Microclimate Geodatabase Construction for deployment to ArcPad

Introduction:

     In this exercise we are tasked with designing a geodatabase that will be used to collected microclimate data across the UW-Eau Claire campus during next weeks class. Creating a geodatabase and adding domains is a very helpful way to ensure that field collection goes as smooth as possible and to reduce time spent correcting entry mistakes afterward. In this exercise I will talk about some basic considerations that need to be examined before going into the field. I will then present a tutorial on how to create a geodatabase using domains. In a future exercise we will actually be going into the field and collecting the microclimate data, exporting the data back into ArcGIS, and creating a map of the data collected.

  

Considerations:

     Before going into the field to collect GPS data it is also a good idea to create a geodatabase. There are many different facors that need to be taken into consideration when creating a geodatabase with domains. First is the different attributes that are going to be collected. Secondly, the precision of the data that is going to be collected. And lastly, the range of values that are expected in the field.

 

Attributes:

     Before going into the field to collect data it is important to decide what data is going to be collected. Since we are going to be collecting microclimate data we will need data for 8 different climate related attributes; temperature at the surface, temperature two meters above the surface, dew point, wind speed, wind direction, wind chill, relative humidity, and ground cover. Figure 1 below is an example of attribute table that built for the Microclimate feature class. Each of the attribute fields will then be filled in with the microclimate data that is collected with the Trimlbe Juno GPS units.

 

Figure 1  Attribute table for the Microclimate feature class.

 

 

 

    

 

Precision:

     Determining how precise the data you are going to collect is very important for reducing computational costs in very large datasets. For our microclimate project there is only going to be small variations in the collected data since the study area is relatively small. Therefore, it is important to collect very precise data. Since I am expecting very little variations throughout campus, we are going to be collecting all temperature related attributes with at least one decimal point. If we were collecting temperature values across a much larger area, say North America, having temperature values with several decimal points would be a waste of storage because we are going to see large variations even when using integer values.

     There are several different field types that can be chosen depending on the precision needed. Figure 2 below is taken from the ArcGIS Resource page. It explains in greater detail the different field types that can be used.

 

 

Figure 2  Different field types that can be chosen for each attribute taken from ArcGIS 10.1 Resources

 

 

 

Range:

     Setting range values for each of the attributes is also very important to ensure data quality. To do this we decide on possible values that could be seen in the field. Since this exercise is taking place on one February day in Wisconsin we can assume the temperature values are going to be somewhere between -30F and 60F. If we were going to be collecting data over the entire year we would have to increase the range to encompass the temperatures we would expect in the summer months (-30F to 120F) These range values are very important because they reduce the likelihood of entering values incorrectly that will lead to errors in the data. For instance, without a range set up for temperature we could easily enter 100 degrees (instead of 10 degrees) which is way higher than it normally would be in March. We can also set range values for humidity that range from 0% to 100%. This prevents us from adding extra digits to the data. You will see in the tutorial below how to set all the range values that we selected for the attributes.

     We can also set coded values to ensure text based attributes are all entered correctly. This can save the user the time of going back through the data and changing text values that may have been misspelled in the field. In the microclimate example we are creating coded values for different ground cover types. Ground cover plays a huge role in climate data. The temperature above snow or ice covered areas is going to be colder than the temperature over blacktopped areas. Figure 3 shows coded values of some common ground cover types we are expected to see around the campus of UW-Eau Claire.

 

Figure 3  Coded values given to ground cover to ensure uniform entry into the geodatabase.

 

 

 

 

 

Part 2: Turorial

 

The video below is a tutorial I created using a screen capturing software. The geodatabase is being created in ArcCatalog 10.2.2 and will be transfered to a Trimble Juno GPS unit to collect the microclimate data. 

 

 

 

UAS Planning Mission

 

Introduction

      Proper planning is very important for any project that takes place in the field, especially when dealing with expensive UAS technologies. Understanding the parameters of the project can allow you to select the most efficient platform which can save the company several thousands of dollars, whereas selecting an incorrect platform can leave you with no useable data and possibly even out of a job. Therefore, this exercise revolves around understanding the properties of different UAS platforms and difference scenarios when each platform should be used. We start of by taking a look at the differences between platforms by using a flight simulator. After the flight simulator is complete we will take a look at a few different scenarios that a geospatial analyst my come across.

