Exercise 4: Distance Azimuth

Introduction

In the past few decades, new technologies such as satellites and GPS units have changed the way that geographical measurements and surveys have been taken. These technologies are crucial to many parts of our everyday lives, such as traveling to a new destination and relying on a smart phone for directions.  Although these technologies have changed the way that the modern world runs, they are simply electronics that can be unreliable and troublesome. Because of this fact, this exercise involves locating features using field methods that have been utilized for centuries. As long as the exact location of a reference point is known, accurate locations of features can be calculated using only simple distance and directional bearing measurements. This exercise incorporates old world measuring techniques with new world mapping technology to accurately locate features.

Methods

The first task in this exercise was locating a study area. An ideal location for this exercise needed to contain at least 100 unique features that could be measured for distance and azimuth. The first location that we came up with was a park since they contain many trees, light poles, benches, tables, and other features. It was also important that this location was close to campus since the winter temperatures are extremely cold in Eau Claire and we did not necessarily want to be outside for longer than we needed to be. We decided upon Randall Park (Figure 1) within the student housing area of Eau Claire. This park was a simple rectangle plot which made it very easy to decide on reference points for the measurements to be made. 

Figure 1. Randall Park is a rectangular city park located in the student housing area just north of the University of Wisconsin-Eau Claire. Close proximity to campus, many features and a simple shape made this park a great candidate for this survey.

Since this exercise involved directional bearing measurements, it is important to know the magnetic declination of the study area. Magnetic declination is the angle on the horizontal plane between magnetic north and true north. Magnetic north is the direction in which a compass points north, and true north is the direction along a meridian towards the North Pole. Magnetic declination is only about 1.07 degrees West in Eau Claire, which is insignificant due to the small scope of this exercise. This declination has such a small effect on the measurements that it is not necessary to calibrate the equipment before measuring azimuth. 

Figure 2. This figure displays the amounts of magnetic declination across the continental United States. Magnetic declination varies not only over distance but also time. The movement of the Earth's inner core causes the magnetic field to gradually shift positions and even completely reverse every several thousand years. 

In order to collect the data quickly and efficiently, we set up tables on notebook paper containing columns for feature numbers, coordinates, distance in meters, and azimuth in degrees. We also collected attribute data to distinguish each feature as a light pole or a bench or a tree, but later during the exercise we realized we could not use this data in such a way.

The instruments utilized in this exercise include a laser rangefinder and a two part radio rangefinder (Figure 3). A compass was also brought along for the azimuth measurements but the laser rangefinder proved to be much quicker and easier to use during data collection.

Figure 3. From left to right: laser rangefinder, compass, two part radio rangefinder. The compass collects azimuth measurements, the radio rangefinder collects distance measurements, and the laser rangefinder can collect both simultaneously.

Since our group consisted of only two members, we decided to have Nathan stand near each feature with the radio rangefinder receiver (Figure 4). Standing at the reference point, I would then activate the device and shout the measurements to Nathan who recorded the numbers in the table. Next, I viewed the location with the laser rangefinder in order to find the azimuth measurement and shouted those numbers back to him as well (Figure 5). To complete measurements for 100 features, we collected data from 3 different reference points in the park in a little over an hour's time. 

After the first few measurements, we realized that the two part radio rangefinder had a maximum range of about 40 meters, so the laser rangefinder was used for any distances which required it. 

Figure 4. Nathan holding the receiver of the two part radio rangefinder. This method was the most accurate but was only useful for measurements under 40 meters in distance.

Figure 5. Me using the laser rangefinder to collect data. This device allowed us to collect both distance and azimuth data quickly and efficiently.

All of the measurements were recorded into the notebook tables (Figure 6) before being entered into a digital spreadsheet (Figure 7). The information in the tables were recorded in separate columns so that ArcGIS software could seamlessly access the information. 

Figure 6. Each page contained data for a separate reference point so that the measurements didn't end up in an incorrect location.

Figure 7. This image displays a portion of the completed data table. The X and Y coordinate fields contain the locations of the three reference points, which were found in ArcMap after data collection.

With the spreadsheets completed and formatted correctly they were imported into a file geodatabase made specifically for this exercise. The data layer was immediately set to the WGS 1984 Geographic Coordinate System since the data was formatted for lat/long units. An aerial imagery basemap was added in order to properly visualize the measurements from the survey. 

