Distance Azimuth Survey

Introduction:

While it is important to have an understanding of the latest technology when conducting field research, it is just as important to understand how to conduct research the "old fashioned way" when technology fails. This was the central theme of this exercise i which the students conducted a Distance Azimuth survey of trees in Putnam Park on the University of Wisconsin- Eau Claire campus. This survey technique is basic but effective, using a range finder and compass to calculate distance and azimuth (measured in degrees between 0-360) respectively students were broken up in groups of three or four to collect the data, which was then uploaded into Arcmap for further processing.

Study Area:

Figure 1
Study Area Map 

The data for this exercise was collected in a small section of Putnam park on the UW-Eau Claire campus near the Davies student center and the Phillips Science Hall. The area is primarily low in elevation as can seen in its swampy terrain. On the other side of the wetland is a walking trail and a large ridge with a stairway leading to upper campus. The study area included a variety of deciduous trees such as birch, basswood, oak and maple. 

Figure 2
Study Area site with swamp (left of image) and ridge (right of image) 

Methods: 

Using a variety of tools, the distance and Azimuth to each tree was collected from a central point and recorded in a field notebook. The data was then entered into an Excel spreadsheet to be uploaded into ArcMap.

To begin, the GPS coordinates were collected for the survey point using a Garmin Etrex GPS device. This step is important because it is necessary to have an accurate point of reference for a survey.

Figure 3
Garmin etrex Device

Once the reference point coordinates were collected, the distance and azimuth was measured from this point to ten nearby trees. This was done using a laser distance finder device. One group member stood on the reference point to take the measurements and then reported them to the other members who recorded them in a field notebook.  The device is easy to use, simply requiring the user to switch to the setting for what they wish to measure (in this case distance and azimuth) and point a laser at the target to obtain a reading.

Figure 4
Example of Distance Finding Device

Unfortunately, one device had some technical issues and was giving inaccurate azimuth readings. As a result, an survey compass needed to be used. Unlike a traditional compass, an survey compass has a small viewfinder the user looks through which shows the azimuth the compass is pointed in.

Figure 5
Example of a Survey grade Compass

The tree diameter was also recorded as well as the tree type. This information is important because it may be used to make observations regarding tree size in relation to its location.

Once the data was collected, it was entered into an Excel spreadsheet with each field getting placed in its own column and uploaded into a geodatabase on ArcMap. Once uploaded the Bearing Distance to Line tool was used to show where the trees were located in reference to the survey point. A tool was then used to convert the vertices of the lines to points so the trees themselves could be symbolized.

Results:

Figure 6
Map of Tree location and Azimuth from the central measurement point.

Once the data was processed in ArcMap, the trees locations could be mapped by converting the vertices of the azimuth lines to points.

Arc Collector: Part 2

Intro:

This lab is a continuation of the previous lab which demonstrated the basics of how to use Arc Collector. Using this knowledge, the goal of this exercise was to formulate a research question that can be solved using ArcCollector, and then to collect data and map the results. This specific project focuses on the condition of sidewalks in the student housing area nearby the UW-Eau Claire campus to answer the question of "which sidewalks are in the greatest need of repair"? This topic is relevant to all of the students of the university because they all utilize the sidewalks to get to campus. When sidewalks are damaged, they can become a hazard to those walking, jogging or using other methods of transportation. By using ArcCollector to pinpoint areas of high damage, a map will be created showing the areas in greatest need of repair.

Study Area:

Figure 1
Section of Student Housing Area

The study area for this exercise is a section of the student housing area north of the UW-Eau Claire campus. This section is a 5 by 5 block area between 1st and 5th avenue and Chippewa and Lake street. The reason this section was chosen is because it is very frequently used by students traveling to campus. This area also contains Randal Park, an area used heavily by students and the public alike. The methods used to collect data in this area can be expanded to larger areas as well.
   

Methods: 

Before data can be collected, a geodatabase needs to be created to house feature classes. In ArcMap, a geodatabase was created and domains were established. Within the geodatabase, a feature class for sidewalk damage was created and given various attributes. Each of these attributes were assigned one of the previous domains.

Figure 2 :
List of Domains under Database Properties

Some domains included a coded values domain to restrict what types of damage may be recorded. Another was a range domain that limited the number of damages that may be recorded.
It is important to make sure that the geodatabase is designed correctly so that the proper data can be collected.

