61 citations as recorded by crossref.

1. Integrating Active and Passive Remote Sensing Data for Forest Age Estimation in Shangri-La City, China F. Cheng et al. https://doi.org/10.3390/f15091622
2. China’s naturally regenerated forests currently have greater aboveground carbon accumulation rates than newly planted forests K. Cheng et al. https://doi.org/10.1038/s43247-025-02323-z
3. Challenges and Strategies for Flood Forecasting in a Changing Environment J. Zhang et al. https://doi.org/10.3724/BNSFC-2025-0076
4. A machine learning model for estimating tree age based on trunk radius and radial growth rate J. Yao et al. https://doi.org/10.1016/j.atech.2026.102525
5. Mapping Forest Tree Species Using Sentinel-2 Time Series by Taking into Account Tree Age B. Yang et al. https://doi.org/10.3390/f15030474
6. Optimized latin hypercube sampling for national-scale forest canopy height mapping S. Zhang et al. https://doi.org/10.1016/j.asr.2026.07.079
7. A dual-pathway framework for mapping forest age in complex mining landscapes by multi-source remote sensing data and tree growth patterns T. Ma et al. https://doi.org/10.1080/15481603.2025.2565858
8. Spatial Pattern of Forest Age in China Estimated by the Fusion of Multiscale Information Y. Xu et al. https://doi.org/10.3390/f15081290
9. Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detection L. Zhai et al. https://doi.org/10.1016/j.ecolind.2024.113043
10. Spatiotemporal Dynamics and Driving Factors of Arbor Forest Carbon Stocks in Yunnan Province, China (2016–2020) J. Wu et al. https://doi.org/10.3390/f16071076
11. Spatial patterns and future potential of tree species richness and structural diversity in China’s forests C. Cheng et al. https://doi.org/10.1038/s41559-025-02922-1
12. Asymmetric response of forest aboveground biomass density to precipitation anomalies in China S. Ma et al. https://doi.org/10.1016/j.jaridenv.2026.105702
13. Assessing the potential of species loss caused by deforestation in a mature subtropical broadleaf forest in central China J. Zhang et al. https://doi.org/10.1016/j.tfp.2024.100673
14. CLM5-FATES模式对中国长白山针阔混交林分布的模拟 Y. Sui & C. Yang https://doi.org/10.3799/dqkx.2025.073
15. Widespread Declining Sensitivity of Chinese Forests to Soil Moisture Under Climate Change (2001–2020) Y. Guan et al. https://doi.org/10.3390/f17010015
16. Grain for Green Project dominates greening in afforested areas rather than that in grass revegetation areas of the Loess Plateau, China—using Deep Crossing LSTM Age network X. Wang et al. https://doi.org/10.1088/1748-9326/adec02
17. Pan‐boreal mapping of forest age and implications for conservation Y. Liu et al. https://doi.org/10.1111/cobi.70346
18. Spatiotemporal evolution and driving mechanisms of urban land use carbon metabolism in the Guangdong-Hong Kong-Macao Greater Bay Area from 2000 to 2023 S. Wang et al. https://doi.org/10.1007/s11430-025-1770-7
19. Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet Z. Chi & K. Xu https://doi.org/10.3390/rs17111926
20. Revealing the spatial distribution of crown base height across China based on close-range Lidar data Z. Yang et al. https://doi.org/10.1016/j.rse.2025.115030
21. Understanding flash drought variations in China's planted and natural forests through machine learning counterfactual analysis X. Yang et al. https://doi.org/10.1016/j.fecs.2026.100462
22. The invasive woodwasp Sirex noctilio Fabricius threatens pine forest carbon storage in China under climate change X. Liu et al. https://doi.org/10.1016/j.tfp.2026.101337
23. An Approach for Mapping Ecotourism Suitability Using Machine Learning: A Case Study of Zhangjiajie, China Q. Huang et al. https://doi.org/10.3390/land13081188
24. Mapping of Chinese fir plantations and stand age distribution in China with time series data Y. Li et al. https://doi.org/10.1016/j.isprsjprs.2026.04.031
25. A global 30 m disturbance-recovery age dataset for young natural and planted forests (1985–2024) Y. Wang et al. https://doi.org/10.5194/essd-18-6225-2026
26. Maximum carbon uptake potential through progressive management of plantation forests in Guangdong Province, China X. Li et al. https://doi.org/10.1038/s43247-024-01977-5
27. Biophysical impact of forest age changes on land surface temperature in China Z. Zhang et al. https://doi.org/10.1016/j.scitotenv.2025.178445
28. Improving Total Carbon Storage Estimation Using Multi-Source Remote Sensing H. Zhou et al. https://doi.org/10.3390/f16030453
29. Uneven decline in the hydrological efficiency of China's natural and plantation forests X. Zhang et al. https://doi.org/10.5194/hess-30-3697-2026
30. Phylogeny contributes to sustaining the functional diversity of temperate forests under environmental change Y. Gu et al. https://doi.org/10.1016/j.foreco.2026.123846
31. Planted forests in China have higher variability than natural forests X. Wang et al. https://doi.org/10.1016/j.foreco.2026.123677
