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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B3-2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1011-2022
© Author(s) 2022. 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-B3-2022-1011-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 May 2022
| 30 May 2022
SEAWEED PRESENCE DETECTION USING MACHINE LEARNING AND REMOTE SENSING
F. Tonion
T.E.R.R.A. S.r.l. Galleria Progresso 5, 30027 San Donà di Piave (VE), Italy
Department of Land, Environment, Agriculture and Forestry (TESAF), University of Padova. Via dell’Università 16, 35020 Legnaro (PD), Italy
F. Pirotti
Department of Land, Environment, Agriculture and Forestry (TESAF), University of Padova. Via dell’Università 16, 35020 Legnaro (PD), 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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