Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3365-2023
© Author(s) 2023. 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-15-3365-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Thirty-meter map of young forest age in China
Yuelong Xiao
College of Surveying and Geo-Informatics, Tongji University, 1239
Siping Road, Shanghai, 200092, China
Qunming Wang
CORRESPONDING AUTHOR
College of Surveying and Geo-Informatics, Tongji University, 1239
Siping Road, Shanghai, 200092, China
Xiaohua Tong
College of Surveying and Geo-Informatics, Tongji University, 1239
Siping Road, Shanghai, 200092, China
Peter M. Atkinson
Faculty of Science and Technology, Lancaster University, Lancaster LA1 4YR, UK
Geography and Environment, University of Southampton, Highfield,
Southampton SO17 1BJ, UK
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Cited
14 citations as recorded by crossref.
- Spatiotemporal patterns of annual clear-cutting distribution in tropical and subtropical regions of China with time series Landsat and CCDC M. Zhou et al. 10.1080/10095020.2024.2446293
- Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in Beijing X. Zhang et al. 10.1016/j.jag.2024.104268
- Global Natural and Planted Forests Mapping at Fine Spatial Resolution of 30 m Y. Xiao et al. 10.34133/remotesensing.0204
- 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
- A 2020 forest age map for China with 30 m resolution K. Cheng et al. 10.5194/essd-16-803-2024
- Detection of Typical Forest Degradation Patterns: Characteristics and Drivers of Forest Degradation in Northeast China Y. Hai et al. 10.3390/rs16081389
- GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method X. Zhang et al. 10.5194/essd-16-1353-2024
- 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
- Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong Province Y. Li et al. 10.1016/j.ecolind.2024.112788
- 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
- Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet Z. Chi & K. Xu 10.3390/rs17111926
- Forest maturation and its drivers on the Qinghai-Xizang Plateau Y. Wang et al. 10.1016/j.agrformet.2025.110642
- Spatial and temporal dynamics of plant water source distribution in China H. Chen et al. 10.1016/j.rse.2025.114843
14 citations as recorded by crossref.
- Spatiotemporal patterns of annual clear-cutting distribution in tropical and subtropical regions of China with time series Landsat and CCDC M. Zhou et al. 10.1080/10095020.2024.2446293
- Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in Beijing X. Zhang et al. 10.1016/j.jag.2024.104268
- Global Natural and Planted Forests Mapping at Fine Spatial Resolution of 30 m Y. Xiao et al. 10.34133/remotesensing.0204
- 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
- A 2020 forest age map for China with 30 m resolution K. Cheng et al. 10.5194/essd-16-803-2024
- Detection of Typical Forest Degradation Patterns: Characteristics and Drivers of Forest Degradation in Northeast China Y. Hai et al. 10.3390/rs16081389
- GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method X. Zhang et al. 10.5194/essd-16-1353-2024
- 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
- Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong Province Y. Li et al. 10.1016/j.ecolind.2024.112788
- 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
- Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet Z. Chi & K. Xu 10.3390/rs17111926
- Forest maturation and its drivers on the Qinghai-Xizang Plateau Y. Wang et al. 10.1016/j.agrformet.2025.110642
- Spatial and temporal dynamics of plant water source distribution in China H. Chen et al. 10.1016/j.rse.2025.114843
Latest update: 24 Jun 2025
Short summary
Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.
Forest age is closely related to forest production, carbon cycles, and other ecosystem services....
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