Geomorphologist with an affinity for remote sensing, statistical modeling, and AI. My work focuses on landslide hazard modeling and hillslope response to wildfire.
Eli Orland
(ASSOCIATE SCIENTIST)
Org Code: 617
NASA/GSFCMail Code: 617
Greenbelt, MD 20771
Employer: UNIVERSITY OF MARYLAND BALTIMORE CO
Brief Bio
Education
Bachelors of Arts - Geology (Minor in Spanish), Middlebury College, Middlebury Vermont
Masters of Science - Earth Sciences, University of Oregon, Eugene, OR
Special Experience
Remote Sensing, Machine Learning, Deep Learning, Programming, Science Communication, Public Policy
Publications
Refereed
Orland, E., D. Kirschbaum, and T. Stanley. 2022. A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data Geophysical Research Letters 49 (18): [10.1029/2022gl099850]
Patton, A. I., J. J. Roering, and E. Orland. 2022. Debris flow initiation in postglacial terrain: Insights from shallow landslide initiation models and geomorphic mapping in Southeast Alaska Earth Surface Processes and Landforms [10.1002/esp.5336]
Orland, E., J. J. Roering, M. A. Thomas, and B. B. Mirus. 2020. Deep Learning as a Tool to Forecast Hydrologic Response for Landslide‐Prone Hillslopes Geophysical Research Letters 47 (16): [10.1029/2020gl088731]