We compared high-resolution drone laser scanning (DLS) and structure from motion (SfM) photogrammetry-derived vegetation heights at the Virginia Tech StREAM Lab to determine Manning's roughness coefficient. We utilized two calibrated approaches and a calculated approach to estimate roughness from the two data sets (DLS and SfM), then utilized them in a two-dimensional (2D) hydrodynamic model (HEC-RAS). The calculated approach used plant characteristics to determine vegetative roughness, while the calibrated approaches involved adjusting roughness values until model outputs approached values of field data (e.g., velocity probe and visual observations). We compared the model simulations to seven actual high-flow events during the fall of 2018 and 2019 using measured field data (velocity sensors, groundwater well height, marked flood extents). We used a t-test to find that all models were not significantly different to water surface elevations from our 18 wells in the floodplain (p > 0.05). There was a decrease in RMSE (-0.02 m) using the calculated compared to the calibrated models. Another decrease in RMSE was found for DLS compared to SfM (-0.01 m). This increase might not justify the increased cost of a DLS setup over SfM (~$150,000 versus ~$2,000), though future studies are needed. Our results inform hydrodynamic modeling efforts, which are becoming increasingly important for management and planning as we experience increasing high-flow events in the eastern United States due to climate change. / Master of Science / We compared high-resolution drone laser scanning (DLS) and structure from motion (SfM) photogrammetry-derived vegetation heights at the Virginia Tech StREAM Lab to improve flood modeling. DLS uses laser pulses to measure distances to create a three-dimensional (3D) point cloud of the landscape. SfM combines overlapping aerial images to create a 3D point cloud. Each method has limitations, such as cost (DLS) and accuracy (SfM). These remote sensing methods have been increasingly used to provide inputs to flood models, due to lower cost, and increased accuracy compared to airplane or satellite-based surveys. Quantifying roughness or resistance to flow can be extremely difficult and results in flood model accuracy problems. We used two forms of a calibrated approach, and a calculated approach to estimate roughness from the two data sets (DLS and SfM) which were then used in a two-dimensional (2D) flood model. We compared the model results to measured field data from seven actual high-flow events in Fall 2018 and 2019. We used statistics to determine compare the various techniques. We found that model results were not significantly different from measured water-surface elevations measured in the floodplain during floods. We also used root mean square error (RMSE) to measure the differences between modeled and observed data. There was slight decrease (-0.02 m) in error when comparing model results using the calculated and calibrated techniques. The error also decreased (-0.01 m) for simulations using the DLS versus SfM data sets. The improved accuracy due to the use of DLS might not be justified based on the increased cost of a DLS setup to SfM (~$150,000 versus ~$2,000), though future studies are needed. Insights from this analysis will help improve flood modeling, particularly as we plan for increasing high-flow events in the eastern Unites States due to climate change.

Recently, products of Unmanned Aerial System (UAS) integrated through SIFT algorithm and dense cloud matching using structure from motion has gained prominence with tree-level inventory maintenance in forestry. Various studies have been carried out by using UAS imagery to quantify and map forest structure of simple coniferous stands. However, most of the previous works employ methodologies that require manual inputs and lack of reproducibility to other forest systmes. Manual detection of trees and calculation of their attributes can be a time-consuming and complicated process which can be overcome with an automated technique applied by forest managers and/or landowners is highly desired to take full advantage of the readily available UAS remote sensing images. This study presents a methodology for automated measurements of tree height, crown area and crown diameter of hardwood species using UAS images. Different UAS platforms were employed to gather digital data of two hardwood plantations at Martell, Indiana. The resulting aerial images were used to generate the Digital Surface Model (DSM) and Digital Elevation Model (DEM) for the forest stand from which the Crown Height Model (CHM) was derived. The canopy height model can be inputted to the web platform deployed through shiny server (https://feilab.shinyapps.io/Crown/) to derive individual tree parameters automatically. The results show that this automated method provides a high accuracy in individual tree identification (F-score> 90%) and tree-level measurements (RMSEht<1.2m and RMSEcrn<1m). Moreover, tree-level parameter estimation for 4,600 trees were calculated in less than 30 minutes based on a post-processed DSM from UAS-SfM derived images with minimal manual inputs. This study demonstrates the feasibility of automated inventory and measure of tree-level attributes in hardwood plantations with UAS images.