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Erschienen in:
Methods, Techniques and Sensors for Precision Flood Monitoring Through Remote Sensing Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Flood Mapping in Vegetated and Urban Areas and Other Challenges: Models and Methods

verfasst von : Nazzareno Pierdicca, Luca Pulvirenti, Marco Chini

Erschienen in: Flood Monitoring through Remote Sensing

Verlag: Springer International Publishing

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Abstract

Floods are the most frequent weather disasters in the world and the most costly in terms of economic losses. Mapping flood extension is fundamental to ascertain the damage and for relief organization. Spaceborne synthetic aperture radar (SAR) systems represent a powerful tool to monitor floods because of their all-weather capability, the very high spatial resolution of the new generation of instruments, and the short revisit time of the present and future satellite constellations. However, mapping flooded vegetated and urban areas still represents a challenging problem.
Modeling different targets both in the presence and in the absence of flood water is a very complex task. In the first part of the chapter we review these challenging conditions, showing their potential effects on radar data and in particular on COSMO-SkyMed images. In some cases the potential of electromagnetic models to predict the radar response is shown.
A second part of the chapter illustrates a number of strategies one can exploit, with examples showing the achievable performances with respect to a simple mapping of dark areas in the SAR image.

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Vorheriges Kapitel Adaptive SAR Image Processing Techniques to Support Flood Monitoring from Earth Observation Data
Nächstes Kapitel Data Fusion Through Bayesian Methods for Flood Monitoring from Remotely Sensed Data
Literatur
1.
Attema, E.P.W., Ulaby, F.T.: Vegetation modelled as a water cloud. Radio Sci. 13, 357–364 (1978)CrossRef
2.
Barber, D.G., Hocheim, K.P., Dixon, R., Mosscrop, D.R., Mcmullan, M.J.: The role of earth observation technologies in flood mapping: A Manitoba case study. Can. J. Remote. Sens. 22, 137–143 (1996)CrossRef
3.
Bazi, Y., Bruzzone, L., Melgani, F.: 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 (2005)CrossRef
4.
Benediktsson, J.A., Pesaresi, M., Arnason, K.: Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans. Geosci. Remote Sens. 41, 1940–1949 (2003)CrossRef
5.
Bovolo, F., Bruzzone, L.: 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 (2007)CrossRef
6.
Bovolo, F., Bruzzone, L., Marconcini, M.: A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure. IEEE Trans. Geosci. Remote Sens. 46(7), 2070–2082 (2008)CrossRef
7.
Bracaglia, M., Ferrazzoli, P., Guerriero, L.: A fully polarimetric multiple scattering model for crops. Remote Sens. Environ. 54, 170–179 (1995)CrossRef
8.
Chini, M., Bignami, C., Stramondo, S., Pierdicca, N.: Uplift and subsidence due to the December 26th, 2004, Indonesian earthquake and tsunami detected by SAR data. Int. J. Remote Sens. 29(13), 3891–3910 (2008)CrossRef
9.
Chini, M., Pierdicca, N., Emery, W.J.: Exploiting SAR and VHR optical images to quantify damage caused by the 2003 bam earthquake. IEEE Trans. Geosci. Remote Sens. 47(1), 145–152 (2009)CrossRef
10.
Chini, M., Pulvirenti, L., Pierdicca, N.: Analysis and interpretation of the COSMO-SkyMed observations of the 2011 Japan tsunami. IEEE Geosci. Remote Sens. Lett. 9(3), 467–471 (2012)CrossRef
11.
Chini, M., Piscini, A., Cinti, F.R., Amici, S., Nappi, R., De Martini, P.M.: The 2011 Tohoku-Oki (Japan) tsunami inundation and liquefaction investigated by optical, thermal and SAR data. IEEE Geosci. Remote Sens. Lett. 10(2), 347–351 (2013)CrossRef
