Ethan Shavers, PhD
Dr. Ethan Shavers is the CEGIS Section Chief and lead researcher on the Remote Sensing and Modeling for Hydrography project. The project research focusses on developing strategies for automated hydrographic feature extraction and landscape characterization.
Dr. Shavers has a BS in Geology and PhD in Environmental Science and GIS, both from Saint Louis University (SLU). His dissertation work focused on remote sensing and spectral analysis of igneous lithologies. His work in the SLU Remote Sensing Lab involved optical instrument integration for unmanned aerial systems and remote sensing applications for precision agriculture. His federal career began as a Mendenhall Fellow in the CEGIS Multi-scale Representation project testing strategies for mapping headwater streams.
Science and Products
Channel cross-section analysis for automated stream head identification
Preserving meander bend geometry through scale
OpenCLC: An open-source software tool for similarity assessment of linear hydrographic features
Scale-specific metrics for adaptive generalization and geomorphic classification of stream features
Streams do work: Measuring the work of low-order streams on the landscape using point clouds
Automated road breaching to enhance extraction of natural drainage networks from elevation models through deep learning
Similarity assessment of linear hydrographic features using high performance computing
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
Channel cross-section analysis for automated stream head identification
Preserving meander bend geometry through scale
OpenCLC: An open-source software tool for similarity assessment of linear hydrographic features
Scale-specific metrics for adaptive generalization and geomorphic classification of stream features
Streams do work: Measuring the work of low-order streams on the landscape using point clouds
Automated road breaching to enhance extraction of natural drainage networks from elevation models through deep learning
Similarity assessment of linear hydrographic features using high performance computing
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.