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Monitoring of beach litter by automatic interpretation of unmanned aerial vehicle images using the segmentation threshold method
This study was aimed at monitoring beach litter using an unmanned aerial vehicle (UAV) in the coastal city of Fuzhou, China. The data analysis shows that the optical images obtained by digital cameras on the UAV can help to identify and monitor beach litter using remote sensing and GIS technologies....
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| Published in: | Marine pollution bulletin 2018-12, Vol.137, p.388-398 |
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| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c465t-8480c7e3e93892e32f35648eefe534b5255ba0a1d6fbe5f782cdd05e649967433 |
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| cites | cdi_FETCH-LOGICAL-c465t-8480c7e3e93892e32f35648eefe534b5255ba0a1d6fbe5f782cdd05e649967433 |
| container_end_page | 398 |
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| container_start_page | 388 |
| container_title | Marine pollution bulletin |
| container_volume | 137 |
| creator | Bao, Zhongcong Sha, Jinming Li, Xiaomei Hanchiso, Terefe Shifaw, Eshetu |
| description | This study was aimed at monitoring beach litter using an unmanned aerial vehicle (UAV) in the coastal city of Fuzhou, China. The data analysis shows that the optical images obtained by digital cameras on the UAV can help to identify and monitor beach litter using remote sensing and GIS technologies. The threshold method can effectively segment the UAV image in the beach area. It is useful for quickly monitoring the distribution of beach litter in the area of interest, and hence it can help to provide effective technical support for the investigation and assessment of coastal beach litter.
•Add knowledge to the literature on use of drones for beach litter monitoring.•Main considerations when selecting drones for aerial photography of beach litter.•Interpretation algorithm of the segmentation threshold method effectively segment the UAV image for monitoring beach litter. |
| doi_str_mv | 10.1016/j.marpolbul.2018.08.009 |
| format | article |
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•Add knowledge to the literature on use of drones for beach litter monitoring.•Main considerations when selecting drones for aerial photography of beach litter.•Interpretation algorithm of the segmentation threshold method effectively segment the UAV image for monitoring beach litter.</description><identifier>ISSN: 0025-326X</identifier><identifier>EISSN: 1879-3363</identifier><identifier>DOI: 10.1016/j.marpolbul.2018.08.009</identifier><identifier>PMID: 30503448</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Aircraft ; Algorithms ; Bathing Beaches ; Beach litter ; Beaches ; Cameras ; China ; Coastal pollution ; Coasts ; Data Analysis ; Data processing ; Digital cameras ; Digital imaging ; Environmental monitoring ; Environmental Monitoring - instrumentation ; Environmental Monitoring - methods ; Environmental Pollution - analysis ; Geographic information systems ; Geographical information systems ; Image processing ; Image Processing, Computer-Assisted ; Image segmentation ; Litter ; Litters (births) ; Methods ; Photography - instrumentation ; Photography - methods ; Pollution ; Remote monitoring ; Remote sensing ; Remote Sensing Technology - instrumentation ; Remote Sensing Technology - methods ; Satellite navigation systems ; Technical services ; Threshold method ; UAV ; Unmanned aerial vehicles</subject><ispartof>Marine pollution bulletin, 2018-12, Vol.137, p.388-398</ispartof><rights>2018 Elsevier Ltd</rights><rights>Copyright © 2018 Elsevier Ltd. All rights reserved.</rights><rights>Copyright Elsevier BV Dec 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c465t-8480c7e3e93892e32f35648eefe534b5255ba0a1d6fbe5f782cdd05e649967433</citedby><cites>FETCH-LOGICAL-c465t-8480c7e3e93892e32f35648eefe534b5255ba0a1d6fbe5f782cdd05e649967433</cites><orcidid>0000-0001-8200-5670</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30503448$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bao, Zhongcong</creatorcontrib><creatorcontrib>Sha, Jinming</creatorcontrib><creatorcontrib>Li, Xiaomei</creatorcontrib><creatorcontrib>Hanchiso, Terefe</creatorcontrib><creatorcontrib>Shifaw, Eshetu</creatorcontrib><title>Monitoring of beach litter by automatic interpretation of unmanned aerial vehicle images using the segmentation threshold method</title><title>Marine pollution bulletin</title><addtitle>Mar Pollut Bull</addtitle><description>This study was aimed at monitoring beach litter using an unmanned aerial vehicle (UAV) in the coastal city of Fuzhou, China. The data analysis shows that the optical images obtained by digital cameras on the UAV can help to identify and monitor beach litter using remote sensing and GIS technologies. The threshold method can effectively segment the UAV image in the beach area. It is useful for quickly monitoring the distribution of beach litter in the area of interest, and hence it can help to provide effective technical support for the investigation and assessment of coastal beach litter.