12.
Chini, M., Albano, M., Saroli, M., Pulvirenti, L., Moro, M., Bignami, C., Falcucci, E., Gori, S., Modoni, G., Pierdicca, N., Stramondo, S.: Coseismic liquefaction phenomenon analysis by COSMO-SkyMed: 2012 Emilia (Italy) earthquake. Int. J. Appl. Earth Obs. Geoinf. 39, 65–78 (2015)CrossRef
13.
Chini, M.: Building damage from multi-resolution, object-based, classification techniques. In: Beer, M., Kougioumtzoglou, I.A., Patelli, E., Au, I.S.-K. (eds.) Encyclopaedia of Earthquake Engineering, pp. 350–360. Springer, Berlin (2015). doi:10.1007/978–3–642-35344-4CrossRef
14.
Chini, M., Papastergios, A., Pulvirenti, L., Pierdicca, N., Matgen, P., Parcharidis, I.: SAR coherence and polarimetric information for improving flood mapping, Proceeding IEEE Igarss 2016, Beijing (China), pp. 7577–7580, (2016)
15.
16.
Cloude, S.R., Pottier, E.: An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans. Geosci. Remote Sens. 35, 68–78 (1997)CrossRef
17.
D’Addabbo, A., Refice, A., Pasquariello, G., Lovergine, F.P., Capolongo, D., Manfreda, S.: A Bayesian network for flood detection combining SAR imagery and ancillary data. IEEE Trans. Geosci. Remote Sens. 54(6.), 7429750), 3612–3625 (2016)CrossRef
18.
Ferrazzoli, P., Guerriero, L.: Radar sensitivity to tree geometry and woody volume: A model analysis. IEEE Trans. Geosci. Remote Sens. 33(2), 360–371 (1995)
19.
Ferro, A., Brunner, D., Bruzzone, L., Lemoine, G.: On the relationship between double bounce and the orientation of buildings in VHR SAR images. IEEE Geosci. Remote Sens. Lett. 8(4), 612–616 (2011)CrossRef
20.
Franceschetti, G., Iodice, A., Riccio, D.: A canonical problem in electromagnetic backscattering from buildings. IEEE Trans. Geosci. Remote Sens. 40(8), 1787–1801 (2002)CrossRef
21.
Freeman, A., Durden, S.L.: A three-component scattering model for polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 36, 963–973 (1998)CrossRef
22.
Giustarini, L., Matgen, P., Hostache, R., Plaza, D., Pauwels, V.R.N., Lannoy, G.J., Keyser, R.D., Pfister, L., Hoffmann, L., Savenije, H.H.G.: Assimilating SAR-derived water level data into a flood model: A case study. Hydrol. Earth Syst. Sci. 15(7), 2349–2365 (2011)CrossRef
23.
Giustarini, L., Hostache, R., Matgen, P., Schumann, G.J.-P., Bates, P.D., Mason, D.C.: A change detection approach to flood mapping in urban areas using TerraSAR-X. IEEE Trans. Geosci. Remote Sens. 51(4), 2417–2430 (2013)CrossRef
24.
Giustarini, L., Vernieuwe, H., Verwaeren, J., Chini, M., Hostache, R., Matgen, P., Verhoest, N.E.C., De Baets, B.: Accounting for image uncertainty in SAR-based flood mapping. Int. J. Appl. Earth Obs. Geoinf. 34, 70–77 (2015)CrossRef
25.
Giustarini, L., Chini, M., Hostache, R., Pappenberger, F., Matgen, P.: Flood hazard mapping combining hydrodynamic modelling and multi annual remote sensing data. Remote Sens. 7(10), 14200–14226 (2015)CrossRef
26.
Giustarini, L., Hostache, R., Kavetski, D., Chini, M., Corato, G., Schlaffer, S., Matgen, P.: Probabilistic flood mapping using synthetic aperture radar data. IEEE Trans. Geosci. Remote Sens. 54, 6958–6969 (2016)CrossRef
27.
Henry, J.-B., Chastanet, P., Fellah, K., Desnos, Y.-L.: Envisat multi-polarized ASAR data for flood mapping. Int. J. Remote Sens. 27(10), 1921–1929 (2006)CrossRef
28.
Hess, L.L., Melack, J.M., Simonett, D.S.: Radar detection of flooding beneath the forest canopy: A review. Int. J. Remote Sens. 11, 1313–1325 (1990)CrossRef
29.
Hess, L.L., Melack, J.M., Novo, E.M.L.M., Barbosa, C.C.F., Gastil, M.: Dual-season mapping of wetland inundation and vegetation for the central Amazon basin. Remote Sens. Environ. 87, 404–428 (2003)CrossRef
30.
Hostache, R., Matgen, P., Schumann, G., Puech, C., Hoffmann, L., Pfister, L.: Water level estimation and reduction of hydraulic model calibration uncertainties using satellite SAR images of floods. IEEE Trans. Geosci. Remote Sens. 47(2), 431–441 (2009)CrossRef
