figure 13).


Looking at this wind speed map we can see that flat open areas tend to be more windy than hilly areas. Upper campus, in the blue, which is flat and very open has the highest wind speeds. The areas in yellow at the bottom of the hill making it more sheltered and lower wind speeds.(Figure 12)

The arrows show the predominant southwesterly wind direction and they are colored for different wind speeds. Again as in the map above we see the low wind speeds at the foot of the hill and higher values on upper campus in flat open areas. (Figure 13)

Discussion

This activity exercised and taught us many skills that should be common knowledge for a geographer. Learning how to use the technology components like ArcMap, ArcPad, Juno GPS units and the Kestrel weather unit is very valuable. Most if not all of the class will use these tools again at some point in the future whether it be for another class of in a job setting they will be familiar with the technology. The troubleshooting and problem solving aspect of the activity is valuable as well. We all know that technology does not always work in the way we expect of want and knowing how to fix those problems is good for building patience and forces you to think outside the box. Communication and team work were also skills used in this activity. We relied on each group to go out and collect data so that we all could complete this assignment and we had to assign groups to certain areas to make sure we covered the needed areas. All these skills will be useful in the future and this is the environment to practice and improve them.

The most important point I am taking away from this activity is that it is essential to plan ahead before doing any kind of field work. If we had not all used the same attributes in our feature classes when we came back and tried to combine the data is would have been a disaster! This planning again increases communication between the groups. We had to come up with standard procedure for how to collect and store the data. It was more work up front but the amount of time it saved us when merging the dating makes it worth the effort. Having assigned areas to collect data in helped to get a better covering of the parts of campus. If everyone had just gone out and collected points in random places the distribution would not have been good which makes the creation of a good map and data representation much harder. To increase the quality of data collection a grid system could be used in the future to assure better spacing of points which would give you a better representation in the map.

Time and temperature change are things that need to be considered when created a climate map like we did in this activity. Fortunately for us the weather was pretty constant and no fronts moved through data collection. If a front would have come through the data readings would probably have been much different before and after giving you a false representation of the actual micro climate of an area. We did have to deal with time of day changing while we were collecting. We started collection in mid afternoon and were approaching sun set when we were done. The position of the sun in the sky makes a lot of difference especially when it comes to temperature. I noticed a slight cooling of temperature in the points recorded as we neared the end of collecting our data. In our case it wasn't enough to make a big difference in the microclimate representation but its should be kept in mind.

Conclusion

This activity may seem like an easy task but it should be taken seriously. There are many places in the work flow that a mistake could be made which will wreck the whole project. Attention to detail an planning ahead are the key to this being a successful activity as well as any field work. Simple tasks such as collecting points with Juno GPS or using the Kestrel get much more complicated when different groups are collaborating to come up with one single product. Communication is the best way to reduce mistakes so that everyone is on the same page. This should again be part of the preplanning.

The technical aspect to this aspect activity would be hard to teach or adequately convey to students without the hands on experience. By having to go out and collect our own data and make our own maps I think the knowledge gained was much greater than it would have been if we had just watched a demonstration of the process. Overall I though this activity went very well. The class worked well as a team with plenty of communication and pre planning leading to a successful group activity.


Activity 6: ArcPad Data Collection I: Deployment of Geodatabase and Arc Project to ArcPad

Introduction

This weeks field activity was all about using the Trimble Juno GPS (Figure 1) units to collect points of interest to look at different microclimate factors. We focused on collecting point s on lower campus this week. Next week we will be creating a microclimate map of the entire campus. We will be going out in groups of two one GPS per group. We will also be using Kestrel units (Figure 2) to collect the microclimate factors such as humidity, temperature, dupoint, wind speed, wind chill and wind direction. We had to go out and take all the points in the same day so that all the conditions were relatively the same.


