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 articles
Abbasi, E., Alavi Moghaddam, M. R., and Kowsari, E.: A systematic and critical review on development of machine learning based-ensemble models for prediction of adsorption process efficiency, J. Clean. Prod., 379, 134588, https://doi.org/10.1016/j.jclepro.2022.134588, 2022.
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2623–2631, Association for Computing Machinery, Anchorage, AK, USA, https://doi.org/10.48550/arXiv.1907.10902, 2019.
Alerskans, E., Zinck, A.-S. P., Nielsen-Englyst, P., and Høyer, J. L.: Exploring machine learning techniques to retrieve sea surface temperatures from passive microwave measurements, Remote Sens Environ., 281, 113220, https://doi.org/10.1016/j.rse.2022.113220, 2022.
Banfield, R. E., Hall, L. O., Bowyer, K. W., and Kegelmeyer, W. P.: A Comparison of Decision Tree Ensemble Creation Techniques, IEEE T. Pattern Anal. Mach. Intell., 29, 173–180, https://doi.org/10.1109/TPAMI.2007.250609, 2007.
Banskota, A., Kayastha, N., Falkowski, M. J., Wulder, M. A., Froese, R. E., and White, J. C.: Forest Monitoring Using Landsat Time Series Data: A Review, Can. J. Remote. Sens., 40, 362–384, https://doi.org/10.1080/07038992.2014.987376, 2014.
Belgiu, M. and Drăguţ, L.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm. Remote Sens., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016.
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.
Bolton, D. K., Tompalski, P., Coops, N. C., White, J. C., Wulder, M. A., Hermosilla, T., Queinnec, M., Luther, J. E., van Lier, O. R., Fournier, R. A., Woods, M., Treitz, P. M., van Ewijk, K. Y., Graham, G., and Quist, L.: Optimizing Landsat time series length for regional mapping of lidar-derived forest structure, Remote Sens Environ., 239, 111645, https://doi.org/10.1016/j.rse.2020.111645, 2020.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Canul-Reich, J., Shoemaker, L., and Hall, L. O.: Ensembles of Fuzzy Classifiers, 2007 IEEE International Fuzzy Systems Conference, London, UK, 2007, https://doi.org/10.1109/FUZZY.2007.4295345, 2007.
Cartus, O., Kellndorfer, J., Rombach, M., and Walker, W.: Mapping Canopy Height and Growing Stock Volume Using Airborne Lidar, ALOS PALSAR and Landsat ETM+, Remote Sens., 4, 3320–3345, https://doi.org/10.3390/rs4113320, 2012.
Chen, D., Loboda, T. V., Krylov, A., and Potapov, P. V.: Mapping stand age dynamics of the Siberian larch forests from recent Landsat observations, Remote Sens Environ., 187, 320–331, https://doi.org/10.1016/j.rse.2016.10.033, 2016.
Cheng, K., Chen, Y., Xiang, T., Yang, H., Liu, W., Ren, Y., Guan, H., Hu, T., Ma, Q., and Guo, Q.: 2020 forest age map for China with 30 m resolution (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.8354262, 2023a.
Cheng, K., Su, Y., Guan, H., Tao, S., Ren, Y., Hu, T., Ma, K., Tang, Y., and Guo, Q.: Mapping China's planted forests using high resolution imagery and massive amounts of crowdsourced samples, ISPRS J. Photogramm. Remote Sens., 196, 356–371, https://doi.org/10.1016/j.isprsjprs.2023.01.005, 2023b.
Dai, M., Zhou, T., Yang, L., and Jia, G.: Spatial pattern of forest ages in China retrieved from national-level inventory and remote sensing imageries, Geogr. Res., 30, 172–184, https://doi.org/10.11821/yj2011010017, 2011 (in Chinese).
de Jong, S. M., Shen, Y., de Vries, J., Bijnaar, G., van Maanen, B., Augustinus, P., and Verweij, P.: Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm, Int. J. Appl. Earth Obs., 97, 102293, https://doi.org/10.1016/j.jag.2020.102293, 2021.
