Marine ecosystem monitoring requires observations of its attributes at different spatial and temporal scales that traditional sampling methods (e.g., RGB imaging, sediment cores) struggle to efficiently provide. Proximal optical sensing methods can fill this observational gap by providing observations of, and tracking changes in, the functional features of marine ecosystems non-invasively. Underwater hyperspectral imaging (UHI) employed in proximity to the seafloor has shown a further potential to monitor pigmentation in benthic and sympagic phototrophic organisms at small spatial scales (mm–cm) and for the identification of minerals and taxa through their finely resolved spectral signatures. Despite the increasing number of studies applying UHI, a review of its applications, capabilities, and challenges for seafloor ecosystem research is overdue. In this review, we first detail how the limited band availability inherent to standard underwater cameras has led to a data analysis “bottleneck” in seafloor ecosystem research, in part due to the widespread implementation of underwater imaging platforms (e.g., remotely operated vehicles, time-lapse stations, towed cameras) that can acquire large image datasets. We discuss how hyperspectral technology brings unique opportunities to address the known limitations of RGB cameras for surveying marine environments. The review concludes by comparing how different studies harness the capacities of hyperspectral imaging, the types of methods required to validate observations, and the current challenges for accurate and replicable UHI research.

Quantifying the structural complexity provided by biogenic habitat structures is important in ecology, conservation and management, and yet remains a challenging task, particularly in deep sea and polar environments, that current photogrammetry tools can alleviate. In this study, we demonstrate how small remotely operated vehicles and compact underwater GoPro® action cameras can be easily integrated into coastal Antarctic surveys to quantify structural complexity of under‐ice benthos via underwater photogrammetry. Forty‐four pairs of 1 m² quadrats at 1 cm resolution, each comprising an orthomosaic and three‐dimensional reconstructions, were analyzed to describe relationships between benthic cover and structural complexity metrics. The study case provided insights into a unique biogenic habitat, highlighting the role of integrating structural complexity metrics in Antarctic benthic surveys. Although no clear relationships between structural complexity and biodiversity were found, high cover of live reef‐building polychaetes was associated with higher levels of structural complexity, particularly fractal dimension (D). Further, broken biogenic structures, product of disturbance events retain habitat structural complexity known to be associated with larvae settlement and biogenic reef growth. This suggests that D can be used as a metric for detecting subtle changes in biogenic structural complexity. We build from available open‐source code, a reproducible scientific workflow that is expected to facilitate the acquisition and analysis of structural complexity metrics. The workflow presented aims to encourage and accelerate the use of photogrammetry tools for benthic studies aiming to quantify biogenic structural complexity across depths and latitudes.

Crustose coralline algae (CCA) are a highly diverse group of habitat‐forming, calcifying red macroalgae (Rhodophyta) with unique adaptations to diverse irradiance regimes. A distinctive CCA phenotype adaptation, which allows them to maximize photosynthetic performance in low light, is their content of a specific group of light‐harvesting pigments called phycobilins. In this study, we assessed the potential of noninvasive hyperspectral imaging (HSI) in the visible spectrum (400–800 nm) to describe the phenotypic variability in phycobilin content of an Antarctic coralline, Tethysphytum antarcticum (Hapalidiales), from two distinct locations. We validated our measurements with pigment extractions and spectrophotometry analysis, in addition to DNA barcoding using the psbA marker. Targeted spectral indices were developed and correlated with phycobilin content using linear mixed models (R² = 0.64–0.7). Once applied to the HSI, the models revealed the distinct phycoerythrin spatial distribution in the two site‐specific CCA phenotypes, with thin and thick crusts, respectively. This study advances the capabilities of hyperspectral imaging as a tool to quantitatively study CCA pigmentation in relation to their phenotypic plasticity, which can be applied in laboratory studies and potentially in situ surveys using underwater hyperspectral imaging systems.

This protocol describes the procedures for deploying a DJI Matrice 350 drone equipped with a dual-gimbal mount to collect simultaneous RGB and 10-band multispectral imagery of 1-hectare ecosystem plots. The sensors DJI Zenmuse P1 and MicaSense RedEdge-P Dual are used. The protocol includes flight planning and data collection procedures to cover a 100 x 100 m (1 ha) area, with a varying margin of 0 to 100 m (up to 300 x 300 m). Georeferencing is achieved using a DJI RTK base station. A key feature of this workflow is the subsequent alignment of imagery from both cameras into a unified model and produce RGB and multispectral orthomosaics (see Data Processing Protocol). The steps outlined here support the creation of UAV protocols and analysis-ready data products for TERN's programs.

In Antarctica’s coastal waters, the seafloor hosts a surprisingly rich diversity of life, shaped by intricate interactions between sea ice and flora and faunal communities. As sea ice forms and melts seasonally, it modulates the availability of light to both the microscopic algae living within and beneath the ice itself (sympagic algae), and, in turn, influences the productivity and biodiversity of the benthic ecosystems below. The complex pathways that connect sea ice dynamics with the benthos are particularly vulnerable to climate-driven changes in sea ice cover. Understanding responses to a rapidly changing icescape, shaped by warming air and ocean conditions, is essential for predicting the future of benthic biodiversity and its ecosystem functions and services, including biogeochemical cycling and carbon storage. Key to this understanding is coupling structural and biological observations across these mirrored ecosystems. We describe a proof-of-concept under-ice hyperspectral imaging platform, HIcyBot, that we deployed in the Ross Sea Marine Protected Area in 2023. HIcyBot is a remotely operated vehicle that leverages emerging sea ice bio-optical models to quantify ice algae biomass and classifies fine-scale seafloor features through integration of underwater hyperspectral imaging, stereophotogrammetry, and acoustic positioning. Through near simultaneous high-resolution characterization of these under-ice realms, the platform introduces a novel, spatially explicit approach to understanding how biodiversity and the ecosystem function beneath Antarctic sea ice and how they are responding to a changing icescape.

This protocol describes the standardised procedure for collecting RGB and multispectral data for on-going ecological monitoring at the 1 to 50-hectare scale. The instructions outlined in this protocol are for the DJI Mavic 3 Multispectral drone with the Real-Time Kinematic (RTK) module installed for direct georeferencing using the DJI D-RTK 2 base station. Instructions include a desktop-based assessment, flight planning, data collection, and a data backup and metadata scheme. The steps outlined here support the creation of drone protocols and analysis-ready data products for the NatureScan project. While this protocol is specific to the use of the DJI Mavic 3 Multispectral drone, much of the content should transfer to other consumer-grade drone configurations.