Figure 1

Figure 2



Methods

Before we went out into the field we had to finish cleaning up our geodatabases and deploy them to the Juno units for use in ArcPad. The first step was to change the symbology of the features to something that is easily recognizable when we are out collecting the data. I chose bright colored circles that stand out on the map. Another thing we did is add a background map this can used for spatial reference of where we were on campus when point was collected. Once we have our ArcPad file ready to go the next step is putting onto the GPS unit. The first step is to go the extensions tab in ArcMap and turn on ArcPad Data Manager and add the ArcPad tool bar to the window. Then click the first button on the toolbar which is the Get Data for ArcPad button. This opens a wizard. Click
next. Click on the Action Menu again and choose Checkout all Geodatabase layers then specify the folder name that it will create. Change the path to your folder. In the select deployment options window click on create the ArcPad data on this computer now. Then click finish. Once this folder is created we are ready to put it on the Juno unit. First connect the Juno to the computer via USB. Open the Juno menu and copy and paste your folder to the SD card of the unit. You are now ready to collect data.
Once this is ready collecting data is easy. We walked around campus stopping every once in a while to collect a point. The unit collects the GPS location and then we can enter the information for the microclimate which you collect with Kestrel unit. Once all of our data points were collected we plugged the Junos back in and simply copied the data file back to our folder.

Results

Below are the resulting maps corresponding to each microclimate factor. (Figure 3-9) In order to make each map I just went in and changed the field to display.



Figure 3
This map shows the wind speed at the 15 points I collected.

Figure 4
This map shows the temperature at a 2 meter height.

Figure 5
This map shows the dupoint values across campus.

Figure 6
This map shows the humidity values.

Figure 7
This map shows the temperature at the surface.

Figure 8
This map shows the different land cover types where the data points were collected.



Figure 9
This is the attribute table showing all the different data fields for each point collected.





Conclusion

For me this activity went really well. Putting in the time last week to make sure my geodatabase was designed correctly definitely made collecting the data this week go much smoother. I forgot to put a couple factors into my geodatabase but next week we will all have the same databases to work out of. Pre planning and preparation made this weeks activity much easier for sure. When it comes to the data I collected it is interesting to see the variance in temperature and other factors across campus. This week was only on lower campus, so I think there will even bigger differences when we collect points on both upper and lower campus.

Activity 5: Microclimate Geodatabase Construction for deployment to ArcPad

Introduction

This week the class was given the task of creating a geodatabase which will be used with ArcPad in the next week or so. This geodatabase is where we will store our points and information for a microclimate map here on campus. Create a geodatabase can a be a easy task if using ArcMap and ArcCatolog but if you want to do it correctly to make data analysis easier it becomes much more in-depth and difficult. This whole activity is in preparation for a future field activity, the better a this activity goes and the more time and attention to detail is used the easier the future field activity part of the exercise will be. One of the biggest parts of field work is the pre-planning,which is what this exercise focuses on. In part one we explain the reasoning behind a geodatabase and part two the actual construction of one.

Part 1

Before going out into to the field the first that needs to be done is preplanning to make sure you have the proper tools and equipment. Just going into the field with no preparation will never end well. A tool that we will be using in the field to create out microclimate map is a Juno GPS unit ( Figure 1). This unit has ArcMap on it which allows us to upload our geodatabase we created and directly edit and add data such as point, line and polygon features to the geodatabase.

Figure 1
This Trimble Juno GPS unit is what we will be using in the field to collect our data points. Using ArcMap on the Juno and importing our created geodatbases we will collect GPS points.

First before we can upload the geodatabase to the Juno unit we have to create the geodatabase. A geodatabase is a common data storage and management framework in connection with ArcGIS. It is basically a big storage bin for your GIS data where you can easily access and edit the data. The creation of a geodatabase that is designed specifically for a field task such a creating a microclimate map is a time intensive process which requires a lot of thought about what exactly you will be doing in the field and what data you will be collecting. Below you can see the steps that ESRI the creators of ArcGIS suggest when creating a geodatabase (Figure 2). As you can see there are many things to consider.

Figure 2
Suggested steps for database creation from ESRI. All of these should be considered when preplanning for a field mission and creating your geodatabase.

