The rapid development in aerial digital cameras in combination with the increased availability of high-resolution Digital Elevation Models (DEMs) provides a renaissance for photogrammetry in forest management planning. Tree height, stem volume, and basal area were estimated for forest stands using canopy height, density, and texture metrics derived from photogrammetric matching of digital aerial images and a high-resolution DEM. The study was conducted at a coniferous hemi-boreal site in southern Sweden. Three different data-sets of digital aerial images were used to test the effects of flight altitude and stereo overlap on an area-based estimation of forest variables. Metrics were calculated for 344 field plots (10 m radius) from point cloud data and used in regression analysis. Stand level accuracy was evaluated using leave-one-out cross validation of 24 stands. For these stands the tree height ranged from 4.8 to 26.9 m (17.8 m mean), stem volume 13.3 to 455 m³ ha⁻¹ (250 m³ ha⁻¹ mean), and basal area from 4.1 to 42.9 m² ha⁻¹ (27.1 m² ha⁻¹ mean) with mean stand size of 2.8 ha. The results showed small differences in estimation accuracy of forest variables between the data-sets. The data-set of digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet), showed Root Mean Square Errors (in percent of the surveyed stand mean) of 8.8% for tree height, 13.1% for stem volume and 14.9% for basal area. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry.

Individual tree crown segmentation from airborne laser scanning (ALS) data often fails to detect all trees depending on the forest structure. This paper presents a new method to produce tree lists consistent with unbiased estimates at area level. First, a tree list with height and diameter at breast height (DBH) was estimated from individual tree crown segmentation. Second, estimates at plot level were used to create a target distribution by using a k-nearest neighbour (k-NN) approach. The number of trees per field plot was rescaled with the estimated stem volume for the field plot. Finally, the initial tree list was calibrated using the estimated target distribution. The calibration improved the estimates of the distributions of tree height (error index (EI) from 109 to 96) and DBH (EI from 99 to 93) in the tree list. Thus, the new method could be used to estimate tree lists that are consistent with unbiased estimates from regression models at field plot level.

The boreal forest is an important global carbon sink, but its response to drought remains uncertain. Here, we compiled biometric- and chamber-based flux data from 50 boreal forest stands to assess the impact of the 2018 European summer drought on net ecosystem production (NEP) across a 68 km² managed landscape in northern Sweden. Our results reveal a non-uniform reduction in NEP (on average by 80 ± 16 g C m⁻² yr⁻¹ or 57 ± 13%) across the landscape, which was greatest in young stands of 20–50 years (95 ± 39 g C m⁻² yr⁻¹), but gradually decreased towards older stands (54 ± 57 g C m⁻² yr⁻¹). This pattern was attributed to the higher sensitivity of forest-floor understorey to drought and its decreasing contribution to production relative to trees during stand development. This suggests that an age-dependent shift in understorey–tree composition with increasing stand age drives the drought response of the boreal forest NEP. Thus, our study advocates the need for partitioning ecosystem responses to improve empirical and modelling assessments of carbon cycle–climate feedbacks in boreal forests. It further implies that the forest age structure may strongly determine the carbon sink response to the projected increase in drought events across the managed boreal landscape.

Boreal forests are important global carbon (C) sinks and, therefore, considered as a key element in climate change mitigation policies. However, their actual C sink strength is uncertain and under debate, particularly for the actively managed forests in the boreal regions of Fennoscandia. In this study, we use an extensive set of biometric‐ and chamber‐based C flux data collected in 50 forest stands (ranging from 5 to 211 years) over 3 years (2016–2018) with the aim to explore the variations of the annual net ecosystem production (NEP; i.e., the ecosystem C balance) across a 68 km² managed boreal forest landscape in northern Sweden. Our results demonstrate that net primary production rather than heterotrophic respiration regulated the spatio‐temporal variations of NEP across the heterogeneous mosaic of the managed boreal forest landscape. We further find divergent successional patterns of NEP in our managed forests relative to naturally regenerating boreal forests, including (i) a fast recovery of the C sink function within the first decade after harvest due to the rapid establishment of a productive understory layer and (ii) a sustained C sink in old stands (131–211 years). We estimate that the rotation period for optimum C sequestration extends to 138 years, which over multiple rotations results in a long‐term C sequestration rate of 86.5 t C ha⁻¹ per rotation. Our study highlights the potential of forest management to maximize C sequestration of boreal forest landscapes and associate climate change mitigation effects by developing strategies that optimize tree biomass production rather than heterotrophic soil C emissions.