Commercial Motivation & Value Proposition

Accurate reporting of greenhouse gas (GHG) emissions and removals from land use, land use change, and forestry (LULUCF) is vital for countries to meet compliance obligations and climate targets. Forests act as essential carbon sinks, absorbing CO₂ from the atmosphere and storing it in biomass, soils, and dead organic matter. Yet, inconsistencies in forest definitions, data collection methods, and monitoring frequency across EU Member States
have created gaps in national GHG inventories.
EO4EU enables the integration of high-resolution Copernicus Sentinel-1 data with detailed in-situ measurements from the LUCAS survey to deliver robust, machine learning-based forest classification. By moving beyond traditional forest maps produced annually and relying on coarse-resolution data, EO4EU allows:

• Higher mapping accuracy (~80% classification accuracy in pilot regions)
• Improved temporal resolution, enabling near real-time forest monitoring
• Harmonised definitions, ensuring consistent reporting aligned with FAO-FRA
• standards
• Faster production cycles, reducing the time to update national forest inventories

The outcome is a set of reliable, standardised and user-defined forest maps that better capture deforestation, degradation, and forest regrowth dynamics, critical for credible GHG reporting and sustainable forest management.

Strategic Commercial Applications

• Continuous Forest Monitoring: Near real-time forest cover updates across EU Member States, supporting prompt policy responses to deforestation and degradation.
• Enhanced National GHG Inventories: Accurate, standardised data feeds directly into LULUCF sector reporting, improving transparency and compliance under the UNFCCC.
• Sustainable Forestry Planning: Detailed maps guide national and local agencies in planning reforestation, afforestation, and conservation initiatives.
• Biodiversity & Land-Use Policy Support: High-resolution maps inform protected area planning, illegal logging detection, and habitat conservation strategies.

Scalability & Exploitation Potential

UC4 is designed to scale to the entire EU and potentially other regions. Leveraging EO4EU’s platform, the service can:

• Be offered as a subscription-based forest monitoring and reporting service to Member States
• Support private forestry and timber companies in sustainable management certification
• Provide data services to NGOs, insurers, and international organisations monitoring land use change

The integration with EO4EU’s high-performance infrastructure and existing TRL7 systems ensures commercial viability and the flexibility to adapt to evolving regulatory requirements and user needs.

Current status

A prototype machine learning model has been successfully developed, achieving ~80% accuracy in classifying forest cover using data from over 8,000 LUCAS points in Austria.
The model utilises monthly Sentinel-1 GRD IW VH band maxima, radiometrically terrain- corrected with Copernicus30m DEM, producing 12 observations per year for each pixel.
Integration with the EO4EU platform is underway, enabling scalable application to other EU regions and different years. The current work also benchmarks spatial and temporal data resolutions to refine classification performance further.

Technical details

Overview

The Forest ecosystems use case will identify information on ecosystem services produced in a managed forest, in particular provisioning services (wood and timber), and regulating services, for hydrology and climate through the water and carbon cycle, respectively.

Countries are required to report GHG emissions and removals from land use, land use change, and forestry (LULUCF) under the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement. Forests act as carbon sinks, absorbing CO₂ from the atmosphere and storing it in biomass, soils, and dead organic matter. Improved forest mapping is crucial for accurately estimating carbon stocks and their changes over time. Without detailed and accurate forest maps, it is difficult to quantify the carbon sequestered by forests or released through deforestation and forest degradation. This accuracy is key for GHG inventories to reflect true emissions and removals. Improved mapping technologies, such as satellite imagery, drones, and remote sensing, allow for better detection of changes in forest cover and forest degradation. Early detection enables corrective measures and ensures that GHG emissions from deforestation are properly accounted for in national inventories.
 

Challenge

Across the EU, different countries and institutions may adopt different criteria for what constitutes a "forest". These definitions differ based on criteria like minimum canopy cover, area size, tree height, and how land use and land cover are classified. For instance, what qualifies as a forest in one country may not meet the thresholds in another, creating disparities in reported forest areas. Further complicating matters, monitoring mechanisms vary widely across Member States. Some rely on advanced satellite data and remote sensing platforms, while others depend on less frequent National Forest Inventories (NFIs) or use differing analytical methods for interpreting forest data. These variations result in inconsistent tracking of forest cover, deforestation, and degradation, making it difficult to standardize reporting under the LULUCF sector for GHG inventories. While the Copernicus satellite program provides valuable data, its snapshots are limited to specific reference years, falling short of the continuous monitoring needed to capture annual changes in forest cover. To ensure accurate and consistent GHG inventories, the EU must harmonize forest definitions and mapping methods, enhance the integration of remote sensing technologies, and develop validated, precise forest cover maps that can be standardized across all countries. 

Solution 

The Forest Ecosystems Use Case (UC4) is designed to generate high-resolution forest cover maps using an advanced machine learning model trained on Earth Observation data. Unlike current annual forest cover maps, this service will offer enhanced classification capabilities, distinguishing forest classes from other land cover types, including agricultural and agroforestry areas. Using timeseries analysis, the machine learning model can identify the structural features of each land cover class, based on phenological characteristics and structural attributes associated with different types of vegetation.  The model is built on precise ground data sourced from the LUCAS (Land Use/Cover Area Frame Survey), which collects detailed information across Europe on tree height, land cover types, and species composition. This high-quality ground data significantly enhances the robustness and accuracy of the model, ensuring reliable classification of forest cover and supporting better management and conservation efforts.

• Earth Observation Data: Sourced from Sentinel-1 (GRD IW VH band), radiometrically terrain-corrected using Copernicus30m DEM, providing high- resolution structural information on forest canopy and land cover dynamics.
• Ground Survey Data: Detailed in-situ measurements from the LUCAS (Land Use/Cover Area Frame Survey), including tree height, land cover types, and species composition, used to train and validate the machine learning model.
• Temporal Data Series: Monthly Sentinel-1 maxima for each pixel (12 images per year), enabling time-series analysis to detect seasonal and structural variations in forest ecosystems.