Azimuth & Distance Survey

Introduction

For this exercise surveying using distance and azimuth was implemented to collect point data. A distance azimuth survey starts with a surveyor measuring the distance and azimuth, angular measurement, an object is from the surveyor. This method is a useful technique to know and have ready in case the power runs out on more sophisticated techniques. The UW-Eau Claire campus mall and a nearby street were surveyed collecting point data for street signs, light poles, major trees, bike racks, benches, tables, and garbage cans. These features were chosen for how numerous and spread out around the study area they are. A total of 100 points were collected from three different origins.

Study Area

The area of UWEC was chosen because of its close proximity to those involved in the surveying and for our general knowledge of the known features. UWEC is located along the Chippewa River but the area surveyed is flat with a small decline in elevation near Little Niagara Creek which runs through the campus. A newly constructed campus mall was finished in 2013. The new mall is surrounded by many university buildings. The section of road surveyed runs alongside a university building. Figure 1 below shows an aerial view of UWEC.

Figure 1. UWEC lower campus located along the Chippewa River in Eau Claire, WI

Methods

Points were collected using two different instrument; a compass which determines azimuth and a laser device which determines both slope distance and azimuth. The two different instruments provide a comparative analysis angle to the survey since both determine azimuths. By using both azimuth and distance an accurate survey of the study area was created. Before the points were collected a phenomenon called magnetic declination was investigated.

Magnetic declination is the angle between magnetic north and true north. Magnetic north is the direction the compass will point to while true north is the direction along the Earth’s surface towards the geographic North Pole. It is important to understand the degree of declination because without understanding what it is any survey using a compass without adjusting for magnetic declination will be inaccurate. NOAA has an application that will calculate the degree of declination for any location. For Eau Claire, WI the degree of declination is approximately 1.36 degrees W (negative). It’s negative because the location of Eau Claire is west of the line-of-declination. Anything easy of the line is positive. This means that 1.36 degrees was subtracted from every azimuth collected from the laser and compass.

Using the laser and compass the 100 total points were collected over two days. 50 points from the first location, 25 from the second and third locations respectively. The information was recorded into a field book then transferred to an Excel spreadsheet (figure 2) using six different fields; Distance (meters), Azimuth (angular degree), Compass (azimuth from the compass), Notes (type of feature), X (x coordinate of the surveying point), Y (y coordinate of the surveying point). The surveying point refers to where the surveyor stood to collect the point data.

Figure 2.  A section of the completed spreadsheet with 6 fields and an ID column. 

The X and Y coordinates were found by using AntiMap, a smart phone application which records point data and creates a spreadsheet capable of being imported to Excel. The coordinates for each of the three surveying points were entered into the X and Y fields in the spreadsheet for the points collected from those three surveying points.

Once all 100 points were entered into a spreadsheet with all relevant information ArcMap was used to display the points. First, in ArcMap, a geodatabase was created to store all of the feature layers that would be created. Next, a basemap from USGS was displayed for the area of UW-Eau Claire from 2013. Then the spreadsheet was imported into ArcMap. A model (figure 3) was created to display the data from the spreadsheet as both point and line data. Figures 4 and 5 show the locations of the tools used to display the data.

Figure 3. The model with the two tools is ran twice. Once to get points and lines derived from the laser and again to get points and lines from using the compass. The model can be saved to be edited later with different inputs and outputs.

Figure 4. Location of the Bearing Distance to Line. This takes the distance in meters from the laser and creates a line from the surveyor point to the feature point on the angle of the azimuth from the laser/compass.

Figure 5. Location of the Feature Vertices to Points. Creates a point of a feature from the azimuth and distance recorded by the laser/compass.

The model significantly decreased the amount of time spent finding and using the tools individually. Since it was able to be saved and used later with different inputs it drastically increase productivity.

