Conducting a Distance Azimuth Survey

Introduction:

Figure 1: Survey Stations along Putnam Trail

While surveying using a grid-based system can be useful for mapping smaller plots, technological advancements with GPS has allowed field surveying to become an easier, faster, and more accurate tool for obtaining and displaying field data for scales of various size. However, the use of any type of technological equipment comes with risk for technological failure while the surveyor is out in the field. Still, the job must get done! For this week's lab, students used various a variety of tools at varying locations along Putnam Trail to survey a series of select trees using the old-school Distance-Azimuth survey method. This method requires the measurement of distance and compass degree between several surveyed points (10 trees) and one pin-point location tied to a latitude and longitude coordinate pair (data collection stations) to be used later for mapping. The surveyed area and stations are pictured in the reference map (Figure 1).

Methods:

Data Collection:

To best familiarize students with a variety of equipment that could be used to obtain distance and azimuth data in the field, students divided into three groups and worked as a team to collect 10 data points from each of the three stations, using the various tools provided as they rotated through.

Figure 2: Image of TruPulse 360 

At Station 1, a TruPulse 360 range laser was used to determine the distance between the surveying pin-point and its selected surrounding data points, as seen in Figure 2. Other necessary tools included a standard compass for measuring the azimuth of the surveyed trees, and a basic GPS unit to measure the coordinates of the central point, or data collector's position. At the data collection point, the latitude and longitude pair read 44.796 deg. N and -91.5016 deg. W. While collecting the data, students alternated roles operating the TruPulse 360 range viewer and compass, measuring the diameter of selected trees, and recording the data until reaching the data collection total of 10 various points. This method was especially accurate in measuring the distances between collection point and tree. Also noteworthy of this tool's function, the measuring units displayed on the reading scope could be easily changed in the equipment's settings to read in either imperial units, metric units, (as used), or in degrees. Some challenges that arose when using this method, however, was the sensitivity of the tool's reading. In some instances, the tool tended measure the distance of a small branch that intercepted the scope on the way to the intended select tree. For this reason, many of the collected data points at this station had to be double, and triple checked for distance integrity of the actually intended data point.

Figure 3: Measuring Diameter of Trees at Breastlevel

Station 2 was the most time consuming because aside from the need for the GPS to denote a specific coordinate point, this station made due without the use of technology completely. Instead, students used a tape measure and compass to determine distances between data collection point at 44.79585 deg. N and -91.50033 deg. W and its surrounding trees. For this reason, the group did not travel nearly as far for plotted points, and the second station appears to be the more clustered data collection group of the three methods. While this method can be especially handy in case of equipment failure, it was also the least accurate of the three methods. Again, students alternated roles between holding and leading the tape measurement, reading the compass azimuth, measuring tree diameters, and recording the resulting data. Figure 3 shows a fellow group mate taking the circumference of a tree at standard breast level in order to find the diameter. Some complications the group faced in gathering data included the struggle to pick trees that were a far enough distance away to appear significant when plotted on a map, but not so far that another tree would block the tape measure's route, causing a curve in the tape and skewing the measurement's reading.

The last station surveyed used a range reader and receiver combination to record the distance. The data collector held the range reader gun at 44.795383 deg. N and -91.499388 deg. W while the person measuring the tree's diameters held the receiving end of the pair. The reader would measure and display the distance between it and the signals picked up by the receiving device. Some of the challenges that arose from this method was the occasional inability for the reader to pick up on the signal sent out by the receiver. This usually just required minor positioning adjustments so that signals would send properly without signal interruption.

Data Normalization and Mapping:

Once all of the data had been collected in the field, it was then necessary to format the data into an Excel file and normalize into a format compatible with ArcMap. A sample snapshot of the final data sheet is displayed below in Figure 4.

Figure 4: Normalized Data Table in Excel

Figure 5: "Bearing Distance to Line"

and "Feature Vertices to Point" 

Commands Location

After creating the table, the routes and distances measured between the three central data collection points and their corresponding trees were imported and plotted onto the map using the Bearing Distance to Line command in ArcToolbox under Data Management >> Features as shown in Figure 5. Figure 5 also references the location of the tool used to place the points where the trees surveyed were stationed at the end of the measured distance line. This tool could be found in the same section of ArcToolbox labeled Feature Vertices to Point. Finally, a topological image was placed beneath the resulting plotted points for reference.Figure 6 illustrates the results generated after the use of both tools in inlay of a basemap.

Figure 6: Tool's Resulting Map Image and Plotted Data

Results:

From the data points and features plotted on the map in the image above, the following map (Map 1) was constructed to further showcase the resulting distribution of each of the three methods and the corresponding trees which were selected in collecting data from each of the central points.

