Articles | Volume 18, issue 9
https://doi.org/10.5194/essd-18-6225-2026
© Author(s) 2026. 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-18-6225-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
01 Sep 2026
Data description article |
| 01 Sep 2026
| 01 Sep 2026
A global 30 m disturbance-recovery age dataset for young natural and planted forests (1985–2024)
Yan Wang
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Hengbin Wang
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Chaoran Liang
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Xiyi Li
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Zhe Liu
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
Key Laboratory for Agricultural Land Quality, Ministry of Natural Resources of the People's Republic of China, Beijing 100083, China
Xiaodong Zhang
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
Key Laboratory for Agricultural Land Quality, Ministry of Natural Resources of the People's Republic of China, Beijing 100083, China
Shaoming Li
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
Key Laboratory for Agricultural Land Quality, Ministry of Natural Resources of the People's Republic of China, Beijing 100083, China
Jinming Yang
Qingdao Agricultural University, No. 700 Changcheng Road, Chengyang, Qingdao, Shandong 266109, China
Yuanyuan Zhao
CORRESPONDING AUTHOR
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
Key Laboratory for Agricultural Land Quality, Ministry of Natural Resources of the People's Republic of China, Beijing 100083, China
Cited articles
Almalki, R., Khaki, M., Saco, P. M., and Rodriguez, J. F.: Monitoring and mapping vegetation cover changes in arid and semi-arid areas using remote sensing technology: A review, Remote Sens., 14, 5143, https://doi.org/10.3390/rs14205143, 2022.
Aszalós, R., Thom, D., Aakala, T., Angelstam, P., Brūmelis, G., Gálhidy, L., Gratzer, G., Hlásny, T., Katzensteiner, K., Kovács, B., Knoke, T., Larrieu, L., Motta, R., Müller, J., Ódor, P., Roženbergar, D., Paillet, Y., Pitar, D., Standovár, T., Svoboda, M., Szwagrzyk, J., Toscani, P., and Keeton, W. S.: Natural disturbance regimes as a guide for sustainable forest management in Europe, Ecol. Appl., 32, e2596, https://doi.org/10.1002/eap.2596, 2022.
Bennett, B. M.: Plantations and Protected Areas: A Global History of Forest Management, MIT Press, Cambridge, MA, https://doi.org/10.7551/mitpress/9780262029933.001.0001, 2015.
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.
Besnard, S., Santoro, M., Herold, M., Cartus, O., Gütter, J., Heinrich, V. H. A., Herault, B., Kassi, J., Koirala, S., N'Guessan, A., Neigh, C., Nelson, J. A., Poulter, B., Weber, U., Zhang, T., and Carvalhais, N.: Global Age Mapping Integration (GAMI) (2.1), GFZ Data Services, https://doi.org/10.5880/GFZ.1.4.2023.006, 2024.
Bright, B. C., Hudak, A. T., Kennedy, R. E., Braaten, J. D., and Henareh Khalyani, A.: Examining post-fire vegetation recovery with Landsat time series analysis in three western North American forest types, Fire Ecol., 15, 1–14, 2019.
Bunce, C.: Correlation, agreement, and Bland–Altman analysis: statistical analysis of method comparison studies, Am. J. Ophthalmol., 148, 4–6, 2009.
Cao, Y., Zhong, D., Shang, R., Ke, Q., Zhang, M., Xie, D., Liu, S., Zhao, C., and Wei, R.: Afforestation as a mitigation strategy: countering climate-induced risk of forest carbon sink in China, Carbon Balance and Management, 20, 18, https://doi.org/10.1186/s13021-025-00308-1, 2025.
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 Sens. Lett., 11, 5–9, 2013.
Chen, W., Chen, J., Liu, J., and Cihlar, J.: Approaches for reducing uncertainties in regional forest carbon balance, Global Biogeochem. Cy., 14, 827–838, 2000.
Cheng, K., Chen, Y., Xiang, T., Yang, H., Liu, W., Ren, Y., Guan, H., Hu, T., Ma, Q., and Guo, Q.: A 2020 forest age map for China with 30 m resolution, Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, 2024a.