An issue discovered when running the model was that when there were different X and Y coordinates the model would fail. To remedy this three separate tables were created; one for each surveying point. This did lead to using the model more than expected. An issue that was encountered by previous class members was that the tools would not work correctly unless the X and Y coordinates had six decimal places. This was an easy change and made the tools run perfectly.

The feature layers created from the model were saved to a geodatabase and the WGS 84 projection was used since latitude and longitude was being used instead of meters. This allowed for the points and lines to be displayed correctly on the basemap instead of not even on the map if using a projected coordinate system.

Results

Once the points were displayed with lines from the surveyor point we noticed that this method of collecting point data is fairly accurate. On a small scale map (figure 6) the points seem very accurate lining up well with where the features really are but on a large scale map (figure 7) the points have a certain amount of error.

Figure 6. Small scale map showing the location of the points and azimuth lines derived from the laser. Note how some points fall on nearby buildings showing there is a lack of accuracy in those areas either from the surveyor or from the basemap. 

Figure 7. Large scale map showing the location of the points and azimuth lines derived from the laser. From this scale most of the points appear to be very accurate with minimal error. Most of the inaccurate points are along the large building in the south west portion of the survey area.

When comparing the points from the laser to those collected from the compass there is a noticeable difference in both large and small scale maps. Figure 8 below shows the compass points compared to the laser points.

Figure 8. Large scale map showing the locations of all points derived from the laser and the compass. If both of the tools recorded the same azimuth for each feature there should be no difference in the locations of any of the points on the map. 

Our X and Y coordinates for the three surveying points are very accurate because of how open of an area we surveyed is. Any error associated with the placement of points on the map may be due to the basemap being improperly georeferenced. Even though this would account for a small change in the location it may end up making a large change in the location of points especially those near buildings.

Discussion

Overall this surveying method of using azimuth and distance is very accurate and compared to more modern techniques such as GPS and survey stations which are used by professionals in the field. This method is reliable in all weather conditions and in virtually any type of environment. One main issue with measuring distance that may have accounted for a majority of error is if the laser actually bounced off the feature or if it bounced off something else. Light poles from a distance are hard to hit with the laser but our results of the locations show that the laser was very accurate in recording the distance.

When comparing the compass to the laser we found that the compass routinely differed by a few degrees for each point with very few being within a degree or two of the laser derived azimuth. A reason for this error may be magnetic disturbances but that should have affected both tools equally unless the laser has something built into it that takes that into account. 

Conclusion

Recording point data can be performed many different ways but using azimuth and distance is one of the more reliable methods. Being able to collect data in a way that is quick, simple, and easy to process makes for increased productivity. We were lucky to have warm weather when we collected the data but even if it was snowing we would have been able to perform the survey. This ability to work in multiple conditions allows for azimuth and distance to be a preferred method of collecting points if more sophisticated methods are not available. If we did not use the laser to find distance we could have used a tape measure. The tape measure distance and the compass azimuth would allow for this entire survey to be completed without any battery power required. To perform a more accurate survey multiple measurements of the features should have been taken to find an aggregate azimuth and distance but since we only had one week to perform the survey and process the data the results we achieved are still fairly accurate.

Unmanned Aerial System Mission Planning

Introduction

The goal of this exercise is to improve critical thinking when planning for different scenarios encountered by geographers. Five different scenarios were given with a goal of devising a plan on how to solve the scenarios.  While planning for the scenarios the use of a UAS (Unmanned Aerial System) was highly recommended to be a big factor in the solving process because the scenarios involved an image of the area to be taken. For each scenario a plan was thought through to include: costs, type of UAS, type of sensor, GIS software, time of year and any other factors that were needed to complete the process.  However, because of the inexperience of the class, only the leg work of the scenarios were thought through to give an overview on how to solve the mission. 

Scenario 1

v  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.

Using UAS to survey for desert tortoise burrows is a much quicker and more cost effective way to discover where the burrows are compared to ground based surveys. There are two main options that can provide high quality data for this kind of survey; LiDAR and supervised classification using aerial imagery.

