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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B2-2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1041-2020
© Author(s) 2020. 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-XLIII-B2-2020-1041-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
12 Aug 2020
| 12 Aug 2020
GLACIER VOLUME CHANGE MONITORING FROM UAV OBSERVATIONS: ISSUES AND POTENTIALS OF STATE-OF-THE-ART TECHNIQUES
M. Di Rita
Leica Geosystems AG, Heerbrugg, Switzerland
D. Fugazza
Department of Environmental Science and Policy, Università degli Studi di Milano, Milan, Italy
V. Belloni
Geodesy and Geomatics Division, DICEA, “La Sapienza” University of Rome, Rome, Italy
G. Diolaiuti
Department of Environmental Science and Policy, Università degli Studi di Milano, Milan, Italy
M. Scaioni
Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Italy
M. Crespi
Geodesy and Geomatics Division, DICEA, “La Sapienza” University of Rome, Rome, Italy
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Earth Syst. Sci. Data, 12, 1805–1821, https://doi.org/10.5194/essd-12-1805-2020, https://doi.org/10.5194/essd-12-1805-2020, 2020
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We have used Sentinel-2 satellite data from 2015 and 2016 to create a new glacier inventory for the European Alps. Outlines from earlier national inventories were used to guide manual corrections (e.g. ice in shadow or under debris cover) of the automatically mapped clean ice. We mapped 4395 glaciers, covering 1806 km2, an area loss of about 14 % (or −1.2 % per year) compared to the last inventory of 2003. We conclude that glacier shrinkage in the Alps has continued unabated since the mid-1980s.
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
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V. Yordanov, D. Fugazza, R. S. Azzoni, M. Cernuschi, M. Scaioni, and G. A. Diolaiuti
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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
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 289–294, https://doi.org/10.5194/isprs-archives-XLII-3-289-2018, https://doi.org/10.5194/isprs-archives-XLII-3-289-2018, 2018
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 527–532, https://doi.org/10.5194/isprs-archives-XLII-3-527-2018, https://doi.org/10.5194/isprs-archives-XLII-3-527-2018, 2018
R. Ravanelli, A. Nascetti, R. V. Cirigliano, C. Di Rico, P. Monti, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1467–1472, https://doi.org/10.5194/isprs-archives-XLII-3-1467-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1467-2018, 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
Antonella Senese, Maurizio Maugeri, Eraldo Meraldi, Gian Pietro Verza, Roberto Sergio Azzoni, Chiara Compostella, and Guglielmina Diolaiuti
The Cryosphere, 12, 1293–1306, https://doi.org/10.5194/tc-12-1293-2018, https://doi.org/10.5194/tc-12-1293-2018, 2018
Short summary
Short summary
We present and compare 11 years of snow data measured by an automatic weather station and corroborated by data from field campaigns on the Forni Glacier in Italy. The methodology we present is interesting for remote locations such as glaciers or high alpine regions, as it makes it possible to estimate the total snow water equivalent (SWE) using a relatively inexpensive, low-power, low-maintenance, and reliable instrument such as the sonic ranger.
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
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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.
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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
Monica Campanelli, Alessandra Mascitelli, Paolo Sanò, Henri Diémoz, Victor Estellés, Stefano Federico, Anna Maria Iannarelli, Francesca Fratarcangeli, Augusto Mazzoni, Eugenio Realini, Mattia Crespi, Olivier Bock, Jose A. Martínez-Lozano, and Stefano Dietrich
Atmos. Meas. Tech., 11, 81–94, https://doi.org/10.5194/amt-11-81-2018, https://doi.org/10.5194/amt-11-81-2018, 2018
Short summary
Short summary
The estimation of precipitable water vapour (W) content is of great interest in both meteorological and climatological studies. Sun photometers allowed the development of W automatic estimations with high temporal resolution. A new methodology, based on the hypothesis that the calibration parameters characterizing the atmospheric transmittance are dependent on vertical profiles of temperature, air pressure and moisture typical of each measurement site, has been presented providing good results.
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
R. Ravanelli, L. Lastilla, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W8, 209–212, https://doi.org/10.5194/isprs-archives-XLII-2-W8-209-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W8-209-2017, 2017
F. Capocchiano, R. Ravanelli, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W8, 75–81, https://doi.org/10.5194/isprs-archives-XLII-2-W8-75-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W8-75-2017, 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
R. Ravanelli, A. Nascetti, M. Di Rita, V. Belloni, D. Mattei, N. Nisticó, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W2, 139–145, https://doi.org/10.5194/isprs-archives-XLII-4-W2-139-2017, https://doi.org/10.5194/isprs-archives-XLII-4-W2-139-2017, 2017
A. Nascetti, M. Di Rita, R. Ravanelli, M. Amicuzi, S. Esposito, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 627–633, https://doi.org/10.5194/isprs-archives-XLII-1-W1-627-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-627-2017, 2017
M. Di Rita, A. Nascetti, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 635–638, https://doi.org/10.5194/isprs-archives-XLII-1-W1-635-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-635-2017, 2017
R. Ravanelli, A. Nascetti, M. Di Rita, L. Nigro, D. Montanari, F. Spagnoli, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W1, 589–592, https://doi.org/10.5194/isprs-archives-XLII-5-W1-589-2017, https://doi.org/10.5194/isprs-archives-XLII-5-W1-589-2017, 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
A. Nascetti, F. Nocchi, A. Camplani, C. Di Rico, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 783–788, https://doi.org/10.5194/isprs-archives-XLI-B7-783-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-783-2016, 2016
F. Fratarcangeli, A. Nascetti, P. Capaldo, A. Mazzoni, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 815–820, https://doi.org/10.5194/isprs-archives-XLI-B7-815-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-815-2016, 2016
M. Di Rita, A. Nascetti, F. Fratarcangeli, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 821–825, https://doi.org/10.5194/isprs-archives-XLI-B7-821-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-821-2016, 2016
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
Roberto Sergio Azzoni, Antonella Senese, Andrea Zerboni, Maurizio Maugeri, Claudio Smiraglia, and Guglielmina Adele Diolaiuti
The Cryosphere, 10, 665–679, https://doi.org/10.5194/tc-10-665-2016, https://doi.org/10.5194/tc-10-665-2016, 2016
Short summary
Short summary
In spite of quite abundant literature focusing on fine debris deposition over snow of glacier accumulation areas, less attention has been paid to the ice of the glacier melting surface. Accordingly, we developed a method for estimating ice albedo from fine debris cover quantified by a semi-automatic method. Our procedure was tested on the surface of the Forni Glacier (Italian Alps), acquiring parallel data sets of in situ measurements of ice albedo and high-resolution images.
C. L. Fyffe, B. W. Brock, M. P. Kirkbride, D. W. F. Mair, N. S. Arnold, C. Smiraglia, G. Diolaiuti, and F. Diotri
The Cryosphere Discuss., https://doi.org/10.5194/tcd-9-5373-2015, https://doi.org/10.5194/tcd-9-5373-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
Dye-tracing of a debris-covered glacier revealed that its hydrological system was not similar to that of a debris-free glacier. Beneath the thick debris covering the lower glacier the drainage system was mainly inefficient, probably due lower sub-debris melt rates causing a lack of the large inputs required to open efficient channels. However, efficient channels opened by the large melt inputs from the debris-free areas did route water from the moulins above the thick debris.
A. Senese, M. Maugeri, E. Vuillermoz, C. Smiraglia, and G. Diolaiuti
The Cryosphere, 8, 1921–1933, https://doi.org/10.5194/tc-8-1921-2014, https://doi.org/10.5194/tc-8-1921-2014, 2014
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