Introduction to Pix4D

Introduction:

Pix4D is a stand alone product for Microsoft Windows and is a premier software for those looking to do photogrammetry. This software is used most often to analyze unmanned aerial vehicle (UAV) data. It can do many functions including creating orthomosaics and construction point clouds. Pix4D is incredibly easy to use because of its user friendly automated tools. It obviously can't be picked up by just anyone, background knowledge on the topic is necessary to make sure everything is done correctly. 

Look at Step 1 (before starting a project). What is the overlap needed for Pix4D to process imagery?

     Terrain type is the determining factor for the amount of overlap. For most instances that don't have snow, water, farm fields, forests, or other difficult or highly vegetated terrain, 75% frontal overlap and 60% side overlap is the lowest recommendation.

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

     Dense vegetation, forest terrains, agricultural fields, or snowy areas require at least 85% frontal overlap and a 75% side overlap. Rivers and lakes can be reconstructed but need land in every picture, oceans however cannot be reconstructed.

What is Rapid Check?

     Rapid check is a processing method in Pix4D that is faster but produces a less quality product. Resolution is reduced and accuracy is lowered, sometimes resulting in incomplete results. This is only recommended for field processing to check that the dataset was collected properly.

Can Pix4D process multiple flights? What does the pilot need to maintain if so?

     Pix4D can process multiple flights only if the the flights have the same height so the images can be processed correctly.

Can Pix4D process oblique images? What type of data do you need if so?

     Yes it can, Oblique imagery is used to reconstruct 3D objects. In order to do so however, the construct to be reconstructed needs to be flown around with the camera at a 45 degree angle. Two more flights are required, at higher heights and harsher camera angles.

Are GCPs necessary for Pix4D? When are they highly recommended?

     Ground control points are unnecessary for Pix4D; however, they are recommended in certain cases because they improve the accuracy of the project. They are nearly necessary when processing images without geolocation. Without GCPs the final model will not be scaled, georeferenced, or oriented correctly.

What is the quality report?

     The quality report is a summary of processed data. After running the processes a quality report is automatically generated and displayed. To find it again click the Process tab at the top of Pix4D and select "Quality Report..." in the drop down menu. The quality report will tell the user the number of calibrated images, the difference between optimized and initial cameras, the georeferencing used, and the matches of each calibrated image. The quality report is necessary to look at to make sure the data is acceptable to use.

Methods:

Starting a new project, first click start new project under the project tab. Name the project and chose the workspace, this is important so all the files are saved automatically in the correct folder. Select the data that needs processing, this will likely be many images from different flights. Verify the coordinate system, then choose a coordinate system that the final project will be in. Lastly chose the type of project that is to be completed.

After all the parameters have been set up the initial processing can begin. The initial processing can take some time depending on the amount of data. It is important to run the initial processing alone before other processes to ensure that the data will work well by checking the quality report afterwards. Figure A below shows the information from the initial processing as well as the name of the project and the time and date it was processed. It also shows the quality check, another part of the quality report. The quality check has 5 criteria, Images, Dataset, Camera Optimization, Matching, and Georeferencing. Green Check marks are good, Yellow Warning signs show caution that the criteria may not be up to par, and Red Warning signs are not good. The median key points per image was met, the dataset has 100% of images enabled, and the median matches per calibrated images were met. For georeferencing, there are no 3D GCPs, this is fine. And the one red warning for Camera Optimization is fine, Dr. Hupy assured us that for this project the camera optimization does not need to be its usual below 5%.

Figure A, the summary and quality check for the initial processing of the Litchfield Mine Data.

Next by selecting the "Processing" side tab, deselect "Initial Processing" and select "Point Cloud and Mesh" and "DSM, Orthomosaic and Index". Then press start. After running the two processes a quality report should be generated again. See Figure B, it will show the are that was processed and show the overlap of the entire area. It has a spectrum of red (bad, 1 image) to green (good, 5+ images). Areas of less overlap can potentially be slightly distorted. It is in good practice to collect enough images, especially on the edges to make sure a good amount of overlap is there to preserve the study are quality.

Figure B, almost all of the image has 5 or more overlapping pictures this is good, and not much of a worry that the edges may be slightly distorted.

Further down in the quality report lies the methods and data regarding the DSM, Orthomosaic and Index Details. IDW interpolation was used.

Figure C shows information regarding the DSM, Orthomosaic, and Index Details.