31.
Iervolino, P., Guida, R., Iodice, A., Riccio, D.: Flooding water depth estimation with high-resolution SAR. IEEE. 53, 2295–2307 (2015)
32.
Kim, S.B., Ouellette, J.D., van Zyl, J.J., Johnson, J.T.: Detection of inland open water surfaces using dual polarization L-band radar for the soil moisture active passive mission. IEEE Trans. Geosci. Remote Sens. 54(6), 3388–3399 (2016)CrossRef
33.
Martinis, S., Twele, A., Voigt, S.: Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Nat. Hazards Earth Syst. Sci. 9, 303–314 (2009)CrossRef
34.
Martinis, S., Kersten, J., Twele, A.: A fully automated TerraSAR-X based flood service. ISPRS J. Photogramm. Remote Sens. 104, 203–212 (2015)CrossRef
35.
Mason, D.C., Giustarini, L., Garcia-Pintado, J., Cloke, H.L.: Detection of flooded urban areas in high resolution Synthetic Aperture Radar images using double scattering. Int. J. Appl. Earth Obs. Geoinf. 28, 150–159 (2014)CrossRef
36.
Matgen, P., Hostache, R., Schumann, G., Pfister, L., Hoffmann, L., Savenije, H.H.G.: Towards an automated SAR-based flood monitoring system: Lessons learned from two case studies. Phys. Chem. Earth. 36, 241–252 (2011)CrossRef
37.
Pal, S.K., Rosenfeld, A.: Image enhancement and thresholding by optimization of fuzzy compactness. Pattern Recogn. Lett. 7, 77–86 (1988)CrossRef
38.
Panegrossi, G., Ferretti, R., Pulvirenti, L., Pierdicca, N.: Impact of ASAR soil moisture data on the MM5 precipitation forecast for the Tanaro flood event of April 2009. Nat. Hazard. Earth Syst. Sci. 11, 3135–3149 (2011)CrossRef
39.
Pesaresi, M., Benediktsson, J.A.: A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Trans. Geosci. Remote Sens. 39, 309–320 (2001)CrossRef
40.
Pierdicca, N., Castracane, P., Pulvirenti, L.: Inversion of electromagnetic models for bare soil parameter estimation from multifrequency polarimetric SAR data. Sensors. 8, 8181–8200 (2008)CrossRef
41.
Pierdicca, N., Chini, M., Pulvirenti, L., Macina, F.: Integrating physical and topographic information into a fuzzy scheme to map flooded area by SAR. Sensors. 8, 4151–4164 (2008)CrossRef
42.
Pierdicca, N., Pulvirenti, L.: Comparing scatterometric and radiometric simulations with geophysical model functions to tune a sea wave spectrum model. IEEE Trans. Geosci. Remote Sens. 46, 3756–3767 (2008)CrossRef
43.
Pierdicca, N., Pulvirenti, L., Chini, M., Guerriero, L., Candela, L.: Observing floods from space: Experience gained from COSMO-SkyMed observations. Acta Astron. 84, 122–133 (2013)CrossRef
44.
Pulvirenti, L., Pierdicca, N., Chini, M., Guerriero, L.: An algorithm for operational flood mapping from synthetic aperture radar (SAR) data based on the fuzzy logic. Nat. Hazard. Earth Syst. Sci. 11(2), 529–540 (2011)CrossRef
45.
Pulvirenti, L., Chini, M., Pierdicca, N., Guerriero, L., Ferrazzoli, P.: Flood monitoring using multi-temporal COSMO-SkyMed data: Image segmentation and signature interpretation. Remote Sens. Environ. 115(4), 990–1002 (2011)CrossRef
46.
Pulvirenti, L., Pierdicca, N., Chini, M., Guerriero, L.: Monitoring flood evolution in agricultural areas using COSMO-SkyMed data: The Tuscany 2009 case study. IEEE J. Select. Topics Appl. Earth Obs. Remote Sens. 6(4), 1199–1210 (2013)
47.
Pulvirenti, L., Pierdicca, N., Boni, G., Fiorini, M., Rudari, R.: Flood damage assessment through multitemporal COSMO-SkyMed data and hydrodynamic models: The Albania 2010 case study. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 7(7), 2848–2855 (2014)CrossRef
48.
Pulvirenti, L., Marzano, F.S., Pierdicca, N., Mori, S., Chini, M.: Discrimination of water surfaces, heavy rainfall and wet snow using COSMO-SkyMed observations of severe weather events. IEEE Trans. Geosci. Remote Sens. 52(2), 858–869 (2014)CrossRef
