The constellation of two Sentinel-1 satellites provides an unprecedented coverage of Synthetic Aperture Radar (SAR) data at high spatial (20 m) and temporal (2 to 6 days over Europe) resolution. The availability of dense time series enables the analysis of the SAR temporal signatures and exploitation of these signatures for classification purposes. Frequent backscatter observations allow derivation of temporally filtered time series that reinforce the effect of changes in vegetation phenology by limiting the influence of short-term changes related to environmental conditions. Recent studies have already shown the potential of multitemporal Sentinel-1 data for forest mapping, forest type classification (coniferous or broadleaved forest) as well as for derivation of phenological variables at local to national scales. In the present study, we tested the viability of a recently published multi-temporal SAR classification method for continental scale forest mapping by applying it over Europe and evaluating the derived forest type and tree cover density maps against the European-wide Copernicus High Resolution Layers (HRL) forest datasets and national-scale forest maps from twelve countries. The comparison with the Copernicus HRL datasets revealed high correspondence over the majority of the European continent with overall accuracies of 86.1% and 73.2% for the forest/non-forest and forest type maps, respectively, and a Pearson correlation coefficient of 0.83 for tree cover density map. Moreover, the evaluation of both datasets against the national forest maps showed that the obtained accuracies of Sentinel-1 forest maps are almost within range of the HRL datasets. The Sentinel-1 forest/non-forest and forest type maps obtained average overall accuracies of 88.2% and 82.7%, respectively, as compared to 90.0% and 87.2% obtained by the Copernicus HRL datasets. This result is especially promising due to the facts that these maps can be produced with a high degree of automation and that only a single year of Sentinel-1 data is required as opposed to the Copernicus HRL forest datasets that are updated every three years.

Extensive use of drainage ditches in European boreal forests and in some parts of North America has resulted in a major change in wetland and soil hydrology and impacted the overall ecosystem functions of these regions. An increasing understanding of the environmental risks associated with forest ditches makes mapping these ditches a priority for sustainable forest and land use management. Here, we present the first rigorous deep learning–based methodology to map forest ditches at regional scale. A deep neural network was trained on airborne laser scanning data (ALS) and 1,607 km of manually digitized ditch channels from 10 regions spread across Sweden. The model correctly mapped 86% of all ditch channels in the test data, with a Matthews correlation coefficient of 0.78. Further, the model proved to be accurate when evaluated on ALS data from other heavily ditched countries in the Baltic Sea Region. This study leads the way in using deep learning and airborne laser scanning for mapping fine-resolution drainage ditches over large areas. This technique requires only one topographical index, which makes it possible to implement on national scales with limited computational resources. It thus provides a significant contribution to the assessment of regional hydrology and ecosystem dynamics in forested landscapes.

Purpose of Review: Mechanized logging operations with ground-based equipment commonly represent European production forestry but are well-known to potentially cause soil impacts through various forms of soil disturbances, especially on wet soils with low bearing capacity. In times of changing climate, with shorter periods of frozen soils, heavy rain fall events in spring and autumn and frequent needs for salvage logging, forestry stakeholders face increasingly unfavourable conditions to conduct low-impact operations. Thus, more than ever, planning tools such as trafficability maps are required to ensure efficient forest operations at reduced environmental impact. This paper aims to describe the status quo of existence and implementation of such tools applied in forest operations across Europe. In addition, focus is given to the availability and accessibility of data relevant for such predictions. Recent Findings: A commonly identified method to support the planning and execution of machine-based operations is given by the prediction of areas with low bearing capacity due to wet soil conditions. Both the topographic wetness index (TWI) and the depth-to-water algorithm (DTW) are used to identify wet areas and to produce trafficability maps, based on spatial information. Summary: The required input data is commonly available among governmental institutions and in some countries already further processed to have topography-derived trafficability maps and respective enabling technologies at hand. Particularly the Nordic countries are ahead within this process and currently pave the way to further transfer static trafficability maps into dynamic ones, including additional site-specific information received from detailed forest inventories. Yet, it is hoped that a broader adoption of these information by forest managers throughout Europe will take place to enhance sustainable forest operations.

Organic horizons including peat deposits are important terrestrial carbon pools, and various chemical, biological, and water exchange processes take place within them. Accurate information on the spatial distribution of organic soils and their properties is important for decision-making and land management. In this study, we present a machine learning approach for mapping the distribution of organic soils and determining the thickness of the peat layer using more than 24,000 peat layer thickness measurements obtained from field data, airborne laser scanning (ALS) data and various indices obtained from therein, as well as other cartographic materials. Our objectives encompassed two primary aims. Firstly, we endeavored to develop updated cartographic materials depicting the spatial distribution of peat layers. Secondly, we aimed to predict the depth of peat layers, thereby enhancing our understanding of soil organic carbon content. Continentality, a wet area map, latitude, a depth to water map with catchment area of 10 ha, and a digital elevation model were the most important covariates for the machine learning model. As a result, we obtained a map with three peat layer thickness classes, an overall classification accuracy of 0.88, and a kappa value of 0.74. This research contributes to a better understanding of organic soil dynamics and facilitates improved assessments of soil organic carbon stocks.

Species-specific basic density (BD) data are necessary to improve the indirect methods of biomass determination. The density of tree components (e.g., bark, branches, roots) is studied much less than that of stem wood. Nevertheless, ignoring the specific BD values of these components in biomass calculations can lead to errors. The study aims to investigate BD variation of aboveground and belowground tree components by studying a total of 162 gray alder (Alnus incana (L.) Moench) and common alder (Alnus glutinosa (L.) Gaertn.) trees. From them, 55 stumps were excavated to determine the BD of the belowground components. Our findings reveal that the volume-weighted BD of the stem (wood and bark) and the branch density of common alder are higher compared to gray alder. Both species have similar bark density, while the BD of belowground components is higher for gray alder. The stem wood density of both species increases upward from the stump to the top. Compared to gray alders, the stems of common alders have more distinct radial within-stem density variation. According to our results, the application of default Alnus spp. wood density values recommended in the IPCC guidelines for the calculation of total biomass and carbon stock is likely causing overestimation. The BD values obtained in our study on alders’ biomass components will allow for more accurate appraisals of total biomass and carbon stock for gray and common alder forests.

When it comes to greenhouse gas (GHG) reduction, the role of water tables in former peat extraction areas has received considerable interest in recent decades. This study analysed the carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) surface-to-atmosphere fluxes from a rewetted and permanently flooded former peat extraction areas in comparison to pristine peatland in hemiboreal Latvia. Measurements of GHG fluxes combined gas sampling using a closed-chamber (opaque) method with the gas chromatography detection method. Among the studied land-use types, the highest annualised CO₂ fluxes (soil heterotrophic and autotrophic respiration) were recorded in rewetted former peat extraction areas with restored vegetation and in undisturbed peatland (4.10 ± 0.21 and 3.45 ± 0.21 t CO₂-C ha⁻¹ yr⁻¹, respectively), with the lowest in flooded former peat extraction areas (0.55 ± 0.05 t CO₂-C ha⁻¹ yr⁻¹); temperature and groundwater level were found to be significant influencing factors. The highest annualised CH₄ fluxes were recorded in undisturbed peatland (562.4 ± 155.8 kg CH₄-C ha⁻¹ yr⁻¹), followed by about two-fold and ~20-fold smaller CH₄ fluxes in flooded and rewetted areas, respectively. N₂O fluxes were negligible in all the studied land-use types, with the highest N₂O fluxes in undisturbed peatland (0.66 ± 0.41 kg N₂O-N ha⁻¹ yr⁻¹).