Assignment 12- Processing UAS Data in Pix4D

Introduction

In the final assignment of the semester, the class was introduced to Pix4D software.  A true orthomosaic of imagery will be created using this software with images from the Litchfield mine.  Before using this software and fully understanding it, it was required to go over the manual to get a basic understanding of the software.  While this software is relatively easy to use at first, there are many processes that one can do to advance their data.  In order to do so, you must learn about how to use the software and requirements your data must meet.  There were certain questions that needed to be answered which are seen below.  The link to the Pix4D manual which helped answer these questions can be found here.

• Look at Step 1 (before starting a project). What is the overlap needed for Pix4D to process imagery?
• High overlap, at least 75%, is required in order for high accuracy results.  This means that image acquisition needs to be carefully planned in order to obtain high end results.  An example of the minimum overlap needed for high accuracy results is seen below in Figure 1.

Figure 1: An example of the minimum amount of overlap needed for high accuracy results.

• What if the user is flying over sand/snow, or uniform fields?
• These areas have very little visual content because of the large uniform areas.  This means high overlap is needed in order to produce good results.  This overlap should be at least 85% frontal overlap and at least 70% side overlap.  The exposure settings should also be set in order to get as much contrast as possible in every image.
• What is Rapid Check?
• This is a way to check that the data output will turn out okay.  It reduces the resolution of the original images, lowers the accuracy and might lead to incomplete results.  However, it produces results much faster.  It is recommended to use in order to get a quick preview of the outputs to make sure everything look the way it is intended to.
• Can Pix4D process multiple flights? What does the pilot need to maintain if so?
• Yes, Pix4D can process multiple flights.  However, when designing the separate flights make sure that: 
• Each flight captures the images with enough overlap
• There's enough overlap between two flight plans (as shown in Figure 2)
• The different flights are taken under almost the same conditions

Figure 2: The amount of overlap needed to process multiple flights vs not enough overlap.

• Can Pix4D process oblique images? What type of data do you need if so?
• Yes it is possible for Pix4D to process oblique images.  These are images that are taken with the camera axis not perpendicular to the ground (as seen in Figure 3).  In order to produce good results, the oblique dataset will need 85% overlap and if possible, fly two rounds around the object.

Figure 3: Vertical vs. oblique images.

• Are GCPs necessary for Pix4D? When are they highly recommended?
• They are not necessary, but they are highly recommended when processing a project with no image geolocation.  If no GCPs are used, then the final results will not have a scale, orientation, or position information.  This means the images cannot be used for measurements, overlay, and comparison.  GCPs are highly recommended because they increase and verify the accuracy of a project.  
• What is the quality report?
• This is automatically displayed after each step of processing.  It checks the data to assure it has high quality.  It gives indicators of whether it is good or not throughout the long and detailed report.  A quick preview of the quality report example can be found by clicking here.

Methods

Now that some background information on Pix4D has been given, it is time to focus back on the Litchfield mine.  The images taken with the UAS were imported in Pix4D to begin the process.  It was recommended to create a smaller study area in order to make the image processing go fast.  However, for the final assignment it was decided to do the whole mine.  There are three options for processing options which are initial processing, point cloud and mesh, and DSM orthomosaic and index. Initial processing was only done first to make sure everything was working correctly. Once this was finished, the quality report was created.  As stated before, this gives an overview of the images and to what quality they turned out.  The images overall has high overlap except for on the edges of the image which wasn't a problem.  Once all of this looked good, point cloud and mesh and DSM orthomosaic and index were run.  These processes took over 10 minutes to run, however the results were well worth it.  A video of the finished results can be seen in the results section.

Results

The mosaic and DSM of the study are was imported into ArcMap and the results are seen below in Figure 4 and 5.  the DSM was also brought into ArcScene which is pictures below in Figure 6.  Pix4D produced highly accurate results which were very pleasing to see and were done with very little work (at least on the students end).  Flying and planning the UAS trip is a different story.

Figure 4: The DSM of the study area as seen in ArcMap.

Figure 5: The mosaiced image of the study area.

Figure 6: The 3D image of the study area as seen in ArcScene.

