Authors: Jing Liu*,
Topics: Coastal and Marine, Quantitative Methods, Environmental Science
Keywords: DEM, LiDAR, sediment elevation, coastal wetlands
Session Type: Paper
Start / End Time: 3:05 PM / 4:45 PM
Room: Taylor, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Riverine-dominated coastal wetlands in Louisiana are experiencing submergence due to sea level rise (SLR) and associated high rate of land subsidence. Inadequate vertical accretion rates have caused a loss of approximately 5,000 km 2 of wetlands over the past century. Specifically, the topography is a fundamental parameter that influences the capacity of coastal wetlands to successfully maintain their sediment surface elevation comparing SLR. Thus, current, high-resolution, and high-accuracy sediment surface elevation mapping is required in coastal wetlands. However, most of the digital elevation models (DEMs) produced to date are simplistic representation derived from older, coarse elevation data. This project aims to evaluate the increased capability of a data fusion approach using light detection and ranging (LiDAR) data, aerial photography, field sediment surface elevation data, and machine learning algorithms to increase the vertical accuracy of DEM in Coastal Louisiana. Finding and lessons from this study will inform future studies along the Louisiana coastal where a detailed understanding of wetlands response to SLR is needed to help prioritize conservation and restoration opportunities.