* Why are proper cartographic skills essential in working with UAS data?
Cartographic skills are essential for working with UAS data because it makes the reader have a better understanding of the data. Having a North Arrow, scale bar, locator map, watermark, and data sources give the reader everything they would need to understand. It is also important to make sure data created and presented can not be stolen as easily, and it also gives the reader the ability to look up anything or look at data used.
* What are the fundamentals of turning either a drawing or an aerial image into a map?
The fundamentals of turning a drawing or an aerial image into a map involve a few different steps. The first step is to add a north arrow, scale bar, watermark, sources and a title. The most important is the north arrow and the scale bar. Although, most of this is not useful without having a locator map as well.
* What can spatial patterns of data tell the reader about UAS data? Provide several examples.
* What are the objectives of the lab?
The objects of the lab include using processed Pix4D to create a map that meets the criteria for the fundamentals of turning an image into a map. The objectives also includes using a hillshade tool, and learning about the difference between a DSM and a DEM. Finally, this lab also includes understanding the patterns found in a orthomosaic, reporting statistics between the DSM and DEM, and understanding how UAS data can be used as a tool to enhance cartography for the user.
Methods
Metadata:
Platform: DJI Phantom 3 Advanced
GPS precision level: sub meters
Drone Sensor: Sony 16 Megapixel Camera
Altitude of project: 60m
Coordinate system: WGS 1984 UTM Zone 15N
Projection: Wisconsin State Plain
Date: 3-7-2016
The first step is to open up the orthomosaic and the DSM into ArcMap, and then create pyramids and generate the statistics for each data set. a DSM is a digital surface map compared to a DEM which is a digital elevation map. A DSM includes many values for elevation which includes trees, plants, and power lines, etc. A DEM involves just the ground surface force creating the raster. Georeference Moasic is using ground control points to lock down the image compared to orthorectified moasic which is when images from a drone are stitched together based on tie points done by a computer. Orthorecified moasic is not nearly as accurate as a georeferenced moasic due to the ground control points. The statistics created from the DSM are important because it will show the value of the ground elevation and give all the data needed that may be presented. Finally, to use a hillshade on a DSM use the search tool on and search for hillshade. In the hillshade window delineate the regions of the DSM. Now the last step is to create a map with all of the fundamentals of map making involved with it. The statistics found within the properties of each group represents the elevation of the ground at different points.
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| Figure 2: This is an oblique view of the sportsfield. The north arrow gives the direction in the top left corner. |
Results
* What types of patterns do you notice on the orthomosaic?
On the Orthomosaic there are a few different patterns the first would be that there is a straight line of trees along with a very gradually increasing slope going from South to North. The map is almost split up right down the middle where it starts to slope down compared to gaining elevation.
* What patterns are noted on the DSM? How do these patterns align with the DSM descriptive statistics? How do the DSM patterns align with patterns with the orthomosaic?
The pattern noticed on the DSM is that there are almost lines going across which indicate the elevation. This aligns with the statistics due to those numbers also showing the elevation. There are also the tree and vegetation that can be seen which lines up with the orthomosaic.
* Describe the regions you created by combining differences in topography and vegetation.
I created a region that involved the top area and then one for the bottom area. I created two separate ones for the vegitation therefore, not allowing it to interfere with the rest of the map.
* What anomalies or errors are noted in the data sets?
The trees and vegetation could make the elevation numbers off due to them being higher up and all being the same height due to how the images were taken. Another error would be having poor data in the top left of the image. This would create errors that could be fixed if there were more pictures to be processed in that area. There are also not any GCP to tie any of the parts down to the actual basemap.
* Where is the data quality the best? Where do you note poor data quality? How might this relate to the application?
The data quality is the best near the middle of the image. This is due to how many images are being stitched together in that area. The poor data is in the top left part of the image. This may be due to having lack of images up there to create a better area of data. This may relate to the application because it may not give an exact elevation compared to the other areas.
Conclusion
UAS data is a great tool to complement any GIS user. It has the ability to take high quality data to create high quality numbers to solve many problems. It is also useful because of the high level of accuracy it has. It does have limitations as it cannot fly during most types of bad weather, and relies on day time to capture data when light is needed. When the user is working with the data the user should know that even if the program can process the data, it does not mean that it will be correct numbers. There is a lot of steps that go into solving a problem and the quick way will most likely be the garbage out method. This data could be combined with GCP to help tie down the points even better than they are with the platforms GPS.
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