Building a Map with UAS Data

Part 1 (Introduction):

Proper cartographic skills are essential in working with UAS data because of the people who will be viewing your data or your maps. If the people who are viewing your maps do not know what they are looking at, the scale of what they are looking at, where the data is located, and other such information, the data is useless to them.

Some items required to turn a drawing or aerial image into a map are a scale bar, a locator map, and a north arrow. Aerial images are not inherently maps. They have to be turned into maps for that classification to be applicable.

Spatial patterns of data can show a reader or user how effective UAS data can be in that area. For example, with UAS data, we can easily gather images and data that depict spatial patterns in areas such as cities, layouts of towns, distribution of trees in a forest, utilization of farmland, and other areas that can have spatial patterns.

The objectives of this lab is to create a map that is functional and distinguishable from a generic image taken from the air.

Part 2 (Methods):

What key characteristics should go into folder and file naming conventions?
File names should include important information regarding the data in the file. For instance, if you are working with data that has a digital terrain model, an orthomosaic, and the ground control points, and all the data was taken on January 15th, 2019 at the "Oxford mines", it would be helpful to include that. Example file names could be 20190115_oxfordmine_dtm, 20190115_oxfordmine_mosaic, and 20190115_oxfordmine_gcp. The first two parts of the file naming would aid in organization and grouping of the data, and the last part would allow you to easily differentiate between the data.

Why is file management so key in working with UAS data?
File management is so key in working with UAS data because of the many file types and extensions used, as well as when it comes to sharing data with other persons or entities. Proper file management would make it easier for other parties to know what they are looking at.

What key forms of metadata should be associated with every UAS mission?
Metadata such as date flown, UAS platform, sensor, altitude captured, ground control GPS, coordinate system, and weather conditions should be included with every UAS mission.

Create a table that provides the key metadata for the data you are working with.
Date Flown:                             June 13th, 2017
UAS Platform:                         M600 Pro
Sensor:                                    Zenmuse X5
Altitude Flown:                        70m
Ground Control GPS:              Trimble UX5
Ground Control Coordinates:  WGS84 UTM Zone 16
UAS Coordinates:                    WGS 84 DD
Pilot:                                         Peter Menet


Add a basemap of your choice. What basemap did you use? Why?
The basemap I chose was the National Geographic World Map because of some of the terrain and natural surface features, such as lakes. The basemap was not pivotal in the map making process.

What is the difference between a DSM and DEM?
The difference between a DSM and a DEM is that one is a digital surface model and the other is a digital elevation model. A digital surface model is taken from the top down, and will show the tops of buildings, trees, and other objects. A digital elevation model, however, will attempt to exclude those features and interpolate where the surface of the earth is, and create an elevation model based off that.


Go into the Properties for the DSM and record the following descriptive statistics.
Cell Size, Units, Projection, Highest Elevation, Lowest Elevation. Enter those statistics into a table. Why are these important?

Cell size (X,Y):	0.02077, 0.02077
Units:	Meter
Projection:	WGS_1984_UTM_Zone_16N
Highest Elevation:	323.089 meters
Lowest Elevation:	281.047 meters

These statistics are important for two reasons. First, it defines the projection of the data, which is important in working with data because if it is projected incorrectly, any analysis derived from that data is inaccurate. Second, the size and elevation data, as well as the units gives scale to our data. With this information, we can exactly determine distances, as well as project the data vertically based on the elevation data.


Generate a Hillshade for the DSM. Then set the original DSM to a color ramp of your choice and set its transparency to your choice over the shaded DSM. What does hillshading do towards being able to visualize relief and topography.
Hillshading allows you to differentiate various elevations based on color changes. As seen in Figure 9, the depicted areas in red are those of higher elevation, whereas those towards the green are of lower elevation. This allows you at a glance to be able to build a sight picture of the elevation changes of the area.

Figure 9: Hillshade on DSM

Use the swipe tool to compare what you see in the orthomosaic to the DSM. How does the orthomosaic relate to what you see in the shaded relief of the DSM?
If you look at Figure 10, the left area in red on the DSM which tells us the elevation is higher relates to the mosaic beneath. Likewise, the white circle on the mosaic appears to be some sort of silo or storage bin. As you can see on the color-shaded DSM, the top part of that circle shows solid red, which would agree, as those storage containers tend to be tall.

