{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:14:48Z","timestamp":1777500888340,"version":"3.51.4"},"reference-count":54,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,5]],"date-time":"2020-03-05T00:00:00Z","timestamp":1583366400000},"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":"<jats:p>Crowdsourced environmental data have the potential to augment traditional data sources during disasters. Traditional sensor networks, satellite remote sensing imagery, and models are all faced with limitations in observational inputs, forecasts, and resolution. This study integrates flood depth derived from crowdsourced images with U.S. Geological Survey (USGS) ground-based observation networks, a remote sensing product, and a model during Hurricane Florence. The data sources are compared using cross-sections to assess flood depth in areas impacted by Hurricane Florence. Automated methods can be used for each source to classify flooded regions and fuse the dataset over common grids to identify areas of flooding. Crowdsourced data can play a major role when there are overlaps of sources that can be used for validation as well providing improved coverage and resolution.<\/jats:p>","DOI":"10.3390\/rs12050834","type":"journal-article","created":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T07:33:46Z","timestamp":1583480026000},"page":"834","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Integration of Crowdsourced Images, USGS Networks, Remote Sensing, and a Model to Assess Flood Depth during Hurricane Florence"],"prefix":"10.3390","volume":"12","author":[{"given":"Carolynne","family":"Hultquist","sequence":"first","affiliation":[{"name":"Geoinformatics and Earth Observation Laboratory, Earth and Environmental Systems Institute (EESI), and the Institute for Computational and Data Sciences, Department of Geography, The Pennsylvania State University, University Park, PA 16802, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6509-0735","authenticated-orcid":false,"given":"Guido","family":"Cervone","sequence":"additional","affiliation":[{"name":"Geoinformatics and Earth Observation Laboratory, Earth and Environmental Systems Institute (EESI), and the Institute for Computational and Data Sciences, Department of Geography, The Pennsylvania State University, University Park, PA 16802, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shintler, L., and Chen, Z. (2017). Damage Assessment of the Urban Environment during Disasters using Volunteered Geographic Information. Big Data for Regional Science, CRC Press. Chapter 18.","DOI":"10.4324\/9781315270838"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1126\/science.1251554","article-title":"Citizen science: Next steps for citizen science","volume":"343","author":"Bonney","year":"2014","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1007\/s10796-017-9734-6","article-title":"Crowdsourcing roles, methods and tools for data-intensive disaster management","volume":"20","author":"Poblet","year":"2018","journal-title":"Inf. Syst. Front."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Crowley, J. (2013). Connecting Grassroots and Government for Disaster Response, Woodrow Wilson International Center for Scholars Commons Lab. Technical Report.","DOI":"10.2139\/ssrn.2478832"},{"key":"ref_5","unstructured":"Shirk, J.L., and Bonney, R. (2015). Citizen Science Framework Review: Informing a Framework for Citizen Science within the US Fish and Wildlife Service."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s12518-011-0056-y","article-title":"Citizen-based sensing of crisis events: Sensor web enablement for volunteered geographic information","volume":"5","author":"Schade","year":"2013","journal-title":"Int. J. Appl. Geomath."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1177\/0162243909337121","article-title":"Buckets of Resistance: Standards and the Effectiveness of Citizen Science","volume":"35","author":"Ottinger","year":"2010","journal-title":"Sci. Technol. Hum. Values"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/feart.2014.00026","article-title":"Citizen science in hydrology and water resources: Opportunities for knowledge generation, ecosystem service management, and sustainable development","volume":"2","author":"Buytaert","year":"2014","journal-title":"Front. Earth Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tidball, K., and Krasny, M. (2012). A role for citizen science in disaster and conflict recovery and resilience. Citizen Science: Public Participation in Environmental Research, Cornell University Press.","DOI":"10.7591\/cornell\/9780801449116.003.0017"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1785\/0220160041","article-title":"Exploiting the Demographics of Did You Feel It?: Responses to Estimate the Felt Area of Moderate Earthquakes in California","volume":"88","author":"Boatwright","year":"2017","journal-title":"Seismol. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Haklay, M. (2013). Citizen Science and Volunteered Geographic Information\u2014Overview and typology of participation. Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice, Springer.","DOI":"10.1007\/978-94-007-4587-2_7"},{"key":"ref_12","unstructured":"Seymour, V., and Regalado, C. (2014, January 24\u201325). Extreme citizen science (excites): One end of the citizen science spectrum. Proceedings of the British Hydrological Society South East Section Meeting, Cardiff, UK."