{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T06:42:32Z","timestamp":1772779352823,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T00:00:00Z","timestamp":1643241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Goodman Foundation","award":["Moreton Bay Seagrass"],"award-info":[{"award-number":["Moreton Bay Seagrass"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Improved development of remote sensing approaches to deliver timely and accurate measurements for environmental monitoring, particularly with respect to marine and estuarine environments is a priority. We describe a machine learning, cloud processing protocol for simultaneous mapping seagrass meadows in waters of variable quality across Moreton Bay, Australia. This method was adapted from a protocol developed for mapping coral reef areas. Georeferenced spot check field-survey data were obtained across Moreton Bay, covering areas of differing water quality, and categorized into either substrate or \u226525% seagrass cover. These point data with coincident Landsat 8 OLI satellite imagery (30 m resolution; pulled directly from Google Earth Engine\u2019s public archive) and a bathymetric layer (30 m resolution) were incorporated to train a random forest classifier. The semiautomated machine learning algorithm was applied to map seagrass in shallow areas of variable water quality simultaneously, and a bay-wide map was created for Moreton Bay. The output benthic habitat map representing seagrass presence\/absence was accurate (63%) as determined by validation with an independent data set.<\/jats:p>","DOI":"10.3390\/rs14030609","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T22:01:57Z","timestamp":1643320917000},"page":"609","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Cloud Processing for Simultaneous Mapping of Seagrass Meadows in Optically Complex and Varied Water"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7228-4028","authenticated-orcid":false,"given":"Eva M.","family":"Kovacs","sequence":"first","affiliation":[{"name":"Remote Sensing Research Centre, School of Earth and Environmental Science, The University of Brisbane, Brisbane, QLD 4072, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0182-1356","authenticated-orcid":false,"given":"Chris","family":"Roelfsema","sequence":"additional","affiliation":[{"name":"Remote Sensing Research Centre, School of Earth and Environmental Science, The University of Brisbane, Brisbane, QLD 4072, Australia"}]},{"given":"James","family":"Udy","sequence":"additional","affiliation":[{"name":"Science Under Sail, Wellington Point, City of Redland, QLD 4160, Australia"}]},{"given":"Simon","family":"Baltais","sequence":"additional","affiliation":[{"name":"The Wildlife Preservation Society of Queensland Bayside Branch (QLD) Inc., Brisbane, QLD 4101, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3960-3522","authenticated-orcid":false,"given":"Mitchell","family":"Lyons","sequence":"additional","affiliation":[{"name":"Remote Sensing Research Centre, School of Earth and Environmental Science, The University of Brisbane, Brisbane, QLD 4072, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2605-6104","authenticated-orcid":false,"given":"Stuart","family":"Phinn","sequence":"additional","affiliation":[{"name":"Remote Sensing Research Centre, School of Earth and Environmental Science, The University of Brisbane, Brisbane, QLD 4072, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.jembe.2007.06.017","article-title":"Impact of light limitation on seagrasses","volume":"350","author":"Ralph","year":"2007","journal-title":"J. Exp. Mar. Biol. Ecol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.coastaleng.2013.11.005","article-title":"The role of seagrasses in coastal protection in a changing climate","volume":"87","author":"Ondiviela","year":"2014","journal-title":"Coast. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1890\/110004","article-title":"A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2","volume":"9","author":"Mcleod","year":"2011","journal-title":"Front. Ecol. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1016\/j.biocon.2011.04.010","article-title":"Extinction risk assessment of the world\u2019s seagrass species","volume":"144","author":"Short","year":"2011","journal-title":"Biol. Conserv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1017\/S0376892902000127","article-title":"The future of seagrass meadows","volume":"29","author":"Duarte","year":"2002","journal-title":"Environ. Conserv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"12377","DOI":"10.1073\/pnas.0905620106","article-title":"Accelerating loss of seagrasses across the globe threatens coastal ecosystems","volume":"106","author":"Waycott","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"617","DOI":"10.3389\/fmars.2020.00617","article-title":"Seagrass Restoration Is Possible: Insights and Lessons from Australia and New Zealand","volume":"7","author":"Tan","year":"2020","journal-title":"Front. Mar. