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.