Cody Holman
Geography 336 Field Methods
Embedded Files
Wednesday November 20th - Wednesday December 11th
Final Project: Vegetation and Microclimate of UWEC Lower Campus
Cody Holman
Geospatial Field Methods Student - University of Wisconsin Eau Claire
Abstract
Vegetation and microclimate (localized weather) are things that we tend to not pay very close attention to daily. You might hear or see what the temperature is expected to be for a particular day, however you probably don’t think about how the type of surface you are standing on, or where you are in a certain geographical region can affect that expected temperature. Similarly, vegetation tends to be overlooked as well. The purpose of this study is to show how vegetation is spread across the lower campus of UWEC, and how different areas of lower campus differ in Humidity, Temperature, and Windspeed.
Background
Vegetation on UWEC lower campus is not necessarily an issue, however I was curious as to how the vegetation was laid out and whether vegetation was grouped or spaced out singularly. Microclimate on lower campus can provide minor issues of comfortability when walking, specifically in the winter months. A similar microclimate study was done in this class, however that was before snow had fallen and limited walking to sidewalks only. It is also a known fact that the footbridge across the river becomes particularly cold in the winter months and this was another point of study I wanted to make regarding microclimate.
Research Objectives
The 2 main objectives for this project are to determine the vegetation layout on lower campus and whether it is grouped or singularly spaced. And second, determine where humidity, windspeeds, and temperature differ the most on lower campus. My hypothesis for vegetation is “The more centralized the location of lower campus the more grouped vegetation will be.” My hypothesis for microclimate is “The closer to the river points are taken, the higher humidity and windspeed will be, and the lower temperature will be.”
Methods
For the Vegetation portion of this project a survey was created using Survey123 on ArcGIS Online. Questions consisted of Point location, Date, Time, Density of vegetation, Size, Health, Whether the vegetation was grouped or not, and Overall rating of vegetation. The app Survey123 was used on my phone to collect data points for the survey. 81 total survey points were taken on lower campus and then compiled on ArcGIS Online. The data was exported as a file geodatabase and downloaded to my own folder. Next, the data points were brought up in ArcMap and points given a unique symbol to separate flower garden/bushes, group of trees and bushes, and group of trees from single trees. A map showing vegetation was created. Next a map with properties set for a table containing Humidity, Temperature at 5cm, Temperature at 200cm, Windspeed, Ground cover type, Last name, and Date was created. This map was then published on ArcGIS Online, where I was able to pull up the map on my phone with the Collector app. 20 Microclimate points were recorded on the Collector app using a Kestrel 3000 Weather Meter (Fig. 1) to measure weather conditions in lower campus. These points were then compiled on ArcGIS Online and downloaded as a file geodatabase. The data for microclimate was used to produce three unique maps. One each for Humidity, Windspeed, and Temperature.
Results
In total 4 maps were created displaying data for vegetation (Fig. 2), Microclimate-Humidity (Fig. 3), Microclimate-Windspeed (Fig. 4) and Microclimate Temperature (Fig. 5). These map displays were chosen to best address the questions of this project. “How is vegetation spread across the lower campus of UWEC?” And, “How do different areas of lower campus differ in Humidity, Temperature, and Windspeed?”
Fig. 1 Kestrel 3000 Weather Meter
Fig. 2 Vegetation in UWEC Lower Campus
Fig. 4 Windspeed In UWEC Lower Campus
Fig. 3 Humidity in UWEC Lower Campus
Fig. 5 Temperature In UWEC Lower Campus
Conclusion
For vegetation (Fig. 2), results support the hypothesis that “The more center the location of lower campus the more grouped the vegetation will be”. You can see that the outside survey points of lower campus are mostly single trees, and more central survey points tend to be grouped vegetation. Problems encountered with the vegetation data collection included not being able to collect data for every piece of vegetation due to the sheer amount of vegetation on lower campus. My phone also died within 1 ½ hours of data collection. For microclimate (Fig. 3-5), results both support and differ from the hypothesis that “The closer to the river points are taken, the higher humidity and windspeed will be, and the lower temperature will be.” Results support that points taken closer to the river show higher windspeeds and lower temperatures. However, humidity is only slightly higher at river points compared to some other lower campus humidity points, and even lower compared to others. Problems with data collection included my phone dying quickly while attempting to obtain microclimate data leading to only 20 points being able to be collected. Sunlight vs. shaded areas also seemed to have a little affect on the temperature values as well, but this seemed to be a minor issue.
