Large-scale estimation of boreal forest leaf area index with airborne laser scanning data

Leaf area index (LAI), defined as half of the two-sided leaf area per unit horizontal ground surface area, is an essential variable that describes forest canopy structure. It is a key input in various biosphere-atmosphere models and an important indicator of biodiversity. Temporally and spatially accurate large-area LAI maps are highly needed, but measuring LAI in the field is labour-intensive and time-consuming, especially over large areas. The aim of this thesis was to investigate the feasibility of estimating LAI at large scales using multiple airborne laser scanning (ALS) datasets. Various ALS-derived penetration indices were first compared with field-measured gap fractions at near-vertical angles. The all-echo penetration index (API) showed the least bias among the indices, making it a suitable input following the semi-physical modelling approach. I also explored the utility of ALS polar metrics following the empirical modelling approach, which were found useful and led to improved model accuracy. Furthermore, the performance of both empirical and semi-physical modelling approaches for LAI estimation was assessed at both regional and nationwide scales. While empirical LAI models achieved slightly higher accuracy, the semi-physical model demonstrated better potential for transferability across regions. The nationwide LAI model accuracy could be further improved by incorporating local plots into model calibration. Finally, we proposed a gamified framework for LAI data collection. It may become a valuable data source for validation and calibration of nationwide LAI models.