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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-3
https://doi.org/10.5194/isprs-archives-XLII-3-527-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLII-3-527-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 Apr 2018
| 30 Apr 2018
ON THE INFLUENCE OF GLOBAL WARMING ON ATLANTIC HURRICANE FREQUENCY
S. R. Hosseini
Politecnico di Milano, Department of Civil and Environmental Engineering (DICA) piazza L. da Vinci 32, 20133 Milano, Italy
M. Scaioni
Politecnico di Milano, Department of Architecture, Built Environment and Construction Engineering (DABC) via Ponzio 31, 20133 Milano, Italy
M. Marani
Università degli studi di Padova, Department of Civil, Environmental and Architectural Engineering via Loredan, 20, 35131 Padova, Italy
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Rasoul Eskandari and Marco Scaioni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3-2024, 131–138, https://doi.org/10.5194/isprs-annals-X-3-2024-131-2024, https://doi.org/10.5194/isprs-annals-X-3-2024-131-2024, 2024
Jianzhuo Xu, Manuel Garramone, and Marco Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W11-2024, 161–166, https://doi.org/10.5194/isprs-archives-XLVIII-4-W11-2024-161-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-W11-2024-161-2024, 2024
Alice Puppin, Davide Tognin, Massimiliano Ghinassi, Erica Franceschinis, Nicola Realdon, Marco Marani, and Andrea D'Alpaos
Biogeosciences, 21, 2937–2954, https://doi.org/10.5194/bg-21-2937-2024, https://doi.org/10.5194/bg-21-2937-2024, 2024
Short summary
Short summary
This study aims at inspecting organic matter dynamics affecting the survival and carbon sink potential of salt marshes, which are valuable yet endangered wetland environments. Measuring the organic matter content in marsh soils and its relationship with environmental variables, we observed that the organic matter accumulation varies at different scales, and it is driven by the interplay between sediment supply and vegetation, which are affected, in turn, by marine and fluvial influences.
Nicola Genzano, Davide Fugazza, Rasoul Eskandari, and Marco Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-2024, 99–106, https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-99-2024, https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-99-2024, 2024
Y. Cao and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 147–153, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-147-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-147-2023, 2023
R. Eskandari and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1235–1241, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1235-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1235-2023, 2023
Guillaume Goodwin, Marco Marani, Sonia Silvestri, Luca Carniello, and Andrea D'Alpaos
Biogeosciences, 20, 4551–4576, https://doi.org/10.5194/bg-20-4551-2023, https://doi.org/10.5194/bg-20-4551-2023, 2023
Short summary
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Seagrass meadows are an emblematic coastal habitat. Their sensitivity to environmental change means that it is essential to monitor their evolution closely. However, high costs make this endeavor a technical challenge. Here, we used machine learning to map seagrass meadows in 148 satellite images in the Venice Lagoon, Italy. We found that adding information such as depth of the seabed and known seagrass location improved our capacity to map temporal change in seagrass habitat.
Y. Cao and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W3-2023, 31–37, https://doi.org/10.5194/isprs-archives-XLVIII-1-W3-2023-31-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W3-2023-31-2023, 2023
J. Xu, M. Garramone, Y. Wang, and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 1645–1652, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1645-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1645-2023, 2023
M. Garramone, D. Jovanovic, D. Oreni, L. Barazzetti, M. Previtali, F. Roncoroni, A. Mandelli, and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-2-2023, 653–660, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-653-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-653-2023, 2023
R. Eskandari and M. Scaioni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-M-1-2023, 79–86, https://doi.org/10.5194/isprs-annals-X-M-1-2023-79-2023, https://doi.org/10.5194/isprs-annals-X-M-1-2023-79-2023, 2023
M. Garramone and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-5-W2-2023, 37–42, https://doi.org/10.5194/isprs-archives-XLVIII-5-W2-2023-37-2023, https://doi.org/10.5194/isprs-archives-XLVIII-5-W2-2023-37-2023, 2023
J. Xu and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-5-W2-2023, 117–124, https://doi.org/10.5194/isprs-archives-XLVIII-5-W2-2023-117-2023, https://doi.org/10.5194/isprs-archives-XLVIII-5-W2-2023-117-2023, 2023
T. Blount, S. Silvestri, M. Marani, and A. D’Alpaos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 57–62, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-57-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-57-2023, 2023
M. Previtali, L. Barazzetti, F. Roncoroni, Y. Cao, and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 411–416, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-411-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-411-2023, 2023
M. Scaioni, A. Malekian, and D. Fugazza
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 293–300, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-293-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-293-2023, 2023
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
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Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
A. Malekian, D. Fugazza, and M. Scaioni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W1-2022, 459–466, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-459-2023, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-459-2023, 2023
R. Eskandari and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W2-2022, 31–38, https://doi.org/10.5194/isprs-archives-XLVIII-4-W2-2022-31-2023, https://doi.org/10.5194/isprs-archives-XLVIII-4-W2-2022-31-2023, 2023
M. Garramone and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 39–45, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-39-2022, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-39-2022, 2022
Y. Cao and M. Scaioni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 219–226, https://doi.org/10.5194/isprs-annals-V-2-2022-219-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-219-2022, 2022
Maria Francesca Caruso and Marco Marani
Nat. Hazards Earth Syst. Sci., 22, 1109–1128, https://doi.org/10.5194/nhess-22-1109-2022, https://doi.org/10.5194/nhess-22-1109-2022, 2022
Short summary
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We comparatively evaluate the predictive performance of traditional and new approaches to estimate the probability distributions of extreme coastal water levels. The metastatistical approach maximizes the use of observational information and provides reliable estimates of high quantiles with respect to traditional methods. Leveraging the increased estimation accuracy afforded by this approach, we investigate future changes in the frequency of extreme total water levels.
