Loading…
EELGRASS MAPPING WITH SENTINEL-2 AND UAV DATA IN PRINCE EDWARD ISLAND (CANADA)
Eelgrass (Zostera marina L.) is a marine angiosperm that grows throughout coastal regions in Atlantic Canada. Eelgrass beds provide a variety of important ecosystem services, and while it is considered an important marine species, little research has been done to understand its distribution and loca...
Saved in:
Published in: | ISPRS annals of the photogrammetry, remote sensing and spatial information sciences remote sensing and spatial information sciences, 2021-06, Vol.V-3-2021, p.125-132 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | 132 |
container_issue | |
container_start_page | 125 |
container_title | ISPRS annals of the photogrammetry, remote sensing and spatial information sciences |
container_volume | V-3-2021 |
creator | Gallant, E. LaRocque, A. Leblon, B. Douglas, A. |
description | Eelgrass (Zostera marina L.) is a marine angiosperm that grows throughout coastal regions in Atlantic Canada. Eelgrass beds provide a variety of important ecosystem services, and while it is considered an important marine species, little research has been done to understand its distribution and location within Atlantic Canada. The purpose of this study was to assess the capability of Sentinel-2 and UAV imagery to map the presence of eelgrass beds within the Souris River in Prince Edward Island. Both imageries were classified using the non-parametric Random Forests (RF) supervised classifier and the resulting classification was validated using sonar data. The Sentinel-2 classified image had a lower validation accuracy at 77.7%, while the UAV classified image had a validation accuracy of 90.9%. The limitations of the study and recommendations for future work are also presented. |
doi_str_mv | 10.5194/isprs-annals-V-3-2021-125-2021 |
format | article |
fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_d18e061e5d01436ab450c440e2d7ca2b</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_d18e061e5d01436ab450c440e2d7ca2b</doaj_id><sourcerecordid>2541984847</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2751-c9915d685db5444bb242f25109077c86eca4bc9a4107aad10448ff2ba48f3db83</originalsourceid><addsrcrecordid>eNpNkV9LwzAUxYMoOHTfISCIPkSTNGmbFyGsdSvUOtZuewxp2krHXGe6Pfjt7R8Rn87hcjj3cn8A3BP8xIlgz3V7tC3Sh4Pet2iDHEQxJYhQPpgLMKFdCgnM8eU_fw2mbbvDGBOPCyHoBCRhGM9XMk3hm1wuo2QOt1G2gGmYZFESxohCmQRwLTcwkJmEUQKXqyiZhTAMtnIVwCiN-8DDTCYykI-34KrqLiqnv3oD1q9hNlug-H0ezWSMDPU4QUYIwgvX50XOGWN5ThmtKCdYYM8zvlsazXIjNCPY07ogmDG_qmiuO3GK3HduQDT2Fo3eqaOtP7X9Vo2u1TBo7IfS9lSbfakK4pfYJSUvMGGOq3PGsWEMl7TwjKZ513U3dh1t83Uu25PaNWfbP1ZRzojwmc-8LvUypoxt2taW1d9WglWPRA1I1IhEbZSjehKqQzIY5weP03pl</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2541984847</pqid></control><display><type>article</type><title>EELGRASS MAPPING WITH SENTINEL-2 AND UAV DATA IN PRINCE EDWARD ISLAND (CANADA)</title><source>Publicly Available Content Database</source><creator>Gallant, E. ; LaRocque, A. ; Leblon, B. ; Douglas, A.</creator><creatorcontrib>Gallant, E. ; LaRocque, A. ; Leblon, B. ; Douglas, A.</creatorcontrib><description>Eelgrass (Zostera marina L.) is a marine angiosperm that grows throughout coastal regions in Atlantic Canada. Eelgrass beds provide a variety of important ecosystem services, and while it is considered an important marine species, little research has been done to understand its distribution and location within Atlantic Canada. The purpose of this study was to assess the capability of Sentinel-2 and UAV imagery to map the presence of eelgrass beds within the Souris River in Prince Edward Island. Both imageries were classified using the non-parametric Random Forests (RF) supervised classifier and the resulting classification was validated using sonar data. The Sentinel-2 classified image had a lower validation accuracy at 77.7%, while the UAV classified image had a validation accuracy of 90.9%. The limitations of the study and recommendations for future work are also presented.