Category: Research

New Publication in Coasts!

Sirianni, H., Sirianni, M.J., Mallinson, D.J., Lindquist, N.L., Valdes-Weaver, L.M., **Moody, M., Henry, B., Coli, C., Rubino, B., Peñalver, M.M., Henne, C. (2022) Quantifying recent storm-induced change on a small fetch-limited barrier island along North Carolina’s Crystal Coast using aerial imagery and LiDAR. Coasts, 2, 302-322. https://doi.org/10.3390/coasts2040015  (Journal Article).

Abstract

Barrier islands within sheltered environments are an important natural defense from severe storm impacts for coastal communities worldwide. Despite their importance, these fetch-limited barrier islands remain understudied and their ability to withstand and recover from storms is not well-understood. Here, we present a case study of Sugarloaf Island in North Carolina that demonstrates the operational use of openly accessible LiDAR and aerial imagery data to quantify synoptic habitat, shoreline, and volumetric change between 2014 and 2020, a period that encompasses four hurricanes and a winter storm event. During this time period, our results show: (1) an 11–13% decrease in marsh and shrub habitat, (2) an average landward shoreline migration of 2.9 m yr−1 and up to 5.2 m yr−1 in extreme areas, and (3) a net volume loss of approximately 9800 m3. The results of this study highlight the importance of storms as a driver of morphologic change on Sugarloaf Island and have implications for better understanding the resiliency of fetch-limited barrier islands to storms. This work helps to enhance prerestoration data availability and supports knowledge-based decision-making regarding habitat change, erosional issues, and the efficacy of nature-based solutions to increase the resiliency of a coastal community in North Carolina.

Figure 4. (A) 2014 DEM relative to Mean Higher High Water (MHHW); (B) 2020 DEM relative to MHHW, (C) difference of DEMs (DoD’s) where the 2014 DEM was subtracted from the 2020 DEM and spatially uniform DEM error removed (+/−0.11 m) to reveal morphological change as erosion (red negative values) and deposition (blue positive values), and (D) corresponding bar graph showing elevation change distributions. Colored arrows in (A,B) depict features described in the text.

Michelle Schlup, Physical Scientist

Lab alumnus Michelle Schlup has accepted a Physical Scientist position with the Army Geospatial Center, US Army Corps of Engineers. Michelle will lend her geospatial expertise to develop innovative solutions to help support one of the largest federal environmental missions: restoring degraded ecosystems; regulating waterways; managing natural resources; and cleaning up contaminated sites from past military activities.

Congratulations, Michelle! We are very proud of you.

Michaelle conducts fieldwork for her master’s thesis.

Ryann Knowles’ Thesis Defense

East Carolina University
Master’s Thesis Defense
Abstract:
Quantifying nearshore bathymetric change using an Unoccupied Surface Vehicle equipped with RTK-GNSS and echosounder: A case study in the Neuse River Estuary, NC
by
Ryann Knowles

The Neuse River Estuary (NRE) located in eastern North Carolina is experiencing shoreline bluff retreat and corresponding nearshore bathymetric change due to increasing storm events such as hurricanes. Monitoring changes in nearshore bathymetry can aid in understanding sediment flux for management and restoration purposes. New remote sensing devices such as small Unoccupied Surface Vehicles (sUSV) allow for on-demand repeat bathymetric surveys of shallow nearshore environments where larger vessels cannot reach. This study uses a sUSV equipped with a single beam echosounder to investigate nearshore morphological changes in the NRE. Two Real-time Kinematic Global Navigation Satellite Systems (RTK-GNSS) and sUSV surveys were carried out in February and April 2022. For each of the two surveys, three Bathymetric Elevation Models (BEMs) were generated using Empirical Bayesian Kriging (EBK), Global Polynomial Interpolation (GPI), and Spline. EBK achieved the best result for both surveys based on conditions observed in the field as well as a vertical Root Mean Square Error (RMSE) of 0.21 m for February and 0.16 m for April. Wave and weather sensors were installed for this study to help determine potential causes of morphological changes. While both months had similar average wind speeds (average 5-10 m/s), their directions were different (Northeast and South directions for February and Southwest direction for April). As can be expected in a wave dominated estuary with these observed wind speeds, wave depth minimum and maximum ranges were small, which ranged 0.02 cm for February and 0.03 cm for April. Short term changes in the nearshore bathymetry were negative resulting in erosion with no estimated deposition. Bathymetry loss ranged from 0.3 to 0.69 m between February and April, and the observed wind and wave data indicate these changes were likely due to another contributing factor such as currents. To assist future work using sUSVs in shallow nearshore estuarine environments, a workflow of best practices when conducting sUSV surveys was also developed in this study. It is anticipated that the results will provide useful information for researchers conducting sUSV surveys as well as understanding the causes of nearshore morphological change in shallow estuarine environments.

