Improvements in forest structural type assessment using airborne laser scanning

Abstract

Accurate forest structural type (FST) assessment provides a valuable support tool to distinguish the different structures in forest stands, achieve sustainable forest management and formulate effective decisions. Data from four research sites within three biogeographical regions-Boreal, Mediterranean and Atlanticwere used in this study, and reliable methodologies were developed for FST assessment. First, the Gini coefficient () of tree size inequality was used for the structural characterisation, and the effects of plot size, stand density and point density of airborne laser scanning (ALS) on the ALS-assisted estimations were evaluated for the Boreal region. Second, four forest structural attributesquadratic mean diameter (), , basal area larger than the mean () and stand density ()from the three biogeographical regions were used to develop regionindependent methods for FST assessment. Lastly, a threshold value to represent maximum entropy was determined and was used to classify the various FST directly from ALS data using L-coefficient of variation and L-skewness of ALS echo heights. Aboveground biomass (AGB) was predicted for each FST and was compared with the AGB predictions without prestratification. The results showed that (a) plot size had a greater effect on the ALS-assisted estimation compared to stand size and point density, and that 250-450 m 2 plot size (radius 9-12 m for circular plots) is the optimal plot size for reliable ALS-assisted estimations, (b) and are the most reliable bivariate descriptors for FST assessment, and single storey, multi-storey and reversed-J type forest structures can be separated by lower, medium and upper and values, respectively, while and are relevant for the separation of young/mature and sparse/dense subtypes, and (c) based on the mathematical proofs, the threshold values calculated from ALS echo heights and tree basal areas to represent maximum entropy should be 0.33 and 0.50, respectively. Moderate improvements were observed in the AGB predictions from FST classified directly from ALS data compared to the full dataset but critical differences were identified in the selection of ALS metrics by the prediction models. For example, higher percentiles were more relevant in uneven-sized structures and open canopy areas, while cover metrics and average percentiles were important in the even-sized structures and closed canopy areas. Thus, these results are very useful in improving our understanding of the relationships that underpin the choice of ALS predictors in structurally complex forests.

Optimizing the airborne laser scanning estimation of basal area larger than mean (BALM): An indicator of cohort balance in forests

“…The optimal plot sizes (s * = 11 − 14 m radii or 380-615 m 2 area) obtained in our study coincide with the recommended plot sizes obtained in other studies such as 13-15 m radii for describing heterogeneity in old stand growth (Lombardi et al, 2015), 9-12 m radii for ALS assisted Gini coefficient estimation (Adnan et al, 2017;Adnan, 2020) and 200-400 m 2 for stand-level stem diameter distribution in Finland (Maltamo et al, 2019). However, there are still differences with the routines of the current ALS forest inventory in Finland.…”