Ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research
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-base...
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ftunivcollcork:oai:cora.ucc.ie:10468/13202 2024-09-15T18:25:29+00:00 Ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research Scarrott, Rory Cawkwell, Fiona Jessopp, Mark John Cronin, Michelle Cusack, Caroline O’Rourke, Eleanor 2022-04 application/pdf https://cora.ucc.ie/handle/10468/13202 en eng University College Cork info:eu-repo/grantAgreement/EC/H2020::RIA/687289/EU/Coastal Waters Research Synergy Framework/Co-ReSyF Scarrott, R. G. 2022. Ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research. PhD Thesis, University College Cork. 226 https://cora.ucc.ie/handle/10468/13202 © 2022, Rory Gordon Scarrott. https://creativecommons.org/licenses/by-sa/4.0/ Northeast Atlantic West European Archipelago ISODATA Remote sensing Hypertemporal Sea surface temperature Spatio-temporal heterogeneity Seascape Ecology Heterogeneity mapping Satellite data Doctoral thesis Doctoral PhD - Doctor of Philosophy 2022 ftunivcollcork 2024-07-29T03:06:03Z 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 ... Doctoral or Postdoctoral Thesis Northeast Atlantic University College Cork, Ireland: Cork Open Research Archive (CORA) |
institution |
Open Polar |
collection |
University College Cork, Ireland: Cork Open Research Archive (CORA) |
op_collection_id |
ftunivcollcork |
language |
English |
topic |
Northeast Atlantic West European Archipelago ISODATA Remote sensing Hypertemporal Sea surface temperature Spatio-temporal heterogeneity Seascape Ecology Heterogeneity mapping Satellite data |
spellingShingle |
Northeast Atlantic West European Archipelago ISODATA Remote sensing Hypertemporal Sea surface temperature Spatio-temporal heterogeneity Seascape Ecology Heterogeneity mapping Satellite data Scarrott, Rory Ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research |
topic_facet |
Northeast Atlantic West European Archipelago ISODATA Remote sensing Hypertemporal Sea surface temperature Spatio-temporal heterogeneity Seascape Ecology Heterogeneity mapping Satellite data |
description |
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 ... |
author2 |
Cawkwell, Fiona Jessopp, Mark John Cronin, Michelle Cusack, Caroline O’Rourke, Eleanor |
format |
Doctoral or Postdoctoral Thesis |
author |
Scarrott, Rory |
author_facet |
Scarrott, Rory |
author_sort |
Scarrott, Rory |
title |
Ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research |
title_short |
Ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research |
title_full |
Ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research |
title_fullStr |
Ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research |
title_full_unstemmed |
Ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research |
title_sort |
ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research |
publisher |
University College Cork |
publishDate |
2022 |
url |
https://cora.ucc.ie/handle/10468/13202 |
genre |
Northeast Atlantic |
genre_facet |
Northeast Atlantic |
op_relation |
info:eu-repo/grantAgreement/EC/H2020::RIA/687289/EU/Coastal Waters Research Synergy Framework/Co-ReSyF Scarrott, R. G. 2022. Ocean-surface heterogeneity mapping: exploiting hypertemporal datasets in support of seascape ecology research. PhD Thesis, University College Cork. 226 https://cora.ucc.ie/handle/10468/13202 |
op_rights |
© 2022, Rory Gordon Scarrott. https://creativecommons.org/licenses/by-sa/4.0/ |
_version_ |
1810466005732818944 |