Mapping Coastal Hazards at an Island Campus with an Unmanned Aircraft System
ISBN 978-85-88783-11-9
Authors
1Starek, M.; 2Rule, R.; 3Giessel, J.; 4Berryhill, J.
1TEXAS A&M UNIVERSITY-CORPUS CHRISTI Email: michael.starek@tamucc.edu
2TEXAS A&M UNIVERSITY-CORPUS CHRISTI Email: rrule@islander.tamucc.edu
3TEXAS A&M UNIVERSITY-CORPUS CHRISTI Email: zac@naismithmarine.com
4TEXAS A&M UNIVERSITY-CORPUS CHRISTI Email: jberryhill@islander.tamucc.edu
Abstract
Small-scale Unmanned Aircraft Systems (UAS) enable the integration of light weight, low-cost imaging payloads providing a cost-effective and efficient means for conducting localized aerial surveys. Through exploitation of high imagery overlap and minimal aiding technology, photogrammetric techniques and structure from motion (SfM) methods can be applied to derive accurate 2D and 3D geospatial data about the observed terrain. Furthermore, small-scale UAS enable rapid response capabilities and provide the potential for collecting repeated observations at high temporal frequency making them an exceptional tool for monitoring highly dynamic environments, such as the coastal zone. Texas A&M University-Corpus Christi (TAMU-CC) is located on Ward Island within Corpus Christi Bay, Texas USA along the Gulf of Mexico. The Island Campus covers approximately 111 hectares including a restored, engineered beach called University Beach. Coastal storms, flooding, and erosion are imminent threats to the campus infrastructure and island geology (shoreline). To improve the University’s ability to monitor and plan for coastal hazards, a small-scale UAS called the SenseFly eBee is now being integrated for periodic surveying of the island campus. The eBee is an ultra-lightweight (~0.7 kg) fully autonomous platform equipped with a Near-Infrared and RGB camera payload. The eBee UAS has a flight endurance of approximately 45 minutes on a fully charged battery and can withstand wind speeds up to about 44 km/hr. Imagery data collected by the UAS are processed to provide high-accuracy and high-spatial resolution (e.g. 3 cm) orthomosaics and digital elevation models (DEMs) of the island campus including the beach. Prior to integrating the data for campus decision making processes, it was necessary to perform an accuracy evaluation. Results show a mean planimetric accuracy of 6 cm for orthomosaiced image products when using control points in combination with the onboard GPS/IMU information. To assess vertical accuracy, survey-grade RTK GPS profiles were collected over a flat, parking lot. RTK GPS profiles were also collected at the sub-aerial beach extending in the cross-shore from the foredune ridge to the shoreline at 10 meter spacing. Results in the parking lot showed a mean absolute vertical accuracy difference of 3.7 cm relative to RTK GPS and < 10 cm difference compared to the beach profiles. Elevation data derived from the UAS were also compared to an airborne light detection and ranging (lidar) survey conducted over the region. The mean absolute elevation difference was approximately 13 cm between the two approaches, and in non-vegetated areas the UAS-derived elevations provided much denser and higher accuracy measurements. Overall, these results show that with adequate mission planning and a data processing workflow, survey-grade 2D and 3D geospatial data can be acquired from small-scale UAS carrying low cost cameras. This presentation will focus on the application of the UAS for monitoring of the TAMU-CC island campus to assess coastal hazards. Firstly, details on the mission design and data processing workflow developed to enable accurate mapping will be presented. Secondly, details on the accuracy of the derived measurement products for monitoring shoreline erosion and volumetric beach change will be presented. Finally, a discussion on how the UAS data products are being integrated to derive new and informative cartographic products for supporting campus decision making and planning will be presented. For example, imagery and topographic data derived from the UAS serve as inputs into a GIS-based sea level rise simulation model developed for the campus to forecast potential impacts. This and other mapping examples integrating the UAS data will be presented.
Keywords
unmanned aircraft system; UAV; coastal hazards