Map 1: Putnam Drive Survey Stations, Methods and Tree Data Points

The first station using the TruPulse 360 was the most effective tool used of the three stations for collecting distance data. It was quick, user friendly, and accurate in its measurements and could be measured with only one user. It also allowed the surveyor to collect these data points without actually having to approach any of the trees. Considering the steeply angled uphill slope of the terrain south of the trail, some of these trees were more difficult to reach by foot when necessary, as in instances when measurements were being taken at the second two surveying points. Station 3, for instance, had the convenience of using technology for measuring these distances as well, but the surveyor still needed a second person to hold the receiver at the tree's location. For this reason, most of the data points collected at the last two survey points were collected north of the trail to avoid any uphill hikes.

Conclusions:

Learning the Distance-Azimuth surveying method is an important skillset to use as a back up tool in the case of equipment or technology failure. Though the results produced are less accurate than those that may be obtained through the use of technology, the method still does a fairly good job of displaying the overall location of collected data points.

The Distance-Azimuth method can also be applied in the Point-Quarter sampling method used for determining the relative concentration of a species in a given habitat, especially those with a less defined shape as is the case with Putnam Trial. During this type of sampling, the same relative technique is used to get an estimate of the overall number of species that are within a given area. To perform sampling, a number of species in the area (in this case, trees) are sampled at random from a central point. Their correlating data is recorded and the trees are each prescribed an identifying number, just as performed in the lab detailed here. The methods begin to differ from here. In the Point-Quarter surveying method, once points are collected, a compass is used to determine and lay out four individual quadrants. The total sampled number of trees observed is multiplied by four (for four quadrants) to get the relative density of the area. This number is multiplied by the total density (calculated from the tree diameters) in order to obtain the absolute density of a species within an area, in this case, the absolute density of each tree species along Putnam Trail.

Overall, this lab equipped students with the necessary knowledge to overcome potentially critical situations that may occur in the field that will enable them to still get the job done! Despite living in an age with ever-advancing technology, learning the basics of the trade and the "old-school" methods used to collect location based data is a handy tool set to have stowed away for the occasional instances in which they just might be needed in the future.

Pix4D Demo

Introduction:

This lab demonstration was designed to introduce students with Pix4DMapper, a program that transforms a series of aerial imagery into a single compiled mosaic capable of rendering both 2D and 3D surface models. 

An online Pix4D Software Manual was provided to familiarize students with the capabilities and limitations of the program's functions. Through exploration of this manual, students were prompted to answer the following questions related to the software:

-What is the overlap needed for Pix4D to process imagery?

        70% Frontal, 60% Side

-What if the user is flying over sand/snow, or uniform fields?

        More overlapped imagery is needed in these instance: 85% Frontal, 70% Side

-What is Rapid Check?
        Rapid Check allows processing to be completed at a much quicker pace, but with a lower accuracy and resulting resolution.

-Can Pix4D process multiple flights? What does the pilot need to maintain if so?
        The software IS able to process multiple flights, provided that there is a thorough amount of overlap between photos and that the photographs were taken under the same conditions (same relative time of day, weather, ground layout, etc.).
-Can Pix4D process oblique images? What type of data do you need if so?
        Oblique images CAN be processed, provided there is enough overlap in and between datasets. 
-Are GCPs necessary for Pix4D? When are they highly recommended?
        GCP's are NOT necessary for Pix4D. They are however highly recommended during tunnel reconstruciton. 
-What is the quality report?
        The quality report works to identify errors within the processing of the given data imagery. 

Methods: 

Following an in class demo, students began by creating a new project in the program, importing the series of 68 provided images titled "Flight 1", and undergoing initial processing before finally completing the processing with Point Cloud and Mesh processing and DSM, Orthomosaic and Index processing.

The results from the initial processing stage generated a quality report (Figure 1) and a single mosaic (Figure 2) that could be used for 3D rendering after completion of the final two processing features (Figure 3). All 68 of the photos in the series were successfully processed.

Figure 1: Quality Report

Figure 2: Flight 1 Mosaic of Litchfeild Mine, Eau Claire, WI

Figure 3: 3D Rendering of Mine Site

Upon the successful completion of 3D rendering, students were able to use the program to produce a fly-by video visual of the 3D terrain surface examined in Figure 3. The resulting video revealing a glimpse of the 3D model from each of the four directional perspectives has been provided in Video 1 below:

Video 1: Flyover of Mine Site

Data Discussion: 

The overall results from the processing of the original 68 aerial photos proved to be successful in stringing together a singular mosaic which was later used for 3D rendering. Contributing largely to the success of this data processing was the high amount of overlapping images used in the data set. The diagram in Figure 4 illustrates where these areas of overlap occurred, as well as the degree to which overlap occurred. The bright green area occupying the majority of the figure is representative of the areas with five or more overlapping images for every pixel. Contrastingly, the red areas at the fringes of the study area depict a scarcity in image overlap per pixel, probably because it lies outside of the mining site.

                   Figure 4: Overlap

Completion of the second two processing stages resulted in a total of two rasters, one as a DEM, the other a Mosaic, as depicted in the map below. The DEM works to symbolize the elevation of the terrain in the area of interest while the mosaic provides the actual visual frame work. Areas with high elevation are pictured in red on the DEM image while areas of low elevation are pictured in blue.

