Mapping Waterbird Habitats with UAV-Derived 2D Orthomosaic Along Belgium’s Lieve Canal
The accurate monitoring of waterbird abundance and their habitat preferences is essential for effective ecological management and conservation planning in aquatic ecosystems. This study explores the efficacy of unmanned aerial vehicle (UAV)-based high-resolution orthomosaics for waterbird monitoring and mapping along the Lieve Canal, Belgium. We systematically classified habitats into residential, industrial, riparian tree, and herbaceous vegetation zones, examining their influence on the spatial distribution of three focal waterbird species: Eurasian coot (Fulica atra), common moorhen (Gallinula chloropus), and wild duck (Anas platyrhynchos). Herbaceous vegetation zones consistently supported the highest waterbird densities, attributed to abundant nesting substrates and minimal human disturbance. UAV-based waterbird counts correlated strongly with ground-based surveys (R2 = 0.668), though species-specific detectability varied significantly due to morphological visibility and ecological behaviors. Detection accuracy was highest for coots, intermediate for ducks, and lowest for moorhens, highlighting the crucial role of image resolution ground sampling distance (GSD) in aerial monitoring. Operational challenges, including image occlusion and habitat complexity, underline the need for tailored survey protocols and advanced sensing techniques. Our findings demonstrate that UAV imagery provides a reliable and scalable method for monitoring waterbird habitats, offering critical insights for biodiversity conservation and sustainable management practices in aquatic landscapes.
Authors
- Marie Anne Eurie Forio (ORCID: https://orcid.org/0000-0001-6675-4751)
- Long Ho (ORCID: https://orcid.org/0000-0002-2999-1691)
- Andrée De Cock (ORCID: https://orcid.org/0000-0001-7911-4219)
- Xingzhen Liu (ORCID: https://orcid.org/0000-0003-2037-0634)
- Kim Pham (ORCID: https://orcid.org/0000-0003-3300-1985)
- Wouter H. Maes (ORCID: https://orcid.org/0000-0002-1592-9299)
- Peter L.M. Goethals (ORCID: https://orcid.org/0000-0003-1168-6776)
- Diego Panique-Casso (ORCID: https://orcid.org/0000-0002-9418-6058)
Institutions
- Ghent University (BE)
Publication Details
- Journal
- Remote Sensing
- Published
- 2025-07-26
- DOI
- https://doi.org/10.3390/rs17152602
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Universiteit Gent
- China Scholarship Council
- HORIZON EUROPE Framework Programme
- Bijzonder Onderzoeksfonds UGent