The fields assigned to the feature class are as follows

Severity: This field records how severe the damage is

Street: A text field to record the street name the damage is on

Number: Number of damage features on a section of sidewalk (i.e two cracks)

Type: Coded values field to record the type of damage (crack, chip or uneven sidewalk)

Once the geodatabase and feature class are created it may be published as a service to ArcGIS online. Once this is done, the data collection in ArcCollector may begin.

Results: 

Figure 3
Map showing types of sidewalk damage

Figure 4
Map of quantified sidewalk damage

http://arcg.is/Oef5v Link to map of damage severity

This study concluded that there is extensive damage to sidewalks in the student housing area near the UW-Eau Claire campus. The greatest damage can be seen on Hudson and Lake streets. This is likely due to these streets being heavily traveled by students yet being further away from major roads such as Water Street or First Avenue. Although it is one of the oldest neighborhoods in the city, the sidewalks around Randal park showed surprisingly little damage. These sidewalks are likely attended to more frequently because the area is used heavily by students as well as the general public. 

Conclusion:

This project is just one example of the capabilities of ArcCollector with data collection. The geodatabase schema for this project was relatively simple and suited the needs of this project. However it was still important to carefully design the geodatabase because in a project with several feature classes with various subtypes and domains it is crucial that the data is well organized. I feel that my research has answered my overall question. In the future I may expand my study over a larger area or include additional attributes to examine.

Arc Collecter

Introduction: 

ArcCollector is a mobile data collection app that allows users to use the advanced GPS capabilities of their mobile device (either Andriod or Apple devices) to collect G.I.S data. This is advantageous because it allows users to collect the data as a GPS unit without requiring receivers or additional equipment. Users can enter data for a variety of attributes in the form of a survey, which can than be exported into a map. Being an Esri product, ArcCollector can export data into other Esri software such as ArcMap

Study Area:

The study area for this assignment was the UW-Eau Claire campus. The class was broken into groups, with each group being assigned a different zone on campus due to time constraints. This report focuses on data collected in zone 7, which consisted of the majority of lower campus including the campus mall and the field behind the Putnam Hall dormitory.

Figure 1
Map of Zones with zone 7 highlighted in yellow

Methods: 

The purpose of this assignment was to use the ArcCollector application to collect microclimate data on the University of Wisconsin-Eau Claire campus. To begin, the ArcCollector app was installed on the students mobile devices. Then ArcCollector was connected to a geodatabase to establish domains. An attribute domain is a sort of rule that determines the type of data that can be assigned to an attribute. For example, for the wind speed attribute, a domain was set so that only numbers between 0-360 may be entered, corresponding with the degrees of a circle. This exercise consisted of 8 attributes, each with an assigned domain. Next, a map was selected from the students ArcGIS online account for data points to be added to.

The next part of the assignment focused on the actual data collection. This was done primarily with a Kestrel 3000, a device that can collect a variety of weather information such as temperature, dew point, heat index and wind speed. A compass was used to determine wind direction. 20 g.p.s points were collected and micro-climate data was recorded for each point. Because the ArcCollector app is connected to an online map, the points appear on the map in real time as they are recorded. The data in the online map could than be exported into ArcMap for a variety of cartographic purposes.

Results: 

 In ArcGIS online, each of the attributes could be mapped to examine patterns across the UWEC campus. Some examples may be seen below.

Figure 2
ArcGIS online map of surface temperatures

One of the attributes mapped was surface temperature across campus. A spatial pattern that the data indicates is that blacktop and concrete surfaces have a higher surface temperature than grass or bare ground surfaces. This is made clear on the map with locations such as parking lots and the campus footbridge. This data was obtained on a sunny day, and seeing that blacktop gets very warm in sunlight, this seems accurate.

Figure 3
ArcGIS online map of Wind Speed

A second attribute mapped was wind speed. A pattern that may be observed on this map is higher wind speeds in open areas. These include areas such as the campus footbridge and outside of towers hall. Some of the lowest wind speeds may be observed in the campus mall. The reasoning behind this is the presence of buildings. Large buildings block the wind and reduce the overall wind speed. The footbridge, an area that exhibits consistently high wind speeds, has no buildings around to block the wind. The campus mall is surround by academic buildings that would block the wind and reduce the overall wind speed.

