DETERMINING THE EFFECT OF MISSION DESIGN AND FLIGHT PATTERN ON THE QUALITY AND ACCURACY OF DEMS AND ORTHOPHOTOS DERIVED FROM UAV IMAGERY

Unmanned Aerial Vehicles (UAVs) are a cost-effective means of collecting imagery that can be used to create high-resolution DEMs (Digital Elevation Models) and orthophotos of field sites. UAVs allow the rapid collection of aerial images across field sites. Additionally, flight planning software allows the precise design of missions with a high-degree of control over flight pattern, camera angle, and image overlap.

Because of the ease of image acquisition and a lack of scientific literature exploring best practices in mission design, most UAV projects err on the side of obtaining large amounts of imagery to ensure a suitable final product. However, acquiring more imagery than necessary leads to considerable time and costs involved in processing the data. Understanding the minimum number of images and ideal mission design required to obtain a specific standard of DEM and orthophoto quality is an important question.

In 2017, imagery of the effects of the April 2017 flooding in the North Fork of the White River watershed, central Missouri, was collected at seven different stream reach corridors ranging from 2.5 to 65 hectares. At each study site, four separate image acquisition missions were flown. Two of the missions were flown in a north-south pattern while the other two were flown in an east-west pattern. One flight in each pattern maintained a nadir camera angle while the other flight had an oblique angle of 30°. In order to georeference and orthorectify the imagery sub-decimeter accuracy dGPS positions were collected at 5-10 visible ground control targets at each study site.

The data were processed into DEMs and orthophotos using various combinations of the imagery missions from each study site. The relative accuracy of each DEM will be determined from the root mean square error (RMSE) of elevation by using the DEM sample heights and known heights. For the orthophotos, relative pixel resolution changes between the various combinations will be used to interpret changes in quality. This will aid in determining which combinations of image acquisition methods yields the best results for the least investment of time and effort.