Skip to main content

Do Remote Sensing Mapping Practices Adequately Address Localized Flooding? A Critical Overview

Abstract

Local-scale flooding (LSF) is usually characterized by much less severe damage compared to extreme flood events; however, it does have marked local environmental influence, especially when it is characterized by regular and frequent occurrence and long duration. Knowledge about the spatial extent of flood-prone areas is essential for flood risk and land management purposes, spatial planning, or emergency response. Flood mapping procedures have been supported by remote sensing for several decades, and progress in remote sensing technology and image processing over the last two decades has made flood extent analysis possible at an unprecedented level of detail. Here we provide an overview of applications of remote sensing technologies for analyzing the extent of flood events and discuss their applicability for LSF. We report on applications of data from the optical visible and reflective infrared spectrum, active microwave spectrum, and airborne laser scanning technology. Additionally, applications of elevation data supporting flood extent mapping are reviewed. The review reveals that in general remote sensing techniques and data types are likely to have similar capabilities and limitations for analyzing LSF as they have for extreme floods. However, data from many current remote sensing sensors are inadequate for LSF analysis, since very high spatial resolution data are required for mapping localized flooding. Finally, airborne laser scanning is found to be an emerging and promising technology in flood-related water surface analysis.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

COSMO-SkyMed Image ©ASI. All rights reserved

Fig. 4

By courtesy of earthobservatory.nasa.gov

Fig. 5

References

  1. Alsdorf DE, Rodríguez E, Lettenmaier DP (2007) Measuring surface water from space. Rev Geophys 45:RG2002. doi:10.1029/2006rg000197

    Article  Google Scholar 

  2. Andresen T, Mott C, Zimmermann S, Schneider T, Melzer A (2002) Object-oriented information extraction for the monitoring of sensitive aquatic environments. In: IGARSS ‘02. pp 3083–3085

  3. Antonarakis AS, Richards KS, Brasington J (2008) Object-based land cover classification using airborne LiDAR. Remote Sens Environ 112:2988–2998. doi:10.1016/j.rse.2008.02.004

    Article  Google Scholar 

  4. Arnesen AS, Silva TSF, Hess LL, Novo EMLM, Rudorff CM, Chapman BD, McDonald KC (2013) Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images. Remote Sens Environ 130:51–61. doi:10.1016/j.rse.2012.10.035

    Article  Google Scholar 

  5. Baatz M, Schäpe A (2000) Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angew Geogr informationsverarbeitung 12:12–23

    Google Scholar 

  6. Barredo J (2007) Major flood disasters in Europe: 1950–2005. Nat Hazards 42:125–148. doi:10.1007/s11069-006-9065-2

    Article  Google Scholar 

  7. Bates PD, Wilson MD, Horritt MS, Mason DC, Holden N, Currie A (2006) Reach scale floodplain inundation dynamics observed using airborne synthetic aperture radar imagery: data analysis and modelling. J Hydrol 328:306–318. doi:10.1016/j.jhydrol.2005.12.028

    Article  Google Scholar 

  8. Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm 65:2–16. doi:10.1016/j.isprsjprs.2009.06.004

    Article  Google Scholar 

  9. Brennan R, Webster TL (2006) Object-oriented land cover classification of lidar-derived surfaces. Can J Remote Sens 32:162–172. doi:10.5589/m06-015

    Article  Google Scholar 

  10. Bretar F (2009) Feature extraction from LiDAR data in urban areas. In: Shan J, Toth CK (eds) Topographic laser ranging and scanning: principles and processing. CRC Press Taylor and Francis Group, Boca Raton, pp 403–419

    Google Scholar 

  11. Brivio PA, Colombo R, Maggi M, Tomasoni R (2002) Integration of remote sensing data and GIS for accurate mapping of flooded areas. Int J Remote Sens 23:429–441

    Article  Google Scholar 

  12. Brzank A, Heipke C, Goepfert J, Soergel U (2008) Aspects of generating precise digital terrain models in the Wadden Sea from lidar–water classification and structure line extraction. ISPRS J Photogramm 63:510–528. doi:10.1016/j.isprsjprs.2008.02.002

    Article  Google Scholar 

  13. Burnett C, Aaviksoo K, Lang S, Langanke T, Blaschke T (2003) An object-based methodology for mapping mires using high resolution imagery. In: Ecohydrological processes in Northern Wetlands, Tallinn. pp 239–244

