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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-3-2020-565-2020
© 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-3-2020-565-2020
© 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
A MACHINE LEARNING APPROACH TO MULTISPECTRAL SATELLITE DERIVED BATHYMETRY
F. Tonion
T.E.R.R.A. S.r.l., Galleria Progresso 5, San Donà di Piave (VE), 30027, Italy
TESAF Department, University of Padua, Viale dell’Università, Legnaro (PD), 35020, Italy
F. Pirotti
TESAF Department, University of Padua, Viale dell’Università, Legnaro (PD), 35020, Italy
CIRGEO, University of Padua, Viale dell’Università, Legnaro (PD), 35020, Italy
G. Faina
COREMA S.r.l., San Giovanni in Persiceto (BO), 40017, Italy
D. Paltrinieri
COREMA S.r.l., San Giovanni in Persiceto (BO), 40017, Italy
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Short summary
Short summary
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.
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