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How we can better understand our forest ecosystems with laser scanning

by Luiza Tyminska and Jean-Matthieu Monnet

If you want to investigate the influence of management on forest resilience after disturbances, you can of course put your walking shoes on and do field measurements. However, how can you evaluate forest areas of several hundreds of square kilometers? In forest science, we consider Airborne Laser Scanning (ALS) a strong solution for mapping forest characteristics – including forests’ internal structure – at high resolution over wide areas. ALS is a remote sensing technology based on the emission of laser pulses. The laser light can penetrate the tree canopy and reflect on objects located inside the forest, or even by the ground. The Earth’s surface is then modelled as point clouds in three dimensions with geometric information on the height of the vegetation, but also on its internal structure. In the project Innovative forest management strategies for a resilient bioeconomy under climate change and disturbances (I-MAESTRO), we used ALS for two purposes: describing the forests to get an initial state for simulations, and analysing forest dynamics with repeated measurements.

We assess the influence of management on forest resilience after disturbances by using forest simulation on three case studies (Bauges in France, Sneznik in Slovenia, and Milicz in Poland). In the beginning, we used a combination of a sample of field plots and ALS data to create the initial map of forests for the simulations. We obtained a fairly realistic description of all trees in the landscape with ALS data using a two-step procedure. In the first step, models were calibrated to estimate stand level variables (basal area, mean diameter, and proportion of broadleaf trees) from the ALS point cloud geometry, using the field measurements. The maps for those stand level variables were then produced for all three case studies at 25 m resolution. In the second step, we generated tree level information on each of these 25 m resolution pixels using a downscaling algorithm we developed as part of our project.

Without any doubt: Technological developments and the increasing availability of remote sensing data including ALS data have brought new opportunities in the study of the dynamics of natural resources and the environment. In the research conducted in I-Maestro, we have shown that ALS data can be successfully applied to forest growth and mortality modelling. With high-resolution lidar data we can determine tree and stand attributes such as height, height increment and changes in tree density with increasing precision. This helps us to better understand forest ecosystem functioning and assess the provision of ecosystem services like wood production, biomass and carbon sequestration under climate change. The ability to use ALS to determine forest attributes over large areas has also been used by the project in predicting forest mortality risk. ALS data were key in explaining how stand attributes, site factors and site productivity influence susceptibility to tree mortality.

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