What the thesis is about
Accurate assessment of forest structural types (FSTs) helps distinguish how forest stands are organised in space - from simple single-storey canopies to complex multi-layered and uneven-aged structures. That knowledge supports sustainable management, inventory design, and decisions about conservation and biomass.
This doctoral research developed practical methods to assess forest structure with airborne laser scanning (ALS / LiDAR). Using field and ALS data from four sites across three biogeographical regions, the work combined structural indicators such as the Gini coefficient, basal area larger than the mean (BALM), quadratic mean diameter, and stand density with three-dimensional canopy information from laser scanning.
What the research showed
Plot size matters most for Gini estimation. Plot size had a stronger effect on ALS-assisted Gini coefficient estimates than stand density or laser point density. An optimal plot size of about 250–450 m² (circular plot radius 9–12 m) gave reliable results.
GC and BALM are the best structure descriptors. Lower, medium, and higher values of the Gini coefficient and BALM separate single-storey, multi-storey, and reversed-J structures. Quadratic mean diameter and stem density help separate young/mature and sparse/dense subtypes.
Maximum-entropy thresholds for 3D data. Mathematical proofs showed that threshold values representing maximum entropy should be 0.33 for ALS echo heights and 0.50 for tree basal areas - useful for classifying structural types directly from laser data.
Structure-aware biomass models. Stratifying stands into FSTs from ALS data brought moderate gains in aboveground biomass prediction and, more importantly, showed that different ALS metrics matter in different structures, for example, higher height percentiles in open and uneven canopies, and cover metrics in closed, even-sized stands.
The three studies behind the dissertation
Plot size, stand density and scan density vs. the Gini coefficient
Examined how plot size, stand density, and ALS point density affect the relationship between laser metrics and the Gini coefficient of tree size inequality in boreal forests - establishing practical guidance for plot design when estimating structural inequality from ALS.
Canadian Journal of Forest Research (2017) →A simple, bioregion-ready approach to forest structure classification
Developed a two-tier classification of forest structural types using inventory variables (QMD, GC, BALM, N) and then predicted those types from ALS metrics across boreal, Mediterranean, and Atlantic forests - showing that the same framework can work across bioregions.
Forest Ecology and Management (2019) →Maximum entropy in 3D height distributions and biomass modelling
Derived maximum-entropy thresholds for three-dimensional remote sensing height distributions and used ALS-based structural stratification to improve aboveground biomass modelling, clarifying which laser predictors matter in different canopy structures.
Remote Sensing of Environment (2021) →Supervisors, examiners and opponent
Research themes
Read the full dissertation
Open-access thesis published in Dissertationes Forestales 306 (2020).
Open via DOI →