</description><identifier>ISSN: 2194-9050</identifier><identifier>ISSN: 2194-9042</identifier><identifier>EISSN: 2194-9050</identifier><identifier>DOI: 10.5194/isprs-annals-V-3-2021-125-2021</identifier><language>eng</language><publisher>Gottingen: Copernicus GmbH</publisher><subject>Aquatic plants ; Autonomous underwater vehicles ; Cameras ; Coastal zone ; Ecosystem services ; Environmental conditions ; Image classification ; Marine ecosystems ; Satellites ; Unmanned aerial vehicles ; Watersheds</subject><ispartof>ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 2021-06, Vol.V-3-2021, p.125-132</ispartof><rights>2021. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2541984847?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590</link.rule.ids></links><search><creatorcontrib>Gallant, E.</creatorcontrib><creatorcontrib>LaRocque, A.</creatorcontrib><creatorcontrib>Leblon, B.</creatorcontrib><creatorcontrib>Douglas, A.</creatorcontrib><title>EELGRASS MAPPING WITH SENTINEL-2 AND UAV DATA IN PRINCE EDWARD ISLAND (CANADA)</title><title>ISPRS annals of the photogrammetry, remote sensing and spatial information sciences</title><description>Eelgrass (Zostera marina L.) is a marine angiosperm that grows throughout coastal regions in Atlantic Canada. Eelgrass beds provide a variety of important ecosystem services, and while it is considered an important marine species, little research has been done to understand its distribution and location within Atlantic Canada. The purpose of this study was to assess the capability of Sentinel-2 and UAV imagery to map the presence of eelgrass beds within the Souris River in Prince Edward Island. Both imageries were classified using the non-parametric Random Forests (RF) supervised classifier and the resulting classification was validated using sonar data. The Sentinel-2 classified image had a lower validation accuracy at 77.7%, while the UAV classified image had a validation accuracy of 90.9%. The limitations of the study and recommendations for future work are also presented.</description><subject>Aquatic plants</subject><subject>Autonomous underwater vehicles</subject><subject>Cameras</subject><subject>Coastal zone</subject><subject>Ecosystem services</subject><subject>Environmental conditions</subject><subject>Image classification</subject><subject>Marine ecosystems</subject><subject>Satellites</subject><subject>Unmanned aerial vehicles</subject><subject>Watersheds</subject><issn>2194-9050</issn><issn>2194-9042</issn><issn>2194-9050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkV9LwzAUxYMoOHTfISCIPkSTNGmbFyGsdSvUOtZuewxp2krHXGe6Pfjt7R8Rn87hcjj3cn8A3BP8xIlgz3V7tC3Sh4Pet2iDHEQxJYhQPpgLMKFdCgnM8eU_fw2mbbvDGBOPCyHoBCRhGM9XMk3hm1wuo2QOt1G2gGmYZFESxohCmQRwLTcwkJmEUQKXqyiZhTAMtnIVwCiN-8DDTCYykI-34KrqLiqnv3oD1q9hNlug-H0ezWSMDPU4QUYIwgvX50XOGWN5ThmtKCdYYM8zvlsazXIjNCPY07ogmDG_qmiuO3GK3HduQDT2Fo3eqaOtP7X9Vo2u1TBo7IfS9lSbfakK4pfYJSUvMGGOq3PGsWEMl7TwjKZ513U3dh1t83Uu25PaNWfbP1ZRzojwmc-8LvUypoxt2taW1d9WglWPRA1I1IhEbZSjehKqQzIY5weP03pl</recordid><startdate>20210617</startdate><enddate>20210617</enddate><creator>Gallant, E.</creator><creator>LaRocque, A.</creator><creator>Leblon, B.</creator><creator>Douglas, A.