 

     There are many different functioning parts to a UAS. The The first part of the UAS is the flying platform itself. This can range anywhere from an RC plane bought on Amazon to a multimillion dollar aircraft similar to the MQ-9 Reaper that is used by the military. Secondly, we need some sensors to collect the data. This can be as simple as a small GoPro camera to Lidar sensors to cameras that collect reflectance outside the visible spectrum. Thirdly, it is nice to have some sort of navigation technology that can be used to autopilot the AUV. Finally, for large scale projects where the UAV will be flown out of site, a streaming camera and screen is needed to keep track of where the UAV actually is in case of an autopilot malfunction. With all these differnt functioning parts UAS can be very expensive, but compared to renting helicopters or airplanes to do fly overs once or twice a year you can now do flyovers once a month or even once a week.

 

 Flight Logs

     The first portion of this exercise involves using the flight simulator Real Flight 7 to fly 4 different UAVs for a minimum of 30 minutes each. This will allow us to become familar with some problems commonly seen in the field. It will also help us become familiar with how different platforms handle and what they can possibly be used for. To start the flight simulations I decided to go with the multi-rotor platforms because I figured they would be easier to handle. After I logged the required time with the mutli-rotors I moved on to the fixed-wing UAVs.

 Multi-rotors

 

     The first UAV I used in the flight simulator was the Explorer 580 quadcopter. It took me a while to get use to how touchy the controls where and I frequently found myself decelerating too fast and smashing into the ground. One thing I noticed about the the multi-rotor platforms is that they became really unstable when decelerating too fast. At one point the quadcopter actually flipped upside down. During calm conditions the multi-rotors basically flew themselves and could stay hovering for a long time without even touching the controller. However, when the wind picked up I noticed both the multi-rotors had difficulty. At one point, with winds upwards of 15 mph, the quadcopter couldn't even fly directly into the wind. It just kept getting taken farther away. At that point I would have had to just land the copter and go pick it up. However, since it was just during a simulation I turned the windspeed back down to 0MPH and was able to fly it back to where I wanted it.

    

 

Figure 1  Explorer 580 from Real Flight 7.0

 

Figure 2  Flight log associated with the Explorer 580

     The Hexacopter 780 handled similarly to the quadcopter. One big difference is that it was more stable in heavy wind conditions. This is due to the added wight of the extra control arms/batteries and the two extra rotors. Since the hexarotor weights a lot more than the quadrotor it is going to have a smaller batter life, but it will also being able to handle a larger payload. Therefore, if a project needs a few different sensors that weight quite a bit I would suggest the hexarotor or octorotor over just a quadrotor.

Figure 3  Example of an average hexacopter.

Figure 4  Flight log assocated with the Hexacopter 780 in Real Flight 7.0

     Overall, the acceleration upward and decleration of both multi-rotors was around 20-30 feet per second and the maximum horizontal speed was right around 30 MPH. With that said, it is obvious that the multi-rotor platforms are good in situations where manueverability and accuracy are key, such as small plots of land. Multi-rotors can also be useful for making low altitude loops around objects, such as buildings, to create a really high quality 3D image.

Fixed-wing

     Fixed-wing UAVs are significantly different than multi-rotor UAVs. They have a much higher maximum speed and can change altitude a lot more quickly. Because they are so much faster they are better suited for projects that have large expanses of land to be covered in a short period of time. Fixed-wing UAVs have to ability to be constructed very large, like the MQ-9 Reaper, which means they have the ability to hold very large payloads, such as LiDar sensors, which are becoming increasingly popular.

Figure 5  L-39 Combat fixed-wind UAV

Figure 6  Flight log of the L-39 Combat

     One negitive aspect of fixed-wing UAVs is that it seems like environmental conditions have a much large impact on the flight than multi-rotors. It is much more difficult to keep the fixed-wind aircrafts parallel to the ground, which would mean possibly having poorly collected data. Another bad thing about fixed-wings is that they need a runway to take off and land. This can make it make using a fixed-wing impossible in certain circumstances.

Figure 7  Photo of real life MQ-9 Reaper

Figure 8  Flight log of the MQ-9 Reaper

 

 

    

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.

