Date of Award

Spring 5-2022

Degree Type

Masters Thesis

Degree Name

Master of Science (MS)


Biological, Environmental, and Earth Sciences

Committee Chair

Gregory Carter

Committee Chair School

Coastal Resilience

Committee Member 2

Carlton Anderson

Committee Member 3

George Raber

Committee Member 3 School

Biological, Environmental, and Earth Sciences


Coastal marshes are influenced by complex interactions among environmental and human factors. Marsh plant communities exist across subtle elevation gradients which are highly influenced by the local tidal regime. To better understand these dynamic conditions, improved methodologies for acquiring accurate elevation values are a necessity in marsh research and management. However, overestimation of marsh elevation values obtained from remote sensing is common due to vegetation characteristics. The goal of this study was to address this problem by determining the optimum altitude and scan angle for Light Detection and Ranging (LiDAR) collection using an Unmanned Aerial System (UAS) within Juncus roemerianus Scheele (Black Needle Rush)-dominated marsh. Furthermore, this study addressed the influences of site characteristics such as water depth, canopy height, and canopy orientation as well as LiDAR classification methods on ground height estimations. To assess accuracy of LiDAR, UAS LiDAR and survey-grade topographic field measurements were collected at two coastal marshes as well as a local control site. At marsh sites, average RMSE values were approximately 52 cm for altitudes less than 75 m, and 14 cm for altitudes greater than 75 m, for nadir scan data. Additionally, LiDAR returns decreased exponentially as altitude increased. Results demonstrate the influence of altitude, scan angle, dominant vegetation, and other site characteristics on the accuracy and density of UAS LiDAR datasets. This study contributes to a better understanding of LiDAR data collection and ground height estimations within coastal marshes that are increasingly crucial in the spatial modeling of these fragile landscapes.