| Popis: |
Coastal wetlands, crucial for global biodiversity and climate adaptation, provide essential ecosystem services such as carbon storage and flood protection. These vital areas are increasingly threatened by both natural and human-induced changes, prompting the need for advanced monitoring techniques. This study employs unmanned aerial systems (UASs) equipped with light detection and ranging (LiDAR) and multispectral sensors to survey diverse wetland types across 8 sites in North Carolina. Utilizing high-resolution elevation data and detailed vegetation analysis, coupled with sophisticated machine learning algorithms, we achieved differentiated and highly precise classifications of wetland types. Classification accuracies varied by type, with estuarine intertidal emergent wetlands showing the highest classification accuracies due to less complex vegetation structure and clearer spectral signatures, especially when collections account for tidal influence. In contrast, palustrine forested and scrub–shrub wetlands presented lower accuracies, often due to the denser, mixed, and more complex vegetation structure and variable inundation levels, which complicate spectral differentiation and ground returns from LiDAR sensors. Overall, our integrated UAS-derived LiDAR and multispectral approach not only enhances the accuracy of wetland mapping but also offers a scalable, efficient, and cost-effective method that substantially advances conservation efforts and informs policy-making for coastal resilience. By demonstrating the usefulness of small-scale aerial data collection in ecological mapping, this study highlights the transformative potential of merging advanced technologies in environmental monitoring, underscoring their critical role in sustaining natural habitats and aiding in climate change mitigation strategies. |
