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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume V-3-2020
https://doi.org/10.5194/isprs-annals-V-3-2020-431-2020
© 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-annals-V-3-2020-431-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
03 Aug 2020
| 03 Aug 2020
MACHINE LEARNING FOR CLASSIFICATION OF AN ERODING SCARP SURFACE USING TERRESTRIAL PHOTOGRAMMETRY WITH NIR AND RGB IMAGERY
H. Bernsteiner
University of Bayreuth, Chair of Geomorphology, 95447 Bayreuth, Germany
N. Brožová
WSL Institute for Snow and Avalanche Research SLF, Flüelastrasse 11, 7260 Davos Dorf, Switzerland
I. Eischeid
Department of Arctic and Marine Biology, UiT The Arctic University of Norway, 9037 Tromsø, Norwegian Polar Institute, Fram Center, Hjalmar Johansens gate 14, 9007 Tromsø, Norway, Aarhus University, Department of Bioscience, Denmark
A. Hamer
University of Manchester, Geography, School of Environment Education & Development, Manchester M13 9PL, UK
S. Haselberger
University of Vienna, Institute of Geography and Regional Research, 1010 Vienna, Austria
Centre de Recherches Pétrographiques et Géochimiques, 15 rue Notre Dame des Pauvres, 54500 Vandoeuvre les Nancy, France
A. Kollert
Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Technikerstr. 21a, 6020 Innsbruck, Austria
T. M. Vandyk
Department of Geography, Royal Holloway University of London, Egham, Surrey, TW200EX, UK
F. Pirotti
CIRGEO Interdepartmental Research Center in Geomatics, TeSAF Department, University of Padova, 35020 Legnaro (PD), Italy
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Strong winds may uproot and break trees and represent a risk for forests. Despite the importance of this natural disturbance and possible intensification in view of climate change, spatial information about wind-related impacts is currently missing on a pan-European scale. We present a new database of wind disturbances in European forests comprised of more than 80 000 records over the period 2000–2018. Our database is a unique spatial source for the study of forest disturbances at large scales.
M. A. Brovelli, P. Boccardo, G. Bordogna, A. Pepe, M. Crespi, M. Munafò, and F. Pirotti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W14, 23–30, https://doi.org/10.5194/isprs-archives-XLII-4-W14-23-2019, https://doi.org/10.5194/isprs-archives-XLII-4-W14-23-2019, 2019
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1105–1111, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1105-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1105-2019, 2019
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1539–1544, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1539-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1539-2019, 2019
F. Pirotti, C. Zanchetta, M. Previtali, and S. Della Torre
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 975–980, https://doi.org/10.5194/isprs-archives-XLII-2-W11-975-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W11-975-2019, 2019
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M. Piragnolo, S. Grigolato, and F. Pirotti
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3-W1, 33–40, https://doi.org/10.5194/isprs-annals-IV-3-W1-33-2019, https://doi.org/10.5194/isprs-annals-IV-3-W1-33-2019, 2019
A. Masiero, F. Fissore, M. Piragnolo, A. Guarnieri, F. Pirotti, and A. Vettore
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 289–293, https://doi.org/10.5194/isprs-archives-XLII-1-289-2018, https://doi.org/10.5194/isprs-archives-XLII-1-289-2018, 2018
F. Fissore and F. Pirotti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 193–200, https://doi.org/10.5194/isprs-archives-XLII-4-193-2018, https://doi.org/10.5194/isprs-archives-XLII-4-193-2018, 2018
M. Piragnolo, G. Lusiani, and F. Pirotti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1381–1387, https://doi.org/10.5194/isprs-archives-XLII-3-1381-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1381-2018, 2018
F. Fissore, F. Pirotti, and A. Vettore
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W8, 99–104, https://doi.org/10.5194/isprs-archives-XLII-2-W8-99-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W8-99-2017, 2017
F. Pirotti, M. Kobal, and J. R. Roussel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 285–290, https://doi.org/10.5194/isprs-archives-XLII-2-W7-285-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-285-2017, 2017
F. Pirotti, F. Sunar, and M. Piragnolo
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 335–340, https://doi.org/10.5194/isprs-archives-XLI-B7-335-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-335-2016, 2016
F. Pirotti, D. Travaglini, F. Giannetti, E. Kutchartt, F. Bottalico, and G. Chirici
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 17–22, https://doi.org/10.5194/isprs-archives-XLI-B7-17-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-17-2016, 2016
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