LiDAR can be used for this project because it collects elevation data in the form of a point-cloud. The LiDAR sensor shoots a laser at the ground and as the beam is reflected back it records the elevation it was reflected at. The LiDAR sensor requires a large UAS because of its weight so most rotary propeller UASs are out of the question but some fixed wing options will work such as in figure 1 below





Figure 1. A fixed wing UAV capable of being equipped with a LiDAR sensor.

Once the LiDAR data has been processed a DEM (digital elevation model) will be created. After knowing how deep the tortoise burrows are a base height should be set that is that many feet/inches above the base height of the data. This will create a DEM with the negative elevation representing the tortoise burrows.

This option is costly but if millions of dollars are being spent on ground based surveys it would be well worth it to use a UAS in this fashion. A second option which will most like be much less expensive would be to fly a UAS and to have it take images of the ground and from these images use a supervised classification to automatically pick out where any tortoise burrows may be.

A supervised classification works by having the user select representative areas using reference sources such as high resolution imagery. The software then characterizes the statistical patterns of the representative areas and classifies the image. The use of a multi-band camera makes the classification scheme much more accurate. This is because the camera records data from a scene as individual color values. From these values a spectral signature can be derived. Using this signature, software such as ERDAS Imagine, will select pixels on the image which are within a specified range of the signature creating an image with one color representing a specific feature such as blue for all water.

This will reduce time in discovering tortoise burrows because the burrows have a unique spectral signature. Since the upturned soil will stand out from the ground it will be easy to select the burrow on an image and specify that all pixels with similar spectral signatures should be classified the same.

This process does involve some ground truthing to verify that the classified burrows are actually burrows and not randomly selected pixels on an image that happen to be similar. Having the person classifying the images will be best because they will know the exact area of where the burrows are.  

A camera that captures imagery in multiple bands that would be excellent for this kind of task is the UltraCam shown in figure 2 below. This camera will produce high quality images with the capability to be used in a supervised classification.

Figure 2. UltraCam camera capable of taking images in panchromatic, red, blue, green, and infrared channels.

Scenario 2

v  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.

Instead of using a helicopter and having someone investigate power line issues it would be much safer and more cost effective to use a rotary UAS (unmanned aerial system). The rotary UAS will be able to fly extremely close to the power line without risk of major damage to the pilot or anyone else if it comes in contact with the line. This is because of how the propellers on the UAS are positioned; they allow for a stable flight with the ability to make sharp turns. Figure 3 shows an image of a rotary UAS. Notice how the propellers are evenly distributed around the center of the vehicle. Pictures of any damage can be taken with ease because the rotary UAS is able to hover in place and can provide not only pictures of the damage but real time video of any issues. 

Figure 3. Rotary propeller UAS with six propellers. This UAS is equipped with a camera for video and picture functionality. The six propellers allow for a stable flight resulting in higher quality images.

A major advantage to using a UAS like this is that you can launch and land the vehicle from virtually anywhere. Not only will this rid the need of an airport but it will also eliminate having to waste time waiting for a helicopter to arrive near the power line. Having a helicopter fly close to power lines creates an issue of pilot safety and also the safety of anyone who may be on the ground. Cameras can take amazingly high quality images from a distance but even then you could receive higher quality by using a similar camera mounted onto a rotary UAS and have it fly in and hover much closer to the power line.

A disadvantage to using the UAS is that typically these types of vehicles have less flight time. This is where a helicopter outdoes the UAS. Even though the flight time may be less the cost of a potential injury to anyone involved in surveying is nonexistent with the UAS since the pilot can be stationed almost anywhere.

Scenario 3

v  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.