Figure 10: Swipe tool between DSM and mosaic

What is the purpose of vertical exaggeration? What settings do you have for your data?
I believe the purpose of vertical exaggeration is to visually accentuate the differences in elevation. There is a bit of vertical exaggeration in Figure 11, and as you can see, the differences in terrain are more pronounced than they normally would be. I did vertical exaggeration based off extent, that way the vertical exaggeration is not too extreme, but there is still enough to stand out to the eye.

Figure 11: Vertical exaggeration

What color ramp did you use? Why?
The color ramp I chose was from a darker green to a bright red, and the reason for this is because a lot of times with elevation changes you see lower areas represented in green, and higher areas in red. This is also true of Ground Proximity Warning Systems (GPWS) as used in manned aircraft. The higher terrain that is close to you is depicted in red, with the lower terrain that is more safely underneath you depicted in green. Also, the green in the image and in other applications gives the appearance of grass or other early terrain.

What are the advantages of using ArcScene to view UAS DSM data vs. the overhead shaded relief in ArcMap. What are the disadvantages?
Advantages of using ArcScene as opposed to the overhead view include a better ability to see the relief in the image. In Figure 11, ArcScene makes it easy to visualize the scene because of its 3D view. A disadvantage I would argue would be accuracy. With ArcMap, it is easy to use the identify tool and view the attribute data of any one point on the DSM to get actual elevation data. With ArcScene, it is more used as a visual aid to represent the data and the scene, and there are areas of the image that ArcScene makes 3D and the image doesn't blend and contains sharp turns and edges and doesn't appear exactly like it would in real life.

Is this export a map? Why or why not?
The export shown in Figure 11 is not a map. A map needs to have scale, and this image does not include any reference to scale, a scale bar, any indication of size or direction, and therefore is not a map. Figures 12 and 13 below are maps, and they include all of these criteria. The first is a map of the mosaic, and the second is a map of the DSM of the same geographic location.

Figure 12: Map of orthomosaic

Figure 13: Map of DSM

Part 3 (Conclusions):

Some things that make UAS data useful as a tool to the cartographer and GIS user is the quality of data that can be capture with a UAS. Between the digital surface model that gives us elevation differences and the orthomosaic that gives us terrain and ground images, those forms of data can be very useful to a cartographer who needs or wants to map a certain area. To the GIS user, both this data and other potential data that can be collected with sensors mounted to a UAS, a wide variety of attribute data can be collected to make a map smart.

Limitations this data have is quality. The sensor and altitude determines a lot about how the quality of the data will be, both with resolution as well as accuracy. Other limitations would be consistency or accuracy. These images were stitched together, and there could be inconsistencies in the stitching process. The user should know these limitations, as well as the information that should be included in the metadata.

Other forms of data this data could be combined with to make it more useful could potentially be still images taken from the ground to help the user identify the scene. I would argue that a wider range of elevation measurements would make this data more useful overall. The ground control points used are useful for the original purpose of the data, but this data could have been used for a wider variety of purposes had it had more accurate and a larger quantity of ground control points and checkpoints.

Geospatial Basics: Working with and Understanding Geospatial Data

Part 1 (Introduction):

Data is not inherently geospatial. Some sort of coordinate system or projection makes the data geospatial. Without some sort of coordinate system, the data is merely that: data. Whether that is an image or an attribute table it is not geospatial if it cannot be located in space.

GIS data is different than a digital map for one main reason: attribute data. GIS is a smart map. It has information and data that accompanies the map or projection. A regular map is simply a map. It does not have any attached data or information or attribute table to do any sort of data processing or analysis from.

Having an understanding of geospatial concepts and fundamentals is important in working with UAS data because a lot of the data we collect with UAS is one sort of spatial data type or another. For example, taking images and creating a mosaic with an unmanned system is a form of raster data. Each pixel holds RGB information in the form of integers, which gives you color information. Also, digital surface models can be created from UAS data, which is floating point raster data that gives you elevation information. These are both spatial data types and are very common in UAS data.

This lab addresses key geospatial concepts such as projections and coordinate systems, as well as types of spatial data.

Part 2 (Methods/Lab Assignment):
Open the Tornadoes folder and find the Tornado_tracks files. List out each file with its extension.
TORNADO_tracks.dbf
TORNADO_tracks.prj
TORNADO_tracks.sbn
TORNADO_tracks.sbx
TORNADO_tracks.shp
TORNADO_tracks.xml
TORNADO_tracks.shx

Why is file management so key in working with UAS data?
File management is so key in working with UAS data because of this reason. As you can see in Figure 1, the file TORNADO_tracks has many various attribute files. Together, they make up the entire data set. File management is important because all of these files are necessary, and with so many files, it can be simple to misplace, mis-copy, or leave behind an important file.

