BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//talks.osgeo.org//WKL8U7
BEGIN:VTIMEZONE
TZID:GMT
BEGIN:STANDARD
DTSTART:20001029T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T020000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-foss4g-it-2023-WKL8U7@talks.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:20240623T203446Z
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.osgeo.org/foss4g-it-2023/talk/WKL8U7/
END:VEVENT
END:VCALENDAR