Articles | Volume 22, issue 17
https://doi.org/10.5194/bg-22-4349-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-4349-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Sep 2025
Research article |
| 04 Sep 2025
| 04 Sep 2025
Forestlines in Italian mountains are shifting upward: detection and monitoring using satellite time series
Lorena Baglioni
CORRESPONDING AUTHOR
Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, Ancona, Italy
Donato Morresi
Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden
Matteo Garbarino
Department of Agricultural, Forest and Food Sciences, University of Turin, Turin, Italy
Carlo Urbinati
Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, Ancona, Italy
Emanuele Lingua
Department of Land, Environment, Agriculture and Forestry, University of Padua, Padua, Italy
Raffaella Marzano
Department of Agricultural, Forest and Food Sciences, University of Turin, Turin, Italy
Alessandro Vitali
Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, Ancona, Italy
Related authors
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Erik Carrieri, Donato Morresi, Fabio Meloni, Nicolò Anselmetto, Emanuele Lingua, Raffaella Marzano, Carlo Urbinati, Alessandro Vitali, and Matteo Garbarino
EGUsphere, https://doi.org/10.5194/egusphere-2024-3757, https://doi.org/10.5194/egusphere-2024-3757, 2024
Short summary
Short summary
Alpine treelines reflect the impacts of climate and land use changes on ecosystems. Using low-cost drones and deep learning, we developed a method to map tree spatial patterns at fine scales across diverse environments. Our results reveal accurate detection and delineation of trees and spatial trends like clustering and size-class interactions. This efficient, adaptable approach enhances forest monitoring, aiding global efforts to assess treeline dynamics and their responses to global change.
M. Balestra, S. Chiappini, A. Vitali, E. Tonelli, F. Malandra, A. Galli, C. Urbinati, E. S. Malinverni, and R. Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 833–839, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-833-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-833-2022, 2022
B. Leblon, F. Ogunjobi Oluwamuyiwa, E. Lingua, and A. LaRocque
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1115–1120, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1115-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1115-2022, 2022
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Short summary
We propose a method for the automated detection of the uppermost forest lines with the aim of supporting their monitoring through a replicable mapping that can be adopted in different geographical contexts and at different scales of analysis according to the available datasets. We adopted a trend analysis of Landsat-based wetness and greenness index time series of the last 40 years, detecting an increase in forest cover along the forest line ecotone in both the Italian Alps and the Apennines.
We propose a method for the automated detection of the uppermost forest lines with the aim of...
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