Seeing more, planning better: AI-powered baselines for climate and agricultural policy

Planting hedgerows could save 112 million tons of CO₂ in the next ten years, according to a recent report published by Agora Agriculture. The think tank estimates that it’s possible to plant 6,000 square kilometers (about 2,300 square miles) of hedgerows in Germany between 2025 and 2045, which would sequester an average of 5 million tons of CO₂ annually over this period. That’s enough to offset nearly a quarter of the country’s arable land emissions.

The EU Common Agricultural Policy recognizes the potential of hedgerows as a carbon capture strategy and has allocated substantial funding to incentivize farmers to plant them. In Germany, a total of €6.2 billion has been set aside for agricultural subsidies, with funding divided across two pillars. The first involves direct payments to farmers who participate in voluntary eco-schemes, including adopting environmentally friendly practices like maintaining hedgerows, flower strips and grasslands. The second is made up of programs for sustainable and environmentally sound farming and rural development.

In principle, the policy looks solid. The environmental benefits have been calculated, and a strong incentivization structure is in place. But in-depth analysis reveals that existing data on hedgerows, both in terms of their scale and placement, is often inaccurate, inconsistent and out of date. Information has been drawn from regional surveys, farmer self-reporting and decades-old baselines that aren’t always reliable. Hedgerow classification metrics often differ, too, as does how frequently models are updated. 

If the EU Common Agricultural Policy’s hedgerow planting subsidies are going to be effective, access to precise and reliable data has to be non-negotiable. With this in mind, research conducted by Dr. Javier Muro and his previous team at Thünen Institute has sought to improve the accuracy of hedgerow mapping through high-resolution satellite imagery. Here, we explore the practical implications of this research for policy development and conservation in Germany and beyond. 

Numbers that can’t be trusted

“Hedgerows and flower strips fulfill critical ecological and environmental functions,” explains Dr. Muro. “They provide essential areas of woody vegetation within vast swathes of what is often intensively farmed land.”

These zones serve as biodiversity corridors and sanctuaries for insects, pollinators and birds, providing habitats for them to feed, nest and reproduce. Below ground, their root systems incorporate atmospheric carbon into the soil, building organic matter that increases both fertility and climate resilience. Above ground, they act as windbreaks that prevent nutrient-rich topsoil from blowing away when fields lie bare.

Over the past 70 years, Germany is estimated to have lost roughly 50% of its hedgerows to farms being consolidated and hedgerows uprooted, a pattern repeated across northern Europe. It’s part of what’s led to a renewed focus on incentivization and subsidy.

At the same time, however, Dr. Muro and his colleagues have found considerable errors in the available figures on hedgerows in Germany. Where federal authorities once estimated that the country had 90,000 kilometers (58,000 miles) of hedgerows, his team’s recent satellite mapping exercise revealed that the actual figure is closer to 293,380 kilometers (182,300 miles). Previous studies have missed more than two-thirds of Germany’s hedgerows, and have therefore miscalculated biomass, habitat, carbon storage, soil protection services and baseline data in the process. 

Hedgerow data in Germany, in other words, is likely unreliable. “As it currently stands, comprehensive, standardized and area-wide information regarding the distribution of hedgerows is often lacking, and the situation is likely similar across other European countries,” says Dr. Muro. This makes it hard to incorporate them in nature conservation plans and national carbon balance models.”

Coming up against a reliability threshold

Environmental policy has always involved some level of estimation and approximation, but things are changing. In recent years, as efforts to mitigate the effects of climate change have become increasingly important, the need for data-led frameworks with enforceable accountability mechanisms has strengthened.

The EU’s Land Use, Land Use Change and Forestry (LULUCF) regulation now requires member states to track and report greenhouse gas emissions and removals from agricultural land with increasing precision. And the Nature Restoration Law, adopted in 2024, sets binding targets for ecosystem restoration, including specific requirements for landscape features like hedgerows. Carbon markets demand verifiable data to establish credit legitimacy. 

Detailed and accurate maps of existing hedgerows are essential for several purposes:

• Identifying priority districts for new planting
• Allocating subsidy funds efficiently across regions
• Verifying that farmers receiving payments are actually creating new hedgerows rather than claiming credit for existing ones
• Measuring whether restoration targets are being met 

This ultimately results in a reliability threshold in policy implementation: a minimum acceptable marker of accuracy, consistency or success to determine if a process, system or tool is reliable. “If you calibrate a model with data that is inaccurate, the results will also be inaccurate,” Dr. Muro adds. “This has real-life implications because policymakers and enforcement agencies won’t commit to binding regulations if they lack confidence in the underlying data.”