When examining the task of finding healthy vegetation over an 8000 acre area the cheapest option I can think of would be to download a LANDSAT image for the area then examine the infrared color band. LANDSAT is an abbreviation for Land Remote-Sensing Satellite which is in orbit around the world with an interval rate of 16 days for the newest satellite (LANDSAT 8). What that means is that every 16 days there will be a new image for the same area. LANDSAT has sensors which are able to record light reflectance from the ground similar to what a normal camera would do but it can also record the infrared energy being emitted which can be used for vegetation analysis because the healthier a plant is the more infrared energy it will emit which will be recorded by the sensor. The files downloaded from LANDSAT represent each band the satellite records light in (red, blue, green, infrared, shortwave infrared, etc.). These bands come in black and white TIFF files which are able to be used/opened in virtually any kind of image manipulation software. The TIFF files are black and white because of how the sensor records the color for each band. For anything blue, such as water, the pixels that make up the water will have a higher pixel value than pixels for land. The same principal applies to green objects such as plants and grass and so on for other colors. The infrared band will give higher pixel values to pixels representing objects that emit more infrared radiation than other objects. The infrared band would be opened using any kind of standard image viewing software. The more white an area is the more infrared energy being emitted thus the healthier the vegetation. In figure 4 below you can see that agricultural fields are much healthier and ready to be harvested than other natural areas in the image.   

Figure 4. A LANDSAT infrared image with healthy vegetation appearing as more white. The circled portions of the image show where the healthiest vegetation is located.

This option is completely free as long as you have an internet connection and a way to unzip the downloaded file then be able to view the files. Although this option saves a lot of money it does have a few downfalls. First, since the satellite is on a 16 day interval you won’t be able to have images be taken on demand and even if you find an image for a date you want there is a chance it could be filled with clouds which would distort or even block the ground altogether. Assuming you go with this method of using the LANDSAT images you may run into an even bigger problem which would make you start over completely; satellite failure. This has already happened to the previous LANDSAT 7 satellite. The images taken from LANDSAT 7 would be of similar quality to LANDSAT 8 but they include a large amount of missing pixel data so all of the images produced are virtually useless for any kind of analysis like checking on the health of a pineapple plantation.

A second option would be to attach an infrared camera onto a fixed wing UAS (unmanned aerial system) and have it fly over the plantation recording infrared radiation producing an image which would be very similar to the one produced by LANDSAT. Figure 5 below shows an infrared camera capable of being attached to a UAS. This option of using a UAS will include a cost of a couple thousand dollars, most of which going to infrared sensor and UAS, but the money saved in not having workers check on the entire plantation’s health might be worth it. By using the UAS you would be able to have on demand infrared images taken of the plantation instead of waiting and hoping that the image from LANDSAT is of high quality. 

Figure 5. An infrared scanner capable of being equipped to a UAS to capture scenes in infrared.

To discover the best time to harvest you could examine the infrared images to see when the plantation is mostly white meaning healthy. By using LANDSAT images you have access to images from previous years so you could start to see a trend in when the plantation is at its peak health and ready to be harvested. The LANDSAT images would give a good approximation of time to see this trend but the use of a UAV with an infrared would give a better look at exactly when the plantation is at peak health. Since LANDSAT is free to use it may not be a bad idea to investigate those images and to use the UAV in conjunction.

Scenario 4

v  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.

First many factors need to be accounted for, the agriculture could be also affected by other factors including a drought, bad soil, and over production.  Also the Niger River is known as being one of the most polluted rivers in the World, thus fixing the oil leaking might not lead to wasted agriculture area or crops.  Many questions will need to be asked before starting the project including: what time of the year is it?  This will affect the river water level and the spread of the oil.  If the Niger River water level is high the disperse of the oil leakage will be effecting the crops more.  Also, the description of the crops should be known, are they being harvested at this time or is the season in a transition?  First an image of the area should be taken to find out where the leakage is occurring.  When looking for an oil leakage, areas of black should be identified, the color of oil.  Also the area of black will be most heavy near the leak and then start to spread out as it travels down the river.  If the river is relatively clear, which should also be known before taking the image, the oil leak should be relatively easy to find.  This image can be taken either by an UAV (unmanned aerial vehicle) controlled by a computer or by a balloon, depending on the expense of the equipment and weather.  The disadvantage of using a UAV to take the image is it will be expensive ranging in the thousands, but it will be the easiest and most efficient way to take the image with the range the UAV can have.  A ‘normal’ high quality camera should be fine for finding the oil leak, no special effects on the camera or image should not have to be used.  The advantage of using a balloon to take the image is it will be very cheap and relatively easy to use compared to flying a UAV.  The disadvantage is the balloon may be hard to control with the wind and the range the balloon has compared to the UAV will be less.  However, a third option can be used, to get more accuracy, to determine the oil leakage by looking at vegetation health using a near infrared sensor. The health of the agriculture should be in most danger surrounding the oil leakage then getting healthier when moving away from the leak.  The near infrared image will show the healthy vegetation appearing in white and the unhealthy vegetation converting from gray to black.  Knowing where the agriculture is most unhealthy will help determine the area of oil spill.  This device will be more expensive and will have to be used by an unmanned aerial system because of the risk of losing the sensor. 