Conclusion:

ArcCollector is a powerful and useful mapping application that allows users to collect field data which can be uploaded to an online map in real time. This could be very useful in a field such as transportation planning. ArcCollector may be used to record areas of roads that are in disrepair. For example, attributes may include road location, surface type, extend of damage (crack, pothole, ect.). This could then be exported into an ArcMap document to illustrate which roads are in the greatest need of repair, allowing city government to prioritize their projects. Overall, ArcMap is a very useful and accessible tool with a wide variety of geospatial applications.

Lab 7: An introduction to survey 123

Introduction:

The goal of this lab was to learn how to create a survey using Survey123 for ArcGIS to collect field survey data. This was done via an online ESRI tutorial. In this tutorial a survey was created for a homeowners association (HOA) with 9 safety checks to determine a communities preparedness in the event of a disaster.

Methods: 

The first step of the tutorial involved creating a survey. On the Survey123 website, a template was filled in with the following information.

Name: HOA emergency preparedness survey

Tag: HOA,emergency prepardness

Summary: This survey is being conducted by the HalloOA to help assess the community's emergency preparedness in the event of a disaster, such as an earthquake.

Figure 1
Template for creating a new survey

After creating the survey, the design tab is opened. This tab contains several options for editing the survey. These include add, edit, appearance and settings. The main functioned used to create the survey is the add function which allows the user to insert a variety of question types into the survey layout.

Figure 2
Types of questions that can be added to a survey

The following question types were included in this survey

Singleline text: Allows for a single line response

Single Choice: One choice can be selected from a list

Dropdown: An answer can be selected from a drop down menu

Date: Allows for answers to be entered in date format

Number: Answers can be intergers or a range of numbers

GeoPoint: Users can place a point on an interactive map

Hints may also be added to guide participants in their responses. Also, rules may be added to make questions appear depending on how previous questions were answered. (If a question is answered YES, the second question appears)

29 total questions were included in the survey. Under the collaborate tab the survey was shared with members of my organization (UWEC Geography and Anthropology) 

Figure 3
Sharing survey under the Collaborate tab

The next step was to complete the survey to gather data. This was done a total of 8 times ( 5 times on web browser and three times on the mobile app). The mobile app allows for users to complete surveys on their smart phone in the field.  All eight responses were provided by myself, however in a real world situation multiple individuals would provide responses. 

In Survey 123 data can be analyzed as a group or individually. Many different charts and statistics related to the survey results can be examined here. 

Figure 4
Example of graph under the analysis tab

The final step involved sharing the data with others. This involved creating a map and a web application. When creating the map, certain data was censored so personal information would not be shared. 

Results: 

The final product was a web application that allows users to examine the survey results on a map. Information about each response appears in a pop-up window when the response is clicked on. This makes it very easy to examine specific data related to each individual survey.

Figure 5
Web Application showing locations of surveys

 

Figure 6
Pop up window showing results for one survey

Conclusion

Survey 123 is a powerful application that allows users to collect survey information for a variety of uses. After obtaining my undergraduate degree I aspire to continue my education and pursue a career in Urban Planning, a field in which survey 123 could potentially be used. For example, a survey could be created asking citizens what type of businesses they would like to see in their neighborhoods and their results could put into consideration when developing an empty plot of land. Overall, this tutorial covered the basics of what is a very useful application for a wide variety of data collection purposes. 

Bad Elf GPS

Intro:

The purpose of this assignment was to use the IOS app Bad Elf GPS pro and a Bad Elf GPS receiver, accurate up to 2.5 meters, to track a route that was taken around campus. This route was then converted into a GPX and KLM dataset and uploaded into ArcMap to be mapped. The Bad Elf GPS unit is able to connect to an Iphone via bluetooth and take advantage of the phones superior computing power. This is important, because for a GPS unit to have similar capabilities it would take years to develop, whereas by connecting to existing technology the developers can instead focus on applications for the GPS rather than the GPS unit itself. 

On the topic of applications, the Bad Elf company has created numerous applications for their GPS software, ranging from a wide variety of uses. A few examples are noted below

Pix4d Capture- This app is meant to serve as a companion to Pix4d photogrammetry software. Using the app, drone data can be accessed with an Iphone or Ipad. Additional features include a drone flight checklist as well as a wide variety of image processing.