Figure 1: Tornado_tracks files

What is the purpose of establishing a folder connection?
In ArcCatalog, a folder connection is made to make it easier to access the files you are working with. After a folder connection is made, you can easily locate and navigate to the files and data you are working with.

Figure 2: File layout in ArcCatalog

What is the difference between viewing the files in Arc Catalog vs. Windows explorer?
Before when viewing the TORNADO_tracks file from windows explorer, we had an entire grouping of files that made up the data. Figure 2 shows that ArcCatalog groups together our data for us. Since ArcMap and ArcCatalog use all the files simultaneously, it makes sense that it would hide the attribute data and just show the shape file title.

Why is it so important, beyond maintaining proper file management/naming, to use Arc Catalog for managing your GIS data?
It is important to use ArcCatalog for managing GIS data to reduce the risk of losing a file. From the layout in Figure 1, it would not be that difficult to misplace one of those files, whereas with ArcCatalog, it organizes it and makes it less likely to lose anything.

What do those icons mean? Hint: Use the preview tab to view the file.
Those icons to the left of each file in Figure 2 give an indication of the type of data of which the file is comprised. For example, to the left of the states shape file, the symbol shows several shapes side by side, which indicates it is a vector polygon data file. To the left of the TORNADO_tracks files, it shows a line with some nodes on it, which indicates it is a vector line file.

List out each of the geospatial data types, and then provide an example file for that data type.
Vector data
     Point: dams00x020.shp
     Line:  TORNADO_tracks.shp
     Polygon:  States.shp
Raster data
     Integer:  20170613_wolfpaving_dsm.tif
     Floating point:  20170613_wolfpaving_transparent_mosaic.tif

What topic/term relates to this description tab? Why is having this information so important in the UAS realm?
Another term that relates to the description tab is metadata. This information is important so other users of the data can have context to the data. They will be able to know the altitude it was flown, the resolution, the sensor and platform used, the weather conditions, and other such factors that could affect the data.

Right click on the 20170613_wolfpaving_dsm.tif raster, and then select ‘properties’. Scroll down to the statistics section. What does it say?
The statistics section gives minimum and maximum values, as well as average and standard deviation. For this specific file, the minimum was 281.047, the maximum 323.089, the average 296.967, and the standard deviation 4.178.

What types of tasks rely on statistics? Why would this information be important for data processing, analysis, and communication with the client? (Think of what was discussed in lecture/demo)
Tasks that rely on statistics are 3D projection in ArcGis, and further analysis of the data could rely on statistics as far as the deliverable to the customer. For example, with the dsm and mosaic of the wolfpaving, this information could be useful in calculating elevation differences, and areas of higher or lower elevation as far as drainage goes.

Cell Size:     0.02077
o Format:     TIFF
o XY Coordinate System:     WGS 1984 UTM Zone 16N
o Linear Unit:     Meter (1.000000)
o Datum:     D_WGS_1984
o Use the Linear unit and write the pixel size in square cm:     4.314 square cm

Referring to your notes from the demo/lecture, list out some different ways to add data.
Some ways to add data include dragging the file from the ArcCatalog menu directly into your layers, using the File>Add Data menu, or using the Add Data icon on the toolbar.

What basemap did you use? Why?
I used the topographic base map. There was not a specific reason for choosing this basemap, it did not matter a whole lot as I overlayed the states shape file above it anyways. I chose the topographic map above the others because it showed elevation changes. Depending on how I was viewing the data, I might have wanted to choose a streets map if I were viewing which cities the tornadoes potentially passed over.

Add the states.shp shapefile. What type of GIS data is this? Justify your answer.
The states shapefile is a vector polygon file. As visible in Figure 3, each state has a boundary line, and using the inspect tool, the state is the one selected and not the line, meaning that it is a vector polygon file rather than a vector line file.

Figure 3: Dams and tornadoes on states

Now add the TORNADO_tracks.shp file. What type of GIS data is this? Justify your answer.
The TORNADO_tracks file is a vector line file. Each of the tracks is a line, and upon inspection, highlights and gives information about a particular line.

