Skip to main content

Advertisement

SpringerLink
Log in

SAR image analysis techniques for flood area mapping - literature survey

  • Review Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Flood area mapping is an integral part of disaster management operation which gets value when the details about inundated region has been made available in real time mode as well as when the much required temporal information is shared to the disaster mitigation authorities at right time. The challenges of such real time flood area mapping operations can be met by spaceborn Synthetic Aperture Radar (SAR) technology which is capable of capturing the critical information of large and hard-to-reach territories during all weather and all time situations. Mapping the flood related information of SAR images require much attention as the pixels associated with the inundated regions exhibit similar reflectance with major part of the pixels associated with high altitude region, shadow, runway and broad road networks. Such challenges have been addressed by worldwide researchers with the help of image processing functions. Many such SAR image based flood area mapping models take the advantages of various image classification approaches as well as in integrating multiple image processing functions mainly to differentiate the inundated pixels from other pixels which exhibits similar reflectance by which the mapping accuracy is enhanced. This paper is dedicated, in understanding and documenting various such significant SAR image based flood area mapping models by highlighting its strengths. Significant SAR image bases flood area mapping models from 1990’s to 2015 has been discussed. The respective references can be used by young researchers who are interested and willing to work in SAR image based flood area mapping techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Bazi Y, Bruzzone L, Melgani F (2005) An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Trans Geosci Remote Sens 43(4):874–887

    Article  Google Scholar 

  • Benz U, Hofmann P, Wilhauck G, Lingenfelder I, Heynen M (2004) Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogramm Remote Sens 58(3–4):239–258

    Article  Google Scholar 

  • Bhatt CM (2014) Near real time flood mapping and monitoring, ISPRS-2014, PreSymposium Tutorials on Flood Monitoring and Modeling, Retrieved from www.nrsc.gov.in/isprs/pdf/3.ISPRS_FloodMappingMonitoring.pdf

  • Bhatt CM, Srinivasa Rao G, Begum A, Manjusree P, Sharma SVSP, Prasanna L, Bhanumurthy V (2013) Satellite images for extraction of flood disaster footprints and assessing the disaster impact: Brahmaputra floods of June–July 2012, Assam, India. Curr Sci 104(12):1692–1700

    Google Scholar 

  • Borghys D, Yvinec Y, Perneel C, Pizurica A, Philips W (2006) Supervised feature-based classification of multi-channel SAR images. Pattern Recogn Lett 27(4):252–258

    Article  Google Scholar 

  • Bovolo F, Bruzzone L (2007) A split-based approach to unsupervised change detection in large-size multitemporal images: application to tsunami-damage assessment. IEEE Trans Geosci Remote Sens 45(6):1658–1670. doi:10.1109/TRGS.2007.895835

    Article  Google Scholar 

  • Buck CH, Monni S (2000) Application of SAR interferometry to flood damage assessment. Proceedings of the Committee on Earth Observation Satellites SAR Workshop, Toulouse, France, SP-450. Retrieved from http://adsabs.harvard.edu/full/2000ESASP.450..473B

  • De Roo A, Van der Knijff J, Horritt MS, Schmuck G, De Jong S (1999) Assessing flood damages of the 1997 Oder flood and the 1995 Meuse flood. Proceedings of the 2nd International Symposium on Operationalisation of Remote Sensing, Enschede, The Netherlands. 3459-3465

  • Dellepiane S, Monni S, Bo G, Buck C (2000) SAR images and interferometric coherence for flood monitoring. Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, USA, 2608–2610

  • Townsend and Foster (2002) Assessing flooding and vegetation structure in forested wetlands using Radarsat SAR imagery, IEEE International Geoscience and Remote Sensing Symposium, IGARSS-2002, 2, 171–1173, Toronto, Canada, June 24-28, 2002

  • Giordano F, Goccia M, Dellepiane S (2005) Segmentation of coherence maps for flood damage assessment. IEEE International Conference on Image Processing, ICIP-2005, Sept. 11-14, 2005. Genova, Italy, IEEE 2, 233–236. doi: 10.1109/ICIP.2005.1530034