Cheng, K., Yang, H., Tao, S., Su, Y., Guan, H., Ren, Y., Hu, T., Li, W., Xu, G., Chen, M., Lu, X., Yang, Z., Tang, Y., Ma, K., Fang, J., and Guo, Q.: Carbon storage through China's planted forest expansion, Nat. Commun., 15, 4106, https://doi.org/10.1038/s41467-024-48546-0, 2024b.
Deng, X., Guo, S., Sun, L., and Chen, J.: Identification of short-rotation eucalyptus plantation at large scale using multi-satellite imageries and cloud computing platform, Remote Sensing, 12, 2153, https://doi.org/10.3390/rs12132153, 2020.
DeVries, B., Decuyper, M., Verbesselt, J., Zeileis, A., Herold, M., and Joseph, S.: Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series, Remote Sens. Environ., 169, 320–334, 2015.
Du, Z., Yu, L., Yang, J., Xu, Y., Chen, B., Peng, S., Zhang, T., Fu, H., Harris, N., and Gong, P.: A global map of planting years of plantations, Sci. Data, 9, 141, 2022.
Erdozain, M., Alberdi, I., Aszalós, R., Bollmann, K., Detsis, V., Diaci, J., Đodan, M., Efthimiou, G., Gálhidy, L., Haase, M., Hoffmann, J., Jaymond, D., Johann, E., Jørgensen, H., Krumm, F., Kuuluvainen, T., Lachat, T., Lapin, K., Lindner, M., Madsen, P., Nichiforel, L., Pach, M., Paillet, Y., Palaghianu, C., Palau, J., Pemán, J., Perić, S., Raum, S., Schueler, S., Skrzyszewski, J., Svensson, J., Teeuwen, S., Vacchiano, G., Vandekerkhove, K., Cañellas, I., Menéndez-Miguélez, M., Werden, L. L. K., Àvila, A., and de-Miguel, S.: The Evolution of Forest Restoration in Europe: A Synthesis for a Step Forward Based on National Expert Knowledge, Current Forestry Reports, 11, 4, https://doi.org/10.1007/s40725-024-00235-3, 2024.
Escuin, S., Navarro, R., and Fernández, P.: Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images, Int. J. Remote Sens., 29, 1053–1073, 2008.
FAO: The State of the World's Forests 2024 – Forest-sector innovations towards a more sustainable future, FAO, Rome, https://doi.org/10.4060/cd1211en, 2024.
Han, C., Liu, Y., Zhang, C., Li, Y., Zhou, T., Khan, S., Chen, N., and Zhao, C.: Effects of three coniferous plantation species on plant‐soil feedbacks and soil physical and chemical properties in semi‐arid mountain ecosystems, Forest Ecosystems, 8, 3, https://doi.org/10.1186/s40663-021-00281-4, 2021.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., and Loveland, T. R.: High-resolution global maps of 21st-century forest cover change, Science, 342, 850–853, 2013.
He, G., Zhang, Z., Zhu, Q., Wang, W., Peng, W., and Cai, Y.: Estimating Carbon Sequestration Potential of Forest and Its Influencing Factors at Fine Spatial-Scales: A Case Study of Lushan City in Southern China, Int. J. Env. Res. Pub. He., 19, 9184, https://doi.org/10.3390/ijerph19159184, 2022.
Huang, Z., Li, X., Du, H., Zou, W., Zhou, G., Mao, F., Fan, W., Xu, Y., Ni, C., and Zhang, B.: An algorithm of forest age estimation based on the forest disturbance and recovery detection, IEEE T. Geosci. Remote Sens., 61, 1–18, 2023.
Ju, J., Roy, D. P., Vermote, E., Masek, J., and Kovalskyy, V.: Continental-scale validation of MODIS-based and LEDAPS Landsat ETM+ atmospheric correction methods, Remote Sens. Environ., 122, 175–184, 2012.
Köhl, M., Neupane, P. R., and Lotfiomran, N.: The impact of tree age on biomass growth and carbon accumulation capacity: A retrospective analysis using tree ring data of three tropical tree species grown in natural forests of Suriname, PLoS One, 12, e0181187, https://doi.org/10.1371/journal.pone.0181187, 2017.