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1175\/BAMS-D-13-00014.1","article-title":"MPING: Crowd-Sourcing Weather Reports for Research","volume":"95","author":"Elmore","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_14","unstructured":"Fritz, A. (2016). Thousands of Birds Got Trapped in Hurricane Hermine\u2019s Eye, WP Company LLC."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s10651-017-0371-5","article-title":"A graphical assessment and spatial clustering of the Deepwater Horizon oil spill impact on Laughing Gulls","volume":"24","author":"Suyundikov","year":"2017","journal-title":"Environ. Ecol. Stat."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1177\/0263774X15614684","article-title":"Fractured knowledge: Mapping the gaps in public and private water monitoring efforts in areas affected by shale gas development","volume":"34","author":"Kinchy","year":"2016","journal-title":"Environ. Plan. C Gov. Policy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"31","DOI":"10.5751\/ES-05263-170431","article-title":"After the Cap: Risk Assessment, Citizen Science and Disaster Recovery","volume":"17","author":"McCormick","year":"2012","journal-title":"Ecol. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7","DOI":"10.2202\/1948-4682.1069","article-title":"Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake","volume":"2","author":"Zook","year":"2010","journal-title":"World Med. Health Policy"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gengler, S., Bogaert, P., Gengler, S., and Bogaert, P. (2016). Integrating Crowdsourced Data with a Land Cover Product: A Bayesian Data Fusion Approach. Remote Sens., 8.","DOI":"10.3390\/rs8070545"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.atmosenv.2018.10.018","article-title":"Comparison of simulated radioactive atmospheric releases to citizen science observations for the Fukushima nuclear accident","volume":"198","author":"Hultquist","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_21","first-page":"41","article-title":"Citizens as Sensors for Natural Hazards: A VGI integration Workflow","volume":"64","author":"Longueville","year":"2010","journal-title":"Geomatica"},{"key":"ref_22","first-page":"1","article-title":"A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management","volume":"29","author":"Herfort","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"32","DOI":"10.5751\/ES-08934-210432","article-title":"Volunteer stream monitoring: Do the data quality and monitoring experience support increased community involvement in freshwater decision making?","volume":"21","author":"Storey","year":"2016","journal-title":"Ecol. Soc."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hadj-Hammou, J., Loiselle, S., Ophof, D., and Thornhill, I. (2017). Getting the full picture: Assessing the complementarity of citizen science and agency monitoring data. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0188507"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.geoforum.2014.01.006","article-title":"Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of \u2018big data\u2019","volume":"52","author":"Shelton","year":"2014","journal-title":"Geoforum"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/JSTARS.2014.2320777","article-title":"Data Analytics for Rapid Mapping: Case Study of a Flooding Event in Germany and the Tsunami in Japan Using Very High Resolution SAR Images","volume":"8","author":"Dumitru","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3141","DOI":"10.1038\/srep03141","article-title":"Quantifying the digital traces of Hurricane Sandy on Flickr","volume":"3","author":"Preis","year":"2013","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hultquist, C., Simpson, M., Huang, Q., and Cervone, G. (2015). Using Nightlight Remote Sensing Imagery and Twitter Data to Study Power Outages. ACM SIGSPATIAL Proc., 1\u20136.","DOI":"10.1145\/2835596.2835601"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"669","DOI":"10.5194\/nhess-13-669-2013","article-title":"Improving remote sensing flood assessment using volunteered geographical data","volume":"13","author":"Schnebele","year":"2013","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.5194\/nhess-14-1007-2014","article-title":"Road assessment after flood events using non-authoritative data","volume":"14","author":"Schnebele","year":"2014","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s11069-016-2704-3","article-title":"Supervised classification of civil air patrol (CAP)","volume":"86","author":"Sava","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1177\/1473871612456122","article-title":"Geovisual analytics to support crisis management: Information foraging for geo-historical context","volume":"11","author":"Tomaszewski","year":"2012","journal-title":"Inf. Vis."