Future Directions
For future studies I would make sure to bring a portable charger for my phone to allow for more time collecting data. I would also do the data collections before snow fall, because snow limits the areas of data collection to only sidewalks. I would also try to find a partner or helper to allow for faster/more data collection. In addition I would do the data collection during an early time of day with the sun shining more uniform on campus to prevent unwanted shaded regions cast from the buildings.
References
Base Map – ESRI, HERE, Garmin, USGS, Intermap, INCREMENT P, NRCan, ESRI Japan, METI, Esri China (Hong Kong), ESRI Korea, ESRI (Thailand), NGCC, (c) OpenStreetMap contributors, and the GIS User Community.
Data- Cody Holman
Kestrel Weather Meter Photo- Kestrel Instruments
Acknowledgments
Thank you to Professor Rahman and the UWEC Department of Geography and Anthropology for providing me with the weather meter and access to the lab to be able to obtain data and produce this project.
Final Poster
Fig. 1 Drone ready for flight sitting on landing pad.
Fig. 2 Handheld controls and live flight video of drone during flight.
Wednesday, November 13th
UAS Image Processing
Cody Holman
Geospatial Field Methods Student - University of Wisconsin Eau Claire
Introduction
This total lab took place over the past two weeks. Last week, Wednesday, November 6th, we met with Martin to learn how drones operate and collect data. We then went into the field and did a drone flight to collect data during a drone flight. After we returned to the lab and learned how to process this data and create maps using Supervised and Unsupervised Classification.
Methods/Discussion
As stated in the introduction we met with Martin to learn how drones operate and collect data, and then went to the field to fly. Before we could go to the field Martin had to submit an application of flight because our planned flight was in a no flight zone due to the proximity of the City of Eau Claire. Once the flight was approved, we went to the field. Initially it seemed that the flight was not going to happen because of the temperature. However, the drone warmed up enough to fly. Once the flight was complete, we returned to the lab and Martin showed us how to download the drone data from the flight and save it into a usable file. He sent us the data for our flight, and the flight he did with the other section of our class.
This week Professor Rahman sent us the data for the drone flight on Nov. 5th, our drone flight Nov. 6th, and some planet imagery of the Eau Claire Area. We learned how to use classification tools such as Supervised and Unsupervised to create maps of the different flights and planet imagery.
I had trouble getting the Unsupervised Classification to run and save into my personal lab folder. After restarting my computer and even trying a different computer I kept having the same issue. Professor Rahman suggested saving it to a general location. This worked and then I just copied the results to my folder. For each of the maps we had to add an attribute field titled “Class” and enter in the proper description for the coded numbers of the maps. You can also see in the Unsupervised Classification for the November 5th flight that a chunk of area is missing from the done flight that is not missing for the November 6th drone flight. I am not sure why this is.
Results
For the final results we were to process the data from the Planet imagery and the two drone flights using Unsupervised Classification and create three maps. The first map is Unsupervised Classification for Planet Imagery (Fig. 3). The second Unsupervised Classification for November 5th flight (Fig. 4). The third being Unsupervised Classification for November 6th flight (Fig. 5). I decided to add the Supervised Classification we did for the Planet Imagery (Fig. 6) because it is more cartographically pleasing compared to the Unsupervised Classification. I also added a locator map to show the overall location of study for this lab (Fig. 7).
Conclusion
Overall, this lab was very fun to participate in. It was very interesting to learn how to create a map using data from a drone that we witnessed flying. The weather for our flight was not optimal, however we were able to complete the flight. The main issue I ran into while processing the data was getting the classifications to run and save to my personal lab folder, however once I put the save location as a general location in the PC, it ran fine. Producing the final maps worked smoothly. One other issue as stated above was the missing area in the unsupervised classification for November 5th flight.
Fig. 3 Unsupervised Classification Planet Imagery
Fig. 4 Unsupervised Classification for November 5th Drone Flight
Fig. 5 Unsupervised Classification for November 6th Drone Flight
Fig. 6 Supervised Classification for Planet Imagery
Fig. 7 Locator Map
Wednesday, October 30th
Spatial Video and Geotagged Photos: An Introduction to gathering geospatial data by using GPS Camera for Qualitative GIS
Fig. 1 GARMIN VIRG GPS Video Camera
Cody Holman
Geospatial Field Methods Student, University of Wisconsin Eau Claire
Introduction
In this lab activity we learned how to use the GARMIN VIRG GPS video camera (Fig. 1) and turn the collected data into geospatial data. We also created an ArcGIS story map for an overview of our project.
