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!