  • Gstaiger V, Huth J, Gebhardt S, Wehrmann T, Kuenzer C (2012) Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data. Int J Remote Sens 33(22):7291–7304

    Article  Google Scholar 

  • Guo H (2000) Spaceborne and airborne SAR for target detection and flood monitoring. Photogramm Eng Remote Sens 66(5):611–617

    Google Scholar 

  • Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, New Jersey

    Google Scholar 

  • Henderson FM (1987) Consistency of open surface detection with L-band SEASAT SAR imagery and confusion with other hydrologic features. Proceedings of the 13th Annual Conference of Remote Sensing Society, University of Nottingham, UK, 69–78

  • 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. Proceedings of International Conference on Recent Advances in Space Technologies, RAST -2003, Istanbul, Turkey, 0-22 Nov. 2003, IEEE 311–316. doi: 10.1109/RAST.2003.1303926

  • Herrera-Cruz V, Koudogbo F (2009) TerraSAR-X Rapid mapping for flood events. Proceedings of the International Society for Photogrammetry and Remote Sensing, Earth Imaging for Geospatial Information, Hannover, Germany, 170–175

  • Hess LL, Melack JM (1994) Mapping wetland hydrology and vegetation with synthetic aperture radar. Int J Ecol Environ Sci 20:197–205

    Google Scholar 

  • Hess LL, Melack JM (2003) Remote sensing of vegetation and flooding on Magela Creek floodplain (Northern Territory, Australia) with the SIR-C synthetic aperture radar. – Hydrobiologia, 500, 65–82

  • Hess LL, Melack JM, Simonett DS (1990) Radar detection of flooding beneath the forest canopy: a review. Int J Remote Sens 11(7):1313–1325

    Article  Google Scholar 

  • Hess LL, Melack JM, Filoso S, Wang Y (1995) Delineation of inundated areas and vegetation along the Amazon floodplain with SIR-C synthetic aperture radar. IEEE Trans Geosci Remote Sens 33(4):896–904

    Article  Google Scholar 

  • Hess LL, Melack JM, Novo EMLM, Barbosa CCF, Gastil M (2003) Dual season mapping of wetland inundation and vegetation for the central Amazon basin. Remote Sens Environ 87:404–428

    Article  Google Scholar 

  • Hirose K, Maruyama, Y, Quy, D-V, Tsukada M, Shiokawa Y (2001) Visualization of flood monitoring in the lower reaches of the Mekong River. Proceeding of 22nd Asian Conference on Remote Sensing, 1, 314-319, Nov. 5-9 2001, Singapore

  • Horritt S (1999) A statistical active contour model for SAR image segmentation. Image Vis Comput 17(3–4):213–224. doi:10.1016/S0262-8856(98)00101-2

    Article  Google Scholar 

  • Horritt MS (2000) Calibration of a two-dimensional finite element flood flow model using satellite radar imagery. Water Resour Res 36 (11): 3279–3291. Retrieved from http://onlinelibrary.wiley.com/doi/10.1029/2000WR900206/pdf

  • Horritt MS (2006) A methodology for the validation of uncertain flood inundation models. J Hydrol 326(1–4):153–165

    Article  Google Scholar 

  • 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(13):2489–2507. doi:10.1080/01431160116902

    Article  Google Scholar 

  • Horritt MS, Mason DC, Cobby DM, Davenport IJ, Bates P (2003) Waterline mapping in flooded vegetation from airborne SAR imagery. Remote Sens Environ 85:271–281

    Article  Google Scholar 

  • Ivins J, Porill J (1995) Active region models for segmenting textures and colours. Image Vis Comput 13(5):431–438

    Article  Google Scholar 

  • Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331

    Article  Google Scholar 

  • Killler J, Illingworth J (1986) J minimum error thresholding. Pattern Recogn 19(1):41–47

    Article  Google Scholar 

  • Kohonen T (1995) Self-organizing maps, 3rd edn. Springer, Heidelberg

    Book  Google Scholar 

  • Kussul N, Shelestov A, Skakun S, Kravchenko O (2008a) Data assimilation technique for flood monitoring and prediction. Int J Inf Theor Appl 15(1):76–82