Methods
To start lab Professor Rahman showed us how to use the GARMIN VIRG GPS video cameras. After, we assembled into our normal groups, and were told to record an approximately 15-minute video, snapping pictures of anything we chose around campus. My group decided to walk the outside of lower campus and snap pictures of the benches that we encountered. After finishing our video, we returned to the lab and plugged in the camera to upload the video data. Next, we separated the video and photos from the data and placed them in our personal folders. The data was then transformed into a point feature layer to display the path of our video on ArcMap.
We then opened Garmin VIRB edit, where our video and map walked could be displayed. This showed where on the map we were at various points in the video. We used the Garmin VIRB edit software while editing our ArcMap, to add points to our map. In the attributes table we added three fields for our points. Our fields including TypeofBench, which consisted of a number scale with 1 being regular, and 2 being unique, Bench which stood for the composition of bench (met = metal or con = concrete), and Location. After all data had been entered into the table, 3 maps were created showing each of these three fields. A story map published to ArcGIS online was also created along with a locator map of our study area.
Discussion/Results
The 3 main maps created were Bench Type (Fig. 2), Bench Composition (Fig. 3), and Bench Location (Fig. 4). The story map can be viewed at https://storymaps.arcgis.com/stories/e314c6de1d974b228ee7adf0b0051564
Finally, a locator map (Fig. 5) was produced.
The creation of all maps went fairly well after data creation was completed. Adding points to our route walked proved difficult in some bench locations because matching the location to the one on Garmin Virb edit was challenging. However, most aspects of this lab went well. One main issue that should be noted is that our pathway appears to enter a building (Davies Student Center) at the beginning of our route. The possible reason for this could be that GPS signal was lost while walking under the overhang in front of the building.
Conclusion
Overall learning to use and using the GARMIN VIRG Video Camera and turning the data we collected into a map proved do be very interesting. It was pleasing to create a map mostly out of data that we collected ourselves. The various programs we used such as Garmin VIRG edit, were also very interesting.
Fig. 2 Bench Type Map
Fig. 3 Bench Composition Map
Fig. 4 Bench Location Map
Fig. 5 Locator Map of study area
Wednesday, October 23rd
Using Arc Collector: An Introduction to gathering geospatial data on a mobile device, such as a tablet or smartphone
Fig. 1 Kestrel 3000 Weather Meter
Cody Holman
Geospatial Field Methods Student, University of Wisconsin Eau Claire
Introduction
In this lab we learned how to set up a geodatabase, domain, and feature classes to be used for our field data collections. We also learned how to publish a created database and feature class to ArcGIS Online using our organization account. In the field we collected weather data using the Kestrel 3000 Weather Meter (Fig. 1) from selected locations around the UWEC campus. Our group collected 20 samples within zone 1 using the Arc Collector app. Zones were determined by Professor Rahman.
Methods
To start lab Professor Rahman showed us how to create a geodatabase and associated feature class data. We then logged into our ArcGIS Organizational Account and published the geodatabase and feature class to ArcGIS Online.
Next, we were instructed to download Arc Collector on our phones and log in by using or UWEC username and passcode. We were then shown zones sectioned off within campus. Our group was assigned zone 1. We then were each given a Kestrel 3000 Weather Meter and instructed to take readings for temperature at 5cm and 200cm, humidity, dew point, ground type, and windspeed at 20 locations within our zone. All readings were entered under the project “microclimate_GEOG336_Fall19” in the Arc Collector app. Our group split into two groups, each collecting data at 10 locations within zone 1.
After data for all 20 locations were collected, we returned to the lab and waited for the other group to finish collecting their data. After the other group finished, we exported the total project data to a file geodatabase in our Lab 7 folder. Data was then brought up in ArcMap.
Discussion/Results
Once the data was in ArcMap, we created multiple maps including Temperature at 5cm (Fig. 2), Temperature at 200cm (Fig. 3), Dew Point (Fig. 4), Humidity (Fig. 5), and Wind Speed (Fig. 6). A Locator Map (Fig. 7) for our study area was also created.