Air Navigation Pro  - This app allows pilots to plan and track flights using real-time gps information. It provides access to a large database of way-points and flight information. If elevation data is added, flights paths and terrain may be viewed in 3d

Cachly- This app is used for the recreational activity geocaching. This involves participants using a gps follow coordinates in order to locate a small item or "geocache" The app shows an interactive map of geocaches around the world as well as an activity log showing when others located the cache.

Methods:

For this assignment the class was divided into groups; one group was the "hiders" and the other were the "locators". The group in charging of hiding was given an Eureka Marco Polo brand tracking receiver that resembled a small USB drive. The unit is intended to be attached to a pets collar so they can be located if they become lost. The other group used the handheld tracking unit to locate the transmitter and used the Bad Elf app/ GPS unit to log the path they took during the search.

Processing UAS Imagery

Objective and Background

The objective of this exercise is to process imagery using Pix4D, a premier software for generating point clouds. This lab builds off of the previous lab which covered the basics of Pix4D. For that exercise the data was already processed, however this lab will focus on how to process this data. This includes calibrating ground control points, creating an image mosaic and exporting the scene as a shapefile to be mapped in ArcMap. The processed data was obtained from a UAS flight at South Middle School in Eau Claire, Wisconsin by Dr. Joseph Hupy of the University of Wisconsin Eau Claire.

Figure 1
Aerial View of Study Area as seen on Google Maps

Methods:

The Pix4d software is relatively easy to use, however a few parameters must be set before the data may be processed. The first step is to create a new project. This can be done on the opening menu screen. 

Figure 2
Pix 4D opening Menu

 After a project was started, a series of images provided by Dr. Hupy were uploaded. A very important step here was to change the camera settings to linear rolling shutter, which is the particular way that the UAS captured this data. Some processing options that can be selected include the ability to generate shapefiles which can than be imported into ArcMap for mapping. For this exercise the option to create a 3D map was selected. The next step was to import ground control points, or known coordinates in the area of study, to improve the accuracy of our final product. The last step is to uncheck the options for point cloud/mesh and DSM/Orthomosaic on the bottom processing bar. The initial processing can then begin. 

Following the initial processing, a quality check report is generated. This shows any potential errors that could affect additional processing. The error in this quality report was corrected in the following step. During this step, the Raycloud editor was opened and the GCPs were calibrated to match with their actual locations, marked by pink squares in the study area. Three calibrations were done for each GCP

Figure 3
Matching GCP in the software (small blue circle under green x) with real world location (pink/black square)

After the GCPs are calculated, the re-optimize option in the processing bar must be used to adjust the images for the corrected GCPs. The second two processing boxes were checked (the first is unchecked to save time) and after some time the processing finished. The final product was then imported into ArcMap to create a final map.

Results

Figure 4
Orthomosaic Map created from processed UAS Data.

Figure 5
DSM map created with processed UAS data

For this exercise two maps were created. The first of these was a map of the Orthomosaic generated from the data that was processed in Pix4D. This map also includes a locator map to show where within the state the study took place. Overall the final product turned out well. there is enough overlap between all of the photos to create a good mosaic. When compared to an actual satellite image, the difference in quality can be observed, however with the orthomosaic overall the quality is good.

Figure 6
Othomosaic (Top) compared to Satellite image (Bottom)

The second map was a DSM map that showed the surface elevation of the study area. A hillshade generated using the DSM which was created during the image processing. This was overlaid on an imagery basemap which was made 40% transparent so it can provide spatial reference but not distract from the DSM. The DSM was given a red to blue diverging color ramp. Areas  of high elevation on this map can be somewhat deceiving because many of those areas are trees.

Overall the Pix4D software is easy to use, however it should be noted that for this lab a very small UAS data set was used. Even with this small size processing took roughly twenty minutes. With larger data sets the processing may take several hours. 

Conclusion

UAS data collection and image processing is becoming a very high demand geospatial field. These last two exercises have provided the basic information on how to use Pix4D, a UAS imagery processing software, to generate point clouds, DSMs and Orthomasics. These were then imported into shapefiles and mapped with ArcMap software. This lab demonstrated only a few of the several applications of UAS data

Lab 4: An introduction to Pix4D

Overview:

This exercise focused on using Pix4D, a premier software used to process UAS data. The software can perform a wide variety of functions, however for this assignment only a few basic functions were used including volume analysis and video fly-through. 

