{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:33:35Z","timestamp":1773246815083,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["NNM11AA01A"],"award-info":[{"award-number":["NNM11AA01A"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In response to Hurricane Florence of 2018, NASA JPL collected quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument, observing record-setting river stages across North and South Carolina. Fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging, stemming from the sensor\u2019s ability to penetrate cloud cover and dense vegetation. This study used random forest classification to generate maps of inundation from L-band UAVSAR imagery processed using the Freeman\u2013Durden decomposition method. An average overall classification accuracy of 87% is achieved with this methodology, with areas of both under- and overprediction for the focus classes of open water and inundated forest. Fuzzy logic operations using hydrologic variables are used to reduce the number of small noise-like features and false detections in areas unlikely to retain water. Following postclassification refinement, estimated flood extents were combined to an event maximum for societal impact assessments. Results from the Hurricane Florence case study are discussed in addition to the limitations of available validation data for accuracy assessments.<\/jats:p>","DOI":"10.3390\/rs13245098","type":"journal-article","created":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T21:47:36Z","timestamp":1639604856000},"page":"5098","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Random Forest Classification of Inundation Following Hurricane Florence (2018) via L-Band Synthetic Aperture Radar and Ancillary Datasets"],"prefix":"10.3390","volume":"13","author":[{"given":"Alexander M.","family":"Melancon","sequence":"first","affiliation":[{"name":"Department of Atmospheric and Earth Science, University of Alabama in Huntsville, 320 Sparkman Dr. NW, Huntsville, AL 35805, USA"}]},{"given":"Andrew L.","family":"Molthan","sequence":"additional","affiliation":[{"name":"NASA Marshall Space Flight Center, Earth Science Branch, Huntsville, AL 35808, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5665-700X","authenticated-orcid":false,"given":"Robert E.","family":"Griffin","sequence":"additional","affiliation":[{"name":"Department of Atmospheric and Earth Science, University of Alabama in Huntsville, 320 Sparkman Dr. NW, Huntsville, AL 35805, USA"}]},{"given":"John R.","family":"Mecikalski","sequence":"additional","affiliation":[{"name":"Department of Atmospheric and Earth Science, University of Alabama in Huntsville, 320 Sparkman Dr. NW, Huntsville, AL 35805, USA"}]},{"given":"Lori A.","family":"Schultz","sequence":"additional","affiliation":[{"name":"NASA Marshall Space Flight Center, Earth Science Branch, Huntsville, AL 35808, USA"}]},{"given":"Jordan R.","family":"Bell","sequence":"additional","affiliation":[{"name":"NASA Marshall Space Flight Center, Earth Science Branch, Huntsville, AL 35808, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,15]]},"reference":[{"key":"ref_1","unstructured":"NOAA National Centers for Environmental Information (NCEI) U.S (2021, April 27). Billion-Dollar Weather and Climate Disasters, Available online: https:\/\/www.ncdc.noaa.gov\/billions\/summary-stats."},{"key":"ref_2","unstructured":"National Research Council (2009). Benefits and Costs of Accurate Flood Mapping. Mapping the Zone: Improving Flood Map Accuracy, The National Academies Press."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.rse.2006.11.012","article-title":"Mapping of Flood Dynamics and Spatial Distribution of Vegetation in the Amazon Floodplain using Multitemporal SAR Data","volume":"108","author":"Martinez","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_4","unstructured":"(2019, June 07). NASA Applied Remote Sensing Training (ARSET) Program: Introduction to Synthetic Aperture Radar, Available online: appliedsciences.nasa.gov\/join-mission\/training\/english\/arset-introduction-synthetic-aperture-radar."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5440","DOI":"10.3390\/rs70505440","article-title":"Mapping Regional Inundation with Spaceborne L-Band SAR","volume":"7","author":"Chapman","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","unstructured":"Flores, A., Herndon, K., Thapa, R., and Cherrington, E. (2019). Spaceborne Synthetic Aperture Radar\u2014Principles, Data Access, and Basic Processing Techniques. SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation, SERVIR Global Science Coordination Office."