•Add knowledge to the literature on use of drones for beach litter monitoring.•Main considerations when selecting drones for aerial photography of beach litter.•Interpretation algorithm of the segmentation threshold method effectively segment the UAV image for monitoring beach litter.</description><subject>Aircraft</subject><subject>Algorithms</subject><subject>Bathing Beaches</subject><subject>Beach litter</subject><subject>Beaches</subject><subject>Cameras</subject><subject>China</subject><subject>Coastal pollution</subject><subject>Coasts</subject><subject>Data Analysis</subject><subject>Data processing</subject><subject>Digital cameras</subject><subject>Digital imaging</subject><subject>Environmental monitoring</subject><subject>Environmental Monitoring - instrumentation</subject><subject>Environmental Monitoring - methods</subject><subject>Environmental Pollution - analysis</subject><subject>Geographic information systems</subject><subject>Geographical information systems</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted</subject><subject>Image segmentation</subject><subject>Litter</subject><subject>Litters (births)</subject><subject>Methods</subject><subject>Photography - instrumentation</subject><subject>Photography - methods</subject><subject>Pollution</subject><subject>Remote monitoring</subject><subject>Remote sensing</subject><subject>Remote Sensing Technology - instrumentation</subject><subject>Remote Sensing Technology - methods</subject><subject>Satellite navigation systems</subject><subject>Technical services</subject><subject>Threshold method</subject><subject>UAV</subject><subject>Unmanned aerial vehicles</subject><issn>0025-326X</issn><issn>1879-3363</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNqFkU-LFDEQxYMo7uzoV9CAFy89Vnf-dPdxWdQVVrwoeAvpdPV0hnQyJumFvfnRzTDjHrwIBSHF772k6hHytoZdDbX8cNgtOh6DG1a3a6DudlAK-mdkU3dtXzEm2XOyAWhExRr584pcp3QAgLZp65fkioEAxnm3Ib-_Bm9ziNbvaZjogNrM1NmcMdLhkeo1h0Vna6j1pXWMmMst-BO7-kV7jyPVGK129AFnaxxSu-g9Jrqmk2eekSbcL-gvwjxHTHNwI10wz2F8RV5M2iV8fTm35Menj99v76r7b5-_3N7cV4ZLkauOd2BaZNizrm-QNRMTkneIEwrGB9EIMWjQ9SinAcXUdo0ZRxAoed_LljO2Je_PvscYfq2YslpsMuic9hjWpJqa92UnLZcFffcPeghr9OV3hZISOtEWxy1pz5SJIaWIkzrGMnp8VDWoU0jqoJ5CUqeQFJSCvijfXPzXYcHxSfc3lQLcnAEsC3mwGFUyFr3B0UY0WY3B_veRP523qc8</recordid><startdate>201812</startdate><enddate>201812</enddate><creator>Bao, Zhongcong</creator><creator>Sha, Jinming</creator><creator>Li, Xiaomei</creator><creator>Hanchiso, Terefe</creator><creator>Shifaw, Eshetu</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7T7</scope><scope>7TN</scope><scope>7TV</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>M7N</scope><scope>P64</scope><scope>SOI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-8200-5670</orcidid></search><sort><creationdate>201812</creationdate><title>Monitoring of beach litter by automatic interpretation of unmanned aerial vehicle images using the segmentation threshold method</title><author>Bao, Zhongcong ; Sha, Jinming ; Li, Xiaomei ; Hanchiso, Terefe ; Shifaw, Eshetu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c465t-8480c7e3e93892e32f35648eefe534b5255ba0a1d6fbe5f782cdd05e649967433</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Aircraft</topic><topic>Algorithms</topic><topic>Bathing Beaches</topic><topic>Beach litter</topic><topic>Beaches</topic><topic>Cameras</topic><topic>China</topic><topic>Coastal pollution</topic><topic>Coasts</topic><topic>Data Analysis</topic><topic>Data processing</topic><topic>Digital cameras</topic><topic>Digital imaging</topic><topic>Environmental monitoring</topic><topic>Environmental Monitoring - instrumentation</topic><topic>Environmental