Mapping the in situ microspatial distribution of ice algal biomass through hyperspectral imaging of sea-ice cores.
Cimoli, Emiliano, Lucieer, Vanessa, Meiners, Klaus M., Chennu, Arjun ORCID: https://orcid.org/0000-0002-0389-5589, Catrisios, Katerina, Ryan, Ken G., Lund-Hansen, Lars Cherten, Martin, Andrew, Kennedy, Fraser and Lucieer, Arko (2020) Mapping the in situ microspatial distribution of ice algal biomass through hyperspectral imaging of sea-ice cores. Scientific Reports, 10 . DOI https://doi.org/10.1038/s41598-020-79084-6.
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Abstract
Ice-associated microalgae make a significant seasonal contribution to primary production and biogeochemical cycling in polar regions. However, the distribution of algal cells is driven by strong physicochemical gradients which lead to a degree of microspatial variability in the microbial biomass that is significant, but difficult to quantify. We address this methodological gap by employing a field-deployable hyperspectral scanning and photogrammetric approach to study sea-ice cores. The optical set-up facilitated unsupervised mapping of the vertical and horizontal distribution of phototrophic biomass in sea-ice cores at mm-scale resolution (using chlorophyll a [Chl a] as proxy), and enabled the development of novel spectral indices to be tested against extracted Chl a (R2 ≤ 0.84). The modelled bio-optical relationships were applied to hyperspectral imagery captured both in situ (using an under-ice sliding platform) and ex situ (on the extracted cores) to quantitatively map Chl a in mg m−2 at high-resolution (≤ 2.4 mm). The optical quantification of Chl a on a per-pixel basis represents a step-change in characterising microspatial variation in the distribution of ice-associated algae. This study highlights the need to increase the resolution at which we monitor under-ice biophysical systems, and the emerging capability of hyperspectral imaging technologies to deliver on this research goal.
Document Type: | Article |
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Programme Area: | PA2 |
Research affiliation: | Integrated Modelling > Data Science and Technology |
Refereed: | Yes |
Open Access Journal?: | Yes |
DOI: | https://doi.org/10.1038/s41598-020-79084-6 |
ISSN: | 2045-2322 |
Date Deposited: | 13 Jan 2021 11:05 |
Last Modified: | 04 Feb 2022 08:05 |
URI: | http://cris.leibniz-zmt.de/id/eprint/4477 |
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