Biogeosciences
Biogeosciences
BG
Articles | Volume 23, issue 2
Articles | Volume 23, issue 2
https://doi.org/10.5194/bg-23-623-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/bg-23-623-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 2
Research article
 | 
22 Jan 2026
Research article |  | 22 Jan 2026

Machine learning-based Alpine treeline ecotone detection on Xue Mountain in Taiwan

Geng-Gui Wang, Min-Chun Liao, Wei Wang, Hui Ping Tsai, and Hsy-Yu Tzeng

Viewed

Total article views: 5,417 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,736 1,295 386 5,417 478 482
  • HTML: 3,736
  • PDF: 1,295
  • XML: 386
  • Total: 5,417
  • BibTeX: 478
  • EndNote: 482
Views and downloads (calculated since 14 Apr 2025)
Cumulative views and downloads (calculated since 14 Apr 2025)

Viewed (geographical distribution)

Total article views: 5,417 (including HTML, PDF, and XML) Thereof 5,247 with geography defined and 170 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 31 Oct 2026
Download
Short summary
 Taiwan is characterized by high mountains density, with over 200 peaks exceeding 3,000 meters in elevation. This study analyzes treeline changes in Xue Mountain using satellite images from 2012 and 2021. By applying machine learning methods, we found forest are growing higher, rising by 32.00 ± 4.00 m, and forest cover expanded by 10.09 hectares. These findings help us understand climate change impacts on mountain ecosystems and support sustainable conservation efforts.
Read more
Share
Special issue
Treeline ecotones under global change: linking spatial patterns...
Altmetrics
Final-revised paper
Preprint