Conclusion

The results seen here are of very high quality which was because of a well planned UAS flight.  It is important when going out into the field to plan beforehand and to also have backup plans.  Things never really truly go as planned (as seen in this class and others).  However, having other plans and preparing before can still produce high end results.  Pix4D was a very useful software to learn and it would have been interesting to use more in the class if there was more time.  It is an easy to use software that has more advanced features that can be used as well.  

Assignment 11- Topographic Survey

Introduction

In assignment 11, the class went outside to use a high precision GPS unit.  20 points were taken and each student was able to collect a point using the GPS unit.  The 20 points were then used with different interpolation methods which produced image displaying elevation of the study area.

Study Area

Below in Figure 1 is the study area outlined in red for this project.  It is a grassy area on the UWEC lower campus in between the buildings.  The points were collected on November 30, 2016.  The temperature was in the mid-30's with light rain.

Figure 1: The study area for this assignment.

Methods

The class went outside to the grassy knoll on the UWEC campus where Dr. Hupy proceeded to explain how to use the GPS.  The GPS is pictured in Figure 2 being used by Heather and Sarah.   By using the random sampling method, groups of two would take a point together for a total of 20.  More points would have been taken, but technical issues only allowed 20.  After everyone received a turn to collect a point and 20 were taken, everyone headed back inside.  The points were then uploaded to a text file for the class.  From there everyone was on their own.  The points were imported into an excel file in order for it to be used in ArcMap.  It was discovered later that the points are in the wrong UTM zone.  Here in Wisconsin is in zone 15, but the points were taken in zone 16.  This produced some differences within the data, however the point still gets across what the data is supposed to look like.  The excel file was imported into ArcMap and then converted into a point feature class.  From there, five interpolation methods were used on the points.  These produced different results.  These interpolation methods were used in a previous lab in which the methods are described.  

Figure 2: Heather and Sarah using the GPS to collect a point.

Results

Below in Figures 3-7 are the images produced using the five interpolation methods.  These results are not 100% accurate because of the zone mix up.  All of these methods created different results which depict the study area in different ways.

Figure 3: IDW interpolation of the study area.

Figure 4: Kriging interpolation of the study area.

Figure 5: Nearest neighbor interpolation of the study area.

Figure 6: Spline interpolation of the study area.

Figure 7: TIN image of the study area.

Conclusion

Ultimately, none of these methods produced a highly accurate representation of the study area.  This is because of the minimal points taken within the study are.  However, nearest neighbor and TIN did a decent job of representing the area.  If more data points were taken these interpolation methods would have produced more accurate results.  The GPS that was used was also so precise that it would have been very accurate in representing the area.  It was a good introduction to using this type of GPS and looking at it's results.

Assignment 10- Arc Collector Part Two: Litter

Introduction

In the second assignment of using Arc Collector, each student created their own projects.  This project focuses on litter in the Eau Claire area.  The main question of this research to see where most of the litter in the study area lies.  There were four zones where data was collected, housing, bars, park, and campus.  The data will hopefully show where more trash cans need to be put in in order to decrease the amount of litter on the ground in Eau Claire.  Data was collected using Arc Collector and then edited in ArcGIS Online as well as ArcMap on a desktop.  For this project, it was very important to have a proper project design in order to produce accurate results.  A set research question was asked and feature classes were created with attributes that made sense to collect.  This in turn produced a successful project.

Study Area

The original study area was set before any data was collected.  This area ended up being too big when data was being collected.  A smaller study area was created based on the points collected.  The original and final study areas can be seen in Figure 1 and 2.  This data was collected between November 17th to the 21st.  The weather throughout these days was quite cold with temperatures ranging from 30-45 degrees (F).

Figure 1: The original study area for this project.

Figure 2: The final study area for this project based upon the data collected.

Methods

Before going out into the field, the feature classes had to be set up.  These classes were area, amount, garbage, description, and notes.  Figure 3 below displays all of these fields.  Domains were set for area, amount, and garbage.  The areas were housing, campus, park, and bars.  Amount was 1, 2-5, 6-10, 11-20, 21-30, and >31.  Garbage meant whether there was a garbage around.  The attributes for this were next to garbage can, within walking distance, visible but far away, and none in sight.  Description did not have a domain because it was used to describe what kind of litter was found.  For example, plastic bag, beer cans, cigarette butts, etc.