De Stefano, C., Fontanella, F., Folino, G., and di Freca, A. S.: A Bayesian Approach for Combining Ensembles of GP Classifiers, Springer Berlin Heidelberg, Berlin, Heidelberg, 26–35, https://doi.org/10.1007/978-3-642-21557-5_5, 2011.
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, https://doi.org/10.1038/s41597-022-01260-2, 2022.
Du, Z., Yu, L., Yang, J., Coomes, D., Kanniah, K., Fu, H., and Gong, P.: Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery, IEEE J.-Stars., 16, 4195–4204, https://doi.org/10.1109/JSTARS.2023.3267796, 2023.
Dutta, K. K., Dutta, K. K., Victor, A., Nathu, A. G., Habib, M. A., and Parashar, D.: Kannada Alphabets Recognition using Decision Tree and Random Forest Models, in: 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 2020 , 534–541, https://doi.org/10.1109/ICISS49785.2020.9315972, 2020.
Fernández-Martínez, M., Vicca, S., Janssens, I. A., Luyssaert, S., Campioli, M., Sardans, J., Estiarte, M., and Peñuelas, J.: Spatial variability and controls over biomass stocks, carbon fluxes, and resource-use efficiencies across forest ecosystems, Trees, 28, 597–611, https://doi.org/10.1007/s00468-013-0975-9, 2014.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: New 1-Km Spatial Resolution Climate Surfaces for Global Land Areas, Int. J. Climatol., 37, 4302–4315, https://doi.org/10.1002/joc.5086, 2017.
Guo, Y., Zhou, Y., Hu, X., and Cheng, W.: Research on Recommendation of Insurance Products Based on Random Forest, in: 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 308–311, https://doi.org/10.1109/MLBDBI48998.2019.00069, 2019.
Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., and Hobart, G. W.: Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics, Remote Sens Environ., 170, 121–132, https://doi.org/10.1016/j.rse.2015.09.004, 2015.
Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., Hobart, G. W., and Campbell, L. B.: Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring, Int. J. Digit. Earth, 9, 1035–1054, https://doi.org/10.1080/17538947.2016.1187673, 2016.
Hua, J., Chen, G., Yu, L., Ye, Q., Jiao, H., and Luo, X.: Improved Mapping of Long-Term Forest Disturbance and Recovery Dynamics in the Subtropical China Using All Available Landsat Time-Series Imagery on Google Earth Engine Platform, IEEE J.-Stars., 14, 2754–2768, https://doi.org/10.1109/JSTARS.2021.3058421, 2021.
Huang, C., Goward, S. N., Masek, J. G., Thomas, N., Zhu, Z., and Vogelmann, J. E.: An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks, Remote Sens. Environ., 114, 183–198, https://doi.org/10.1016/j.rse.2009.08.017, 2010.
Huang, Z., Li, X., Du, H., Zou, W., Zhou, G., Mao, F., Fan, W., Xu, Y., Ni, C., Zhang, B., Chen, Q., Chen, J., and Hu, M.: An Algorithm of Forest Age Estimation Based on the Forest Disturbance and Recovery Detection, IEEE T. Geosci. Remote, 61, 1–18, https://doi.org/10.1109/TGRS.2023.3322163, 2023.
Jerome, H. F.: Greedy function approximation: A gradient boosting machine, Ann. Stat., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001.
Kennedy, R. E., Yang, Z., and Cohen, W. B.: Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr – Temporal segmentation algorithms, Remote Sens. Environ., 114, 2897–2910, https://doi.org/10.1016/j.rse.2010.07.010, 2010.
Kennedy, R. E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W. B., and Healey, S.: Implementation of the LandTrendr Algorithm on Google Earth Engine, Remote Sens., 10, 691, https://doi.org/10.3390/rs10050691, 2018.
Kim, H., Crow, W., Li, X., Wagner, W., Hahn, S., and Lakshmi, V.: True global error maps for SMAP, SMOS, and ASCAT soil moisture data based on machine learning and triple collocation analysis, Remote Sens Environ., 298, 113776, https://doi.org/10.1016/j.rse.2023.113776, 2023.
