Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3365-2023
https://doi.org/10.5194/essd-15-3365-2023
Data description article
 | 
02 Aug 2023
Data description article |  | 02 Aug 2023

Thirty-meter map of young forest age in China

Yuelong Xiao, Qunming Wang, Xiaohua Tong, and Peter M. Atkinson

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Cited articles

Arévalo, P., Bullock, E. L., Woodcock, C. E., and Olofsson, P.: A Suite of Tools for Continuous Land Change Monitoring in Google Earth Engine, Front. Clim., 2, 111051, https://doi.org/10.3389/fclim.2020.576740, 2020. 
Besnard, S., Koirala, S., Santoro, M., Weber, U., Nelson, J., Gütter, J., Herault, B., Kassi, J., N'Guessan, A., Neigh, C., Poulter, B., Zhang, T., and Carvalhais, N.: Mapping global forest age from forest inventories, biomass and climate data, Earth Syst. Sci. Data, 13, 4881–4896, https://doi.org/10.5194/essd-13-4881-2021, 2021. 
Betts, M. G., Yang, Z., Hadley, A. S., Smith, A. C., Rousseau, J. S., Northrup, J. M., Nocera, J. J., Gorelick, N., and Gerber, B. D.: Forest degradation drives widespread avian habitat and population declines, Nature Ecology & Evolution, 6, 709–719, https://doi.org/10.1038/s41559-022-01737-8, 2022. 
Bullock, E. L., Woodcock, C. E., and Olofsson, P.: Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis, Remote Sens. Environ., 238, 110968, https://doi.org/10.1016/j.rse.2018.11.011, 2020. 
Champion, I., Germain, C., Da Costa, J. P., Alborini, A., and Dubois-Fernandez, P.: Retrieval of Forest Stand Age From SAR Image Texture for Varying Distance and Orientation Values of the Gray Level Co-Occurrence Matrix, IEEE Geosci. Remote S., 11, 5–9, https://doi.org/10.1109/LGRS.2013.2244060, 2014. 
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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 %.
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