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 Atlantic – were used in this study, and reliable methodologies were developed for FST assessment. First, the Gini coefficient (𝐺𝐶) 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 𝐺𝐶 estimations were evaluated for the Boreal region. Second, four forest structural attributes – quadratic mean diameter (𝑄𝑀𝐷), 𝐺𝐶, basal area larger than the mean (𝐵𝐴𝐿𝑀) and stand density (𝑁) – from the three biogeographical regions were used to develop region-independent 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 pre-stratification.
The results showed that (a) plot size had a greater effect on the ALS-assisted 𝐺𝐶 estimation compared to stand size and point density, and that 250–450 m2 plot size (radius 9–12 m for circular plots) is the optimal plot size for reliable ALS-assisted 𝐺𝐶 estimations, (b) 𝐺𝐶 and 𝐵𝐴𝐿𝑀 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 𝐺𝐶 and 𝐵𝐴𝐿𝑀 values, respectively, while 𝑄𝑀𝐷 and 𝑁 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.
The doctoral dissertation of MSc Syed Adnan, entitled Improvements in forest structural type assessment using airborne laser scanning will be examined at the Faculty of Science and Forestry on the 11th of December online. The opponent in the public examination will be Professor Arne Nothdurft, University of Natural Resources and Life Sciences, Austria, and the custos will be Professor Matti Maltamo, University of Eastern Finland. The public examination will be held in English.
Link to the event (coming)