The first tool that was used in this exercise was the Distance Bearing to Line tool. This tool creates line data based on distance, azimuth, and XY coordinates from the reference point (Figure 8), all found from within the imported table. The result is a series of lines originating from each reference point to the feature locations around them. The next tool used was Feature Vertices to Points, which simply placed a point at the end of each line created from the previous tool (Figure 9). Theoretically, these points should be placed at the exact location of each of the features that we surveyed. 

Figure 8. When finding magnetic bearing or azimuth, magnetic north is displayed as 0 degrees. The exact location of a feature can then be found by examining the distance and bearing from the reference point in relation to magnetic north. 

Figure 9. This aerial image shows the reference points and the corresponding feature locations from the survey. To accurately cover the most features, we divided the survey into three parts from three reference points. 

Discussion

A problem that we ran into during data collection was that the two part radio rangefinder did not operate correctly for any distances beyond 40 meters. This made data collection more difficult because it was very quick for us to have Nathan hold the receiver at each feature while I found the distance from the reference point. The radio rangefinders worked great even through thick brush, which was very common in the park between the features and reference points. Some features were too far for the radio rangefinders but too thin to target with the laser rangefinder, so Nathan would stand at the feature and I would measure him instead. Even this method proved difficult as the snow was 3 feet deep, making it challenging for Nathan to reach each feature. 

One point for this exercise that should be discussed is the importance of accuracy for the reference points. It was crucial to locate reference points that would be visible from aerial photographs. For this reason, we chose to use the intersections of the outer sidewalks as the reference points. After surveying from two of the corners of the park, we decided that we could collect the most features from the center of the park, and it would also be easy to locate the area in an aerial image as well. Once the data layer was set to the WGS 1984 Geographic Coordinate System, the reference points were placed and their locations were recorded to six decimal places. This accuracy was absolutely necessary because a simple difference of .1 decimal degrees meant a land difference of about 8 km. 

As visible from Figure 9, some of the feature locations reach out of the park and into the middle of Third Street. This occurrence can not be explained, but it may have to do with problems within the relationship of the basemap and the coordinate system. It was quite difficult to steady the laser rangefinder due to gusts of wind and shaking hands, and this fact may contribute to the inaccuracy of the feature locations as well. A solution to this problem would have been to utilize a tripod during data collection. 

The last problem we ran into during the analysis was that the description field somehow influenced the placement of the distance-azimuth line features. We had included nominal data that separated each feature as a light pole or tree or bench, but when we included this information in the tools it completely changed the locations. Because of this problem, we decided to not include the nominal data into the final analysis. 

Conclusion

While the use of a GPS would have made this type of data collection much quicker, this exercise proves that collecting accurate locational data can be done without needing advanced satellites and equipment, and can ideally be done with only a compass and tape measurer. In a dense forest, it becomes difficult for a GPS unit to receive signals from a satellite. By using the distance azimuth method, the measurements are as accurate as you make them, and cannot be influenced by any interference. 

Exercise 3: UAS Mission Planning

Introduction

One of the most important aspects of unmanned aerial systems mission planning is the ability to assess the situation you have been given, develop a thorough plan, and communicate it to individuals and businesses that need to understand the process by which you will provide a solution for them.

• The mission plan should maximize productivity without endangering operators or equipment.
• In order to utilize the proper equipment, the mission plan should taken into account, weather, landscape, potential hazards, and the scope of the solution needed for clients.

Unmanned aerial systems (UAS) are typically divided into two broad categories, fixed-wing and rotary. In order to choose the correct UAS, you must understand their capabilities and limitations.

Fixed-Wing

A fixed-wing UAS (as seen in Figure 1) is capable of flight using wings that generate lift caused by the forward air-speed of the vehicle.

Figure 1: This picture provides an example of the size of a fixed-wing UAS. This particular model takes flight after being physically released by hand into the wind.

Flight Time: up to 26 hours              Speed: up to 175 mph              Payload: up to 350 lb.     

   

        Capabilities                                                                 Limitations

good for high altitude remote sensing                        limited ability to turn

long flight duration                                                     may require runway to launch or land
                                              
greater carrying capacity                                            inability to hover

high speed for greater coverage area                          limited close-up inspection capability   

Rotary

A rotary-wing UAS (as seen in Figure 2) uses lift generated by wings called rotor-blades (propellers) to revolve around a mast.

