Laser scanners sweep forest depths for new ecosystem insights

Scanning forests with lasers can provide three-dimensional models of forests, creating a unique way to look at these ecosystems. Researchers at the University of Helsinki are pioneers in applying this technical solution to measuring ecosystems.

This technique, referred to as terrestrial laser scanning (TLS), opens up new opportunities in measuring forest structure, monitoring disturbances and simulating
ecosystems. 

“TLS provides us with a new way of looking at forests – not just from above, but from within. It helps us understand tree growth, forest responses to disturbances and the effects of forest structure on biodiversity and capacity for recovery,” says Associate Professor Eduardo Maeda from the University of Helsinki. 

Maeda notes that a
deeper understanding helps researchers, decision-makers and communities to make increasingly justified decisions – and, ultimately, to better reconcile human activity with the environment.

The Terrestrial Ecosystem Dynamics Research Group (Tree-D Lab), headed by Associate Professor Maeda, is a pioneer in the study of changes in the ecosystem. 

In addition to terrestrial laser scanning, the researchers utilise satellite
data, sensors installed in drones and field measurements. In an article recently published in the Nature Communications journal, the group describes the potential of terrestrial laser scanning. 

The Tree-D Lab researchers have applied the TLS technique across the world in a range of ecosystems. In the tropics, such as Southeast Asia and the Amazon, laser scanning
and machine learning have been used to assess the effects of forest fragmentation and logging. 

These investigations have revealed changes at the edges of clear-cut areas where the conditions are different from those in the middle of large forest areas. 

In Finland, research has provided new information on the effects of forest management on forest structure and growth, supporting increasingly sustainable practices and national forest monitoring.