Overall this lab was pretty straight forward. The main part was learning to use the Kestrel 3000 Weather Meter to collect the needed data. One issue that was apparent when I first attempted to create my temperature maps, was 2 outlier points that made for large temperature groups. The 2 points were at 67-68 degrees and were not consistent with the rest of the temperature data. I decided to eliminate these two location data points from my maps. After doing so, the temperature maps turned out much more pleasing to the eye, and groupings seemed more sensible. All other final maps were created with no issue.
Conclusion
This lab taught us how to publish a created geodatabase, domain, and associated feature class to ArcGIS Online. We also learned how to use the Kestrel 3000 Weather Meter and Arc Collector to collect micro climate data. Creating maps of this data gave us more practice creating maps using data that we collected ourselves in the field. Overall, this lab gave us more experience with field data collection, and using devices, such at the Kestrel 3000 Weather Meter, in the field.
Fig. 2 Temperature Data at 5cm above ground
Fig. 3 Temperature Data at 200cm above ground
Fig. 4 Dew Point Data
Fig. 5 Humidity Data
Fig. 6 Wind Speed Data
Fig. 7 Study Area Location
Wednesday, October 16th
Collecting Data Using Survey123 ArcGIS Online
Fig. 1 General Tree Data map
Fig. 2 General Biodiversity Map
Cody Holman
Geospatial Field Methods Student, University of Wisconsin Eau Claire
Introduction
During this lab we used Survey123 to collect data of 10 trees at various locations on campus. The location was determined by Professor Rahman. We then created our own survey and collected data outside of campus. I created a biodiversity survey and chose to use a section of trail at Carson Park as my location to survey.
Methods
To start lab, we were instructed to download the Survey123 app on our phones and find the UWEC_Geog336_Survey123_Fall2019_Section2 survey. Once we were on this survey, Professor Rahman gave us the coordinates on campus of where we were to collect our data. My location was in front of the Davies Building. I then collected data/photos of 10 trees located in this area and sent them to the survey. Once the rest of the class had finished their data collections, Professor Rahman sent us the data in a .zip file. We then exported this data to a file geodatabase and created a map of the tree data on campus (Fig.1).
For the second part of lab we were instructed to create our own survey with Survey123 of our choice. Being a Biology (Emphasis in Ecology and Environmental Biology) student I decided to make a Biodiversity Survey. I used the questions of: Date, Time, Location (coordinates), How many species were in the area, Type of organism, Quality of organism, Size of Organism, Whether the organism was on the ground or in the air, Image of organism, and lastly notes on the organism. I chose to use a portion of a trail at Carson Park along Half Moon Lake as my area of focus. I walked along this trail surveying the different kinds of organisms I found. I expected to find different species of trees/shrubs, birds, and possibly squirrels or other mammals. Once my survey was complete (11 entries) I headed back to the lab. Once in the lab, I exported the data for my Biodiversity Survey as a file geodatabase to my Lab 6 folder. The file geodatabase was then added to ArcMap to create a map of my survey data (Fig. 2).
Discussion/Results
For the first part of the lab, collecting tree data on campus, most aspects worked as planned. The collecting of data went well, except that a few location points for trees were not in the correct location for some students. One location point that stands out, was one that was located in the middle of the Chippewa River (Fig. 3). For my Biodiversity Survey, I did not collect as many varieties of organisms as I thought I would have. The majority of my Organism turned out to be trees. I did collect some data of fungus such as mosses and a mushroom. When I created the survey, I thought that I would be able to collect data on organisms such as ducks or other types of birds, and also mammals such as squirrels. A reason for this lack of “Biodiversity” in my survey could be due to the time of surveying. I surveyed the area late in the evening. A second reason for the lack of “Biodiversity” could be due to the weather conditions at the time of surveying. At the time of surveying, it was cold and drizzling, which could explain why birds and squirrels were not visible when I collected data. I was able to collect enough data to create final maps. A map of Tree Type and another of Structure of Tree was created for the tree data surveyed during class time (Fig. 4) and (Fig. 5). A locator map of the study area of my survey was created (Fig. 6). Final maps including Biodiversity Type of Life on a Section of Carson Park (Fig. 7) and a map of the Size of Organism on a section of Carson Park (Fig 8) was created.
