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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., Water Management in Baltic Forests

Boreal peatlands represent a biogeochemically unique and diverse environment in high-latitude landscape. They represent a long-term globally significant sink for carbon dioxide and a source of methane, hence playing an important role in regulating the global climate. There is an increasing interest in deciphering peatland biogeochemical processes to improve our understanding of how anthropogenic and climate change effects regulate the peatland biogeochemistry and greenhouse gas balances. At present, most studies investigating land-atmosphere exchanges of peatland ecosystems are commonly based on single-tower setups, which require the assumption of homogeneous conditions during upscaling to the landscape. However, the spatial organization of peatland complexes might feature large heterogeneity due to its varying underlying topography and vegetation composition. Little is known about how well single site studies represent the spatial variations of biogeochemical processes across entire peatland complexes. The recently established Kulbäcksliden Research Infrastructure (KRI) includes five peatland study sites located less than 3 km apart, thus providing a unique opportunity to explore the spatial variation in ecosystem-scale processes across a typical boreal peatland complex. All KRI sites are equipped with eddy covariance flux towers combined with installations for detailed monitoring of biotic and abiotic variables, as well as catchment-scale hydrology and hydrochemistry. Here, we review studies that were conducted in the Kulbäcksliden area and provide a description of the site characteristics as well as the instrumentation available at the KRI. We highlight the value of long-term infrastructures with ecosystem-scale and replicated experimental sites to advance our understanding of peatland biogeochemistry, hydrology, ecology, and its feedbacks on the environment and climate system.

Drained wetlands can constitute a large source of greenhouse gas emissions, but the drainage networks in these wetlands are largely unmapped, and better maps are needed to aid in forest production and to better understand the climate consequences. We develop a method for detecting ditches in high resolution digital elevation models derived from LiDAR scans. Thresholding methods using digital terrain indices can be used to detect ditches. However, a single threshold generally does not capture the variability in the landscape, and generates many false positives and negatives. We hypothesise that, by combining the digital terrain indices using supervised learning, we can improve ditch detection at a landscape-scale. In addition to digital terrain indices, additional features are generated by transforming the data to include neighbouring cells for better ditch predictions. A Random Forests classifier is used to locate the ditches, and its probability output is processed to remove noise, and binarised to produce the final ditch prediction. The confidence interval for the Cohen's Kappa index ranges [0.655, 0.781] between the evaluation plots with a confidence level of 95%. The study demonstrates that combining information from a suite of digital terrain indices using machine learning provides an effective technique for automatic ditch detection at a landscape-scale, aiding in both practical forest management and in combatting climate change. © 2022 The Authors, open access

Improving our ability to detect changes in terrestrial and aquatic systems is a grand challenge in the environmental sciences. In a world experiencing increasingly rapid rates of climate change and ecosystem transformation, our ability to understand and predict how, when, where, and why changes occur is essential for adapting and mitigating human behaviours. In this context, long-term field research infrastructures have a fundamentally important role to play. For northern boreal landscapes, the Krycklan Catchment Study (KCS) has supported monitoring and research aimed at revealing these changes since it was initiated in 1980. Early studies focused on forest regeneration and microclimatic conditions, nutrient balances and forest hydrology, which included monitoring climate variables, water balance components, and stream water chemistry. The research infrastructure has expanded over the years to encompass a 6790 ha catchment, which currently includes 11 gauged streams, ca. 1000 soil lysimeters, 150 groundwater wells, >500 permanent forest inventory plots, and a 150 m tall tower (a combined ecosystem-atmosphere station of the ICOS, Integrated Carbon Observation System) for measurements of atmospheric gas concentrations and biosphere-atmosphere exchanges of carbon, water, and energy. In addition, the KCS has also been the focus of numerous high resolution multi-spectral LiDAR measurements and large scale experiments. This large collection of equipment and data generation supports a range of disciplinary studies, but more importantly fosters multi-, trans-, and interdisciplinary research opportunities. The KCS attracts a broad collection of scientists, including biogeochemists, ecologists, foresters, geologists, hydrologists, limnologists, soil scientists, and social scientists, all of whom bring their knowledge and experience to the site. The combination of long-term monitoring, shorter-term research projects, and large-scale experiments, including manipulations of c