Assignment #4: Distance Azimuth Survey

Introduction

For this exercise, we were to learn how to do surveying without a GPS.  We were to use a rangefinder to measure slope distance and azimuth and then record points to create our survey.  The Azimuth compass starts at 0 degrees as true north, and then going in a clockwise rotation, 90 degrees is found to be east, 180 degrees as south, and 270 degrees as west.  Slope Distance is more accurate than straight distance when measuring distances along the Earth’s surface as it takes into account the curvature of the Earth in its measurement.  If you know the coordinates of one starting point and are able to take several slope distance and azimuth readings of other stationary objects visible from the starting point, you can accurately map where they are.

Methods

We used a TruPulse 360 Laser Rangefinder.  This device allows you to take a reading of a stationary object and immediately get its slope distance and azimuth from the starting location.

The TruPulse 360 Laser Rangefinder

We proceeded outside to the location of our first starting point, in front of the railing in the center of the stairs in front of the Schofield Administration building.  We took twenty-five readings from this point.  To take a reading, the instrument was held up to the eye and directed at a stationary target.  The crosshairs of the instrument we held in place on the object while the button on top of the device was held down for a couple of seconds.  This captured the distance to the object and displayed it as a digital display on the viewing window of the device.  Next, by pushing a button on the side of the device (the down arrow), twice, the device was switched from taking the distance reading to taking the azimuth reading.  We did this work as a group, so we rotated between one person using the instrument and taking a measurement, another person recording the information in a notebook, and the third person helping the instrument-user keep track of the objects being recorded. 

Zach using the TruPulse at our starting location

We took twenty-five readings at the first location and then moved on.  Owing to the thick layer of snow on the ground, we were forced to take readings of items with enough height to be visible.  The majority of the readings we took were off of trees.  We were able to capture readings from park benches, heating vents, road signs, and light poles as well.  We walked across the way and stood at our second point, at the edge of a railing near the UWEC campus footbridge.  From this position, we took an additional twenty-five readings.  When this was done with, we moved along a path in front of the walking bridge and took our remaining twenty five readings. 

The view from our third location

Once these were collected we returned and copied the readings from our notebook into Excel.  If we had used a laptop or tablet device in the field to record our readings and had been able to directly record them into Excel we could have been able to skip this step.  Anyway, we got our Excel spreadsheet set up.  The next step was to import the Excel spreadsheet into Arc Map.  First we set up a geodatabase.  This was done by going into Arc Map, opening up the Catalog tab (typically found along the right side of the interface) and navigating to the folder where we want to save our work.  Then, by right-clicking on the folder, we choose “Create new file geodatabase.”  This opens the Table to Table dialog box (figure).  For Input Rows, use the folder to find the spreadsheet and select the sheet where the data is kept.  Output Location is already filled in, and the only other thing to do is to provide a name for the imported table in the Output Table box.  Click OK and the table is imported.

Next we need to create a feature to display the numbers visually in Arc Map.  We will use two Tools from the Arc Toolbox: Bearing Distance To Line and Feature Vertices To Points.  Bearing Distance To Line is found in Arc Toolbox under Data Management Tools, Features, Bearing Distance To Line. 

The Bearing Distance To Line Tool and its location in the Arc Toolbox

To use this tool, specify the imported spreadsheet for Input Table by using the dropdown arrow and matching the fields up from the spreadsheet for each of the four boxes likewise.  The Azimuth column goes in the Bearing Field box.  Click OK and the feature is created.

The created feature after running the Bearing Distance To Line Tool

Since what we want to show are the ends of these lines on the map, as they are the objects that we took our readings off of in the field, we need to use the Feature Vertices To Points Tool.  This Tool is found in the same folder in Arc Toolbox as Bearing Distance To Line. 

The Feature Vertices To Points Tool and its location in Arc Toolbox

For Feature Vertices To Points, simply choose the feature created in the previous step for the Input Features box with the dropdown arrow and click OK.  Points are now created at the ends of all the lines.

The Point features created after running the Tool

Finally, our basemap of satellite imagery is added to Arc Map.  The colors of the lines and points can now be changed to stand out better against the basemap.

The finished survey



Discussion and Results

The survey turned out all right, although there were a few problems with it.  First of all, there are some points that are way off, appearing to be in the center of buildings or in the water.  These points could have resulted from not having the rangefinder totally steady when taking measurments.  If we had used a tripod to steady the rangefinder we could have greatly improved the accuracy.  Second, the starting locations on our map appear just slightly off from where they should be, by a matter of a couple of feet.  This is not horrible, but could also have been more accurate.  All in all, this was a good exercise.  We learned how to create a survey of an area without the use of GPS.  If GPS is not available, this method would be very valuable.