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/0034-4257(95)00140-9","article-title":"Understanding the Radar Back-Scattering from Flooded and Nonflooded Amazonian Forests: Results from Canopy Backscatter Modeling","volume":"54","author":"Wang","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1080\/01431168708954756","article-title":"An Explanation of Enhanced Radar Backscattering from Flooded Forests","volume":"8","author":"Richards","year":"1987","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1111\/jawr.12082","article-title":"Coastal Flood Inundation Monitoring with Satellite C-band and L-band Synthetic Aperture Radar Data","volume":"49","author":"Ramsey","year":"2013","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"and Dragu, L. Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Brieman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.03.002","article-title":"Random Forest and Rotation Forest for Fully Polarized SAR Image Classification using Polarimetric and Spatial Features","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An Assessment of the Effectiveness of a Random Forest Classifier for Land Cover Classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1109\/TGRS.2013.2258675","article-title":"and Kiranyaz, S. Integrating Color Features in Polarimetric SAR Image Classification","volume":"52","author":"Uhlmann","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good Practices for Estimating Area and Assessing Accuracy of Land Change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_16","unstructured":"Rose, A.N., McKee, J.J., Urban, M.L., Bright, E.A., and Sims, K.M. (2019, November 13). Oak Ridge National Laboratory LandScan 2018 Global Population Database, Available online: https:\/\/landscan.ornl.gov."},{"key":"ref_17","unstructured":"(2019, November 13). Microsoft. U.S. Building Footprints. 13 June 2018. Available online: https:\/\/github.com\/Microsoft\/USBuildingFootprints."},{"key":"ref_18","unstructured":"(2019, November 16). USGS National Transportation Dataset (NTD) Downloadable Data Collection, Available online: https:\/\/catalog.data.gov\/dataset\/usgs-national-\u00a0transportation-dataset-ntd-downloadable-data-collection1."},{"key":"ref_19","unstructured":"Stewart, S., and Berg, R. (2019, July 28). National Hurricane Center Tropical Cyclone Report: Hurricane Florence, Available online: https:\/\/www.nhc.noaa.gov\/data\/tcr\/AL062018_Florence.pdf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Feaster, T.D., Weaver, J.C., Gotvald, A.J., and Kolb, K.R. (2018). Preliminary Peak Stage and Streamflow Data for Selected U.S. Geological Survey Streamgaging Stations in North and South Carolina for Flooding Following Hurricane Florence.","DOI":"10.3133\/ofr20181172"},{"key":"ref_21","unstructured":"Armstrong, T. (2019, July 29). Hurricane Florence: 14 September 2018, Available online: https:\/\/www.weather.gov\/ilm\/HurricaneFlorence."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2018.09.006","article-title":"A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies","volume":"146","author":"Yang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MAES.2007.4365860","article-title":"UAVSAR: New NASA Airborne SAR System for Research","volume":"22","author":"Rosen","year":"2007","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_24","unstructured":"Lou, Y. (2019, August 01). Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Available online: https:\/\/airbornescience.nasa.gov\/instrument\/UAVSAR."},{"key":"ref_25","unstructured":"(2019, June 08). UAVSAR Data, Available online: https:\/\/uavsar.jpl.nasa.gov\/cgi-bin\/data.pl."},{"key":"ref_26","unstructured":"Cantalloube, H., and Nahum, C. (1999, January 26\u201329). How to Compute a Multi-Look SAR Image?. Proceedings of the Working Group on Calibration and Validation, Toulouse, France."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1080\/01431161.2017.1420938","article-title":"SAR-based Detection of Flooded Vegetation - A Review of Characteristics and Approaches","volume":"39","author":"Tsyganskaya","year":"2018","journal-title":"International Journal of Remote Sensing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7615","DOI":"10.3390\/rs70607615","article-title":"A Collection of SAR Methodologies for Monitoring Wetlands","volume":"7","author":"White","year":"2015","journal-title":"Remote Sens."},{"key":"ref_29","unstructured":"Woodhouse, I.H. (2006). The Scattering Matrix. Introduction to Microwave Remote Sensing, CRC Press."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e2020EA001554","DOI":"10.1029\/2020EA001554","article-title":"Rice Inundation Assessment Using Polarimetric UAVSAR Data","volume":"8","author":"Huang","year":"2021","journal-title":"Earth Space Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A Three-Component Scattering Model for Polarimetric SAR Data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","unstructured":"Chapman, B. (2019, June 10). Classifying Inundation from UAVSAR Polarimetric Data, Available online: https:\/\/uavsar.jpl.nasa.gov\/science\/workshops\/workshop2015.html."},{"key":"ref_33","unstructured":"Pottier, E. (2019, April 04). PolSARpro v6.0 (Biomass Edition) Software. Available online: https:\/\/ietr-lab.univ-rennes1.fr\/polsarpro-bio\/."