Articles | Volume 16, issue 2
https://doi.org/10.5194/essd-16-803-2024
© Author(s) 2024. 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-16-803-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
07 Feb 2024
Data description paper |
| 07 Feb 2024
| 07 Feb 2024
A 2020 forest age map for China with 30 m resolution
Kai Cheng
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Yuling Chen
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Tianyu Xiang
College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Haitao Yang
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Weiyan Liu
State Forestry and Grassland Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, China
Yu Ren
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Institute of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Hongcan Guan
School of Tropical Agriculture and Forestry, Hainan University, Haikou 570100, China
Tianyu Hu
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
Qin Ma
School of Geography, Nanjing Normal University, Nanjing 210023, China
Qinghua Guo
CORRESPONDING AUTHOR
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Institute of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
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Cited
28 citations as recorded by crossref.
- Integrating Active and Passive Remote Sensing Data for Forest Age Estimation in Shangri-La City, China F. Cheng et al. 10.3390/f15091622
- A 2020 forest age map for China with 30 m resolution K. Cheng et al. 10.5194/essd-16-803-2024
- Spatiotemporal Dynamics of Forest Carbon Sinks in China’s Qinba Mountains: Insights from Sun-Induced Chlorophyll Fluorescence Remote Sensing Y. Lian et al. 10.3390/rs17081418
- China’s naturally regenerated forests currently have greater aboveground carbon accumulation rates than newly planted forests K. Cheng et al. 10.1038/s43247-025-02323-z
- China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022 R. Shang et al. 10.5194/essd-17-3219-2025
- Attributing long-term forest disturbance events across the northeast forest region of China by analyzing Landsat time-series observations with machine learning (1986–2023) X. Yin et al. 10.1016/j.ecolind.2025.113997
- Mapping Forest Tree Species Using Sentinel-2 Time Series by Taking into Account Tree Age B. Yang et al. 10.3390/f15030474
- Spatial Pattern of Forest Age in China Estimated by the Fusion of Multiscale Information Y. Xu et al. 10.3390/f15081290
- Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detection L. Zhai et al. 10.1016/j.ecolind.2024.113043
- Spatiotemporal Dynamics and Driving Factors of Arbor Forest Carbon Stocks in Yunnan Province, China (2016–2020) J. Wu et al. 10.3390/f16071076
- Assessing the potential of species loss caused by deforestation in a mature subtropical broadleaf forest in central China J. Zhang et al. 10.1016/j.tfp.2024.100673
- Sustainable growth of China’s forest biomass carbon storage since 2002: Facing threats and loss risks Q. Lv et al. 10.1016/j.geosus.2025.100340
- Grain for Green Project dominates greening in afforested areas rather than that in grass revegetation areas of the Loess Plateau, China—using Deep Crossing LSTM Age network X. Wang et al. 10.1088/1748-9326/adec02
- Carbon sequestration potential of tree planting in China L. Yao et al. 10.1038/s41467-024-52785-6
- Spatio-temporal dynamics of future aboveground carbon stocks in natural forests of China Y. Zhang et al. 10.1016/j.fecs.2025.100293
- Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet Z. Chi & K. Xu 10.3390/rs17111926
- Automated machine learning integrating multi-source satellite observations to predict gross and net CO2 fluxes of coastal wetlands in China N. Ngoc Tu et al. 10.1088/1748-9326/ade731
- Forest carbon storage and sink estimates under different management scenarios in China from 2020 to 2100 J. Qin et al. 10.1016/j.scitotenv.2024.172076
- Ecosystem engineering and global changes are increasingly enhancing China’s terrestrial carbon sinks M. Zhang et al. 10.1016/j.resconrec.2025.108514
- An Approach for Mapping Ecotourism Suitability Using Machine Learning: A Case Study of Zhangjiajie, China Q. Huang et al. 10.3390/land13081188
- Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data Y. Chen et al. 10.5194/essd-16-5267-2024
- Maximum carbon uptake potential through progressive management of plantation forests in Guangdong Province, China X. Li et al. 10.1038/s43247-024-01977-5
- Biophysical impact of forest age changes on land surface temperature in China Z. Zhang et al. 10.1016/j.scitotenv.2025.178445
- Improving Total Carbon Storage Estimation Using Multi-Source Remote Sensing H. Zhou et al. 10.3390/f16030453
- The impact of global change on the slow decline in human-wild boar (Sus scrofa) conflicts in central China from 2018 to 2022 T. Jiang et al. 10.1016/j.gecco.2025.e03790
- Remote Sensing Classification and Mapping of Forest Dominant Tree Species in the Three Gorges Reservoir Area of China Based on Sample Migration and Machine Learning W. Zhang et al. 10.3390/rs16142547
- Afforestation as a mitigation strategy: countering climate-induced risk of forest carbon sink in China Y. Cao et al. 10.1186/s13021-025-00308-1
- A 2020 forest age map for China with 30 m resolution K. Cheng et al. 10.5194/essd-16-803-2024
27 citations as recorded by crossref.
