Supporting Environmental Insight for Scotland’s National LiDAR Programme
New open-source tools & pipelines for mapping vegetation, structure & landscape change in complex landscapes
Aerial LiDAR scans send out thousands of laser beams every second to create a detailed 3D picture of the landscape. Researchers at the Centre for Landscape Regeneration have developed a set of data-processing steps designed to help extract clearer and more accurate information from LiDAR data in difficult-to-read environments, such as dense vegetation or uneven terrain.
These free-to-use workflows were developed to improve the analysis of data collected using laser scanning, allowing policy makers and planners to better understand complex landscapes. Working closely with the Rural and Environment Science and Analytical Services Division of the Scottish Government, which funds and manages research in areas such as the environment, agriculture, food, rural affairs and natural resources, this work was led by Will Flynn, Aland Chan and David Coomes, with contributions from Sudina Thapa, Jessica Williams, and Amandine Debus.
The research supports Scotland's National Land LiDAR Programme, helping to ensure that the high-resolution national datasets being collected can be translated into meaningful ecological and environmental insights.
Why this research matters
Airborne LiDAR is widely used in environmental mapping, but standard processing tools often struggle in dense or structurally complex ecosystems-particularly where vegetation is small, layered, or under forest canopy.
This limits the ability to extract key ecological information at scale.
This research addresses that gap by developing targeted, landscape-aware methods based on data from a LiDAR survey of the Cairngorms National Park we commissioned in 2023, making the insights especially relevant to Scottish environments, while offering potential benefits to future research.
What the research delivers
A set of new and improved LiDAR processing approaches that enable more accurate mapping of:
- Shrub height across landscapes
- Regenerating trees (natural woodland regeneration)
- Deadwood detection and structure
- Broader vegetation and geomorphological attributes
Several of these approaches have been developed into software packages available on GitHUB, designed for deployment on LiDAR datasets. This includes programmes associated with Scotland’s national terrestrial LiDAR mapping initiative, launched in 2025. The initiative aims to generate large volumes of high-resolution spatial data to support large-scale landscape monitoring and analysis, improving decision-making across a range of environmental applications such as land-use planning and the long-term adaptation of landscapes to environmental change.
Real-world applications
By improving how structural information about landscapes is captured from the LiDAR data, these methods can be used in many environmental and policy areas, including:
- Habitat quality assessment
- Estimating carbon stored in ecosystems
- Modelling wildfire risk
- Analysing natural water flow & drainage patterns for flood management and mitigation
- Supporting landscape monitoring & ecological restoration planning
These methods are especially useful for nationwide monitoring projects in the UK and elsewhere in the world,
Watch the webinar
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A webinar led by Aland Chan, with support from David Coomes, was held in April 2026. The webinar is suitable for researchers, practitioners, and anyone interested in applying LiDAR data in ecological and landscape analysis.
In the session, Aland introduces the methods behind the research and demonstrates how the tools are applied in practice, including a live walkthrough of one of the pipelines to assess shrub height (lidarSHM package).
This webinar covers:
- Methods for estimating shrub height
- Approaches to mapping regenerating trees
- Techniques for detecting deadwood
- A live demonstration of LiDAR processing workflows
Access the research
A full report on this work is also available: “Maximizing the utility of Scotland's National LiDAR Scan: A Review of Data and Code Available from the Centre for Landscape Regeneration”
DOI: https://doi.org/10.33774/coe-2026-70x5q-v2
If you have any questions or comments please do get in touch: clr_admin@plantsci.cam.ac.uk