Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near-infrared and green signals
Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non‐navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (
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| Veröffentlicht in: | Earth surface processes and landforms 2010-05, Vol.35 (6), p.640-650 |
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| creator | Allouis, Tristan Bailly, Jean-Stéphane Pastol, Yves Le Roux, Catherine |
| description | Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non‐navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters ( |
| doi_str_mv | 10.1002/esp.1959 |
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However, few studies have focused on continental waters, in particular, on very shallow waters (<2 m) where it is difficult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near‐infrared (NIR), Raman] of the SHOALS‐1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The first method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classification and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (−4·2 cm verusu −4·5 cm) and a smaller standard deviation (25·3 cm verusu 33·5 cm). The Raman processing method provides very scattered results (standard deviation of 40·3 cm) with the lowest mean error (−3·1 cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1 m for the green/NIR approach and 0·5 m for the statistical approach, compared to 1·5 m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method. Copyright © 2010 John Wiley & Sons, Ltd.</description><identifier>ISSN: 0197-9337</identifier><identifier>EISSN: 1096-9837</identifier><identifier>DOI: 10.1002/esp.1959</identifier><identifier>CODEN: ESPLDB</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>bathymetric LiDAR ; Bgi / Prodig ; Cartography, space figuration ; coastal waters ; Image interpreation. 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Process. Landforms</addtitle><description>Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non‐navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (<2 m) where it is difficult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near‐infrared (NIR), Raman] of the SHOALS‐1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The first method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classification and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (−4·2 cm verusu −4·5 cm) and a smaller standard deviation (25·3 cm verusu 33·5 cm). The Raman processing method provides very scattered results (standard deviation of 40·3 cm) with the lowest mean error (−3·1 cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1 m for the green/NIR approach and 0·5 m for the statistical approach, compared to 1·5 m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method. Copyright © 2010 John Wiley & Sons, Ltd.</description><subject>bathymetric LiDAR</subject><subject>Bgi / Prodig</subject><subject>Cartography, space figuration</subject><subject>coastal waters</subject><subject>Image interpreation. Remote sensing</subject><subject>minimum depth</subject><subject>precision</subject><subject>principal components analysis</subject><subject>signal processing</subject><issn>0197-9337</issn><issn>1096-9837</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNp10E9v0zAYBnALgUQZSHwEXxAcyLBjO4mPI4xtqBpo48_Repu8bg2J3dnpSi98dlxa7cbple2fHvl9CHnJ2SlnrHyHaX3KtdKPyIwzXRW6EfVjMmNc14UWon5KnqX0kzHOZaNn5E8bxjVEl4KnwdK5-3B2Q7dwjzbEka5j6DAl55d0xGkV-kTzPb3HuKNpBcMQthlPGOkCptUum_yw-edvYAT_lnqEWDhvI0TsKfieLiOip8ktPQzpOXli88AXx3lCvn08_9peFvPPF1ft2bwAqaQuKsss5B_3omGVxgXvpK3VohFl32itJNTSouhU1_OesUVT9txqwIrtD0wwcUJeH3LzRncbTJMZXepwGMBj2CRTS1XJklUqyzcH2cWQUkRr1tGNEHeGM7Nv2OSGzb7hTF8dQyF1MOQdfefSgy-F0rJu9pHFwW3dgLv_5pnz2y_H3KN3acLfDx7iL1PVolbmx_WFaW-_v7_-1AozF38BUWKarA</recordid><startdate>201005</startdate><enddate>201005</enddate><creator>Allouis, Tristan</creator><creator>Bailly, Jean-Stéphane</creator><creator>Pastol, Yves</creator><creator>Le Roux, Catherine</creator><general>John Wiley & Sons, Ltd</general><general>Wiley</general><scope>BSCLL</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope></search><sort><creationdate>201005</creationdate><title>Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near-infrared and green signals</title><author>Allouis, Tristan ; Bailly, Jean-Stéphane ; Pastol, Yves ; Le Roux, Catherine</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a4549-6f0fa114d38069eb1c4f75b832d89954a74fe3c5cd1d00b82d1f9ae6000b80303</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>bathymetric LiDAR</topic><topic>Bgi / Prodig</topic><topic>Cartography, space figuration</topic><topic>coastal waters</topic><topic>Image interpreation. Remote sensing</topic><topic>minimum depth</topic><topic>precision</topic><topic>principal components analysis</topic><topic>signal processing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Allouis, Tristan</creatorcontrib><creatorcontrib>Bailly, Jean-Stéphane</creatorcontrib><creatorcontrib>Pastol, Yves</creatorcontrib><creatorcontrib>Le Roux, Catherine</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Earth surface processes and landforms</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Allouis, Tristan</au><au>Bailly, Jean-Stéphane</au><au>Pastol, Yves</au><au>Le Roux, Catherine</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near-infrared and green signals</atitle><jtitle>Earth surface processes and landforms</jtitle><addtitle>Earth Surf. Process. Landforms</addtitle><date>2010-05</date><risdate>2010</risdate><volume>35</volume><issue>6</issue><spage>640</spage><epage>650</epage><pages>640-650</pages><issn>0197-9337</issn><eissn>1096-9837</eissn><coden>ESPLDB</coden><abstract>Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non‐navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (<2 m) where it is difficult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near‐infrared (NIR), Raman] of the SHOALS‐1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The first method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classification and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (−4·2 cm verusu −4·5 cm) and a smaller standard deviation (25·3 cm verusu 33·5 cm). The Raman processing method provides very scattered results (standard deviation of 40·3 cm) with the lowest mean error (−3·1 cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1 m for the green/NIR approach and 0·5 m for the statistical approach, compared to 1·5 m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method. Copyright © 2010 John Wiley & Sons, Ltd.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/esp.1959</doi><tpages>11</tpages></addata></record> |
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| subjects | bathymetric LiDAR Bgi / Prodig Cartography, space figuration coastal waters Image interpreation. Remote sensing minimum depth precision principal components analysis signal processing |
| title | Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near-infrared and green signals |
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