     The main consideration to take into account for this project is the overall area that needs to be covered. Since we are looking to cover large expanses over Lake Michigan it will be beneficial to use a fast flying fixed-wind UAV. Another important thing to consider is the payload size. Multi-rotors are better at carrying heavier payloads because they have more engines and are more stable. However, the two devices that are to be added, an ozone monitor similar to the one seen in Figure X and a weather station similar to the Kestrel seen in Figure X, are very lightweight. Since the UAV will be flown out of sight, a nose camera with direct streaming capabilities must be installed on the front of the craft. Additionally, by installing a GPS unit you could set a pre-recorded path that the UAV would travel using autopilot.

 

 

 

 

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 the most important aspect of the project is the manueverability of the UAV. In order to determine whether everything is in operational order a very stable UAV is needed to get as close as possible to the power equipment and take high quality pictures/video. Therefore, I would have to recommend a multi-rotor platform with an attached HD camera. Since some of the towers are fairly tall the system might require a streaming nose camera that would be used to navigate as close as possible to the tower.

     I feel the best way to check the towers and certain areas of lines would be to have a team drive a vehicle to each tower and then deploy the multi-rotor UAV to collect the data. After the data has been collected they will land the UAV, load it into the vehicle, and drive to the next location. This will reduce the amount of flight time and battery power needed to determine the functionality of the towers/lines. Since multi-rotors can be very small in size they are able to be deployed basically anywhere, which will save the cost of having to rent full size helicopters to check out the lines/towers.

    

 

Conclusion

     In conclusion, there are many different considerations that need to be taken into account when determining which UAS platform to use during a project. One thing is for sure, UAS systems are much more cost effective than manned aerial systems and provide higher quality results than satellite imagery. Therefore, any project manager should seriously consider using UAS systems over any other data collection platform. 

 

Development of a Field Navigation Map

Introduction

     In this exercise we will be creating two different maps that will be used in navigating throughout the Priory in a future exercise. The Priory is located three miles south of the University of Wisconsin- Eau Claire along Interstate 94 on Priory Road (Figure 1). Formerly St. Bedes Monestary, the Priory was purchased by the UW-Eau Claire Foundation in 2011 and is currently being leased to the University of Wisconsin- Eau Claire for expaneded educational purposes. The Priory consists of 3 buildings totaling around 80,000 square feet of space on a 112 acre forested lot. The land is heavily wooded and contains a combination of flat terrain along with very steep ridges with a slope up to 63 degrees.

Figure 1  The Priory is found south of the city of Eau Claire, WI.

 

Methods

     An important part in navigating in the field is knowing your pace count. A pace is defined as the length of two strides by an individual. To determine pace count we set up a 100 meter stretch of sidewalk outside Phillips Hall, using a normal stride we counted how many times our right foot hit the ground throughout the 100 meters. My pace count is 67 paces per 100 meters, or 1.49 meters per pace. Knowing this information will provide a fairly accurate method for measuring distance traveled from one point to another in the field.

 

      The next step is determing what data to include on the maps. Although there was a lot of data provided in the Priory Geodatabase, I decided to gather data from outside sources. The first map I created (Figure 4

) contains the boundary of the Priory and a three inch aerial imagery provided by the City of Eau Claire. This map is projected with a UTM Zone 15 projection and contains a grid spaced at 50 meter intervals. The UTM coordinate system is a projected coordinate system that breaks the earth into 60 zones (Figure 2). The UTM coordinate system is designed to minimize distortion in the selected zone. In this case, the Priory is located within zone 15.

Figure 2  Universal Transverse Mercator (UTM) zones throughout the United States.

      The second map i created was a map showing the slope of the landscape throughout the Priory (Figure 5). Although there was a DEM available in the Priory Geodatabase, I found that the resolution was too coarse to calculate accurate slopes so I decided to use the much more accurate DEM created by the City of Eau Claire using LIDAR data. I then ran the Slope Tool, found in the Spatial Analyst Toolbox, to get an accurate map of the slope within the Priory. I used the same UTM Zone 15 projection as the aerial map to keep both maps as similar as possible. I then added a grid in decimal degrees which will aid in locating objects that have a geographic coordinate system, which is basically an angular measurement from the equator or prime meridean (Figure 3).

 

Figure 3  Illustration showing how decimal degrees are calculated.

                          

 

 

Results

      

Figure 4 Navigation map of the Priory using 3 inch aerial imagery and a projected UTM Zone 15 coordinate system.

Figure 5 Navigation map of the Priory using slope calculated from LIDAR data, provided by the City of Eau Claire,
using a decimal degree grid and a UTM Zone 15 projection.