Figure 3: The fields and their attributes used in collecting litter data.

After the fields were all set up, they were imported into ArcGIS online.  This allowed the class to download their individual maps onto their phones or what ever they were collecting data with.  From there, each student was reading go and collect data.  Below in Figures 4 and 5 are screenshots of what the collector app looks on the phone used (Samsung Galaxy S6).  

Figure 4: A screenshot of the Arc Collector app interface when collecting data.

Figure 5: The interface in Arc Collector when entering a point.

 The data points were collected over a few days.  The points were taken while walking down the sidewalk.  Most of the litter was either on the sidewalk or in yards by the sidewalk.  This means that the points aren't in the exactly precise spot where the litter was found, however it is very close.  When it came to a point that represented more than one piece of litter it means that it was obvious when standing in one spot that there were multiple pieces of litter together.  If the pieces were separated by some distance than separate points were logged.  In total, 69 points were collected.

Results

The results of this assignment are shown below in Figure 6.  Figure 6 displays the amount of litter at each point that was collected.  It can be seen that there is a lot of litter within the housing and bar areas.  There is less on campus and in parks.  The greater amount of litter in housing and bars could be attributed to a lot of different causes.  For example, intoxicated people throwing empty cans and bottles on the ground, bar patrons throwing cigarette butts on the ground, the people living in these areas only pay rent rather than owning so they don't care about the status of litter in their yard.  There are many reasons.  There could be less litter in parks and campus because of the higher number of trash cans available, workers who are paid to clean up these areas, people don't see litter on the ground so they don't litter, etc...  The causes for the greater or lesser amount of litter in these areas can be attributed to many different reasons.  

Figure 6: The amount of litter at each data point that was collected within the study area.

Figure 7 shows the points where litter was collected in proximity to where a garbage is located.  This map is only partially accurate.  The garbage cans in the park area, bar area, and on campus were actual garbage cans that were meant to stay there.  The garbage cans in the housing area were garbage cans that belonged to the houses.  After looking over the data, it makes sense to exclude these individuals garbage cans.  On one of the data collecting days was trash day so this skewed the data in the housing area as well.  Ignoring the green points in the housing area it is obvious that there is no permanent garbage cans for residents to throw away their litter.  This could be because people would dump their trash in the available bins rather than paying for a trash service.   In the other the areas, there were more trash cans readily available, but the amount of litter varied.  In the bar areas, the most litter on the ground was typically cigarette butts.  People typically stand outside bars and smoke and throw their cigarette butts on the ground.  If bars provided ashtrays maybe the amount of cigarette butts on the ground would reduce.  As for the litter on campus, most of the litter is right by a trash can or within walking distance.  It was found that there was minimal litter on campus, except for right by the trash cans.  The litter typically on the ground is gum and small pieces of paper.  Perhaps people miss the bin when throwing away their litter or those who empty the bins spill some when emptying them.  There are many causes for the litter on the ground along with many solutions to avoid more litter on the ground.

Figure 7: The data points collected displaying the proximity of litter to garbage cans.

A web map was also created for this project that was created on ArcGIS online.  That map is shown below.  It displays the same map as in Figure 6.

Conclusions

This assignment was incredibly useful for future use of data collecting.  It required the class to set up feature classes all with their own attributes.  It was very important to set this all up correctly or the whole assignment wouldn't have worked.  The project proved that there is more litter in the bar and housing areas than in park and on campus.  More trash cans could be put in in bar and housing areas, however even these may not help.  Other solutions to cleaning up the litter within these areas is to encourage the residents to pick up litter in their yards and clean up after themselves.  If this project were to be done again, the only thing that would be changed would be to create points for garbage cans.  This would be useful to see them in proximity to where the litter was.  It would also be useful to only mark where permanent trash cans are rather that household trash bins as well.  This project could be expanded into a larger project if someone wanted to do more research on litter in the Eau Claire area and how to clean up these areas.

