Satellite Intelligence for Woodland and Nature Recovery | Case Study

Overview

The South of Scotland is Scotland's national Natural Capital Innovation Zone (NCIZ) — a launchpad for innovative natural capital projects that attract responsible investment and create scalable solutions to the twin global challenges of biodiversity loss and climate change.

As the region scales up native woodland creation to meet national Net Zero targets, monitoring thousands of hectares for survival, carbon capture, and biodiversity is a significant challenge. Traditional field surveys are labour-intensive and difficult to repeat at scale.

This initiative was a collaborative feasibility study funded by South of Scotland Enterprise (SOSE). Between December 2025 and March 2026, project stakeholders, led by Dumfries and Galloway Woodlands (DGW) and D-CAT explored how intelligence derived from freely available satellite Earth Observation (EO) data can provide an automated, cost-optimised approach to monitor woodland health across diverse landscapes for a broad group of stakeholders.

Challenge

Across the South of Scotland, native woodland creation is central to achieving climate, biodiversity, and economic goals. However, ensuring that each hectare delivers maximum value is complex. Landowners and project developers must balance carbon sequestration potential with biodiversity outcomes, long-term resilience to climate variability and economic viability of management practices.

Regional stakeholders need a scalable and cost-efficient approach that enables them to verify the success of funded planting initiatives without relying on constant manual inspections, ensuring that sites are establishing as intended. At the same time, they must be able to quantify carbon stocks with credible, defensible estimates that support funding applications and meet increasingly rigorous reporting standards. Alongside this, there is a growing need to monitor resilience across diverse ownership types, allowing early identification of risks such as fire, drought, disease, and windblow before they escalate into significant issues.

In this project, the specific challenge set by DGW was how to optimise both the economic and environmental returns from native woodland creation across the region. This required more than just better data. It required a shift in how woodland creation is approached — moving toward a model where planning, monitoring, and long-term performance are connected through consistent, scalable insight. At a more granular level, that meant identifying what data would deliver real value to stakeholders, understanding the trade-offs between freely available and commercial EO data, and delivering a cost-optimised solution across large and diverse landscapes.

Approach

The study combined stakeholder engagement with technical investigation to define what an effective EO-based monitoring framework should look like in practice. Rather than starting with technology, the work began with outcomes: what DGW need to know to make better decisions.

With climate change becoming an increasing priority to understand and adapt to, DGW also sought insights on resilience to fire, flooding and water issues and changing weather patterns. In parallel, the project explored how these insights could be operationalised — moving from analysis to a repeatable service capable of supporting regional programmes as well as individual site.

Seven priority use case applications were therefore identified and tested for the region:

Grant Verification: Independent monitoring of tree survival to support grant compliance.
Woodland Laboratory: Assessing how different management practices affect canopy health.
Biodiversity Connectivity: Mapping how new plantings bridge habitat gaps at a landscape scale.
Flood Resilience: Using moisture indicators to show how woodlands mitigate runoff risk.
Carbon Reporting: Estimating biomass to provide cost-effective alternatives to field surveys.
Fire Risk: Mapping fuel loads and temperature trends to identify high-risk areas.
Weather Trends: Tracking local drivers like temperature and rainfall that influence woodland health.

Solution

Satellite data from both freely available sources and high-resolution commercial providers was analysed through D-CAT's Fusion Platform^®, with the focus being on the processing of open data to meet the cost-optimised objectives of the project. This enabled a direct comparison of cost, spatial detail, and usability across different woodland creation and management scenarios and identified a range of data services most suited to being combined into a tiered, fit-for-purpose monitoring solution.

Specific D-CAT data services explored by the study included vegetation health monitoring, canopy tracking, biodiversity analysis, forest growth / deforestation, connectivity mapping and carbon stock estimation. In addition, fire risk and fuel load, water detection and depth mapping, runoff, weather trends and impact analyses were also conducted.