    Google Scholar 

  • Kussul N, Shelestov A, Skakun S (2008b) Grid system for flood extent extraction from satellite images. Earth Sci Inf 1(3–4):105–117. doi:10.1007/s12145-008-0014-3

    Article  Google Scholar 

  • Kussul N, Shelestov A, Skakun S, Li G (2008b) Inter grid test bed for flood monitoring. Geophys Res Abstr, 10. doi: EGU2008-A-00385

  • Kussul N, Shelestov A, Skakun S (2008c) Intelligent Computations for flood monitoring. International Journal of Advanced Research in Artificial Intelligence, Book 2 Advanced Research in Artificial Intelligence, Retrieved from http://hdl.handle.net/10525/1029

  • Kussul N, Shelestov A, Skakun S (2009) Grid and sensor web technologies for environmental monitoring. Earth Sci Inf 2:37–51

    Article  Google Scholar 

  • Li J, Chen W, Touzi R (2007) Optimum RADARSAT-1 configurations for wetlands discrimination: a case study of the Mer Bleue peat bog. Can J Remote Sens 33(Suppl 1):S46–S55. doi:10.5589/m07-046

    Article  Google Scholar 

  • Lohmann G (1991) An evidential reasoning approach to the classification of satellite images, Part of lecture notes in computer science, Symbolic and Quantitative Approached to uncertainty. In: Kruse R, Siegel P (ed) Proceedings of European conference ECSQAU, Marseille, France, October 1991, 227 – 231

  • Malnes E, Guneriussen T, Høgda KA (2002) Mapping of flood-area by RADARSAT in Vannsjø, Norway. Proceedings of the 29th International Symposium on Remote Sensing of the Environment, Buenos Aires, Argentina. Retrieved with permission from www.researchgate.net

  • Manavalan, Rao YS (2014) DEM and SAR image based flood feature extraction techniques to map the deep and shallow flood inundated regions of known as well as remote disaster regions. Geocarto Int 29(7):745–757

    Article  Google Scholar 

  • Marinelli L, Michel R, Beaudoin A, Astier, J (1997) Flood mapping using ERS tandem coherence image: a case study in south France. – Proceedings of the 3rd ERS Symposium, Florence, Italy, 531–536. Retrieved with permission from www.researchgate.net

  • Martinis S (2010) Automatic near real-time flood detection in high resolution X-band synthetic aperture radar satellite data using context-based classification on irregular graphs, Dissertation of der Fakultät für Geowissenschaften, der Ludwig-Maximilians-Universität München, Germany

  • Martinis S, Rieke C (2015) Backscatter analysis using multi-temporal and multi-frequency SAR data in the context of flood mapping at river Saale. Ger Open Access J Remote Sens 7:7732–7752. doi:10.3390/rs70607732

  • Martinis S, Twele A, Voigt S (2009) Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. J Nat Hazards Earth Syst Sci 19:303–314

    Article  Google Scholar 

  • Martinis S, Twele A, Voigt S (2011) Unsupervised extraction of flood-induced backscatter changes in SAR data using markov image modeling on irregular graphs. IEEE Trans Geosci Remote Sens 49:251–263

    Article  Google Scholar 

  • Martinis S, Kersten J, Twele A (2015) A fully automated TerraSAR-X based flood service. ISPRS J Photogramm Remote Sens 104:203–212

    Article  Google Scholar 

  • Mason DC, Davenport IJ (1996) Accurate and efficient determination of the shoreline in ERS-1 images. IEEE Trans Geosci Remote Sens 34(5):1243–1253

    Article  Google Scholar 

  • 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(12):3932–3943

    Article  Google Scholar 

  • Mason DC, Speck R, Devereux B, Schumann GJP, Nea JC, Bates PD (2010) Flood detection in urban areas using TerraSAR-X. IEEE Trans Geosci Remote Sens 48(2):882–894

    Article  Google Scholar 

  • Mason DC, Schumann GJP, Neal JC, Garcia-Pintado J, Bates PD (2012) Automatic near real-time selection of flood water levels from high resolution Synthetic Aperture Radar images for assimilation into hydraulic models: a case study. Remote Sens Environ 124:705–716