Leng, Y., Li, W., Ciais, P., Sun, M., Zhu, L., Yue, C., Chang, J., Yao, Y., Zhang, Y., Zhou, J., Li, Z., Wang, X., Xi, Y., and Peng, S.: Forest aging limits future carbon sink in China, One Earth, 7, 822–834, https://doi.org/10.1016/j.oneear.2024.04.011, 2024.
Li, P., Li, H., Si, B., Zhou, T., Zhang, C., and Li, M.: Mapping planted forest age using LandTrendr algorithm and Landsat 5–8 on the Loess Plateau, China, Agr. Forest Meteorol., 344, 109795, https://doi.org/10.1016/j.agrformet.2023.109795, 2024.
Liang, B., Wang, J., Zhang, Z., Zhang, J., Zhang, J., Cressey, E. L., and Wang, Z.: Planted forest is catching up with natural forest in China in terms of carbon density and carbon storage, Fundamental Research, 2, 688–696, https://doi.org/10.1016/j.fmre.2022.04.008, 2022.
Liao, W., Ye, D., Yuan, R., Zhang, Y., and Deng, Q.: Financial compensation for natural forest logging ban: Standard calculation based on willingness to accept, Sci. Prog., 106, 368504221145563, https://doi.org/10.1177/00368504221145563, 2023.
Lu, J., Huang, C., Schleeweis, K., Zou, Z., and Gong, W.: Tree age estimation across the US using forest inventory and analysis database, Forest Ecol. Manage., 584, 122603, https://doi.org/10.1016/j.foreco.2025.122603, 2025.
Maltman, J. C., Hermosilla, T., Wulder, M. A., Coops, N. C., and White, J. C.: Estimating and mapping forest age across Canada's forested ecosystems, Remote Sens. Environ., 290, 113529, https://doi.org/10.1016/j.rse.2023.113529, 2023.
Maza, M., Lara, J. L., and Losada, I. J.: Predicting the evolution of coastal protection service with mangrove forest age, Coast. Eng., 168, 103922, https://doi.org/10.1016/j.coastaleng.2021.103922, 2021.
Murphy, B. A., Rollinson, C. R., Dietze, M. C., Staudhammer, C. L., VonHedemann, N. R., Schultz, C. A., Kleindl, W. J., and Desai, A. R.: Insights into nature‐based climate solutions: Managing forests for climate resilience and carbon stability, J. Geophys. Res.-Biogeo., 130, e2024JG008391, https://doi.org/10.1029/2024JG008391, 2025.
Noss, R. F.: Assessing and monitoring forest biodiversity: a suggested framework and indicators, Forest Ecol. Manag., 115, 135–146, 1999.
Oldekop, J. A., Rasmussen, L. V., Agrawal, A., Bebbington, A. J., Meyfroidt, P., Bengston, D. N., Blackman, A., Brooks, S., Davidson-Hunt, I., Davies, P., Dinsi, S. C., Fontana, L. B., Gumucio, T., Kumar, C., Kumar, K., Moran, D., Mwampamba, T. H., Nasi, R., Nilsson, M., Pinedo-Vasquez, M. A., Rhemtulla, J. M., Sutherland, W. J., Watkins, C., and Wilson, S. J.: Forest-linked livelihoods in a globalized world, Nat. Plants, 6, 1400–1407, https://doi.org/10.1038/s41477-020-00814-9, 2020.
Pan, Y., Chen, J. M., Birdsey, R., McCullough, K., He, L., and Deng, F.: Age structure and disturbance legacy of North American forests, Biogeosciences, 8, 715–732, https://doi.org/10.5194/bg-8-715-2011, 2011.
Poulter, B., Aragao, L., Adela, N., Bellassen, V., Ciais, P., Kato, T., Lin, X., Nachin, B., Luyssaert, S., Pederson, N., Peylin, P., Piao, S., Saatchi, S., Schepaschenko, D., Schelhaas, M., and Shivdenko, A.: The global forest age dataset (GFADv1.0), PANGAEA, https://doi.org/10.1594/PANGAEA.889943, 2018.