Lavanya, K., Bajaj, S., Tank, P., and Jain, S.: Handwritten digit recognition using hoeffding tree, decision tree and random forests – A comparative approach, in: 2017 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India, 2017, 1–6, https://doi.org/10.1109/ICCIDS.2017.8272641, 2017.
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, Agric. For. Meteorol., 344, 109795, https://doi.org/10.1016/j.agrformet.2023.109795, 2024.
Lin, G., Xia, B., Zeng, Z., and Huang, W.: The Relationship between NDVI, Stand Age and Terrain Factors of Pinus elliottii Forest, in: 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing, 2008, December , 232–236, https://doi.org/10.1109/ETTandGRS.2008.302, 2008.
Lin, X., Shang, R., Chen, J. M., Zhao, G., Zhang, X., Huang, Y., Yu, G., He, N., Xu, L., and Jiao, W.: High-resolution forest age mapping based on forest height maps derived from GEDI and ICESat-2 space-borne lidar data, Agric. For. Meteorol., 339, 109592, https://doi.org/10.1016/j.agrformet.2023.109592, 2023.
Liu, X., Su, Y., Hu, T., Yang, Q., Liu, B., Deng, Y., Tang, H., Tang, Z., Fang, J., and Guo, Q.: Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data, Remote Sens. Environ., 269, 112844, https://doi.org/10.1016/j.rse.2021.112844, 2022.
Lundberg, S. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, arXiv [preprint], arXiv:1705.07874, https://doi.org/10.48550/arXiv.1705.07874, 2017.
Lundberg, S. M., Erion, G. G., and Lee, S.-I.: Consistent Individualized Feature Attribution for Tree Ensembles, arXiv [preprint], arXiv:1802.03888, https://doi.org/10.48550/arXiv.1802.03888, 2019.
Luther, J. E., Fournier, R. A., van Lier, O. R., and Bujold, M.: Extending ALS-Based Mapping of Forest Attributes with Medium Resolution Satellite and Environmental Data, Remote Sens., 11, 1092, https://doi.org/10.3390/rs11091092, 2019.
Maltamo, M., Kinnunen, H., Kangas, A., and Korhonen, L.: Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning, For. Ecosyst., 7, 44, https://doi.org/10.1186/s40663-020-00254-z, 2020.
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.
Matasci, G., Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., Hobart, G. W., and Zald, H. S.: Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots, Remote Sens. Environ., 209, 90–106, https://doi.org/10.1016/j.rse.2017.12.020, 2018.
Matloob, F., Ghazal, T. M., Taleb, N., Aftab, S., Ahmad, M., Khan, M. A., Abbas, S., and Soomro, T. R.: Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review, IEEE Access, 9, 98754–98771, https://doi.org/10.1109/ACCESS.2021.3095559, 2021.
Mekruksavanich, S., Jantawong, P., Hnoohom, N., and Jitpattanakul, A.: Hyperparameter Tuning in Convolutional Neural Network for Face Touching Activity Recognition using Accelerometer Data, in: 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), Bangkok, Thailand, 2022, 101–105, https://doi.org/10.1109/RI2C56397.2022.9910262, 2022.
Montesano, P. M., Cook, B. D., Sun, G., Simard, M., Nelson, R. F., Ranson, K. J., Zhang, Z., and Luthcke, S.: Achieving accuracy requirements for forest biomass mapping: A spaceborne data fusion method for estimating forest biomass and LiDAR sampling error, Remote Sens. Environ., 130, 153–170, https://doi.org/10.1016/j.rse.2012.11.016, 2013.
Niu, Y., Squires, V., and Jentsch, A.: Jentsch. Risks of China's increased forest area, Science, 379, 447–448, https://doi.org/10.1126/science.adg0210, 2023.
Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., Ciais, P., Jackson, R. B., Pacala, S. W., McGuire, A. D., Piao, S., Rautiainen, A., Sitch, S., and Hayes, D.: A Large and Persistent Carbon Sink in the World, Forests, Science, 333, 988–993, https://doi.org/10.1126/science.1201609, 2011.