Discussion

     I believe the two maps I created above will be sufficient for any future navigation exercise we partake in. I believe that less is more when in comes to map creation. Adding a bunch of different data to a map can cause it to become cluttered and difficult to read. With that said, I believe that only two different sets of data are needed to navigate through any terrain. The first set of data is a map with an aerial image. This map shows the overall landscape of the area. It provides information about vegetation and other physical features, such as rivers or buildings, that are present. The next piece of information that is necessary is slope. A map showing the slope of a landscape can be useful in determining the easiest way to navigate over a steep ridge or around a steep valley.
    
     I am a little worried that my maps will not print very well and will therefore be difficult to read while in the field. I feel as though adding more information to the maps, such as contour lines, would only increase the likelihood of the maps appearing too cluttered and messy. In reality, all a person should really need in navigating is the angle of the sun and a compass, so an aerial map and a slope map should be enough information to correctly navigate through the Priory.

Conclusion

      In conclusion, one must take into account several different factors when creating a navigation map. It is important to select a proper projection to eliminate major sources of distortion. It is important to have a detailed grid that will aid in keeping correct distance/direction. It is also important to keep the map uncluttered so it can easily be read while in the field. As previously stated, I have some experience using a compass and paces for navigating from my time in Boy Scouts so I look forward to seeing how much of that information I retained over the years.

Visualizing and Refining Terrain Surface

 

 

Introduction

      The goal of this exercise is to create 3D terrain models, of the landscape created in exercise 1, using various interpolation tools found in ArcMap. The interpolation methods used in this exercise include IDW, Kriging, Natural Neighbor, and Spline. We also use the elevation values of the sampled points to create a Triangulated Irregular Network (TIN). Based on the 3D terrain models we created we are able to determine whether or not our sampling technique was sufficient to produce accurate terrains.

 

     After our original 3D terrains are created we are tasked with determining which areas, if any, need to be resampled to increase the accuracy of the terrain model. In many cases, areas with great variability in elevation will need more sampling points than areas that are relatively flat.

Methods

      We being the process of creating 3D terrains of our landscape by adding the tabular data, from exercise 1, into ArcMap using the "Creat Feature Class from XY Table" feature. This tool creates a point feature class where each point is given its elevation value based on its xy locatioin. Figure 1 below shows the xy coordinate grid that will be used in the interpolation process to great the 3D terrains.

 

Figure 1.  XY grid based on the 10 cm by 10 cm sampling technique used to aquire elevation values.

 

 



     After the point grid was created we used different tools in the Raster Conversion toolset to create 3D terrains of the surface. In this lab we used IDW, Kriging, Natural Neighbor, Spline, and TIN interpolation methods to create 3D terrain surfaces.   

 

 

     The first interpolation method used to create a 3D surface was the IDW, or inverse distance weighted method (Figure 2). IDW calculates elevation values of cells based on the elevation of surrounding elevation values. IDW uses the assumption that points closer will have more of an influence in determining interpolated elevation values than cells farther away. Overall, the IDW model created a terrain with a lot of dimples and does not look like our original landscape.

 

Figure 2.  3D terrain model created using the IDW method.

 

 

 

 

 

 

     The second interpolation method used was the Kriging Method (Figure 3). The Kriging method is a predictive model that uses distance or direction between sample points to create a spatial correlation, which will then be used to estimate elevation.

Figure 3.  3D terrain model created using the Kriging method of raster interpolation.

 

 

 

 

     Natural Neighbor (Figure 4) uses a weighted average of proportionate elevation values of the nearest known points to create a 3D terrain. The interpolated elevation values have a range found between the sampled points, and therefore cannot replicate peaks or valleys that are not already represented by sample inputs.

Figure 4.  3D terrain model created using the Natural Neighbor method of raster interpolation.

 

     Spline uses a mathematical equation to create a surface that passes directly through the exact value of each known point. This equation is aimed at minimizing the curvature of the surface which leads to the smoothest surface of all the interpolation methods. Since our landscape has very smooth features the spline model seems to best replicate the real life landscape.

 

Figure 5.  3D model created using the Spline method of raster interpolation.

 

     Triangulated Irregular Networks are a vector based surface model. TINs take elevation values of known data points and create a series of triangles to create a 3D surface (Figure 6). 

