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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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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. |
format | Online Article Text |
id | pubmed-5862293 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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