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DELINEATION WATER OF PEARL RIVER BASIN USING LANDSAT IMAGES FROM GOOGLE EARTH ENGINE
Surface water plays an important role in ecological circulation. Global climate change and urbanization affect the distribution and quality of water. In order to obtain surface water information quickly and accurately, this study uses Google Earth Engine (GEE) as a data processing tool, 309 Landsat...
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description | Surface water plays an important role in ecological circulation. Global climate change and urbanization affect the distribution and quality of water. In order to obtain surface water information quickly and accurately, this study uses Google Earth Engine (GEE) as a data processing tool, 309 Landsat 8 series images from 2016 to 2019 are selected to calculate 4 different water indexes, including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEIsh) and Multi- Band Water Index (MBWI) to extract surface water in Pearl River Basin. In order to remove the influence of other ground objects, Normalized Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Digital Surface Model (DSM) are combined with the above four water indexes, and threshold segmentation is used to eliminate the influence of vegetation, buildings and mountains. Finally, take the advantage of morphological filtering algorithm to eliminate non-water pixels. The results show that GEE is able to extract surface water in a very short time; AWEIsh has the highest overall accuracy of 94.12%, which is 7.20% higher than the classical NDWI method; There is no significant difference in the width and shape of rivers from 2015 to 2018; The locations of the rivers extracted by the four methods are consistent with the 1 : 100,000 river system basic data of 2015 provided by the Ministry of Water Resources of the People’s Republic of China. |
doi_str_mv | 10.5194/isprs-archives-XLII-3-W10-5-2020 |
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L. ; Lou, P. Q. ; Tang, T. Y.</creator><creatorcontrib>Bi, L. ; Fu, B. L. ; Lou, P. Q. ; Tang, T. Y.</creatorcontrib><description>Surface water plays an important role in ecological circulation. Global climate change and urbanization affect the distribution and quality of water. In order to obtain surface water information quickly and accurately, this study uses Google Earth Engine (GEE) as a data processing tool, 309 Landsat 8 series images from 2016 to 2019 are selected to calculate 4 different water indexes, including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEIsh) and Multi- Band Water Index (MBWI) to extract surface water in Pearl River Basin. In order to remove the influence of other ground objects, Normalized Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Digital Surface Model (DSM) are combined with the above four water indexes, and threshold segmentation is used to eliminate the influence of vegetation, buildings and mountains. Finally, take the advantage of morphological filtering algorithm to eliminate non-water pixels. The results show that GEE is able to extract surface water in a very short time; AWEIsh has the highest overall accuracy of 94.12%, which is 7.20% higher than the classical NDWI method; There is no significant difference in the width and shape of rivers from 2015 to 2018; The locations of the rivers extracted by the four methods are consistent with the 1 : 100,000 river system basic data of 2015 provided by the Ministry of Water Resources of the People’s Republic of China.</description><identifier>ISSN: 2194-9034</identifier><identifier>ISSN: 1682-1750</identifier><identifier>EISSN: 2194-9034</identifier><identifier>DOI: 10.5194/isprs-archives-XLII-3-W10-5-2020</identifier><language>eng</language><publisher>Gottingen: Copernicus GmbH</publisher><subject>Algorithms ; Climate change ; Data analysis ; Data processing ; Earth ; Earth surface ; Image segmentation ; Landsat ; Landsat satellites ; Mountains ; Remote sensing ; River basins ; Rivers ; Satellite imagery ; Surface water ; Urbanization ; Vegetation ; Vegetation index ; Water circulation ; Water purification ; Water quality ; Water resources</subject><ispartof>International archives of the photogrammetry, remote sensing and spatial information sciences., 2020, Vol.XLII-3/W10, p.5-10</ispartof><rights>2020. 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Y.</creatorcontrib><title>DELINEATION WATER OF PEARL RIVER BASIN USING LANDSAT IMAGES FROM GOOGLE EARTH ENGINE</title><title>International archives of the photogrammetry, remote sensing and spatial information sciences.</title><description>Surface water plays an important role in ecological circulation. Global climate change and urbanization affect the distribution and quality of water. In order to obtain surface water information quickly and accurately, this study uses Google Earth Engine (GEE) as a data processing tool, 309 Landsat 8 series images from 2016 to 2019 are selected to calculate 4 different water indexes, including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEIsh) and Multi- Band Water Index (MBWI) to extract surface water in Pearl River Basin. In order to remove the influence of other ground objects, Normalized Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Digital Surface Model (DSM) are combined with the above four water indexes, and threshold segmentation is used to eliminate the influence of vegetation, buildings and mountains. Finally, take the advantage of morphological filtering algorithm to eliminate non-water pixels. The results show that GEE is able to extract surface water in a very short time; AWEIsh has the highest overall accuracy of 94.12%, which is 7.20% higher than the classical NDWI method; There is no significant difference in the width and shape of rivers from 2015 to 2018; The locations of the rivers extracted by the four methods are consistent with the 1 : 100,000 river system basic data of 2015 provided by the Ministry of Water Resources of the People’s Republic of China.