Piao, S., He, Y., Wang, X., and Chen, F.: Estimation of China's terrestrial ecosystem carbon sink: Methods, progress and prospects, Sci. China Earth Sci., 65, 641–651, https://doi.org/10.1007/s11430-021-9892-6, 2022.
Ren, Y., Wei, X., Zhang, L., Cui, S., Chen, F., Xiong, Y., and Xie, P.: Potential for forest vegetation carbon storage in Fujian Province, China, determined from forest inventories, Plant Soil, 345, 125–140, https://doi.org/10.1007/s11104-011-0766-2, 2011.
Rodman, K. C., Andrus, R. A., Veblen, T. T., and Hart, S. J.: Disturbance detection in landsat time series is influenced by tree mortality agent and severity, not by prior disturbance, Remote Sens. Environ., 254, 112244, https://doi.org/10.1016/j.rse.2020.112244, 2021.
Rodriguez, J. J., Kuncheva, L. I., and Alonso, C. J.: Rotation Forest: A New Classifier Ensemble Method, IEEE T. Pattern Anal. Mach. Intell., 28, 1619–1630, https://doi.org/10.1109/TPAMI.2006.211, 2006.
Rokach, L.: Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography, Comput. Stat. Data Anal., 53, 4046–4072, https://doi.org/10.1016/j.csda.2009.07.017, 2009.
Sandha, S. S., Aggarwal, M., Fedorov, I., and Srivastava, M.: Mango: A Python Library for Parallel Hyperparameter Tuning, in: ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, 3987–3991, https://doi.org/10.1109/ICASSP40776.2020.9054609, 2020.
Schumacher, J., Hauglin, M., Astrup, R., and Breidenbach, J.: Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data, For. Ecosyst., 7, 60, https://doi.org/10.1186/s40663-020-00274-9, 2020.
Sharma, M. and Parton, J.: Height–diameter equations for boreal tree species in Ontario using a mixed-effects modelling approach, For. Ecol. Manag., 249, 187–198, https://doi.org/10.1016/j.foreco.2007.05.006, 2007.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res.-Biogeo., 116, G04021, https://doi.org/10.1029/2011JG001708, 2011.
Su, Y., Guo, Q., Hu, T., Guan, H., Jin, S., An, S., Chen, X., Guo, K., Hao, Z., Hu, Y., Huang, Y., Jiang, M., Li, J., Li, Z., Li, X., Li, X., Liang, C., Liu, R., Liu, Q., Ni, H., Peng, S., Shen, Z., Tang, Z., Tian, X., Wang, X., Wang, R., Xie, Z., Xie, Y., Xu, X., Yang, X., Yang,Y., Yu, L., Yue, M., Zhang, F., and Ma, K.: An updated Vegetation Map of China (1:1 000 000), Sci. Bull., 65, 1125–1136, https://doi.org/10.1016/j.scib.2020.04.004, 2020.
Sun, B., Cui, W., Liu, G., Zhou, B., and Zhao, W.: A hybrid strategy of AutoML and SHAP for automated and explainable concrete strength prediction, Case Stud. Constr. Mater., 19, e02405, https://doi.org/10.1016/j.cscm.2023.e02405, 2023.
Tesfagergish, S. G., Kapočiūtė-Dzikienė, J., and Damaševičius, R.: Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning, Appl. Sci., 12, 8662, https://doi.org/10.3390/app12178662, 2022.
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.
Tong, X., Brandt, M., Yue, Y., Ciais, P., Rudbeck Jepsen, M., Penuelas, J.,Wigneron, J., Xiao, X., Song, X.-P., Horion, S., Rasmussen, K., Saatchi, S., Fan, L., Wang, K., Zhang, B., Chen, Z., Wang, Y., Li, X., and Fensholt, R.: Forest management in southern China generates short term extensive carbon sequestration, Nat. Commun., 11, 129, https://doi.org/10.1038/s41467-019-13798-8, 2020.
Tubiello, F. N., Conchedda, G., Casse, L., Hao, P., De Santis, G., and Chen, Z.: A new cropland area database by country circa 2020, Earth Syst. Sci. Data, 15, 4997–5015, https://doi.org/10.5194/essd-15-4997-2023, 2023.