Conclusion
Overall, the use of Survey123 was a good tool to collect and upload data to be used in creating a map using ArcGIS. Some small errors and inconveniences were encountered as mentioned above, however these did not prevent the ability to create final maps for the two surveys completed in this lab activity.
Fig. 4 Tree Type on UWEC Campus
Fig. 5 Structure of Tree on UWEC Campus
Fig. 6 Study Area Location Map
Fig. 7 Biodiversity Type of Life on a Section of Carson Park
Fig. 8 Size of Organism on a Section of Carson Park
Fig. 3 Tree Point Located in Chippewa River
Wednesday, October 9th.
Total Station System and Collecting Field Data Measurements with Eau Claire County Surveying Office.
Fig. 1 Data Collector
Fig. 2 Tripod for leveling GPS
Fig. 3 GPS on top of pole
Fig. 4 Radio set up
Fig. 5 Total Station
Cody Holman
GIS Field Methods Student, University of Wisconsin Eau Claire
Total Station System and Collecting Field Data Measurements with Eau Claire County Surveying Office.
Introduction
Surveying and GIS go hand in hand. All the data we use in ArcGIS has to be recorded. Surveying is a way to collect much of the data such as topographic data for an area. In lab 5 we work with the Eau Claire County Surveyors Office to collect topographic data on part of campus.
Methods
At the start of class members from the Eau Claire County Surveyors Office gave a quick talk about what they do and the equipment that we would be using. We then went down to the center of campus and began setting up some of the equipment. The Surveyor explained what each piece of equipment did as he set them up.
To begin we used the data collector (Fig. 1) with the tripod (Fig. 2). On the data collector we started a new job. The surveyor called the tripod the training wheels, because the only time it is used is for teaching people or when topography is very fast changing. Attached to the top of the pole above the data collector was the GPS (Fig. 3). To start we worked down the area with everybody taking a turn using the data collector and practicing the keeping the GPS level when recording location points. We would take a recording about every 25 to 50 feet depending on how “flat” the topography was. The distances were measured by paces. Every “pace” was about 5 feet according to the surveyor.
Next, we took off the tripod and worked our way back towards the start location recording points as we went. When we reached the starting location, we set up the radio (Fig. 4) and Total Station (Fig. 5). These tools all communicate together and allowed for us to take faster recordings and cover a larger area.
Once we finished recording points at our area, we returned to the start location and packed all the equipment away. The surveyor said that he would send us the data for our covered area. The next day we received the .csv file and Eau Claire City parcel shapefile. The shapefile and .csv file were uploaded to ArcMap. Create TIN was used to create a TIN of the data. The TIN was then turned into a raster, which was used to create 2ft contour lines. The contour lines were labeled, and along with the data points and TIN overlaid to an areal image.
Results/Discussion
The final product on ArcMap was an overlaid Tin with 2ft contour lines, on top of an aerial image (Fig. 6). Overall, we were able to get the data we needed to complete the lab activity. At first it was sort of confusing as to what we were doing and how the equipment worked. However, by the end of collection data and after asking the surveyor a few questions, I had a fairly good understanding of how the data is gathered and how each piece of equipment works.
Conclusion
This lab activity helped answer one of the very first questions I had about GIS when I first started taking classes in this field. That question was “Where does all of this data/information that we are using come from?”. The answer we were given as a class was that it was all public data, everybody can use it. But, after doing this lab activity I gained a deeper understanding as to where the data originates.
Fig. 6 Final Product (Tin with 2ft contours overlaid on areal image.)
Fig. 1 TruPulse 360B Laser Range Finder.
Fig. 2 Suunto Compass.
Fig. 3 Bosch Laser Range Finder.
Fig. 4 GPS
Fig. 5 Bearing Distance To Line Tool preferences.
Fig. 6 Feature Vertices To Points tool preferences.
Wednesday, October 2nd.
Conducting a Distance Azimuth Survey
Cody Holman
GIS Field Methods Student, University of Wisconsin Eau Claire
Introduction
Many maps show more than just location of objects. Many of them show information such as surveying. In this field activity we use the basic surveying technique of Azimuth. Although basic, Azimuth works in many situations. We will use this method to survey point features such as tree and light posts.