Go into the hydrofeatures folder, and add the dams00x020.shp file. What type of GIS data is this? Justify your answer.
The last data that is overlayed in figure 3 is the dams shape file. These data are represented by the diamonds on the image. This type of file is a vector point data file, as each dam is at one particular location, and using the inspect tool, it displays the attribute data behind each individual point.

Right click on the Tornado_tracks file, and go to properties, then click on the source tab. Write down the Coordinate System:
NAD_1983_UTM_Zone_16N

Now write the coordinate system for the other two data layers.
Dams: GCS_North_American_1983
States: USA_Contiguous_Equidistant_Conic

Are all of these coordinate systems the same? Why might that be an issue?
These coordinate systems are not all the same. This could be an issue because of differences in the projections. Both the tornado file and the states file are projected coordinate systems, and the dams file is a global coordinate system. The slight differences in coordinate systems could easily skew the data being analyzed, and not only that, if the datum is not the same between coordinate systems, you would have even further skewed data.

How might the need for metadata relate to coordinate systems.
The original coordinate system is needed just as much as metadata is needed to analyze data. If you are analyzing data that is projected on a different coordinate system, your interpretations will be wrong. If you are lacking metadata that shows altitude the images were captured at, it could be more difficult to put a scale to the image to properly use the data.

Think of some different types of attribute data that could be used in conjunction with UAS data and list it here with a use example.
Figure 4 displays attribute data associated with the TORNADO_tracks file. This data includes the date, time, state, F scale of the tornado, length, deaths, and other information that is associated with each individual line in the data. Other attribute data that could be used in conjunction with UAS could be wind information, temperature, humidity, and similar data that could influence or be used in the analysis of the data.

Figure 4: TORNADO_tracks attribute data

Bring the orthomosaic and the DSM into ArcMap. Uncheck the DSM for now, and make sure the Orthomosaic is the top layer.
What type of data is this?
The orthomosaic is raster data.

What is the format?
The format is TIFF.

What is the projection?
The projection is a UTM projection on the WGS_1984_UTM_Zone_16N coordinate system.

Add the XYWOLF_PAVING_UTM16_massaged.shp file.
What is the projection?
The projection is a UTM projection.

Does this projection match the Ortho? Why is this so important?
Yes this projection matches the orthomosaic. This is really important because this current file is the ground control points for the mosaic. These two files are very closely related, and if these projections did not match, the data would be worthless.

Zoom in over a few of the GCP points. Do the points line up with the markers on the ground?
These ground control points do line up with the markers on the ground.

Figure 5: Ground Control Point

A few tools that will come in use as you within ArcMap. Measure: 
Measure several features on the map. How might this type of tool be useful in working with UAS data?
The measure tool could be useful for measuring distances between points of interest, or in measuring areas of the image that would be useful in data analysis.

Figure 6: Measuring tool

Identify: Use the identify tool on several of the GCP points. Also, turn on the DSM and identify pixels on that layer. How might this tool come in handy when working with UAS data?
The identify tool could come in handy in viewing the attribute data of a certain point or part of the file. Figure 7 shows the floating point value of a pixel in the wolfpaving_dsm file, which shows the particular elevation at that pixel.

Figure 7: Identify tool

Swipe: Use the swipe tool to move between the Orthomosaic and the DSM. How might this tool be useful when working with UAS data?
Figure 8 shows the swipe tool being used between the wolfpaving_dsm and the mosaic. This tool could be useful not only with these data types, but in others as well such as various spectrum images. This tool allows you to quickly swipe back and forth between two sets of data, allowing you to quickly compare and contrast areas of the data being analyzed.

Figure 8: Swipe tool

Part 3 (Conclusions):

Depending on the GIS user, UAS data could be very beneficial in the data analysis process and the production of a smart map. The data that can be collection through the use of a UAS is unique to other forms of data collection.

Some limitations that UAS data has includes limitations with resolution, whether that is from the sensor aboard the UAS, or the altitude that was flown. Other limitations include weather limitations. For instance, the wind conditions could affect the collection of data. The user should know about how the data was collected, especially through the use of a metadata file. The metadata will give useful information regarding the data and sensor package.

UAS data could be combined with ground station data such as other forms of surveying, or other ground sensor depending on the mission. For example, if a person is trying to analyze irrigation levels in a particular field, the sensor data could be combined with rain gauges on the ground as well as ground elevation surveying to form a more accurate 3D model of the field, potentially to better analyze drainage conditions.