Once all the processes have been run, an image is generated and it can be found in the "Volumes" side tab. This 3D image can be manipulated using the tools across the top of Pix4D. It can be viewed directly from the top down, or the trackball navigation mode can be used to look at it from literally any angle. Figure D below shows the Litch field Mine image that was processed.

Figure D is a 3D image of Litchfield Mine made in Pix4D

In Pix4D volumes can be calculated of the sand hills that can be seen in the image above. With the image in top down mode, click new volumes and digitize around a hill. Left clicking places a vertices and right clicking places a final vertices to complete the polygon. Then click compute, the volume and area of the hill will be calculated. Figure E below is what the digitized hill base should look like.

Figure E, Digitized hill base.

Figure F, shows the hill from the side after the Area and Volume have been computed.

The next step is to export the volume data. Above the data on the left hand side is a box with an arrow pointing up and to the right. Click this and select the volumes to be exported, then click export and save the image as a shape file so that it can be opened in ESRI ArcMap. The hill calculated had a total volume of 779.33 cubic meters with a buffer of 15.40 cubic meters. This volume could be more exact if GCPs had been placed, lowering the margin of error.

The last thing to do was to create maps. In ArcMap the DSM and Mosaic rasters that were created when the Pix4D processes were run were opened. From there the necessary manipulation was done to create a viewer friendly Digital Surface Model map and a Mosaic Map. The maps can be seen in the results section below.

Results:

Figure G, Digital Surface Model of Litchfield Mine

Figure H is a Red, Green, Blue Mosaic of the Litchfield Mine.

Conclusions:

All in all, Pix4D is a ridiculously user friendly software that made myself and my classmates, who know very little about processing UAV data, very easy. All of the tools used in this lab were easy to understand and if questions arose, it was easy to find answers using the help button. As stated this by the professor this lab did not dive into the full extent of this software as we didn't even place GCPs for the lab, and we only computed one hill. It is very effective for new users and it would be fun and challenging to use this software more with new UAV data.

Grassy Knoll Assignment

Introduction:

This lab was designed to teach the user how to use a dual frequency survey GPS to collect data and create continuous rasters in ArcMap with the data collected. The lab was centered right in the middle of campus and used a familiar land feature to make the students comfortable with the equipment.

Study Area:

Figure A: the study area denoted by the red rectangle in the middle of UWEC campus.

Methods:

Using the dual frequency survey grade GPS on a small grassy knoll on campus mall, the group was able to get 19 data points (Figure B below) The students broke up in to groups of 2 so each person could learn how to use the equipment effectively. Each group gathered at least 2 GPS points that had X,Y,Z data. This is done by first leveling the tall GPS post with its 3 legs. Then gather the point on the handheld touch screen GPS monitor. The class gathered data points at random, meaning the class used a stratified sampling method. The data was compiled into a text file and given to the class by our professor. Individually each student had to normalize the data. After having an X, Y, and Z column it was time to import the table to ArcMap. From there a feature class was created from the XY data ready to be interpolated. 5 rasters were created for the 5 types of interpolation, IDW, Kriging, Natural Neighbor, Spline, and TIN. From there the rasters were then put into ArcScene to be viewed as 3 dimensional.

Figure B: Dr. Hupy teaching us the ins and outs of the survey grade GPS

Results/ Discussion:

The Interpolations ran smoothly without a hiccup, and were translated successfully in ArcScene.

Figure C: IDW interpolation

The IDW interpolation put too much emphasis on the low points and high points giving the almost level parts divots and the top of the hill a point. Neither of which exist in real life.

Figure D: Kriging Interpolation

The kriging gave a very accurate representation of the hill, nice and steady slope and even shows where the slope is most drastic.

Figure E: Natural Neighbor Interpolation

Nearest neighbor was great because, not the best, however it did use the outer most points to make the shape of the actual area we were dealing with and not a rectangle.

Figure F: Spline Interpolation

Spline worked quite well in accurately showing how the hill wasn't perfectly even. It showed the wave of the base of the hill nicely.

Figure G: TIN Interpolation

The TIN was great as it used triangles to show depth and variation. It really shows the slope better than the others, however it makes it seem more drastic and puts a point at the top of the hill.

Conclusion:

This field assignment was great in teaching the class how to really use a survey grade GPS, as we had tried to do in the first week but failed due to time problems. These skills can be transferred to the real world easily because a GPS unit like this is quite common for gathering data. The technology worked very well and the rain didn't stop our class from learning well.

