New remote sensing methods enable individual tree-level forest monitoring

Researcher Olli Nevalainen has developed new remote sensing methods capable of providing detailed data at the individual tree level. Using drone-based photogrammetry and multi-wavelength terrestrial laser scanning, the research allows for the assessment of forest resources and carbon sequestration variations within different parts of a single tree without the need for ground measurements.

The study utilized one of the world’s first multi-wavelength laser scanners, which enables three-dimensional assessments of object properties by measuring reflected radiation across multiple light wavelengths. This technology can detect variations in chlorophyll levels, providing insights into tree health and photosynthetic capacity, and can produce 3D maps of chlorophyll distribution in tree canopies.

Additionally, the research introduced automated methods for identifying and classifying individual trees from drone-collected data. These methods can estimate standard forest resource metrics, such as tree height, trunk diameter, and volume, at both the individual tree and stand levels. Such data is considered vital for climate research, forestry, and the creation of precise digital twins of forests.