Constructing Maps with UAS Data

Why are proper cartographic skills essential in working with UAS data?

Properly displaying UAS data with cartographic elements will help make the data more accessible and understandable to an audience. Raw UAS data is useless to the audience. Cartographic skills allows the author to direct the audience’s attention to the main focus of the map. Also, it needs to be clear where this imagery was taken as UAS allows for the collection of data in most landscapes.

What are the fundamentals of turning either a drawing or an aerial image into a map?

A drawing or aerial imagery must contain the seven basic cartographic elements: title, author, legend, scale, north arrow, data source, and date of production. A title should be able to describe what and where your map is. The legend needs to be well organized and easy to interpret. Scale should be in the metric system unless you audience requires imperial. Data source is crucial, just like you cite sources in a paper you need to cite where your data came from. And of course it must include the author so that they receive credit as well as the date it was produced. 

What can spatial patterns of data

Spatial patterns are very important when delineating regions or objects. We use visual ques to help identify tree, vehicles, roads, grass, etc… These visual ques include texture, shade, shape, color, pattern, value.

The objectives of this lab is to lay out the basics for developing proper maps with UAS data. It is important to develop and refine cartographic skills in relation UAS data in the context of a GIS. In this lab we will be working with various UAS data and GIS software to construct cartographically please maps. 

Methods

Flash Flight Logs

For this part of the lab I opened a KMZ file in google earth and in ArcMap.

What components are missing from this map?

While google maps helps us visualize the flight this image is not useful as a map as it is missing important components. It is missing a title, a scale, legend, author, and date of production. This is not an appropriate map to use.

Advantage of viewing in Google Earth.

Flight log data in google earth displays height and the path the aircraft took. ArcMap displays a 2D version of the flight with distinguished ‘U’ shapes representing the craft turning for another row of data collection. The line cutting through these rows signifies that data collection is complete and the then returns straight to the designated landing area.

When I was done viewing the data in ArcMap I saved the flight log as a LML file that is later converted into a compatible file type for ArcGIS: .gpx. I did this using the “KML to layer tool”

Tlogs

In this part of the lab we worked on converting a Tlog (telemetry log) that stores data about the flight path into a KML. To do this I opened up Mission Planner and selected the telemetry Logs tab on the left side of the screen. From here I selected the Tlog to KML and imported the desired Tlog file for conversion. From here I used the same “KML to Layer” tool in ArcMap to make a compatible file for the GIS.

GEMs Geotiffs

For this part of the lab I added 6 raster layers into ArcMap. For this data I used the “Calaculate Statistics” tool that yields Min, Max, Mean, and Standard Deviation values for each layer based on pixel vales. After the tool is run, the resulting statistics can be found in the layers Properties.

Pix4D Data Products

 For the Pix4D imagery we also used the "Calculate Statistics" tool

What is the difference between a DSM and an Orthomosaic?

Results

Flight Logs

Flight log data in google earth displays height and the path the aircraft took. ArcMap displays a 2D version of the flight with distinguished ‘U’ shapes representing the craft turning for another row of data collection. The line cutting through these rows signifies that data collection is complete and the then returns straight to the designated landing area.

Based on the tight “U” turns the UAS takes the aeriel vehicle is likely a multirotor versus a fixed wing. A multirotor will have better maneuverability – when taking into account the size of the study area as well, a multirotor makes more sense.

Line spacing will vary based on the sensors ability to capture a larger area from its focal point. For instance some cameras have a larger degree of view and so the resulting image will contain more of the area then say that of a camera with a smaller view and is more constricted to what is below or in front of it instead of surrounding it. In order to make a mosaic the images must overlap due to distortion at the edges of an image. As a sensor rises in altitude, line spacing will increase, the lower a sensor is creates a decrease in line spacing. Again this relates to how much of the area a camera can take in as well as the amount of distortion to occur at the edges of an image. 

Geotiff

When observing the GEM RGB orthomosaic distortion can be seen around the edges of the imagery when overlain on a basemap. For instance looking up at the track (North) you can see that the images are not quite aligned. Also, this isn’t a very smooth image. The edges are sharp and at different angles. When I zoom in more in ArcMap, the basemap imagery becomes blurry where as the RGB image remains detailed. It should also be mentioned that in the basemap imagery there is no community garden. 

Figure X: This is a map of the image produced in ArcScene. Because
Arcscene doesn't allow for scale representation I created a Fishnet polyline feature
class to represent the scale in ArcMap. Each grid is 20 x 20 meters.

Conclusion

Using UAS data is a useful tool to a cartographer because it supplies a much more detailed image when mapping small areas. When processed and managed correctly you will have an accurate representation of an area. Not only does UAS sensor supply good RGB imagery there are other sensors that are sensitive to other wavelengths allowing a cartographer to use different imagery that portrays different data. For instance Using NDVI data can help with vegetation health assessments.

Of course this data does have its limitations as well. Working with any kind of imagery will cause distortion the further from the center you go. Overlapping of images helps to minimize this but it is important to note that an image will always have some amount of distortion. One way to tackle this problem is to use Ground Control Points, but we will discuss this later in the semester. Dealing with UAS data also takes a lot of computing power. Because so many images are collected and then stitched together you’ll probably be dealing with long processing times. Not to mention that that time may also be affected by the type of software you are using to process you data. For instance open source software versus proprietary software or free software. Not all data is pristine either. For instance when applying the hillshade to the DSM data the image became textured and less smooth. It infact didn’t represent what was actually happening on the surface.