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.
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.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
04 Jul 2025
Data description paper |
| 04 Jul 2025
| 04 Jul 2025
China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022
Rong Shang
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Academy of Carbon Neutrality, Fujian Normal University, Fuzhou, 350117, China
Xudong Lin
CORRESPONDING AUTHOR
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Jing M. Chen
CORRESPONDING AUTHOR
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Department of Geography and Planning, University of Toronto, Toronto, Ontario, ON M5S 3G3, Canada
Yunjian Liang
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Keyan Fang
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Mingzhu Xu
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Yulin Yan
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Weimin Ju
International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
Guirui Yu
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Nianpeng He
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Li Xu
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Liangyun Liu
International Research Center of Big Data for Sustainable Development Goals, Beijing, 100094, China
Jing Li
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
Wang Li
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
Jun Zhai
Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment of the People's Republic of China, Beijing, 100094, China
Zhongmin Hu
College of Ecology and Environment, Hainan University, Haikou, 570228, China
Data sets
China’s annual forest age dataset at 30-m spatial resolution from 1986 to 2022 Rong Shang et al. https://doi.org/10.6084/m9.figshare.24464170
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.
Forest age is critical for carbon cycle modeling and effective forest management. Existing...
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