Albrecht, F., Zussner, M., Perger, C., Durauer, M., See, L., McCallum, I.,
Fritz, S., and Kainz, W.: Using student volunteers to crowdsource land cover
information, in: GI_Forum 2014. Geospatial Innovation for Society –
Conference Proceedings, edited by: Vogler, R., Car, A., Strobl, J., and
Griesebner, G., Austrian Academy of Sciences Press, Vienna, Austria,
314–317, 2014.
Buytaert, W., Zulkafli, Z., Grainger, S., Acosta, L., Alemie, T. C.,
Bastiaensen, J., De Bièvre, B., Bhusal, J., Clark, J., Dewulf, A.,
Foggin, M., Hannah, D. M., Hergarten, C., Isaeva, A., Karpouzoglou, T.,
Pandeya, B., Paudel, D., Sharma, K., Steenhuis, T., Tilahun, S., Van
Hecken, G., and Zhumanova, M.: Citizen science in hydrology and water
resources: opportunities for knowledge generation, ecosystem service
management, and sustainable development, Front. Earth Sci., 2, 26,
https://doi.org/10.3389/feart.2014.00026, 2014.
Cervone, G., Sava, E., Huang, Q., Schnebele, E., Harrison, J., and
Waters, N.: Using Twitter for tasking remote-sensing data collection and
damage assessment: 2013 Boulder flood case study, Int. J. Remote Sens., 37,
100–124, https://doi.org/10.1080/01431161.2015.1117684, 2016.
Conrad, C. C. and Hilchey, K. G.: A review of citizen science and
community-based environmental monitoring: issues and opportunities, Environ.
Monit. Assess., 176, 273–291, https://doi.org/10.1007/s10661-010-1582-5, 2011.
Craglia, M., Ostermann, F., and Spinsanti, L.: Digital Earth from vision to
practice: making sense of citizen-generated content, Int. J. Digit. Earth, 5,
398–416, https://doi.org/10.1080/17538947.2012.712273, 2012.
Dashti, S., Palen, L., Heris, M. P., Anderson, K. M., Anderson, T. J., and
Anderson, S.: Supporting disaster reconnaissance with social media data?:
a design-oriented case study of the 2013 Colorado floods, in: Proceedings of
the 11th International ISCRAM Conference, Pennsylvania, USA, May 2014,
632–641, 2014.
de Vos, L., Leijnse, H., Overeem, A., and Uijlenhoet, R.: The potential of
urban rainfall monitoring with crowdsourced automatic weather stations in
Amsterdam, Hydrol. Earth Syst. Sci., 21, 765–777,
https://doi.org/10.5194/hess-21-765-2017, 2017.
Degrossi, L. C., Porto De Albuquerque, J., Fava, M. C., and Mendiondo, E. M.:
Flood Citizen Observatory?: a crowdsourcing-based approach for flood risk
management in Brazil, in: Proceedings of the International Conference on
Software Engineering and Knowledge Engineering, SEKE, 570–575, 2014.
Di Baldassarre, G., Schumann, G., Brandimarte, L., and Bates, P.: Timely low
resolution SAR imagery to support floodplain modelling: a case study review,
Surv. Geophys., 32, 255–269, https://doi.org/10.1007/s10712-011-9111-9, 2011.
Dong, J., Xiao, X., Sheldon, S., Biradar, C., Duong, N. D., and Hazarika, M.:
A comparison of forest cover maps in mainland Southeast Asia from multiple
sources: PALSAR, MERIS, MODIS and FRA, Remote Sens. Environ., 127, 60–73,
https://doi.org/10.1016/j.rse.2012.08.022, 2012.
Dorn, H., Vetter, M., and Höfle, B.: GIS-based roughness derivation for
flood simulations: a comparison of orthophotos, LiDAR and crowdsourced
geodata, Remote Sens.-Basel, 6, 1739–1759, https://doi.org/10.3390/rs6021739, 2014.
Evers, M., Jonoski, A., Almoradie, A., and Lange, L.: Environmental science
and policy collaborative decision making in sustainable flood risk
management?: a socio-technical approach and tools for participatory
governance, Environ. Sci. Policy, 55, 335–344,
https://doi.org/10.1016/j.envsci.2015.09.009, 2016.
Fava, M. C., Santana, G., Bressiani, D. A., Rosa, A., Horita, F. E. A., and
Mendiondo, E. M.: Integration of information technology systems for flood
forecasting with hybrid data sources, in: 6th International Conference on
Flood Management, 1–11, São Paulo, Brazil, 16–18 September 2014, 2014.