</creator><general>Copernicus GmbH</general><general>Copernicus Publications</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>DOA</scope></search><sort><creationdate>20210617</creationdate><title>EELGRASS MAPPING WITH SENTINEL-2 AND UAV DATA IN PRINCE EDWARD ISLAND (CANADA)</title><author>Gallant, E. ; LaRocque, A. ; Leblon, B. ; Douglas, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2751-c9915d685db5444bb242f25109077c86eca4bc9a4107aad10448ff2ba48f3db83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aquatic plants</topic><topic>Autonomous underwater vehicles</topic><topic>Cameras</topic><topic>Coastal zone</topic><topic>Ecosystem services</topic><topic>Environmental conditions</topic><topic>Image classification</topic><topic>Marine ecosystems</topic><topic>Satellites</topic><topic>Unmanned aerial vehicles</topic><topic>Watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gallant, E.</creatorcontrib><creatorcontrib>LaRocque, A.</creatorcontrib><creatorcontrib>Leblon, B.</creatorcontrib><creatorcontrib>Douglas, A.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>ISPRS annals of the photogrammetry, remote sensing and spatial information sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gallant, E.</au><au>LaRocque, A.</au><au>Leblon, B.</au><au>Douglas, A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>EELGRASS MAPPING WITH SENTINEL-2 AND UAV DATA IN PRINCE EDWARD ISLAND (CANADA)</atitle><jtitle>ISPRS annals of the photogrammetry, remote sensing and spatial information sciences</jtitle><date>2021-06-17</date><risdate>2021</risdate><volume>V-3-2021</volume><spage>125</spage><epage>132</epage><pages>125-132</pages><issn>2194-9050</issn><issn>2194-9042</issn><eissn>2194-9050</eissn><abstract>Eelgrass (Zostera marina L.) is a marine angiosperm that grows throughout coastal regions in Atlantic Canada. Eelgrass beds provide a variety of important ecosystem services, and while it is considered an important marine species, little research has been done to understand its distribution and location within Atlantic Canada. The purpose of this study was to assess the capability of Sentinel-2 and UAV imagery to map the presence of eelgrass beds within the Souris River in Prince Edward Island. Both imageries were classified using the non-parametric Random Forests (RF) supervised classifier and the resulting classification was validated using sonar data. The Sentinel-2 classified image had a lower validation accuracy at 77.7%, while the UAV classified image had a validation accuracy of 90.9%. The limitations of the study and recommendations for future work are also presented.</abstract><cop>Gottingen</cop><pub>Copernicus GmbH</pub><doi>10.5194/isprs-annals-V-3-2021-125-2021</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2194-9050 |
ispartof | ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 2021-06, Vol.V-3-2021, p.125-132 |
issn | 2194-9050 2194-9042 2194-9050 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_d18e061e5d01436ab450c440e2d7ca2b |
source | Publicly Available Content Database |
subjects | Aquatic plants Autonomous underwater vehicles Cameras Coastal zone Ecosystem services Environmental conditions Image classification Marine ecosystems Satellites Unmanned aerial vehicles Watersheds |
title | EELGRASS MAPPING WITH SENTINEL-2 AND UAV DATA IN PRINCE EDWARD ISLAND (CANADA) |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T02%3A01%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=EELGRASS%20MAPPING%20WITH%20SENTINEL-2%20AND%20UAV%20DATA%20IN%20PRINCE%20EDWARD%20ISLAND%20(CANADA)&rft.jtitle=ISPRS%20annals%20of%20the%20photogrammetry,%20remote%20sensing%20and%20spatial%20information%20sciences&rft.au=Gallant,%20E.&rft.date=2021-06-17&rft.volume=V-3-2021&rft.spage=125&rft.epage=132&rft.pages=125-132&rft.issn=2194-9050&rft.eissn=2194-9050&rft_id=info:doi/10.5194/isprs-annals-V-3-2021-125-2021&rft_dat=%3Cproquest_doaj_%3E2541984847%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c2751-c9915d685db5444bb242f25109077c86eca4bc9a4107aad10448ff2ba48f3db83%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2541984847&rft_id=info:pmid/&rfr_iscdi=true |