Using the UAV to take an image of the Niger River Delta to find the oil leak is the best option in this scenario.  It will on the higher end of the cost but with a serious problem, like an oil leak, the best option should be used.  Also using a near infrared scanner to look at vegetation could also be used along with the UAV.  After these steps are taken and clean images are produced the oil leak should be able to be found and fixed, helping the revenue and stopping the contamination of crops.  Two links that sell UAS; the first is less expensive of less quality and the second being more expensive and having more options of UAVs.

Figure 6. A UAV being placed in the air ready to fly and capture images. This UAS is a fixed wing UAS which enables it fly longer distances but sacrificing sharp turns.

Scenario 5

v  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.

In order to determine the volume of fill removed from an open pit mine a 3 dimensional image of the mine is required to produce a DEM (digital elevation model) of the mine. Obtaining these 3 dimensional images can be done through a Photogrammetry camera systems mounted on a fixed wing UAS. A fixed wing UAS will use a long flight path with the ability to make multiple passes of the same area at a higher rate than a rotary UAS. Photogrammetry camera systems have automated film advance and exposure controls, as well as long continuous rolls of film. Aerial photographs should be taken in continuous sequence with an approximate 60% overlap. This overlap area of adjacent images enables 3 dimensional analysis for extraction of point elevations and contours. Once the images have been shot by the fixed wing UAS, a technique called least squares stereo matching can be used to produce a dense array of x, y, z data. This is commonly called a point cloud. A point cloud is a set of data points displayed in a coordinate system to represent the external surface of an object as shown in figure 7. An interpolation technique such as kriging is used to "smooth" the surface of the data.

Figure 7. A point cloud representation of a section of forest. The spaces in the images show where data was not collected. Since the data is in the form of points there is bound to be numerous gaps in the data. An interpolation technique is used to fill in the gaps using data from the points to estimate the data for the gaps. 

 A DEM image like the one below (figure 8) can then be modeled in ArcGIS to accurately reflect contours of the mine as well as the elevation levels of the mine.

Figure 8. An example of a 3 dimensional DEM with the color gradient representing elevation; the more red the higher, the more blue, the lower.

Since the elevation of the mine will be known from the first DEM created, subsequent missions with the UAS to create new point clouds will reflect elevation changes. From these elevation changes in the mine a volume analysis can be run to determine how much fill has been removed. 

Obtaining an elevation point cloud with a fixed wing UAS equipped with a photogrammetry camera system, is much faster and efficient than manually surveying the mine. UAS missions can be done as often as needed with relative ease, saving your company large amounts of time and ultimately money. This method is not as accurate as using LIDAR data, but it is much cheaper and less taxing on the computer creating the DEMs. If you were to take weekly readings of the mine using LIDAR you would spend a fortune on data collection. I see photogrammetry as your most viable option if you are set on taking weekly volume tests.

Digital Terrain Models

Introduction

After completion of the data collection the next task was to display the points collected as a digital terrain model (DTM)Using ArcMap and ArcScene five different DTMs were created to represent the terrain. Five different forms of interpolation were used to discover which interpolation best represented the snow model that was created in the planter box.

