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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. [ABSTRACT FROM AUTHOR]

Comparisons between field data and available maps show that 64% of wet areas in the boreal landscape are missing on current maps. Primarily forested wetlands and wet soils near streams and lakes are missing, making them difficult to manage. One solution is to model missing wet areas from high-resolution digital elevation models, using indices such as topographical wetness index and depth to water. However, when working across large areas with gradients in topography, soils and climate, it is not possible to find one method or one threshold that works everywhere. By using soil moisture data from the National Forest Inventory of Sweden as a training dataset, we show that it is possible to combine information from several indices and thresholds, using machine learners, thereby improving the mapping of wet soils (kappa = 0.65). The new maps can be used to better plan roads and generate riparian buffer zones near surface waters. [ABSTRACT FROM AUTHOR]

Drainage of forested wetlands for increased timber production has profoundly altered the hydrology and water quality of their downstream waterways. Some ditches need network maintenance (DNM), but potential positive effects on tree productivity must be balanced against environmental impacts. Currently, no clear guidelines exist for DNM that strike this balance. Our study helps begin to prioritise DNM by: (1) quantifying ditches by soil type in the 68 km2 Krycklan Catchment Study in northern Sweden and (2) using upslope catchment area algorithms on new high-resolution digital elevation models to determine their likelihood to drain water. Ditches nearly doubled the size of the stream network (178-327 km) and 17% of ditches occurred on well-draining sedimentary soils, presumably making DNM unwarranted. Modelling results suggest that 25-50% of ditches may never support flow. With new laser scanning technology, simple mapping and modelling methods can locate ditches and model their function, facilitating efforts to balance DNM with environmental impacts. [ABSTRACT FROM AUTHOR]

Riparian buffer zones (RBZs) are an important instrument for environmental policies for water and biodiversity protection in managed forests. We investigate the variation of the cost of implementing RBZs within different property size classes across the size range of non-industrial forest owner properties in Southern Sweden. Using the Heureka PlanWise decision support system, we quantified the cost of setting aside RBZs or applying alternative management in them, as the relative loss of harvest volume and of net present value per property. We did this for multiple simulated as well as real-world property distributions. The variation of cost distribution among small properties was 4.2–6.9 times higher than among large properties. The interproperty cost inequality decreased non-linearly with increasing property size and levelled off from around 200 ha. We conclude that RBZs, due to the irregular distribution of streams, cause highly unequal financial consequences for owners, with some small property owners bearing a disproportionally high cost. This adds to previous studies showing how environmental considerations differentially affect property owners. We recommend decision makers to stimulate the uptake of RBZs by alleviating these inequalities between forest owners by including appropriate cost sharing or compensation mechanisms in their design. [ABSTRACT FROM AUTHOR]

Streams are important emitters of CO2 but extreme spatial variability in their physical properties can make upscaling very uncertain. Here, we determined critical drivers of stream CO2 evasion at scales from 30 to 400 m across a 52.5 km2 catchment in northern Sweden. We found that turbulent reaches never have elevated CO2 concentrations, while less turbulent locations can potentially support a broad range of CO2 concentrations, consistent with global observations. The predictability of stream pCO2 is greatly improved when we include a proxy for soil‐stream connectivity. Catchment topography shapes network patterns of evasion by creating hydrologically linked "domains" characterized by high water‐atmosphere exchange and/or strong soil‐stream connection. This template generates spatial variability in the drivers of CO2 evasion that can strongly bias regional and global estimates. To overcome this complexity, we provide the foundations of a mechanistic framework of CO2 evasion by considering how landscape process domains regulate transfer and supply. [ABSTRACT FROM AUTHOR]

