Geodaysit 2023

Probabilistic approach to the mapping of flooded areas through the analysis of historical time series of SAR intensity and coherence.
06-14, 15:30–15:45 (Europe/London), Sala Biblioteca @ PoliBa

Giacomo Caporusso(1), Alberto Refice(1), Domenico Capolongo(2), Rosa Colacicco(2), Raffaele Nutricato(3), Davide Oscar Nitti(3), Francesco P. Lovergine(1), Fabio Bovenga(1), Annarita D’Addabbo(1)
1 IREA-CNR – Bari, Italy
2 Earth and Geoenvironmental Sciences Dept., University of Bari, Italy
3 GAP srl, Bari, Italy

As part of the analysis of flood events, ongoing studies aim to identify methods of using optical and SAR data in order to be able to map in an ever more precise way the flooded areas that are defined following a flood. At the same time, institutions 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 the hazard and risk approaches defined, for example, at European or National regulatory level.
As far as flood hazards are concerned, hydraulic modeling is currently the most widely used reference for responding to forecasting 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.
A statistical approach to the analysis of historical series of satellite images can take into consideration the study of the probability connected to the presence/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. In particular, for the study of time series of the variables considered, algorithmic approaches of a probabilistic nature are suitable, such as the Bayesian model and the Theory of Extreme Values.
The 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, relating to a test area.
In 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.
Sentinel-1 SAR images were considered, from which the intensity and interferometric coherence variables 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.
Regarding the methodology, the probabilistic modeling of the InSAR intensity and coherence time stacks is cast in a Bayesian framework. It is assumed that floods are temporally impulsive events lasting a single, or a few consecutive acquisitions. The Bayesian framework also allows to consider ancillary information such as the above-mentioned hydrogeomorphology and satellite acquisition geometry, which allow to characterize the a priori probabilities in a more realistic way, especially for areas with low probability of flooding. According 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 (intensity or coherence) at a certain pixel and at a certain time t as a function of the a priori and conditioned probabilities, through the Bayes equation:
p(F|v) = p(v|F)p(F) / (p(v|F)p(F) + p(v|NF)p(NF)),
with 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.
The flood likelihood can be estimated on permanent water bodies, while, to estimate the likelihood of areas potentially affected by flood events, the residuals of the historical series are considered with respect to a regular temporal modeling of the variable v.
Gaussian processes (GP) are used to fit the time series of the variable v. GPs are valid alternatives to parametric models, in which data trends are modeled by "learning" their stochastic behavior by optimizing some "hyperparameters" of a given autocorrelation function (kernel). The residuals with respect to this model can be used to derive conditional probabilities and then plugged into the Bayes equation.
The availability of the flood maps will allow to tackle the forecasting aspect in the next future, taking the time series of satellite images as a reference.

- dal 01/09/2013 ad oggi: Docente di ruolo di scuola superiore Classe A037 - Scienze e tecnologie delle costruzioni, tecnologie e tecniche di rappresentazione grafica – (ruolo ottenuto tramite superamento di concorso pubblico per esami e titoli relativo bandito ai sensi del D.D.G. n. 82 del 24/09/2012) in posizione di congedo per assegno di ricerca presso in CNR IREA di Bari;
- Dicembre 2020: docente di Topografia del corso di preparazione all’esame di abilitazione per la professione di Geometra presso il Collegio dei Geometri e Geometri Laureati della Provincia BT;
- Dicembre 2019: docente di Topografia del corso di preparazione all’esame di abilitazione per la professione di Geometra presso il Collegio dei Geometri e Geometri Laureati della Provincia BT;
- Dicembre 2018: docente di Topografia del corso di preparazione all’esame di abilitazione per la professione di Geometra presso il Collegio dei Geometri e Geometri Laureati della Provincia BT;
- A.A. 2018/2019 Docente a Contratto presso il Politecnico di Bari della disciplina di Trattamento delle Osservazioni Topografiche ICAR/06 6CFU Cds L7;
- 05/05/2013 – 31/08/2013 software tester presso la MERMEC Group di Monopoli.