Greetings.

My name is Connor Yoder.
In this post I will introduce myself, as well as give several assessments of the electronic portfolios from past classes and how I plan to use those assessments in the creation of my electronic portfolio.

I will start out with the assessments of past portfolios in a list format, including whose portfolio was assessed and what semester and class for which the portfolio was produced.

Jacob Charneski, Geography 390, Spring 2017:
Browsing through Jacob's portfolio, there are a couple things I thought he did well, and several things I think could be improved upon.
First, he did an adequate job describing the environments and the process of the data collection. Entries were descriptive, and included informative and applicable images relating to the lab and data.
Some things could also be improved upon as well. For instance, it would be helpful for pictures to have labels to help improve the descriptiveness and tie the image to a certain aspect of the lab. Another thing I noticed were spelling and grammatical errors. Lastly, the alternative links on the portfolio page were void of content, and included the, in my opinion, unprofessional and distracting comment box.

Eli Fredrickson, Geography 390, Spring 2017:
Similar to Jacob's portfolio Eli described the labs decently well, but I thought there could have been more content and descriptions in the results are of some of the entries. Unlike Jacob's, this portfolio included descriptions on each of the images, although the first images themselves were not of the best quality, and of those taken with a still camera, the angles were skewed and awkward.
Of the overall portfolio, I would have hoped to have seen more neutral colors that to me seem more professional, instead of the pale yellows and rust oranges that were prominent.

Ryan Ferguson, AT 409, Fall 2018:
Ryan's portfolio was formatted differently than the other portfolios I have viewed so far. I like the tile aspect of Ryan's portfolio because not every entry is displayed simultaneously. You can pick and choose which entry you want to view at a time, and each entry has an associated image beside it. The layout gives the portfolio a more professional look in my opinion, due to it being less cluttered and more streamlined.

Evan Hockridge, AT 409, Fall 2018:
With Evan's portfolio, I like that he actually utilized the figures and maps tab. Most of the other portfolios I have viewed have left that page blank. Not only did Evan fill that tab, he added extra tabs beyond the normal, such as unmanned system experience, software experience, and other interests. I like that he did this because it adds more to his portfolio than just the labs for class. It gives interested parties more information to gather about Evan's experiences as well as interests.

Ian Wiley, AT 409, Fall 208:
Ian's portfolio entries I thought were adequate. Each post had a representative title about the lab, and each post included relevant imagery to add to the context of each post. The thing I am not super fond of on Ian's portfolio page is the large image of him at the top. I think an image of Ian is acceptable in the right spot, for instance, the about me or contact info page, but the image is distracting from the content and I feel like the image detracts from the overall quality of the portfolio.

In summary, for my portfolio, I think I want something similar to Ryan Ferguson, in more of a tile format for each entry with a relevant image as the thumbnail. Also, with the others, I want each post to include an acceptable quantity of relevant imagery to complete the context of each lab. I will be sure to fill my secondary pages with the figures from each post, as I thought several of the portfolios were lacking in that. Also, I think an image of myself is not a bad idea, so in the contact info page, I will most likely include a small image of myself. As for the overall design, I want to try to stick to more neutral or earth colors, simply to keep the portfolio looking as professional as I can. I see E-portfolios becoming more and more important in the ever-evolving digital age in which we currently live, and as technology and sensors improve, I also see the UAS curriculum evolving in that way to reflect and adapt to the technology.

Finally, a bit about me. My background with Unmanned Aerial Systems extends a little before high school. Naturally, I jumped on the "toy" drone aspect when unmanned systems started gaining popularity. While unmanned systems are definitely not toys by any means, this was how they were portrayed when I entered the hobby. Since then, I started at Purdue in Professional Flight, but began Unmanned Aerial Systems as a minor to continue and expand my knowledge of unmanned systems, although by now, I had an acceptable level of understanding of flight characteristics as well as components and a small amount of flight experience with regards to aerial imagery and videography. I cannot pinpoint one particular moment that led to an interest in unmanned systems, but I would say a combination of interest in manned aviation and unmanned aviation culminated into the interest I have today. As far as a career in unmanned systems, if I chose to pursue a career in unmanned systems, I would love to work with a film group, for example, Copterkids, LLC. That would be my ideal career in unmanned systems is with the film industry, although I know there are a lot of fields in the industry that are growing that will need educated and qualified individuals to fill the positions.