Methods
To start we formed into the same groups as the previous field activities. One of our group members was gone attending a field trip, so our group consisted of only two of us. During the class introduction it began raining pretty hard outside. We practiced using the TruPulse 360B rangefinder (Fig. 1) in the lab. This rangefinder can give you multiple readings including distance and Azimuth readings. We also practiced with a Suunto compass (Fig. 2) which is a non-electronic way to get Azimuth values. Other tools used included a Bosch laser range finder (Fig. 3) solely for distance, and a GPS (Fig. 4) to find longitude and latitude. A clipboard and paper table were used to record data from the field.
After gathering and practicing with all of the tools, we headed outside. It was still raining pretty hard when we got outside and we decided to take our measurements from locations where we would not get too wet and could keep the tools and our paper table dry. The first location was in the courtyard of Philips Hall. Instead of actually going into the courtyard where we could get more measurements, we took our measurements from inside the doorway. The second location was just on the outside of Phillips Hall next to the parking lot. Again, we stayed back under large trees to try to stay out of the rain. The locations were not the best choices for taking our measurements, but were the best locations we could think of to work around the rain. At our first location in the courtyard of Philips Hall we took distance and azimuth measurements for 5 trees. The measurements were first taken with the TruPulse 360B rangefinder. Next we retook the distance measurements with the Bosch laser finder, and the azimuth measurements with the Suunto compass. Coordinates were found using the GPS. Next, we moved to the second location and repeated the same procedure. We took measurements of 4 trees and a light post in the parking lot. Coordinates were again found using the GPS.
After gathering our measurements, we returned to the lab. Once in the lab we entered our data into a excel table. We needed to look at the other groups information to get some points that we missed. An issue that we noticed was that our coordinate measurements were the same for both locations. We went onto google maps and found our coordinates for the locations we were at and entered those coordinates into our table. When our table was complete, we saved it as a .csv file and uploaded it to ArcGIS.
When the file was uploaded to ArcGIS we initially made a feature class out of the data. However, we then ran into problems trying to use the Bearing Distance to Line tool. We then used the raw table data table in the tool, and still ran into a problem. Lines would appear in ArcMap, but they were bunched together, and not in the right location on the map. We again had to fix the X,Y coordinates in our table and run the tool again. If the tool preferences were not set correctly the lines would appear bunched and in the wrong location on the map. Fig. 5 displays the preferences on the Bearing Distance to Line Tool that allowed our data to appear correctly and in the correct location. We then used the Feature Vertices to Points tool (Fig. 6) to put points on the end of our measurement lines. We then overlaid our data to a base map and created a final product.
Results/Discussion
The general map shows Phillips Hall and our location with measurements (Fig. 7). This map was taken and turned into a more cartographically pleasing map (Fig. 8). Unlike the previous activities in prior weeks, we ran into quite a few problems in this lab. The most time consuming problem was figuring out why our data was portraying in a bunched up group. Another problem we had was our coordinate measurements. The GPS gave us the same coordinate reading for both of our locations. However as stated in the methods we used google earth to get the coordinates. The TruPulse 360 was the most efficient and consistent tool to use for azimuth and distance readings. The Suunto compass and Bosch range finder worked, however they were not as consistent. An overall problem that affected every aspect of the lab, was the rain. It discouraged us from obtaining a lot of data such as tree type and diameter. Overall, we were able to complete the lab and create our final product.
Conclusion
The purpose of this lab was to conduct an azimuth distance survey. We ran into problems in the field such as rain, and also problems in the lab while processing data. These problems were able to be overcome and a final product was created.
Fig. 8 Final Cartographic Pleasing map.
Fig. 7 General Map and Data Locations.
Wednesday, September 25th.
Visualizations of the terrain survey data using ArcGIS both in 2D and 3D models
Cody Holman
Geospatial Field Methods Student, University of Wisconsin Eau Claire
Introduction
Maps and models can be very useful for people who cannot go directly to the location of interest. Another benefit is that you can compare different locations by sitting behind your computer, or looking at a poster with multiple maps on them. In this lab activity we took our data from last week's sandbox model, and made 2D and 3D models using ArcGIS. We also returned to the field to try improved methods of molding and measuring.
Methods
To start, our .csv file from last week was loaded into ArcMap and saved it as a feature class in a geodatabase. The file was then added to a blank ArcMap. Under tools, spatial analysis, and interpolation, 5 different methods were used to display the data. The 4 methods in the toolbox were IDW (Fig.1), Natural Neighbors (Fig. 2), Kringing (Fig. 3), and Spline (Fig. 4). TIN (Fig. 5) was also used, by searching for the tool in the search menu. All of these methods were saved into the lab 3 geodatabase. ArcScene was then opened and each method displayed and turned into a 3D model (Fig. 6-10).
