{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T20:42:33Z","timestamp":1782247353755,"version":"3.54.5"},"reference-count":89,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NOAA","award":["NA18 OAR4170073"],"award-info":[{"award-number":["NA18 OAR4170073"]}]},{"name":"NOAA","award":["R\/HCE-07"],"award-info":[{"award-number":["R\/HCE-07"]}]},{"DOI":"10.13039\/100005522","name":"California Sea Grant College Program Project","doi-asserted-by":"publisher","award":["NA18 OAR4170073"],"award-info":[{"award-number":["NA18 OAR4170073"]}],"id":[{"id":"10.13039\/100005522","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005522","name":"California Sea Grant College Program Project","doi-asserted-by":"publisher","award":["R\/HCE-07"],"award-info":[{"award-number":["R\/HCE-07"]}],"id":[{"id":"10.13039\/100005522","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NOAA\u2019s National Sea Grant College Program, the U.S. Department of Commerce","award":["NA18 OAR4170073"],"award-info":[{"award-number":["NA18 OAR4170073"]}]},{"name":"NOAA\u2019s National Sea Grant College Program, the U.S. Department of Commerce","award":["R\/HCE-07"],"award-info":[{"award-number":["R\/HCE-07"]}]},{"name":"William and Linda Frost Fund in the Cal Poly College of Science and Mathematics","award":["NA18 OAR4170073"],"award-info":[{"award-number":["NA18 OAR4170073"]}]},{"name":"William and Linda Frost Fund in the Cal Poly College of Science and Mathematics","award":["R\/HCE-07"],"award-info":[{"award-number":["R\/HCE-07"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Shallow estuarine habitats are globally undergoing rapid changes due to climate change and anthropogenic influences, resulting in spatiotemporal shifts in distribution and habitat extent. Yet, scientists and managers do not always have rapidly available data to track habitat changes in real-time. In this study, we apply a novel and a state-of-the-art image segmentation machine learning technique (DeepLab) to two years of high-resolution drone-based imagery of a marine flowering plant species (eelgrass, a temperate seagrass). We apply the model to eelgrass (Zostera marina) meadows in the Morro Bay estuary, California, an estuary that has undergone large eelgrass declines and the subsequent recovery of seagrass meadows in the last decade. The model accurately classified eelgrass across a range of conditions and sizes from meadow-scale to small-scale patches that are less than a meter in size. The model recall, precision, and F1 scores were 0.954, 0.723, and 0.809, respectively, when using human-annotated training data and random assessment points. All our accuracy values were comparable to or demonstrated greater accuracy than other models for similar seagrass systems. This study demonstrates the potential for advanced image segmentation machine learning methods to accurately support the active monitoring and analysis of seagrass dynamics from drone-based images, a framework likely applicable to similar marine ecosystems globally, and one that can provide quantitative and accurate data for long-term management strategies that seek to protect these vital ecosystems.<\/jats:p>","DOI":"10.3390\/rs15092321","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T01:33:40Z","timestamp":1682645620000},"page":"2321","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Application of Deep Learning for Classification of Intertidal Eelgrass from Drone-Acquired Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4509-4157","authenticated-orcid":false,"given":"Krti","family":"Tallam","sequence":"first","affiliation":[{"name":"Biology Department, Stanford University, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1135-4069","authenticated-orcid":false,"given":"Nam","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Computer Science & Software Engineering Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1661-8529","authenticated-orcid":false,"given":"Jonathan","family":"Ventura","sequence":"additional","affiliation":[{"name":"Computer Science & Software Engineering Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6310-3427","authenticated-orcid":false,"given":"Andrew","family":"Fricker","sequence":"additional","affiliation":[{"name":"Social Sciences Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sadie","family":"Calhoun","sequence":"additional","affiliation":[{"name":"Social Sciences Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"O\u2019Leary","sequence":"additional","affiliation":[{"name":"Wildlife Conservation Society, Mombasa 