AI-Powered Carbon Monitoring: Insights from a Planetary Scientist

In this exclusive interview, Flávia de Souza Mendes, Senior Program Manager of Forest and Land Use at Planet, shares how AI is revolutionizing forest carbon monitoring. Planet, with its fleet of over 200 satellites imaging Earth’s landmass daily, uses AI to create scalable, repeatable, and transparent carbon maps. These tools enable stakeholders to monitor carbon stocks, assess project risks, and comply with emerging regulations like the EU Deforestation Regulation. Flávia’s insights highlight how AI enhances consistency, reduces fieldwork, and supports policy frameworks, offering practical guidance for organizations navigating the carbon market.

Introduction: AI at the Core of Carbon Monitoring

Carbon management in forests is critical for addressing climate change, but traditional methods—relying on labor-intensive field measurements—are slow, costly, and limited in scope. Flávia de Souza Mendes, a scientist and Senior Program Manager of Forest and Land Use at Planet, works at the intersection of remote sensing, policy, and product development to bridge this gap. Planet operates the world’s largest fleet of Earth-imaging satellites, capturing daily imagery of the planet’s landmass. By integrating AI, Planet transforms this data into actionable carbon monitoring tools that support governments, companies, and carbon project developers.

“AI makes forest carbon data scalable and repeatable,” Flávia explains. In this conversation, she explores how AI enables high-resolution carbon mapping, improves transparency in the carbon market, and supports emerging policy frameworks, offering a roadmap for organizations looking to leverage these technologies.

Scalable Carbon Mapping with AI

Traditional carbon monitoring involves field teams measuring tree heights and diameters, a process that is time-consuming and impractical for large or remote areas. Planet’s AI-driven approach overcomes these limitations by combining satellite imagery with diverse data sources like terrain and LiDAR to produce harmonized carbon maps. “AI allows us to extract information on canopy height, carbon density, or cover and update it annually or quarterly,” Flávia says.

Planet offers two key products: a 30-meter Forest Carbon Diligence product, which provides a long-term baseline dating back over a decade, and a 3-meter Forest Carbon Monitoring product, updated every three months. The 30-meter product helps assess historical trends, such as carbon stock changes or deforestation risks, while the 3-meter product enables fine-grained detection of events like selective logging or early signs of fire. These maps allow stakeholders to monitor existing carbon projects or evaluate new areas for project development, ensuring informed decisions based on comprehensive data.

Enhancing Transparency and Consistency

The carbon market has faced credibility challenges, with concerns about inaccurate carbon estimates undermining trust. AI addresses this by improving consistency and transparency. “AI applies the same calibrated approach across regions, supporting fairness and comparability,” Flávia notes. Unlike manual methods, which vary by operator and region, AI-driven models use standardized processes to estimate carbon, reducing human error and bias.

Planet’s AI outputs include quality flags, prediction intervals, and per-pixel uncertainty metrics, allowing users to assess data reliability. For example, if a region’s data has high uncertainty due to limited training data, users can opt for additional field measurements to complement the AI model. This transparency builds confidence in carbon estimates, critical for compliance with frameworks like Article 6 of the Paris Agreement, which emphasizes standardized, science-based methodologies.

Supporting Policy and Compliance

AI-driven carbon maps are increasingly vital for emerging regulatory frameworks. The EU Deforestation Regulation (EUDR), for instance, requires companies to verify that commodities like soy or coffee are not linked to deforestation after December 2020. Planet’s carbon maps help governments and companies confirm whether an area was forested, assess carbon stocks, and ensure compliance. “These maps support regulations by tracking changes, not just the status,” Flávia explains.

For carbon projects, AI enables dynamic Monitoring, Reporting, and Verification (MRV) systems. By detecting changes like selective logging or fire risks in near real-time, Planet’s 3-meter product supports early intervention, ensuring projects meet certification standards. Governments can also use these maps to plan carbon offset strategies, calculating how much carbon must be preserved elsewhere to compensate for authorized deforestation.

Balancing Automation with Human Expertise

While AI offers scalability, Flávia emphasizes that it complements, not replaces, human expertise. “Automation brings consistency, but human knowledge provides context,” she says. Field measurements remain essential for validating AI models, especially in regions with limited high-quality training data. For example, errors in traditional fieldwork—such as inconsistent measurements or inaccessible plots—can introduce inaccuracies that AI can help quantify.

Planet’s AI models estimate uncertainties per pixel, allowing users to identify areas needing additional field data. “If the confidence is low, you can collect a few samples to improve the model,” Flávia advises. This hybrid approach ensures that AI-driven maps are both scalable and grounded in local context, enhancing their utility for carbon project developers and policymakers.

Overcoming Challenges in AI Adoption

Implementing AI for carbon monitoring comes with challenges, particularly around sectoral trust. Recent controversies in the voluntary carbon market have made stakeholders wary of new technologies. “There’s a fear that AI could add risk or confusion,” Flávia acknowledges. However, she argues that AI, when properly validated, reduces risk by making assumptions and uncertainties transparent, unlike hidden errors in manual methods.

Another challenge is data quality. “AI is only as good as the data it’s trained on,” Flávia says. In many regions, high-quality training data is scarce, leading to higher uncertainties in AI outputs. To address this, Planet integrates multiple data sources—optical, radar, and LiDAR—to improve predictions. Flávia recommends that organizations use diverse, high-quality datasets to maximize AI’s effectiveness, avoiding reliance on a single source like optical imagery.

The Future of AI in Carbon Management

Looking ahead, Flávia sees AI playing a central role in three key areas. First, near real-time updates will enable rapid detection of events like fires or illegal logging, supporting early intervention and performance-based carbon credit systems. Second, AI will enhance accountability by providing transparent data with uncertainty metrics, ensuring carbon assets are accurately tracked over time. Third, AI will streamline carbon project development, reducing timelines from years to months by automating MRV processes and supporting new standards under frameworks like Article 6.

Flávia also envisions AI supporting broader applications, such as assessing fire risk probabilities or evaluating forest health to prioritize conservation efforts. As regulations evolve and trust in AI grows, these tools will become standard, enabling a more robust and transparent carbon market.

FAQ: AI in Carbon Monitoring

Q: How does AI improve carbon monitoring compared to traditional methods?
A: AI enables scalable, repeatable carbon maps that cover entire regions, updated quarterly or annually, reducing the need for costly, infrequent field measurements. It improves consistency and transparency with standardized methods and uncertainty metrics.

Q: What are the main applications of AI in carbon management at Planet?
A: Planet uses AI for 30-meter long-term carbon baselines to assess project feasibility and 3-meter quarterly monitoring to detect changes like selective logging or fire risks. These maps also support compliance with regulations like the EUDR.

Q: How can organizations measure ROI from AI in carbon monitoring?
A: ROI comes from reduced fieldwork costs, faster project timelines, and improved compliance through accurate, transparent data. For example, AI maps help assess project risks upfront, saving resources on unsuitable sites.

Q: What challenges do organizations face when adopting AI for carbon monitoring?
A: Key challenges include sectoral distrust due to past carbon market issues and limited high-quality training data in some regions. Transparent validation and diverse data sources can address these concerns.

Q: How should SMEs approach AI adoption for carbon management?
A: SMEs should partner with providers like Planet to access validated AI-driven carbon maps, integrate diverse data sources, and combine AI outputs with local expertise to ensure accuracy and relevance.

Q: What’s the future of AI in carbon management?
A: AI will enable near real-time monitoring, enhance accountability with transparent data, and streamline project development, aligning with evolving standards like Article 6 to create a more trusted carbon market.