Assignment #3: Unmanned Aerial System Mission Planning

Introduction

Unmanned Aerial Systems are used in a variety of scenarios.  In this lab, we have been given five real life scenarios that unmanned aerial systems could be used in.  We are to look at these scenarios and critically think how we would plan our attack by using an unmanned aerial system.  Since these are vague scenarios there are some questions that arise as we are mission planning.  This lab is designed to help familiarize us with one of the most up and coming job markets in technology and to gain knowledge of what kinds of unmanned aerial systems are on the market.

Types of Unmanned Aerial Systems

Multicopters- Multicopter Unmanned Aerial Systems are perfect for tight fit areas like power lines or for a quiet approach.  Multicopters can have two or more wings.  Multicopters are used to fly short times from 10 to 30 minutes in length.  These multicopters can be landed or can take off in a relatively small area.  These are best used in tight spaces, for they can hover, go faster or slower.  Here is an example of a flight being performed by a multicopter: DIY IRIS

Fixed wing- Fixed wing craft are perfect for mapping large areas. They can reach a higher altitude and stay in the air for a longer period of time compared to multicopters. This makes them perfect for terrain modelling or aerial mapping. A downside is that the do require some space for takeoff and landing, whereas multicopters do not.

Lighter than air- Lighter than air craft are similar to balloons and blimps. The main area is a section filled with a gas that is lighter than the surrounding air (like helium). Like multicopters, these are capable of taking off and landing vertically, making them very versatile. They are also usually cheaper than the other two. The biggest problem is the lack of control. Any wind would throw a balloon or blimp type craft off course, limiting the situations they could be used it.

Scenarios

Scenario 1
A military testing range is having problems engaging in conducting its training exercises due to the presence of desert tortoises. They currently spend millions of dollars doing ground based surveys to find their burrows. They want to know if you, as the geographer can find a better solution with UAS.

Questions before Starting:

• How big is the survey area?
• What time of year will the survey be done?
• Where do desert tortoises burrow?
• When are desert tortoises most active?

 

For example, desert tortoises are most active during the morning and evening hours of the spring, when the desert air is coolest (they hibernate during the winter). Locating tortoises on the military testing range would be easiest during these times, as they would be out of their burrows looking for food.

One way of locating the creatures would be to use a gas-powered, fixed wing UAV equipped with a thermal imaging sensor and a video camera. A fixed wing craft would be able to cover more area faster than most rotary craft, and using a gas engine as opposed to an electric would enable the craft to stay in the air longer. Thermal sensors would pick up heat given off by the tortoise while it is outside of its burrow.

One problem that might arise is if the tortoise shell warms from the heat of the desert sun, therefore blending in with the surrounding rocks on a thermal imaging sensor. This is why the time of day would be very important, and the thermal camera would be supplemented with a normal video camera.

The survey area could be cut down by focusing on areas near some kind of vegetation. The animals burrows would have to be somewhat near an area where they could get food, since they do not move very quickly. Beginning the search in this area would be a good start.

When tortoises are spotted, the technician could plot a point using a GIS program, or the UAS flight software. When the craft has finished its flight, we could send out a truck equipped with gear for collecting tortoises to those locations marked, and relocate the animals. Tortoises would not have moved far from the position they were in when the UAS spotted them. Additional reading on tortoises can be found here: Tortoise Information.

A desert tortoise eating local vegetation.

A desert tortoise in its burrow.

Scenario 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.

Questions before Starting:

• How tall are the power lines?
• How far is it between the towers?
• How many miles of power line are we looking at? 2 miles or 30 miles?
• What is the weather like?

 

If the weather is bad, for example if there is any wind or precipitation, the unmanned aerial system will have a harder time operating and not running into a power line.  

Since we don’t know how far we have to plan for, it would be best to imagine that we need to look at a long stretch of power line.  With a long stretch of power line we will need an unmanned aerial system that can fly for a long time.  Gas powered or dual battery UAS systems will be our best bet because it will allow us to view greater amounts of power line and help save the electric company money.  With power lines being close together, we will need a small, versatile UAS system that has a video camera with the option to take pictures.  I think being able to fly the UAS down the power line with a video/still camera would be our best option.  When flying down the power line if you notice a problem you could take a still picture and see what equipment you would need to fix the problem. There are many UAS available, but the best one for this job would look like the 3DR RTF X8 with a GoPro camera attached. You can check out this UAS here and check out the camera here.  

Here is an example of a power line tower that was surveyed by a UAS.

Scenario 3
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.