Assignment 10

Introduction:

The purpose of this field activity was to set up and use ArcCollector through ESRI and ArcGIS Online. While this was done in the previous exercise, it was already set up and the data was the only thing that needed to be collected. In this exercise a question was asked, Geodatabase was created, the domains were made for the feature class, and the parameters were set up to be uploaded into ArcGIS online. The question for the project was: Do Student or Non-Student residents on 7th Avenue, Eau Claire Wisconsin, take better care of their lawns? The criteria were: student or non student, number of residents in the house/ apartment, quality of the lawn maintenance, and number of unwanted things (trash/garbage/tipped over furniture/other assorted items) seen in the yard.

Study Area:

The study area was 7th Avenue, Eau Claire, Wisconsin; from Water Street north to Broadway Street; as seen in Figure A.

Figure A is a screen shot from my cellphone of the study area on ArcCollector.

Methods:

The very first thing that was done was to create a new file geodatabase. Then it is time to create the domains, the domains are what will go into the feature class and what will be available to select once it is time to remotely gather data. The domains used were Demographic, which determined if the residents were students or non-students, this was a text field. Next was Garbage, an integer field used to record how many pieces of garbage were in each yard. Thirdly, Landscaping, a text field that leveled, allowing the user to pick from poorly maintained, somewhat maintained, and well maintained to describe the lawn's condition. Lastly, Residents, a short integer field describing the amount of people in the house. After the domains were set, a feature class was created in order to take the data in the field.
Next was to create the area in which the data was collected, while this step was not necessary, it seemed to make it easier when selecting how far my study should go based on time around the Thanksgiving holiday season. This step was just to create a feature class of a single polygon to encompass the study area in case any rasters were to be made so they could be clipped to the proper size. Once these two feature classes were done, they were uploaded to ArcGIS online and applied to a basemap. Then saved and downloaded to the Android smartphone.
The last step was to go outside in the cold of November and gather data on the smart phone. This step was the easiest because counting garbage and knocking on the doors of houses to ask a few questions isn't hard work.

Results/Discussion:

Figure B

Figure B above shows the amount of garbage left in the yard and if they house had students or non students living in it. As you can see, while one house of non-student residents had a lot of trash, the other 4 did not have much unwanted debris in their yards.

Figure C

In Figure C the number of residents was put with the amount of trash in the yard. This did not really seem like there was much of a correlation that you can visibly see. Trash seems equally distributed despite the number of residents.

Figure D

Lastly Figure D shows the quality of landscaping and the house's demographic. Only one house got the ranking of poorly maintained and it happened to be lived in by students, this however is not really a significant finding because in the student ghetto area, the landlords often take care of the yard maintenance.

Conclusion:

Project design is everything, if the work is not put in at the grass roots of a project then it won't turn out well and it won't go nearly as smoothly. Setting up domains correctly is a major key to success when working on a project like this. If mistakes are made during that set up, they will be irreversible once the geographer is out in the field. There doesn't really seem to be much of any correlation between students and bad lawns and extra garbage, however this question could have been answered much better if the study area had been increased. ArcCollector is literally like the coolest program ever, provided you have access to ArcGIS online.

Assignment 9

Introduction:

This particular lab used ArcCollector, a smartphone app connected to Esri ArcGIS Online. Using ArcCollector the class was able to gather data remotely from the field and have it saved to ArcGIS Online and updated in real time. This collection method allows different groups to split up and gather data and have it compiled together without the hassle of manually normalizing the data after collection. This assignment had the groups splitting up and collecting data on the micro climate of the UWEC campus. The groups collected Temperature, Dew Point Temperature, Wind Speed, and Wind Direction. The class was split up into groups of 2 and assigned a Zone to gather data in. Figure A below shows the different zones. November 9th was the date of collection, it was cloudy most of the time and decently chilly, but oddly warm for early November.

Figure A shows UWEC campus split into different zones as well as the points collected by all the groups.