Poulter, B., Aragão, L., Andela, N., Bellassen, V., Ciais, P., Kato, T., Lin, X., Nachin, B., Luyssaert, S., Pederson, N., Peylin, P., Piao, S., Pugh, T., Saatchi, S., Schepaschenko, D., Schelhaas, M., and Shivdenko, A.: The global forest age dataset and its uncertainties (GFADv1.1), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.897392, 2019.
Racine, E. B., Coops, N. C., St-Onge, B., and Bégin, J.: Estimating forest stand age from LiDAR-derived predictors and nearest neighbor imputation, Forest Sci., 60, 128–136, 2014.
Reyes-Palomeque, G., Dupuy, J., Portillo-Quintero, C., Andrade, J., Tun-Dzul, F., and Hernández-Stefanoni, J.: Mapping forest age and characterizing vegetation structure and species composition in tropical dry forests, Ecol. Indic., 120, 106955, https://doi.org/10.1016/j.ecolind.2020.106955, 2021.
Ryu, J.-H., Han, K.-S., Hong, S., Park, N.-W., Lee, Y.-W., and Cho, J.: Satellite-based evaluation of the post-fire recovery process from the worst forest fire case in South Korea, Remote Sens., 10, 918, https://doi.org/10.3390/rs10060918, 2018.
Shang, R., Chen, J. M., Xu, M., Lin, X., Li, P., Yu, G., He, N., Xu, L., Gong, P., Liu, L., Liu, H., and Jiao, W.: China's current forest age structure will lead to weakened carbon sinks in the near future, The Innovation, 4, 100515, https://doi.org/10.1016/j.xinn.2023.100515, 2023.
Shi, X., Wang, T., Lu, S., Chen, K., He, D., and Xu, Z.: Evaluation of China's forest carbon sink service value, Environ. Sci. Pollut. R., 29, 44668–44677, https://doi.org/10.1007/s11356-022-18958-w, 2022.
Smolina, A., Illarionova, S., Shadrin, D., Kedrov, A., and Burnaev, E.: Forest age estimation in northern Arkhangelsk region based on machine learning pipeline on Sentinel-2 and auxiliary data, Sci. Rep., 13, 22167, https://doi.org/10.1038/s41598-023-49207-w, 2023.
Steel, E. A., Hinckley, T. M., Richards, W. H., and D'Amore, D. V.: Chapter 3 – Forests then and now: managing for ecosystem benefits, services to humans, and healthy forests across scales, in: Future Forests, edited by: McNulty, S. G., Elsevier, 49–64, https://doi.org/10.1016/B978-0-323-90430-8.00009-5, 2024.
Strandgard, M. and Béland, M.: Economics of forest biomass for bioenergy: Potential site preparation savings from coarse woody harvesting residue removal in a short-rotation Eucalyptus globulus (Labill.) plantation, Silva Balcanica, 22, 45–55, 2021.
Su, Y., Zhang, C., Ciais, P., Zeng, Z., Cescatti, A., Shang, J., Chen, J. M., Liu, J., Wang, Y.-P., and Yuan, W.: Asymmetric influence of forest cover gain and loss on land surface temperature, Nat. Clim. Change, 13, 823–831, 2023.
Tang, S., Tian, Q., Xu, K., Xu, N., and Yue, J.: Age information retrieval of Larix gmelinii forest using Sentinel-2 data, Natl. Remote Sens. Bull., 24, 1511–1524, 2020.
Thompson, I., Mackey, B., McNulty, S., and Mosseler, A.: Forest resilience, biodiversity, and climate change, Secretariat of the Convention on Biological Diversity (Montreal, Canada), 1–67, https://www.cbd.int/doc/publications/cbd-ts-43-en.pdf (last access: 28 August 2026), 2009.
Tian, H., Zhu, J., Lei, X., Jian, Z., Chen, X., Zeng, L., Huang, G., Liu, C., and Xiao, W.: Models considering the theoretical stand age will underestimate the future forest carbon sequestration potential, Forest Ecol. Manage., 562, 121982, https://doi.org/10.1016/j.foreco.2024.121982, 2024.
Tian, L., Liao, L., Tao, Y., Wu, X., and Li, M.: Forest age mapping using landsat time-series stacks data based on forest disturbance and empirical relationships between age and height, Remote Sens., 15, 2862, https://doi.org/10.3390/rs15112862, 2023.