49.
Pulvirenti, L., Chini, M., Pierdicca, N., Boni, G.: Use of SAR data for detecting floodwater in urban and agricultural areas: The role of the interferometric coherence. IEEE Trans. Geosci. Remote Sens. 54(3), 1532–1544 (2016)CrossRef
50.
Quartulli, M., Datcu, M.: Stochastic geometrical modeling for built-up area understanding from a single SAR intensity image with meter resolution. IEEE Trans. Geosci. Remote Sens. 42, 1996–2003 (2004)CrossRef
51.
Schlaffer, S., Matgen, P., Hollaus, M., Wagner, W.: Flood detection from multi-temporal SAR data using harmonic analysis and change detection. Int. J. Appl. Earth Obs. Geoinform. 38, 15–24 (2015)CrossRef
52.
Schlaffer, S., Chini, M., Dettmering, D., Wagner, W.: Mapping Wetlands in Zambia using seasonal backscatter signatures derived from ENVISAT ASAR time series. Remote Sens. 8, 1–24 (2016)CrossRef
53.
Schlaffer, S., Chini, M., Giustarini, L., Matgen, P.: Probabilistic mapping of flood-induced backscatter changes in SAR time series. Int. J. Appl. Earth Obs. Geoinf. 56, 77–87 (2017)CrossRef
54.
Schumann, G.J.-P., Frye, S., Wells, G., Adler, R., Brakenridge, R., Bolten, J., Murray, J., Slayback, D., Policelli, F., Kirschbaum, D., Wu, H., Cappelaere, P., Howard, T., Flamig, Z., Clark, R., Stough, T., Chini, M., Matgen, P., Green, D., Jones, B.: Unlocking the full potential of earth observation during the 2015 Texas flood disaster. Water Resour. Res. 52, 3288–3293 (2016)CrossRef
55.
Shimada, T., Kawamura, H., Shimada, M.: An L-band geophysical model function for SAR wind retrieval using JERS-1 SAR. IEEE Trans. Geosci. Remote Sens. 41, 518–531 (2003)CrossRef
56.
Soille, P.: Morphological Image Analysis – Principles and Applications. Springer, Berlin (2003)
57.
Stramondo, S., Bignami, C., Chini, M., Pierdicca, N., Tertulliani, A.: Satellite radar and optical remote sensing for earthquake damage detection: Results from different case studies. Int. J. Remote Sens. 27(20), 4433–4447 (2006)CrossRef
58.
Thiele, Cadario, E., Schulz, K., Thönnessen, U., Soerge, U.: Building recognition from multi-aspect high-resolution InSAR data in urban areas. IEEE Trans. Geosci. Remote Sens. 45(11), 3583–3593 (2007)CrossRef
59.
Ticconi, F., Pulvirenti, L., Pierdicca, N.: Models for scattering from rough surfaces. In: Zhurbenko, V. (ed.) Electromagnetic Waves, Croatia. In Tech. ISBN: 978–953–307–304–0 (2011)
60.
Ulaby, F.T., Sarabandi, K., McDonald, K., Whitt, M., Dobson, M.C.: Michigan microwave canopy scattering model. Int. Remote Sens. 11(7), 1223–1253 (1990)CrossRef
61.
Wang, Y., Hess, L.L., Filoso, S., Melack, J.M.: Understanding the radar backscattering from flooded and nonflooded Amazonian forests: Results from canopy backscatter modeling. Remote Sens. Environ. 54, 324–332 (1995)CrossRef
62.
Weydahl, D.J.: Flood monitoring in Norway using ERS-1 SAR images. In: Geoscience and Remote Sensing Symposium, 1996. IGARSS ‘96, Vol.1, pp. 151–153. Lincoln (1996)
63.
Wood, M., Hostache, R., Neal, J., Wagener, T., Giustarini, L., Chini, M., Corato, G., Matgen, P., Bates, P.: Calibration of channel depth and friction parameters in the LISFLOOD-FP hydraulic model using medium resolution SAR data. Hydrol. Earth Syst. Sci. 20(12), 4983–4997 (2016)CrossRef
64.
65.
Zebker, H.A., Villasenor, J.: Decorrelation in interferometric radar echoes. IEEE Trans. Geosci. Remote Sens. 30(5), 950–959 (1992)CrossRef
Metadaten
Titel
Flood Mapping in Vegetated and Urban Areas and Other Challenges: Models and Methods
verfasst von
Nazzareno Pierdicca
Luca Pulvirenti
Marco Chini
Copyright-Jahr
2018
Buch
Flood Monitoring through Remote Sensing

Print ISBN: 978-3-319-63958-1

Electronic ISBN: 978-3-319-63959-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-63959-8_7