Proximal Sensing For Scalable Mapping Of Shallow Coastal Ecosystems

Talk on elucidating the community structure of coral reefs and mangrove forests through dense and detailed maps and inventories.


Shallow coastal habitats, such as shallow coral reefs and mangrove forests, provide invaluable services to surrounding ecosystems and coastal human populations, but are under constant threat from direct and indirect anthropogenic stressors. 

To better manage, protect and restore these ecosystems, qualitative and quantitative ecological information about their biotic communities and abiotic environment is required. 

This doctoral dissertation discusses the shortcomings of traditional surveying methods for coral reefs and mangrove forests when providing such information, on both the proximal sensing scale (e.g., underwater or on-ground sensors) and remote sensing scale (e.g., air- or spaceborne sensors). 

Furthermore, it shows that through well designed AI workflows working with detailed spatial and spectral imaging, more detail and new insights on these ecosystems can be drawn, while reducing uncertainty. Moving away from sparse sampling towards dense thematic mapping provides a closer view of the underlying biodiversity in shallow coastal ecosystems, capturing intra-group composition and configuration patterns, without neglecting rare species or small specimens. An environmental correlation analysis shows that more detailed sampling helps unveil the mechanisms and drivers of shifts in community composition and configuration, as well as the co-occurrence of species and substrate classes in an island-wide coral reef ecosystem. The modern capabilities of AI workflows also enable a shift from studies with purely areal coverage percentage towards analysis focused on individual organisms. This not only facilitates in-depth spatial and temporal investigations of individuals within populations, but also reduces the error in ecosystem accounting calculations.

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