Assignment 9- Arc Collector Part One: Microclimates

Introduction

This assignment focuses around using Arc Collector.  Arc Collector is a program that can be used on smart phones and tablets.  It is used to collect and update data in the field and log location.  It is a progressive way to use everyday technology in the field to collect data.  The class got familiar with the app whether it was on a phone or tablet.  After this, Dr. Hupy explained the attributes each group needed to collect.  These were temperature, dew point, wind speed, wind direction, and group number.  Each group collected around 10 or more points and then returned to class.  From there, each student created maps depicting the data the class collected as a whole.  The purpose of using Arc Collector this week was to prepare the class for the next assignment.  The next assignment will also be using Arc Collector, however, each student will develop a research question and collect their own data.  

Study Area

Campus was divided into five sections for this assignment.  Figure 1 below shows the five different zones.  Two or three groups were put into each zone to collect data.  The data was taken between 3-5 p.m. on November 9, 2016 which was sunny and mid 50's for the temperature.  Our group (Sarah and I) was located in zone 2.  Figure 2 also display the study area and also shows the different data points collected within the zones by each group.  As it can be seen in Figure 2, there were no data points taken in zone 4.  There is also no group 5, which were most likely the ones assigned to zone 4.   These were simple mistakes, which did not alter the data very much.

Figure 1: Study area with the five separate zones.

Figure 2: Data points that were collected and sorted out by group number.

Methods

The group set off to zone 2 to collect data points just after Dr. Hupy explained how to use Arc Collector.  He helped us all to set up our own maps and then explained how to collect a point.  In order to collect a point, you had to click the plus button lower down on the screen of the phone or tablet you're collecting data on.  From there, the attributes will come up and the data can be entered.  Once this is finished, all that had to be done was to click the check button to add the point to the map. The group collected points in many different areas to get varying results.  These points were taken in the sun, shade, between buildings, under vents, and by water.  Arc Collector was relatively easy to use and updated in real time.  This allowed each group to see where others were collecting points.  The wind data was collected using a compass and a simple air data meter.  These two tools helped to find the wind data that was then entered into Arc Collector.

Each group collected their points and then headed back to the lab.  From there, Dr. Hupy instructed the class on how to download the data from ArcGIS online.  It was all quite simple and easy to understand.  Figure 2 below shows that different attributes that groups gathered.  The attributes highlighted in blue were from our group.

Figure 3: Attributes collected from the different groups.

Results

The data for this assignment was interesting to work with.  Below, two maps are depicting temperature and wind direction within the study area.  Figure 4 displays temperature on campus.  The temperature was created using the IDW interpolation tool in ArcMap.  Other interpolation methods were tried out, however IDW looked the most accurate.  The cooler areas on campus mostly lie within heavily wooded areas or by water of some sort.  The hotter areas are located around large buildings on campus which pump out hot air.  If more points were taken, this map could show even more accurate temperature patterns within the UWEC campus.  There were no points for zone 4, so even though it is a small area, the temperatures here are only estimations.

Figure 4: Temperature patterns on the UWEC campus.

Figure 5 displays the wind direction on campus.  This was done through measuring wind direction in the field using degrees.  The arrows are pointed in the direction that the wind was coming from.  Zone 4, once again, did not have any data collected for it so there is no information in the area.  In the other zones, the arrows create small patterns that can be seen.  It looks like in zone 3 the wind was blowing mostly East.  Above in zone 2 it looks to be blowing more Northeast.  It then shifts North in zone 1 after coming over the Chippewa river.  It's easier to find patterns in the study area as a whole rather than just focusing on one zone.

Conclusion

This lab demonstrated the basics of how to use Arc Collector.  This was then used in the field to collect data on the wind and temperature.  Collector produced satisfying results and achieved the main purpose of the lab, which was to collect data using the technology available to most people today; our cell phones and tablets.  This was good practice for the next assignment which is very similar to this one.

Assignment 8- Navigation with a Map and Compass

Introduction

For this assignment, the class headed out to the Priory.  The Priory is a University owned area about 3.4 miles from the main campus.  Figure 1 below shows the route from campus to the Priory.

