Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices

Soil moisture has important implications for drought and flooding forecasting, forest fire prediction and water supply management. However, mapping soil moisture has remained a scientific challenge due to forest canopy cover and small-scale variations in soil moisture conditions. When accurately scaled, terrain indices constitute a good candidate for modelling the spatial variation of soil moisture conditions in many landscapes. In this study, we evaluated seven different terrain indices at varying digital elevation model (DEM) resolutions and user-defined thresholds as well as two available soil moisture maps, using an extensive field dataset (398 plots) of soil moisture conditions registered in five classes from a survey covering a (68 km2) boreal landscape. We found that the variation in soil moisture conditions could be explained by terrain indices, and the best predictors within the studied landscape were the depth to water index (DTW) and a machine-learning-generated map. Furthermore, this study showed a large difference between terrain indices in the effects of changing DEM resolution and user-defined thresholds, which severely affected the performance of the predictions. For example, the commonly used topographic wetness index (TWI) performed best on a resolution of 16 m, while TWI calculated on DEM resolutions higher than 4 m gave inaccurate results. In contrast, depth to water (DTW) and elevation above stream (EAS) were more stable and performed best on 1–2 m DEM resolution. None of the terrain indices performed best on the highest DEM resolution of 0.5 m. In addition, this study highlights the challenges caused by heterogeneous soil types within the study area and shows the need of local knowledge when interpreting the modelled results. The results from this study clearly demonstrate that when using terrain indices to represent soil moisture conditions, modelled results need to be validated, as selecting an unsuitable DEM resolution or user-defined threshold can give ambiguous and even incorrect results

Virtual landscape-scale restoration of altered channels helps us understand the extent of impacts to guide future ecosystem management

Human modification of hydrological connectivity of landscapes has had significant consequences on ecosystem functioning. Artificial drainage practices have fundamentally altered northern landscapes, yet these man made channels are rarely considered in ecosystem management. To better understand the effects of drainage ditches, we conducted a landscape-scale analysis across eleven selected study regions in Sweden. We implemented a unique approach by backfilling ditches in the current digital elevation model to recreate the prehistoric landscape, thus quantifying and characterizing the channel networks of prehistoric (natural) and current (drained) landscapes. Our analysis detected that 58% of the prehistoric natural channels had been converted to ditches. Even more striking was that the average channel density increased from 1.33 km km(-2) in the prehistoric landscape to 4.66 km km(-2) in the current landscape, indicating the extent of ditching activities in the northern regions. These results highlight that man-made ditches should be accurately mapped across northern landscapes to enable more informed decisions in ecosystem management

Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape

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 Naive 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

Detecting ditches using supervised learning on high-resolution digital elevation models

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

Delineating the distribution of mineral and peat soils at the landscape scale in northern boreal regions

To meet the sustainable development goals and enable sustainable management and protection of peatlands, there is a strong need for improving the mapping of peatlands. Here we present a novel approach to identify peat soils based on a high-resolution digital soil moisture map that was produced by combining airborne laser scanning-derived terrain indices and machine learning to model soil moisture at 2 m spatial resolution across the Swedish landscape. As soil moisture is a key factor in peat formation, we fitted an empirical relationship between the thickness of the organic layer (measured at 5479 soil plots across the country) and the continuous SLU (Swedish University of Agricultural Science) soil moisture map (R2= 0.66, p  0.001). We generated categorical maps of peat occurrence using three different definitions of peat (30, 40, and 50 cm thickness of the organic layer) and a continuous map of organic layer thickness. The predicted peat maps had a higher overall quality (MCC = 0.69–0.73) compared to traditional Quaternary deposits maps (MCC = 0.65) and topographical maps (MCC = 0.61) and captured the peatlands with a recall of ca. 80 % compared to 50 %–70 % on the traditional maps. The predicted peat maps identified more peatland area than previous maps, and the areal coverage estimates fell within the same order as upscaling estimates from national field surveys. Our method was able to identify smaller peatlands resulting in more accurate maps of peat soils, which was not restricted to only large peatlands that can be visually detected from aerial imagery – the historical approach of mapping. We also provided a continuous map of the organic layer, which ranged 6–88 cm organic layer thickness, with an R2 of 0.67 and RMSE (root mean square error) of 19 cm. The continuous map exhibits a smooth transition of organic layers from mineral soil to peat soils and likely provides a more natural representation of the distribution of soils. The continuous map also provides an intuitive uncertainty estimate in the delineation of peat soils, critically useful for sustainable spatial planning, e.g., greenhouse gas or biodiversity inventories and landscape ecological research