M. Garramone, E. Tonelli, and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-5-W1-2022, 95–102, https://doi.org/10.5194/isprs-archives-XLVI-5-W1-2022-95-2022, https://doi.org/10.5194/isprs-archives-XLVI-5-W1-2022-95-2022, 2022
Piera Belotti, Fabio Conzi, Chiara Dell’Orto, Maurizio Federici, Luigi Fregonese, Gabriele Garnero, Emilio Guastamacchia, Franco Guzzetti, Livio Pinto, and Marco Scaioni
Proc. Int. Cartogr. Assoc., 4, 12, https://doi.org/10.5194/ica-proc-4-12-2021, https://doi.org/10.5194/ica-proc-4-12-2021, 2021
Y. Cao and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 449–456, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-449-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-449-2021, 2021
M. Previtali, M. Garramone, and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 229–235, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-229-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-229-2021, 2021
M. Scaioni, L. Longoni, L. Zanzi, V. Ivanov, and M. Papini
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-3-W1-2020, 131–138, https://doi.org/10.5194/isprs-archives-XLIV-3-W1-2020-131-2020, https://doi.org/10.5194/isprs-archives-XLIV-3-W1-2020-131-2020, 2020
M. Garramone, N. Moretti, M. Scaioni, C. Ellul, F. Re Cecconi, and M. C. Dejaco
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-4-W1-2020, 77–84, https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-77-2020, https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-77-2020, 2020
M. Rutzinger, K. Anders, M. Bremer, B. Höfle, R. Lindenbergh, S. Oude Elberink, F. Pirotti, M. Scaioni, and T. Zieher
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 243–250, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, 2020
M. Previtali, L. Barazzetti, and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 533–539, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-533-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-533-2020, 2020
Y. Cao, M. Previtali, and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 651–657, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-651-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-651-2020, 2020
D. Backes, M. Smigaj, M. Schimka, V. Zahs, A. Grznárová, and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1017–1024, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1017-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1017-2020, 2020
M. Di Rita, D. Fugazza, V. Belloni, G. Diolaiuti, M. Scaioni, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1041–1048, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1041-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1041-2020, 2020
Pietro Teatini, Cristina Da Lio, Luigi Tosi, Alessandro Bergamasco, Stefano Pasqual, Paolo Simonini, Veronica Girardi, Paolo Zorzan, Claudia Zoccarato, Massimiliano Ferronato, Marcella Roner, Marco Marani, Andrea D'Alpaos, Simonetta Cola, and Giuseppe Zambon
Proc. IAHS, 382, 345–351, https://doi.org/10.5194/piahs-382-345-2020, https://doi.org/10.5194/piahs-382-345-2020, 2020
Short summary
Short summary
An in-situ loading test was carried out in the Lazzaretto Nuovo salt-marsh in the Venice Lagoon, Italy. The test was aimed at characterizing the geotechnical properties of soils forming the marsh sedimentary body deposits. In fact porosity and compressibility are of paramount importance to quantify consolidation versus accretion and relative sea level rise. The fate of coastal marshlands in the next future will strongly depend of these processes.