Fohringer, J., Dransch, D., Kreibich, H., and Schröter, K.: Social media
as an information source for rapid flood inundation mapping, Nat. Hazards
Earth Syst. Sci., 15, 2725–2738, https://doi.org/10.5194/nhess-15-2725-2015, 2015.
Foody, G. M., See, L., Fritz, S., Van der Velde, M., Perger, C., Schill, C.,
and Boyd, D. S.: Assessing the accuracy of Volunteered Geographic Information
arising from multiple contributors to an internet based collaborative
project, T. GIS, 17, 847–860, https://doi.org/10.1111/tgis.12033, 2013.
Friedrichs, A., Busch, J. A., Henkel, R., Heuermann, R., John, C., and
Zielinski, O.: Measuring fluorescence by means of smartphones with the new
Citclops-Application, in: Ocean Optics XXII, Portland, USA, 26–31 October
2014, 2014.
Fritz, S., McCallum, I., Schill, C., Perger, C., Grillmayer, R., Achard, F.,
Kraxner, F., and Obersteiner, M.: Geo-Wiki.Org: the use of crowdsourcing to
improve global land cover, Remote Sens.-Basel, 1, 345–354,
https://doi.org/10.3390/rs1030345, 2009.
Fritz, S., McCallum, I., Schill, C., Perger, C., See, L., Schepaschenko, D.,
van der Velde, M., Kraxner, F., and Obersteiner, M.: Geo-Wiki: an online
platform for improving global land cover, Environ. Modell. Softw., 31,
110–123, https://doi.org/10.1016/j.envsoft.2011.11.015, 2012.
Gallart, F., Llorens, P., Latron, J., Cid, N., Rieradevall, M., and Prat, N.:
Validating alternative methodologies to
estimate the regime of temporary
rivers when flow data are unavailable, Sci. Total Environ., 565, 1001–1010,
https://doi.org/10.1016/j.scitotenv.2016.05.116, 2016.
Gharesifard, M. and Wehn, U.: To share or not to share: drivers and barriers
for sharing data via online amateur weather networks, J. Hydrol., 535,
181–190, https://doi.org/10.1016/j.jhydrol.2016.01.036, 2016.
Grimaldi, S., Li, Y., Pauwels, V. R. N., and Walker, J. P.: Remote
sensing-derived water extent and level to constrain hydraulic flood
forecasting models: opportunities and challenges, Surv. Geophys., 37,
977–1034, https://doi.org/10.1007/s10712-016-9378-y, 2016.
Haklay, M.: Citizen Science and Volunteered Geographic Information: overview
and typology of participation, in: Crowdsourcing Geographic Knowledge:
Volunteered Geographic Information (VGI) in Theory and Practice, edited by:
Sui, D., Elwood, S., and Goodchild, M., 105–122, Springer Netherlands, 2013.
Hamshaw, S. D., Bryce, T., Rizzo, D. M., O'Neil-Dunne, J., Frolik, J., and
Dewoolkar, M. M.: Quantifying streambank movement and topography using
unmanned aircraft system photogrammetry with comparison to terrestrial laser
scanning, River Res. Appl., 33, 1354–1367, https://doi.org/10.1002/rra.3183, 2017.
Heipke, C.: Crowdsourcing geospatial data, ISPRS J. Photogramm., 65,
550–557, https://doi.org/10.1016/j.isprsjprs.2010.06.005, 2010.
Higgins, C. I., Williams, J., Leibovici, D. G., Simonis, I., Davis, M. J.,
Muldoon, C., Van Genuchten, P., and O'Hare, G.: Citizen OBservatory WEB
(COBWEB): a generic infrastructure platform to facilitate the collection of
citizen science data for environmental monitoring, Int. J. Spat. Data
Infrastructures Res., 11, 20–48, https://doi.org/10.2902/1725-0463.2016.11.art3, 2016.
Hochmair, H. H. and Zielstra, D.: Analysing user contribution patterns of
drone pictures to the dronestagram photo sharing portal, J. Spat. Sci., 60,
79–98, https://doi.org/10.1080/14498596.2015.969340, 2015.
Horita, F. E. A., Degrossi, L. C., Assis, L. F. F. G., Zipf, A., and Porto De
Albuquerque, J.: The use of Volunteered Geographic Information and
crowdsourcing in disaster management: a systematic literature review, in:
Proceedings of the Nineteenth Americas Conference on Information Systems,
Chicago, USA, 15–17 August 2013, 2013.
