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
Related authors
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Yuelong Xiao and Qunming Wang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-489, https://doi.org/10.5194/essd-2025-489, 2025
Preprint under review for ESSD
Short summary
Short summary
A C-PFM method was proposed to produce the 30 m annual maps for planted forests (PF) and natural forests (NF) across China from 1990 and 2020. The resulting dataset can serve as valuable scientific data for policymakers, researchers, and forest managers, guiding appropriate planting, environment enhancement, and carbon sequestration efforts.
Huan Xie, Yifan Wang, Xiongfeng Yan, Ming Yang, Yaqiong Wang, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 1589–1594, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1589-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1589-2025, 2025
Peiqi Ye, Rong Huang, Puzuo Wang, Yusheng Xu, Zhen Ye, Yongjiu Feng, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 1641–1646, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1641-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1641-2025, 2025
Lingxiao Zhang, Rong Huang, Yusheng Xu, Zhen Ye, Changjiang Xiao, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 1699–1705, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1699-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1699-2025, 2025
Yi Zhang, Genyi Wan, Dayong Liu, Tao Tao, Changjiang Xiao, Zhen Ye, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 1727–1732, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1727-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1727-2025, 2025
Yuan Sun, Huan Xie, Xiaohua Tong, Qi Xu, Binbin Li, Changda Liu, Min Ji, and Hao Tang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 1421–1426, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1421-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1421-2025, 2025
Chen Liu, Rong Huang, Huan Xie, Tao Tao, Yongjiu Feng, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 959–966, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-959-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-959-2025, 2025
Binbin Li, Huan Xie, Shijie Liu, Zhen Ye, Zhonghua Hong, Qihao Weng, Yuan Sun, Qi Xu, and Xiaohua Tong
Earth Syst. Sci. Data, 17, 205–220, https://doi.org/10.5194/essd-17-205-2025, https://doi.org/10.5194/essd-17-205-2025, 2025
Short summary
Short summary
We refined the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) with Ice, Cloud, and Land Elevation Satellite 2 data to release a new dataset (IC2-GDEM). It has superior global elevation quality to ASTER GDEM. Its seamless integration with historical ASTER GDEM datasets is essential for longitudinal environmental studies. As a complementary data source to other GDEMs, it enables more reliable and comprehensive scientific discoveries.
Leilei Jiao, Yusheng Xu, Rong Huang, Zhen Ye, Sicong Liu, Shijie Liu, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 629–635, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-629-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-629-2024, 2024
Jiarui Cao, Rong Huang, Zhen Ye, Yusheng Xu, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 51–56, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-51-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-51-2024, 2024
Zhige Wang, Ce Zhang, Kejian Shi, Yulin Shangguan, Bifeng Hu, Xueyao Chen, Danqing Wei, Songchao Chen, Peter M. Atkinson, and Qiang Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-315, https://doi.org/10.5194/essd-2024-315, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
The irreversible trend in global warming underscores the necessity for accurate monitoring of atmospheric carbon dynamics on a global scale. This study generated a global dataset of column-averaged dry-air mole fraction of CO2 (XCO2) at 0.05° resolution with full coverage using carbon satellite data and a deep learning model. The dataset accurately depicts global and regional XCO2 patterns, advancing the monitoring of carbon emissions and understanding of global carbon dynamics.
S. Xu, R. Huang, Y. Xu, Z. Ye, H. Xie, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 771–776, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-771-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-771-2023, 2023
Haoxuan Yang, Qunming Wang, Wei Zhao, and Peter Atkinson
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-426, https://doi.org/10.5194/essd-2022-426, 2023
Preprint withdrawn
Short summary
Short summary
A random forest (RF) model was proposed to extend the superior SMAP dataset (named RF_SMAP) from 1979 to 2015, using the corresponding CCI time-series. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring.
Haoxuan Yang, Qunming Wang, Wei Zhao, and Peter M. Atkinson
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-137, https://doi.org/10.5194/essd-2022-137, 2022
Preprint withdrawn
Short summary
Short summary
A random forest (RF) model was proposed to extend the superior SMAP dataset (named RF_SMAP) from 1979 to 2015, using the corresponding CCI time-series. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring.
H. Zhang, B. Xie, S. Liu, R. Ding, Z. Ye, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 79–84, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-79-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-79-2022, 2022
Q. Xu, H. Xie, Y. Sun, X. Liu, Y. Guo, P. Huang, B. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 309–314, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-309-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-309-2022, 2022
H. Zhang, Y. Shang, X. Tong, J. Chen, W. Ma, M. Li, Y. Lu, and H. Chen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 619–625, https://doi.org/10.5194/isprs-annals-V-3-2022-619-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-619-2022, 2022
S. Luo, Y. Cheng, Z. Li, Y. Wang, K. Wang, X. Wang, G. Qiao, W. Ye, Y. Li, M. Xia, X. Yuan, Y. Tian, X. Tong, and R. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 491–496, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-491-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-491-2021, 2021
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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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