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
11 citations as recorded by crossref.
- 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
- 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
- Mapping Forest Tree Species Using Sentinel-2 Time Series by Taking into Account Tree Age B. Yang et al. 10.3390/f15030474
- 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
- Spatial Pattern of Forest Age in China Estimated by the Fusion of Multiscale Information Y. Xu et al. 10.3390/f15081290
- Carbon sequestration potential of tree planting in China L. Yao et al. 10.1038/s41467-024-52785-6
- 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
- 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
- A 2020 forest age map for China with 30 m resolution K. Cheng et al. 10.5194/essd-16-803-2024
10 citations as recorded by crossref.
- 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
- 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
- Mapping Forest Tree Species Using Sentinel-2 Time Series by Taking into Account Tree Age B. Yang et al. 10.3390/f15030474
- 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
- Spatial Pattern of Forest Age in China Estimated by the Fusion of Multiscale Information Y. Xu et al. 10.3390/f15081290
- Carbon sequestration potential of tree planting in China L. Yao et al. 10.1038/s41467-024-52785-6
- 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
- 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
1 citations as recorded by crossref.
Latest update: 23 Nov 2024
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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