 

Figure 2: This picture provides an example of the size of a rotary wing UAS. For this particular model, four propellors revolve around masts propelling it into the air

Flight Time: up to 3 hours            Speed: up to 37 mph (approx.)            Payload: up to 75 lb.

       Capabilities                                                                        Limitations


vertical capability to hover and "stare"                                limited flight time

agile enough to come within feet of visual target                 limited payload

deliver payloads with precision                                           low operating speed

allows perfect camera pictures and angles                                               

each propeller can operate independently

not as effected by side-winds

good for facility inspections                       

more portable

vertical launch doesn't require runway

Mission Scenarios

This activity presents the following five scenarios. For each scenario we will provide a recommendation for how to configure the UAS and carry out a mission that will provide the required information to the concerned party.

Mission 1: Operation Desert Tortoise
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.

Mission 2: Operation Power Tower
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 things from the closest airport. 

Mission 3: Operation Healthy Pineapple
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.

Mission 4: Operation Leaky Pipe
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.

Mission 5: Operation Earth Removal
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.

Mission Plans

For each of the five mission scenarios listed above, a mission plan has been developed. Each mission number and name will link back to the mission scenario for your review of the mission scenario.

Mission 1: Operation Desert Tortoise

In order to ascertain the needs of this mission we analyzed the following aspects:

1. The behavioral patterns of the desert tortoise and the geologic composition of their habitat 
2. The area coverage required for the mission 
3. The cost of current measures used to locate desert tortoises
4. The weather patterns affecting data collection
5. The equipment necessary to locate desert tortoises

Behavioral Patterns of the Desert Tortoise

Geologic Composition
The desert tortoise spends most of its life in burrows/shelters (as seen if figures 4-6) in order to regulate body temperature and reduce water loss. Sandy loam soils are the preferred location for these burrows/shelters due to their high resistance to flooding and high water holding capacity compared to other sandy soils. 

Figure 3: This is a pyramid explaining the different categorization of soils based on the quantity of silt, sand and clay it is composed of (states as a percentage). Sandy loam soil (outlined in red) consists of less than 7% clay, less than 50% silt, and between 43 and 50% sand. This soil is the preferred location for desert tortoise burrows.

Also, they are primarily found on the steep, rocky slopes of hillsides. The slopes may be littered with granitic or volcanic boulders and are often covered with dense vegetation. The palo verde-saguaro cactus is the most frequently occupied habitat, although some tortoises are found in oak woodlands and dense stands of bunch grass.

Vegetation
In addition, their burrows tend to be located in areas containing wildflowers such as wishbone bushes, lotus, loco weeds, spurges, blazing stars, lupines, Indian wheat, forget-me-nots, desert dandelions, gilias, phacelias, coreopsis, and many other species. They also eat annual and perennial grasses and fresh pads and buds of some species of cactus. They do not eat shrubs such as creosote bush and burro bush. 

Shelter Types
Shelters for the desert tortoise can be categorized into three different types based on the resources present on the landscape and the current needs of the tortoise. These three types are burrows, rock shelters, and pallets (as seen in Figures 4-6).

Burrow

Figure 4: Built into the side of a rocky slope, this burrow represents a "permanent" shelter for the desert tortoise used in all seasons. A burrow is typically between 2.5 and 10 feet in length.

    

Pallet 

Figure 5: This is an example of a pallet which equates to a "shallow" burrow that just barely covers the shell of the tortoise in spring, summer and fall. These temporary burrows or pallets use whatever cover is available in an area and can be fragile. They may be used for shelter for a few days while a tortoise is foraging in a particular area. A temporary burrow usually lasts from a few weeks to a season and disintegrates.

Rock Shelter

Figure 6: This is a rock shelter due to the composition of the material overhead as opposed to the sand/rock slope of the burrow in Figure 3. This is also a more permanent shelter for a desert tortoise.

Each tortoise usually has more than one burrow. In fact, desert tortoises may have between 5 and 25 burrows per year. The number of burrows the tortoise uses may depend on age and sex, as well as on the season. When burrows are constructed in soil, they are the size and shape of the tortoise -- half moon for the roof and flat on the bottom. Small tortoises have small burrows and large tortoises have large burrows.

