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Ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research
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Date
2022-04
Authors
Scarrott, Rory
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Publisher
University College Cork
Published Version
Abstract
Seascape ecology provides us with a framework to explore the distribution of marine life throughout the world’s oceans. Studies often require both biological and environmental data from a variety of sources, which are increasingly complemented by data acquired using remote sensing and satellite-based sensors. Advances in remote sensing technology have equipped researchers with the capability to visualise environmental conditions, with great precision over large spatial scales. As records of environmental conditions, satellite-derived image data have proven useful in seascape ecology. Indeed, single-date and multi-date satellite imagery are already widely used in support of oceanographic and fisheries research and monitoring. However, with increases in the temporal frequency of imagery, a greater diversity of ocean surface features can be studied, modelled, and understood in terms of their development over time.
This research examines the interface between oceanography, marine ecology, and remote sensing. It focuses on the use of a subset of temporally-rich satellite-derived imagery known as hypertemporal data, and its potential utility for seascape ecology studies. These high temporal resolution datasets are characterised by being: univariate in nature (e.g. Sea Surface Temperature); comprised of frequent, equally-spaced discrete time slices; precisely co-registered; and radiometrically consistent between images (i.e. they are measured using the same sensors, or are derived from inter-validated sensor systems).
An in-depth review analyses nearly 25 years of available literature on the use of satellite-derived hypertemporal datasets. In general, they have been more widely used in terrestrial environments where in-situ validation costs are lower, and boundaries and features have greater permanency. By contrast, hypertemporal datasets have been under-utilised in ocean sciences. The review examines and describes the range of methodologies that have been adapted specifically for hypertemporal applications, in both marine and terrestrial contexts. It also identifies the priority research that should be done to maximise data usage for the ocean arena. In particular, the review highlighted the importance of prioritising data-driven methodologies in the near-term, and developing methods to map spatio-temporal heterogeneity (i.e. spatial and temporal variations in physical, chemical, or biological conditions) within datasets.
A data-driven method to map Spatio-Temporal Heterogeneity (STH) using hypertemporal datasets is then presented. The Ocean-surface Heterogeneity MApping (OHMA) algorithm uses unsupervised ISODATA clustering, an ensemble approach, and a data-driven optimisation process, to identify where boundaries between different ocean regions frequently occur. OHMA effectively produces a suite of complementary datasets – a single STH image dataset that highlights the frequency at which boundaries between regions occur, and an optimised spatio-temporal classification of the ocean surface (both spatial cluster distributions, and their associated temporal profiles). A Sea Surface Temperature (SST) dataset, comprising weekly images of the North Atlantic Ocean from 2011, was used to develop the method. Validation, using Irish Marine Institute temperature measurements acquired along vessel tracks while the vessel is “Underway”, demonstrated an association between higher STH values and areas where locally extreme temperature transitions were more likely to occur. Generalised Linear Modelling of STH versus a combination of possible ocean-surface heterogeneity characteristics (fronts and currents), and oceanographically-known drivers (bathymetry), showed that the drivers of STH vary regionally. This highlighted the utility of OHMA to generate higher-level products. These contain spatial and temporal information that cannot be produced using only in-situ or infrequent satellite data.
Following development of the OHMA methodology, the research examined its performance at a range of spatial and temporal scales, and the considerations needed to integrate STH mapping into seascape ecology analyses. Two OHMA-applications on SST data used relatively well-understood waters around the West European Archipelago, and ecologically important thermohaline and tidal front systems, to explore the methodology’s potential. The first OHMA-application focused on the algorithm’s performance at higher spatial scales, and sub-annual timeframes. STH values, derived from a 48-day hypertemporal SST dataset from summer 2019, were compared to a difference grid generated from a network of Conductivity, Temperature and Depth (CTD) measurements, and hydrographic information derived from Underway and CTD cast data. Higher value STH features were also compared to a number of well-documented tidal fronts, and a thermohaline front. Statistically significant positive correlations were identified between STH values and a difference in surface water characteristics between CTD sites. While the approach conveyed detailed spatio-temporal information on tidal fronts, it performed poorly at framing the thermohaline front. Examining the hydrographic information revealed the complexity in interpreting these associations. The study highlighted the value of using STH datasets to objectively characterise the spatio-temporal behaviour of fronts, and the surface structure of stratified shelf seas. It also suggested an annual cycle of STH should be analysed, to clarify whether thermohaline front information could be gathered.