Monitoring - methods</topic><topic>Environmental Pollution - analysis</topic><topic>Geographic information systems</topic><topic>Geographical information systems</topic><topic>Image processing</topic><topic>Image Processing, Computer-Assisted</topic><topic>Image segmentation</topic><topic>Litter</topic><topic>Litters (births)</topic><topic>Methods</topic><topic>Photography - instrumentation</topic><topic>Photography - methods</topic><topic>Pollution</topic><topic>Remote monitoring</topic><topic>Remote sensing</topic><topic>Remote Sensing Technology - instrumentation</topic><topic>Remote Sensing Technology - methods</topic><topic>Satellite navigation systems</topic><topic>Technical services</topic><topic>Threshold method</topic><topic>UAV</topic><topic>Unmanned aerial vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bao, Zhongcong</creatorcontrib><creatorcontrib>Sha, Jinming</creatorcontrib><creatorcontrib>Li, Xiaomei</creatorcontrib><creatorcontrib>Hanchiso, Terefe</creatorcontrib><creatorcontrib>Shifaw, Eshetu</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Oceanic Abstracts</collection><collection>Pollution Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Marine pollution bulletin</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bao, Zhongcong</au><au>Sha, Jinming</au><au>Li, Xiaomei</au><au>Hanchiso, Terefe</au><au>Shifaw, Eshetu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Monitoring of beach litter by automatic interpretation of unmanned aerial vehicle images using the segmentation threshold method</atitle><jtitle>Marine pollution bulletin</jtitle><addtitle>Mar Pollut Bull</addtitle><date>2018-12</date><risdate>2018</risdate><volume>137</volume><spage>388</spage><epage>398</epage><pages>388-398</pages><issn>0025-326X</issn><eissn>1879-3363</eissn><abstract>This study was aimed at monitoring beach litter using an unmanned aerial vehicle (UAV) in the coastal city of Fuzhou, China. The data analysis shows that the optical images obtained by digital cameras on the UAV can help to identify and monitor beach litter using remote sensing and GIS technologies. The threshold method can effectively segment the UAV image in the beach area. It is useful for quickly monitoring the distribution of beach litter in the area of interest, and hence it can help to provide effective technical support for the investigation and assessment of coastal beach litter.
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| ispartof | Marine pollution bulletin, 2018-12, Vol.137, p.388-398 |
| issn | 0025-326X 1879-3363 |
| language | eng |
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| source | ScienceDirect Freedom Collection 2022-2024 |
| subjects | Aircraft Algorithms Bathing Beaches Beach litter Beaches Cameras China Coastal pollution Coasts Data Analysis Data processing Digital cameras Digital imaging Environmental monitoring Environmental Monitoring - instrumentation Environmental Monitoring - methods Environmental Pollution - analysis Geographic information systems Geographical information systems Image processing Image Processing, Computer-Assisted Image segmentation Litter Litters (births) Methods Photography - instrumentation Photography - methods Pollution Remote monitoring Remote sensing Remote Sensing Technology - instrumentation Remote Sensing Technology - methods Satellite navigation systems Technical services Threshold method UAV Unmanned aerial vehicles |
| title | Monitoring of beach litter by automatic interpretation of unmanned aerial vehicle images using the segmentation threshold method |
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