Methodology

Using the data we had five three-dimensional DTMs were created using ArcMap and ArcScene. First the Excel spreadsheet was imported as a layer into ArcMap then the data was displayed using the "Display X,Y data" tool which created a grid showing the 420 points we collected. No projection was used because the points were collected using a custom coordinate system. Below in figure 1 you can see how the points are displayed in ArcMap a program used for mapping data. The points line up perfectly with each other with no missing points inbetween meaning that data collection was accurate as far as the x and y axes go.

Figure 1. data points displayed in ArcMap. There is no projection used with the data points because this is just a model; not a real life interpretation of a landscape.

The file was then exported as a "shapefile" so that it could be used as reference for the other tools which were used to create the DTMs. The five different types of DTMs created were Natural Neighbors, Kriging, Spline, Triangularted Irregular Network (TIN), and Inverse distance weighted (IDW). Each of these surfaces interpolated the data from each of the points to fill in the gaps between the points creating continuous surfaces with no gaps representing the terrain of the snow model that was created earlier.

Natural Neighbors

The algorithm used by the Natural Neighbors interpolation tool finds the closest subset of input samples to a query point and applies weights to them based on proportionate areas to interpolate a value. What this means is that it calculates what the height between the points should be by estimating the slope from point to point and those that surround it. Below is the surface created by the Natural Neighbors tool. This method produced a very natural looking surface with smooth elevation transitions.

Figure 2. Natural Neighbors DTM. This method produced a smooth looking surface with prominent peaks in the data. These peaks are created from the interpolation method and do not accurately represent the model.

Kriging

The next surface was created using the Kriging method. Kriging is based on statistical models that include autocorrelation—that is, the statistical relationships among the measured points. What this means is that it uses the surrounding points to determine the height and does not use weights in this determination. Figure 3 shows the surface created using the kriging method. Notice how there seems to be erroneous data around each of the points. This is caused from the way kriging interpolates data. For the amount of points used and how dynamic the landscape was kriging is not the best method for this.

Figure 3. Kriging DTM. The features appear well on the model but closer inspection shows that each original data point can be seen resulting in a very odd looking high resolution model.

Spline

The spline algorithm makes sure that the surface generated goes through each of the original points and smooths the surface by minimizing the amount of curvature of the created surface. This means that the resulting surface may not as accurately represent sharp changes in elevation. Figure 4 shows the model created from the spline technique. Peaks are smoothed over but not as generalized as kriging. The steepness of the ridges and peaks is not as drastic as in the natural neighbors method. Spline is by far the best representation for this data set resulting in an accurate representation of the model created in the snow.

Figure 4. Spline DTM. This method created the most accurate model for all five of the interpolation methods. 

Tin

A triangulated irregular network is pretty much exactly what the name implies. By using a set of points with elevation the tool creates triangles with each point being the corner of a triangle with no overlapping triangles. Since the points were collected in a normal array the tin looks a little odd but is still functional to represent the elevation. With a tin sharp changes in elevation are a little awkward to read. Figure 5 displays the TIN DTM. The triangles are fairly apparent in the overhead view with changes in elevation being overgeneralized. This over-generalization is because of how the triangles do not overlap.

Figure 5. TIN DTM show sharp changes in elevation and an over-generalization of the landscape. Since the interpolation relied on triangles that do not overlap there is a lot of error associated with this model for the terrain it is describing.

IDW

IDW determines cell values using a linearly weighted combination of a set of sample points. The resulting DTM appears very spiky showing that this method assumes that the variable being mapped decreases in influence with distance from its sampled location. This method resulted in a very inaccurate model. Figure 6 shows that each data point stands out considerably more than the interpolated data. In areas where there should be level surfaces between points there are drops in elevation.

Figure 6. IDW DTM. This DTM is the worst representation of the data that has been created. The data points should not be as apparent as they are in the model.