Before any project can be started, UAS data from the field is needed. This includes orthorectified imagery and ground control points. The higher the quality of the data, the better the project will be. Additionally, Pix4D requires a certain level of overlap between images. The recommended values are 75% front overlap and 60% side overlap. If flying over snow, sand or uniform fields these values should be increased to 85% frontal overlap and 60% side overlap.It is also recommended that the camera platform is kept at a constant height as much as possible.

When in the field, the Pix4D rapid check processing system can be used. This processes images more rapidly to determine if sufficient coverage was obtained, but sacrifices accuracy to do so.

The Pix4D software can process data in a variety of ways. For example, data from multiple flights may be processed together provided that both datasets were collected from the same height, in similar weather conditions and have enough overlap. The software can also process oblique imagery, but that is beyond the scope of this exercise.

When processing data in Pix4D, ground control points are not required, but can help with georeferencing and the accuracy of the final product. 

The final feature users should be aware of is the quality report. This is a report generated by the software after processing data to give an overview of what happened, including errors in processing and processing parameters. 

Using Pix4D software

Two techniques that can be performed with Pix4D include volume measurement and video capture. The volume measurement tool can be found under the volume tab. Once a project is loaded, control points can be placed around whatever object is being measured. In this exercise, we measured the volume of a large gravel pit.

The second function, video capture, can be accessed from the ray cloud tab. This function allows the user to create a video where the camera moves through the project scene. To do this, the camera can be adjusted and waypoints are created along a route for the different camera views. The camera then moves through this route. An example may be seen below in figure one. 

 

Figure 1

Video capture from Pix4D

Creating a Map from Pix4D Software

The final section of this exercise involved making two maps in ArcMap using data obtained by Dr. Joseph Hupy from the University of Wisconsin-Eau Claire. ArcScene was also used in creating these maps. 

Figure 2
Map of Ortho-Mosaic created from UAS imagery

The first map created was of an Ortho-Mosaic that was generated by Dr. Hupy. The file was edited in ArcScene to eliminate the black background and uploaded into ArcMap. One thing that was noted was that the image quality changed after uploading the file into ArcMap. The original file may be seen below.

Figure 3
Ortho-Mosaic image prior to being uploaded to ArcMap

The second map created is of a digital surface model, or DSM of the mine site. A DSM shows ground surface features as well as elevation, which is often obtained from Lidar data. A hillshade was taken from the DSM using the hillshade tool. This was then made partially transparent and overlaid on the DSM to better show surface features. To display elevation, a blue-red color ramp was chosen with blue representing low elevations and red representing high elevations. One thing to note is that many areas of "high elevation" displayed on the map are actually just trees.

Figure 4
DSM map of mine site

Summary and Conclusion

Pix4D is capable of performing numerous functions, and this introduction exercise just scratched the surface. The main objective of this lab was to learn how to enter data into a Pix4D project and perform basic processing operations. These basic functions are very important to have a good grasp on since they will be used again more in depth in future UAS labs.

Creating a Navigation Map

Objective and Background

The objective of this exercise was to design a navigation map of the Priory, an area of land owned by the University of Wisconsin-Eau Claire, for a future course assignment. A secondary objective was to gain a better understanding of how projections and coordinate systems may effect navigation.

A coordinate system is a sort of grid that uses a series of numbers to determine a point in space. For example, on earth location is determined using latitude and longitude values.Different coordinate systems may be used depending on what is being mapped, what projection is being used and other factors.These maps use two different coordinate systems

WGS-1984 Web Mercator Auxilary Shpere- This coordinate system has become the standard for web mapping applications and is the system utilized by Google Maps. This coordinate system has many advantages and disadvantages that are associated with the traditional Mercator projection.

NAD1927-UTM zone 15N- This coordinate system is part of the Universal Transverse Mercator or UTM system that breaks the earth into 60 north-south zones, each covering six degrees of latitude. Each zone can be mapped with a specific transverse Mercator projection with very little distortion.

Methods:

There were a few key terms that needed to be understood prior to creating the navigation grid

1. Define Projection- This tool changes the information about the current projection, however it does not change the projection itself. You basically give it a new name. The project tool is need to actually change the projection.