Questions before Starting:

• What time of year is it?
• Harvest or growing season?
• Is there a history of problems (like parasites, bacteria, fungus, etc.) on this plantation?

For a situation like this, the best thing to do would be to start with the most recent NDVI (normalized difference vegetation index) satellite data of the area. The plantation is too large to start combing the entire area with a small, unmanned craft. Using recent NDVI satellite data, we could see what areas are not as healthy. Then, once we have narrowed down our survey area, we could send out a UAS to gather data on those specific parts of the plantation. With video and infrared devices on board, we could find out why those areas are having trouble (if it is a soil issue, pest issue, etc), and plan accordingly.

An example of the NDVI.

Another, cheaper option would be to use a balloon after analysing the NDVI data. The same type of multispectral cameras could be fitted to a balloon to get images from above the plantation. The problem with this option is that you wouldn’t have as much control over a balloon as you would a fixed wing or rotary device.

A gas powered fixed wing craft may be the best option, as it would be able to cover more area and remain in the air for longer periods of time, as compared to a rotary craft. After analysing the satellite images and narrowing down the area, we could send out a fixed wing craft that would circle the areas in question and gather multispectral data. Here is a company that is doing this exact thing right now.

Scenario 4

An oil pipeline running through the Niger River delta is showing some signs of leaking. This is impacting both agriculture and loss of revenue to the company.

Questions before Starting:

• How many miles of pipeline do we have to monitor?
• How much oil they are losing from the leaking?

There are more ways to start like remotely sensed or LIDAR images.  The first, but most expensive option would be to use LIDAR data.  LIDAR data are images have a closer resolution, have more depth and detail than remote sensing images from LandSat or other satellites.

A good, cheap way would be to use the latest NDVI (normalized difference vegetation index) or LandSat image of the area to see where vegetation is dying or where a leak might be. If the pipeline is in two or more images you can mosaic the images together to be able to look at the full pipeline.

In order to use an UAS for this situation we would have to use a very quiet and undetectable system.  Niger has been through a lot of changes in the past few decades which has led to violent outbreaks.  You can read about Niger and it’s history here.  

Scenario 5

A mining company wants to get a better idea of the volume they remove each week. They don’t have the money for LiDAR, but want to engage in 3D analysis (Hint: look up point cloud)

Questions before Starting:

• How large is the mine?
• How in depth does the photo have to be?

Since they don’t have the money for LiDar they could use DroneMapper to create 3D landscape images.  DroneMapper software can create 3D images from 2D aerial photos.  We could send up a UAV  to take still images of the mining site at the end of each week for the mining company.  Those still images could then be sent in to DroneMapper for the conversion.  Another way would be to have a balloon with a camera mounted on it take the pictures.  An advantage of using a balloon instead of a UAV would be that it would cut down further on cost.  Further information about the DroneMapper service can be found here and information about their image requirements can be found here.

Assignment #2: Development of survey data into a Digital Terrain Model & comparison of model to actual surface and resampling of surface

Introduction

This project was a continuation of the first project.  The idea was to take our data that we had collected and generate a map of the terrain.  There were several options available for how to generate the map, as the grid points we had collected did not provide a complete picture of the elevation; the spaces between the grid points needed to be filled in, and there are several methodologies that can be applied to do this.

Methods

To start, we needed to get our elevation point data into ArcGIS.  The data file that we had created in Microsoft Excel first had to be saved with the extension .dbf (Database File).  This allows it to be imported into ArcMap.  Once in ArcMap, the x, y, and z data had to be interpolated.  Interpolation gives data to the areas between the grid points, known as cells.  Interpolation will make our map have a continuious surface.  ArcMap has several options for how the interpolation is to be done.  The five methods are IDW, Natural Neighbor, Kriging, Spline, and TIN.  We chose to use each of the five methods to generate five separate maps, and then determine which gave the best looking results. 

The IDW Method

Inverse Distance Weighted interpolation, or IDW, draws on grid points that are close to the cell more than on grid points that are far away, as it makes the assumption that the nearer the points are to the cell, the more accurately they can be used to predict the cell’s value.  It does, however, draw on grid points that are farther away than the immediate area when making its calculation.

The map generated by the IDW method.  This map is very choppy, and gives the appearance of many little pointed hills all over the landscape surface, which were not present on the actual landscape surface.

The Natural Neighbor Method

Natural Neighbor interpolation defines a local area around the cell and uses only those grid point values to calculate its cell value. 

The map generated by the Natural Neighbor method.

The Kriging Method

Kriging interpolation, unlike the other methods, examines the z data for the surrounding grid points when making its cell value calculation. 