How precision mapping differs

The advances that Dr. Muro has made in precision mapping offer a solution. His approach uses high-resolution satellite data from PlanetScope, captured at multiple points across the growing season. This data is then analyzed with deep learning models that can distinguish hedgerows from other landscape features. By incorporating near-infrared bands alongside visible light, the system captures vegetation signatures invisible to the human eye, enabling it to detect narrow woody strips that traditional-resolution satellites miss entirely.

The result is Germany’s first comprehensive precision hedgerow monitoring at a national scale. On a practical level, it has the potential to improve environmental planning at several levels.

Real-time monitoring

Traditional environmental monitoring produces snapshots at specific intervals. It offers insight into the state of a particular landscape at discrete moments in time, but fails to indicate what’s happening in the months and years in between. High-resolution satellite constellations like PlanetScope provide near-daily global coverage and enable continuous observation. This makes it easier to track how hedgerows change throughout the year, and offers critical information about their status and health. 

Carbon credit markets

Carbon offset markets depend on someone paying for verifiable emissions reductions or carbon sequestration that wouldn’t have occurred otherwise. But without comprehensive baseline mapping, verification becomes nearly impossible. Credit buyers need to be confident that they’re paying for real, additional sequestration. And sellers need transparent metrics to demonstrate this additionality. 

Dr. Muro’s high-resolution model offers the surety carbon credit markets need. His three-meter resolution imagery captures the extent and width of hedgerows, while the temporal monitoring enables him to revisit areas to track growth, maintenance and degradation.

Detailed scenario modeling

Precise baseline data enables scenario modeling that was previously impossible. With accurate maps, planners can model interventions, test assumptions and optimize resource allocation before spending billions on implementation. 

“Our approach allows us to plan ahead for different climate change scenarios,” says Dr. Muro. “It gives us all the data we need to anticipate what will happen depending on how much global temperatures increase. Better data means better scenario simulations.”

Multi-benefit optimization

Landscape features like hedgerows deliver multiple ecosystem services simultaneously. They improve biodiversity, maximize carbon sequestration and minimize soil erosion all at once. But the optimal configuration for one benefit may differ from another. Sometimes carbon and biodiversity goals conflict, or soil protection competes with agricultural productivity.

Precise data makes these trade-offs explicit and quantifiable. And by mapping hedgerows accurately, it’s easier to optimize hedgerow policy so that it can achieve as many objectives as possible given the unique circumstances at play. 

Key results

Through precision mapping, Dr. Muro and his team revealed several key insights that would likely have a significant impact on how effectively various policies are executed. 

One of the first is that hedgerow distribution across Germany is strikingly uneven. Some districts show coverage as high as 14% of agricultural land, while others register barely 1%. The north-south gradient follows historical patterns and wind pressure dynamics, with northern regions like Schleswig-Holstein developing dense hedgerow networks centuries ago as windbreaks. Southern Germany’s hillier terrain and fragmented landscapes, on the other hand, never required the same systematic planting.

This means that districts with extensive arable land but minimal hedgerow coverage should be seen as high-priority intervention zones, particularly where soil erosion is severe and wind exposure is high.

The mapping also reveals structural patterns. “Our research demonstrated that Germany’s northwestern regions have high average hedgerow height, which is good for carbon storage, but low variability,” Dr. Muro explains. “This low variability isn’t great for biodiversity, which requires a greater structural diversity. Other regions revealed lower heights but greater structural diversity.”

This level of detail plays into the multi-benefit optimization described above, and is likely to influence how policymakers advise and engage with farmers.

The maps also support connectivity analysis. By overlaying hedgerow distribution with locations of nature reserves and high-value habitats, conservation planners can identify which new hedgerows would create functional corridors for species to move between. Not all hedgerows are equally valuable for biodiversity. A single isolated strip does far less than one that links existing habitat zones. Spatially explicit mapping allows policymakers to have a firm handle on the effect that hedgerow layout is likely to have on different spaces, rather than purely focusing on the number of kilometers planted. 

“Finally, precision data also allows us to show farmers exactly where and how hedgerows will affect their farms in terms that are most important to them, like yield, cost and compensation,” Dr. Muro concludes. “It’s an essential part of the policy buy-in process, and is likely to ensure that policies have a measurable impact when they’re implemented.”

Putting policy into practice

When environmental monitoring relies on incorrect data, it delivers misallocated subsidies, unreliable carbon accounting and flawed conservation targets. With stakes measured in billions of euros and millions of tons of carbon dioxide, the ramifications of this are severe. Precise mapping, on the other hand, creates the foundation for effective carbon markets, meaningful agricultural subsidies and targeted conservation. 

The technology exists to source up-to-date, consistent and reliable data, as Dr. Muro’s research clearly demonstrates. With it, policies become robust and their effects more acute. Implemented effectively, farmers, communities, organizations and governments inch closer to improving biodiversity, mitigating the effects of climate change, and nurturing a healthier agricultural and natural world.