Relevant Observations for Mission Planning
Based on these observations regarding the behavioral patterns of the desert tortoise our mission plan to locate desert tortoises will take into account the following geological and geographical features:

• Sandy Loam as the ideal soil for desert tortoise burrows
• The presence of vegetation such as grasses, wildflowers, and cacti as essential to the location of desert burrows due to dietary needs
• Tendency for shelters to be located along rocky slopes
• Variation of desert tortoise shelters into three categories - burrows, pallets and rock shelters

Coverage Area Required for Mission


While we have not been given information about the size of the military testing facility, based on the nature of operations at such a site, it is safe to assume a wide coverage area. Collecting data from a large circumferential area will require an unmanned aerial system with long flight range. It will also be preferential for the UAS to be capable of high speeds to complete a mission of this magnitude in a reasonable amount of time.

With a coverage area of this scope, it is important to make use of ground control points. Ground control points are points on the surface of known location. These locations are normally found by measuring the coordinates using a GPS. These points will allow us to geo-reference the image during post mission analysis.

Cost of Current Methods for Locating Desert Tortoises

From the information we have been supplied, the cost of locating desert tortoises currently is in the millions of dollars. Every time military testing takes place, data must be collected and desert tortoise location determined. Due to the high costs associated with such an endeavor options such as LiDAR remote sensing technology and high resolution photography equipment should be considered if additional precision can be gained through their use.

Weather Patterns Affecting Data Collection

Every time that military testing takes place, it is required that the location of all desert tortoises be determined. Because desert tortoises will have between 5 and 25 burrows that, potentially, vary from year to year, data collection will take place on a regular basis. As such, weather is not a long-term consideration. Data collection will take place subject to the needs of the military testing facility as opposed to the ideal long-term timing of data collectors such as seasonal considerations.

However, in the short-term, the absence of factors such as rain, strong winds, and low visibility conditions will be required for the accurate collection of data.

Equipment Options for Data Collection
This section will be used to present the abilities of equipment options available for accurate data collection based on the details of this project. How they will be used will be discussed in the Mission Plan.

This will include unmanned aerial system options as well as sensors, cameras, GPS units, or other data collection options recommended.

Unmanned Aerial System
Fixed-Wing Aircraft

Falcon Eye fixed-wing UAV
A fixed-wing UAS (as seen in Figure 1) is capable of flight using wings that generate lift caused by the forward air-speed of the vehicle.

Figure 7:This is a picture of the Falcon Eye fixed-wing UAS . This particular model requires a runway for launch. 

                               Flight Time: up to 24 hours                  Speed: up to 112 mph   

Cruising Altitude: up to 18000 ft.         Payload: up to 220 lb.     

   

        Capabilities                                                                 Limitations

good for high altitude remote sensing                         limited ability to turn

long flight duration                                                     may require runway to launch or land
                                              
greater carrying capacity                                            inability to hover

high speed for greater coverage area                          limited close-up inspection capability


Reasons for Selecting this UAS
Due to the significant coverage area required to collect data from, a fixed wing UAS, such as the Falcon Eye will be necessary. Whereas rotary UAS models only have up to 3 hours of flight time, this fixed-wing aircraft will allow up to 24 hours of flight time for data collection. In addition, at a speed of 112 mph, the Falcon Eye will be able to collect data in a reasonable amount of time. Because data collection will take place at a military testing facility, it is understood that a runway will be available.

Hyperspectral Imaging Sensor

A hyperspectral imaging sensor (as seen in Figure 8) collects and processes information from across the electromagnetic spectrum.

Figure 8: This image is a hyperspectral imaging sensor. It will be mounted onto our fixed-wing UAS to provide data by remotely sensing the surface of the Earth in our study area. This information will guide us to the location of various types of soils and vegetation for identifying the location of desert tortoise burrows.

The hyperspectral imaging sensor divides light into many bands including the visible spectrum and those beyond the range of the human eye including infrared, ultraviolet, etc. (as seen in Figure 9).

Figure 9: This is a diagram of the bands of the electromagnetic spectrum as viewed by hyperspectral imaging sensors. As stated on the diagram, vegetation can be differentiated in the Red Band of the spectrum. Soil types can be discriminated in the Longwave Infrared Band. Using the hyperspectral signature attached to the soil and vegetation types we are looking for, we will be able to locate potential desert tortoise burrows.