  14. Chesnaud C, Refregier P, Boulet V (1999) Statistical region snake-based segmentation adapted to different physical noise models. IEEE Trans Pattern Anal Mach Intell 21:1145–1157. doi:10.1109/34.809108

    Article  Google Scholar 

  15. Chormanski J, Okruszko T, Ignar S, Batelaan O, Rebel KT, Wassen MJ (2011) Flood mapping with remote sensing and hydrochemistry: a new method to distinguish the origin of flood water during floods. Ecol Eng 37:1334–1349. doi:10.1016/j.ecoleng.2011.03.016

    Article  Google Scholar 

  16. Collin A, Long B, Archambault P (2010) Salt-marsh characterization, zonation assessment and mapping through a dual-wavelength LiDAR. Remote Sens Environ 114:520–530. doi:10.1016/j.rse.2009.10.011

    Article  Google Scholar 

  17. Congalton RG, Green K (2009) Assessing the accuracy of remotely sensed data: principles and practices. CRC Press, Boca Raton

    Google Scholar 

  18. Copernicus (2016) Copernicus—The European Earth Observation Programme Website. European commission. http://ec.europa.eu/growth/sectors/space/copernicus/index_en.htm. Accessed 10 Feb 2016

  19. Corr DG, Keyte GE, Whitehouse S (1995) Studies of decorrelation in multi-temporal SAR imagery. In: Geoscience and Remote Sensing Symposium, 1995. IGARSS ‘95. ‘Quantitative Remote Sensing for Science and Applications’, International, 10–14 Jul 1995. vol 1022, pp 1026–1028. doi:10.1109/igarss.1995.521128

  20. Crasto N, Hopkinson C, Forbes DL, Lesack L, Marsh P, Spooner I, van der Sanden JJ (2015) A LiDAR-based decision-tree classification of open water surfaces in an Arctic delta Remote Sens Environ 164:90–102. doi:10.1016/j.rse.2015.04.011

    Google Scholar 

  21. Crist EP, Kauth RJ (1986) The Tassled Cap de-mystified. Photogrammetric Engineering & Remote Sensing 52:81–86

    Google Scholar 

  22. Davranche A, Poulin B, Lefebvre G (2013) Mapping flooding regimes in Camargue wetlands using seasonal multispectral data. Remote Sens Environ 138:165–171. doi:10.1016/j.rse.2013.07.015

    Article  Google Scholar 

  23. De Groeve T (2010) Flood monitoring and mapping using passive microwave remote sensing in Namibia Geomatics. Natural Hazards Risk 1:19–35. doi:10.1080/19475701003648085

    Article  Google Scholar 

  24. De Groeve T, Riva P (2009) Global real-time detection of major floods using passive microwave remote sensing. pp 1–4

  25. De Moel H, Van Alphen J, Aerts JCJH (2009) Flood maps in Europe—methods, availability and use. Nat Hazards Earth Syst Sci 9:289–301. doi:10.5194/nhess-9-289-2009

    Article  Google Scholar 

  26. De Roo A, Van Der Knijff J, Horritt M, Schmuck G, De Jong S (1999) Assessing flood damages of the 1997 Oder flood and the 1995 Meuse flood. In: Proceedings of the second international ITC symposium on operationalization of remote sensing, Enschede, The Netherlands, 16–20 Aug 1999

  27. Dissanska M, Bernier M, Payette S (2009) Object-based classification of very high resolution panchromatic images for evaluating recent change in the structure of patterned peatlands. Can J Remote Sens 35:189–215. doi:10.5589/m09-002

    Article  Google Scholar 

  28. EC (2007) Directive 2007/60/EC of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risks. Official Journal of the European Communities 288/27

  29. EM-DAT (2015) EM-DAT: the OFDA/CRED international disaster database. Université Catholique de Louvain. http://www.emdat.be/. Accessed 25 Feb 2015

  30. EXCIMAP (2007) Handbook on good practices for flood mapping in Europe, European exchange circle on flood mapping

  31. Feyisa GL, Meilby H, Fensholt R, Proud SR (2014) Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sens Environ 140:23–35. doi:10.1016/j.rse.2013.08.029

    Article  Google Scholar 

  32. Frazier PS, Page KJ (2000) Water body detection and delineation with Landsat TM data. Photogramm Eng Remote Sens 66:1461–1467

    Google Scholar 

  33. Grenier M, Demers A-M, Labrecque S, Benoit M, Fournier RA, Drolet B (2007) An object-based method to map wetland using RADARSAT-1 and Landsat ETM images: test case on two sites in Quebec, Canada. Can J Remote Sens 33:S28–S45. doi:10.5589/m07-048