J. Wang, H. B. Zheng, and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 235–243, https://doi.org/10.5194/isprs-archives-XLII-3-W10-235-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W10-235-2020, 2020
A. Mostafavi, M. Scaioni, and V. Yordanov
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W18, 765–772, https://doi.org/10.5194/isprs-archives-XLII-4-W18-765-2019, https://doi.org/10.5194/isprs-archives-XLII-4-W18-765-2019, 2019
V. Yordanov, D. Fugazza, R. S. Azzoni, M. Cernuschi, M. Scaioni, and G. A. Diolaiuti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1803–1810, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1803-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1803-2019, 2019
V. Yordanov, A. Mostafavi, and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 1165–1172, https://doi.org/10.5194/isprs-archives-XLII-2-W11-1165-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W11-1165-2019, 2019
R. Brumana, V. Pracchi, F. Rinaudo, A. Grimoldi, M. Scaioni, L. Cantini, and M. Previtali
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 1–2, https://doi.org/10.5194/isprs-archives-XLII-2-W11-1-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W11-1-2019, 2019
L. Díaz-Vilariño, E. Frías, M. Previtali, M. Scaioni, and J. Balado
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 489–494, https://doi.org/10.5194/isprs-archives-XLII-2-W11-489-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W11-489-2019, 2019
D. Wujanz, L. Barazzetti, M. Previtali, and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W9, 779–786, https://doi.org/10.5194/isprs-archives-XLII-2-W9-779-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W9-779-2019, 2019
M. Previtali, L. Díaz-Vilariño, and M. Scaioni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 507–514, https://doi.org/10.5194/isprs-archives-XLII-4-507-2018, https://doi.org/10.5194/isprs-archives-XLII-4-507-2018, 2018
M. Scaioni, J. Crippa, M. Corti, L. Barazzetti, D. Fugazza, R. Azzoni, M. Cernuschi, and G. A. Diolaiuti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1029–1036, https://doi.org/10.5194/isprs-archives-XLII-2-1029-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1029-2018, 2018
M. Scaioni, B. Höfle, A. P. Baungarten Kersting, L. Barazzetti, M. Previtali, and D. Wujanz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1503–1510, https://doi.org/10.5194/isprs-archives-XLII-3-1503-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1503-2018, 2018
Davide Fugazza, Marco Scaioni, Manuel Corti, Carlo D'Agata, Roberto Sergio Azzoni, Massimo Cernuschi, Claudio Smiraglia, and Guglielmina Adele Diolaiuti
Nat. Hazards Earth Syst. Sci., 18, 1055–1071, https://doi.org/10.5194/nhess-18-1055-2018, https://doi.org/10.5194/nhess-18-1055-2018, 2018
Short summary
Short summary
This paper describes the surveys we performed in 2014 and 2016 by means of UAVs and terrestrial photogrammetry to monitor the Forni Glacier, one of the largest glaciers in the Italian Alps. We investigated the hazards related to the glacier collapse, which have been increasing recently due to the high ice melting rate. Our approach is feasible and low cost and we will repeatedly monitor the glacier to provide rapid hazard detection services to help the tourism sector.
M. Scaioni, L. Barazzetti, M. Corti, J. Crippa, R. S. Azzoni, D. Fugazza, M. Cernuschi, and G. A. Diolaiuti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W4, 445–452, https://doi.org/10.5194/isprs-archives-XLII-3-W4-445-2018, https://doi.org/10.5194/isprs-archives-XLII-3-W4-445-2018, 2018
M. Scaioni, J. Crippa, V. Yordanov, L. Longoni, V. I. Ivanov, and M. Papini
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W4, 453–460, https://doi.org/10.5194/isprs-archives-XLII-3-W4-453-2018, https://doi.org/10.5194/isprs-archives-XLII-3-W4-453-2018, 2018
Xing Chen, Mukesh Kumar, Stefano Basso, and Marco Marani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-65, https://doi.org/10.5194/hess-2018-65, 2018
Preprint withdrawn
M. Cogliati, E. Tonelli, D. Battaglia, and M. Scaioni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5-W1, 9–16, https://doi.org/10.5194/isprs-annals-IV-5-W1-9-2017, https://doi.org/10.5194/isprs-annals-IV-5-W1-9-2017, 2017
G. López-Pazos, J. Balado, L. Díaz-Vilariño, P. Arias, and M. Scaioni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5-W1, 35–41, https://doi.org/10.5194/isprs-annals-IV-5-W1-35-2017, https://doi.org/10.5194/isprs-annals-IV-5-W1-35-2017, 2017
M. Scaioni, J. Crippa, L. Longoni, M. Papini, and L. Zanzi
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5-W1, 63–70, https://doi.org/10.5194/isprs-annals-IV-5-W1-63-2017, https://doi.org/10.5194/isprs-annals-IV-5-W1-63-2017, 2017
M. Scaioni, M. Corti, G. Diolaiuti, D. Fugazza, and M. Cernuschi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1547–1554, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1547-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1547-2017, 2017
M. Scaioni, E. Rosina, A. L’Erario, and L. Dìaz-Vilariño
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W1, 153–160, https://doi.org/10.5194/isprs-archives-XLII-5-W1-153-2017, https://doi.org/10.5194/isprs-archives-XLII-5-W1-153-2017, 2017
V. Yordanov, M. Scaioni, M. T. Brunetti, M. T. Melis, A. Zinzi, and P. Giommi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B6, 17–24, https://doi.org/10.5194/isprs-archives-XLI-B6-17-2016, https://doi.org/10.5194/isprs-archives-XLI-B6-17-2016, 2016
M. Scaioni, P. Giommi, M. T. Brunetti, C. Carli, P. Cerroni, G. Cremonese, G. Forlani, P. Gamba, M. Lavagna, M. T. Melis, M. Massironi, G. Ori, F. Salese, A. Zinzi, G. Xie, Z. Kang, R. Shi, Y. Sun, and Y. Wu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B6, 71–78, https://doi.org/10.5194/isprs-archives-XLI-B6-71-2016, https://doi.org/10.5194/isprs-archives-XLI-B6-71-2016, 2016
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
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