    Article  Google Scholar 

  • Matgen P, Schumann G, Henry JB, Hoffmann L, Pfister L (2007) Integration of SAR derived river inundation areas, high precision topographic data and a river flow model toward near real-time flood management. Int J Appl Earth Obs Geoinf 9(3):247–263

    Article  Google Scholar 

  • McMillan A, Morley JG, Adams BJ, Chesworth S (2006) Identifying optimal SAR imagery specifications for urban flood monitoring: a hurricane Katrina case study. Proceedings of 4th International Workshop on Remote Sensing for Disaster Response, Cambridge University, England, Sept. 25-26, 2006. Retrieved with permission from www.researchgate.net

  • Nico G, Pappalepore M, Pasquariello G, Refice S, Samarelli S (2000) Comparison of SAR amplitude vs. coherence flood detection methods – a GIS application. Int J Remote Sens 21(8):1619–1631

    Article  Google Scholar 

  • Oberstadler R, Hoè Nsch H, Huth D (1997) Assessment of the mapping capabilities of ERS-1 SAR data for flood mapping: a case study in Germany. Hydrol Process 11:1415–1425

    Article  Google Scholar 

  • Parmuchi MG, Karszenbaum H, Kandus K (2002) Mapping wetlands using multi-temporal RADARSAT-1 data and a decision-based Classifier. Can J Remote Sens 28(2):175–186

    Article  Google Scholar 

  • Peng W, Pan C, Prinet V (2004) Unsuperived change detection for flood analysis in SAR images. Proceedings of the 6th Asian Conference on Computer Vision, Jeju Island, Korea, 652–657

  • Pierdicca L, Pulvirenti N, Chini M, Guerriero L, Candela L (2013) Observing floods from space: experience gained from COSMO-SkyMed observations. Acta Astronaut 84:122–133

    Article  Google Scholar 

  • Puech C, Hostache R, Raclot, Matgen P (2007) Estimation of flood water levels by merging DTM and satellite imagery using hydraulics laws through AI to enhance the estimates. Hydrospace, Geneva, Nov. 12-14, 2007. Retrieved from http://earth.esa.int/hydrospace07/participants/77039/pres_77039.pdf

  • Pulvirenti L, Pierdicca N, Chini M (2010) Analysis of Cosmo-SkyMed observations of the 2008 flood in Myanmar. Ital J Remote Sens 42(1):79–90

    Article  Google Scholar 

  • Pulvirenti L, Chini M, Pierdicca L, Guerriero L, Ferrazzoli P (2011a) Flood monitoring using multi-temporal COSMO-SkyMed data: Image segmentation and signature interpretation. Remote Sens Environ 115(4):990–1002

    Article  Google Scholar 

  • Pulvirenti L, Pierdicca L, Chini M, Guerriero L (2011b) 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 

  • Pulvirenti L, Pierdicca N, Guerriero L (2013) Monitoring flood evolution in vegetated areas using COSMO-SkyMed data: the Tuscany 2009 case study. IEEE J Sel Top Appl Earth Obs Remote Sens 6(4):1939–1404

    Article  Google Scholar 

  • Rémi A, Hervé Y (2007) Large scale change detection techniques dedicated to flood monitoring using ENVISAT Wide Swath mode data. Proceedings of the ENVISAT Symposium 2007, Montreux, Switzerland, SP-636. Retrieved with permission from www.researchgate.net

  • Rennó CD, Nobre AD, Cuartas LA, Soares JAV, Hodnett MG, Tomasella J, Waterloo MJ (2008) HAND, a new terrain descriptor using SRTMDEM: mapping terra-firme rainforest environments in Amazonia. Remote Sens Environ 112(9):3469–3481

    Article  Google Scholar 

  • Sanyal J, Lu XX (2003) Application of remote sensing in flood management with special reference to Monsoon Asia: a Review. Nat Hazards 33:283–301

    Article  Google Scholar 

  • Schlaffer S, Matgen P, Hollaus M, Wagner W (2015) Flood detection from multi-temporal SAR data using harmonic analysis and change detection. Int J Appl Earth Obs Geoinf 38:15–24