Figure 1: Directions to the study area (Google Maps).

The class met in the parking lot and discussed the activity.  Each group was given five points within the woods behind the Priory.  The groups were also given printed out maps, which were made by one of the group members that were made for last weeks assignment.  The groups also received a GPS and a compass.  Dr. Hupy gave some instructions and set the class free.  Each group was supposed to navigate to the five points they received.  The navigation tools were limited and each person had a specific job to do while in the woods.  The activity took less than two hours to complete.

Study Area

Figure 2 below shows the study area inside the dark blue square.  The points were spread out within this area.

Figure 2: Study area of the Priory.

Methods

Before the groups dispersed, Dr. Hupy instructed the class on how to use the compass and paper map.  He said to line the edge of the compass along the line being used to navigate.  Then, adjust the dial so the arrow aligns with the orienting arrow.  The person holding the compass was then supposed to hold it flat against their chest and follow it while keeping the needle in the orienting arrow.  Figure 3 is the GPS the group used to verify the points and Figure 4 shows a compass similar to the one the class used.  

Figure 3: The GPS used to verify the given coordinates.

Figure 4: A similar compass to what the class used.

Before heading out into the woods, the group found the five points on the map and marked them in order to navigate better.  Figure 5 shows the five points marked for group 3.  Figure 6 displays the five points coordinates that the group attempted to navigate to.  The five points were really spread out which made the assignment a bit more difficult.

Figure 5: The five points marked for navigation.

Figure 6: The five coordinates.

 Navigating with the compass took to time to get used to.  The group figured out easily how to use the compass, but using it while hiking through the woods made things complicated.  The terrain in the woods was rougher than expected and it was tough to walk in a straight line and keep the needle of the compass in the orienting arrow.  The group had to stop many times within the woods in order to adjust the compass and ensure we were headed in the correct direction.  Figure 7 and 8 shows group members Andrew and Heather using the compass to set up which direction the group should go.  Like it was stated before, the terrain was difficult to navigate through.  There were a lot of leaves on the ground, fallen trees, and many twigs that kept hitting all of us in the face. Figure 9, 10, and 11 display some of the terrain that the group came across.

Figure 7: Andrew using the compass to figure
out how to navigate towards the first point.

Figure 8: Heather using the compass to navigate
towards one of the given points.

Figure 9: The terrain the group encountered.

Figure 10: Andrew and Heather dictating which direction to head next while in the woods.

Figure 11: Andrew at the bottom of the ravine where one of the points was thought to be.

Discussion

Throughout this process there were some errors and mistakes made.  The first is shown in Figure 12.  The maps the group used were my own and the legend said the contour intervals were 5 feet when it was actually supposed to say meters.  This didn't produce any complications the field.  The other mistakes made were thinking certain were our own when they weren't.  The group came across many trees marked by pink tape (Figure 13, indicators of the points we were searching for) which were debated over whether they were ours or not.  When looking at the maps it seemed as though the group was in the right area, however the GPS said otherwise.

Figure 12: Map mistake.

Figure 13: One of the markers the group
came across in the woods.

Results

Below in Figure 14 shows the group's tracking points which were taken by the GPS.  Figure 15 shows a line of these tracks along with direction.  The tracks show the the group got close to the points, but sometimes not to the actual point.  There was pink tape on some trees in that area that seemed like the right point, but the GPS points were off.  The group got to the areas which seemed close enough.  We couldn't decide if the GPS was off, we were off, or the tape had fallen off the trees and we couldn't find the right spot.

Figure 14: GPS tracking points in the woods.

Figure 15: GPS tracking points with direction to the given points.

 Conclusion

This activity was very educational, frustrating, and fun.  It is very useful to know all of these navigation skills, especially when technology fails.  Navigation without technology is difficult, but possible with some practice.

