Harnessing Artificial Intelligence, Machine Learning and Deep Learning for Sustainable Forestry Management and...

Area of Science:

• Forestry and Environmental Science
• Artificial Intelligence in Ecology

Background:

• Forest ecosystems provide vital ecological, environmental, and economic benefits.
• Sustainable forest management (SFM) is crucial but faces challenges in balancing use and conservation, exacerbated by climate change and resource depletion.

Purpose of the Study:

• To review the transformative potential of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in SFM.
• To summarize current AI applications, limitations, and future perspectives in modernizing SFM.

Main Methods:

• Review of current research and technological advancements in AI, ML, and DL for SFM.
• Analysis of AI applications in predictive analytics, modeling, and decision support systems for forest management.

Main Results:

• AI, ML, and DL enable accurate forecasting of forest dynamics, including carbon sequestration, species distribution, and ecosystem health.
• AI-powered decision support systems integrate real-time data for adaptive forest management strategies.
• Identified limitations of AI in SFM and proposed solutions.

Conclusions:

• AI, ML, and DL hold immense potential to modernize SFM and address current challenges.
• Further research and implementation of these technologies are essential for effective forest conservation and management.