Nicolas Cattaneo
Forsker
Forfattere
Marta Vergarechea C Antón-Fernández J.U Jepsen Ole Petter Laksforsmo Vindstad Nicolas Cattaneo J.J Camarero Rasmus AstrupSammendrag
resilience. In Norway, birch species (Betula pendula and Betula pubescens) dominate large areas of boreal forest, yet large-scale patterns of their age distribution and growth dynamics remain poorly quantified. Using increment core data from 2818 trees sampled across the Norwegian National Forest Inventory, spanning five vegetation zones (58–71◦N) and a broad productivity gradient, we analyzed the drivers of birch age structure and growth variation across age classes and historical cohorts. Intermediate-aged trees (35–80 years) dominated most regions, whereas older individuals were scarce, particularly on productive sites, reflecting the combined effects of forest management and the life-history strategy of fast-growing pioneer species. When compared at equivalent biological ages, younger trees consistently showed higher basal area increment (BAI) than older trees, with differences strongest during early development and on productive sites. Cohort analyses showed a pronounced long-term increase in juvenile growth: mean BAI during the first ten years after reaching breast height increased steadily across successive cohorts over the past 150 years. This increase became more pronounced after ~1960 and was consistent across vegetation zones and site productivity classes. Although sampling and survivor bias cannot be fully excluded, the consistency across environmental gradients points to broad-scale changes in early growth dynamics of birch forests in Norway. These results underscore the importance of considering both age structure and cohort-related variation when interpreting forest dynamics and planning future management.
Forfattere
Stefano Puliti Binbin Xiang Maciej Wielgosz Eivind Handegard Nicolas Cattaneo Marta Vergarechea Terje Gobakken Juha Hyyppä Erik Næsset Mikko Vastaranta Tuomas Yrttimaa Rasmus AstrupSammendrag
Accurately determining the age of individual trees is important for understanding forest dynamics, tree growth, site productivity and describing ecological processes. Traditional methods, such as dendrochronological coring, are invasive, labor-intensive, and costly. This study investigates the use of deep learning (DL) to predict tree age from high-density laser scanning data as a scalable, non-invasive alternative. The dataset includes approximately 1700 tree point clouds from approx. 1 K trees across Norway, Sweden, and Finland, encompassing Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) and a broad range of tree age and developmental stages, from young seedlings (1 year) to old trees (∼350 years). Data were collected using terrestrial, mobile, and high-density airborne laser scanning platforms, enabling the development of sensor-agnostic models. We evaluated multiple modelling approaches, from linear regression to transformer architectures, using both training-from-scratch and fine-tuning strategies. Models fine-tuned starting from pre-trained weights from ForestFormer3D's U-Net as well as the transformer architecture (PointTransformerV3) trained from scratch, proved effective for age regression (RMSE ≤23 years). Although our analysis was limited to two tree species, we demonstrated that a single joint age-estimation model can be successfully trained for both species. We demonstrate that models trained on high-resolution data can generalize to lower-resolution, less costly inputs, provided that data augmentations that mimic reduced resolutions are included during training. This study presents a data-driven framework for estimating tree age without destructive sampling. The findings support the potential for AI-based methods to complement or replace traditional age estimation techniques in forest inventory and monitoring.
Sammendrag
Det er ikke registrert sammendrag
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