2. Project/Project Raster- These two tools are used to actually change the map projection in the map document. Project Raster is obviously used for rasters and the Project tool is used for vector data.

3. Contour- This tool was used to create contour lines in the map document. This was done using elevation information from Lidar data of taken from the study area. In these maps, each contour line represents a 5 foot change in elevation.

After creating the contour lines, the next step was to create a navigation grid using ArcMap software. The grid can be created under data frame properties, which can be accessed by right clicking within the data frame. Our first map created was a navigation map that used a gradicule grid. The grid lines were spaced one second apart and the decimal degree labels were given four decimal points. 

The second map used a measured grid with each grid line being 50 meters apart. Instead of using decimal degrees, this units on this map stand for how many meters north of the equator and west of the prime meridian a point is. The final step to creating the maps was adding all of the necessary map elements

Results

  

Figure 1
Priory Navigation Map in Decimal Degrees

For the navigation map I choose to have my contour interval set at 5 feet, therefore every yellow line represents a change in elevation of roughly five feet. The grid spacing of every one second is ideal because the map is not too cluttered however there are still plenty of coordinates to navigate by. For this maps background I choose to use an areal image of the study area with about 70% transparency. This provided some visual reference to the map without making it appear too cluttered.

Figure 2
UTM Priory Map

This map is similar in design to the first map, however it uses a different coordinate system. This graph has a grid with spacings every 50 meters. Because this map does not utilize decimal degrees, degrees minutes and seconds are not measured. This will be the main map used for navigating during the lab exercise. One design error I would correct is the map background. I used two separate black and white images with about 70% transparency and there is a significant amount of overlap between the two images. In the future I would perform a mosaic of the two images in a program such as Erdas Imagine.

Conclusions: 
Conclusions that may be drawn from this assignment include the fact that making navigation maps can be quite challenging. The cartographer needs to understand which coordinate systems and projections to use in order to insure that the map remains accurate. There is a fine balance between the map being too cluttered or hard to read and being to vague and impossible to navigate by.

Sandbox Visualization

Overview and Objective:

This lab is a continuation of the first lab that involved creating a survey grid to survey a sandbox. The objective for this lab was to use the data that was collected in the survey to create a 3D model of the sandbox environment. The X,Y and Z values were compiled into a spreadsheet; a process known as data normalization. Each X,Y and Z value in a row corresponds to 1 point on the survey grid. A secondary objective was to gain an understanding of different interpolation methods and use different methods to create each model. Interpolation is the process of a computer program calculated the estimated value of areas in between points on a grid.

Fig 1.0
Normalized Data in Excel Spreadsheet

Methods:

In this lab five interpolation methods were used to create a 3D model using ArcMap and ArcScene software. The methods used are as follows. The descriptions are based on those provided by ESRI


Spline- The Spline interpolation method uses a mathematical function to estimate values in-between given points and provide a smooth surface. The spline method is best used when a data set has a large number of sample points. Because a spline surface passes directly through the points, the more points there are the smoother the surface will be.

Kriging-This interpolation method uses an advanced equation to investigate the spatial correlation between data points to estimate a surface. Kriging works well with scattered data.

Natural Neighbor- Finds closest subset of input samples to a point and applys weights based on proportionate areas to interpolate a value. An advantage of this method is that it can work with equally or irregularly spaced data

IDW- Also known as inverse distance weighted, estimates cell values by averaging values of sample points in neighboring cells. IDW works better with closely grouped data.

TIN-Interpolates by triangulating sets of vertices. A surface is generated from non-overlapping triangles. More triangles are generated in areas with more surface variation, such as a slope that changes in elevation.

After collecting the data, the values were placed into a spreadsheet, which may be seen in figure one. Each X,Y and Z value represents one point. This Excel document was then placed imported a geodatabase in Arc GIS software. Then, using the add XY data function, a grid of the data points was generated. Interpolation tools were then used on the grid to generate a terrain model. These were transformed 3-D models using the Z-coordinate values with Arc Scene software. Elevation surfaces were set to float on a custom surface. The models were oriented with north being at the top of the image. To represent scale I drew a straight line at the base of the model created a label that displayed the overall length. It is important to include the scale and orientation because when doing an experiment or exercise the process needs to be repeatable  Once the scenes were generated they were exported as JPEG images. These images were then loaded into an Adobe Illustrator document along with map elements from ArcMap to create a final map.

