Sirianni, H., Richter, J., Sirianni, M., Pettyjohn, S. (2024) Shoreline classification maps and ground truth data for the Neuse River Estuary, North Carolina. Scientific Data, 11(103). https://doi.org/10.1038/s41597-024-02954-5

https://www.nature.com/articles/s41597-024-02954-5

Congrats to Sarah for her outstanding presentation at the 2023 American Geophysical Union (AGU) Annual Meeting in San Francisco, CA! Her research, titled “Quantifying Storm-Induced Bluff Erosion Using Aerial Imagery and Lidar: A Case Study of the Neuse River Estuary, North Carolina, USA,” not only drew attention but also sparked thought-provoking discussions among her peers.

What’s truly remarkable is that this isn’t even part of her master’s thesis; Sarah conducted this work as part of her research assistantship in the Coastal Geography & Terrain Analysis Lab. Way to go, Sarah!

This photo captures part of our academic family tree! Sarah Pettyjohn (L), Hannah Sirianni (M), Caiyun Zhang (R). Taken while at the AAG 2023 annual meeting in Denver, Colorado.

NOAA’s National Sea Grant College Program and the U.S. Coastal Research Program (USCRP) are backing our project “Co-developing a community and data-driven framework for coastal protection decision-making.” This work is in collaboration with Rachel Gittman (PI) and co-pIs Hannah Sirianni, Nadine Heck, Siddharth Narayan, Scott Leahy, Frank Lopez, Sarah Spiegler.

To learn more, check out the news release here: https://ncseagrant.ncsu.edu/news/2023/01/north-carolina-sea-grant-advances-shoreline-protection-and-coastal-resilience/

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

Watch Jessica Richter present her research at the NC Space/Sea Grant as a lightening talk.

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.

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

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!