    Article  Google Scholar 

  34. Grenier M, Demers AM, Labrecque S, Benoit M, Fournier RA, Drolet B (2007) An object-based method to map wetland using RADARSAT-1 and Landsat ETM images: test case on two sites in Quebec. Canada Can J Remote Sens 33:S28–S45

    Article  Google Scholar 

  35. Hagg W, Sties M (1998) Monitoring the Oder/Germany flood with ERS, RADARSAT and optical data. In: Geoscience and remote sensing symposium proceedings, 1998. IGARSS ‘98. 1998 IEEE International, 6–10 Jul 1998. vol 1613, pp 1614–1616. doi:10.1109/igarss.1998.691641

  36. Hakala T, Suomalainen J, Kaasalainen S, Chen Y (2012) Full waveform hyperspectral LiDAR for terrestrial laser scanning. Opt Express 20:7119–7127

    Article  PubMed  Google Scholar 

  37. Hengl T (2006) Finding the right pixel size. Comput Geosci-UK 32:1283–1298. doi:10.1016/j.cageo.2005.11.008

    Article  Google Scholar 

  38. Henry JB, Chastanet P, Fellah K, Desnos YL (2006) Envisat multi-polarized ASAR data for flood mapping. Int J Remote Sens 27:1921–1929. doi:10.1080/01431160500486724

    Article  Google Scholar 

  39. Heremans R, Willekens A, Borghys D, Verbeeck B, Valckenborgh J, Acheroy M, Perneel C (2003) Automatic detection of flooded areas on ENVISAT/ASAR images using an object-oriented classification technique and an active contour algorithm. In: Proceedings of international conference on recent advances in space technologies, 2003. RAST ‘03., 20–22 Nov 2003. pp 311–316. doi:10.1109/rast.2003.1303926

  40. Herrera-Cruz V, Koudogbo F, Herrera V (2009) TerraSAR-X rapid mapping for flood events. In: Proceedings of the international society for photogrammetry and remote sensing (earth imaging for geospatial information), Hannover, Germany. pp 170–175

  41. Hirschboeck KK (1988) Flood hydroclimatology. In: Baker VR, Kochel RC, Patton PC (eds) Flood geomorphology. Wiley, New York, pp 27–49

    Google Scholar 

  42. Höfle B, Hollaus M, Hagenauer J (2012) Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data. ISPRS J Photogramm 67:134–147. doi:10.1016/j.isprsjprs.2011.12.003

    Article  Google Scholar 

  43. Höfle B, Pfeifer N (2007) Correction of laser scanning intensity data: Data and model-driven approaches. ISPRS J Photogramm 62:415–433. doi:10.1016/j.isprsjprs.2007.05.008

    Article  Google Scholar 

  44. Höfle B, Vetter M, Pfeifer N, Mandlburger G, Stötter J (2009) Water surface mapping from airborne laser scanning using signal intensity and elevation data. Earth Surf Proc Land 34:1635–1649. doi:10.1002/esp.1853

    Article  Google Scholar 

  45. Horritt M (1999) A statistical active contour model for SAR image segmentation. Image Vis Comput 17:213–224. doi:10.1016/s0262-8856(98)00101-2

    Article  Google Scholar 

  46. Horritt MS, Mason DC, Luckman AJ (2001) Flood boundary delineation from Synthetic Aperture Radar imagery using a statistical active contour model. Int J Remote Sens 22:2489–2507. doi:10.1080/01431160116902

    Article  Google Scholar 

  47. Hudson PF, Colditz RR (2003) Flood delineation in a large and complex alluvial valley, lower Pánuco basin. Mexico J Hydrol 280:229–245. doi:10.1016/S0022-1694(03)00227-0

    Article  Google Scholar 

  48. Hyyppa J, Hyyppa H, Leckie D, Gougeon F, Yu X, Maltamo M (2008) Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. Int J Remote Sens 29:1339–1366. doi:10.1080/01431160701736489

    Article  Google Scholar 

  49. Jain SK, Singh RD, Jain MK, Lohani AK (2005) Delineation of flood-prone areas using remote sensing techniques. Water Resour Manage 19:333–347

    Article  Google Scholar 

  50. Jansen LJM, Groom G, Carrai G (2008) Land-cover harmonisation and semantic similarity: some methodological issues. J Land Use Sci 3:131–160. doi:10.1080/17474230802332076