Zone 1 is mostly open space with some tree cover. It also has 2 large parking lots and Hass Fine Arts Building. It is also right next to the Chippewa River and has a bike path through a wooded area. The southern and eastern portions are the flood plain of the river and are a lower elevation from the rest of the zone. Zone 2 is mostly campus with many sidewalks and roads and open grass areas as well as 6 campus buildings. It extends northward into a residential area. Its section adjacent to the river is elevated above the water level. Zone 3 (my zone) contains 3 campus buildings and a large heavily wooded bog/stream area. known as Putnam Park West. A stream runs right through the middle as well. It also contains a massive parking lot, a small parking lot and a fair amount of sidewalks. Zone 4 was the center of campus, it is mostly planted grass and sidewalks. It contains two buildings two resident halls and a road, as well as the same stream in zone 3. Zone 5 is very diverse, its northern section is the heavily wooded Putnam Park North which is at a lower elevation next to the river with a floodplain. The southern and eastern portions are UWEC upper campus which is elevated above the other 4 zones. This area has 7 resident halls, the Hilltop Center, the Central Heating Plant and many parking lots roads and sidewalks. It also has a fair amount of open grass.

Methods:

At the start of class each person in the class logged on to ArcGIS Online and set up the study area provided by Dr. Hupy. Then downloaded the ArcCollecter app on their smartphones and linked them to the map with the zones. The class then split into groups of 2 and set out for the different zones. In order to collect the data each group was issued a Kestral weather meter which measured Temperature (Fahrenheit), Dew Point, and Windspeed (MPH). Using a compass and pointing north each group manually guestimated the direction of the wind in degrees, 0 being due north. Lastly we used our smart phones with the GPS location services on. This recorded our locations of where we collected each point. Using the Kestral we measured the above data and recorded it onto the smartphone in the ArcCollector app. 
After all the points were collected each person downloaded the data into ArcMap into their own geodatabase. Then 3 maps were created with the data. Using Inverse Distance Weighted (IDW) Interpolation the recorded data (temperature, dew point, and wind speed) were used as the Z value to create rasters. Each raster was then clipped to the Micro Climate Zone feature class to fit just our study area. For the final map that shows Wind Speed and Direction the collected points were set to be displayed with Wind Direction as the attribute. Then in the Symbology tab the were displayed with geographical direction selected. Finally the Symbol was changed from a dot to an arrow.

Results:

Figure B, the first image below is the map of temperatures around the UWEC Campus. This image is very straight forward and not out of the ordinary. The temperatures are warmer when the collected near buildings and in the middle of parking lots and on sidewalks. This is because the concrete and buildings have been absorbing heat all day. The colder areas are grassy and away from buildings. The coldest area (southeast) was recorded by my group in the middle of the wooded bog area.

Figure B shows surface temperatures on UWEC Campus. Note the coldest area in the southeast was heavily wooded with a bog.

Figure C below shows the Dew Point temperatures around campus. The data collected shows a pattern of higher dew points in the middle of the map and lower dew points on the northeast and southwest. This is most likely directly related to the stream, Little Niagara, that runs right through campus.

Figure C shows the Dew Point Temperatures with a diagonal pattern following Little Niagara.

Figure D shows both the Wind Temperature and the Direction from which it was blowing. It is easy to see how the buildings around campus affected what way to wind was blowing and how strong because some acted as shields from the wind and some amplified its speed. 

Figure D shows Wind Speed and Direction. Points that had no wind direction were defaulted to due North.

Conclusion:

This lab was great, it was so cool that I told my friend studying GIS at another school all about it. It is incredible that Esri has an app that allows users to connect directly using the internet and smartphone. It was an fantastic way to collect data and have accurate data points. The data transfer and collection was very easy and user friendly and overall quite efficient. Unfortunately the rasters lost some resolution and became more coarse when clipped from a large square to just the study area. I could not remember how to combat this so they are coarser than I would like, but still acceptable. 

Assignment 8

Introduction:

This is the second part of the Navigation lab, started the previous week. Last week my group, group 4, created UTM and Decimal degree maps to use for navigating out in the woods by the UWEC Priory. As said before, technology will fail you and you need to be able to navigate without the use of a cell phone or a GPS unit. Out in the woods we found that terrain is difficult and navigation is even harder.

Study Area:

The study area was the UWEC Priory, an apartment style residence hall and Nature academy/day care for children. It is roughly 3 miles south of the Eau Claire campus and is surrounded by a 120 acre forested area that is full of elevation changes. The priory woods was heavily wooded and the area we were in changed elevation quite frequently. There were many downed trees and small creek that ran through our area. It was a great autumn day with a few clouds in the sky.