Date: June 27, 2022
Time: 10:00 am
Place: https://ecu.webex.com/ecu/j.php?MTID=m200716ca1baf2fd63e9723450e18d482
Meeting number: 2620 113 6592
Password: qKutbBTA273 (75882282 from phones)
Join by phone: +1-415-655-0003 US Toll; Access code: 2620 113 6592

Advisor: Dr. Hannah Sirianni
Committee: Drs. Scott Lecce and Thad Wasklewicz

New Publication in Drones!

Guan, S., Sirianni, H., Wang, W., Zhu, Z. (2022) sUAS monitoring of Coastal Environments: A Review of Best Practices from Field to Lab. Drones. 6(6), 142, https://doi.org/10.3390/drones6060142 (Journal Article: IF: 5.532).

Abstract

This review article aims to synthesize and illustrate best practices used to collect and process sUAS data of coastal environments. To assist with this objective, we reviewed recent review articles that focus on sUAS applications in coastal environments and include topics such as regulations, sensors, platforms, calibration, validation, and data processing. Based on the best practices identified from these review articles in addition to the current literature, we illustrate a step-by-step workflow that can be used for either conducting sUAS surveys or sUAS data processing, or both.

Figure 2. A 17-step workflow using best practices for processing sUAS photo-based surveys of coastal environments.

Jessica Richter’s Thesis Defense

East Carolina University
Master’s Thesis Defense
Abstract:
Shoreline mapping in the Neuse River Estuary, NC using object-based ensemble analysis, aerial imagery,
and LiDAR
by
Jessica Richter

The Neuse River Estuary (NRE), NC has been impacted by a significant portion of the extreme storms that have swept coastal North Carolina in the past three decades. Shoreline classification maps are critical to understanding the context and magnitude of storm-induced erosion along the NRE shoreline. This study assessed the ability of an object-based ensemble analysis to map natural and engineered shoreline types observed within the NRE. Object-based ensemble analysis has emerged as a successful framework to improve image classification but has yet to be tested in classifying an estuarine shoreline environment. This approach used in-situ reference data, high-resolution aerial imagery, and LiDAR point data to train an ensemble of five machine learning algorithms (Random Forest, Support Vector Machine, LibLINEAR, Artificial Neural Network, and k-Nearest Neighbors). The object-based ensemble produced the highest overall classification accuracy at 76.4% (Kappa value = 0.66), 6.3% higher than the top performing pixel-based model, justifying its use to produce the final shoreline classification map. The results of the object-based ensemble analysis were then used to assess shoreline change and erosion vulnerability and produced comparable erosion rates to those observed in past studies. This demonstrates that an object-based ensemble approach is an effective way to map shoreline classifications in the NRE and should continue to be explored within other shoreline management applications.

Date: April 14, 2022
Time: 10:30 -11 am
Place: https://ecu.webex.com/ecu/j.php?MTID=m3a40a39d91d53d68a52b95a0f2ab7b87
Meeting number: 2620 747 3807
Password: XesqWcPQ222 (93779277 from phones)
Join by phone: +1-415-655-0003 US Toll; Access code: 2620 747 3807
Advisor: Dr. Hannah Sirianni
Committee: Drs. Thad Wasklewicz, Yong Wang and Burrell Montz

Dense Fog Impacts Field Efforts

Feb 2, 2022 Fog Field Crew. Phil (far left), Robert, Ryann, and Liz (far right).

A dense fog surprised the field crew on a cold February morning causing them to wait a few hours before they could survey. This delay in precious fieldwork time was a disappointment, but the field crew didn’t let that stop them from trying and having a good time!

New Lab Member, Liz Schunk!

Liz Schunk joins the Lab as an undergraduate research assistant. Liz is a senior double majoring in GIScience & Technology and Geography (concentration in environmental systems). She is also the vice president of ECU’s Beta Iota chapter of Gamma Theta Upsilon. Liz is interested in coastal and marine wildlife/habitat conservation and management. She plans to combine these interests along with her Lab experience using geospatial technologies to help develop original ideas for a master’s thesis. Liz plans to enter a master’s program upon her graduation, and her experience in the lab will provide her the training and support she needs to make her applications competitive.

Watch MaRS take off!

A fine resolution camera is needed to capture the topography of beaches and sand dunes. Otherwise the glare from the sun will make it difficult to resolve features necessary for creating tie points when conducting structure-from-motion photogrammetry. Here, we configured our best drone for measuring topographic complexity of beaches and sand dunes. Meet the Matrice 600 Pro aircraft equipped with a Ronin-MX gimbal carrying a Sony A7III camera, also known as MaRS. Grad student Phil Van Wagoner pilots the MaRS while grad student Jessica Richter watches as the visual observer.