    Article  Google Scholar 

  51. Jensen JR (2007) Remote sensing of the environment: an earth resource perspective. Prentice Hall series in geographic information science, 2nd edn. Pearson Prentice Hall, Upper Saddle River

    Google Scholar 

  52. Jiang Z, Qi J, Su S, Zhang Z, Wu J (2011) Water body delineation using index composition and HIS transformation. Int J Remote Sens 33:3402–3421. doi:10.1080/01431161.2011.614967

    Article  Google Scholar 

  53. Kaartinen H et al (2012) An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sens-Basel 4:950–974

    Article  Google Scholar 

  54. Kaasalainen S, Pyysalo U, Krooks A, Vain A, Kukko A, Hyyppa J, Kaasalainen M (2011) Absolute radiometric calibration of ALS intensity data: effects on accuracy and target classification. Sensors-Basel 11:10586–10602. doi:10.3390/S111110586

    Article  PubMed  PubMed Central  Google Scholar 

  55. Kankaku Y, Osawa Y, Suzuki S, Watanabe T (2009) The overview of the L-band SAR onboard ALOS-2. In: Progress in electromagnetics research symposium, Moscow, Russia, 18–21 Aug 2009

  56. Keshava N, Mustard JF (2002) Spectral unmixing. IEEE Signal Process Mag 19:44–57

    Article  Google Scholar 

  57. Kraus K, Pfeifer N (1998) Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J Photogramm 53:193–203

    Article  Google Scholar 

  58. Lang MW, McCarty GW (2009) Lidar intensity for improved detection of inundation below the forest canopy. Wetlands 29:1166–1178

    Article  Google Scholar 

  59. LDCM (2015) Landsat Data Continuity Mission (LDCM)—Landsat 8. United States Geological Survey. http://landsat.usgs.gov/landsat8.php. Accessed 20 Mar 2015

  60. Li W et al (2013) A Comparison of land surface water mapping using the normalized difference water index from TM, ETM + and ALI. Remote Sens-Basel 5:5530

    Article  Google Scholar 

  61. Lu SL, Wu BF, Yan NN, Wang H (2011) Water body mapping method with HJ-1A/B satellite imagery. Int J Appl Earth Obs 13:428–434. doi:10.1016/j.jag.2010.09.006

    Article  Google Scholar 

  62. Malinowski R, Groom G, Schwanghart W, Heckrath G (2015) Detection and delineation of localized flooding from WorldView-2 multispectral data. Remote Sens-Basel 7:14853–14875. doi:10.3390/rs71114853

    Article  Google Scholar 

  63. Malinowski R, Höfle B, Koenig K, Groom G, Schwanghart W, Heckrath G (2016) Local-scale flood mapping on vegetated floodplains from radiometrically calibrated airborne LiDAR data. ISPRS J Photogramm 119:267–279. doi:10.1016/j.isprsjprs.2016.06.009

    Article  Google Scholar 

  64. Mallinis G, Gitas IZ, Giannakopoulos V, Maris F, Tsakiri-Strati M (2011) An object-based approach for flood area delineation in a transboundary area using ENVISAT ASAR and LANDSAT TM data. Int J Digital Earth. doi:10.1080/17538947.2011.641601

    Google Scholar 

  65. Mandlburger G, Hauer C, Wieser M, Pfeifer N (2015) Topo-bathymetric LiDAR for monitoring river morphodynamics and instream habitats—a case study at the Pielach River. Remote Sens-Basel 7:6160–6195. doi:10.3390/rs70506160

    Article  Google Scholar 

  66. Marcus WA, Fonstad MA (2008) Optical remote mapping of rivers at sub-meter resolutions and watershed extents. Earth Surf Proc Land 33:4–24. doi:10.1002/esp.1637

    Article  Google Scholar 

  67. Mason DC, Horritt MS, Dall’Amico JT, Scott TR, Bates PD (2007) Improving river flood extent delineation from synthetic aperture radar using airborne laser altimetry. IEEE Trans Geosci Remote Sens 45:3932–3943. doi:10.1109/tgrs.2007.901032

    Article  Google Scholar 

  68. Mason DC, Speck R, Devereux B, Schumann GJP, Neal JC, Bates PD (2010) Flood detection in urban areas using TerraSAR-X. IEEE Trans Geosci Remote Sens 48:882–894. doi:10.1109/tgrs.2009.2029236

    Article  Google Scholar 

  69. McCoy RM (2004) Field methods in remote sensing. Guilford Press, New York

    Google Scholar 

  70. McFeeters SK (1996) The use of the normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 17:1425–1432. doi:10.1080/01431169608948714