99470\u201380100, Kenya"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauri\u00e7a","family":"Fitzgibbons","sequence":"additional","affiliation":[{"name":"Department of Food and Environmental Sciences, California Polytechnic State University, San Luis Obispo, CA 93407, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian","family":"Robbins","sequence":"additional","affiliation":[{"name":"Physics Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1907-001X","authenticated-orcid":false,"given":"Ryan K.","family":"Walter","sequence":"additional","affiliation":[{"name":"Physics Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.tree.2019.04.004","article-title":"The Role of Vegetated Coastal Wetlands for Marine Megafauna Conservation","volume":"34","author":"Sievers","year":"2019","journal-title":"Trends Ecol. Evol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1806","DOI":"10.1126\/science.1128035","article-title":"Depletion, Degradation, and Recovery Potential of Estuaries and Coastal Seas","volume":"312","author":"Lotze","year":"2006","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1111\/j.1749-6632.2009.04496.x","article-title":"Understanding and Managing Human Threats to the Coastal Marine Environment","volume":"1162","author":"Crain","year":"2009","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"151","DOI":"10.5334\/aogh.2831","article-title":"Human Health and Ocean Pollution","volume":"86","author":"Landrigan","year":"2020","journal-title":"Ann. Glob. Health"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10980-005-1063-3","article-title":"Integrating Patch and Boundary Dynamics to Understand and Predict Biotic Transitions at Multiple Scales","volume":"21","author":"Peters","year":"2006","journal-title":"Landsc. Ecol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1007\/s10021-007-9036-9","article-title":"Toward Conceptual Cohesiveness: A Historical Analysis of the Theory and Utility of Ecological Boundaries and Transition Zones","volume":"10","author":"Yarrow","year":"2007","journal-title":"Ecosystems"},{"key":"ref_7","unstructured":"Kark, S. (2013). Ecological Systems, Springer."},{"key":"ref_8","unstructured":"Short, F., and Green, E. (2003). World Atlas of Seagrasses, Univesity of California Press."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.aquabot.2005.07.007","article-title":"Seagrass recovery in the Delmarva Coastal Bays, USA","volume":"84","author":"Orth","year":"2006","journal-title":"Aquat. Bot."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Evans, S.M., Griffin, K.J., Blick, R.A.J., Poore, A., and Verg\u00e9s, A. (2018). Seagrass on the brink: Decline of threatened seagrass Posidonia australis continues following protection. PLoS ONE, 13, Available online: https:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0190370.","DOI":"10.1371\/journal.pone.0190370"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4096","DOI":"10.1111\/gcb.15684","article-title":"Long-Term Declines and Recovery of Meadow Area across the World\u2019s Seagrass Bioregions","volume":"27","author":"Dunic","year":"2021","journal-title":"Glob. Change Biol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.marpolbul.2017.09.030","article-title":"Review: Host-Pathogen Dynamics of Seagrass Diseases under Future Global Change","volume":"134","author":"Sullivan","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/0272-7714(89)90083-8","article-title":"Sediment stabilization by Halophila decipiens in comparison to other seagrasses","volume":"29","author":"Fonseca","year":"1989","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3389\/fmars.2017.00013","article-title":"Export from Seagrass Meadows Contributes to Marine Carbon Sequestration","volume":"4","author":"Duarte","year":"2017","journal-title":"Front. Mar. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e12566","DOI":"10.1111\/conl.12566","article-title":"Seagrass Meadows Support Global Fisheries Production","volume":"12","author":"Unsworth","year":"2018","journal-title":"Conserv. Lett."},{"key":"ref_16","unstructured":"Ainis, A., Erlandson, J., Gill, K., Graham, M., and Vellanoweth, R. (2019). An Archaeology of Abundance: Reevaluating the Marginality of California\u2019s Islands, University Press of Florida."