Assignment 7- Development of a Field Navigation Map

Introduction

For this assignment, two maps needed to be created in order to complete a navigation activity in the next lab.  When navigating, one needs two sets of tools.  The first being the tools to complete the actual navigation, which cover the simple to the advanced, like the sun, a map, GPS, etc.  The second thing being some type of location system, like a coordinate system.  These systems are based on latitude and longitude, which isn't always the best method to use.  On a smaller scale, it is better to use a state plane or UTM system.  The two maps created for this assignment are using UTM and Geographic Coordinate System of Decimal Degrees.

Study Area

The maps that were created for this assignment are of the Priory, which is a University owned area with a dorm and children's nature preserve.  The area is about 3.4 miles away from the UW- Eau Claire campus. Figure 1 below shows the directions from campus to the priory.  Figure 2 shows that actual Priory study area.

Figure 1: Diretions to the study area (Google Maps).

Figure 2: Study area at the priory (Google Maps).

Methods

For these maps, a different coordinate system was used for each.  For the first map, the coordinate system is NAD 1983 UTM Zone 15N.  This is a common coordinate system which is catered to certain zones to produce an accurate representation of the land.  Figure 3 below shows the zones in the lower 48 states of the U.S.  The state of Wisconsin is broken up into zones 15 and 16.  The whole system is broken in 60 zones which are each 6 degrees of longitude wide.  The zones are also split into north and south at the equator.

Figure 3: UTM zones in the U.S. (http://i.stack.imgur.com/OD3g3.jpg)

For the second map, Geographic Coordinate System WGS 1984 was used.  This distorts the maps when it is used in a smaller scale like this assignment.  This coordinate system uses latitude and longitude to determine locations.  The location is stated in decimal degrees, but this can cause issues.  Decimal degrees are not good to use when measuring distance because it isn't truly a form of measurement.  Figure 4 displays how GCS is measured.

Figure 4: GCS system (http://help.arcgis.com/en/geodatabase/10.0/sdk/arcsde/images/coordsys01.jpg)

The final map for the UTM zones is shown below in Figure 5.  This map is overlayed with a grid which displays the critical number and the beginning number.  The contour layers used in this map have a 5 meter spacing to indicate changes in elevation.  This will help with navigation in the following assignment.  Because this map is a projected coordinate system, it means that it has a projection which is a transverse mercator.  The GCS map not have a projection because it is unprojected which is causing the distortion in the image.  The GCS map has the same contour intervals but are distorted along with the image.  The UTM map will most likely be more useful in the field when it comes to navigating.  However, the GCS map will most likely be used for navigation or something else as well.

Figure 5: UTM navigation map of the Priory.

Figure 6: GCS navigation map of the Priory.

Another aspect of this assignment was pace counting, which was not done.  This was due to bad weather the previous week.  Pace counting helps to determine how far you're going when navigating with a paper map.  Pace count is done by walking 100 meters and measuring the number of paces it takes to get to this distance.

Discussion

 This activity was very informative.  It was all rather new and should produce interesting results in the field, especially navigating with paper maps.  The maps should hopefully prove useful in the field.  Some aspects of the map may be off in some sort and may throw some things off when navigating, but these confusions or downfalls of the maps will be discussed in the following lab.  the two maps that were created for this assignment are similar in many ways while also being completely different.  Changing a coordinate system can create large changes, especially when the coordinate system is either projected or unprojected.  I would have been useful to measure out a pace count beforehand, but this can be done tomorrow during the lab.

Conclusion

These maps were challenging to create in some ways, but have provided a new insight into simple navigation.  Working with classmates and looking at previous blogs has helped to further knowledge on this subject.  However, actually working in the field and learning from personal mistakes will most likely create the greatest amount of learning.  There will most likely be changes that will be needed to make to the maps, bu this will be discussed in the following blog.

Assignment 6- Conducting a Distance Azimuth Survey

Introduction

In this assignment, the class was required to conduct a survey.  This was done behind Phillips hall along Putnam Drive.  From there, the class broke up into three groups. There would have been more groups, but there was limited equipment. The class was supposed to collect 30 tree locations along with their species, diameter, azimuth, and distance.  This is a simple way to do a survey of a small area when other more advanced technology fails.  This is not ideal, but it can work for small study areas.  The three different groups were given a GPS point for the certain area they were supposed to stand and gather the points.  From there, the person standing in this spot measured the azimuth and distance of a tree from where they were standing using survey instruments.  10 points were collected from each of the three groups to produce 30 different tree locations and their attributes.