    Article  Google Scholar 

  71. Mercer B (2004) DEMs created from airborne IFSAR—an update In: International Archives of Photogrammetry and Remote Sensing, vol 35

  72. Nico G, Pappalepore M, Pasquariello G, Refice A, Samarelli S (2000) Comparison of SAR amplitude vs. coherence flood detection methods—a GIS application. Int J Remote Sens 21:1619–1631. doi:10.1080/014311600209931

    Article  Google Scholar 

  73. Otepka J, Ghuffar S, Waldhauser C, Hochreiter R, Pfeifer N (2013) Georeferenced point clouds: a survey of features and point cloud management. ISPRS Int J Geo-Inf 2:1038–1065

    Article  Google Scholar 

  74. Otsu N (1979) A threshold selection method from gray-level histograms systems. IEEE Trans Man Cybern 9:62–66. doi:10.1109/tsmc.1979.4310076

    Article  Google Scholar 

  75. Ouma YO, Tateishi R (2006) A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: an empirical analysis using Landsat TM and ETM + data. Int J Remote Sens 27:3153–3181. doi:10.1080/01431160500309934

    Article  Google Scholar 

  76. Pajares G (2015) Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogramm Eng Remote Sens 81:281–329. doi:10.14358/PERS.81.4.281

    Article  Google Scholar 

  77. Petrie G, Toth CK (2009) Introduction to laser ranging, profiling, and scanning. In: Shan J, Toth CK (eds) Topographic laser ranging and scanning: principles and processing. CRC Press Taylor and Francis Group, Boca Raton, pp 1–28

    Google Scholar 

  78. Pierdicca N, Chini M, Pulvirenti L, Macina F (2008) Integrating physical and topographic information into a fuzzy scheme to map flooded area by SAR. Sensors-Basel 8:4151–4164

    Article  PubMed  PubMed Central  Google Scholar 

  79. Pierdicca N, Pulvirenti L, Chini M, Guerriero L, Candela L (2013) Observing floods from space: experience gained from COSMO-SkyMed observations. Acta Astronaut 84:122–133. doi:10.1016/j.actaastro.2012.10.034

    Article  Google Scholar 

  80. Pulvirenti L, Chini M, Pierdicca N, Guerriero L, Ferrazzoli P (2011) Flood monitoring using multi-temporal COSMO-SkyMed data: Image segmentation and signature interpretation. Remote Sens Environ 115:990–1002. doi:10.1016/j.rse.2010.12.002

    Article  Google Scholar 

  81. Pulvirenti L, Pierdicca N, Chini M, Guerriero L (2011) An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic. Nat Hazards Earth Syst Sci 11:529–540. doi:10.5194/nhess-11-529-2011

    Article  Google Scholar 

  82. Robertson LD, Douglas JK, Davies C (2011) Spatial analysis of wetlands at multiple scales in Eastern Ontario using remote sensing and GIS. In: 32nd Canadian symposium on remote sensing, Sherbrooke, Quebec, 13–16 June 2011

  83. Rutzinger M, Hofle B, Hollaus M, Pfeifer N (2008) Object-based point cloud analysis of full-waveform airborne laser scanning data for urban vegetation classification. Sensors-Basel 8:4505–4528. doi:10.3390/S8084505

    Article  PubMed  PubMed Central  Google Scholar 

  84. Sanyal J, Lu XX (2004) Application of remote sensing in flood management with special reference to monsoon Asia: a review. Nat Hazards 33:283–301

    Article  Google Scholar 

  85. Schäfer ML, Lundström JO (2011) Detection of temporary flooded areas with potential floodwater mosquito production using imaging radar. Int J Remote Sens 33:1943–1953. doi:10.1080/01431161.2011.604053

    Article  Google Scholar 

  86. Schmidt A, Rottensteiner F, Sörgel U (2013) Water-land-classification in coastal areas with full waveform lidar data. Photogramm Fernerkund Geoinf 2013:71–81

    Article  Google Scholar 

  87. Schumann G, Bates PD, Horritt MS, Matgen P, Pappenberger F (2009) Progress in integration of remote sensing-derived flood extent and stage data and hydraulic models. Rev Geophys 47:RG4001. doi:10.1029/2008RG000274