Methods:

We met in the priory parking lot and got our data sheets. These sheets contained our navigational landmarks with their decimal degree coordinates. We marked them on our maps that Dr. Hupy printed out for each group. These were the maps created in the previous week's lab. We were then issued a handheld GPS unit and a compass (figure A below) to navigate with. Next was to find our individual pace counts. I did not partake due to a previous injury and was walking with a limp, hence my paces would be drastically different on different terrain. The other two group members found their pace counts for 50 meters, we then doubled them for a 100 meter pace count. We set out to the corner of the parking lot and assigned roles. We had a pace counter, azimuth controller, and pace count recorder. The pacer walked to a landmark and counted the paces, the azimuth controller told them the land mark and made sure they stayed on track, and the counter recorded the steps and converted the steps into meters. Using the compass we lined up where we needed to go on the map and converted the cm into meters and began our journey (figure B). To find the bearing direction line up the starting point and the point on the map, make sure the map is oriented north. Then line the red lines on the compass north and south by turning the dial. When those lines are oriented north-south the degree on the outer edge should tell you the direction you have to travel when the compass is pointed north. Red in the shed. Then pacer then walks in that direction counting their steps and the recorded records them and converts them to meters.

Figure A, a navigation compass

Figure B, Payden being the Azimuth Controller

while Sarah Ventures off in the correct

direction.

Results and Discussion:

The GPS unit mapped our progress and where we traveled but it didn't seem very accurate I believe this was because some of the valleys and ravines and the tall canopies messing with the signal. While we were standing still the GPS was moving. We actually found the first point with ease figure C shows Sarah posing with our first point.

Figure C, Sarah at navigation point 1 for group 4.

We had troubles finding point 2, but we came within the vicinity. When we tried navigating to points 3 and 4 we didn't travel northward enough. The elevation changes were changing the pace counts and the group was not taking that into account quite enough when attempting to navigate to the points. As we spent time trying to navigate through the thorny terrain full of relief the sun began to set and we decided that instead of getting lost in the woods at night we would pack it in before finding point 5. Figure D is a map of the points we were supposed to navigate to.

Figure D shows The Priory woods with the Navigation Points for group 4

Conclusion:

Of the two maps used to navigate the UTM map was the map we chose to use since we were able to easily mark the points on that map. The grid lines however could have been finer, this would have made navigation easier and our placement of the points more accurate. We believe that our points were off because of the grid being too coarse. Either way this assignment was great especially because it taught a bunch of people how to navigate with a map and compass even if they didn't make it to their targeted areas, they now have the knowledge to do so.

Assignment 7

Introduction:

This week's activity was to create field navigation maps that will allow each group in the class to navigate part of the University known as the Priory, using just our map and a compass. Each group of 3 made two maps and picked the best/easiest map to use when navigating. The purpose of making two maps was to have each in a different coordinate system. One map having a UTM Projected CS and the other having a Geographic CS in decimal degrees. When making the maps it was crucial to make sure the maps only had what was necessary for navigation. A map with too much will be crowded and confusing, but a map with too little will get you nowhere, literally.

Methods:

Starting with a blank work space in ArcMap the Priory Geodatabase, provided by our professor, was saved  into a personal folder. This geodatabase contained a bunch of information that could be used or not used when making the two navigation maps. First the Navigation Boundary feature class was added to the blank map. It was then made hollow and its outline enlarged and made red. Then an elevation raster was added. Using the clip tool, it was then resized to fit the Navigation Boundary. After clipping the elevation raster it was made into contour lines using the Spatial Analyst tool Contour. It was contoured to 2 meters because that seemed like a distance everyone can judge. Next the new 2 meter contour feature class was projected into the NAD 1983 UTM Zone 15 coordinate system. Then a nice backdrop was added to show detail underneath the contour lines, a true colour image was chosen.
Once all the data was added it was time to make the map. First was to change the page set up to landscape 11x17, this ensures that the maps would be formatted to be printed no matter whose map in our group got chosen. Next was to add the grid, this was a bit difficult. Under properties in the grid tab, select new grid, then choose measured grid. Confirm all layers are in the correct UTM CS and set the grid to be at increments of  50 meters on both axes. After the grid was figured out, the north arrow, title, legend, watermark, projection, and other information were added. This created the UTM map shown in Figure A in the results section below. For the Decimal Degree Map I started over in ArcMap with a blank map, added the NON projected 2 meter contour feature class, my GCS Navigation Boundary and a true colour image of the Priory. With all of these being in Geographic Coordinate System North American 1983 it allowed for a Decimal Degree grid to be placed on the map in layout mode. This map can also be seen below as Figure B in the results section.

