Balancing moose (Alces alces) populations with forest production objectives remains a major challenge in Swedish boreal forestry. Browsing damage is commonly assessed using empirical indices, while the underlying availability of browsable shoot biomass is rarely quantified mechanistically. We developed a mechanistic, data-driven model of browse availability in young Scots pine (Pinus sylvestris) by linking shoot-level allometry, crown architecture, and tree height development. Empirical data were collected from nine unbrowsed trees, for which current-year shoots were measured by branch whorl, including shoot length, shoot number, and wet/dry biomass ratio. Shoot scaling with crown position was analysed to quantify crown allometry and distribution. Crown architecture was coupled with a height–age growth model to simulate vertical crown development and estimate shoot biomass accessible to moose browsing within a defined browsing height for moose. Simulations were conducted across site productivity classes and scaled to the stand level. Shoot biomass, length and diameter increased with tree height and declined exponentially with distance from the apex with 50%, 33% and 13% per whorl, respectively. At the crown level, both total shoot length and biomass followed unimodal distributions, peaking near the vertical midpoint of the crown. Simulations showed that browse availability increased rapidly with tree growth, peaked at tree heights around 3.5 m (15–25 years depending on site productivity), and subsequently declined. Peak shoot biomass ranged from 330 to 450 g per tree, with high-productivity sites yielding up to 35% more biomass. When scaled to stands ≤2.5 m in height at 2,000 stems ha⁻¹, simulated browse availability was 100–200 kg ha⁻¹. These results show that crown architecture in young Scots pine follows consistent scaling rules that can be expressed as functions of tree height and crown position. The model provides a mechanistic connection between tree growth and herbivore-accessible forage and implications for management is discussed.
In GRIP on LIFE IP, public authorities in Sweden work together with forest owners’ associations, non-governmental organisations and researchers to improve environmental consideration to waters and wetlands in the forest landscape, while continuing an active forest management. Our goal is to improve the environment and conditions for animals and plants living in the forest’s watercourses and wetlands.
Northern peatlands provide a globally important carbon (C) store. Since the beginning of the 20th century, however, large areas of natural peatlands have been drained for biomass production across Fennoscandia. Today, drained peatland forests constitute a common feature of the managed boreal landscape, yet their ecosystem C balance and associated climate impact are not well understood, particularly within the nutrient-poor boreal region. In this study, we estimated the net ecosystem carbon balance (NECB) from a nutrient-poor drained peatland forest and an adjacent natural mire in northern Sweden by integrating terrestrial carbon dioxide (CO2 ) and methane (CH4 ) fluxes with aquatic losses of dissolved organic C (DOC) and inorganic C based on eddy covariance and stream discharge measurements, respectively, over two hydrological years. Since the forest included a dense spruce-birch area and a sparse pine area, we were able to further evaluate the effect of contrasting forest structure on the NECB and component fluxes. We found that the drained peatland forest was a net C sink with a 2-year mean NECB of -115 ± 5 g C m-2 year-1 while the adjacent mire was close to C neutral with 14.6 ± 1.7 g C m-2 year-1 . The NECB of the drained peatland forest was dominated by the net CO2 exchange (net ecosystem exchange [NEE]), whereas NEE and DOC export fluxes contributed equally to the mire NECB. We further found that the C sink strength in the sparse pine forest area (-153 ± 8 g C m-2 year-1 ) was about 1.5 times as high as in the dense spruce-birch forest area (-95 ± 8 g C m-2 year-1 ) due to enhanced C uptake by ground vegetation and lower DOC export. Our study suggests that historically drained peatland forests in nutrient-poor boreal regions may provide a significant net ecosystem C sink and associated climate benefits.
Forests worldwide contain unique cultural traces of past human land use. Increased pressure on forest ecosystems and intensive modern forest management methods threaten these ancient monuments and cultural remains. In northern Europe, older forests often contain very old traces, such as millennia-old hunting pits and indigenous Sami hearths. Investigations have repeatedly found that forest owners often fail to protect these cultural remains and that many are damaged by forestry operations. Current maps of hunting pits are incomplete, and the locations of known pits have poor spatial accuracy. This study investigated whether hunting pits can be automatically mapped using national airborne laser data and deep learning. The best model correctly mapped 70% of all the hunting pits in the test data with an F1 score of 0.76. This model can be implemented across northern Scandinavia and could have an immediate effect on the protection of cultural remains.
Cloud formations often obscure optical satellite-based monitoring of the Earth’s surface, thus limiting Earth observation (EO) activities such as land cover mapping, ocean color analysis, and cropland monitoring. The integration of machine learning (ML) methods within the remote sensing domain has significantly improved performance for a wide range of EO tasks, including cloud detection and filtering, but there is still much room for improvement. A key bottleneck is that ML methods typically depend on large amounts of annotated data for training, which are often difficult to come by in EO contexts. This is especially true when it comes to cloud optical thickness (COT) estimation. A reliable estimation of COT enables more fine-grained and application-dependent control compared to using pre-specified cloud categories, as is common practice. To alleviate the COT data scarcity problem, in this work, we propose a novel synthetic dataset for COT estimation, which we subsequently leverage for obtaining reliable and versatile cloud masks on real data. In our dataset, top-of-atmosphere radiances have been simulated for 12 of the spectral bands of the Multispectral Imagery (MSI) sensor onboard Sentinel-2 platforms. These data points have been simulated under consideration of different cloud types, COTs, and ground surface and atmospheric profiles. Extensive experimentation of training several ML models to predict COT from the measured reflectivity of the spectral bands demonstrates the usefulness of our proposed dataset. In particular, by thresholding COT estimates from our ML models, we show on two satellite image datasets (one that is publicly available, and one which we have collected and annotated) that reliable cloud masks can be obtained. The synthetic data, the newly collected real dataset, code and models have been made publicly available.
