AI effective in sustainable forest management, but cost and trust issues limit use

While the study acknowledges that AI will not replace human expertise in forestry, it argues that the technology can enhance decision-making, optimize resource allocation, and support sustainable supply chains. If deployed responsibly, AI has the potential to reconcile environmental conservation with economic development - two goals often seen in conflict.

Artificial intelligence, machine learning, and deep learning technologies are transforming sustainable forestry management, offering tools to combat deforestation, monitor forest health, and enhance biodiversity conservation. But despite these advancements, technical, institutional, and ethical barriers continue to obstruct their widespread implementation, according to a comprehensive review published this week in Plants.

The review, titled "Harnessing Artificial Intelligence, Machine Learning and Deep Learning for Sustainable Forestry Management and Conservation: Transformative Potential and Future Perspectives," was conducted by researchers at Nanjing Forestry University in China. Synthesizing global developments from recent years, the study outlines how AI-powered tools are reshaping forest monitoring, predictive analytics, wildfire detection, and carbon sequestration strategies. However, it also highlights persistent challenges related to data quality, transparency, governance, and economic feasibility.

The findings arrive at a time of rising environmental urgency. Forest ecosystems - essential for carbon sequestration, biodiversity, and climate resilience - are facing mounting threats from illegal logging, invasive species, and changing weather patterns. According to the FAO, global deforestation exceeds 10 million hectares annually. The review underscores that AI, while not a cure-all, presents a transformative opportunity - if applied responsibly and equitably.

One of the most significant contributions of AI to forestry is remote sensing. By integrating satellite imagery, drone footage, and LiDAR data, AI algorithms can assess forest health, identify deforestation hotspots, and map species distribution with unprecedented speed and precision. The review cites examples such as Hitachi Vantara's collaboration with Rainforest Connection, which uses bioacoustic sensors and machine learning to detect illegal logging activity up to five days in advance.

Deep learning models, particularly convolutional neural networks (CNNs), have shown exceptional accuracy in tree species classification, canopy analysis, and disease detection. In one application, CNN-based systems trained on RGB aerial imagery successfully identified tree crowns and predicted biomass in tropical and temperate forests, streamlining inventory and conservation efforts.

In predictive forest health monitoring, AI models analyze spectral data, environmental variables, and historical records to forecast disease outbreaks and pest infestations. Machine learning algorithms like Random Forest, SVMs, and VGG16 have been used to detect citrus disease, rice blast, and other threats with high reliability. These tools support proactive interventions that reduce ecological damage and economic losses.

AI is also reshaping wildfire detection and response. Patrolling drones equipped with vision-based AI models such as YOLOv3 are now capable of detecting smoke and fire in real time, enabling quicker containment. Machine learning-enhanced weather indices provide granular, hourly fire risk forecasts, improving the accuracy of early warning systems.

Beyond short-term crisis management, AI contributes to long-term conservation planning. Algorithms analyze camera trap data, satellite images, and ecological models to optimize wildlife corridors, monitor endangered species, and track invasive threats. Tools like CAPTAIN, BIOCLIM, and reinforcement learning systems have been deployed to balance biodiversity preservation with land-use needs.

In the context of climate mitigation, AI models are used to estimate forest carbon stocks and assess the impact of climate change on ecosystem resilience. Techniques such as LiDAR-driven biomass modeling and deep learning-based carbon density mapping help quantify and manage carbon sequestration. The review notes that models using platforms like Sentinel-2 and Landsat 8 have proven especially useful in evaluating above-ground biomass.

Despite this progress, the review identifies several barriers limiting AI’s effectiveness in real-world forest management. Chief among them is the “black box” nature of many deep learning models. Lack of explainability undermines stakeholder trust, particularly among conservationists, Indigenous communities, and policymakers who demand transparency in land-use decisions.

Data quality is another persistent issue. Many AI systems rely on large, annotated datasets for training, yet forested regions often lack comprehensive, high-resolution data. In low-income countries, technological constraints and limited internet infrastructure further hinder deployment.

Ethical and societal concerns also loom large. The review raises questions about data ownership, surveillance, and the risk of marginalizing local knowledge systems. AI’s potential to disrupt existing labor structures in forestry-dependent communities must also be carefully managed, the authors argue.

From an economic standpoint, the high cost of AI infrastructure, coupled with the complex regulatory requirements for drone and satellite use, poses a challenge for smallholders and local governments. While pilot projects show promise, scaling up remains difficult without public-private partnerships and policy support.

To overcome these barriers, the researchers propose a multi-pronged strategy. Key recommendations include:

• Investing in open-access forestry datasets to improve algorithm training
• Developing explainable AI models to increase trust among stakeholders
• Integrating traditional ecological knowledge into AI workflows
• Providing capacity-building programs for forest managers and communities
• Establishing ethical and legal frameworks to govern data use and surveillance

The authors also stress the importance of international cooperation. As forests transcend borders, so too must the technologies designed to protect them. Shared research platforms, regional monitoring initiatives, and harmonized regulatory policies are needed to ensure equitable AI development.

While the study acknowledges that AI will not replace human expertise in forestry, it argues that the technology can enhance decision-making, optimize resource allocation, and support sustainable supply chains. If deployed responsibly, AI has the potential to reconcile environmental conservation with economic development - two goals often seen in conflict.

As climate change accelerates and biodiversity loss intensifies, the integration of AI into sustainable forestry is no longer a question of possibility, but of policy, ethics, and political will.