Characterization of an alpine tree line using airborne LiDAR data and physiological modeling

Area of Science:

• Ecology
• Forestry
• Remote Sensing
• Climate Change Science

Background:

• The alpine tree line's position is crucial for understanding plant stress, tree development, and climate change impacts.
• Monitoring tree line dynamics is challenging due to cost, technical limitations, and the absence of a clear boundary.
• Remote sensing (LiDAR) and process-based models offer potential solutions for mapping and understanding tree line ecotones.

Purpose of the Study:

• To investigate the combined utility of remote sensing and ecosystem modeling for delineating the alpine tree line.
• To map tree height and density using airborne LiDAR data across altitudinal gradients in the Swiss National Park.
• To assess the influence of seasonal climate variations on tree line position using a process-based model.

Main Methods:

• Airborne Light Detection and Ranging (LiDAR) data acquisition for mapping tree height and stand density.
• Application of a process-based forest growth model to evaluate climatic drivers of photosynthesis.
• Analysis of altitudinal gradients and seasonal variations in environmental variables within the Swiss National Park.

Main Results:

• Both LiDAR data and the ecosystem model accurately predicted the tree line within a 50 m altitudinal zone.
• Tree cover, rather than tree height, was identified as the primary discriminator of the tree line at higher elevations.
• Seasonal temperatures (fall and spring) significantly influenced altitudinal differences, while evaporative demand affected lower-altitude growth.

Conclusions:

• Remote sensing and process-based modeling provide complementary data for accurate alpine tree line delineation.
• The combination of these methods offers enhanced insights into vulnerable forest/grassland transition zones.
• Aspect was found to be an insignificant driver of tree line variations in the studied region.