Abstract: Streams are intricate components of the landscape system that vary across temporal and spatial scales while transporting and storing water, sediment, energy, nutrients as well as aquatic and terrestrial species from one part of the system to another. Such changes have traditionally been captured with extensive expert assessment and/or remote sensing analysis (i.e. photo interpretation). In collaboration with the Vermont Agency of Natural Resources River Management Program, this study aims to enhance the capabilities of traditional remote sensing studies by incorporating Light Detection and Ranging (LiDAR) data in the geomorphic assessment of fluvial channels to quantify stream adjustment properties and gain insight into a stream's state of dynamic equilibrium with greater accuracy than traditional methods. A series of 18 digital elevation models (DEM) were generated using three interpolation methods (inverse distance weighting (IDW), natural neighbor (NN), and ordinary kriging), varying raster grid cell sizes (1, 2 and 3m) and different amounts of LiDAR data (bare earth data alone and bare earth with additional reflective data that reduce the mean point spacing) and compared with survey data (n = 689) to determine the optimal interpolation parameters for an agricultural study area, a portion of Allen Brook watershed in northern Vermont. Through analytical comparison, 1m IDW with the additional reflective data was the optimal method for minimizing error metrics but 1m NN (with additional reflective data) was best for retaining maximum elevation range, computational simplicity, and identifying small stream channels.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).