Modern advances in cloud computing and machine-leaning algorithms are shifting the manner in which Earth-observation (EO) data are used for environmental monitoring, particularly as we settle into the era of free, open-access satellite data streams. Wetland delineation represents a particularly worthy application of this emerging research trend, since wetlands are an ecologically important yet chronically under-represented component of contemporary mapping and monitoring programs, particularly at the regional and national levels. Exploiting Google Earth Engine and R Statistical software, we developed a workflow for predicting the probability of wetland occurrence using a boosted regression tree machine-learning framework applied to digital topographic and EO data. Working in a 13,700 km2 study area in northern Alberta, our best models produced excellent results, with AUC (area under the receiver-operator characteristic curve) values of 0.898 and explained-deviance values of 0.708. Our results demonstrate the central role of high-quality topographic variables for modeling wetland distribution at regional scales. Including optical and/or radar variables into the workflow substantially improved model performance, though optical data performed slightly better. Converting our wetland probability-of-occurrence model into a binary Wet-Dry classification yielded an overall accuracy of 85%, which is virtually identical to that derived from the Alberta Merged Wetland Inventory (AMWI): the contemporary inventory used by the Government of Alberta. However, our workflow contains several key advantages over that used to produce the AMWI, and provides a scalable foundation for province-wide monitoring initiatives.

Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping

Towards optimizing riparian buffer zones: Ecological and biogeochemical implications for forest management

Browning of surface waters, as a result of increasing dissolved organic carbon and iron concentrations, is a widespread phenomenon with implications to the structure and function of aquatic ecosystems. In this article, we provide an overview of the consequences of browning in relation to ecosystem services, outline what the underlying drivers and mechanisms of browning are, and specifically focus on exploring potential mitigation measures to locally counteract browning. These topical concepts are discussed with a focus on Scandinavia, but are of relevance also to other regions. Browning is of environmental concern as it leads to, e.g., increasing costs and risks for drinking water production, and reduced fish production in lakes by limiting light penetration. While climate change, recovery from acidification, and land-use change are all likely factors contributing to the observed browning, managing the land use in the hydrologically connected parts of the landscape may be the most feasible way to counteract browning of natural waters.

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Browning of freshwaters: Consequences to ecosystem services, underlying drivers, and potential mitigation measures

Sustainable Forest Operations (SFO): A new paradigm in a changing world and climate

The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the N...

https://www.tandfonline.com/doi/pdf/10.1080/02827581.2017.1416666

Remote sensing and forest inventories in Nordic countries – roadmap for the future

Trafficking wet soils within and near stream and lake buffers can cause soil disturbances, i.e. rutting and compaction. This - in turn - can lead to increased surface flow, thereby facilitating the leaking of unwanted substances into downstream environments. Wet soils in mires, near streams and lakes have particularly low bearing capacity and are therefore more susceptible to rutting. It is therefore important to model and map the extent of these areas and associated wetness variations. This can now be done with adequate reliability using a high-resolution digital elevation model (DEM). In this article, we report on several digital terrain indices to predict soil wetness by wet-area locations. We varied the resolution of these indices to test what scale produces the best possible wet-areas mapping conformance. We found that topographic wetness index (T-WI) and the newly developed cartographic depth-to-water index (D-TW) were the best soil wetness predictors. While the T-WI derivations were sensitive to scale, the D-TW derivations were not and were therefore numerically robust. Since the D-TW derivations vary by the area threshold for setting stream flow initiation, we found that the optimal threshold values for permanently wet areas varied by landform within the Krycklan watershed, e.g. 1-2 ha for till-derived landforms versus 8-16 ha for a coarse-textured alluvial floodplain.

/pdf/evaluating-digital-terrain-indices-for-soil-wetness-mapping-1uf4kwmof0.pdf

Evaluating digital terrain indices for soil wetness mapping – a Swedish case study

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 s ...