A longstanding challenge in stream ecology is to understand how landscape configuration organizes spatial patterns of ecosystem function via lateral groundwater connections. We combined laboratory bioassays and field additions of a metabolic tracer (resazurin) to test how groundwater‐stream confluences, or "discrete riparian inflow points" (DRIPs), regulate heterotrophic microbial activity along a boreal stream. We hypothesized that DRIPs shape spatial patterns and rates of aquatic heterotrophic microbial activity by supplying labile dissolved organic matter (DOM) to streams. Laboratory bioassays showed that the potential influence of DRIPs on heterotrophic activity varied spatially and temporally, and was related to their DOM content and composition. At the reach scale, DRIP‐stream confluences elevated the spatial heterogeneity and whole‐reach rates of heterotrophic activity, especially during periods of high land–water hydrological connectivity. Collectively, our results show how the arrangement of lateral groundwater connections influence heterotrophic activity in streams with implications for watershed biogeochemical cycles. [ABSTRACT FROM AUTHOR]

The history of mining and smelting and the associated pollution have been documented using lake sediments for decades, but the broader ecological implications are not well studied. We analyzed sediment profiles covering the past ~10,000 years from three lakes associated with an iron blast furnace in central Sweden, as an example of the many small-scale furnaces with historical roots in the medieval period. With a focus on long-term lake-water quality, we analyzed multiple proxies including geochemistry, pollen and charcoal, diatom composition and inferred pH, biogenic silica (bSi), visible near-infrared spectroscopy (VNIRS)-inferred lake-water total organic carbon (LW-TOC), and VNIRS-inferred sediment chlorophyll (sed-Chl). All three lakes had stable conditions during the middle Holocene (~5000 BCE to 1110 CE) typical of oligo-dystrophic lakes: pH 5.4–5.6, LW-TOC 15–18 mg L−1. The most important diatom taxa include, for example, Aulacoseira scalaris, Brachysira neoexilis, and Frustulia saxonica. From ~1150 CE, decreases in LW-TOC, bSi, and sed-Chl in all three lakes coincide with a suite of proxies indicating disturbance associated with local, small-scale agriculture, and the more widespread use of the landscape in the past (e.g. forest grazing, charcoal production). Most important was a decline in LW-TOC by 30–50% in the three lakes prior to the 20th century. In addition, the one lake (Fickeln) downstream of the smelter and main areas of cultivation experienced a shift in diatom composition (mainly increasing Asterionella formosa) and a 0.6 pH increase coinciding with increasing cereal pollen and signs of blast furnace activity. The pH did not change in the other two lakes in response to disturbance; however, these lakes show a slight increase (0.3–0.5 pH units) because of modern liming. LW-TOC has returned to background levels in the downstream lake and remains lower in the other two. [ABSTRACT FROM AUTHOR]

• A new method for accurate high-resolution soil moisture mapping in forests. • LIDAR-derived terrain indices proved effective for mapping of forest soil moisture. • Extreme Gradient Boosting was the best algorithm for mapping soil moisture. • The continuous soil moisture map would significantly contribute to practical forestry. • Maps were relatively less accurate near roads, on coarse sediments, and in steep terrains. Spatially extensive high-resolution soil moisture mapping is valuable in practical forestry and land management, but challenging. Here we present a novel technique involving use of LIDAR-derived terrain indices and machine learning (ML) algorithms capable of accurately modeling soil moisture at 2 m spatial resolution across the entire Swedish forest landscape. We used field data from about 20,000 sites across Sweden to train and evaluate multiple ML models. The predictor features (variables) included a suite of terrain indices generated from a national LIDAR digital elevation model and ancillary environmental features, including surficial geology, climate and land use, enabling adjustment of soil moisture class maps to regional or local conditions. Extreme gradient boosting (XGBoost) provided better performance for a 2-class model, manifested by Cohen's Kappa and Matthews Correlation Coefficient (MCC) values of 0.69 and 0.68, respectively, than the other tested ML methods: Artificial Neural Network, Random Forest, Support Vector Machine, and Naïve Bayes classification. The depth to water index, topographic wetness index, and 'wetland' categorization derived from Swedish property maps were the most important predictors for all models. The presented technique enabled generation of a 3-class model with Cohen's Kappa and MCC values of 0.58. In addition to the classified moisture maps, we investigated the technique's potential for producing continuous soil moisture maps. We argue that the probability of a pixel being classified as wet from a 2-class model can be used as a 0–100% index (dry to wet) of soil moisture, and the resulting maps could provide more valuable information for practical forest management than classified maps. [ABSTRACT FROM AUTHOR]