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Data Fusion Through Bayesian Methods for Flood Monitoring from Remotely Sensed Data

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Flood Monitoring through Remote Sensing

Part of the book series: Springer Remote Sensing/Photogrammetry ((SPRINGERREMO))

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Abstract

Producing high-precision flood maps requires integrating and correctly classifying information coming from heterogeneous sources. Methods to perform such integration have to rely on different knowledge bases. A useful tool to perform this task consists in the use of Bayesian methods to assign probabilities to areas being subject to flood phenomena, fusing a priori information and modeling with data coming from radar or optical imagery. In this chapter we review the use of Bayesian networks, an elegant framework to cast probabilistic descriptions of complex systems, applied to flood monitoring from multi-sensor, multi-temporal remotely sensed and ancillary data.

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Notes

  1. 1.

    The contributing area, or drainage area, of a given river point is the sum of the hydrologically connected area upslope that point. A contributing area can be generally calculated for every landscape point by iteratively summing unitary “parcels” or pixels upslope a given point (see, e.g., [2]).

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Acknowledgements

COSMO-SkyMed images are courtesy of Italian Space Agency. Ing. L. Candela, of the Italian Space Agency (ASI), is kindly acknowledged for support in data acquisition. InSAR processing was performed by Dr. D. O. Nitti of GAP s.r.l.

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Authors and Affiliations

  1. ISSIA-CNR, Bari, Italy

    Annarita D’Addabbo, Alberto Refice & Guido Pasquariello

  2. Department of Earth and Environmental Sciences, University of Bari, Bari, Italy

    Domenico Capolongo

  3. Department of European and Mediterranean Cultures: Architecture, Environment, Cultural Heritage (DiCEM), University of Basilicata, Potenza, Italy

    Salvatore Manfreda

Authors
  1. Annarita D’Addabbo
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  2. Alberto Refice
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  3. Domenico Capolongo
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  4. Guido Pasquariello
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  5. Salvatore Manfreda
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Corresponding author

Correspondence to Annarita D’Addabbo .

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Editors and Affiliations

  1. Istituto di Studi sui Sistemi, Intelligenti per l’Automazione (ISSIA), Consiglio Nazionale delle Ricerche (CNR), Bari, Italy

    Alberto Refice

  2. Istituto di Studi sui Sistemi, Intelligenti per l’Automazione (ISSIA), Consiglio Nazionale delle Ricerche (CNR), Bari, Italy

    Annarita D'Addabbo

  3. Department of Earth and Environmental Sciences, University of Bari, Bari, Italy

    Domenico Capolongo

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D’Addabbo, A., Refice, A., Capolongo, D., Pasquariello, G., Manfreda, S. (2018). Data Fusion Through Bayesian Methods for Flood Monitoring from Remotely Sensed Data. In: Refice, A., D'Addabbo, A., Capolongo, D. (eds) Flood Monitoring through Remote Sensing. Springer Remote Sensing/Photogrammetry. Springer, Cham. https://doi.org/10.1007/978-3-319-63959-8_8

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