Tian, X., Van der Tol, C., Su, Z., Li, Z., Chen, E., Li, X., Yan, M., Chen, X., Wang, X., and Pan, X.: Simulation of forest evapotranspiration using time-series parameterization of the Surface Energy Balance System (SEBS) over the Qilian Mountains, Remote Sens., 7, 15822–15843, 2015.
Trisasongko, B., Panuju, D., and Iman, L.: Estimating tree age of rubber stands using spaceborne L-band synthetic aperture radar, IOP Conference Series: Earth and Environmental Science, IPB International Convention Center, Bogor, Indonesia, 012079, https://doi.org/10.1088/1755-1315/418/1/012079, 2020.
United States Forest Service: Forest Inventory and Analysis Database: FIA DataMart, https://research.fs.usda.gov/products/dataandtools/fia-datamart (last access: 1 June 2026), 2024.
U.S. Department of State's Office of the Geographer: LSIB 2017: Large Scale International Boundary Polygons, Simplified, USDOS/LSIB_SIMPLE/2017 [data set], https://developers.google.com/earth-engine/datasets/catalog/USDOS_LSIB_SIMPLE_2017 (last access: 28 August 2026), 2017.
Vermote, E., Roger, J.-C., Franch, B., and Skakun, S.: LaSRC (Land Surface Reflectance Code): Overview, application and validation using MODIS, VIIRS, LANDSAT and Sentinel 2 data's, IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, 8173–8176, https://doi.org/10.1109/IGARSS.2018.8517622, 2018.
Vilén, T., Gunia, K., Verkerk, P., Seidl, R., Schelhaas, M.-J., Lindner, M., and Bellassen, V.: Reconstructed forest age structure in Europe 1950–2010, Forest Ecol. Manage., 286, 203–218, 2012.
Wang, Y., Wang, H., Liang, C., Li, X., Liu, Z., Zhang, X., Li, S., Yang, J., and Zhao, Y.: Global Natural and Planted Forest Age dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.22030377, 2026.
Xiao, Y., Wang, Q., Tong, X., and Atkinson, P. M.: Thirty-meter map of young forest age in China, Earth Syst. Sci. Data, 15, 3365–3386, https://doi.org/10.5194/essd-15-3365-2023, 2023.
Xiao, Y., Wang, Q., and Zhang, H. K.: Global Natural and Planted Forests Mapping at Fine Spatial Resolution of 30 m, J. Remote Sens., 4, 0204, https://doi.org/10.34133/remotesensing.0204, 2024.
Yu, Z., Liu, S., Li, H., Liang, J., Liu, W., Piao, S., Tian, H., Zhou, G., Lu, C., and You, W.: Maximizing carbon sequestration potential in Chinese forests through optimal management, Nat. Commun., 15, 3154, https://doi.org/10.1038/s41467-024-47143-5, 2024.
Zhang, J., Zhang, P., Wang, R., Liu, Y., and Lu, S.: Identifying the coupling coordination relationship between urbanization and forest ecological security and its impact mechanism: Case study of the Yangtze River Economic Belt, China, J. Environ. Manage., 342, 118327, https://doi.org/10.1016/j.jenvman.2023.118327, 2023.
Zhang, Y., Yao, Y., Wang, X., Liu, Y., and Piao, S.: Mapping spatial distribution of forest age in China, Earth Space Sci., 4, 108–116, 2017.
Zou, H. and Song, Y.: Influence of forest vegetation restoration on carbon increment after mining, Sci. Rep., 13, 19565, https://doi.org/10.1038/s41598-023-45332-8, 2023.
Short summary
Forests are vital for biodiversity, carbon storage and ecosystem services. We developed a 30 m global forest-age dataset (1985–2024) that distinguishes natural and planted forests with different recovery dynamics. The dataset integrates long-term satellite observations and provides pixel-level uncertainty information, enabling improved understanding of global forest age patterns and supporting carbon, ecosystem and forest management studies.
Forests are vital for biodiversity, carbon storage and ecosystem services. We developed a 30 m...
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