IDEM: Volunteer Stream Monitoring Training Manual, Hoosier Riverwatch Indiana
Department of Environmental Management, Indianapolis, USA, 2015.
Iwao, K., Nishida, K., Kinoshita, T., and Yamagata, Y.: Validating land cover
maps with Degree Confluence Project information, Geophys. Res. Lett., 33,
L23404, https://doi.org/10.1029/2006GL027768, 2006.
Jiang, H., Feng, M., Zhu, Y., Lu, N., Huang, J., and Xiao, T.: An automated
method for extracting rivers and lakes from Landsat imagery , Remote
Sens.-Basel, 6, 5067–5089, https://doi.org/10.3390/rs6065067, 2014.
Johnson, P., Ricker, B., and Harrison, S.: Volunteered Drone Imagery:
challenges and constraints to the development of an open shared image
repository, in: HICSS '17 Proc. 50th Hawaii Int. Conf. Syst. Sci., 4–7
January 2017, 1995–2004, 2017.
Kim, S., Robson, C., Zimmerman, T., Pierce, J., and Haber, E. M.: Creek
watch: pairing usefulness and usability for successful citizen science, in:
Proceedings of the 2011 Annual Conference on Human Factors in Computing
Systems – CHI '11, p. 2125, ACM Press, New York, USA, 7–12 May 2011, 2011.
Klonner, C., Marx, S., Usón, T., Porto de Albuquerque, J., and
Höfle, B.: Volunteered geographic information in natural hazard analysis:
a systematic literature review of current approaches with a focus on
preparedness and mitigation, ISPRS Int. J. Geo-Inf., 5, 103,
https://doi.org/10.3390/ijgi5070103, 2016.
Kotovirta, V., Toivanen, T., Järvinen, M., Lindholm, M., and Kallio, K.:
Participatory surface algal bloom monitoring in Finland in 2011–2013,
Environ. Syst. Res., 3, 24, https://doi.org/10.1186/s40068-014-0024-8, 2014.
Kutija, V., Bertsch, R., Glenis, V., Alderson, D., Parkin, G., Walsh, C. L.,
Robinson, J., and Kilsby, C.: Model validation using crowd-sourced data from
a large pluvial flood, in: 11th International Conference on Hydroinformatics,
New York, USA, 17–21 August 2014, 2014.
Le Boursicaud, R., Pénard, L., Hauet, A., Thollet, F., and Le Coz, J.:
Gauging extreme floods on YouTube: application of LSPIV to home movies for
the post-event determination of stream discharges, Hydrol. Process., 30,
90–105, https://doi.org/10.1002/hyp.10532, 2016.
Le Coz, J., Patalano, A., Collins, D., Guillén, N. F.,
García, C. M., Smart, G. M., Bind, J., Chiaverini, A., Le
Boursicaud, R., Dramais, G., and Braud, I.: Crowdsourced data for flood
hydrology: feedback from recent citizen science projects in Argentina, France
and New Zealand, J. Hydrol., 541, 766–777,
https://doi.org/10.1016/j.jhydrol.2016.07.036, 2016.
Leibovici, D. G., Evans, B., Hodges, C., Wiemann, S., Meek, S., Rosser, J.,
and Jackson, M.: On data quality assurance and conflation entanglement in
crowdsourcing for environmental studies, ISPRS Ann. Photogramm. Remote Sens.
Spat. Inf. Sci., II-3/W5, 195–202,
https://doi.org/10.5194/isprsannals-II-3-W5-195-2015, 2015.
Lesiv, M., Moltchanova, E., Schepaschenko, D., See, L., Shvidenko, A.,
Comber, A., and Fritz, S.: Comparison of data fusion methods using
crowdsourced data in creating a hybrid forest cover map, Remote Sens.-Basel,
8, 261, https://doi.org/10.3390/rs8030261, 2016.
Li, Z., Wang, C., Emrich, C. T., and Guo, D.: A novel approach to leveraging
social media for rapid flood mapping: a case study of the 2015 South Carolina
floods, Cartogr. Geogr. Inf. Sc., 45, 97–110,
https://doi.org/10.1080/15230406.2016.1271356, 2017.
Lintott, C. J., Schawinski, K., Slosar, A., Land, K., Bamford, S.,
Thomas, D., Raddick, M. J., Nichol, R. C., Szalay, A., Andreescu, D.,
Murray, P., and Vandenberg, J.: Galaxy Zoo: morphologies derived from visual
inspection of galaxies from the Sloan Digital Sky Survey, Mon. Not. R.