For the second portion of the activity, we were to assess our digital models and decide where data was lacking. Our group decided to amplify our features and recreate portions of the sandbox model (Fig. 11). We then measured our grid, recording data into a paper X-Y table, with Z again being elevation. Back in the lab we entered our data into a excel spreadsheet following the same guidelines as before. The file was saved as a .csv file and uploaded into ArcMap. The same process was followed with the new data, and 5 3D models created.
Results/Discussion
The final result of this activity is the 5 3D models created: IDW (Fig. 12), Natural Neighbors (Fig. 13), Kringing (Fig. 14), Spline (Fig. 15), and TIN (Fig. 16). The best interpolation technique for our survey is IDW. This method looks the most accurate when elevation from features is adjusted properly in properties.
It would be beneficial in future activities to take more elevation measurements. Adding more grids to the same area could help the final digital model become much more accurate. Lack of materials, such as tacks, limited us in the size of our grids.
Conclusion
Overall all parts of this lab went smoothly. Last week's activities allowed for continued work in creating digital elevation surfaces. No major problems were encountered in this week's activity. One minor problem occurred when the first interpolation method was attempted. I tried saving the outcome into a general file, rather than a geodatabase, and kept receiving error messages. This problem was solved by simply changing the save location.
Fig. 1 IDW 2D
Fig. 3 Kringing 2D
Fig. 5 TIN 2D
Fig. 7 Natural Neighbors 3D
Fig. 9 Spline 3D
Fig. 11 Amplifying features and recreating portions of sandbox model.
Fig. 13 Natural Neighbors
Fig. 15 Spline
Fig. 2 Natural Neighbors 2D
Fig. 4 Spline 2D
Fig. 6 IDW 3D
Fig. 8 Kringing 3D
Fig. 10 TIN 3D
Fig. 12 IDW
Fig. 14 Kringing
Fig. 16 TIN
Wednesday, September 18th. Field activity #1 Creation of a Digital Elevation Surface
Fig. 1 Sand was hand molded to create a plain, ridge, valley, depression, and hill/mountain.
Fig. 2 Tacks placed in 6 cm spaces around the sand box and string being run to create a grid over the model.
Fig. 3 Elevation being measured from base of each coordinate to the string grid.
Fig. 4 X, Y, Z, excel spreadsheet containing the elevation data collected in the field. Saved as a .csv file.
Cody Holman
GIS Field Methods Student, University of Wisconsin Eau Claire
Introduction
Elevation is a major component of life for people, and has been for most of history. It is a factor in where people travel, farm, and even live. To determine elevation, you find the distance the earth is above or below sea level. In this lab activity we created a sandbox model to mimic a geographical area, and took elevation measurements which will be used to create a digital elevation surface using ArcGIS tools.
Methods
To start the class separated into two groups, and we separated one sandbox into two sections. For our model we needed a plain, ridge, valley, depression, and hill/mountain. We molded the sand to create each of these criteria (Fig. 1). After our models were completed, we then placed tacks every 6cm around the box, and ran string from the tacks to create a grid over the top of the sandbox (Fig. 2). The grid allowed for us to have a X, Y, and Z coordinate system. Elevation was measured using a meter stick (measurements taken in cm). Measuring from the bottom of the sand model, to the string grid, which we assigned as the sea level (Fig. 3). This means that all our measurements would be negative, because they are all below sea level.
Results/Discussion
All elevation measurements were recorded in the field on a paper X-Y table, with Z being the elevation measured. When all measurements were completed, we returned to the lab and entered all data into an excel spreadsheet (Fig. 4). The excel spreadsheet was then saved as a .csv file, which will allow for it to be imported to ArcGIS. All techniques for collecting data worked well. Only issue is that all elevation levels are below sea level (under the grid) so all values are negative. This should only have a minor impact, if any, on the activity moving forward.
Conclusion
The proper data was collected in this activity to be able to continue with our creation of a Digital Elevation Surface in next week's activity. Next week we will use the excel table we created to create a digital 2D and 3D model of our created model using ArcGIS. Overall, all parts of this activity worked smoothly. We encountered one mishap while entering our data into the excel spreadsheet, and that was neglecting to make all values negative. This mishap was easily fixed.
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