Chinese Academy of Forestry | China
Jiang Liu is a Ph.D. candidate at the Chinese Academy of Forestry (2022–present) with a strong academic foundation, having earned a Master of Science (2019–2022) and a Bachelor of Engineering (2015–2019) from Southwest Forestry University. His research primarily focuses on forest management decisions, forestry information technology, machine learning, and image recognition, with extensive expertise in Python, MATLAB, R, C++, and Java. Professionally, he has contributed to numerous national and provincial projects, including key roles in the “Intelligent Multifunctional Management Decision-Making Technology for Larch Plantation Forests” under China’s National Key Research and Development Program, precision forest quality enhancement services, ecological product value realization, and carbon sequestration enhancement technologies, as well as participating in research on gene network mechanisms and adaptive evolution of Pinaceae species under environmental changes. Jiang has led projects on birdsong classification using ensemble learning and automatic recognition technologies, demonstrating his capability in integrating machine learning into forestry applications. His scholarly contributions include high-impact publications in journals such as Forests, Scientific Reports, Ecological Informatics, and World Forestry Research, covering topics from forest management type identification and stand age effects to blockchain applications in forestry and bird species classification. His excellence has been recognized with numerous awards, including the Outstanding Postgraduate Cadre, First-Class Doctoral Scholarship, provincial and national-level academic honors, and recognition for Party and leadership roles, reflecting both his academic rigor and community engagement. Jiang Liu’s career embodies a multidisciplinary approach that bridges forestry science, artificial intelligence, and ecological management, emphasizing practical applications that enhance forest productivity, conservation, and sustainability, positioning him as a leading young researcher in the field of forest management decisions.
Profiles: Scopus | ORCID | ResearchGate
Featured Publications
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Liu, J., Huang, X., Chen, S., Zheng, P., Han, D., & Liu, W. (2025). Effects of stand age gradient and thinning intervention on the structure and productivity of Larix gmelinii plantations. Forests, 16(10), 1552. https://doi.org/10.3390/f16101552
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Liu, J., Chen, J., Chen, S., & Wu, K. (2024). Forest management type identification based on stacking ensemble learning. Forests, 15(5), 887. https://doi.org/10.3390/f15050887
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Xie, S., Zhang, Y., Lv, D., Chen, X., Lu, J., & Liu, J. (2023). A new improved maximal relevance and minimal redundancy method based on feature subset. Journal of Supercomputing. https://doi.org/10.1007/s11227-022-04763-2 Citations: 29
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Deng, W., Xu, Q., Liu, J., Lu, Y., Fan, M., & Liu, X. (2022). Image classification method of longhorn beetles of Yunnan based on bagging and CNN. In Proceedings of the 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2022). IEEE. https://doi.org/10.1109/PRAI55851.2022.9904155
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Deng, W., Xu, Q., Liu, J., Lu, Y., & Fan, M. (2022). Image recognition method of longhorn beetles of Yunnan based on Gabor and CNN. In Proceedings of the 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2022). IEEE. https://doi.org/10.1109/PRAI55851.2022.9904015