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7732","DOI":"10.3390\/rs70607732","article-title":"Backscatter Analysis using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany","volume":"7","author":"Martinis","year":"2015","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2819","DOI":"10.1080\/01431169308904311","article-title":"Simulated and Observed LHH Radar Backscatter from Tropical Mangrove Forests","volume":"14","author":"Wang","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lin, Y., Yun, S., Bhardwaj, A., and Hill, E. (2019). Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew. Remote Sens., 11.","DOI":"10.3390\/rs11151778"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.rse.2012.10.035","article-title":"Monitoring Flood Extent in the Lower Amazon Floodplain using ALOS\/PALSAR ScanSAR Images","volume":"130","author":"Arnesen","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tiner, R.W., Lang, M.W., and Klemas, V.V. (2015). Remote Sensing of Wetlands: Applications and Advances, Taylor & Francis Group.","DOI":"10.1201\/b18210"},{"key":"ref_39","unstructured":"Planet Labs Inc. (2019, July 16). Disaster Data. Available online: https:\/\/planet.com\/disasterdata\/."},{"key":"ref_40","unstructured":"(2019, July 16). September 2018: Hurricane Florence, Available online: https:\/\/oceanservice.noaa.gov\/news\/sep18\/florence-storm-imagery.html."},{"key":"ref_41","unstructured":"(2019, July 16). Beechcraft King Air 350CER, Available online: http:\/\/www.omao.noaa.gov\/learn\/aircraft-operations\/aircraft\/hawker-beechcraft-king-air-350er."},{"key":"ref_42","unstructured":"Brown de Colstoun, E.C., Huang, C., Wang, P., Tilton, J.C., Tan, B., Phillips, J., Niemczura, S., Ling, P.Y., and Wolfe, R.E. (2019, October 15). Global Man-Made Impervious Surface (GMIS) Dataset from Landsat, Available online: https:\/\/sedac.ciesin.columbia.edu\/data\/set\/ulandsat-gmis-v1."},{"key":"ref_43","unstructured":"(2020, May 06). Height Above Nearest Drainage (HAND) for CONUS. Available online: https:\/\/www.hydroshare.org\/resource\/69f7d237675c4c73938481904358c789\/."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jhydrol.2011.03.051","article-title":"Height Above Nearest Drainage - A Hydrologically Relevant New Terrain Model","volume":"404","author":"Nobre","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1029\/96WR03137","article-title":"A New Method for the Determination of Flow Directions and Upslope Areas in Grid Digital Elevation Models","volume":"33","author":"Tarboton","year":"1997","journal-title":"AGU Water Sources Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"9655","DOI":"10.3390\/rs70809655","article-title":"An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms","volume":"7","author":"Colditz","year":"2015","journal-title":"Remote Sens."},{"key":"ref_47","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_48","first-page":"9655","article-title":"Delineation of Inundated Area and Vegetation Along the Amazon Floodplain with the SIR-C Synthetic Aperture Radar","volume":"7","author":"Hess","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","unstructured":"Vaughan, C., and Molthan, A. Personal Communication."},{"key":"ref_50","unstructured":"Warner, J.D. (2020, December 09). Scikit-Fuzzy Version 0.4.2. Available online: https:\/\/zenodo.org\/record\/3541386\/export\/hx."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","article-title":"Fuzzy Sets","volume":"8","author":"Zadeh","year":"1965","journal-title":"Inform. Control"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.isprsjprs.2014.07.014","article-title":"A Fully Automated TerraSAR-X Based Flood Service","volume":"104","author":"Martinis","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"529","DOI":"10.5194\/nhess-11-529-2011","article-title":"An Algorithm for Operational Flood Mapping from Synthetic Aperture Radar (SAR) Data using Fuzzy Logic","volume":"11","author":"Pulvirenti","year":"2011","journal-title":"Natl. Hazards Earth Syst. Sci."},{"key":"ref_54","unstructured":"European Space Agency (2021, January 21). Radar Course 2: Bragg Scattering. Available online: https:\/\/earth.esa.int\/web\/guest\/missions\/esa-operational-eo-missions\/ers\/instruments\/sar\/applications\/radar-courses\/content-2\/-\/asset_publisher\/qIBc6NYRXfnG\/content\/radar-course-2-bragg-scattering."},{"key":"ref_55","unstructured":"FEMA Mapping and Analysis Center (2021, January 18). Pacific Northwest National Laboratory Rapid Infrastructure Flood Tool for Hurricane Florence. Available online: https:\/\/www.arcgis.com\/home\/item.html?id=a3163b34af324c099a5e9f4b97a9523a."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5098\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:48:49Z","timestamp":1760168929000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5098"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,15]]},"references-count":55,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245098"],"URL":"https:\/\/doi.org\/10.3390\/rs13245098","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,15]]}}}