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2725","DOI":"10.5194\/nhess-15-2725-2015","article-title":"Social media as an information source for rapid flood inundation","volume":"15","author":"Fohringer","year":"2015","journal-title":"Nat. Hazards Earth Syst. Sci. (NHESS)"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1016\/j.jhydrol.2016.07.036","article-title":"Crowdsourced data for flood hydrology: Feedback from recent citizen science projects in Argentina, France and New Zealand","volume":"541","author":"Patalano","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_35","first-page":"29","article-title":"Opportunities provided by geographic information systems and volunteered geographic information for a timely emergency response during flood events in Cologne, Germany","volume":"91","author":"Tzavella","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_36","unstructured":"Doherty, P., Arkison, E., Zelman-Fahm, D., San Souci, J., Leon, M., and Torpey, H. (2020, January 12). Volunteers Contribute to Hurricane Florence Crowdsourcing and Flood Estimation Efforts. Available online: https:\/\/www.giscorps.org\/napsg_243\/."},{"key":"ref_37","unstructured":"(2020, January 12). ArcNews: Weathering Hurricane Florence. Available online: https:\/\/www.napsgfoundation.org\/arcnews-weathering-hurricane-florence\/."},{"key":"ref_38","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 at Selected U.S. Geological Survey Streamgaging Stations in North and South Carolina for Flooding Following Hurricane Florence, September 2018.","DOI":"10.3133\/ofr20181172"},{"key":"ref_39","unstructured":"USGS (2020, January 12). USGS Flood Event Viewer: Providing Hurricane and Flood Response Data, Available online: https:\/\/www.usgs.gov\/mission-areas\/water-resources\/science\/usgs-flood-event-viewer-providing-hurricane-and-flood-response."},{"key":"ref_40","unstructured":"FilteredInstruments.csv (2020, January 12). United States Geological Survey, Available online: https:\/\/stn.wim.usgs.gov\/FEV\/#FlorenceSep2018."},{"key":"ref_41","unstructured":"Pacific Northwest National Lab (2020, January 12). RIFT Model Flood Extent, Available online: https:\/\/disasters.geoplatform.gov\/publicdata\/NationalDisasters\/HurricaneFlorence\/Data\/PNNL_RIFT_FloodExtent\/."},{"key":"ref_42","unstructured":"Pacific Northwest National Lab (2020, January 12). PNNL RIFT Flood Depth Grids, Available online: https:\/\/communities.geoplatform.gov\/disasters\/pnnl-rift-flood-products-midwest-flooding\/."},{"key":"ref_43","unstructured":"NASA-JPL\/Caltech ARIA Team (2020, January 12). NASA Disaster\u2019s Website: Hurricane Florence 2018, Available online: https:\/\/disasters.nasa.gov\/hurricane-florence-2018\/hurricane-florence-resources-aria-flood-extent-maps."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Clement, M., Kilsby, C., and Moore, P. (2017). Multi-temporal synthetic aperture radar flood mapping using change detection. Flood Risk Manag.","DOI":"10.1111\/jfr3.12303"},{"key":"ref_45","unstructured":"NASA-JPL\/Caltech ARIA Team (2020, January 12). JPL ARIA Data Share Site: Hurricane Florence 2018, Available online: https:\/\/aria-share.jpl.nasa.gov\/201809-Hurricane_Florence\/FPM\/."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1016\/j.jhydrol.2016.04.062","article-title":"Filling the observational void: Scientific value and quantitative validation of hydrometeorological data from a community-based monitoring programme","volume":"538","author":"Walker","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1007\/s11069-016-2198-z","article-title":"Hyper-resolution mapping of regional storm surge and tide flooding: Comparison of static and dynamic models","volume":"82","author":"Ramirez","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Brenton, P., von Gaval, S., Vogel, E., and Lecoq, M.E. (2018). Technology infrastructure for citizen science. Citizen Science: Innovation in Open Science, Society and Policy, UCL Press.","DOI":"10.2307\/j.ctv550cf2.12"},{"key":"ref_49","unstructured":"Haklay, M.M., Antoniou, V., Basiouka, S., Soden, R., and Mooney, P. (2014). Crowdsourced Geographic Information Use in Government, World Bank\u2019s Global Facility for Disaster Reduction and Recovery (GFDRR). Technical Report."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5334\/dsj-2015-002","article-title":"The Challenges of Data Quality and Data Quality Assessment in the Big Data Era","volume":"14","author":"Cai","year":"2015","journal-title":"Data Sci. J."},{"key":"ref_51","unstructured":"Liu, S. (2020, January 12). GeoDC - 2019 03 - Sophia Liu - USGS Support to FEMA Crowdsourcing Unit. Available online: https:\/\/www.youtube.com\/watch?v=XcUpOn0NmGI."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hultquist, C., and Cervone, G. (2017). Citizen monitoring during hazards: Validation of Fukushima radiation measurements. GeoJournal.","DOI":"10.1007\/s10708-017-9767-x"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1177\/0963662513497324","article-title":"Citizen science as seen by scientists: Methodological, epistemological and ethical dimensions","volume":"23","author":"Riesch","year":"2014","journal-title":"Public Underst. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1080\/1369118X.2013.848918","article-title":"Hacker science versus closed science: Building environmental monitoring infrastructure","volume":"17","author":"Hemmi","year":"2014","journal-title":"Inf. Commun. Soc."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/834\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:04:14Z","timestamp":1760173454000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/834"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,5]]},"references-count":54,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["rs12050834"],"URL":"https:\/\/doi.org\/10.3390\/rs12050834","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,5]]}}}