Biogeosciences
Biogeosciences
BG
Articles | Volume 22, issue 21
Articles | Volume 22, issue 21
https://doi.org/10.5194/bg-22-6509-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/bg-22-6509-2025
© 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

Identifying alpine treeline species using high-resolution WorldView-3 multispectral imagery and convolutional neural networks

Laurel A. Sindewald, Ryan Lagerquist, Matthew D. Cross, Theodore A. Scambos, Peter J. Anthamatten, and Diana F. Tomback

Viewed

Total article views: 837 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
686 127 24 837 49 17 31
  • HTML: 686
  • PDF: 127
  • XML: 24
  • Total: 837
  • Supplement: 49
  • BibTeX: 17
  • EndNote: 31
Views and downloads (calculated since 28 May 2025)
Cumulative views and downloads (calculated since 28 May 2025)

Viewed (geographical distribution)

Total article views: 837 (including HTML, PDF, and XML) Thereof 837 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Nov 2025
Download
Short summary
We used high-resolution satellite imagery and artificial intelligence models to identify six tree and shrub species commonly found at alpine treeline in the Rocky Mountains with accuracies from 44.1% to 86.2%. We are the first to attempt species identification using satellite imagery in treeline systems, where trees are small and difficult to identify remotely. Our work provides a method to identify species with satellite imagery over a broader geographic range than can be achieved with drones.
Read more
Share
Special issue
Treeline ecotones under global change: linking spatial patterns...
Altmetrics
Final-revised paper
Preprint