Mapping stress in submerged aquatic vegetation using multispectral imagery and structure from motion photogrammetry

Amritha Nair; Fleur Visser; Ian Maddock; Jonas Schoelynck

doi:10.1117/12.3034015

20 November 2024 Mapping stress in submerged aquatic vegetation using multispectral imagery and structure from motion photogrammetry

Amritha Nair, Fleur Visser, Ian Maddock, Jonas Schoelynck

Author Affiliations +

Proceedings Volume 13191, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI; 131910P (2024) https://doi.org/10.1117/12.3034015
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

Submerged aquatic vegetation (SAV) forms an integral component of ecologically healthy aquatic systems. Besides providing a thriving habitat for underwater fauna, it also regulates the water flow, stabilises sediment, and improves biogeochemical cycling. However, they have been subject to different kinds of pressures ranging from grazing by invasive species, increased flow velocities, and rising temperatures leading to observable changes in their canopy structure and composition. Only fewer studies have looked into the possibility of mapping stress in SAV, mainly due to the difficulty of applying optical remote sensing techniques in submerged environments resulting from the strong attenuation of visible and particularly infrared light in water. In this project the main aim is to identify stress in SAV from multispectral imagery. To achieve this a highly detailed model of a submerged plant canopy was obtained using Structure from Motion photogrammetry (SfM).

These preliminary results show that it is possible to detect variation in spectral reflectance of SAV as a result of stress when above water radiation is corrected for submergence depth based on a 3D canopy model obtained through SfM.

Conference Presentation

Citation Download Citation

Amritha Nair, Fleur Visser, Ian Maddock, and Jonas Schoelynck "Mapping stress in submerged aquatic vegetation using multispectral imagery and structure from motion photogrammetry", Proc. SPIE 13191, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI, 131910P (20 November 2024); https://doi.org/10.1117/12.3034015

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