Study Area

Below, Figure 1 gives an idea of where the data points were collected from.  The red square box is where Phillips hall is.  Further back on Putnam Drive shows the three different data collection points.  These point were spread out enough where there would be no overlapping data points collected.

Figure 1: Phillips hall and the three data collection points.

Methods

For each tree, latitude and longitude, distance, azimuth, diameter, tree type and group number were recorded.  The lat and long were only three different spots which were where the tree data points were collected.  The person standing there then measured the distance and azimuth from their spot to a tree.  This was manually recorded.  Someone else in the group would then go to the tree and measure it's diameter.  Finally, the group had to debate the species of tree and record this.  Below in Figure 2 shows the excel sheet with the data that was collected from each group. 

Figure 2: Excel spreadsheet displaying the data collection is assignment 6.

These attributes were chosen to collect because they were deemed the most important.  The attributes give a unique description for each data point that was collected.  This is crucial in order to create a unique identifier in a map later for each tree. 

Discussion

This assignment was less technological and advanced that most other assignments.  These data collection methods are supposed to be used in circumstances where technology fails.  There was minimal technology used in the lab, which made it less advanced.  Less advanced does not mean lesser work, though.  The data that was collected was for the most part was accurate and reliable, as long as the person collecting the data points stayed in the same location.  The person collecting data points kept changing so the collection point changed just slightly which may skew data points a bit, but not in a major way.  This was the only problem that was was encountered during this assignment, though many issues could have come up if the groups were not careful.

Conclusion

This assignment was successful overall.  It helped to have three different groups so everyone could work with different people while collecting data points.  There were very minimal problems encountered while doing this assignment.  Perhaps, if more data points were collected with more attributes then most errors could have been made.  However, very few mistake were made.  It was good to work in groups and work through how the equipment was used with each other.  This assignment was very informative and it would be useful to use the field method techniques in another lab.

Assignment 5- Visualizing and Refining Terrain Data

Introduction

This assignment required the data from the previous sandbox assignment.  In the previous assignment, each group was to create a landscape in a sandbox.  The sandbox was 114 cm on each side and it was divided into 6 cm boxes to create a grid across the sandbox.  Below in Figure 1 is a picture showing the grid.

Figure 1: The grid overlaying the sandbox for this assignment.

Each square was measured, recorded, and then input into an excel file.  It was realized later that a measurement for each square was not needed.  This is because sampling wasn't taken into consideration.  Systematic sampling was used in a way.  This type of sampling is organized distribution of measurements, which means not having to measure each square.  This was sort of done in the previous assignment.  There were some squares where it was obvious they would have the same measurements as the previous squares.  Otherwise, each square was measured, which ended up creating very precise results.  There is nothing wrong with very precise results especially considering that it's a small area of study.  However, that degree of preciseness just wasn't always needed.

When the measurements for the previous lab were being input into an excel file, data normalization had to be taken into consideration.  Data normalization is important when inputting data.  It is the process of organizing columns and tables of database in order to improve data integrity.  If the measurements were input wrong into the excel file, that could make the results turn out completely different.  Luckily, the results turned out correct the first time and the measurements taken were accurate.

In this lab, the measurements were input into ArcMap and a point feature class was created.  From there, interpolation methods were used to create the digital elevation model (DEM) of the sandbox.  There are multiple different interpolation methods that produce different looking results.  Each were tried out until it was decided which method was most accurate to display the sandbox data.

Methods

1. IDW

• IDW (Inverse Distance Weighting) was the first interpolation method that was tried out.  This method uses weighted distance, which means the average needs to be between the highest and lowest numbers.  This method estimates values by averaging the values in the same area of each processing cell.  When a point is closer to the center of an estimated cell, it will have a greater influence on it's appearance.  This means IDW cannot create ridges or valleys if they have not been measured thoroughly.  This method relies on sampling that is distributed evenly in order to produce accurate results.  Below in Figure 2 the sandbox data is being displayed using IDW interpolation. This method looks a bit too precise.  Perhaps if the measurements followed the systematic sampling better rather than taking a measurement for each square, then the resulting image might look more smooth.

