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  • Antarctic Landfast sea ice (fast ice) is important climatologically, biologically and for logistics for short time-scale anomalies. Until recently, there hasn’t been an accurate, high-resolution fast ice extent dataset which can support an analysis on drivers of fast ice and most studies only investigate fast ice on limited regions of Antarctica in a limited time scale. There is a need to extend the spatial and temporal studying coverage to provide detailed information on the Antarctic coast over a longer period. This is the first detailed analysis to identify and quantify correlation between the environmental anomaly and fast ice anomaly mainly in the east Antarctic coast. By examining regional/local fast ice extent in in east Antarctic coast in the context of the broader and/or remote-teleconnected atmospheric circulation/properties using spatial correlation techniques, a strong correlation between NINO3 region and Lützow-Holm Bay fast ice and similar and significant correlation of regional scale factors from Lützow-Holm Bay to Mawson Coast mainly are found. The results of this thesis suggest that the pack ice, atmospheric factors and oceanic factors are important for interpreting fast ice anomalies. To identify and quantify correlation between the pack ice, temperature at 2m, wind at 10m, snow fall anomaly, sea surface temperature anomaly, ocean heat content anomaly and fast ice anomaly, backward multiple linear regression is conducted to demonstrate some predictive fast ice driver information by quantifying the correlation between different drivers and fast ice anomaly. The multiple linear regression also suggests that oceanic influences including pack ice are generally more important than atmospheric influences. Future experiments could be conducted to interpret fast ice anomalies in the context of the ocean mainly.

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    Dataset collected at Cape Evans, Antarctica, November 2023 as part of a long-term NIWA benthic monitoring program under the Antarctica New Zealand event number K882A. The dataset includes multiple sea-ice and seafloor hyperspectral imaging transects (10-40 meters long) coupled with normal red, green, and blue (RGB) imagery from a dual camera machine vision system. The data were acquired using the remotely operated vehicle (ROV) HIcyBot system, funded by the Australian Centre for Excellence in Antarctic Research (ACEAS). A GNSS-integrated USBL transponder equipped onto the ROV allowed every frame of the high frequency hyperspectral imager to be timestamped via GPS clock to acoustically provided underwater position and attitude. The dataset also includes hyperspectral imaging scans of sampled/retrieved organisms found at the seafloor, to support habitat mapping algorithm development (e.g., algae, urchins, sea-stars, etc.). The ROV was tested as part of an ACEAS Program 2 subcomponent that involved the design of the new under-ice hyperspectral imaging and photogrammetric payload mounted onto the HIcyBot ROV. The overarching goal of the systems was to be able to acquire information of under the sea-ice sympagic and benthic communities (e.g., biomass and photophysiology) and deliver a multi-scale array of biophysical data that can be assimilated with known information in the region and monitor fine-scale change. Through the analysis of new and existing bio-optical under-ice data, the dataset aims to ultimately envisions the delivery of new monitoring tools and algorithms that can provide support for modelling efforts and reveal complex biophysical processes under a changing Antarctic Sea ice. ***NOTE DATA TO BE EMBARGOED UNTIL 01/01/2025***