- Integrating Active and Passive Remote Sensing Data for Forest Age Estimation in Shangri-La City, China F. Cheng et al. 10.3390/f15091622
- A 2020 forest age map for China with 30 m resolution K. Cheng et al. 10.5194/essd-16-803-2024
- Spatiotemporal Dynamics of Forest Carbon Sinks in China’s Qinba Mountains: Insights from Sun-Induced Chlorophyll Fluorescence Remote Sensing Y. Lian et al. 10.3390/rs17081418
- China’s naturally regenerated forests currently have greater aboveground carbon accumulation rates than newly planted forests K. Cheng et al. 10.1038/s43247-025-02323-z
- China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022 R. Shang et al. 10.5194/essd-17-3219-2025
- Attributing long-term forest disturbance events across the northeast forest region of China by analyzing Landsat time-series observations with machine learning (1986–2023) X. Yin et al. 10.1016/j.ecolind.2025.113997
- Mapping Forest Tree Species Using Sentinel-2 Time Series by Taking into Account Tree Age B. Yang et al. 10.3390/f15030474
- Spatial Pattern of Forest Age in China Estimated by the Fusion of Multiscale Information Y. Xu et al. 10.3390/f15081290
- Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detection L. Zhai et al. 10.1016/j.ecolind.2024.113043
- Spatiotemporal Dynamics and Driving Factors of Arbor Forest Carbon Stocks in Yunnan Province, China (2016–2020) J. Wu et al. 10.3390/f16071076
- Assessing the potential of species loss caused by deforestation in a mature subtropical broadleaf forest in central China J. Zhang et al. 10.1016/j.tfp.2024.100673
- Sustainable growth of China’s forest biomass carbon storage since 2002: Facing threats and loss risks Q. Lv et al. 10.1016/j.geosus.2025.100340
- Grain for Green Project dominates greening in afforested areas rather than that in grass revegetation areas of the Loess Plateau, China—using Deep Crossing LSTM Age network X. Wang et al. 10.1088/1748-9326/adec02
- Carbon sequestration potential of tree planting in China L. Yao et al. 10.1038/s41467-024-52785-6
- Spatio-temporal dynamics of future aboveground carbon stocks in natural forests of China Y. Zhang et al. 10.1016/j.fecs.2025.100293
- Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet Z. Chi & K. Xu 10.3390/rs17111926
- Automated machine learning integrating multi-source satellite observations to predict gross and net CO2 fluxes of coastal wetlands in China N. Ngoc Tu et al. 10.1088/1748-9326/ade731
- Forest carbon storage and sink estimates under different management scenarios in China from 2020 to 2100 J. Qin et al. 10.1016/j.scitotenv.2024.172076
- Ecosystem engineering and global changes are increasingly enhancing China’s terrestrial carbon sinks M. Zhang et al. 10.1016/j.resconrec.2025.108514
- An Approach for Mapping Ecotourism Suitability Using Machine Learning: A Case Study of Zhangjiajie, China Q. Huang et al. 10.3390/land13081188
- Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data Y. Chen et al. 10.5194/essd-16-5267-2024
- Maximum carbon uptake potential through progressive management of plantation forests in Guangdong Province, China X. Li et al. 10.1038/s43247-024-01977-5
- Biophysical impact of forest age changes on land surface temperature in China Z. Zhang et al. 10.1016/j.scitotenv.2025.178445
- Improving Total Carbon Storage Estimation Using Multi-Source Remote Sensing H. Zhou et al. 10.3390/f16030453
- The impact of global change on the slow decline in human-wild boar (Sus scrofa) conflicts in central China from 2018 to 2022 T. Jiang et al. 10.1016/j.gecco.2025.e03790
- Remote Sensing Classification and Mapping of Forest Dominant Tree Species in the Three Gorges Reservoir Area of China Based on Sample Migration and Machine Learning W. Zhang et al. 10.3390/rs16142547
- Afforestation as a mitigation strategy: countering climate-induced risk of forest carbon sink in China Y. Cao et al. 10.1186/s13021-025-00308-1
1 citations as recorded by crossref.
Latest update: 29 Aug 2025
Short summary
To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
To quantify forest carbon stock and its future potential accurately, we generated a 30 m...
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