Artificial Intelligence (AI) is a broad and rapidly evolving field within computer science focused on developing smart systems capable of augmenting, automating, and accelerating tasks that typically require human intelligence. AI aims to build tools that can solve problems, recognize patterns, process natural language, learn from data, and prepare decision alternatives for human users. To achieve this, it draws on diverse computational approaches—including automated reasoning, anomaly detection, neural networks, and heuristic search—to extract patterns, estimate future conditions, and perform functions traditionally associated with human cognition. Recent advancements in data science, coupled with rapid developments in digital technologies and remote sensing, have greatly expanded the potential for AI applications in forestry. From emerging sensor platforms to algorithms capable of processing and interpreting big data, AI offers new opportunities to enhance the monitoring, management, and conservation of forest ecosystems facing increasingly complex global challenges.

Despite its potential, the research progress and practical adoption of AI in forestry have been noticeably slower than in sectors with higher economic returns. Uneven geographic distribution of research efforts and gaps in forestry education further limit adoption, highlighting the need for wider regional engagement, stronger collaborations between industry and academia, and modernized curricula that equip future foresters with digital and analytical competencies. In addition, growing doubts about reliance of AI-generated outputs raises concerns about data quality, transparency, and the importance of scientific validation. Addressing these limitations will be critical to ensuring the reliable, responsible, and effective use of AI in sustainable forest management.

Against this backdrop, this Research Topic aims to bring together the most up-to-date, rigorous, and reliable applications of AI in forestry to understand, characterize, monitor, and simulate forest ecosystems and their dynamics in a rapidly changing world. We invite submissions that enhance forest inventories, advance forest health assessments and disturbance detection, characterize and map ecosystem services (including biodiversity values), optimize forest planning or harvest scheduling, or address Sustainable Development Goal (SDG) data gaps through AI-based remote sensing or geospatial analysis. Studies emphasizing responsibility, validated, and transparent AI use in the forestry sector are especially welcome.

Possible contributions may include, but are not limited to:

• Active or passive remote sensing of forests
• Tree species identification or forest stand delineation
• Detection of deforestation and illicit logging
• Automated afforestation and restoration planning
• Forest biomass and biomass-carbon modeling
• Hazard assessment, forest fire detection, and wildlife monitoring
• Monitoring the supply, flow and/or demand of forest ecosystem services (including biodiversity)
• Operations research methods for forest planning, harvest scheduling, or simulation-based scenario analysis
• Automated drone flight planning, harvester or robot trajectory optimization in forest environments
• Topics related to precision forestry, climate-smart forestry, and operational forest management.

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Artificial Intelligence Applications in Forestry