New paradigm of forestry and grassland research driven by artificial intelligence

Authors

• ZHANG Huaiqing, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; National Forestry and Grassland Science Data Center, Beijing 100091, ChinaFollow
• Jiaojun ZHU, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
• Yang LIU, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; National Forestry and Grassland Science Data Center, Beijing 100091, China
• Tingdong YANG, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; National Forestry and Grassland Science Data Center, Beijing 100091, China
• Tian GAO, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
• Jing ZHANG, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; National Forestry and Grassland Science Data Center, Beijing 100091, China
• Xueyan ZHU, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; National Forestry and Grassland Science Data Center, Beijing 100091, China
• Yan CHEN, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; National Forestry and Grassland Science Data Center, Beijing 100091, China
• Xian JIANG, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; National Forestry and Grassland Science Data Center, Beijing 100091, China
• Zeyu CUI, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; National Forestry and Grassland Science Data Center, Beijing 100091, China
• Jingwei TAN, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; National Forestry and Grassland Science Data Center, Beijing 100091, China
• Kexin LEI, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; National Forestry and Grassland Science Data Center, Beijing 100091, China

Keywords

AI for science, artificial intelligence-driven scientific research, forestry and grassland science, transformation of research paradigms, reconstruction of disciplinary systems

Abstract

Addressing the current limitations in forestry and grassland research, particularly in cross-scale system cognition, complex process mechanism representation, and multi-scenario simulation, this study proposes an artificial intelligence-driven paradigm reconstruction framework, to shift the research model from experience-oriented approaches toward data- and intelligence-driven integration. On this basis, the study systematically establishes a multidimensional mapping between artificial intelligence and forestry and grassland research across research objects, processes, and objectives, and clarifies their intrinsic coupling mechanisms and technical pathways. Furthermore, it develops a foundational capability system to support the new paradigm from four key dimensions: data, computing power, models, and applications. By examining typical application scenarios—including ecological governance, resource management, forest cultivation, and industrial development—the study further explores the practical modes and application potential of artificial intelligence in forestry and grassland science. This work is expected to provide a useful reference for promoting the intelligent and systematic transformation of forestry and grassland research in China.

First page

1500

Last Page

1511

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

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Recommended Citation

Huaiqing, ZHANG; ZHU, Jiaojun; LIU, Yang; YANG, Tingdong; GAO, Tian; ZHANG, Jing; ZHU, Xueyan; CHEN, Yan; JIANG, Xian; CUI, Zeyu; TAN, Jingwei; and LEI, Kexin (2026) "New paradigm of forestry and grassland research driven by artificial intelligence," Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 41 : Iss. 7 , Article 17.
DOI: https://doi.org/10.3724/j.issn.1000-3045.20260203003
Available at: https://bulletinofcas.researchcommons.org/journal/vol41/iss7/17