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Carl J Legleiter, Ph.D.
As a member of the USGS Geomorphology and Sediment Transport Laboratory, Dr. Legleiter conducts research on remote sensing of rivers, specifically retreival of water depth and other channel attributes from hyperspectral image data. Current studies focus on the Sacramento River in California, the Kootenai River in Idaho, an experimental channel in Korea, and several rivers in Alaska.
Dr. Legleiter is a recent addition to the USGS Geomorphology and Sediment Transport Laboratory, joining the Survey in January 2016. Previously, Legleiter was employed as an Associate Professor of Geography at the University of Wyoming. He earned a doctoral degree from the University of California Santa Barbara, graduating in 2009.
Dr. Legleiter's research focuses on the application of remote sensing to rivers, specifically hyperspectral imaging of sand- and gravel-bed channels. His interests also encompass fluvial geomorphology and sediment transport as he seeks to understand the mechanisms by which the movement of water and sediment direct the morphologic evolution of rivers. For this reason, Legleiter conducts studies of channel change, for which remote sensing has proven to be a powerful tool. Current work is focused on developing new methods of measuring river discharge via remote sensing.
Remote sensing has emerged as a powerful tool for characterizing river systems across a broad range of scales and with far greater efficiency than conventional field methods. Dr. Legleiter's research at the USGS Geomorphology and Sediment Transport Laboratory (GSTL) focuses on developing innovative techniques for inferring various channel attributes from remotely sensed data. For example, previous studies have established methods for retrieving information on water depth from hyperspectral images and/or bathymetric (green) LiDAR. Several recent publications describe techniques methods for calibrating image-derived depth estimates when field data are not available. In collaboration with colleagues at the GSTL, Legleiter is now working to develop innovative approaches for remote sensing of river discharge using a combination of depth retrieval from passive optical image data and water surface velocity estimation from thermal videography.