Assessing methods of identifying open water bodies using Landsat 8 OLI imagery
Remote sensing is an effective technology for monitoring water resources. However, many methods in remote sensing imagery used to identify open bodies of water have often been shown to produce varying water body classification results for the same bodies of water. Therefore, it is necessary to have...
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description | Remote sensing is an effective technology for monitoring water resources. However, many methods in remote sensing imagery used to identify open bodies of water have often been shown to produce varying water body classification results for the same bodies of water. Therefore, it is necessary to have measures to assess these methods. In this study, we used datasets collected in the field to assess methods for identifying open water bodies using images from the Landsat 8 Operational Land Imager. From this, we clarified the difference in the performance between the use of spectral reflectance images and that of digital number (DN) value images for the classification of water bodies. The results showed that the normalized difference water index (NDWI), calculated using green and near-infrared bands (NDWI
Green/NIR
) with reflectance, captured correct control points with an accuracy of greater than 95 % and was therefore the superior method. The result of a comparison in performance in terms of the NDWI between reflectance images and DN value images was consistent with their initial definitions. The NDWI indices calculated by the initial definitions yielded more reasonable results in the classification of water bodies. The optimized threshold, calibrated and validated by 737 field control points, generated water classification results with a higher confidence in this study. We think that it might be better to set the optimized threshold of NDWI
Green/NIR
to −0.05 instead of the value of zero used in many studies. However, more optimized thresholds for other regions need to be calibrated and confirmed if data are available. Our results indicated that NDWI methods are more suitable for water body classification than single-band methods when the frequency histogram method is used. |
doi_str_mv | 10.1007/s12665-016-5686-2 |
format | Article |
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Green/NIR
) with reflectance, captured correct control points with an accuracy of greater than 95 % and was therefore the superior method. The result of a comparison in performance in terms of the NDWI between reflectance images and DN value images was consistent with their initial definitions. The NDWI indices calculated by the initial definitions yielded more reasonable results in the classification of water bodies. The optimized threshold, calibrated and validated by 737 field control points, generated water classification results with a higher confidence in this study. We think that it might be better to set the optimized threshold of NDWI
Green/NIR
to −0.05 instead of the value of zero used in many studies. However, more optimized thresholds for other regions need to be calibrated and confirmed if data are available. Our results indicated that NDWI methods are more suitable for water body classification than single-band methods when the frequency histogram method is used.</description><identifier>ISSN: 1866-6280</identifier><identifier>EISSN: 1866-6299</identifier><identifier>DOI: 10.1007/s12665-016-5686-2</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Biogeosciences ; Classification ; Data collection ; Earth and Environmental Science ; Earth Sciences ; Environmental Science and Engineering ; Geochemistry ; Geology ; Histograms ; Hydrology/Water Resources ; Landsat ; Original Article ; Reflectance ; Remote sensing ; Terrestrial Pollution ; Water bodies ; Water monitoring ; Water resources</subject><ispartof>Environmental earth sciences, 2016-05, Vol.75 (10), p.1, Article 873</ispartof><rights>Springer-Verlag Berlin Heidelberg 2016</rights><rights>Environmental Earth Sciences is a copyright of Springer, 2016.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a382t-8fc834d5f68bced78b7234e7610a5e8ddcb1c141d198c99b06e407c7be7b18373</citedby><cites>FETCH-LOGICAL-a382t-8fc834d5f68bced78b7234e7610a5e8ddcb1c141d198c99b06e407c7be7b18373</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12665-016-5686-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12665-016-5686-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Liu, Zhaofei</creatorcontrib><creatorcontrib>Yao, Zhijun</creatorcontrib><creatorcontrib>Wang, Rui</creatorcontrib><title>Assessing methods of identifying open water bodies using Landsat 8 OLI imagery</title><title>Environmental earth sciences</title><addtitle>Environ Earth Sci</addtitle><description>Remote sensing is an effective technology for monitoring water resources. However, many methods in remote sensing imagery used to identify open bodies of water have often been shown to produce varying water body classification results for the same bodies of water. Therefore, it is necessary to have measures to assess these methods. In this study, we used datasets collected in the field to assess methods for identifying open water bodies using images from the Landsat 8 Operational Land Imager. From this, we clarified the difference in the performance between the use of spectral reflectance images and that of digital number (DN) value images for the classification of water bodies. The results showed that the normalized difference water index (NDWI), calculated using green and near-infrared bands (NDWI
Green/NIR
) with reflectance, captured correct control points with an accuracy of greater than 95 % and was therefore the superior method. The result of a comparison in performance in terms of the NDWI between reflectance images and DN value images was consistent with their initial definitions. The NDWI indices calculated by the initial definitions yielded more reasonable results in the classification of water bodies. The optimized threshold, calibrated and validated by 737 field control points, generated water classification results with a higher confidence in this study. We think that it might be better to set the optimized threshold of NDWI
