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.