- Dal 01/11/2019 ad oggi: Dottorando presso il Politecnico di Bari in Rischio e Sviluppo Ambientale, Territoriale ed Edilizio, XXXV Ciclo, matricola 580018, tutor Prof.ssa Eufemia Tarantino. Tema di ricerca: “Il SAR come strumento di analisi dei disastri dell’Ambiente e del Territorio”. Conclusione del corso prevista per il 31/01/2023.
- ABILITAZIONE NELLA CLASSE DI CONCORSO DI A072 PER SCUOLA SUPERIORE - Topografia Generale, Costruzioni Rurali e Disegno (confluita nella classe A-37 – Scienze e Tecnologie delle Costruzioni, Tecnologie e Tecniche di Rappresentazione Grafica) conseguita in seguito alla partecipazione al TFA indetto con del D.M. prot.n. 487 del 20 giugno 2014. nella provincia/regione Bari, presso il Politecnico di Bari in data 13/07/2015, con punti 99/100.
- ABILITAZIONE ALL’ESERCIZIO DELLA PROFESSIONE DI INGEGNERE CIVILE presso il Politecnico di Bari in data 12/04/2011 con la votazione di 221/240.
- DIPLOMA DI LAUREA SPECIALISTICA IN INGEGNERIA CIVILE conseguito presso il Politecnico di Bari in data 22/04/2010 con punti 110/110 e lode.
- DIPLOMA DI LAUREA TRIENNALE IN INGEGNERIA CIVILE conseguito presso il Politecnico di Bari in data 07/11/2006 con punti 110/110 e lode.
- DIPLOMA DI SCUOLA SUPERIORE DI GEOMETRA conseguito l’11/07/2001 presso l’ITG Nervi di Barletta e di aver riportato la seguente votazione totale 100/100.

certificazione di lingua inglese livello B2 conseguita il 20/11/2014 presso il Trinity College London.
- ICDL Essentials IT 2461137 31/08/2020
- ICDL Certificato ECDL Base IT 2461137 31/08/2020
- ICDL Certificato ECDL Standard IT 2461137 31/08/2020
- ICDL Certificato ECDL Full Standard IT 2461137 31/08/2020
- ICDL Certificato IT Security IT 2461137 31/08/2020
1. Caporusso, G., Gallo, C., & Tarantino, E. (2022). Change Detection Analysis Using Sentinel-1 Satellite Data with SNAP and GEE Regarding Oil Spill in Venezuela. In International Conference on Computational Science and Its Applications (pp. 387-404). Springer, Cham.
2. 2Caporusso, G., Dell’Olio, M., & Tarantino, E. (2022). Use of the Sentinel-1 Satellite Data in the SNAP Platform and the WebGNOME Simulation Model for Change Detection Analyses on the Persian Gulf Oil Spill. In International Conference on Computational Science and Its Applications (pp. 369-386). Springer, Cham.
3. Capolupo, A., Monterisi, C., Sonnessa, A., Caporusso, G., & Tarantino, E. (2021, September). Modeling land cover impact on albedo changes in Google Earth Engine environment. In International Conference on Computational Science and Its Applications (pp. 89-101). Springer, Cham.
4. Saponaro, M., Capolupo, A., Caporusso, G., & Tarantino, E. (2021). Influence of Co-Alignment Procedures on the Co-Registration Accuracy of Multi-Epoch SFM Points Clouds. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 231-238.
5. Rząsa, K., Caporusso, G., Ogryzek, M. P., & Tarantino, E. (2021). Spatial planning systems in Poland and Italy-comparative analysis on the example of Olsztyn and Bari. Acta Scientiarum Polonorum Administratio Locorum, 20(2).
6. Capolupo, A., Monterisi, C., Caporusso, G., & Tarantino, E. (2020, July). Extracting Land Cover Data Using GEE: A Review of the Classification Indices. In International Conference on Computational Science and Its Applications (pp. 782-796). Springer, Cham.
7. Saponaro, M., Capolupo, A., Caporusso,G., Reina, A., Fratino, U., ., & Tarantino, E, (2020) Exploring UAV and cloud platform potentialities for detecting geomorphological changes in coastal environment. In XV Protection and Restoration of the Environment Conference
8. Saponaro, M., Capolupo, A., Caporusso, G., Borgogno Mondino, E., & Tarantino, E. (2020). Predicting the Accuracy of Photogrammetric 3d Reconstruction from Camera Calibration Parameters Through a Multivariate Statistical Approach. In XXIV ISPRS Congress (Vol. 43, pp. 479-486). ISPRS.
9. Giacomo, C., Ettore, L., Rino, L., Rosa, L., Rocchina, G., Girolamo, D. M., & Patrizia, S. (2020, September). The hyperspectral prisma mission in operations. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 3282-3285). IEEE.

He is senior researcher at National Research Council (CNR) of Italy at IREA institute. His main research interests concern advanced processing techniques for SAR imaging and SAR interferometry, and the application of multi-temporal / multi-frequencies analysis to ground monitoring, change detection, and ground parameter estimation. From 2003 to 2015 he has been lecturer on SAR / InSAR principles and techniques at the Master on "Spaceborne Remote Sensing" of Bari University. Since 2020 he is adjunct professor on “Satellite Systems for Remote Sensing and Geolocation” at Polytechnic University of Bari. He has co-authored more than 150 among journal papers, book chapters and conference communications, and serves as paper reviewer for several international peer-reviewed journals. He is vice chair of the IEEE Geoscience and Remote Sensing South Italy Chapter.

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