    Article  Google Scholar 

  • Song Y-S, Sohn H-G, Park C-H (2007) Efficient water area classification using RADARSAT-1 SAR Imagery in a high relief mountainous environment. Photogramm Eng Remote Sens 73(3):285–296

    Article  Google Scholar 

  • Tan Q, Bi S, Hu J, Liu Z (2004) Measuring lake water level using multi-source remote sensing images combined with hydrological statistical data. IEEE Geoscience and Remote Sensing Symposium, IGARSS-2004, 7, 4885–4888. Anchorage, AK, Sept. 20-24, 2004. doi: 10.1109/IGARSS.2004.1370258

  • Townsend PA (2001) Mapping seasonal flooding in forested wetlands using multi-temporal Radarsat SAR. Photogramm Eng Remote Sens 67(7):857–864

    Google Scholar 

  • Townsend PA (2002) Estimating forest structure in wetlands using multitemporal SAR. Remote Sens Environ 79(2-3):288–304

    Article  Google Scholar 

  • Townsend PA, Walsh SJ (1998) Modelling flood plain inundation using integrated GIS with radar and optical remote sensing. Geomorphology 21(3–4):295–312

    Article  Google Scholar 

  • Toyra J, Pietroniro A, Martz LW, Prowse TD (2002) A multi-sensor approach to wetland flood monitoring. Hydrol Process 16(8):1569–1581. doi:10.1002/hyp.1021

    Article  Google Scholar 

  • Tso B, Olsen C (2005) Combining spectral and spatial information into hidden Markov models for unsupervised image classification. Int J Remote Sens 26(10):2113–2133

    Article  Google Scholar 

  • Voormansik K, Praks J, Antropov O, Jagomagi J, Zalite K (2014) Flood mapping with TerraSAR-X in forested regions in Estonia. IEEE J Sel Top Appl Earth Obs Remote Sens 7(2):562–577

    Article  Google Scholar 

  • Wang Y (2004) Seasonal change in the extent of inundation on floodplains detected by JERS-1 Synthetic Aperture Radar data. Int J Remote Sens 25(13):2497–2508

    Article  Google Scholar 

  • Wang Y, Imhoff ML (1993) Simulated and observed L-band HH radar backscatter from tropical mangrove forests. Int J Remote Sens 14(15):2819–2828

    Article  Google Scholar 

  • Wang Y, Colby JD, Mulcahy KA (2002) An efficient method for mapping flood extent in a coastal flood plain using Landsat TM and DEM data. Int J Remote Sens 23(18):3681–3696

    Article  Google Scholar 

  • Wegmüller U, Werner CL, Nüesch D, Borgeaud M (1995) Forest mapping using ERS repeat-pass SAR interferometry. Earth Obs Q 49:4–7

    Google Scholar 

  • Williams DJ, Shah M (1992) A fast algorithm for active contours and curvature estimation. CVGIP: Image Understing 55(1):14–26

  • Wood (2001) The use of Earth observing satellites for Hazard support: assessments & scenarios - final report, National Oceanic & Atmospheric Administration, USA. Retrieved from http://ceos.esa.int/plenary16/papers/plenary16_doc14_dmsg_final/final_report/DMSG_final.html

  • Yamada Y (2001) Detection of Flood-inundated area and relation between the area and Micro-Geomorphology using SAR and GIS. IEEE Geoscience and Remote Sensing Symposium, IGARSS 2001, 7, 3282–3284, Sydney, NSW, July 9-13, 200. doi: 10.1109/IGARSS.2001.978329

  • Zhou C, Luo J, Yang C, Li B, Wang S (2000) Flood monitoring using multi-temporal AVHRR and RADARSAT imagery. Photogramm Eng Remote Sens 66(5):633–638

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Development of Advanced Computing, Bangalore, India

    R. Manavalan

Authors
  1. R. Manavalan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Manavalan.

Additional information

Communicated by: H. A. Babaie

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manavalan, R. SAR image analysis techniques for flood area mapping - literature survey. Earth Sci Inform 10, 1–14 (2017). https://doi.org/10.1007/s12145-016-0274-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-016-0274-2

Keywords

Search

Navigation