Final Critique:

In all, the results of this lab are meant to demonstrate the functions and capabilities of Pix4DMapper as well as the potential benefits its use. While this demonstration only revealed a small fraction of the software's potential, it is evident that this program could prove powerful in the geospatial world, analyzing everything ranging from land use and agriculture to architectural and structural projects in urban planning. 

Using Survey123

Introduction:

There are a number of survey sets which Geographers often consult for the purposes of collecting data, the gold standard of surveys and go-to consult often being the US Census records. Sometimes, however, the surveys that are popularly referenced for consult do not host the information sought for in the data collection process. In these instances, developing personalized surveys are a key way for Geographers to reach out to the community for data collection of a particular topic of interests. Survey123 provides a user-friendly platform that allows students to quickly formulate and customizes online surveys for a community. This lab provided students with a tutorial (through https://learn.arcgis.com/en/gallery/) on how to use Survey123 and manipulate details within its program for further use in future labs.

Methods: 

The tutorial was formatted in step by step exercises for students to replicate directly, and ultimately, produce a final product which, in the case of the example, would serve the Homeowner Association in evaluating the disaster preparedness of community homes in a given study area. The tutorial was divided into a series of four lessons overall, including: "Create a survey," "Complete and submit the survey," "Analyze survey data," and "Share your survey data."

During the first lesson under "Create a survey," students worked at constructing the overall survey survey form, focusing on the questions the survey will ask, and the standardized types of responses a surveyor may use to answer. The platform is set up in an easy to manage format that allows the survey builder to simply "Add" (Figure 1) a question with presets per each response type (numerical, multiple choice (single or multi-answer), text), and then use "Edit" (Figure 2) to formulate the actual question and modify any necessary response options.

Figure 1: Add a Question Type

Figure 2: Edit the Question and Associated Response Options

Once the survey was built and complete, students were able to move on to the "Complete and submit the survey" lesson portion of the tutorial. In this section, students perform a number of runs through their survey's final product, and submit them for use. In the case of this lab, the survey conducted a total number of eight times before evaluation.

The third lesson in the tutorial, "Analyze survey data." allowed students to visualize the statistical results of their collected survey evaluations. The Survey123 platform allows students to visualize their collective results to each question through the use of columns charts (Figure 3), bar charts (Figure 4), pie charts (Figure 5), and proportional symbol mapping (Figure 6). One of each of these methods is provided with the people per household question in the four figures below. Each question also hosts some statistical result tables posted below visual illustrations of the data collected, some include a number of statistics while others include simple percentages for certain responses.

Figure 3: Survey Question Results- Column Bar Graph

Figure 4: Survey Question Results- Bar Graph Chart

Figure 5: Survey Question Results- Pie Chart Graph

Figure 6: Survey Question Results-Proportional Mapping Graph

The last lesson on "Share your survey data" walked students through the process of publicly publishing their survey to be taken by the target community through the web. The final tutorial illustrates how the program allows for the generation of pop-up configuration maps with data collection points linked to text of their associated surveys as shown below in Figure 7. The survey creator can choose which survey elements they choose to remain in the pop-up configuration links within the maps so that personal information is not revealed to the public eye.

Figure 7: Data Collection Points and Pop-Up Survey Configurations

Conclusion:

Overall, experimenting with Survey123 through the tutorial provided was effective in helping students to learn the potential of the program, how to navigate its layout and manipulate its functions. Survey123 will prove useful in conducting personalized research which requires a self-generated survey to obtain new data about a population.

Sources:

Survey123
https://learn.arcgis.com/en/projects/get-started-with-survey123/lessons/create-a-survey.htm
ESRI

Development of a Field Navigation Map

Introduction:

The process of navigating a given territory often relies on grid systems and sight-based location points in order to be successful. There are a number of grid systems that can be utilized for the purpose of referencing location during navigation. In generating a pair of field navigation maps for the Priory of Eau Claire, Wisconsin, students will demonstrate two of these grid systems in projection, one using a UTM Projected Coordinate System and the other using a traditional world Geographic Coordinate System of Decimal Degrees. The resulting maps and their use will be expanded on in later labs. 

Methods/Results:

For the first map illustrated in Figure 1, I decided to set both my data frame properties and my individual feature projections to Transverse Mercator reflecting the UTM to the 15N Zone projected coordinate system (where the city of Eau Claire falls on the UTM system). I then formatted the grid in the layout properties to mark latitude and longitude points as reference points for the mapped area. 

The second map denoted as Figure 2, conversely, uses a world Geographic Coordinate System (GCS_WGS_1984) to map the area of focus and provides its units as decimal degrees when translated onto a grid system.

While the map in Figure 1 is beneficial for preserving the shape of the illustrated area, the map in Figure 2 is better for preserving direction, which could translate as being the optimal choice map between the two for the purpose of navigation. The resulting maps are posted below: 

Figure 1: UTM Coordinate System

Figure 2: World Geographic Coordinate System

Sources:

ESRI and ArcMap

Geodatabase info provided in class from Priory