Marine Habitat Mapping Using Bathymetric LiDAR Data: A Case Study from Bonne Bay, Newfoundland
Marine habitats provide various benefits to the environment and humans. In this regard, an accurate marine habitat map is an important component of effective marine management. Newfoundland’s coastal area is covered by different marine habitats, which should be correctly mapped using advanced techno...
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Veröffentlicht in: | Water (Basel) 2022-12, Vol.14 (23), p.3809 |
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description | Marine habitats provide various benefits to the environment and humans. In this regard, an accurate marine habitat map is an important component of effective marine management. Newfoundland’s coastal area is covered by different marine habitats, which should be correctly mapped using advanced technologies, such as remote sensing methods. In this study, bathymetric Light Detection and Ranging (LiDAR) data were applied to accurately discriminate different habitat types in Bonne Bay, Newfoundland. To this end, the LiDAR intensity image was employed along with an object-based Random Forest (RF) algorithm. Two types of habitat classifications were produced: a two-class map (i.e., Vegetation and Non-Vegetation) and a five-class map (i.e., Eelgrass, Macroalgae, Rockweed, Fine Sediment, and Gravel/Cobble). It was observed that the accuracies of the produced habitat maps were reasonable considering the existing challenges, such as the error of the LiDAR data and lacking enough in situ samples for some of the classes such as macroalgae. The overall classification accuracies for the two-class and five-class maps were 87% and 80%, respectively, indicating the high capability of the developed machine learning model for future marine habitat mapping studies. The results also showed that Eelgrass, Fine Sediment, Gravel/Cobble, Macroalgae, and Rockweed cover 22.4% (3.66 km2), 51.4% (8.39 km2), 13.5% (2.21 km2), 6.9% (1.12 km2), and 5.8% (0.95 km2) of the study area, respectively. |
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In this regard, an accurate marine habitat map is an important component of effective marine management. Newfoundland’s coastal area is covered by different marine habitats, which should be correctly mapped using advanced technologies, such as remote sensing methods. In this study, bathymetric Light Detection and Ranging (LiDAR) data were applied to accurately discriminate different habitat types in Bonne Bay, Newfoundland. To this end, the LiDAR intensity image was employed along with an object-based Random Forest (RF) algorithm. Two types of habitat classifications were produced: a two-class map (i.e., Vegetation and Non-Vegetation) and a five-class map (i.e., Eelgrass, Macroalgae, Rockweed, Fine Sediment, and Gravel/Cobble). It was observed that the accuracies of the produced habitat maps were reasonable considering the existing challenges, such as the error of the LiDAR data and lacking enough in situ samples for some of the classes such as macroalgae. The overall classification accuracies for the two-class and five-class maps were 87% and 80%, respectively, indicating the high capability of the developed machine learning model for future marine habitat mapping studies. The results also showed that Eelgrass, Fine Sediment, Gravel/Cobble, Macroalgae, and Rockweed cover 22.4% (3.66 km2), 51.4% (8.39 km2), 13.5% (2.21 km2), 6.9% (1.12 km2), and 5.8% (0.95 km2) of the study area, respectively.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w14233809</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Algae ; Algorithms ; Animal behavior ; Aquatic habitats ; Case studies ; Classification ; Climatic changes ; Coastal management ; Coastal zone ; Decision trees ; Gravel ; Habitats ; Lasers ; Machine learning ; Mapping ; Marine technology ; Optical radar ; Polygons ; Remote sensing ; Remote sensing systems ; Satellites ; Sensors ; Vegetation</subject><ispartof>Water (Basel), 2022-12, Vol.14 (23), p.3809</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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The overall classification accuracies for the two-class and five-class maps were 87% and 80%, respectively, indicating the high capability of the developed machine learning model for future marine habitat mapping studies. The results also showed that Eelgrass, Fine Sediment, Gravel/Cobble, Macroalgae, and Rockweed cover 22.4% (3.66 km2), 51.4% (8.39 km2), 13.5% (2.21 km2), 6.9% (1.12 km2), and 5.8% (0.95 km2) of the study area, respectively.