</description><subject>Algorithms</subject><subject>Climate change</subject><subject>Data analysis</subject><subject>Data processing</subject><subject>Earth</subject><subject>Earth surface</subject><subject>Image segmentation</subject><subject>Landsat</subject><subject>Landsat satellites</subject><subject>Mountains</subject><subject>Remote sensing</subject><subject>River basins</subject><subject>Rivers</subject><subject>Satellite imagery</subject><subject>Surface water</subject><subject>Urbanization</subject><subject>Vegetation</subject><subject>Vegetation index</subject><subject>Water circulation</subject><subject>Water purification</subject><subject>Water quality</subject><subject>Water resources</subject><issn>2194-9034</issn><issn>1682-1750</issn><issn>2194-9034</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2020</creationdate><recordtype>conference_proceeding</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkU1PwzAMhisEEgj4D5G4cAnke82NAl0XqbRoK4xblKYNdAI6kg2Jf0-3AeISx_br15aeKDrH6IJjyS67sPQBGm9fus82wKdcKUjhHCPIIUEE7UVHZNBBiSjb__c_jE5DWCCEMBOCI34UVbdproo0qVRZgHlSpVNQjsF9mkxzMFWPQ3qdzFQBHoYnA3lS3M6SCqi7JEtnYDwt70BWllmegmGimoC0yAa3k-jAmdfQnv7E4-hhnFY3E5iXmbpJcmgpIwgaYaW1Maa8xtQ4x2IZYx43TrTEMGcdGzXMudgiPjKC1CQeiZgRJxsS05aN6HGkdr5NbxZ66bs34790bzq9LfT-WRu_6uxrq5E1womaYSEJa4STspFY4FZQRzmu68HrbOe19P3Hug0rvejX_n04XxPKCZJcYjmornYq6_sQfOv-tmKkN2j0Fo3-RaM3aDTV801bb9DQb3MWf0Q</recordid><startdate>20200207</startdate><enddate>20200207</enddate><creator>Bi, L.</creator><creator>Fu, B. L.</creator><creator>Lou, P. Q.</creator><creator>Tang, T. Y.</creator><general>Copernicus GmbH</general><general>Copernicus Publications</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TN</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>F1W</scope><scope>H96</scope><scope>HCIFZ</scope><scope>L.G</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>20200207</creationdate><title>DELINEATION WATER OF PEARL RIVER BASIN USING LANDSAT IMAGES FROM GOOGLE EARTH ENGINE</title><author>Bi, L. ; Fu, B. L. ; Lou, P. Q. ; Tang, T. Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3420-a6c9cc8135b13aff4898158df6e2a4fcf47d4ff8c057a62b2876842f9d283e473</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Climate change</topic><topic>Data analysis</topic><topic>Data processing</topic><topic>Earth</topic><topic>Earth surface</topic><topic>Image segmentation</topic><topic>Landsat</topic><topic>Landsat satellites</topic><topic>Mountains</topic><topic>Remote sensing</topic><topic>River basins</topic><topic>Rivers</topic><topic>Satellite imagery</topic><topic>Surface water</topic><topic>Urbanization</topic><topic>Vegetation</topic><topic>Vegetation index</topic><topic>Water circulation</topic><topic>Water purification</topic><topic>Water quality</topic><topic>Water resources</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bi, L.</creatorcontrib><creatorcontrib>Fu, B. 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L.</au><au>Lou, P. Q.</au><au>Tang, T. Y.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>DELINEATION WATER OF PEARL RIVER BASIN USING LANDSAT IMAGES FROM GOOGLE EARTH ENGINE</atitle><btitle>International archives of the photogrammetry, remote sensing and spatial information sciences.</btitle><date>2020-02-07</date><risdate>2020</risdate><volume>XLII-3/W10</volume><spage>5</spage><epage>10</epage><pages>5-10</pages><issn>2194-9034</issn><issn>1682-1750</issn><eissn>2194-9034</eissn><abstract>Surface water plays an important role in ecological circulation. Global climate change and urbanization affect the distribution and quality of water. In order to obtain surface water information quickly and accurately, this study uses Google Earth Engine (GEE) as a data processing tool, 309 Landsat 8 series images from 2016 to 2019 are selected to calculate 4 different water indexes, including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEIsh) and Multi- Band Water Index (MBWI) to extract surface water in Pearl River Basin. In order to remove the influence of other ground objects, Normalized Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Digital Surface Model (DSM) are combined with the above four water indexes, and threshold segmentation is used to eliminate the influence of vegetation, buildings and mountains. Finally, take the advantage of morphological filtering algorithm to eliminate non-water pixels. The results show that GEE is able to extract surface water in a very short time; AWEIsh has the highest overall accuracy of 94.12%, which is 7.20% higher than the classical NDWI method; There is no significant difference in the width and shape of rivers from 2015 to 2018; The locations of the rivers extracted by the four methods are consistent with the 1 : 100,000 river system basic data of 2015 provided by the Ministry of Water Resources of the People’s Republic of China.</abstract><cop>Gottingen</cop><pub>Copernicus GmbH</pub><doi>10.5194/isprs-archives-XLII-3-W10-5-2020</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Climate change Data analysis Data processing Earth Earth surface Image segmentation Landsat Landsat satellites Mountains Remote sensing River basins Rivers Satellite imagery Surface water Urbanization Vegetation Vegetation index Water circulation Water purification Water quality Water resources |
title | DELINEATION WATER OF PEARL RIVER BASIN USING LANDSAT IMAGES FROM GOOGLE EARTH ENGINE |
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