Green/NIR
to −0.05 instead of the value of zero used in many studies. However, more optimized thresholds for other regions need to be calibrated and confirmed if data are available. Our results indicated that NDWI methods are more suitable for water body classification than single-band methods when the frequency histogram method is used.</description><subject>Biogeosciences</subject><subject>Classification</subject><subject>Data collection</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environmental Science and Engineering</subject><subject>Geochemistry</subject><subject>Geology</subject><subject>Histograms</subject><subject>Hydrology/Water Resources</subject><subject>Landsat</subject><subject>Original Article</subject><subject>Reflectance</subject><subject>Remote sensing</subject><subject>Terrestrial Pollution</subject><subject>Water bodies</subject><subject>Water monitoring</subject><subject>Water resources</subject><issn>1866-6280</issn><issn>1866-6299</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1UMtOwzAQtBBIVKUfwM0SZ4Mfie0cq4pHpYhe4Gwl9qakoknxpkL9exyCEBf2sqvVzOzOEHIt-K3g3NyhkFrnjAvNcm01k2dkJqzWTMuiOP-dLb8kC8QdT6WEKriekeclIiC23ZbuYXjrA9K-oW2Abmib07juD9DRz2qASOs-tID0-A0vqy5gNVBLN-WatvtqC_F0RS6a6h1h8dPn5PXh_mX1xMrN43q1LFmlrByYbbxVWcgbbWsPwdjaSJWB0YJXOdgQfC28yEQQhfVFUXMNGTfe1GBqYZVRc3Iz6R5i_3EEHNyuP8YunXTJbC654tYmlJhQPvaIERp3iOnReHKCuzE5NyXnUnJuTM7JxJETBxO2S57-KP9L-gKm7XAW</recordid><startdate>20160501</startdate><enddate>20160501</enddate><creator>Liu, Zhaofei</creator><creator>Yao, Zhijun</creator><creator>Wang, Rui</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M2P</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope></search><sort><creationdate>20160501</creationdate><title>Assessing methods of identifying open water bodies using Landsat 8 OLI imagery</title><author>Liu, Zhaofei ; Yao, Zhijun ; Wang, Rui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a382t-8fc834d5f68bced78b7234e7610a5e8ddcb1c141d198c99b06e407c7be7b18373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Biogeosciences</topic><topic>Classification</topic><topic>Data collection</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Environmental Science and Engineering</topic><topic>Geochemistry</topic><topic>Geology</topic><topic>Histograms</topic><topic>Hydrology/Water Resources</topic><topic>Landsat</topic><topic>Original Article</topic><topic>Reflectance</topic><topic>Remote sensing</topic><topic>Terrestrial Pollution</topic><topic>Water bodies</topic><topic>Water monitoring</topic><topic>Water resources</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Zhaofei</creatorcontrib><creatorcontrib>Yao, Zhijun</creatorcontrib><creatorcontrib>Wang, Rui</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Science Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science 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>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>Environmental earth sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Zhaofei</au><au>Yao, Zhijun</au><au>Wang, Rui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessing methods of identifying open water bodies using Landsat 8 OLI imagery</atitle><jtitle>Environmental earth sciences</jtitle><stitle>Environ Earth Sci</stitle><date>2016-05-01</date><risdate>2016</risdate><volume>75</volume><issue>10</issue><spage>1</spage><pages>1-</pages><artnum>873</artnum><issn>1866-6280</issn><eissn>1866-6299</eissn><abstract>Remote sensing is an effective technology for monitoring water resources. However, many methods in remote sensing imagery used to identify open bodies of water have often been shown to produce varying water body classification results for the same bodies of water. Therefore, it is necessary to have measures to assess these methods. In this study, we used datasets collected in the field to assess methods for identifying open water bodies using images from the Landsat 8 Operational Land Imager. From this, we clarified the difference in the performance between the use of spectral reflectance images and that of digital number (DN) value images for the classification of water bodies. The results showed that the normalized difference water index (NDWI), calculated using green and near-infrared bands (NDWI
Green/NIR
) with reflectance, captured correct control points with an accuracy of greater than 95 % and was therefore the superior method. The result of a comparison in performance in terms of the NDWI between reflectance images and DN value images was consistent with their initial definitions. The NDWI indices calculated by the initial definitions yielded more reasonable results in the classification of water bodies. The optimized threshold, calibrated and validated by 737 field control points, generated water classification results with a higher confidence in this study. We think that it might be better to set the optimized threshold of NDWI
Green/NIR
to −0.05 instead of the value of zero used in many studies. However, more optimized thresholds for other regions need to be calibrated and confirmed if data are available. Our results indicated that NDWI methods are more suitable for water body classification than single-band methods when the frequency histogram method is used.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12665-016-5686-2</doi><oa>free_for_read</oa></addata></record> |
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subjects | Biogeosciences Classification Data collection Earth and Environmental Science Earth Sciences Environmental Science and Engineering Geochemistry Geology Histograms Hydrology/Water Resources Landsat Original Article Reflectance Remote sensing Terrestrial Pollution Water bodies Water monitoring Water resources |
title | Assessing methods of identifying open water bodies using Landsat 8 OLI imagery |
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