Discussion

Out of all of the surfaces I would suggest not using IDW, TIN, or Kriging because of the way they displayed the data. It was very easy to see each of the data points on the DTMs created by those methods. I prefer the spline method the most for this type of terrain analysis because of how smooth the final product looked. Natural Neighbors looked great visually but upon further inspection there were some places of inaccuracy (too sharp of elevation changes) that may have been improved upon by performing a second collection of data.

Conclusion

Creating the DTMs required no work between members of the group compared to collecting the data. Using the tools in ArcMap to create the surfaces was very simple and required no assistance from the help files as to how to run the tools. ArcScene allowed the DTMs to be displayed in a 3-D environment to better show the elevation of the surface which was very beneficial in examining the accuracy of the surface. It would be interesting to see how a recollection of data at a higher frequency would benefit (or harm) the resulting DTMs.

Data Collection for a DTM

Introduction

When my team was charged with the task of creating a complex terrain out of snow to be represented as a Digital Terrain Model (DTM) we first brainstormed as to how we were going to accomplish this task. The underlying goal of the assignment was to force us to be able to conceptually capture topographic and locational data by creating a grid system and applying it in the field. The features created include: cliffs, valleys, mountains and ridges.

Methodology

For this project we first sculpted the terrain out of snow.  Thankfully for us we already had an outline for the terrain thanks to the planter box which was full of snow that we used. The first step was to clear the top of the box of snow so that we had a level surface of snow which would serve as the maximum height for the terrain. Next we continued to dig out snow to create the rest of the terrain to represent the features mentioned above. After we sculpted the landscape to our satisfaction we began to lay out a grid of string over the planter box to serve as a Cartesian coordinate system (figure 1.) that we would later use to collect the elevation and location data. In figure 1. below you can see the grid layout over the model. The grid formed 420 intersections which we used for data collection.

Figure 1. 3x3 inch grid over the snow terrain model.

We used a 3inch by 3inch grid to gather an accurate representation of the terrain totaling in 420 total points recorded (30rows by 14 columns). To record the points we used a meter stick and stuck it in the box until it hit snow then we measured the depth of the snow using the intersections of the grid as the location of the measurement. Figure 2 below shows the data collection method. As the depth was measured it was entered directly into an Excel spreadsheet and normalized with an X, Y, and Z, field so that it could be displayed within ArcMap. Figure 3 below shows a screenshot of the data once entered into Excel.

Figure 2. Data collection using a meter stick measureing the depth to the nearest tenth of a centimeter.

Figure 3. A screenshot of the data collected showing the X, Y, and Z (elevation) fields.

Discussion

Communication and cooperation was key in completing the creation and collection of data for the terrain model. Using a grid with more intersections would greatly improve the quantity of the data and most likely the quality of the DTM. None-the-less our data collection was efficient with no errors in the collection and the grid was measured accurately to give us accurate X and Y fields. The only things that could have made our survey more accurate would be to increase the number of points collected as well as using a more accurate form of measuring depth. With a meter stick we measured to tenths of a centimeter but if we could get to hundredths that would increase the portrayal of the depths.

Working in the cold was almost unbearble but in figure 2 above you can see that the person (me) entering data into his laptop is wearing a face mask and wearing gloves while using a pencil to push buttons in order to enter the data without completely freezing. Working in warmer conditions would have made this collection of data a lot more bearable and probably more accurate.

Conclusions

The team worked well together to collect data but I would have liked to have met more with them after the data was collected to ensure that everyone understood the data and why it was collected how it was. We communicated well online and during the collection of data so I believe this will not be an issue. Even though we do not have a "sea-level" elevation what we have is sufficient to be worked with in any kind of geographic information systems (GIS) platform and we can always set a "sea-level" once we begin to symbolize the DTMs. Learning to create an improvised surveying technique is a very useful and interesting skill to have. I would love to perform this exercise more in the future for my own purposes but on a much larger scale.