Verbesselt, J., Hyndman, R., Newnham, G., and Culvenor, D.: Detecting trend and seasonal changes in satellite image time series, Remote Sens Environ., 114, 106–115, https://doi.org/10.1016/j.rse.2009.08.014, 2010a.
Verbesselt, J., Hyndman, R., Zeileis, A., and Culvenor, D.: Phenological change detection while accounting for abrupt and gradual trends in satellite image time series, Remote Sens. Environ., 114, 2970–2980, https://doi.org/10.1016/j.rse.2010.08.003, 2010b.
Wang, S., Chen, J. M., Ju, W. M., Feng, X., Chen, M., Chen, P., and Yu, G.: Carbon sinks and sources in China's forests during 1901–2001, J. Environ. Manage., 85, 524–537, https://doi.org/10.1016/j.jenvman.2006.09.019, 2007.
Wang, Y., Wang, X., Wang, K., Chevallier, F., Zhu, D., Lian, J., He, Y., Tian, H., Li, J., Zhu, J., Jeong, S., and Canadell, J. G.: The size of the land carbon sink in China, Nature, 603, E7–E9, https://doi.org/10.1038/s41586-021-04255-y, 2022.
Wei, Z., Meng, Y., Zhang, W., Peng, J., and Meng, L.: Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau, Remote Sens Environ., 225, 30–44, https://doi.org/10.1016/j.rse.2019.02.022, 2019.
Xia, J., Xia, X., Chen, Y., Shen, R., Zhang, Z., Liang, B., Wang, J., and Yuan, W.: Reconstructing Long-Term Forest Age of China by Combining Forest Inventories, Satellite-Based Forest Age and Forest Cover Data Sets, J. Geophys. Res.-Biogeo., 128, e2023JG007492, https://doi.org/10.1029/2023JG007492, 2023.
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.
Yang, Y., Erskine, P. D., Lechner, A. M., Mulligan, D., Zhang, S., and Wang, Z.: Detecting the dynamics of vegetation disturbance and recovery in surface mining area via Landsat imagery and LandTrendr algorithm, J. Clean. Prod., 178, 353–362, https://doi.org/10.1016/j.jclepro.2018.01.050, 2018.
Yu, Z., Zhao, H., Liu, S., Zhou, G., Fang, J., Yu, G., Tang, X., Wang, W., Yan, J., Wang, G., Ma, K., Li, S., Du, S., Han, S., Ma, Y., Zhang, D., Liu, J., Liu, S., Chu, G., Zhang, Q., and Li, Y.: Mapping forest type and age in China's plantations, Sci. Total Environ., 744, 140790, https://doi.org/10.1016/j.scitotenv.2020.140790, 2020.
Zhang, C., Ju, W., Chen, J. M., Li, D., Wang, X., Fan, W., Li, M., and Zan, M.: Mapping forest stand age in China using remotely sensed forest height and observation data, J. Geophys. Res.-Biogeo., 119, 1163–1179, https://doi.org/10.1002/2013JG002515, 2014.
Zhang, H., Jin, Y., Shen, X., Li, G., and Zhou, D.: Rising Air Temperature and Its Asymmetry Under Different Vegetation Regions in China, Sci. Geol. Sin., 38, 272–283, https://doi.org/10.13249/j.cnki.sgs.2018.02.014, 2018.
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, https://doi.org/10.1002/2016EA000177, 2017.
Zhang, Z., Zhang, F., Wang, L., Lin, A., and Zhao, L. : Biophysical climate impact of forests with different age classes in mid- and high-latitude North America, For. Ecol. Manag., 494, 119327, https://doi.org/10.1016/j.foreco.2021.119327, 2021.
Zhu, Z.: Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications, ISPRS J. Photogramm. Remote, 130, 370–384, https://doi.org/10.1016/j.isprsjprs.2017.06.013, 2017.
Zhu, Z. and Woodcock, C. E.: Continuous change detection and classification of land cover using all available Landsat data, Remote Sens. Environ., 144, 152–171, https://doi.org/10.1016/j.rse.2014.01.011, 2014.
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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