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.

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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
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Review on Sustainable Forestry with Artificial Intelligence

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Review on Sustainable Forestry with Artificial Intelligence

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article en
4 citations

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 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.

ACM Journal on Computing and Sustainable SocietiesVol. 3(4)
University of Moratuwa (LK), UNSW Sydney (AU), Asian Institute of Technology (TH), University of Jeddah (SA), Cardiff University (GB)
Openalex Percentile: Top 8%
Remote Sensing and LiDAR Applications
3.20
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