Landscape process domains drive patterns of CO2 evasion from river networks

This study focuses on avoiding negative effects on surface waters using new techniques for identifying wet areas near surface waters. This would aid planning and designing of forest buffer zones and off-road forestry traffic. The temporal variability in the geographical distribution of the stream network renders this type of planning difficult. A field study was performed in the 68 km2 Krycklan Catchment to illustrate the variability of a boreal stream network. The perennial stream length was 140 km while the stream length during high-flow conditions was 630 km. Comparing the field-measured stream network to the network presented on current maps showed that 58% of the perennial and 76% of the fully expanded network was missing on current maps. Similarly, cartographic depth-to-water maps showed that associated wet soils constituted 5% of the productive forest land during baseflow and 25% during high flow. Using a new technique, maps can be generated that indicate full stream networks, as well as seasonally active streams and associated wet soils, thus, forestry planning can be performed more efficiently and impacts on surface waters can be reduced.

/pdf/mapping-temporal-dynamics-in-a-forest-stream-network-7bp0e9xhou.pdf

Mapping Temporal Dynamics in a Forest Stream Network—Implications for Riparian Forest Management

Groundwater discharge along streams exerts an important influence on biogeochemistry and thermal regimes of aquatic ecosystems. A common approach for predicting locations of shallow lateral groundwater discharge is to use digital elevation models (DEMs) combined with upslope contributing area algorithms. We evaluated a topography-based prediction of subsurface discharge zones along a 1500 m headwater stream reach using temperature and water isotope tracers. We deployed fiber-optic distributed temperature sensing instrumentation to monitor stream temperature at 0.25 m intervals along the reach. We also collected samples of stream water for the analysis of its water isotope composition at 50 m intervals on five occasions representing distinct streamflow conditions before, during, and after a major rain event. The combined tracer evaluation showed that topography-predicted locations of groundwater discharge were generally accurate; however, predicted magnitude of groundwater inflows estimated from upslope contributing area did not always agree with tracer estimates. At the catchment scale, lateral inflows were an important source of streamflow at base flow and peak flow during a major rain event; however, water from a headwater lake was the dominant water source during the event hydrograph recession. Overall, this study highlights potential utility and limitations of predicting locations and contributions of lateral groundwater discharge zones using topography-based approaches in humid boreal regions.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016WR019804

Evaluating topography-based predictions of shallow lateral groundwater discharge zones for a boreal lake–stream system

With the introduction of high resolution digital elevation models it is possible to use digital terrain analysis to extract small streams. In order to map streams correctly it is necessary to remove errors and artificial sinks in the digital elevation models. This step is known as pre-processing and will allow water to move across a digital landscape. However, new challenges are introduced with increasing resolution since the effect of anthropogenic artifacts such as road embankments and bridges increases with increased resolution. These are problematic during the pre-processing step since they are elevated above the surrounding landscape and act as artificial dams. The aims of this study was to evaluate the effect of different pre-processing methods such as breaching and filling on digital elevation models with different resolutions (2 m, 4 m 8 m and 16 m) and to evaluate which pre-processing methods that most accurately route water across road impoundments at actual culvert locations. A unique dataset with over 30000 field mapped road culverts was used to assess the accuracy of stream networks derived from digital elevation models using different pre-processing methods. Our results showed that the accuracy of stream networks increases with increasing resolution. Breaching created the most accurate stream networks on all resolutions while filling was the least accurate. Burning streams from the topographic map across roads from the topographic map increased the accuracy for all methods and resolutions. In addition, the impact in terms of change in area and absolute volume between original and pre-processed digital elevation models, was smaller for breaching than for filling. With the appropriate methods it is possible to extract accurate stream networks from high resolution digital elevation models with extensive road networks thus provide forest managers with stream networks that can be used when planning operations in wet areas or areas near streams to prevent rutting, sediment transport, and mercury export.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.11385

William Lidberg

Papers

Evaluating digital terrain indices for soil wetness mapping – a Swedish case study

Landscape process domains drive patterns of CO2 evasion from river networks

Mapping Temporal Dynamics in a Forest Stream Network—Implications for Riparian Forest Management

Evaluating topography-based predictions of shallow lateral groundwater discharge zones for a boreal lake–stream system

Evaluating preprocessing methods of digital elevation models for hydrological modelling