Crucially, the solution is designed to scale — supporting everything from individual landowners to regional programmes — without compromising on consistency or scientific integrity.

Results

By analysing historical time-series data (2021-2025), the study proved that 10m-resolution imagery provides the full range of insights sought at sufficient resolution for all use cases, except for connectivity of fine features such as hedgerows and saplings where D-CAT data services can be used with commercial satellite imagery to provide the spatial detail required.

A multitude of insights were gained through the study, all of which provided DWG with practical examples of value from EO-derived imagery and informed them regarding use case applicability, required update frequency, sufficient spatial resolution, and cost optimisation. Just a few examples of the intelligence layers delivered are shown below along with commentary on their use cases and benefits.

Left: Automated identification of forests (bright green) within a mixed use estate; Right: Automated land classification. Both facilitate use cases 1 and 3, saving time and money.

Left two images: Before and after storm Arwen of December 2021 showing dense tree canopy (deep green) and less dense canopy (light green) processing 10m-resolution imagery. Next image: Tree detection processing after storm Arwen using higher-resolution imagery. Right: Wind direction map at a point in time through the storm.

Left: Moisture after rainfall before loss of trees; Right: Saturated ground after similar rainfall with no tree canopy. Illustrates the protection of soils provided by forestry.

In this example, the site has a mixture of different forestry as well as pastures and cropping. Automated processing enabled a carbon stock map to be delivered for the full area, quickly and cost-effectively.

Scalable, repeatable analysis like this was noted as being of value for reporting as well as forest management.

Looking Ahead

This project proposed two structures to deliver data and derive intelligence that could be embedded across the NCIZ and future projects:

Woodland Intelligence Capability:

A regional monitoring capability that automatically acquires and processes satellite data to provide continuous, independent intelligence into woodland condition and change across all woodland ownership types. Delivered through D-CAT's Fusion Platform^®.

Woodland Intelligence Dashboards:

A proposed regional tool that turns complicated satellite information into simple, useful metrics that help stakeholders make decisions. Instead of looking at raw data, users see clear updates on essential "Key Performance Indicators" (KPIs), such as how much the forest canopy is changing or spotting "hotspots" where trees are struggling. Because the core Woodland Intelligence data is shared through an automatic digital connection (an API), it is flexible enough to create custom versions of the dashboard for different stakeholder groups. This means that local communities, landowners, and investors can each have a specific view that shows them exactly the data they need for their specific roles.

Conclusion

Through close collaboration with DGW, and support from SOSE, D-CAT has shown how satellite intelligence can transform woodland management into a precision-driven, measurable investment in the future. The region can reduce monitoring costs and improve transparency, and this collaboration positions the NCIZ as a leader in evidence-based stewardship for all stakeholders, ensuring natural assets are developed and protected for future generations.

Access to affordable and reliable natural capital intelligence derived from satellite data provides value across the regional NCIZ ecosystem:

Landowners & Managers: From private estates to community groups, owners gain tools for proactive management and establishment verification.
Investors & Finance: Natural capital investors require reliable and affordable "Monitoring, Reporting, and Verification" (MRV) data to de-risk projects and validate carbon or biodiversity credits.
Public Agencies: Organisations like SOSE and local councils can use data to verify grants and inform Local Development Plans.
Communities & Trustees: Charitable trusts like DGW use the data to provide objective evidence of environmental progress to funders and local members.

Specifically, the study demonstrated clear value in integrating satellite intelligence into woodland management and creation programmes, delivering:

Improved Decision-Making

Stakeholders gained clearer visibility into where and how woodland creation efforts would deliver the greatest returns.

Cost-Effective Monitoring at Scale

Free EO data was shown to be highly effective for broad monitoring, with commercial data adding value in targeted scenarios.

Enhanced Environmental Outcomes

Better-informed site selection and management support stronger outcomes for carbon capture, biodiversity restoration and ecosystem resilience.

Foundation for Operational Deployment

The project established a practical pathway to scale monitoring services across the region.