    Article  Google Scholar 

  88. Schumann G, Di Baldassarre G, Bates PD (2009) The utility of spaceborne radar to render flood inundation maps based on multialgorithm ensembles. IEEE Trans Geosci Remote Sens 47:2801–2807. doi:10.1109/tgrs.2009.2017937

    Article  Google Scholar 

  89. Schumann GJP, Neal JC, Mason DC, Bates PD (2011) The accuracy of sequential aerial photography and SAR data for observing urban flood dynamics, a case study of the UK summer 2007 floods. Remote Sens Environ 115:2536–2546. doi:10.1016/j.rse.2011.04.039

    Article  Google Scholar 

  90. Shahbazi M, Théau J, Ménard P (2014) Recent applications of unmanned aerial imagery in natural resource management. GISci Remote Sens 51:339–365. doi:10.1080/15481603.2014.926650

    Article  Google Scholar 

  91. Shan J, Toth CK (2008) Topographic laser ranging and scanning: principles and processing. CRC Press, Boca Raton

    Book  Google Scholar 

  92. Silva TF, Costa MF, Melack J, Novo ELM (2008) Remote sensing of aquatic vegetation: theory and applications. Environ Monit Assess 140:131–145. doi:10.1007/s10661-007-9855-3

    Article  PubMed  Google Scholar 

  93. Smeeckaert J, Mallet C, David N, Chehata N, Ferraz A (2013) Large-scale classification of water areas using airborne topographic lidar data. Remote Sens Environ 138:134–148. doi:10.1016/j.rse.2013.07.004

    Article  Google Scholar 

  94. Smith LC (1997) Satellite remote sensing of river inundation area, stage, and discharge: a review. Hydrol Process 11:1427–1439

    Article  Google Scholar 

  95. Thomas RF, Kingsford RT, Lu Y, Hunter SJ (2011) Landsat mapping of annual inundation (1979–2006) of the Macquarie Marshes in semi-arid Australia. Int J Remote Sens 32:4545–4569. doi:10.1080/01431161.2010.489064

    Article  Google Scholar 

  96. Tuxen K, Kelly M (2008) Multi-scale functional mapping of tidal marsh vegetation using object-based image analysis. In: Blaschke T, Lang S, Hay G (eds) Object-based image analysis. Lecture Notes in geoinformation and cartography. Springer, Berlin, Heidelberg, pp 415–442. doi:10.1007/978-3-540-77058-9_23

  97. Verstraeten G, Poesen J (1999) The nature of small-scale flooding, muddy floods and retention pond sedimentation in central Belgium. Geomorphology 29:275–292. doi:10.1016/S0169-555x(99)00020-3

    Article  Google Scholar 

  98. Wagner W (2010) Radiometric calibration of small-footprint full-waveform airborne laser scanner measurements: Basic physical concepts. ISPRS J Photogramm 65:505–513. doi:10.1016/j.isprsjprs.2010.06.007

    Article  Google Scholar 

  99. Wang Y (2002) Mapping extent of floods: what we have learned and how we can do better. Nat Hazards Rev 3:68–73. doi:10.1061/(asce)1527-6988(2002)3:2(68)

    Article  Google Scholar 

  100. Wang Y, Hess LL, Filoso S, Melack JM (1995) Understanding the radar backscattering from flooded and nonflooded Amazonian forests: results from canopy backscatter modeling. Remote Sens Environ 54:324–332. doi:10.1016/0034-4257(95)00140-9

    Article  Google Scholar 

  101. Xu HQ (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27:3025–3033

    Article  Google Scholar 

  102. Zwenzner H, Voigt S (2009) Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data. Hydrol Earth Syst Sci 13:567–576. doi:10.5194/hess-13-567-2009

    Article  Google Scholar 

Download references

Acknowledgements

The funding of this work for Radosław Malinowski, Geoff Groom and Goswin Heckrath, by a research grant from the Danish AgriFish Agency is gratefully acknowledged (Grant Number: 923063). Wolfgang Schwanghart acknowledges the support by the Potsdam Research Cluster for Georisk Analysis, Environmental Change and Sustainability (PROGRESS) for his contribution to this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Radek Malinowski.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Endorsed by Goswin Heckrath.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Malinowski, R., Groom, G.B., Heckrath, G. et al. Do Remote Sensing Mapping Practices Adequately Address Localized Flooding? A Critical Overview. Springer Science Reviews 5, 1–17 (2017). https://doi.org/10.1007/s40362-017-0043-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40362-017-0043-8

Keywords

  • Flood
  • Inundation
  • Landsat
  • LiDAR
  • Remote sensing
  • SAR