One of the most important aspects of geodatabase design are Domains. ArcHelp gives a very good definition of what these domains are. "Domains are rules that describe the legal values of a field type, providing a method for enforcing data integrity. Attribute domains are used to constrain the values allowed in any particular attribute for a table or feature class. If the features in a feature class or nonspatial objects in a table have been grouped into subtypes, different attribute domains can be assigned to each of the subtypes. A domain is a declaration of acceptable attribute values. Whenever a domain is associated with an attribute field, only the values within that domain are valid for the field. In other words, the field will not accept a value that is not in that domain". To sum up that definition, we use domains to limit what values can be entered for a certain feature which helps to keep the data accurate and cuts down on data entry errors. Take a temperature data set for example. When creating the domain you would set a temperature range of -20 to 100 degrees. If by accident you enter a value outside this range like 1,000 it will not accept the value and ask you to enter an new value. This helps keep the data in reasonable range eliminating outliers or very extreme values that will skew your data.

When creating domains you have a variety of different fields you can sign. These include: short integer, long integer, float, double, text, and date. Below is some explanation of these fields provided by ArcHelp (Figure 3).



Figure 3
These are a few of the field type options for domains. Short integer and float are typically the most popular options. They respectively  allow for either whole number values or fractional values to be entered. These two types as well as text will be used in this assignment. Text lets you enter words such as grass or asphalt for land cover values.

Part 2

For this second part of the exercise I will be showing you how to create a geodatabase and also set domains to customize the database for the specific field work task. There a multiple steps to this process. They are as follows:
1. Preplanning for geodatabase creation and field data collection
2. Creation of a new geodatabase
3.  Setting of domains based on preplanning info
4. Creation of the feature class that will be used to collect data
5. Import the project into ArcMap

Step one is all about thinking about the project at hand. We are creating a microclimate map. A microclimate is a small area that is different from the are surrounding it based on factors like temperature and humidity. These areas can be small like the courtyard area in Philips which is typically warmer and less windy than the areas around the outside of Phillips hall. A large microclimate would be a city where the temperature is usually more moderate and doesn't experience bitter cold like rural areas do.

In order to create a microclimate map many factors need to be taken into consideration. In our case will be looking at temperature (ground level and at 2 meter height), wind speed, wind direction, relative humidity, dew point, and land cover type. All this data will be collected here on the UWEC campus and from which we will create our microclimate map.

For steps 2- 4 I will walk you through step by step in the tutorial below.

Conclusion

Creating a geodatabase itself is not a very challenging task however, paying close attention to detail is the challenging part. If this is not done correctly work in the field will not be successful. This is all part of preplanning for a field mission. If everything has been planned out well before the field portion begins, data collection can and should go smoothly. We will see how well I planned ahead in the design of my geodatabase when we go out to collect data in the next couple of weeks.

Activity 4: Unmanned Aerial System Mission Planning

Introduction

The use of Unmanned Aerial Vhicles (UAV) is and up- and- coming area in the world of technology. UAVs are remotely‐piloted aircraft fitted with sensors that can be used in many civilian applications. Some examples of regional industries interested in UAV technology include: agriculture, for precision agriculture operations; engineering firms, for surveys and inspections of structures such as bridges; mining operations, for volumetric analysis of materials excavated; and insurance companies, for hazard assessment and mitigation strategies when the technology proves applicable. Traditionally imagery of the earth’s surface has been collected through sensors and detectors mounted on a piloted aircraft or satellite. These missions require a large amount of planning, highly trained personnel, sensors, and fuel which make this method very expensive.  In some cases the data collected may not be accurate, or is distorted, which leads to the use of additional resources and limits their applicability. Unmanned Aerial Systems have several advantages over these techniques. UAVs can be deployed rapidly and with little planning compared to traditional missions. Through advances in technology, UAVs can carry many of the same sensors as piloted aircraft and satellites. This technology allows for safer collection methods and also reduces the amount of intense training compared to what would be needed for a piloted aircraft mission. For these reasons the use of UAVs is capable of revolutionizing the geospatial industry.

Part 1: Flight Simulator and Flight Logs

For the first part of this assignment we were to use the RealFlight 7.5 flight simulators we have here one campus. These simulators of for training in preparation for flying R/C planes and miltirotor platforms. I have used this program previous to this assignment as well as flown the real R/C machines and I am very impressed with how realistic the simulator is. As part of this weeks assignment and introduction to Unmanned Aerial System we had to fly 4 different platforms for 30 minutes each. We also were to adjust the flight conditions such as wind speed and direction to see how the platforms handle in various conditions. Below is the log of my four different platforms and flights (Figure 2). There are two flying wing platforms and two multirotor platforms. 

