Semester of Graduation
Spring 2019
Department
Civil and Environmental Engineering
Document Type
Thesis
Abstract
The onset of airborne light detection and ranging (lidar) has resulted in expansive, precise digital elevation models (DEMs). DEMs are essential for modeling complex systems, such as the coastal land margin of Louisiana. They are used for many applications (e.g. tide, storm surge, and ecological modeling) and by diverse groups (e.g. state and federal agencies, NGOs, and academia). However, in a marsh environment, it is difficult for airborne lidar to produce accurate bare-earth measurements and even accurate elevations are rarely verified by ground truth data. The accuracy of lidar in marshes is limited by the sensor’s resolution and by the laser’s ability to penetrate dense vegetation. The first objective of this work is to measure elevation using Real Time Kinematic (RTK) instruments and compare them to elevations from lidar-derived DEMs. This error evaluation (elevationDEM – elevationRTK = error) will be performed in a variety of marsh types with differing vegetation. This evaluation shows that the surveyed marshes produce minimal DEM error in relation to other published work but are still likely to result in misleading hydrodynamic and wetland modeling outcomes. The second objective is to correct lidar-derived DEMs by applying and improving upon previously published methods. The techniques will be improved through the use of additional remote sensing inputs and by understanding the ecological factors that influence the spatial and temporal distribution, composition and productivity of marsh plant species.
Recommended Citation
Lauve, William M., "Assessment and Correction of Lidar-derived DEMs in the Coastal Marshes of Louisiana" (2019). LSU Master's Theses. 4900.
https://repository.lsu.edu/gradschool_theses/4900
Committee Chair
Hagen, Scott
DOI
10.31390/gradschool_theses.4900
Included in
Applied Statistics Commons, Civil Engineering Commons, Environmental Engineering Commons, Environmental Monitoring Commons, Oceanography Commons, Other Civil and Environmental Engineering Commons, Statistical Models Commons