Results

The end result of this exercise was a map showing the 3-D terrain model for each interpolation method. These may be seen below.

Kriging

The Kriging method did not do an adequate job  modeling the terrain. The major features are shown, however several smaller depressions are not shown in this model but are in models generated using different interpolation methods. On a positive note, the surface generated by the Kriging method looks more realistic.

Figure 2
Map with Kriging Interpolation

Natural Neighbor

I believe that the natural neighbor interpolation does a nice job of representing the contours of the sandbox surface. Smaller ridges and depressions may be seen which do not appear in other models. The smooth surface in between features is accurate as to what the sandbox actually looked like. 

Figure 3
Surface Model using Natural Neighbor Interpolation

IDW

The IDW interpolation method is not the best method to use. Because of the way the points are weighted, small bumps in the sand box look peaked. The depressions on the surface are more difficult to make out as well with this model.

Figure 4
Model using IDW interpolation

Spline

The spline interpolation method proved very useful in showing small changes in the terrain. For example, in the center of the map there are three depressions that form a triangle. The depression at the top is actually two smaller depressions, which was harder to make out in the other models. One aspect of this model that should be changed would be the color ramp. The contrast between red and blue accurately shows changes in elevation, however the highest points and lowest points are both represented as black, which may be confusing

Figure 5
Surface Model Using Spline Interpolation

T.I.N

The Tin model did a great job visualizing changes in elevation. This can especially be seen on the slope in the northeast corner. one downside to this map is that it looks more computer generated and not as "pretty" as the other models.

Figure 5
T.I.N model

Overall the Spline and Natural neighbor interpolation methods produced the best terrain models. All of the models included a very small amount of variance between what was actually in the sandbox and what was generated in the model. This could be due to measurement error. The initial exercise was done in single digit temperatures and it's possible this contributed to human error when measuring.

Conclusion

The overarching goal of this exercise was to understand the fundamentals of data creation by gathering data in the field and importing the data into a geodatabase in order to generate a model. The survey we performed was very similar to professional surveys in that we divided an area of land into smaller, equally sized sections to map it. The differences include the scale of the project and the tools used. Because we were in a sandbox, hand measuring tools were fine. In the field professional survey equipment needs to be used in order to insure accuracy. The grid pattern we used is ideal for relatively flat land. This can be seen in the landscape of the rural mid-western United States. In other areas, such as the east coast, the different terrain was not conducive to a grid like survey system. 

Interpolation may be used in many different ways besides those in this exercise.  For example, bi-linear interpolation is used in remote sensing to resample images.

Understanding Survey Grids

Background and Objectives:

The objective of this exercise was to gain a better understanding of survey grids and construct a survey grid in a sandbox landscape. A secondary objective was to understand how different sampling techniques may be used in field research. Spatial sampling involves determining how measurements will be taken across a given study area. Three sampling types were discussed

Random: Random samples are taken across the study area in order to reduce bias

Systematic: Samples are taken at systemic intervals throughout the whole study area

Stratified: Samples are taken within small groups to portray a portion of the whole study area

This exercise was completed outdoors between 1:00 and 4:00 pm with a temperature of 3 degrees Fahrenheit

Methods:

To construct our survey, the following items were used

1. Meter Stick
2. Thumb Tacks
3. Measuring Tape
4. String

To begin, the perimeter of the sand box was measured using the measuring tap. Each side was found to be 114 centimeters in length. A systematic sampling method was choosen, and each side was divided into 19 separate 6 centimeter segments. Thumb Tacks were used to mark off each six centimeter segment. String was then woven in between the thumb tacks to create a grid. An X and Y axis was assigned to the grid, and the X/Y coordinates for each section were recorded in a spreadsheet.

The next step involved measuring the height above "sea level" to obtain a Z coordinate for each section. For the purposes of this assignment, sea level was considered to be the string that made up the grid, with features below the string being below sea level. Using a meter stick, this height was measured to the nearest half centimeter and recorded for each section and entered into a spreadsheet.

Results:

Our survey consisted of 361 individual data points. According to the data obtained, the average height of our sandbox landscape was approximately 6 centimeters below sea level. The highest point just made contact with the string and was recorded to be at sea level. The standard deviation is 2.38 meaning that the vast majority of measurements fall within 2.38 centimeters of the average.