Biogeosciences
Biogeosciences
BG
Articles | Volume 22, issue 21
Articles | Volume 22, issue 21
https://doi.org/10.5194/bg-22-6393-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/bg-22-6393-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 21
Research article
 | 
06 Nov 2025
Research article |  | 06 Nov 2025

Very-high resolution aerial imagery and deep learning uncover the fine-scale patterns of elevational treelines

Erik Carrieri, Donato Morresi, Fabio Meloni, Nicolò Anselmetto, Emanuele Lingua, Raffaella Marzano, Carlo Urbinati, Alessandro Vitali, and Matteo Garbarino

Viewed

Total article views: 4,587 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,983 1,452 152 4,587 144 597
  • HTML: 2,983
  • PDF: 1,452
  • XML: 152
  • Total: 4,587
  • BibTeX: 144
  • EndNote: 597
Views and downloads (calculated since 06 Dec 2024)
Cumulative views and downloads (calculated since 06 Dec 2024)

Viewed (geographical distribution)

Total article views: 4,587 (including HTML, PDF, and XML) Thereof 4,566 with geography defined and 21 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 08 Jun 2026
Download
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 treelines at fine scales across diverse environments. Our results reveal accurate detection and delineation of trees maps over 90 ha of treeline ecotones. This efficient, adaptable approach enables enhanced ecological analyses of treeline processes, aiding global efforts to assess treeline dynamics and their responses to global change.
Read more
Share
Special issue
Treeline ecotones under global change: linking spatial patterns...
Altmetrics
Final-revised paper
Preprint