Advanced Insights into Coastal and Estuarine Environments: Key Fine Targets Analysis through Automated 2D and 3D Techniques

Coastal inshore areas, recognized as invaluable yet vulnerable, are experiencing shifts between various states due to gradual environmental changes and artificial disturbance. These transitions, however, are often imperceptible with large-scale mapping or through on regional in situ surveying when using traditional techniques. Advanced 2D and 3D technologies, particularly high-resolution remote sensing (HRRS) and LiDAR, offer novel perspectives that unveil fine details and precise vertical 3D structure of coastal ingredients. These technologies enable early, rapid, and accurate identification of significant transient or persistent patterns. Additionally, machine learning (ML), encompassing parametrized algorithms, ensemble learning (EL), and deep learning (DL), provides a unique advantage for automated observation.

 

This work aims to advance the observation of key fine components in coastal inshore areas by designing automated methods and frameworks. It considers both natural and human-made sources as targets. with the focus of Poaceae and marine debris.

 

First, an automated 3D recognition of stalks and leaves for Poaceae in coastal mudflats. Poaceae species (Giant reed and reed) in coastal mudflats hold ecological importance and serve as indicators. However, obtaining their phenotypic parameters like stalks and leaves is challenging. Our new automated, parametrized algorithm recognizes stalks and leaves of individual Poaceae plants in coastal wetlands using terrestrial LiDAR point clouds, leveraging radiometric and geometric features.

                                                                                   

Second, a new framework for comprehensive surveying of coastal Fairy Circles (FCs). FCs, predominantly formed by Poaceae, are self-organized patterns linked to recovery processes and salt-marsh resilience. Our new framework aims for automated surveying of coastal FCs, utilizing ML methods (which includes state-of-the-art foundation model, EL, and DL methods) on 2D and 3D data (satellite-borne and airborne). It is grounded in clear principles of FCs' definition and dynamics, potentially revolutionizing our understanding of coastal FCs behavior.

 

Third, an automated method for 2D and 3D recognition of marine debris across complex scenarios. Marine debris in coastal environments poses significant ecological and environmental issues and has garnered widespread concern. Our new method detects and extracts marine debris from terrestrial LiDAR point clouds or UAV HRRS imagery, combining calibrated radiometric data with geometric features.

                         

Fourth, we have developed a series of mathematical models for instrumentation and data processing to achieve these goals. We proposed generalized rigorous model to mathematically correct the density variation in terrestrial LiDAR point clouds, the novel distribution pattern features, and a model to eliminate the specular effect on UAV LiDAR point cloud intensity.