Review on Sustainable Forestry with Artificial Intelligence
Sustainable forest management (SFM) is essential for preserving biodiversity, maintaining ecosystem services, and mitigating climate change. This systematic review synthesizes global trends and innovations in SFM practices, analyzing peer-reviewed literature from 2015 to 2025 to identify effective strategies and emerging technologies. The review examines a diverse range of approaches, including forest health index, forest health sensing techniques, emphasizing remote sensing, ground-based monitoring, and the application of machine learning (ML) and artificial intelligence (AI). Moreover, the review highlights SFM practices, including ecosystem-based approaches, community and indigenous involvement, carbon sequestration strategies, and local and global policy frameworks. By integrating technological advancements with policy-driven initiatives, this study provides a comprehensive understanding of current trends and innovations in forest management, offering valuable insights for researchers, policymakers, and practitioners.
Authors
- Benoît Goossens (ORCID: https://orcid.org/0000-0003-2360-4643)
- Pablo Orozco‐terWengel (ORCID: https://orcid.org/0000-0002-7951-4148)
- Dulani Meedeniya (ORCID: https://orcid.org/0000-0002-4520-3819)
- Omer Rana (ORCID: https://orcid.org/0000-0003-3597-2646)
- Oktay Karakuş (ORCID: https://orcid.org/0000-0001-8009-9319)
- Charith Perera (ORCID: https://orcid.org/0000-0002-0190-3346)
- Adisorn Lertsinsrubtavee (ORCID: https://orcid.org/0000-0002-3814-5913)
- Preechai Mekbungwan (ORCID: https://orcid.org/0000-0002-6444-9857)
- Deepak Mishra (ORCID: https://orcid.org/0000-0002-3225-6495)
- Rayan Binlajdam
- Charuni Kosala
Institutions
- University of Moratuwa (LK)
- UNSW Sydney (AU)
- Asian Institute of Technology (TH)
- University of Jeddah (SA)
- Cardiff University (GB)
Publication Details
- Journal
- ACM Journal on Computing and Sustainable Societies
- Published
- 2025-08-26
- DOI
- https://doi.org/10.1145/3759259
- Citations
- 4
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 3.20