Figure 1
Flight log for time spent using RealFlight Simulator.

The first aircraft I used was the Hexacopter 780 (Figure 2). This is a 6 rotor aircraft with many mode options that make it much easier to fly. This aircraft is equipped with a GPS unit and RTL, self level mode and  loiter mode. The GPS mode allows you to flip a switch and have the aircraft fly along a predetermined flight path. This allows you to fly a very accurate grid pattern which is very important in real life applications. Another part of the GPS is RTL feature mode or return to launch where it will take over in autopilot and fly back to the launch point where it automatically lands itself. The self level mode makes flying easier because it always keeps the aircraft upright and very stable. If you were in acrobat mode you would be able to tilt as far as you wanted in any direction allowing you to do flips which are very difficult to control for novice pilots. Another nice feature of this aircraft is the loiter or altitude hold feature. This allows you to flip a switch and have the aircraft stop at that height. If for some reason you are falling at a very fast rate and can not slow the descent, placing the aircraft in loiter will take over and stop the descent. The altitude hold keeps the aircraft the same height off of the ground as you fly which is very handy when taking pictures and needing them to all be the same resolution. These hexacopter platforms are very stable and smooth operating. The other multirotor platform I flew was the Octocopter 1000. This aricraft was very similar to the hexacoptor. It also is very stable. Both of these platforms have a greater ability to carry instruments and sensors than other smaller platforms. They are also very maneuverable. You can move in any direction with this type of platform unlike a flying wing where you always have to be moving forward. This allows for fitting into tighter areas and getting closer to items when taking pictures. FPV or first person view is another nice feature on this platform. With first person view there is a camera facing forward on the platform that is transmitting a live video feed to a set of video goggles or a laptop screen. This lets you see what is in front of you even if you can not see the aircraft from the ground. Slower speed gives the pilot more time to react when flying these platforms which leads to less crashes. As you can see from my flight logs I had fewer crashed with the multirotor than the flying wing platform. You can also see however that sometimes the autopilot modes don't always work as you want. Knowing how to fly these manually and not rely on autopilot all the time is key to being a good pilot.

There are some drawbacks to these platforms. One is that the more rotors you have the faster the battery dies and the shorter your flights will be. Also there is no glide option with these like there is with the flying wing platforms where you can turn off the rotors to conserve battery and glide on the wind. Another disadvantage is the fact that you can move in any direction. Sometimes if you lose orientation of the aircraft and can't tell what is forward you can end up flying sideways into trees and such.

Figure 2
Hexacopter

Figure 3
Octocopter

The 3rd aircraft I used was the AR-6 Endeavor (Figure 4). This is a flying wing platform. It was extremely maneuverable and flew extremely fast. It didn't have any fancy features like the mulitrotor platforms did so it was completely manually operated. When I turned up the wind speed this plane got tossed around quite a bit and was harder to maneuver which I why I flew into a crane and crashed. The other flying wing aircraft I flew was an Harrier (Figure 5). Like the first aircraft it is very maneuverable, easy to fly and fast. When I increased the wind this aircraft reformed better than the other fixed wing but it still was a bit harder to control than the multirotors were in wind. Platfoms like these are best for when you need to cover a very large area in a short amount of time. They are much faster moving than multirotors and also have the ability to stay in the air much longer. However without all the fancy modes and features that the multirotor platforms have they can be less stable and harder to fly for novice pilots. This instability causes problems in real life too when trying to gather imagery. These platforms are harder to launch than multirotor platforms as well. With a multirotor you can set it anywhere and take off vertically. With flying wing aircraft you have to throw or propel them forward some how to get them into the air. Landing these vehicles is also more of a challenge. I had more trouble with these aircraft as I expected I would. They are much more sensitive to flying conditions as well as movements of the joystick. 

Figure 4 Endeavor

Figure 5 Harrier

   

  

Overall I found the multirotor platforms to be easier to fly and maneuver. From previous experience I figured this would be the case but all of these platforms handles differently so you never know. Adding and adjusting the flight conditions definitely makes flying more challenging. Again no matter what aircraft you are flying you need to know how to manually fly the aircraft. Auto pilot does not always function properly.

