Research

My research interests include both development of statistical methods, as well as applications to environmental problems including carbon monitoring, national forest inventory, and remote sensing. Specifically, my work focuses on spatio-temporal statistics, dynamic models, small area estimation, and Bayesian hierarchical modeling.

Publications

Elliot S. Shannon, Andrew O. Finley, Paul B. May and Sudipto Banerjee. 2025. Investigating spatio-temporal models for forest inventory data under increasing information. In prep.

Naresh Khanal, Raju Pokharel, Elliot S. Shannon, Jagdish Poudel, Emily Silver and Emily Silver. 2025. Historical trend of market coverage and competition of various wood products in Michigan. To be submitted to Forestry.

Elliot S. Shannon, Andrew O. Finley and Paul B. May. 2025. Quantifying impacts of natural gas development on forest carbon. To be submitted to PNAS. bioRxiv preprint.

Elliot S. Shannon, Andrew O. Finley, Paul B. May, Grant M. Domke, Hans-Erik Andersen, George C. Gaines III, Arne Nothdurft and Sudipto Banerjee. 2025. Leveraging national forest inventory data to estimate forest carbon density status and trends for small areas. Submitted to Forest Ecology and Managment. ArXiv preprint.

Elliot S. Shannon, Andrew O. Finley, Grant M. Domke, Paul B. May, Hans-Erik Andersen, George C. Gaines III and Sudipto Banerjee. 2025. Toward spatio-temporal models to support national-scale forest carbon monitoring and reporting. Environmental Research Letters. DOI: 10.1088/1748-9326/ad9e07.

Naresh Khanal, Raju Pokharel, Jagdish Poudel, Shivan Gc, Elliot S. Shannon and Andrew O. Finley. 2024. Analysis of location, feedstock availability, and economic impacts of potential mass timber processing facilities in Michigan. Forest Policy and Economics. DOI: 10.1016/j.forpol.2024.103203.

Elliot S. Shannon, Andrew O. Finley, Daniel J. Hayes, Sylvia N. Noralez, Aaron R. Weiskittel, Bruce D. Cook and Chad Babcock. 2024. Quantifying and correcting geolocation error in spaceborne LiDAR forest canopy observations using high spatial accuracy data: A Bayesian model approach. Environmentrics. DOI: 10.1002/env.2840.