GSA Annual Meeting in Phoenix, Arizona, USA - 2019

Paper No. 174-8
Presentation Time: 10:05 AM


LOWRY, Christopher S.1, CRAMER, Jennifer M.2, FENNELLY IV, Patrick E.3, DANTU, Karthik4, RANA, Rakeshsingh2 and KOMMINENI, Avinash4, (1)Department of Geology, University at Buffalo, 126 Cooke Hall, Buffalo, NY 14260, (2)Department of Geology, University at Buffalo, The State University of New York, 126 Cooke Hall, Buffalo, NY 14260, (3)Geology, University at Buffalo, 12 Capen Hall, Amherst, NY 14068, (4)Department of Computer Science and Engineering, University at Buffalo, The State University of New York, 338 Davis Hall, Buffalo, NY 14260

Low altitude remote sensing using drones is an emerging tool to map geomorphic features, evaluate ecosystem health, identify springs, and quantify stream velocity. As with any new toolset there are challenges that have to be overcome but there are also exciting opportunities. The objectives of this research are to investigate the use of drones to quantify groundwater surface water interaction using image processing based differential stream gauging. This research is based on off-the-shelf large-scale particle image velocimetry (LSPIV) software coupled with consumer grade drones. Combining these technologies, our goal is to develop quantitative methods to study groundwater surface water interactions. Through this research, we analyze the application and errors associated with these drone-based methods, and describe future use cases. We review complementary image processing methods using thermal cameras and citizen science-based observations stations to identify and quantify groundwater surface water interactions. Results show while these methods present new opportunities, direct application of current algorithms results in errors in differential discharge under the best conditions of 10-50%, which can pose problems for investigations of short stream reaches. Finally, results address federal regulations that can impact the use of application of methods at the watershed scale. In the future, we hope to improve image-processing and drone-positioning techniques for better accuracy as well as efficacy.