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UID:pretalx-foss4g-it-2023-3JY9P3@talks.osgeo.org
DTSTART;TZID=GMT:20230613T143000
DTEND;TZID=GMT:20230613T144500
DESCRIPTION:Multi-temporal SAR interferometry (MTInSAR)\, by providing both
mean displacement maps and displacement time series over coherent objects
on the Earth’s surface\, allows analysing wide areas\, identifying grou
nd displacements\, and studying the phenomenon evolution on long time scal
es. This technique has also been proven to be very useful for detecting an
d monitoring instabilities affecting both terrain slopes and man-made obje
cts. In this contest\, an automatic and reliable characterization of MTInS
AR displacements trends is of particular relevance as pivotal for the dete
ction of warning signals related to pre-failure of natural and artificial
structures. Warning signals are typically characterised by high rates and
non-linear kinematics. The Sentinel-1 (S1) C-band mission from the Europea
n Space Agency (ESA) as well as the high-resolution X-band COSMO-SkyMed (C
SK) constellations from Italian Space Agency\, both shorten the revisit ti
mes up to a few days\, thus being very promising for detecting non-linear
displacement trends related to warning signals. However\, a detailed analy
sis of MTInSAR displacement products looking for specific trends\, is ofte
n hindered by the large number of coherent targets (up to millions) to be
inspected by expert users to recognize different signal components and als
o possible artifacts\, such as\, for instance\, those related to phase unw
rapping errors. \n\nThis work concerns the development of methods able to
fully exploit the content of MTInSAR products\, by automatically identifyi
ng relevant changes in displacement time series and to classify the target
s on the ground according to their kinematic regime. We introduced a new s
tatistical test based on the Fisher distribution with the aim of evaluatin
g the reliability of a parametric displacement model fit with a determined
statistical confidence. We also proposed a new set of rules based on the
statistical characterization of displacement time series\, which allows di
fferent polynomial approximations for MTInSAR time series to be ranked. Th
e method was applied to model warning signals. Moreover\, in order to meas
ure the degree of regularity of a given time series\, an innovative index
was introduced based on the fuzzy entropy\, which basically evaluates the
gain in information by comparing signal segments of different lengths. Thi
s fuzzy entropy index\, without postulating any a priori model\, allows hi
ghlighting time series which show interesting trends\, including strong no
n linearities\, jumps related to phase unwrapping errors\, and the so-call
ed partially coherent scatterers. These procedures were used for analysing
MTInSAR products derived by processing both S1 and CSK datasets acquired
over Southern Italian Apennine (Basilicata region)\, in an area where seve
ral landslides occurred in the recent past. Both approaches were very effe
ctive in supporting the analysis of ground displacements provided by MTInS
AR\, since they helped focusing on a smaller set of coherent targets ident
ifying areas or structures on the ground which deserved further detailed g
eotechnical investigations. Moreover\, the joint exploitation of MTInSAR d
atasets acquired at different wavelengths\, resolutions\, and revisit time
s provided valuable insights\, with CSK more effective over man-made struc
tures\, and S1 over outcrops.\n\nSpecifically\, the work presents an examp
le of slope pre-failure monitoring on Pomarico landslide\, an example of s
lope post-failure monitoring on Montescaglioso landslide\, and few example
s of structures (such as buildings and roads) affected by instability rela
ted to different causes. Our analysis performed on CSK MTInSAR products o
ver Pomarico was able to capture the building deformations preceding the l
andslide and the collapse. This allows the understanding of the phenomenon
evolution\, highlighting a change in velocities that occurred two years b
efore the collapse. This variation probably influenced the dynamics of the
landslide leading to the collapse of an area considered to be at a medium
-risk level by the regional landslide risk map. Results from the analysis
performed on S1 MTInSAR products were instead useful to identify post-fail
ure signals within the Montescaglioso landslide body. The selected trends
confirm the stability of the landslide area with some local displacements
due to restoration works. In this case\, the value of the MTInSAR displace
ment time series analysis emerges in the assessment phase of post-landslid
e stability\, resulting in a useful support tool in the planning of safety
measures in landslide areas. \n\n**Acknowledgments** - This work was supp
orted in part by the Italian Ministry of Education\, University and Resear
ch\, D.D. 2261 del 6.9.2018\, Programma Operativo Nazionale Ricerca e Inno
vazione (PON R&I) 2014–2020 under Project OT4CLIMA\; and in part by ASI
under the Project “CRIOSAR: Applicazioni SAR multifrequenza alla criosfe
ra”\, grant agreement N. 2021-12-U.0.
DTSTAMP:20240623T213051Z
LOCATION:Sala Videoconferenza @ PoliBa
SUMMARY:Analysis of DInSAR Displacement time series for monitoring slope in
stability - Davide Oscar Nitti\, Fabio Bovenga\, Raffaele Nutricato\, Albe
rto Refice\, Ilenia Argentiero\, Guido Pasquariello\, Giuseppe Spilotro
URL:https://talks.osgeo.org/foss4g-it-2023/talk/3JY9P3/
END:VEVENT
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:20240623T213051Z
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/
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