Submerged Aquatic Vegetation Survey in Southwest Louisiana

The GRI UAS team recently conducted work in southwest Louisiana in support of an ongoing MSU research project focused on evaluating coastal restoration techniques in the northern Gulf of Mexico. This research, funded by the National Academy of Science Gulf Research Program, is investigating the effectiveness of a wetland restoration technique known as marsh terracing. Marsh terraces are linear islands of sediment constructed within open water areas of coastal wetlands. They are designed to preserve and create new marsh in a number of ways including the reduction of wave energy and associated shoreline erosion, as well as the promotion of submerged aquatic vegetation (SAV) growth.

The work conducted by the GRI UAS team specifically focused on assessing how effective marsh terraces are at promoting SAV growth. The team collected UAS images of the water surface in locations with constructed marsh terraces as well as nearby reference sites without terraces, to determine if there was a measurable difference in the abundance of SAV between the terraced and unterraced sites. Additionally, researchers also conducted manual raking surveys of SAV presence at these sites to ground truth UAS observations and evaluate the effectiveness of UAS collected imagery for surveying SAV in wetland environments.

Water Quality Observations for Enhanced Understanding and Sustainable Management of Oyster Reefs in Mississippi Sound

Oyster reefs provide substantial ecological value to coastal environments as biodiverse habitats as well as significant economic value to coastal communities as commercial fisheries. Unfortunately, oyster reef acreage as well as the volume and value of commercial oyster harvests in Mississippi waters have declined sharply in recent years, which has been attributed to a range of environmental stressors including impaired water quality. Therefore, we are using field sampling and remote sensing data collected from traditional platforms as well as novel autonomous aerial and surface vehicles to quantify the spatiotemporal variability of water quality at oyster reef sites in Mississippi Sound.

Water quality sampling, Autonomous Surface Vessel (ASV) surveys and Unmanned Aerial Systems (UAS) imaging was conducted from July 26 to 30, 2021 in the western Mississippi Sound proximal to oyster reefs. These data will not only inform the state of the water quality in the region, but also will provide input data for the calibration and validation of UAS and satellite algorithms. The water sampling included collection of water samples and profiles of in situ parameters (temperature, salinity, dissolved oxygen, pH-oxygen reduction potential, photosynthetically available radiation), and remote sensing reflectance measurements. The water samples were collected from 41 locations for suspended particulate matter (SPM), chlorophyll a, cyanobacteria-specific pigment phycocyanin (PC), colored dissolved organic matter (CDOM), absorption, algal toxins, coastal acidification, nutrients, microscopic, bacterial counts, and toxic metal analyses.

The ASV survey provided measurements of Conductivity/salinity, Temperature, pH, Chlorophyll a, Phycocyanin, Phycoerythrin, Colored dissolved organic matter (CDOM), Turbidity at 595 nm, Turbidity at 700 nm, Backscattering at 470 nm, 532 nm, and 650 nm, and Dissolved Oxygen (DO) concentrations collected every second during deployment. During field deployments, all data collected by the ASV are transmitted via cellular uplink to a server in the laboratory at MSU, and the plots and maps of the dataset collected by the ASV are displayed on a website in real time (https://water.geosci.msstate.edu/monitor/). This website is also used to query historical data, generate plots and maps, and/or download the data in multiple formats including Microsoft Excel files or shape files.

Multispectral imagery data were collected with a multi-rotor UAS with 8 cm resolution from a height of 122 m during water sample collection, and remote sensing algorithms will be calibrated and validated by developing quantitative relationships between water quality data and UAS imagery. The algorithms developed will be applied to this multispectral imagery, the imagery collected during the water sampling trips, and a timeseries of satellite data to obtain synoptic information on harmful algal blooms, SPM, CDOM, and other water quality indicators. Based on the in situ, ASV surveys, UAS-derived, and satellite-derived water quality data, the potential impacts of water quality on the oyster population will be determined in the western Mississippi Sound.

LiDAR Data Collection over KLMS Louisville Winston County Airport

The GRI UAS team recently had the opportunity to work with a local Civil Engineering firm to help assess the status of one of their projects. Clearwater Consultants were in the process of expanding the runway at the Louisville Winston County Airport and needed a topographic survey completed. Clearwater reached out to the GRI to look at the feasibility of collecting LiDAR over their project area of roughly 200 acres. At the point the team flew the site, the construction crews had already moved a large volume of dirt and extended the runway. They mainly wanted the survey to ensure the different grades and slopes matched the plans drawn prior to starting the construction.

Traditionally a topographic survey would be completed by a person walking the area and collecting GPS points on a 50 to a 100 foot grid. Those points, containing XYZ coordinates, could then be imported into some modelling software to create a Triangular Irregular Network (TIN) surface to make a model of the jobsite. As one could image walking 200 acres in varying terrain and with some thick vegetation can be fairly time consuming and taxing.

However, utilizing the GeoCue TrueView 410 on a DJI Matrice 600 UAV allowed the for all 200 acres to be mapped in less than 5 hours. Also, instead of collecting a single point every 50 to 100 feet, the team was able to achieve roughly 10 points per square foot. This greater point density allows the project managers to have a far more accurate representation of the terrain and all its varying features.