Astron. Soc., 389, 1179–1189, https://doi.org/10.1111/j.1365-2966.2008.13689.x, 2008.
Lowry, C. S. and Fienen, M. N.:
CrowdHydrology: crowdsourcing hydrologic data and engaging citizen scientists,
Ground Water,
51, 151–156, https://doi.org/10.1111/j.1745-6584.2012.00956.x, 2013.
Mazzoleni, M., Alfonso, L., Chacon-Hurtado, J., and Solomatine, D.:
Assimilating uncertain, dynamic and intermittent streamflow observations in
hydrological models, Adv. Water Resour., 83, 323–339,
https://doi.org/10.1016/j.advwatres.2015.07.004, 2015.
Mazzoleni, M., Verlaan, M., Alfonso, L., Monego, M., Norbiato, D., Ferri, M.,
and Solomatine, D. P.: Can assimilation of crowdsourced data in hydrological
modelling improve flood prediction?, Hydrol. Earth Syst. Sci., 21, 839–861,
https://doi.org/10.5194/hess-21-839-2017, 2017.
McDougall, K.: Using volunteered information to map the Queensland floods,
in: Surveying and Spatial Science Biennial Conference, Wellington, New
Zealand, 21–25 November 2011, 13–23, 2011.
McDougall, K. and Temple-Watts, P.: The use of LidAR and Volunteered
Geographic Information to map flood extents and inundation, in: ISPRS Annals
of Photogrammetry, Remote Sensing and Spatial Information Sciences,
Melbourne, Australia, 25 August–1 September 2012, 251–256, 2012.
Merkuryeva, G., Merkuryev, Y., Sokolov, B. V., Potryasaev, S.,
Zelentsov, V. A., and Lektauers, A.: Advanced river flood monitoring,
modelling and forecasting, J. Comput. Sci.-Neth., 10, 77–85,
https://doi.org/10.1016/j.jocs.2014.10.004, 2015.
Michelsen, N., Dirks, H., Schulz, S., Kempe, S., Al-Saud, M., and
Schüth, C.: YouTube as a crowd-generated water level archive, Sci. Total
Environ., 568, 189–195, https://doi.org/10.1016/j.scitotenv.2016.05.211, 2016.
Miller-Rushing, A., Primack, R., and Bonney, R.: The history of public
participation in ecological research, Front. Ecol. Environ., 10, 285–290,
https://doi.org/10.1890/110278, 2012.
Montargil, F. and Santos, V.: Citizen observatories: concept, opportunities
and communication with citizens in the first EU experiences, in: Beyond
Bureaucracy: Towards Sustainable Governance Informatisation, edited by:
Paulin, A. A., Anthopoulos, L. G., and Reddick, C. G., 167–184, Springer
International Publishing, Cham., 2017.
Muller, C. L., Chapman, L., Johnston, S., Kidd, C., Illingworth, S.,
Foody, G., Overeem, A., and Leigh, R. R.: Crowdsourcing for climate and
atmospheric sciences: current status and future potential, Int. J. Climatol.,
35, 3185–3203, https://doi.org/10.1002/joc.4210, 2015.
Oxendine, C. E., Schnebele, E., Cervone, G., and Waters, N.: Fusing
non-authoritative data to improve situational awareness in emergencies, in:
Proceedings of the 11th International ISCRAM Conference, Pennsylvania, USA,
May 2014 762–766, 762–766, 2014.
Padawangi, R., Turpin, E., Herlily, Prescott, M. F., Lee, I., and
Shepherd, A.: Mapping an alternative community river: the case of the
Ciliwung, Sustain. Cities Soc., 20, 147–157,
https://doi.org/10.1016/j.scs.2015.09.001, 2016.
Poser, K. and Dransch, D.: Volunteered Geographic Information for disaster
management with application to rapid flood damage estimation, Geomatica, 64,
89–98, 2010.
Rosser, J. F., Leibovici, D. G., and Jackson, M. J.: Rapid flood inundation
mapping using social media, remote sensing and topographic data, Nat.
Hazards, 87, 103–120, https://doi.org/10.1007/s11069-017-2755-0, 2017.
Salk, C. F., Sturn, T., See, L., Fritz, S., and Perger, C.: Assessing quality
of volunteer crowdsourcing contributions: lessons from the Cropland Capture
game, Int. J. Digit. Earth, 9, 410–426, https://doi.org/10.1080/17538947.2015.1039609,
2016.