</description><subject>Accuracy</subject><subject>Algae</subject><subject>Algorithms</subject><subject>Animal behavior</subject><subject>Aquatic habitats</subject><subject>Case studies</subject><subject>Classification</subject><subject>Climatic changes</subject><subject>Coastal management</subject><subject>Coastal zone</subject><subject>Decision trees</subject><subject>Gravel</subject><subject>Habitats</subject><subject>Lasers</subject><subject>Machine learning</subject><subject>Mapping</subject><subject>Marine technology</subject><subject>Optical radar</subject><subject>Polygons</subject><subject>Remote sensing</subject><subject>Remote sensing systems</subject><subject>Satellites</subject><subject>Sensors</subject><subject>Vegetation</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNUNtKw0AQXUTBon3wDxZ8EkzdW5KNb2mrVmgV1L4aZpPduqW5uNlQ8vemVMSZYWYYzjkDB6ErSiacJ-RuTwXjXJLkBI0YiXkghKCn__ZzNG7bLRlCJFKGZIQ-V-BspfEClPXg8QqaxlYbvG4PfQr-qy-1dzbHSztP3_AcPNzjFM-g1fjdd0WPjatLPK2rQWUK_S1-0XtTd1Wxg6q4RGcGdq0e_84LtH58-JgtguXr0_MsXQY559QHhWFKSqUjqgQjglKlhFCcRhRYmA8lNVGaMwDgxgAvjBE8LEQcqTDRmvMLdH3UbVz93enWZ9u6c9XwMmOxkGEkuJADanJEbWCnM1uZ2jvIhyx0afO60sYO91RSRiIaR2Qg3BwJuavb1mmTNc6W4PqMkuxgefZnOf8BwGhxDg</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Amani, Meisam</creator><creator>Macdonald, Candace</creator><creator>Salehi, Abbas</creator><creator>Mahdavi, Sahel</creator><creator>Gullage, Mardi</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-1670-151X</orcidid><orcidid>https://orcid.org/0000-0002-6057-1847</orcidid><orcidid>https://orcid.org/0000-0002-9495-4010</orcidid><orcidid>https://orcid.org/0000-0002-9957-8659</orcidid></search><sort><creationdate>20221201</creationdate><title>Marine Habitat Mapping Using Bathymetric LiDAR Data: A Case Study from Bonne Bay, Newfoundland</title><author>Amani, Meisam ; 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In this regard, an accurate marine habitat map is an important component of effective marine management. Newfoundland’s coastal area is covered by different marine habitats, which should be correctly mapped using advanced technologies, such as remote sensing methods. In this study, bathymetric Light Detection and Ranging (LiDAR) data were applied to accurately discriminate different habitat types in Bonne Bay, Newfoundland. To this end, the LiDAR intensity image was employed along with an object-based Random Forest (RF) algorithm. Two types of habitat classifications were produced: a two-class map (i.e., Vegetation and Non-Vegetation) and a five-class map (i.e., Eelgrass, Macroalgae, Rockweed, Fine Sediment, and Gravel/Cobble). It was observed that the accuracies of the produced habitat maps were reasonable considering the existing challenges, such as the error of the LiDAR data and lacking enough in situ samples for some of the classes such as macroalgae. The overall classification accuracies for the two-class and five-class maps were 87% and 80%, respectively, indicating the high capability of the developed machine learning model for future marine habitat mapping studies. The results also showed that Eelgrass, Fine Sediment, Gravel/Cobble, Macroalgae, and Rockweed cover 22.4% (3.66 km2), 51.4% (8.39 km2), 13.5% (2.21 km2), 6.9% (1.12 km2), and 5.8% (0.95 km2) of the study area, respectively.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w14233809</doi><orcidid>https://orcid.org/0000-0002-1670-151X</orcidid><orcidid>https://orcid.org/0000-0002-6057-1847</orcidid><orcidid>https://orcid.org/0000-0002-9495-4010</orcidid><orcidid>https://orcid.org/0000-0002-9957-8659</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Algae Algorithms Animal behavior Aquatic habitats Case studies Classification Climatic changes Coastal management Coastal zone Decision trees Gravel Habitats Lasers Machine learning Mapping Marine technology Optical radar Polygons Remote sensing Remote sensing systems Satellites Sensors Vegetation |
title | Marine Habitat Mapping Using Bathymetric LiDAR Data: A Case Study from Bonne Bay, Newfoundland |
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