The second application examined the use of multiple temporal scales within a preliminary seascape ecology study, comparing SST-derived STH to a number of ecosystem indicators concerning primary producer abundance (phytoplankton) and fishing fleet activity. In doing so, it also further examined temporal scale considerations when using hypertemporal data. STH datasets were produced from different temporal inputs (an annual 7-day SST dataset versus 30/31-day daily SST datasets over 2018). SST-STH, model-derived phytoplankton concentration, and apparent commercial fishing effort data (derived from a behavioural classification of Automatic Identification System data, recording vessel tracks) were compared for February, March, July, and August using a correlation-based approach. Positive associations were found between SST-STH and fishing effort. This study demonstrated there could be important, usable, biophysical linkages between trophic indicators, and the higher-level heterogeneity conveyed by a STH dataset. It also highlighted the challenges the OHMA approach faced when used on multi-temporal, rather than hypertemporal, data. Additionally, it clarified SST-STH maps could convey information on thermohaline fronts when OHMA was used on SST datasets at annual timeframes, or datasets framing times when continental shelf seas are not thermally stratified.
The thesis culminates in a discussion of how hypertemporal data could be used to support seascape ecology applications, using the development of the OHMA approach to provide evidence and guidance. Discussions highlight the key advances the research has made. Namely, the research has clarified that there are benefits to exploiting the hypertemporal data resource within seascape ecology research. The high-resolution data records subtle changes that can be used to better characterise ocean surface habitats. The research also produced a methodology that delivers objectively-derived, high-level heterogeneity information from hypertemporal data, demonstrated its use within a seascape ecology study, and described it along with best practice guidance for use. The limitations of the methodology are also clearly outlined, and include a cautionary note concerning the use of OHMA on non-hypertemporal datasets where the image number, or dataset variability, may be insufficient for the process to produce the full range of possible products. Concerning validation, the research highlighted the need for in-situ data to be collected which is spatio-temporally comparable to hypertemporal datasets, to improve quality assessment of, and confidence in, information products derived from hypertemporal datasets. Within this research in particular, the in-situ data should allow for estimates of spatio-temporal heterogeneity to be derived, which are comparable to those which can be derived from hypertemporal datasets.
The discussion also emphasises a range of near-term (1-5 year) future research avenues, which include (i) exploiting available hypertemporal datasets from the ESA Climate Change Initiative, (ii) using OHMA-derived STH maps to stratify in-situ sampling of ocean-surface features, (iii) demonstrating the use of OHMA coupled with time-series analysis of ocean-surface parameters, and (iv) examining multi-trophic interactions between primary producers, predators, and STH features in waters around the West European Archipelago and the North Atlantic. Such near-term studies would enhance our understanding of the methodology, and use hypertemporal data to expand our understanding of ocean-surface structure at different spatial and temporal scales. In addition, they would use spatio-temporal heterogeneity mapping to diversify the range of hypertemporal methodologies being deployed on ocean-surface parameters, further enabling seascape ecology studies to exploit the hypertemporal data resource.
Description
Keywords
Northeast Atlantic , West European Archipelago , ISODATA , Remote sensing , Hypertemporal , Sea surface temperature , Spatio-temporal heterogeneity , Seascape , Ecology , Heterogeneity mapping , Satellite data
Citation
Scarrott, R. G. 2022. Ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research. PhD Thesis, University College Cork.