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Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation

As the sampling frequency and resolution of Earth observation imagery increase, there are growing opportunities for novel applications in population monitoring. New methods are required to apply established analytical approaches to data collected from new observation platforms (e.g., satellites and...

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Autores principales: Moxley, Jerry H., Bogomolni, Andrea, Hammill, Mike O., Moore, Kathleen M. T., Polito, Michael J., Sette, Lisa, Sharp, W. Brian, Waring, Gordon T., Gilbert, James R., Halpin, Patrick N., Johnston, David W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862293/
https://www.ncbi.nlm.nih.gov/pubmed/29599542
http://dx.doi.org/10.1093/biosci/bix059
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author Moxley, Jerry H.
Bogomolni, Andrea
Hammill, Mike O.
Moore, Kathleen M. T.
Polito, Michael J.
Sette, Lisa
Sharp, W. Brian
Waring, Gordon T.
Gilbert, James R.
Halpin, Patrick N.
Johnston, David W.
author_facet Moxley, Jerry H.
Bogomolni, Andrea
Hammill, Mike O.
Moore, Kathleen M. T.
Polito, Michael J.
Sette, Lisa
Sharp, W. Brian
Waring, Gordon T.
Gilbert, James R.
Halpin, Patrick N.
Johnston, David W.
author_sort Moxley, Jerry H.
collection PubMed
description As the sampling frequency and resolution of Earth observation imagery increase, there are growing opportunities for novel applications in population monitoring. New methods are required to apply established analytical approaches to data collected from new observation platforms (e.g., satellites and unmanned aerial vehicles). Here, we present a method that estimates regional seasonal abundances for an understudied and growing population of gray seals (Halichoerus grypus) in southeastern Massachusetts, using opportunistic observations in Google Earth imagery. Abundance estimates are derived from digital aerial survey counts by adapting established correction-based analyses with telemetry behavioral observation to quantify survey biases. The result is a first regional understanding of gray seal abundance in the northeast US through opportunistic Earth observation imagery and repurposed animal telemetry data. As species observation data from Earth observation imagery become more ubiquitous, such methods provide a robust, adaptable, and cost-effective solution to monitoring animal colonies and understanding species abundances.
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spelling pubmed-58622932018-03-29 Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation Moxley, Jerry H. Bogomolni, Andrea Hammill, Mike O. Moore, Kathleen M. T. Polito, Michael J. Sette, Lisa Sharp, W. Brian Waring, Gordon T. Gilbert, James R. Halpin, Patrick N. Johnston, David W. Bioscience Biologist's Toolbox As the sampling frequency and resolution of Earth observation imagery increase, there are growing opportunities for novel applications in population monitoring. New methods are required to apply established analytical approaches to data collected from new observation platforms (e.g., satellites and unmanned aerial vehicles). Here, we present a method that estimates regional seasonal abundances for an understudied and growing population of gray seals (Halichoerus grypus) in southeastern Massachusetts, using opportunistic observations in Google Earth imagery. Abundance estimates are derived from digital aerial survey counts by adapting established correction-based analyses with telemetry behavioral observation to quantify survey biases. The result is a first regional understanding of gray seal abundance in the northeast US through opportunistic Earth observation imagery and repurposed animal telemetry data. As species observation data from Earth observation imagery become more ubiquitous, such methods provide a robust, adaptable, and cost-effective solution to monitoring animal colonies and understanding species abundances. Oxford University Press 2017-08-01 2017-06-14 /pmc/articles/PMC5862293/ /pubmed/29599542 http://dx.doi.org/10.1093/biosci/bix059 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of the American Institute of Biological Sciences. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Biologist's Toolbox
Moxley, Jerry H.
Bogomolni, Andrea
Hammill, Mike O.
Moore, Kathleen M. T.
Polito, Michael J.
Sette, Lisa
Sharp, W. Brian
Waring, Gordon T.
Gilbert, James R.
Halpin, Patrick N.
Johnston, David W.
Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation
title Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation
title_full Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation
title_fullStr Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation
title_full_unstemmed Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation
title_short Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation
title_sort google haul out: earth observation imagery and digital aerial surveys in coastal wildlife management and abundance estimation
topic Biologist's Toolbox
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862293/
https://www.ncbi.nlm.nih.gov/pubmed/29599542
http://dx.doi.org/10.1093/biosci/bix059
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