{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,4]],"date-time":"2024-08-04T18:35:46Z","timestamp":1722796546837},"reference-count":46,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T00:00:00Z","timestamp":1581033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"The use of unmanned aerial systems (UAS) over the past years has exploded due to their agility and ability to image an area with high-end products. UAS are a low-cost method for close remote sensing, giving scientists high-resolution data with limited deployment time, accessing even the most inaccessible areas. This study aims to produce marine habitat mapping by comparing the results produced from true-color RGB (tc-RGB) and multispectral high-resolution orthomosaics derived from UAS geodata using object-based image analysis (OBIA). The aerial data was acquired using two different types of sensors\u2014one true-color RGB and one multispectral\u2014both attached to a UAS, capturing images simultaneously. Additionally, divers\u2019 underwater images and echo sounder measurements were collected as in situ data. The produced orthomosaics were processed using three scenarios by applying different classifiers for the marine habitat classification. In the first and second scenario, the k-nearest neighbor (k-NN) and fuzzy rules were applied as classifiers, respectively. In the third scenario, fuzzy rules were applied in the echo sounder data to create samples for the classification process, and then the k-NN algorithm was used as the classifier. The in situ data collected were used as reference and training data. Additionally, these data were used for the calculation of the overall accuracy of the OBIA process in all scenarios. The classification results of the three scenarios were compared. Using tc-RGB instead of multispectral data provides better accuracy in detecting and classifying marine habitats when applying the k-NN as the classifier. In this case, the overall accuracy was 79%, and the Kappa index of agreement (KIA) was equal to 0.71, which illustrates the effectiveness of the proposed approach. The results showed that sub-decimeter resolution UAS data revealed the sub-bottom complexity to a large extent in relatively shallow areas as they provide accurate information that permits the habitat mapping in extreme detail. The produced habitat datasets are ideal as reference data for studying complex coastal environments using satellite imagery.<\/jats:p>","DOI":"10.3390\/rs12030554","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T16:50:28Z","timestamp":1581094228000},"page":"554","source":"Crossref","is-referenced-by-count":19,"title":["Comparison of True-Color and Multispectral Unmanned Aerial Systems Imagery for Marine Habitat Mapping Using Object-Based Image Analysis"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6464-2008","authenticated-orcid":false,"given":"Apostolos","family":"Papakonstantinou","sequence":"first","affiliation":[{"name":"Department of Marine Sciences, Marine Remote Sensing Group, University of the Aegean, University Hill, 81100 Mytilene, Greece"}]},{"given":"Chrysa","family":"Stamati","sequence":"additional","affiliation":[{"name":"Department of Marine Sciences, Marine Remote Sensing Group, University of the Aegean, University Hill, 81100 Mytilene, Greece"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1916-1600","authenticated-orcid":false,"given":"Konstantinos","family":"Topouzelis","sequence":"additional","affiliation":[{"name":"Department of Marine Sciences, Marine Remote Sensing Group, University of the Aegean, University Hill, 81100 Mytilene, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,7]]},"reference":[{"key":"ref_1","first-page":"138","article-title":"Remote sensing for Marine Spatial Planning and Integrated Coastal Areas Management: Achievements, challenges, opportunities and future prospects","volume":"4","author":"Ouellette","year":"2016","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Husson, E., Ecke, F., and Reese, H. (2016). Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images. Remote Sens., 8.","DOI":"10.3390\/rs8090724"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Husson, E., Reese, H., and Ecke, F. (2017). Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation. Remote Sens., 9.","DOI":"10.3390\/rs9030247"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/j.ocecoaman.2006.01.001","article-title":"Linking marine protected areas to integrated coastal and ocean management: A review of theory and practice","volume":"48","author":"Belfiore","year":"2005","journal-title":"Ocean Coast. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1093\/icesjms\/fsp214","article-title":"The role of marine habitat mapping in ecosystem-based management","volume":"66","author":"Cogan","year":"2009","journal-title":"ICES J. Mar. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"54","DOI":"10.4018\/IJAGR.2019010103","article-title":"Coastal Management Using UAS and High-Resolution Satellite Images for Touristic Areas","volume":"10","author":"Papakonstantinou","year":"2019","journal-title":"Int. J. Appl. Geospatial Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Topouzelis, K., Doukari, M., Papakonstantinou, A., Stamatis, P., Makri, D., and Katsanevakis, S. (2017, January 6). Coastal Habitat Mapping in the Aegean Sea Using High Resolution Orthophoto Maps. Proceedings of the Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017), Paphos, Cyprus.","DOI":"10.1117\/12.2279140"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Harris, P.T., and Baker, E.K. (2012). GeoHab Atlas of Seafloor Geomorphic Features and Benthic Habitats. Seafloor Geomorphology as Benthic Habitat, Elsevier.","DOI":"10.1016\/B978-0-12-385140-6.00064-5"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1191\/0309133305pp455ra","article-title":"Remote sensing for large-area habitat mapping","volume":"29","author":"McDermid","year":"2005","journal-title":"Prog. Phys. Geogr."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Papakonstantinou, A., Topouzelis, K., and Doukari, M. (2017, January 6). UAS close range remote sensing for mapping coastal environments. Proceedings of the Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017), Paphos, Cyprus.","DOI":"10.1117\/12.2278988"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Su, L., and Gibeaut, J. (2017). Using UAS hyperspatial RGB imagery for identifying beach zones along the South Texas Coast. Remote Sens., 9.","DOI":"10.3390\/rs9020159"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ventura, D., Bonifazi, A., Gravina, M.F., Belluscio, A., and Ardizzone, G. (2018). Mapping and Classification of Ecologically Sensitive Marine Habitats Using Unmanned Aerial Vehicle (UAV) Imagery and Object-Based Image Analysis (OBIA). Remote Sens., 10.","DOI":"10.3390\/rs10091331"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ventura, D., Bonifazi, A., Gravina, M.F., and Ardizzone, G.D. (2017). Unmanned Aerial Systems (UASs) for Environmental Monitoring: A Review with Applications in Coastal Habitats. Aerial Robots\u2014Aerodynamics, Control and Applications, IntechOpen.","DOI":"10.5772\/intechopen.69598"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G.J. (2008). Geographic Object-Based Image Analysis (GEOBIA): A New Name for a New Discipline. Lecture Notes in Geoinformation and Cartography, Springer.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.ecss.2016.01.030","article-title":"A low-cost drone based application for identifying and mapping of coastal fish nursery grounds","volume":"171","author":"Ventura","year":"2016","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Makri, D., Stamatis, P., Doukari, M., Papakonstantinou, A., Vasilakos, C., and Topouzelis, K. (2018, January 6). Multi-Scale Seagrass Mapping in Satellite Data and the Use of UAS in Accuracy Assessment. Proceedings of the Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018), Paphos, Cyprus.","DOI":"10.1117\/12.2326012"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ecss.2017.11.001","article-title":"Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone","volume":"200","author":"Duffy","year":"2018","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_19","unstructured":"R Core Development Team (2019). A Language and Environment for Statistical Computing, R Core Development Team. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_20","unstructured":"Leutner, B., and Horning, N. (2017). RStoolbox: Tools for Remote Sensing Data Analysis R Package, Version 0.2.4, R Core Development Team."},{"key":"ref_21","unstructured":"Leutner, B., and Horning, N. (2017). Package \u2018RStoolbox,\u2019 R Foundation for Statistical Computing, Version 0.1, R Core Development Team."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.envsoft.2011.11.014","article-title":"GRASS GIS: A multi-purpose open source GIS","volume":"31","author":"Neteler","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_23","unstructured":"Team, G.D. (2020, February 06). Geographic Resources Analysis Support System (GRASS GIS) Software, Version 7.2. Available online: https:\/\/grass.osgeo.org."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3951","DOI":"10.1121\/1.2936060","article-title":"Acoustic data fusion devoted to underwater vegetation mapping","volume":"123","author":"Noel","year":"2008","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_25","unstructured":"(2013). ArcGIS Desktop Release 10.3.1, Environmental Systems Research Institute."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Meier, L., Tanskanen, P., Fraundorfer, F., and Pollefeys, M. (2011, January 9\u201313). PIXHAWK: A System for Autonomous Flight using Onboard Computer Vision. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980229"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13","DOI":"10.5194\/isprsarchives-XXXVIII-1-C22-13-2011","article-title":"The Pixhawk Open-Source Computer Vision Framework for Mavs","volume":"XXXVIII-1\/C22","author":"Meier","year":"2011","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_28","unstructured":"(2019, December 19). ArduPilot, Mission Planner, What Is Mission Planner. Available online: http:\/\/ardupilot.org\/planner\/docs\/mission-planner-overview.html."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Doukari, M., Batsaris, M., Papakonstantinou, A., and Topouzelis, K. (2019). A Protocol for Aerial Survey in Coastal Areas Using UAS. Remote Sens., 11.","DOI":"10.3390\/rs11161913"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sturdivant, E. (2017). Sturdivant, E.J.; Lentz, E.E.; Thieler, E.R.; Farris, A.S.; Weber, K.M.; Remsen, D.P.; Miner, S.; Henderson, R.E. UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9101020"},{"key":"ref_31","unstructured":"Torres, J.C., Arroyo, G., Romo, C., and De Haro, J. (2012, January 12\u201314). 3D Digitization using Structure from Motion. Proceedings of the CEIG-Spanish Computer Graphics Conference, Ja\u00e9n, Spain."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"Structure-from-Motion photogrammetry: A low-cost, effective tool for geoscience applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_33","unstructured":"Agisoft, L.L.C. (2020, February 06). Agisoft PhotoScan, Professional Edition, Version 1.4.1. Available online: https:\/\/www.agisoft.com\/pdf\/photoscan-pro_1_4_en.pdf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.3390\/rs5052164","article-title":"Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud","volume":"5","author":"Mathews","year":"2013","journal-title":"Remote Sens."},{"key":"ref_35","unstructured":"Dellaert, F., Seitz, S.M., Thorpe, C.E., and Thrun, S. (2000, January 15). Structure from motion without correspondence. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000 (Cat. No. PR00662), Hilton Head Island, SC, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12518-013-0120-x","article-title":"UAV for 3D mapping applications: A. review","volume":"6","author":"Nex","year":"2013","journal-title":"Appl. Geomatics"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_38","first-page":"175","article-title":"Detection of floating plastics from satellite and unmanned aerial systems (Plastic Litter Project 2018)","volume":"79","author":"Topouzelis","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","unstructured":"(2014). Trimble eCognition\u00ae Reference Book, Trimble Germany GmbH."},{"key":"ref_40","unstructured":"Reuter, R. (2010). Land Cover\/Use Mapping Using Object Based Classification of SPOT Imagery. Proceedings of EARSeL Remote Sensing for Science, Education, and Natural and Cultural Heritage, University of Oldenburg."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"762","DOI":"10.3390\/a6040762","article-title":"Very high resolution satellite image classification using fuzzy rule-based systems","volume":"6","author":"Jabari","year":"2013","journal-title":"Algorithms"},{"key":"ref_42","unstructured":"Shani, A. (2006). Landsat Image Classification Using Fuzzy Sets Rule Base Theory. [Master\u2019s Thesis, San Jose State University]."},{"key":"ref_43","unstructured":"Doukari, M., Papakonstantinou, A., and Topouzelis, K. (2018, January 12\u201315). Fighting the Sunglint Removal in UAV Images. Proceedings of the 11th International Conference of the Hellenic Geographical Society (ICHGS-2018), Lavrion, Greece."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hodgson, A., Kelly, N., and Peel, D. (2013). Unmanned aerial vehicles (UAVs) for surveying Marine Fauna: A dugong case study. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0079556"},{"key":"ref_45","first-page":"5564","article-title":"Coastline Change Detection Using Unmanned Aerial Vehicles and Image Processing","volume":"26","author":"Topouzelis","year":"2017","journal-title":"Fresenius Environ. Bull."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Papakonstantinou, A., Topouzelis, K., and Pavlogeorgatos, G. (2016). Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5060075"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/554\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T04:42:59Z","timestamp":1719204179000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/554"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,7]]},"references-count":46,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12030554"],"URL":"https:\/\/doi.org\/10.3390\/rs12030554","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,7]]}}}