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Phinn, S., Roelfsema, C., Kovacs, E., Canto, R., Lyons, M., Saunders, M., and Maxwell, P. (2018). Mapping, monitoring and modelling seagrass using remote sensing techniques. Seagrasses of Australia, Springer.","DOI":"10.1007\/978-3-319-71354-0_15"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1071\/MF17380","article-title":"Principles and practice of acquiring drone-based image data in marine environments","volume":"70","author":"Joyce","year":"2019","journal-title":"Mar. Freshw. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"24479","DOI":"10.1109\/ACCESS.2017.2764998","article-title":"Visual Discrimination and Large Area Mapping of Posidonia Oceanica Using a Lightweight AUV","volume":"5","author":"Burguera","year":"2017","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.1016\/j.rse.2008.01.020","article-title":"Regional-scale seagrass habitat mapping in the Wider Caribbean region using Landsat sensors: Applications to conservation and ecology","volume":"112","author":"Wabnitz","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Traganos, D., Aggarwal, B., Poursanidis, D., Topouzelis, K., Chrysoulakis, N., and Reinartz, P. (2018). Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas. Remote Sens., 10.","DOI":"10.3390\/rs10081227"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"074041","DOI":"10.1088\/1748-9326\/ab7d06","article-title":"The global distribution of seagrass meadows","volume":"15","author":"Leonard","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3413","DOI":"10.1016\/j.rse.2007.09.017","article-title":"Mapping seagrass species, cover and biomass in shallow waters: An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia)","volume":"112","author":"Phinn","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.marpolbul.2004.10.031","article-title":"Mapping Water Quality and Substrate Cover in Optically Complex Coastal and Reef Waters: An Integrated Approach","volume":"51","author":"Phinn","year":"2005","journal-title":"Mar. Pollut. Bull."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/08920759609362279","article-title":"A Review of Remote Sensing for the Assessment and Management of Tropical Coastal Resources","volume":"24","author":"Green","year":"1996","journal-title":"Coast. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13550","DOI":"10.1038\/s41598-020-70318-1","article-title":"The role of seagrass vegetation and local environmental conditions in shaping benthic bacterial and macroinvertebrate communities in a tropical coastal lagoon","volume":"10","author":"Alsaffar","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_19","unstructured":"Short, F.C.R., and Short, C. (2001). Seagrass Taxonomy and Identification Key. Global Seagrass Research Methods, Elsevier Science B.V."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1080\/2150704X.2018.1468101","article-title":"Seagrass habitat mapping: How do Landsat 8 OLI, Sentinel-2, ZY-3A, and Worldview-3 perform?","volume":"9","author":"Kovacs","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1080\/01431161003692057","article-title":"Remote sensing of seagrasses in a patchy multi-species environment","volume":"32","author":"Knudby","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ecss.2013.08.026","article-title":"Challenges of remote sensing for quantifying changes in large complex seagrass environments","volume":"133","author":"Roelfsema","year":"2013","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.rse.2014.05.001","article-title":"Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach","volume":"150","author":"Roelfsema","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/14498596.2009.9635166","article-title":"An integrated field and remote sensing approach for mapping seagrass cover, Moreton Bay, Australia","volume":"54","author":"Roelfsema","year":"2009","journal-title":"J. Spat. Sci."},{"key":"ref_25","unstructured":"HLW (2015). Ecosystem Health Monitoring Program, Dataset, Healthy Land and Water."},{"key":"ref_26","unstructured":"Roelfsema, C., Loder, J., Host, R., and Kovacs, E. (2018). Benthic Inventory of Reefal Areas of Inshore Moreton Bay, Queensland, Australia, Reef Check Australia."},{"key":"ref_27","unstructured":"Wong, M.H. (2004). A comparison of issues and management approaches in Moreton Bay, Australia and Chesapeake Bay, USA. Developments in Ecosystems, Volume 1: Wetlands Ecosystems in Asia: Function and Management, Elsevier."