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106910","DOI":"10.1016\/j.ecss.2020.106910","article-title":"Large-scale erosion driven by intertidal eelgrass loss in an estuarine environment","volume":"243","author":"Walter","year":"2020","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"163","DOI":"10.3354\/meps13426","article-title":"Ecosystem-level effects of large-scale disturbance in kelp forests","volume":"656","author":"Norderhaug","year":"2020","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"plaa005","DOI":"10.1093\/aobpla\/plaa005","article-title":"Small spaces, big impacts: Contributions of micro-environmental variation to population persistence under climate change","volume":"12","author":"Denney","year":"2020","journal-title":"AoB Plants"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1111\/1365-2745.12682","article-title":"Forty years of seagrass population stability and resilience in an urbanizing estuary","volume":"105","author":"Shelton","year":"2017","journal-title":"J. Ecol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"61","DOI":"10.3354\/meps14248","article-title":"Northeast Pacific eelgrass dynamics: Interannual expansion distances and meadow area variation over time","volume":"705","author":"Munsch","year":"2023","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1139\/facets-2020-0020","article-title":"From Coast to Coast to Coast: Ecology and Management of Seagrass Ecosystems across Canada","volume":"6","author":"Murphy","year":"2021","journal-title":"Facets"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, B., Hawthorne, T.L., Searson, H., and Duffy, E. (October, January 26). High-Resolution UAV Mapping for Investigating Eelgrass Beds Along the West Coast of North America. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Virtual.","DOI":"10.1109\/IGARSS39084.2020.9324230"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"693","DOI":"10.3389\/fmars.2019.00693","article-title":"Editorial: Integrating Emerging Technologies Into Marine Megafauna Conservation Management","volume":"6","author":"Dutton","year":"2019","journal-title":"Front. Mar. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1111\/j.1749-6632.2009.04494.x","article-title":"Behavioral Indicators for Conserving Mammal Diversity","volume":"1162","author":"Morris","year":"2009","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/esr01007","article-title":"Using small drones to photo-identify Antillean manatees: A novel method for monitoring an endangered marine mammal in the Caribbean Sea","volume":"41","author":"Ramos","year":"2020","journal-title":"Endanger. Species Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"173","DOI":"10.3354\/meps14009","article-title":"Estimating the cost of growth in southern right whales from drone photogrammetry data and long-term sighting histories","volume":"687","author":"Christiansen","year":"2022","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1146\/annurev-marine-010318-095323","article-title":"Unoccupied Aircraft Systems in Marine Science and Conservation","volume":"11","author":"Johnston","year":"2019","journal-title":"Annu. Rev. Mar. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4","DOI":"10.2747\/1548-1603.48.1.4","article-title":"Image Processing and Classification Procedures for Analysis of Sub-decimeter Imagery Acquired with an Unmanned Aircraft over Arid Rangelands","volume":"48","author":"Laliberte","year":"2011","journal-title":"GIScience Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"704","DOI":"10.2112\/JCOASTRES-D-17-00088.1","article-title":"Deploying Fixed Wing Unoccupied Aerial Systems (UAS) for Coastal Morphology Assessment and Management","volume":"34","author":"Seymour","year":"2018","journal-title":"J. Coast. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.ecss.2014.08.012","article-title":"Study of wave runup using numerical models and low-altitude aerial photogrammetry: A tool for coastal management","volume":"49","author":"Casella","year":"2014","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"737","DOI":"10.5194\/acp-8-737-2008","article-title":"Capturing Vertical Profiles of Aerosols and Black Carbon over the Indian Ocean Using Autonomous Unmanned Aerial Vehicles","volume":"8","author":"Corrigan","year":"2008","journal-title":"Atmos. Chem. Phys."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.marpolbul.2018.01.061","article-title":"A UAV and S2A data-based estimation of the initial biomass of green algae in the South Yellow Sea","volume":"128","author":"Xu","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6880","DOI":"10.3390\/rs5126880","article-title":"Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments","volume":"5","author":"Mancini","year":"2013","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"McIntyre, E.M., and Gasiewski, A.J. (2007, January 23\u201328). An ultra-lightweight L-band digital Lobe-Differencing Correlation Radiometer (LDCR) for airborne UAV SSS mapping. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4422992"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1002\/rse2.157","article-title":"Mapping the world\u2019s coral reefs using a global multiscale earth observation framework","volume":"6","author":"Lyons","year":"2020","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1080\/08920753.2018.1474066","article-title":"A Resilience Framework for Chronic Exposures: Water Quality and Ecosystem Services in Coastal Social-Ecological Systems","volume":"46","author":"Merrill","year":"2018","journal-title":"Coast. Manag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1111\/mms.12328","article-title":"Photogrammetry of Blue Whales with an Unmanned Hexacopter","volume":"32","author":"Durban","year":"2016","journal-title":"Mar. Mamm. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"580","DOI":"10.3389\/fmars.2019.00580","article-title":"Coral Reef Monitoring, Reef Assessment Technologies, and Ecosystem-Based Management","volume":"6","author":"Obura","year":"2019","journal-title":"Front. Mar. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112107","DOI":"10.1016\/j.rse.2020.112107","article-title":"Adopting deep learning methods for airborne RGB fluvial scene classification","volume":"251","author":"Carbonneau","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/rse2.205","article-title":"Machine learning to detect marine animals in UAV imagery: Effect of morphology, spacing, behaviour and habitat","volume":"7","author":"Dujon","year":"2021","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gray, P.C., Ridge, J.T., Poulin, S.K., Seymour, A.C., Schwantes, A.M., Swenson, J.J., and Johnston, D.W. (2018). Integrating Drone Imagery into High Resolution Satellite Remote Sensing Assessments of Estuarine Environments. Remote Sens., 10.","DOI":"10.3390\/rs10081257"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kentsch, S., Cabezas, M., Tomhave, L., Gro\u00df, J., Burkhard, B., Caceres, M.L.L., Waki, K., and Diez, Y. (2021). Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning. Sensors, 21.","DOI":"10.3390\/s21020471"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"McKenzie, L.J., Langlois, L.A., and Roelfsema, C.M. (2022). Improving Approaches to Mapping Seagrass within the Great Barrier Reef: From Field to Spaceborne Earth Observation. Remote Sens., 14.","DOI":"10.3390\/rs14112604"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Parsons, M., Bratanov, D., Gaston, K.J., and Gonzalez, F. (2018). UAVs, Hyperspectral Remote Sensing, and Machine Learning Revolutionizing Reef Monitoring. Sensors, 18.","DOI":"10.3390\/s18072026"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"juac008","DOI":"10.1093\/jue\/juac008","article-title":"Application of UAV remote sensing and machine learning to model and map land use in urban gardens","volume":"8","author":"Wagner","year":"2022","journal-title":"J. Urban Ecol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"106629","DOI":"10.1016\/j.compag.2021.106629","article-title":"GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing","volume":"193","author":"Wang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Benmokhtar, S., Robin, M., Maanan, M., and Bazairi, H. (2021). Mapping and Quantification of the Dwarf Eelgrass Zostera noltei Using a Random Forest Algorithm on a SPOT 7 Satellite Image. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10050313"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ha, N.T., Manley-Harris, M., Pham, T.D., and Hawes, I. (2020). A Comparative Assessment of Ensemble-Based Machine Learning and Maximum Likelihood Methods for Mapping Seagrass Using Sentinel-2 Imagery in Tauranga Harbor, New Zealand. Remote Sens., 12.","DOI":"10.3390\/rs12030355"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1007\/s12237-020-00881-3","article-title":"Defining the Zostera marina (Eelgrass) Niche from Long-Term Success of Restored and Naturally Colonized Meadows: Implications for Seagrass Restoration","volume":"44","author":"Oreska","year":"2021","journal-title":"Estuaries Coasts"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rappazzo, B.H., Eisenlord, M.E., Graham, O.J., Aoki, L.R., Dawkins, P.D., Harvell, D., and Gomes, C. (2021, January 2\u20139). EeLISA: Combating Global Warming Through the Rapid Analysis of Eelgrass Wasting Disease. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.","DOI":"10.1609\/aaai.v35i17.17779"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wicaksono, P., Aryaguna, P.A., and Lazuardi, W. (2019). Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms. Remote Sens., 11.","DOI":"10.3390\/rs11111279"},{"key":"ref_53","first-page":"697","article-title":"Hyperspectral remote sensing of vegetation and agricultural crops","volume":"80","author":"Thenkabail","year":"2014","journal-title":"Photogramm. Eng. Remote Sens. TSI"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Price, D.M., Felgate, S.L., Huvenne, V.A.I., Strong, J., Carpenter, S., Barry, C., Lichtschlag, A., Sanders, R., Carrias, A., and Young, A. (2022). Quantifying the Intra-Habitat Variation of Seagrass Beds with Unoccupied Aerial Vehicles (UAVs). Remote Sens., 14.","DOI":"10.3390\/rs14030480"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, R., and Chang, L. (2022). A Study on the Dynamic Effects and Ecological Stress of Eco-Environment in the Headwaters of the Yangtze River Based on Improved DeepLab V3+ Network. Remote Sens., 14.","DOI":"10.3390\/rs14092225"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.ecss.2018.08.026","article-title":"Hydrodynamics in a shallow seasonally low-inflow estuary following eelgrass collapse","volume":"213","author":"Walter","year":"2018","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2250","DOI":"10.1007\/s12237-021-00917-2","article-title":"Effects of Estuary-Wide Seagrass Loss on Fish Populations","volume":"44","author":"Goodman","year":"2021","journal-title":"Estuaries Coasts"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_59","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Mahajan, A., and Chaudhary, S. (2019, January 12\u201314). Categorical Image Classification Based On Representational Deep Network (RESNET). Proceedings of the 2019 3rd International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.","DOI":"10.1109\/ICECA.2019.8822133"},{"key":"ref_61","unstructured":"Smith, L.N. (2018). A disciplined approach to neural network hyper-parameters: Part 1\u2014Learning rate, batch size, momentum, and weight decay. arXiv."},{"key":"ref_62","unstructured":"You, K., Long, M., Wang, J., and Jordan, M.I. (2019). How Does Learning Rate Decay Help Modern Neural Networks?. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Lipton, Z.C., Elkan, C., and Narayanaswamy, B. (2014). Thresholding Classifiers to Maximize F1 Score. arXiv.","DOI":"10.1007\/978-3-662-44851-9_15"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., and Wu, X. (2018). Artificial Mangrove Species Mapping Using Pl\u00e9iades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms. Remote Sens., 10.","DOI":"10.3390\/rs10020294"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez Prentice, R., Villoslada Peci\u00f1a, M., Ward, R.D., Bergamo, T.F., Joyce, C.B., and Sepp, K. (2021). Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands. Remote Sens., 13.","DOI":"10.3390\/rs13183669"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"5098","DOI":"10.3390\/rs70505098","article-title":"Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series","volume":"7","author":"Blakey","year":"2015","journal-title":"Remote Sens."},{"key":"ref_68","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_69","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1515\/bot-2018-0017","article-title":"Application of deep learning techniques for determining the spatial extent and classification of seagrass beds, Trang, Thailand","volume":"62","author":"Yamakita","year":"2019","journal-title":"Bot. Mar."},{"key":"ref_70","unstructured":"Anderson, R. (2022, November 01). High Resolution Remote Sensing of Eelgrass (Zostera Marina) in South Slough, Oregon. Available online: https:\/\/scholarsbank.uoregon.edu\/xmlui\/handle\/1794\/25612."},{"key":"ref_71","unstructured":"Forsey, D., Leblon, B., LaRocque, A., Skinner, M., and Douglas, A. (2020, January 15\u201316). Eelgrass Mapping in Atlantic Canada Using Worldview-2 Imagery. Proceedings of the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Gottingen, Germany."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Hobley, B., Arosio, R., French, G., Bremner, J., Dolphin, T., and Mackiewicz, M. (2021). Semi-Supervised Segmentation for Coastal Monitoring Seagrass Using RPA Imagery. Remote Sens., 13.","DOI":"10.20944\/preprints202103.0780.v1"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"101430","DOI":"10.1016\/j.ecoinf.2021.101430","article-title":"Semantic segmentation of seagrass habitat from drone imagery based on deep learning: A comparative study","volume":"66","author":"Jeon","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Li, Y., Bai, J., Zhang, L., and Yang, Z. (2022). Mapping and Spatial Variation of Seagrasses in Xincun, Hainan Province, China, Based on Satellite Images. Remote Sens., 14.","DOI":"10.3390\/rs14102373"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Bhatnagar, S., Gill, L., and Ghosh, B. (2020). Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities. Remote Sens., 12.","DOI":"10.3390\/rs12162602"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Qin, H., Li, X., Yang, Z., and Shang, M. (2015, January 19\u201322). When underwater imagery analysis meets deep learning: A solution at the age of big visual data. Proceedings of the OCEANS 2015\u2014MTS\/IEEE Washington, Washington, DC, USA.","DOI":"10.23919\/OCEANS.2015.7404463"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"147","DOI":"10.3354\/meps106147","article-title":"Patch dynamics of eelgrass Zostera marina","volume":"106","author":"Olesen","year":"1994","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1007\/s00227-005-0011-8","article-title":"Production dynamics of the eelgrass, Zostera marina in two bay systems on the south coast of the Korean peninsula","volume":"147","author":"Lee","year":"2005","journal-title":"Mar. Biol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.ecolmodel.2018.01.001","article-title":"Agent Based Modelling (ABM) of eelgrass (Zostera marina) seedbank dynamics in a shallow Danish estuary","volume":"371","author":"Lange","year":"2018","journal-title":"Ecol. Model."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1002\/lno.12022","article-title":"Coastal ecosystem engineers and their impact on sediment dynamics: Eelgrass\u2013bivalve interactions under wave exposure","volume":"67","author":"Meysick","year":"2022","journal-title":"Limnol. Oceanogr."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.4319\/lo.2013.58.6.2293","article-title":"Estuarine ecosystem function response to flood and drought in a shallow, semiarid estuary: Nitrogen cycling and ecosystem metabolism","volume":"58","author":"Bruesewitz","year":"2013","journal-title":"Limnol. Oceanogr."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"5250","DOI":"10.1038\/s41598-019-41676-2","article-title":"The ups and downs of a canopy-forming seaweed over a span of more than one century","volume":"9","author":"Boudouresque","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"11629","DOI":"10.1038\/s41598-020-67736-6","article-title":"Projections of global-scale extreme sea levels and resulting episodic coastal flooding over the 21st Century","volume":"10","author":"Kirezci","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"161","DOI":"10.3354\/aei00301","article-title":"Resilience of dynamic coastal benthic ecosystems in response to large-scale finfish farming","volume":"11","author":"Keeley","year":"2019","journal-title":"Aquac. Environ. Interact."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Politi, T., Zilius, M., Castaldelli, G., Bartoli, M., and Daunys, D. (2019). Estuarine Macrofauna Affects Benthic Biogeochemistry in a Hypertrophic Lagoon. Water, 11.","DOI":"10.3390\/w11061186"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1146\/annurev-marine-032720-095144","article-title":"Marine Heatwaves","volume":"13","author":"Oliver","year":"2021","journal-title":"Annu. Rev. Mar. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"750265","DOI":"10.3389\/fmars.2021.750265","article-title":"Marine Heatwaves in the Chesapeake Bay","volume":"8","author":"Mazzini","year":"2022","journal-title":"Front. Mar. Sci."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1890\/11-1083.1","article-title":"Predicted eelgrass response to sea level rise and its availability to foraging Black Brant in Pacific coast estuaries","volume":"22","author":"Shaughnessy","year":"2012","journal-title":"Ecol. Appl."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"101594","DOI":"10.1016\/j.hal.2019.03.012","article-title":"Dynamic CO2 and pH levels in coastal, estuarine, and inland waters: Theoretical and observed effects on harmful algal blooms","volume":"91","author":"Raven","year":"2020","journal-title":"Harmful Algae"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2321\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:25:09Z","timestamp":1760124309000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2321"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,28]]},"references-count":89,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092321"],"URL":"https:\/\/doi.org\/10.3390\/rs15092321","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,28]]}}}