In the rolling hills and rugged terrain of Spain, a quiet revolution is underway, one that’s helping cattle farmers make better decisions about how they use their land.
At the heart of it is lidar, a remote sensing technology that’s proving to be more than just a tool for mapping landscapes. It’s becoming a critical asset in agricultural policy and practice.
Spain’s national mapping program, known as the Plan Nacional de Ortofotografía Aérea (PNOA), was launched by the Instituto Geográfico Nacional (IGN) in 2004 to ensure a cohesive and high-definition record of the nation using aerial imagery. In 2009, the program expanded to include lidar, enabling precise elevation mapping. Today the program covers the entire country at 5 points/m2 lidar density, and 25cm imagery. The data is open and freely available, and it’s being used by a wide range of public agencies. But one of the most impactful applications is in agriculture, where lidar helps determine how European Union subsidies are distributed to cattle farmers.
A key part of this process is the Pasture Subsidizability Coefficient (CSP), an indicator used within the Common Agricultural Policy (CAP) to determine what portion of a pasture parcel is eligible for subsidies. The CSP is expressed as a percentage and reflects the proportion of pasture area that is actually usable for grazing livestock. It excludes areas that can’t be used—such as steep slopes, dense scrub, or tree cover—and lidar plays a central role in identifying those features.
The CSP is calculated using a formula that considers the percentage of usable soil, deductions for non-grazable vegetation, and the slope of the terrain (see sidebar for additional details). Lidar provides the high-resolution elevation and vegetation data needed to make those calculations accurate and consistent across the country.
The lidar data provides a detailed 3D view of the terrain, capturing not just elevation but also vegetation density and structure. This allows analysts to model where animals can realistically move and feed. It’s a level of precision that satellite imagery alone can’t offer, especially in mountainous or forested regions.
The Ministry of Agriculture in Spain employs lidar technology to evaluate where livestock can feasibly graze. Terrain features such as steep slopes or dense vegetation are factored in, since animals are unable to access those areas. As a result, only navigable land is considered eligible for agricultural subsidies.
While this may seem like a small detail, it carries significant financial implications. By leveraging lidar for more precise land assessments, Spain has successfully avoided substantial penalties from the European Union. A government audit confirmed that the approach has led to considerable savings.
And it’s not just about compliance. For farmers, this kind of data can support better land management. Knowing which parts of a pasture are accessible and which aren’t helps optimize grazing patterns, reduce overuse, and improve animal welfare.
The lidar program also includes imagery collection, producing colorized point clouds and orthomosaics. These datasets complement each other to provide comprehensive coverage. Lidar is flown in years when there is no imagery acquisition, helping to fill in the gaps where the imagery program hasn’t yet reached.
A pivotal factor in reaching remote grazing lands for the PNOA program was the use of the Teledyne Optech Galaxy lidar system. Specifically designed for wide-area mapping, the Galaxy delivered consistent point density and high vertical accuracy—even across Spain’s most topographically challenging regions.
This is due to Galaxy’s SwathTRAK capability which allowed aerial contractors to cover the mountainous regions in less flight hours, cost, and carbon emissions. It also allowed them to collect these areas more safely than fixed-FOV systems, which require pilots to frequently descend into valleys and climb slopes. Galaxy dynamically changes its field of view and maintains a constant swath width despite the high peaks and deep valleys. Take for example this example from Asturias where the elevation range is about 850m, yet the swath width and consistent.
As lidar becomes more accessible and its applications better understood, its role in agriculture is likely to grow. For now, Spain’s experience offers a compelling example of how a technology often associated with urban planning and infrastructure is making a real difference in rural life.
Calculating the CSP
The Common Agricultural Policy (CAP) includes a Pasture Subsidy Eligibility Coefficient (CSP) that determines the percentage of a pasture parcel eligible for subsidies. This coefficient excludes areas with excessive slope, buildings, unproductive elements, bare or rocky soil, and non-herbaceous vegetation such as shrubs or trees.
The CSP calculation involves the following factors:
Soil Factor: Measures vegetative activity using the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 and Geosat imagery.
Slope Factor: Affects pasture productivity and livestock accessibility, calculated from the Digital Terrain Model (DTM) obtained via LiDAR flights.
Vegetation Factor: Derived from LAS file classification (LiDAR data), grouping pixels by pulse height and class (e.g., soil, penetrable vegetation, shrubs, trees).
Species Factor: Adjusts the vegetation factor based on whether non-herbaceous vegetation (shrubs, trees) is considered grazeable.
The CSP is calculated per SIGPAC plot by rasterizing each factor and combining them. The formula used is: CSP = Soil Factor × Slope Factor × Vegetation-Species Factor
The final CSP value is stored as an attribute in the SIGPAC database and recalculated automatically if the plot geometry changes.
This information is provided by Tragsatec (Tecnologías y Servicios Agrarios, S.A., S.M.E., M.P.), a publicly owned Spanish company and subsidiary of the Tragsa Group under SEPI (Sociedad Estatal de Participaciones Industriales).
Malek Singer is a Senior Product Manager at Teledyne Geospatial, specializing in the research, development, and productization of advanced geospatial acquisition technologies, with a particular emphasis on lidar.