Ditches show systematic impacts on soil and vegetation properties across the Swedish forest landscape

Novel mapping methods using AI have led to improved mapping of the extent of drainage systems, but the full scope of the effects of drainage on ecosystems has yet to be understood. By combining ditches mapped with remote sensing and AI methods with soil data from the Swedish Forest Soil Inventory, and vegetation data from the National Forest Inventory we identified 4 126 survey plots within 100 m of a ditch. The inventory data span across three biomes; the northern boreal zone, the hemiboreal zone, and the temperate zone. We explored if soils and vegetation close to ditches were indeed different from the surrounding landscape. The large number of plots spread widely across the Swedish forest landscape spanning different physiographic regions, climates, topography, soils, and vegetation made it possible to identify the general effect of drainage on soil properties, tree productivity, and plant species composition. We found a surprisingly large amount of ditches on mineral soils (50-70%, depending on the definition of peatlands). Forest growth was affected, with higher growth rates of trees closer to ditches, particularly Norway spruce. Sphagnum mosses - a key indicator of wet soils - were less common near ditches, where they were replaced by feather mosses. The soil bulk density was higher closer to ditches, as was the concentration of metals that are typically associated with organic matter (Al), while concentrations of metals with a lower affinity for organic material decreased toward ditches (Na, K, Mg). The results from mineral soils and peat soils often differed. For example, N and tree volume increased toward ditches, but on different levels for peat and mineral soils, while the thickness of the humus layer and Pleurozium schreberi cover showed opposite patterns for the different soils. Clearly, ditches have affected the entire Swedish forest landscape, driving it towards a drier, more spruce-dominated productive forested ecosystem and away from wetland ecosystems like mires and littoral areas along streams. Furthermore, the biogeochemistry of the soils and understory species cover near ditches have changed, potentially irreversibly, at least within human time frames, and have implications for restoration goals and the future of forestry

Tree growth potential and its relationship with soil moisture conditions across a heterogeneous boreal forest landscape

Forest growth varies across landscapes due to the intricate relationships between various environmental drivers and forest management. In this study, we analysed the variation of tree growth potential across a landscape scale and its relation to soil moisture. We hypothesised that soil moisture conditions drive landscape-level variation in site quality and that intermediate soil moisture conditions demonstrate the highest potential forest production. We used an age-independent difference model to estimate site quality in terms of maximum achievable tree height by measuring the relative change in Lorey's mean height for a five year period across 337 plots within a 68 km2 boreal landscape. We achieved wall-to-wall estimates of site quality by extrapolating the modelled relationship using repeated airborne laser scanning data collected in connection to the field surveys. We found a clear decrease in site quality under the highest soil moisture conditions. However, intermediate soil moisture conditions did not demonstrate clear site quality differences; this is most likely a result of the nature of the modelled soil moisture conditions and limitations connected to the site quality estimation. There was considerable unexplained variation in the modelled site quality both on the plot and landscape levels. We successfully demonstrated that there is a significant relationship between soil moisture conditions and site quality despite limitations associated with a short study period in a low productive region and the precision of airborne laser scanning measurements of mean height

Scale-dependent groundwater contributions influence patterns of winter baseflow stream chemistry in boreal catchments

Funded by •KCS •Swedish Science Foundation (VR) SITES •European Research Council. Grant Number: GA 335910 VEWAPeer reviewedPublisher PD