학술논문
Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision
Document Type
Conference
Author
Luo, Feng; Liu, Ling; Wang, G. Geoff; Kumar, Vijay; Ashton, Mark S.; Abernethy, Jacob; Afghah, Fatemeh; Browning, Matthew H. E. M; Coyle, David; Dames, Philip; O'Halloran, Tom; Hays, James; Heisl, Patrick; Jiang, Chenfanfu; Khanal, Puskar; Krovi, Venkat Narayan; Kuebbing, Sara; Li, Nianyi; Liang, JingJing; Liu, Ninghao; McNulty, Steve; Oswalt, Christopher M.; Pederson, Neil; Terzopoulos, Demetri; Woodall, Christopher W.; Wu, Yongkai; Yang, Jian; Yang, Yin; Zhao, Liang
Source
2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI) COGMI Cognitive Machine Intelligence (CogMI), 2023 IEEE 5th International Conference on. :1-10 Nov, 2023
Subject
Language
Abstract
Forests and forest ecosystems are vital to our social, economic, and environmental well-being. However, climate change and climate-driven disturbances (CDDs) are undermining the health and resilience of forests worldwide and pose significant uncertainty to sustainable forest management. Climate-smart forestry (CSF) remains a grand challenge in practice due to our limited knowledge of how forests respond to climate change and our abilities to collect related information to empower decision making. Rapid advances in artificial intelligence (AI) can offer a timely opportunity to address the challenges in CSF. We argue that the AI-enabled, next-generation CSF can be achievable through synergistically coordinated and transdisciplinary efforts that develop and advance foundational and use-inspired AI technologies that can lead to building next-generation forest decision support systems.