Results/Discussion:

Figure A is the UTM map of the Priory area

Figure B is the GCS map of the Priory area

The goal of making these maps was to create maps that would aid us in navigating the priory. 2 meter contour lines were used because I believe that seeing the change in elevation, a compass, and the aerial imagery should be enough to help us navigate and do whatever zany activity is going on in lab that day to come. The UTM map may be better in general because meters are easier to understand and it has a finer grid.

Conclusion:

This assignment was very method heavy with not much instruction. We were just given a list of criteria and told to do the best we can with a shove in the right direction, which is probably what real life GIS people do most of the time. This assignment really required thought and skills to remember elements that go in to making a good map, and not just some run of the mill map for a blog post, but a map that each group has to use. Not to mention this map is going to be used to help us practice a skill that everyone should have, simple map/compass navigation.

Assignment 6

Introduction:

The goal of this assignment was to do a field survey of trees in Putnam Park on the UWEC campus area (Figure A below) using Azimuth angles and the distance between a fixed point and the trees being surveyed. This is especially important because there may be a situation where all you may have is range finder and little handheld GPS. As my professor says "Technology will fail you" so the class needs to be prepared to collect data without nice equipment like a survey grade GPS or a drone. An important part of this lab was data normalization because the class split into 3 groups in order to get all of the data. Working together the class decided that the data should have x, y, distance, Azimuth, Tree type, Diameter at breast height (DBH), and point number. X field is the longitude, Y field is the latitude, Distance (meters) is the distance between the group and the tree, and  Azimuth is the angle to the tree being surveyed. Tree type is what kind of tree we thought it was, DBH is the tree's diameter, and the point number denotes what set of data the trees belonged to.

Figure A, the red box denotes the study area.

Methods:

Materials

• Tree Diameter Measuring Tape
• Compass
• Rangefinder
• Handheld GPS
• Field Notebook
• Smart Phone

The first step was to collect the GPS location off of the handheld GPS. These GPS coordinates will be used for all the data at point 1. The GPS coordinates for point 2 are all the same (point 2 located about 50 meters west of point 1), and the GPS coordinates for point 2 are all the same (point 3 located about 50 meters east of point 1). See the data below in Figure B. Then a tree is selected and the compass is used to find the angle to a certain degree. Then the range finder is used to find the distance in meters between the fixed point and the tree. Next is tree identification, using smart phones the groups were able to identify the trees easily. Lastly a class member used the Tree Diameter Measuring Tape to measure the tree's diameter at breast height. These steps were then repeated for about 10 trees at points 1, 2, and 3. After all the data was collect it needed to be entered into an excel spread sheet. 

Figure B is the data. The x,y values differ for every point, but are the same within those points.

The data is then imported into an ArcMap Geodatabase using the import single table function. Once the data is a table in ArcMap it is now time to run some tools. The first tool used was the Bearing Distance to Line command. The table was used for the data and all the fields were correlated, x to x, y to y, distance to distance, azimuth to azimuth, and tree type to characteristic. Once the lines are created the tool Feature Vertices to Point command was used. This tool put a point at the end of each line, these points were the trees. To finish it off the feature classes were added to the map and so was a base map.

Results/Discussion:

Figure C is the final azimuth survey map

The final survey map is accurate and shows the correct distances from the point center to each tree. and the GPS points are all accurate. This however did not come with ease, the data needed manipulation because the handheld GPS that was used messed up the points. Figure D below shows the initial map made before the data was fixed.

Figure D shows the bad map made form the initial data.

In Figure D the data at point 2 was in the parking lot, the data for point 1 was too far to the east, and the data for point 3 was located a few miles south of our study area. To remedy this the XY data was changed by using the GPS information gathered from the base map. Once the excel file was fixed the tools were reran and the new feature classes were added, and looked good.

Figure E is the final survey map, but it shows the individual tree types sorted by different colours.

Conclusion:

This distance azimuth survey turned out decently successful despite the setback from the bad GPS data. That was easily remedied by using ArcMap to find the correct lat/long data. It was much different than any other survey the class has done thus far, and quite different from using a Survey Grade GPS, but as said before, those tools will not always be available. Taking measurements by hand turned out to work well, and the fact that this lab went swimmingly was quite rewarding.