Vermont EPSCoR Publications and Products
Interactions between human and natural systems along rural road networks: The case of the Lake Champlain basin. In: 2018 CERM (Catskill Environmental Research & Monitoring) Conference. 2018 CERM (Catskill Environmental Research & Monitoring) Conference. Highmount, NY: Ashokan Watershed Stream Management Program; 2018. Available from: http://ashokanstreams.org/wp-content/uploads/2016/09/5-Wemple_CERM2018.pdf
. Impact of an Extreme Storm Event on River Corridor Bank Erosion and Phosphorus Mobilization in a Mountainous Watershed in the Northeastern United States. Journal of Geophysical Research - Biogeosciences [Internet]. 2018 . Available from: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018JG004497
. Identification of patterns of hysteresis in suspended sediment-discharge relationships to infer watershed sediment dynamics. In: Lake Champlain Research Conference. Lake Champlain Research Conference. Burlington, VT: Lake Champlain Basin Program; 2018. Available from: http://www.lcbp.org/water-environment/data-monitoring/lake-champlain-research-conference/
. Hydrological Event Detection & Analysis (HEDA) tool for streamflow and water quality time series. CUAHSI Conference on Hydroinformatics [Internet]. 2019 . Available from: https://www.cuahsi.org/uploads/pages/img/2019_Conference_on_Hydroinformatics_Program.pdf
. High Frequency Turbidity Monitoring to Quantify Sediment Loading in the Mad River. 2014 NEAEB Conference. 2014 [cited 0BC].
. . Fluvial Processes in Motion: Measuring Bank Erosion and Suspended Sediment Flux using Advanced Geomatics and Machine Learning. Burlington, VT: University of Vermont; 2017.
. Evaluating visual classification of suspended sediment – discharge hysteresis via crowd-sourcing and in-stream monitoring. 2018 AGU (American Geophysical Union) Fall Meeting [Internet]. 2018 . Available from: https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/370154
. Estimates of Sediment Loading from Streambank Erosion Using Terrestrial LIDAR sediment in rivers using artificial neural networks: Implications for development of sediment budgets. In: American Geophysical Union, Fall Meeting. American Geophysical Union, Fall Meeting. San Francisco, CA; 2013.
. Data Imputation for Multivariate Time Series Sensor Data With Large Gaps of Missing Data. IEEE Sensors Journal [Internet]. 2022 ;22(11):10671 - 10683. Available from: https://ieeexplore.ieee.org/document/9755143
. Critical Zone network cluster research: Using Big Data approaches to assess ecohydrological resilience across scales. In: 2020 AGU (American Geophysical Union) Fall Meeting. 2020 AGU (American Geophysical Union) Fall Meeting. Virtual: American Geophysical Union (AGU); 2020. Available from: https://agu.confex.com/agu/fm20/webprogram/Paper748076.html
Comparison of Unmanned Aircraft Systems (UAS) to LIDAR for Streambank Erosion Measurement at the Site-Specific Scale. Vermont Geospatial Forum 2017 [Internet]. 2017 . Available from: http://vcgi.vermont.gov/event/forum_2017/poster_gallery
. Automating the Classification of Hysteresis in Event Concentration-Discharge Relationships. In: SEDHYD 2019. SEDHYD 2019. Reno, NV: SEDHYD, INC.; 2019. Available from: https://www.sedhyd.org/2019/openconf/modules/request.php?module=oc_program&action=view.php&id=70&file=1/70.pdf
. Applying Deep Learning to Event Concentration-Discharge Hysteresis Patterns to Reveal Differences in Sediment Dynamics across Contrasting Watersheds. 2018 AGU (American Geophysical Union) Fall Meeting [Internet]. 2018 . Available from: https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/355901
. Application of unmanned aircraft system (UAS) for monitoring bank erosion along river corridors. Geomatics, Natural Hazards and Risk [Internet]. 2019 ;10(1):1285 - 1305. Available from: https://www.tandfonline.com/doi/full/10.1080/19475705.2019.1571533
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