The map generated by the Kriging method.

The Spline Method

Spline interpolation uses a mathematical function to minimize surface curvature when creating its cell value.  This results in an overall smoother landscape. 

The map generated by the Spline method.  The smoothness is clearly evident.

The TIN Method

TIN interpolation creates triangles out of three grid points and determines cell values based on these triangles. 

The map generated by the TIN method.  This map has a striking visual appearance that separates it from the other methods, owing to its use of triangles.  This map clearly shows the classes of elevation that it generated.  This makes it easy to see how the features relate to one another.  However, it makes the top of the ridge appear to have three little unconnected peaks in the yellow class, when the actual feature had a more continuous area of that elevation.

Discussion and Results

Ultimately, the Spline method produced the best map for our purposes.  IDW and TIN produced maps that did not replicate the actual conditions of the smooth, snow landscape.  The Kriging and Natural Neighbor methods produced maps that were almost identical, but were not as good as the Spline method.  The Spline method produced a map that most represented the actual landscape terrain that we had created.

This project was immensely valuable.  With x,y,z data from any landscape source (including actual life-sized field work) one can employ this methodology to generate a proper map showing the elevation.  

Assignment #1: Creation of Digital Elevation Surface using critical thinking skills and improvised survey techniques

Introduction

The goal of this project was to familiarize ourselves with the method of creating a landscape terrain map. We were instructed to build a small-scale landscape with a number of physical features and then develop a system for taking elevation points on the plot and implement the system to record the elevation data.

Methods

First we had to build the terrain up from scratch.  A garden planter box was to be our study area.  We chose to have the top of the planter act as our baseline.  The box had dirt at the bottom and snow filling it.  We leveled off the snow to our baseline and then sculpted our landscape features.  We were required to have a hill, a ridge, a plain, a depression, and a valley.  From one end of the box to the other are our hill, plain, a rather large mountain with a depression at its top, our valley and finally our ridge.  We are pleased with our efforts.

Our finished landscape.  Note our hill in the bottom left, our plain in the bottom right, our mountain with depression in the center, our valley beyond it, and our ridge feature near the top.

Next we had to devise our method for surveying the landscape.  We decided to make a grid on the surface with 5 centimeter increments.  We marked the grid lines on the edge of the planter in marker.   The plot’s dimensions are 110 cm by 235 cm, so we had 22 marks width-wise and 47 marks lengthwise, each mark spaced five centimeters apart, equaling 1,034 grid points.  The bottom right corner of the box plot was the origin point with x,y coordinates of 0,0.  X values went from 0 on the right to 110 on the left.  Y values went from 0 on the bottom to 235 on the top.  When this was completed, we were faced with the somewhat daunting task of taking the measurements for each of those points.

We began by laying a string across the surface of the landscape at each lengthwise grid line, from 0 cm at the bottom (where the hill is) to 235 cm (the end with the ridge).  This marked a line on the snow surface.  We then began our first method of taking the measurements, which was to hold a meter stick vertically over each grid point and press down into the snow until the stick reached the hard-packed baseline, and then read the depth off of the meter stick.  The data was recorded as x,y,z points, where x and y describe the two dimensional grid point and z describes the height.  

Here we are using our first method for measuring the elevation of our grid points.  This method only employed two team members at a time.  Jeremy, seen in this picture, took the measurement and read it off, while another team member recorded it.

We employed this method to measure the elevation of the hill and plain.  This method would not have been effective for measuring the mountain, so we adopted a new method. This new method involved two meter sticks.  One was held vertically on the edge of the planter at each lengthwise grid line, while the second was held horizontally and extended out to the width-wise grid point.  The elevation of the horizontal meter stick in relation to the vertical meter stick was recorded.  

Here we are using our second method.  This method was much more efficient, and required the use of more of our team members.  Shown here, from left to right, are Blake, recording the data directly into Excel, Cody, holding the vertical meter stick steady, Jeremy, positioning the horizontal meter stick, and Zach, reading and calling out the measurement for Blake.

This method was better in two ways.  It was more precise, as we were not estimating the depth of our baseline with every measurement as we had with the first method, and it was also quicker, as we were able to simply slide the horizontal meter stick along to each new point.

Discussion and Results

The day that we did this work, it was 10 degrees outside and there was a decent freezing wind to contend with.  As a result, we needed to take several breaks from the measuring process to go inside and warm up.  This increased the amount of time that this process took. 

The skills used in this exercise are applicable for use in a real life situation, albeit on a much larger scale and with more accurate tools for measurement.  The use of a grid ensures that points are taken everywhere on the plot and at equal distances, which will help out with the mapping.