Figure 2: IDW interpolation from the side.

2. Kriging

• This interpolation method is more advanced and intense.  It generates an image from the set of z-values.  This method produces very precise results.  It produces even more precise results when more measurements are taken, like this scenario.  This method creates an output that flows together better than some other choppy looking methods.  Below in Figure 3 the kriging interpolation method can be seen.  This looks a like a very accurate and realistic representation of the sandbox landscape.

Figure 3: Kriging interpolation from the side.

3. Natural Neighbor

• In this interpolation method, it finds the closest subset of input samples to a point.  It then applies weights to them, which is based on areas to interpolate a value.  This technique will not produce any unrepresented data.  If data is missing it is averaged with the data it has to produce a model that is close enough to the real thing.  This can be seen below in Figure 4.  This method is similar to IDW, but without some many divots.  This method smooths out the data more, producing a better looking result.

Figure 4: Natural Neighbor interpolation from the side.

4. Spline

• This method estimates values using a mathematical function.  This function minimizes surface curvature, which produces a very smooth looking surface which still is accurately representing the data.  The great number of measurements means the smother the surface of the output.  This can be seen below in Figure 5.  It is of course smoothed out more than the sandbox, but the features seem to be displaying the most accurate representation compared to other interpolation methods.

Figure 5: Spline interpolation from the side.

5. TIN

• The final interpolation method is a triangular irregular network (TIN).  A TIN is a vector-based digital geographic data.  In this method, it is create by triangulating a set of vertices with vector data.  The vertices are connected through edges to form a network of triangles.  This is seen below in Figure 6.  This method is practically the same as the above methods, but it is just a different form of data (vector vs. raster).  This still produced a very accurate representation of the sandbox landscape.

Figure 6: TIN from the side.

Discussion

After working with all the interpolation methods, it was decided that the spline method (Figure 4) produced the most accurate representation of the landscape.  This method is smooth and the proportions to the actual landscape were accurate rather than too low or too high.  It didn't have weird bumps or unexplainable results.  A runner up was the TIN (Figure 5).  This also displayed a very accurate representation of the sandbox.  however, it just seemed too bumpy.  It wasn't smooth enough to show an accurate output.  If it were smoother it would be just as good, if not better, than spline.  Next was the kriging method (Figure 2).  This type of interpolation produced an interesting result.  It looked like a real life landscape rather than the sandbox.  This did produce an intriguing result, but it just wasn't accurate with the heights and depths.  They are in the correct areas, but the mountains aren't high enough and the valleys aren't low enough.  Natural neighbor (Figure 3)ranked next because it looks similar to the spline method, but the output had odd shapes all over the place.  This result wasn't accurate compared to the actual landscape.  Finally, IDW (Figure 1) was ranked last.  This is because the output turned out to be rather unsatisfying.  It does look like the landscape in some ways, but the measurements for this interpolation method were too accurate for it to display smoothly.

The data that was used for this assignment, which was collected two weeks ago, was very accurate.  This was nice because it produced fantastic results.  The interpolation methods were interesting to work with because they all look so different representing the same thing.  If different sampling methods were used, some interpolation methods may have worked better than the others.  But, overall the results were satisfactory.

Conclusion

This assignment produced great results and was very good experience.  There were some mistakes made, like not precisely following a sampling method.  This didn't necessarily mess up the results, but it would have been easier to take less measurements.  In assignment 1, the landscape was created, a grid was overlayed, each square on the grid was measured and then recorded.  These measurements were input into an excel file.  In assignment 2, the excel file was input into ArcMap which created a point feature class.  From there, interpolation methods were used to produce different looking DEMs.  Ultimately the spline interpolation method was chose as the best method because it displayed the most accurate representation of the sandbox landscape.  This was really good experience when it come to collecting geospatial data.  It helped to work as a team and talk out questions and concerns about the assignment.  A lot of knowledge was gained through these assignments.