Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 17, issue 7
Articles | Volume 17, issue 7
https://doi.org/10.5194/essd-17-3219-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-3219-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 7
Data description paper
 | 
04 Jul 2025
Data description paper |  | 04 Jul 2025

China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022

Rong Shang, Xudong Lin, Jing M. Chen, Yunjian Liang, Keyan Fang, Mingzhu Xu, Yulin Yan, Weimin Ju, Guirui Yu, Nianpeng He, Li Xu, Liangyun Liu, Jing Li, Wang Li, Jun Zhai, and Zhongmin Hu

Viewed

Total article views: 1,339 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,099 187 53 1,339 25 39
  • HTML: 1,099
  • PDF: 187
  • XML: 53
  • Total: 1,339
  • BibTeX: 25
  • EndNote: 39
Views and downloads (calculated since 16 Jan 2025)
Cumulative views and downloads (calculated since 16 Jan 2025)

Viewed (geographical distribution)

Total article views: 1,339 (including HTML, PDF, and XML) Thereof 1,308 with geography defined and 31 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Jul 2025
Download
Short summary
Forest age is critical for carbon cycle modeling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's annual forest age dataset (CAFA), spanning 1986–2022 at a 30 m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger errors for undisturbed forests. CAFA can enhance carbon cycle modeling and forest management in China.
Read more
Share
Altmetrics
Final-revised paper
Preprint