Using visual images obtained by the hyperspectral imaging sensor, the red band will allow us to identify the vegetation associated with desert tortoise burrows. In addition, the longwave infrared band will allow us to establish the location of sandy loam soil throughout the region in question.This soil type is the typical location of desert tortoise burrows. These features are easily identified because each type of soil, vegetation, etc. has been cataloged with a specific spectral signature based on the amount of light (energy) reflected. These signatures are read on a chart produced from information gathered by the hyperspectral sensor (as seen in Figures 10 and 11)

Figure 10: This chart shows how different features such as grass, soil, and water reflect different percentages of light and thus can be identified. By locating specific areas of vegetation we will be able to narrow down the potential locations of desert tortoises.

Figure 11: This chart shows the reflectance patterns specific to Sandy Loam variations. Through hyperspectral remote sensing, the location of all sandy loam in the area in question will be located for the identification of desert tortoise burrows.

Post Mission Analysis

Aerial images taken during the mission will be imported into a remote sensing program in which the desired spectral signatures can be measured, identified and differentiated. Using our predetermined ground control points, we can anchor down certain locations on the aerial imagery in order to establish a coordinate system. Once these signatures have been located, the images can then be imported and projected into a GIS in order to find the coordinates of potential desert tortoise habitats.

***ADJUSTMENT TO CURRENT FORMAT***

The extent of this mission planning exercise has been exceeded as far as what you see in mission 1. As a result my instructor has advised providing only the essential details as far as what sensors, UAS, and other equipment recommended to complete the mission.

Mission 2: Operation Power Tower

For Operation Power Tower, a UAS is needed to examine various power lines and electrical towers. We decided that a rotary UAS will be the preferred method in order to hover and stare at various components on each electrical power. A small high resolution camera will be mounted onto the UAS in order to provide a live video feed to the pilot and concerned party. UAS mission planning software will be used to set predetermined stops at each tower to minimize flight time and maximize mission efficiency due to the short battery life of the rotary UAS.

Mission 3: Operation Healthy Pineapple

Operation Healthy Pineapple required an analysis on the health of a large scale pineapple plantation. Because this mission will be covering a distance of 8000 acres, it calls for a long flight duration and high altitude, which makes a fixed wing UAS the ideal vehicle. In order to measure the health of the pineapple, a multispectral sensor will be mounted to the UAS. This sensor will allow us to examine the different spectral signatures found within the red band of the visible spectrum to identify healthy pineapple and unhealthy pineapple. 

To determine the optimal harvest time for pineapple, it is recommended that multiple data collection runs be made over the course of the standard harvesting season. 

Mission 4: Operation Leaky Pipe

Operation Leaky Pipe involves locating the source of oil pollution within the Niger River Delta. To find this information, we will be using a two stage process including two different UAS vehicles. The first stage of the mission will require a fixed wing UAS equipped with a hyperspectral scanner to locate areas of oil pollution in the river delta using the green band of the visible spectrum and potential areas of impacted crops using the red band. By using remote sensing software, areas with these spectral signatures will be located in the river delta until the furthest location of pollution upriver is found. 

In the second stage of the mission a rotary UAS will hover to the upriver location identified by the fixed wing UAS. The rotary UAS will be equipped with a small high resolution camera which will provide a live feed to the pilot and concerned parties and a GPS unit in order to locate the oil leak. Locating the oil leak will also help us understand the scope of crop damage than can be attributed to the oil leak as opposed to damaged crops further upstream. 

Mission 5: Operation Earth Removal

Operation Earth Removal required volumetric analysis in order to measure the amount of material removed from the mine each day. Using a rotary UAS will allow us to adjust the vehicle until we find the desired elevation and angles that will provide the most accurate measurements to calculate volumetric output. At the end of each workday, the UAS will fly over the mine and generate point cloud data using a point cloud sensor. Point cloud data can be used to generate a 3D representation of the mine, which can be compared to the mine measurements of the previous day to determine the difference in volume.

