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UID:pretalx-foss4g-it-2023-WKL8U7@talks.staging.osgeo.org
DTSTART;TZID=GMT:20230614T153000
DTEND;TZID=GMT:20230614T154500
DESCRIPTION:Giacomo Caporusso(1)\, Alberto Refice(1)\, Domenico Capolongo(2
 )\, Rosa Colacicco(2)\, Raffaele Nutricato(3)\, Davide Oscar Nitti(3)\, Fr
 ancesco P. Lovergine(1)\, Fabio Bovenga(1)\, Annarita D’Addabbo(1)\n1 IR
 EA-CNR – Bari\, Italy\n2 Earth and Geoenvironmental Sciences Dept.\, Uni
 versity of Bari\, Italy\n3 GAP srl\, Bari\, Italy\n\nAs part of the analys
 is of flood events\, ongoing studies aim to identify methods of using opti
 cal and SAR data in order to be able to map in an ever more precise way th
 e flooded areas that are defined following a flood. At the same time\, ins
 titutions responsible for territorial security have concrete needs of both
  monitoring tools capable of describing the susceptibility to flooding and
  of forecast tools for events with a fixed return time\, consistent with t
 he hazard and risk approaches defined\, for example\, at European or Natio
 nal regulatory level.\nAs far as flood hazards are concerned\, hydraulic m
 odeling is currently the most widely used reference for responding to fore
 casting needs\, while the concrete value of remote sensing support emerges
  in the monitoring context\, given the possibility of examining historical
  series of images referring to any portion of the territory.\nA statistica
 l approach to the analysis of historical series of satellite images can ta
 ke into consideration the study of the probability connected to the presen
 ce/absence of water in the area\, through the analysis of specific indices
  derived from multi- and hyperspectral optical images (NDVI\, NDWI\, LSWI)
  and/or intensity\, coherence and radar indices derived from SAR images. I
 n particular\, for the study of time series of the variables considered\, 
 algorithmic approaches of a probabilistic nature are suitable\, such as th
 e Bayesian model and the Theory of Extreme Values.\nThe objective of this 
 work is the assessment of a methodology to return the historical series of
  the probability of flooding\, as well as the corresponding maps\, relatin
 g to a test area. \nIn this context we present some results related to the
  study of an agricultural area near the city of Vercelli (Northern Italy)\
 , characterized by the presence of widespread rice fields and affected by 
 a major flood of the Sesia river in October 2020.\nSentinel-1 SAR images w
 ere considered\, from which the intensity and interferometric coherence va
 riables can be deduced. The hydrogeomorphological support consist of slope
 \, Height Above the Nearest Drainage (HAND)\, and Land Cover maps. Through
  the Copernicus Emergency Management\, the flood maps relating to the 2020
  event were acquired\, to validate the results.\nRegarding the methodology
 \, the probabilistic modeling of the InSAR intensity and coherence time st
 acks is cast in a Bayesian framework. It is assumed that floods are tempor
 ally impulsive events lasting a single\, or a few consecutive acquisitions
 . The Bayesian framework also allows to consider ancillary information suc
 h as the above-mentioned hydrogeomorphology and satellite acquisition geom
 etry\, which allow to characterize the a priori probabilities in a more re
 alistic way\, especially for areas with low probability of flooding. Accor
 ding to this approach it is possible to express the posterior probability 
 p(F|v) for the presence of flood waters (F) given the variable v (intensit
 y or coherence) at a certain pixel and at a certain time t as a function o
 f the a priori and conditioned probabilities\, through the Bayes equation:
 \np(F|v) = p(v|F)p(F) / (p(v|F)p(F) + p(v|NF)p(NF))\,\nwith p(F) and p(NF)
  = 1 − p(F) indicating respectively the a priori probability of flood or
  no flood\, while p(v|F) and p(v|NF) are the likelihoods of v\, given the 
 two events.\nThe flood likelihood can be estimated on permanent water bodi
 es\, while\, to estimate the likelihood of areas potentially affected by f
 lood events\, the residuals of the historical series are considered with r
 espect to a regular temporal modeling of the variable v.\nGaussian process
 es (GP) are used to fit the time series of the variable v. GPs are valid a
 lternatives to parametric models\, in which data trends are modeled by "le
 arning" their stochastic behavior by optimizing some "hyperparameters" of 
 a given autocorrelation function (kernel). The residuals with respect to t
 his model can be used to derive conditional probabilities and then plugged
  into the Bayes equation.\nThe availability of the flood maps will allow t
 o tackle the forecasting aspect in the next future\, taking the time serie
 s of satellite images as a reference.
DTSTAMP:20260516T141046Z
LOCATION:Sala Biblioteca @ PoliBa
SUMMARY:Probabilistic approach to the mapping of flooded areas through the 
 analysis of historical time series of SAR intensity and coherence. - Giaco
 mo Caporusso\, Davide Oscar Nitti\, Fabio Bovenga\, Raffaele Nutricato\, A
 lberto Refice\, Domenico Capolongo\, Rosa Colacicco\, Francesco P. Lovergi
 ne\, Annarita D’Addabbo(
URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/WKL8U7/
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