32. 2000–2023年粤港澳大湾区城市土地利用碳代谢时空演变与驱动机制 少. 王 et al. https://doi.org/10.1360/SSTe-2025-0048
33. A 2020 forest age map for China with 30 m resolution K. Cheng et al. https://doi.org/10.5194/essd-16-803-2024
34. Spatiotemporal Dynamics of Forest Carbon Sinks in China’s Qinba Mountains: Insights from Sun-Induced Chlorophyll Fluorescence Remote Sensing Y. Lian et al. https://doi.org/10.3390/rs17081418
35. China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022 R. Shang et al. https://doi.org/10.5194/essd-17-3219-2025
36. Attributing long-term forest disturbance events across the northeast forest region of China by analyzing Landsat time-series observations with machine learning (1986–2023) X. Yin et al. https://doi.org/10.1016/j.ecolind.2025.113997
37. Species trait dissimilarity dominates the mixing effects of forest plantations across stand development stages Q. Wang et al. https://doi.org/10.1016/j.fecs.2026.100497
38. Abiotic Factors Exert a Predominant Influence on the Annual Aboveground Biomass Dynamics of Chinese Abies Mill. Forests Relative to Biotic Factors Z. Gao et al. https://doi.org/10.3390/f17040466
39. Untangling the drivers of gross primary productivity in Chinese Fagaceae-dominated forests S. Yang et al. https://doi.org/10.3389/ffgc.2026.1693013
40. Sustainable growth of China’s forest biomass carbon storage since 2002: Facing threats and loss risks Q. Lv et al. https://doi.org/10.1016/j.geosus.2025.100340
41. Carbon sequestration potential of tree planting in China L. Yao et al. https://doi.org/10.1038/s41467-024-52785-6
42. Spatio-temporal dynamics of future aboveground carbon stocks in natural forests of China Y. Zhang et al. https://doi.org/10.1016/j.fecs.2025.100293
43. Network-enabled dynamic modeling unveils divergent land-carbon futures and governance pathways in a megaregion Z. Liu & S. Wang https://doi.org/10.1016/j.jclepro.2026.148185
44. Automated machine learning integrating multi-source satellite observations to predict gross and net CO2 fluxes of coastal wetlands in China N. Ngoc Tu et al. https://doi.org/10.1088/1748-9326/ade731
45. Natural forests show stronger carbon sink resistance to extreme drought than plantations in Northeast China Y. Ding et al. https://doi.org/10.1016/j.agrformet.2026.111266
46. Forest carbon storage and sink estimates under different management scenarios in China from 2020 to 2100 J. Qin et al. https://doi.org/10.1016/j.scitotenv.2024.172076
47. Ecosystem engineering and global changes are increasingly enhancing China’s terrestrial carbon sinks M. Zhang et al. https://doi.org/10.1016/j.resconrec.2025.108514
48. Extraction of eucalyptus age and estimation of its aboveground biomass in China with the integration of empirical model and machine learning algorithm C. Tang et al. https://doi.org/10.1016/j.fecs.2026.100440
49. Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data Y. Chen et al. https://doi.org/10.5194/essd-16-5267-2024
50. Edge effects on forest dynamics in China from 2000 to 2020: Evidence from satellite remote sensing J. Chen et al. https://doi.org/10.1016/j.rse.2025.115187
51. Photon-Counting Lidar Remote Sensing: Current progress and future trends L. Wu et al. https://doi.org/10.1109/MGRS.2026.3660928
52. Long-Term Forest Disturbance Mapping in the Qinling Mountains Using Landsat–Sentinel Annual Composites: A Regional Assessment of LandTrendr Performance Y. Wang et al. https://doi.org/10.3390/rs18111802
53. A 20-year assessment of AGBD accumulation and its drivers in China's broad-leaved forests across temperature zones S. Ma et al. https://doi.org/10.1016/j.envres.2025.123344
54. Disentangling the effects of the Grain for Green project on ecosystem carbon sequestration on the Loess Plateau J. Wang et al. https://doi.org/10.1016/j.catena.2025.109668
55. 1 km annual forest cover and plant functional type dataset for China from 1981 to 2023 B. Liu et al. https://doi.org/10.5194/essd-18-1103-2026
56. The impact of global change on the slow decline in human-wild boar (Sus scrofa) conflicts in central China from 2018 to 2022 T. Jiang et al. https://doi.org/10.1016/j.gecco.2025.e03790
57. Contrasting drought vulnerability of natural and planted forests in drylands X. Dong et al. https://doi.org/10.1016/j.foreco.2025.123311
58. How resilient are China’s forest conservation and restoration efforts against drought risks? Empirical evidence from nearly two decades of forest management in China N. Shuai et al. https://doi.org/10.1016/j.agrformet.2026.111110
59. 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. https://doi.org/10.3390/rs16142547
60. Plantation expansion reshapes water and carbon patterns in Northeast China's forests: A high carbon sink at the cost of inefficient water use Y. Zhao et al. https://doi.org/10.1016/j.ecolind.2026.115026
61. Afforestation as a mitigation strategy: countering climate-induced risk of forest carbon sink in China Y. Cao et al. https://doi.org/10.1186/s13021-025-00308-1