Part 2: Scenarios

1.  An atmospheric chemist is looking to place an ozone monitor, and other meteorological instruments onboard a UAS. She wants to put this over Lake Michigan, and would like to have this platform up as long as possible, and out several miles if she can.

For this scenario I would suggest the use of a flying wing platform. You are going to want to use a larger aircraft with a wing span of approx. 2 meters. This larger surface area on the wings will allow for a longer time in the air because of increased uplift on the wings. This increased uplift will also allow for an increased weight limit so the use of multiple sensors on the aircraft should not be a problem. You will want to make sure that there is a breeze of 10 to 15 MPH winds in order to increase the gliding capability and reduce battery use, maximizing time in the air. At times you will not be able to see the aircraft from where you are piloting it, therefore you will want to install a first person view camera on the nose of the aircraft pointed slightly downwards. This will allow you to see what is in front of the aircraft at all times even if line of site from the ground is cut off. In order to do this you will need to install video transmitters with a range of at least 5 miles. In order to ensure that you do not lose control of the aircraft you are also going to want to use a higher frequency and power controller good up to at least 6 miles. As a precaution I would also install a GPS tracker on the plane in case your line of site is cut off and your FPV does not work for some reason. That GPS tracker would allow you to at least direct the plane back to your location or recovery in the event of a crash. For launching the aircraft you will need a bungy cord launching station this plane will be too big to launch by hand. Once the plane is in the air I would advise that you control it from a watercraft on the lake. This increases your chances of being able to see the aircraft at all times and it also reduces the chance of losing signal of the FPV or the aircraft itself. I would advise the installation of an altimeter to aid with staying at a consistent altitude and suggest an altitude of approx. 75 meters. This will give you more accurate meteorological readings because the effects of the large water body will be reduced at that height. It will also give you a more steady wind flow which you can use to create uplift and keep your aircraft in the air longer. Try to keep the nose of the plane into the wind and do not fly across the wind because this could cause the aircraft to role causing loss of control. If you follow these suggestions you should achieve your goal and be able to collect a large amount of data. Based on the above criteria I suggest using a FE RVJet Aerobot. It has a large wing span, is light weight, has a large area for batteries sensors and other payload items and can be quickly set up and deployed. This item is a bit pricey coming in at about 3,000 dollars but it is capable of doing everything you need it to.

2. 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.

For this scenario I would suggest the use of a multirotor platform. You are going to need an aircraft that is capable of hovering in close proximity to power lines and towers and that is also very maneuverable and can fit into tight spaces. It needs to be stable and capable of carrying multiple cameras. You should have a first person view camera mounted to the front to aid in piloting the aircraft. You also will want a camera mounted to a gimbal which you can rotate up down left and right to ensure all needed pictures can be taken. Installing GPS tracker and an altimeter are also highly necessary. The altimeter will allow you do altitude hold which will greatly aid in stability of the aircraft  when taking photos. In order to use these features you are going to half to have autopilot technology on the aircraft. When trying to figure out the best way to get the supplies to the towers I would use the same platform. Using the camera on the gimbal you can take aerial photos to look for efficient routes to the towers. In order to do this I would suggest using the autopilot technology with a flight path planning program. This will assure that the flight path is straight and the resolution of the images is consistent through use of the altimeter. I would suggest flying at an altitude of 100 to 150 meters to get a large area in each photo but also be able to see details like roads and the towers. Based on the above criteria I suggest using a Turbo Ace MATRIX. It gives you this agility and maneuverability needed to inspect the towers and lines while also giving the stability and range to take good quality aerial photos for finding paths to the towers. Priced at around 3,000 dollars this is a very reasonable alternative to what you are currently paying to fly helicopter to inspect your lines. 

    Conclusion

     UAVs are very versatile and the options of what kind you can build are endless. As seen in these two scenarios the uses for UAV technology are ever expanding and very diverse. This whole exercise is both part 1 and 2 are very important when trying to understand UAVs. By seeing the different behaviors of the platforms in the flight simulator and coming up with a list of positives and negatives of each we are able to make educated decisions on the various scenarios we were presented with in part two. This is great activity to get the students to not only learn something new but to be able to apply that knowledge to real life situations. Some of the students may end up working in this field of study once they are done here at UWEC, this exercise is a little taste and a good intro to what they may work with on a day to day basis. 

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