},{"key":"ref_28","unstructured":"Maxwell, P., Connolly, R., Roelfsema, C., Burfeind, D., Udy, J., O\u2019Brien, K., Saunders, M., Barnes, R., Olds, A.D., and Hendersen, C.J. (2019). Seagrasses of Moreton Bay Quandamooka: Diversity, ecology and resilience. Moreton Bay Quandamooka & Catchment: Past, Present, and Future, Moreton Bay Foundation Ltd."},{"key":"ref_29","unstructured":"EHMP (2010). Ecosystem Health Monitoring Program (EHMP) 2008\u201309, Healthy Land and Water. Annual Technical Report."},{"key":"ref_30","unstructured":"Beaman, R.J. (2017). High-Resolution Depth Model for the Great Barrier Ree-30 m, Commonwealth of Australia (Geoscience Australia)."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_32","unstructured":"USGS (2021). Landsat 8\u20139 Calibration and Validation (Cal\/Val) Algorithm Description Document (ADD), United States Geological Society."},{"key":"ref_33","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":"J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Achanta, R., and Susstrunk, S. (2017, January 21\u201326). Superpixels and Polygons using Simple Non-Iterative Clustering. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.520"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the accuracy of remotely sensed data: Principles and practices. Mapping Science, CRC Press. [2nd ed.].","DOI":"10.1201\/9781420055139"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.scitotenv.2015.04.061","article-title":"Unravelling complexity in seagrass systems for management: Australia as a microcosm","volume":"534","author":"Kilminster","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Goodman, J., Purkis, S., and Phinn, S.R. (2013). Validation. Coral Reef Remote Sensing: A Guide for Multi-Level Sensing Mapping and Assessment, Elsiver.","DOI":"10.1007\/978-90-481-9292-2"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"643381","DOI":"10.3389\/fmars.2021.643381","article-title":"Workflow for the Generation of Expert-Derived Training and Validation Data: A View to Global Scale Habitat Mapping","volume":"8","author":"Roelfsema","year":"2021","journal-title":"Front. Mar. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1061\/(ASCE)0733-9372(1988)114:6(1436)","article-title":"Secchi Disk Transparency and Turbidity","volume":"114","author":"Effler","year":"1988","journal-title":"J. Environ. Eng. Asce"},{"key":"ref_41","unstructured":"HLW (2021). Trends over Time, Healthy Land and Water."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2005.02.017","article-title":"Retrospective seagrass change detection in a shallow coastal tidal Australian lake","volume":"97","author":"Dekker","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/01431161.2014.990649","article-title":"The application of remote sensing to seagrass ecosystems: An overview and future research prospects","volume":"36","author":"Hossain","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Koedsin, W., Intararuang, W., Ritchie, R.J., and Huete, A. (2016). An Integrated Field and Remote Sensing Method for Mapping Seagrass Species, Cover, and Biomass in Southern Thailand. Remote Sens., 8.","DOI":"10.3390\/rs8040292"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1002\/rse2.98","article-title":"Mapping with Confidence; delineating seagrass habitats using Unoccupied Aerial Systems (UAS)","volume":"5","author":"Nahirnick","year":"2019","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"362","DOI":"10.3389\/fmars.2017.00362","article-title":"Remote Sensing of Seagrass Leaf Area Index and Species: The Capability of a Model Inversion Method Assessed by Sensitivity Analysis and Hyperspectral Data of Florida Bay","volume":"4","author":"Hedley","year":"2017","journal-title":"Front. Mar. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Dierssen, H.M., Bostrom, K.J., Chlus, A., Hammerstrom, K., Thompson, D.R., and Lee, Z. (2019). Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California. Remote Sens., 11.","DOI":"10.3390\/rs11141664"},{"key":"ref_48","unstructured":"McKenzie, L. (2003). Guidelines for the Rapid Assessment and Mapping of Tropical Seagrass Habitats State of Queensland, State of Queensland, Department of Primary Industries."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/609\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:08:51Z","timestamp":1760134131000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/609"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,27]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030609"],"URL":"https:\/\/doi.org\/10.3390\/rs14030609","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,27]]}}}