We are proud and happy to announce that Jaime Candelas Bielza successfully defended his PhD within SmartForest on
“Advancing tree species composition prediction in boreal forests with remote sensing”
Congratulations Jaime!
Accurate tree species composition information is crucial for forest management, influencing harvest scheduling, regeneration choices, and silvicultural treatments. In operational forest management inventories, species composition is typically estimated through manual photo interpretation, a costly, subjective method prone to systematic errors.
🎯 The main objective of this work was to develop an objective, automated approach for predicting species composition using remote sensing data and to assess the effect of uncertainty in its prediction.
🌲 🌳 This thesis demonstrates the potential of integrating remotely sensed data and statistical modelling to improve forest inventory practices. By combining airborne laser scanning and multi-season Sentinel-2 data and selecting appropriate modelling techniques, tree species composition can be predicted with improved accuracy and reliability compared to current operational methods.
🌲 🌳 These advancements provide scalable and cost-efficient alternatives to conventional methods, reducing reliance on manual aerial photo interpretation to support forest management decisions. Furthermore, the demonstrated impact of prediction uncertainty on economic valuation emphasizes the need for accurate species composition and site index predictions in forestry planning.
🌳 🌲 Ultimately, this research contributed to the development of objective, data-driven methods that can improve the precision, efficiency and adaptability of operational forest management inventories.
👉 read the already published article from the thesis here:
Image credits Marie-Claude Jutras-Perreault and Katrin Zimmer