Exercise 2: Visualizing and Refining the Terrain Survey

Introduction

Last week our group created a landscape using a large sandbox filled with snow, created a coordinate system to survey the landscape, measured the depths, and created a digital table filled with coordinates and depths for each of our 1056 zones. This week we are going to use that data to create a digital representation of our landscape. We will use a variety of different interpolation methods and analyze which ones resemble our landscape with the most precision. 

Methodology

Our data from last week was recorded in a large table with an X and Y axis which resembled our sandbox. In order for the GIS to use this data, it needed to be reformatted into an XYZ columned format. 

A portion of the original large table

A portion of the reformatted XYZ table

With the new GIS friendly table, 5 different interpolation methods were executed with the data: inverse distance weighted (IDW), kriging, natural neighbors, spline, and a triangulated irregular network (TIN). IDW is an interpolation technique that estimates cell values based on the parameter that the further a sampled point is from the cell being evaluated, the less weight it has in the calculation of the cell's value. Kriging an interpolation technique in which the surrounding measured values are weighted to derive a predicted value for an unmeasured location. Weights are based on the distance between the measured points, the prediction locations, and the overall spatial arrangement among the measured points. Natural neighbors is a method that calculates the value for an interpolation point by estimating using weighted values of the closest surrounding points in the triangulation. Spline is technique in which cell values are estimated using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points. The final method is known as TIN and it uses the XYZ data to connect a series of triangles between each data point. This process forms geographic space into contiguous, nonoverlapping triangles. 

IDW

Kriging

Natural Neighbors

Spline

TIN

After examining the various interpolation techniques, we decided that it would be best to resurvey some portions of our landscape. The walls of our valley appear to be separated near the center of the landscape, and the lake to the east of the mountain seems to be half the size and not as deep as it should be. 

Once again, we ventured back out into the frigid courtyard for another round of data collection, focusing on the valley and lake where the data seemed a bit inaccurate. We agreed that the best method to do this would be to split each of the existing zones into 4 smaller zones. This was done by using another set of string and measuring all of the affected areas in increments of 2.5cm. 

Pink string was used for the 2.5cm increment resurvey

We updated the table to include the newly surveyed data zones and began the interpolation process once more with more accurate data, bringing our zone total to 1442. 

Dot density of the original survey

Dot density of the resurvey locations

Week 2 IDW

Week 2 Kriging

Week 2 Natural Neighbor

Week 2 Spline

Week 2 TIN

After examining the various interpolation techniques for the newly resurveyed data, I believe that kriging and natural neighbor techniques were most effective, with kriging producing the most similar landscape. These two processes replicated our data the best out of the five, even though none of them were perfect. We then took all of the rasters we created and viewed them in a 3D modeling software called ArcScene. By setting the base height to the surface elevation values assigned to each cell, we could create stunning 3D models of our landscape and view the survey from a completely different angle. 

3D model of the IDW interpolation method

3D model of the Kriging interpolation method 

3D model of the natural neighbor interpolation method 

3D model of the spline interpolation method

3D model of the TIN interpolation method

Discussion

I am quite pleased with the results of our resurvey. We located areas in which quality data was lacking, we brainstormed a resurvey process, carried the process through, and created several 3D models of what was once a pile of snow. Our group continued to function as a fantastic team in which everyone played a crucial role. Collecting data outside in subzero temperatures was not easy, so we managed to divide the workload throughout the week very nicely.

It was challenging to try and explain why each interpolation method was different from one another. Besides the triangulated irregular network, I was unfamiliar with all of the interpolation techniques. While it was easy to just plug in the right values and create different rasters, it would have been nice to have some background knowledge of each of the different interpolation methods before this exercise. 


Conclusion

In conclusion, kriging appears to be the best method in recreating our landscape. Kriging preserved both the sharp edges and the rounded hills of our landscape. The IDW process lifted each data point too high and created spikes all over the landscape. The natural neighbor method was great for shallow elevation change, but it created too many sharp peaks when the elevation rose. The spline method was more smooth compared to the IDW, but it created the same tall spikes throughout the landscape. Finally, the TIN showed elevation very well, but the triangulated network couldn't replicate the same results in flatter areas of the landscape. 

If there was a contest for which method replicated the snow landscape the best, I believe that kriging would win for our purposes. Maybe the different methods exist because each is better at displaying a certain characteristic of the landscape, such as flatness, roundness, or rapid changes in elevation.