Schnebele, E. and Cervone, G.: Improving remote sensing flood assessment
using volunteered geographical data, Nat. Hazards Earth Syst. Sci., 13,
669–677, https://doi.org/10.5194/nhess-13-669-2013, 2013.
Schnebele, E., Cervone, G., Kumar, S., and Waters, N.: Real time estimation
of the Calgary floods using limited remote sensing data, Water, 6, 381–398,
https://doi.org/10.3390/w6020381, 2014.
See, L., Schepaschenko, D., Lesiv, M., McCallum, I., Fritz, S., Comber, A.,
Perger, C., Schill, C., Zhao, Y., Maus, V., Siraj, M. A., Albrecht, F.,
Cipriani, A., Vakolyuk, M., Garcia, A., Rabia, A. H., Singha, K.,
Marcarini, A. A., Kattenborn, T., Hazarika, R., Schepaschenko, M., van der
Velde, M., Kraxner, F., and Obersteiner, M.: Building a hybrid land cover map
with crowdsourcing and geographically weighted regression, ISPRS J.
Photogramm., 103, 48–56, https://doi.org/10.1016/j.isprsjprs.2014.06.016, 2015a.
See, L., Fritz, S., Perger, C., Schill, C., McCallum, I., Schepaschenko, D.,
Duerauer, M., Sturn, T., Karner, M., Kraxner, F., and Obersteiner, M.:
Harnessing the power of volunteers, the internet and Google Earth to collect
and validate global spatial information using Geo-Wiki, Technol. Forecast.
Soc., 98, 324–335, https://doi.org/10.1016/j.techfore.2015.03.002, 2015b.
See, L., Mooney, P., Foody, G., Bastin, L., Comber, A., Estima, J.,
Fritz, S., Kerle, N., Jiang, B., Laakso, M., Liu, H., Milčinski, G.,
Nikšič, M., Painho, M., Pódör, A., Olteanu-Raimond, A.-M.,
and Rutzinger, M.: Crowdsourcing, Citizen Science or Volunteered Geographic
Information? The Current State of Crowdsourced Geographic Information, SPRS
Int. J. Geo-Inf., 5, 55, https://doi.org/10.3390/ijgi5050055, 2016.
Shaad, K., Ninsalam, Y., Padawangi, R., and Burlando, P.: Towards high
resolution and cost-effective terrain mapping for urban hydrodynamic
modelling in densely settled river-corridors, Sustain. Cities Soc. 20,
168–179, https://doi.org/10.1016/j.scs.2015.09.005, 2016.
Smith, L., Liang, Q., James, P., and Lin, W.: Assessing the utility of social
media as a data source for flood risk management using a real-time modelling
framework, J. Flood Risk Manag., 10, 370–380, https://doi.org/10.1111/jfr3.12154,
2015.
Starkey, E., Parkin, G., Birkinshaw, S., Large, A., Quinn, P., and
Gibson, C.: Demonstrating the value of community-based (“citizen science”)
observations for catchment modelling and characterisation, J. Hydrol., 548,
801–817, https://doi.org/10.1016/j.jhydrol.2017.03.019, 2017.
Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., and Kim, S.:
Flood inundation modelling: a review of methods, recent advances and
uncertainty analysis, Environ. Modell. Softw., 90, 201–216,
https://doi.org/10.1016/j.envsoft.2017.01.006, 2017.
Uhrner, U., Grosso, G., Romain, A. C., Hutsemekers, V., Delva, J., Kunz, W.,
De Groof, A., Arnaud, Y., Valoggia, P., Johannsen, L., Stevenot, B., and
Ledent, P.: Development of an environmental information system for odour
using citizen and technology innovative sensors and advanced modeling, in:
CEUR Workshop Proceedings, vol. 1322, Aachen, Germany, 2013.
Walker, D., Forsythe, N., Parkin, G., and Gowing, J.: Filling the
observational void: scientific value and quantitative validation of
hydrometeorological data from a
community-based monitoring programme,
J. Hydrol., 538, 713–725, https://doi.org/10.1016/j.jhydrol.2016.04.062, 2016.
Yang, Y. Y. and Kang, S. C.: Crowd-based velocimetry for surface flows, Adv.
Eng. Inform., 32, 275–286, https://doi.org/10.1016/j.aei.2017.03.007, 2017.
Yu, D., Yin, J., and Liu, M.: Validating city-scale surface water flood
modelling using crowd-sourced data, Environ. Res. Lett., 11, 124011,
https://doi.org/10.1088/1748-9326/11/12/124011, 2016.