 

 

 

Figure 6.  Triangulated Irregular Network created using the elevation of each point in the sampled grid.

 

     After each of the 3D terrains were created we noticed certain areas that were not modelled accurately. Therefore, we resampled the areas that were not modelled correctly (Figure 7). Instead of the original 10 cm by 10 cm grid we resampled the area with a 5 cm by 5 cm grid to add elevation points to create a more accurate terrain model (Figure 8).

Figure 7.  Resampling the mouth of the valley. This area was resampled using 5 cm by 5 cm grid.

 

  

Figure 8.  The feature class created showing the area that was resampled using the 5 cm by 5 cm grid.

 

 

 

     After the area was resampled we used the same technique described above to import the xy table into ArcMap. We then ran the Spline interpolation tool to create a new, more accurate 3D terrain model of the landscape (Figure 9). Although this model is not exactly identical to the original landscape it is the most accurate.

 

Figure 9.  3D terrain model created using the Spline method after the landscape was resampled.

 

Discussion

     After the original sampling technique was turned into 3D models it was clear that some features were not interpolated correctly due to a lack of data points. Therefore, it was necessary to add more sampling points to those specific areas to ensure a more accurate 3D model. The areas that needed the most sampling points were areas that had large variability in elevation while more gradually changing areas did not require additional sampling points to be modelled accurately.

     In addition to proper sampling of an area, several different user-defined options are available for each interpolation technique. For instance, in each of the interpolation methods we are able to change the output cell size. I believe changing the output cell size would remove the dimples found throughout the IDW model and in the valley of Spline model. For IDW and Kriging we are able to set how each elevation value will be weighted, which will also affect the output terrain.

     After the area was resampled we chose to remodel the landscape using the Spline interpolation. This method was chosen because it creates the smoothest surface, which represents the smoothness of the actual landscape the best. If our landscape had more angular featues it may have been necesary to choose a different interpolation method.

 

 

Conclusion



     In conclusion, the most important factor in determining the accuracy of the terrain model was choosing an appropriate grid to sample from. If our landscape had more variability in the features we would have had to use a smaller grid to capture all of the features. However, The 10 cm by 10 cm sampling grid did a nice job capturing all of the features in the landscape, except a small area at the mouth of the valley. Since this area had the largest variation in elevation we had to resample using a small 5 cm by 5cm grid. This change allowed the valley to be modelled accurately enough for this project.

Creating a Terrain Surface

Introduction

     Many different geographic projects involve collecting survey data in the field. In this lab we created a 3D landscape in the snow, set up a coordinate system, and surveyed the area to collect elevation values across the landscape. Although this exercise was done on a very small scale, a similar approach can be used to create terrain surfaces of much larger landscapes.

Methods

     To begin this exercise, we created a 3 dimensional landscape in the planter boxes, measuring approximately 1.2 meters by 2.36 meters, in the courtyard of Phillips Hall. The landscape was required to contain a ridge, a hill, a depression, a valley, and a plain (Figure 1).

Figure 1  Planter box with the landscape created out of snow.

 

 

 

     Next, we set up a grid system to collect the elevation values. Since the area was fairly small and the landscape contained gradually changing features we set up a grid of 10 cm by 10 cm. This ensured a detailed terrain (Figure 2).

Figure 2  Coordinate system created with sting in 10 cm intervals.

 

 

     Since the edges of the planter box were higher than the landscape itself we set the datum to the height of the edges and measured down to the landscape to get elevation values (Figure 3). This led to negative elevation values (Figure 4).

Figure 3  Measuring the depth to the landscape from the datum (edge of the planter box).

 

 

Results

 

     Table 1 shows the elevation values that were measured from the top of the planter box. Since the landscape was below the datum all the elevation values are negative.

Table 1  Elevation values that were collected at each 10 cm interval.

Discussion

     Overall, our landscape was pretty flat with a few gradually changing formations. Since there was not a lot of complexity in the landscape we were able to use regular intervals of 10 cm to attain an accurate surface model. If the landscape had areas with significant elevation changes we would have had to increase the spatial resolution of the grid to collect more data points.

Conclusion

     In conclusion, this exercise provided a set of basic skills that will be the foundation for this class. It helped us think rationally about the task at hand and what was required before we actually went out into the field. This is very important in all field projects. If you can conceptualize the project before going into the field one can prepare for different problems they might come across, which will lead to better quality data.