Binlajdam, Rayan, Meedeniya, Dulani, Kosala, Charuni, Karakus, Oktay  ORCID: https://orcid.org/0000-0001-8009-9319, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646, Orozco Ter Wengel, Pablo ORCID: https://orcid.org/0000-0002-7951-4148, Goossens, Benoit ORCID: https://orcid.org/0000-0003-2360-4643, Lertsinsrubtavee, Adisorn, Mekbungwan, Preechai, Mishra, Deepak and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346
2025.
Review on sustainable forestry with artificial intelligence.
ACM Journal on Computing and Sustainable Societies
10.1145/3759259
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Abstract
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 sustainable forest management 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.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | In Press |
| Schools: | Schools > Computer Science & Informatics Schools > Biosciences |
| Publisher: | Association for Computing Machinery (ACM) |
| ISSN: | 2834-5533 |
| Date of First Compliant Deposit: | 29 August 2025 |
| Date of Acceptance: | 3 August 2025 |
| Last Modified: | 10 Sep 2025 21:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/180751 |
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