Seismic lines are among the most widespread anthropogenic disturbances in Alberta's boreal peatlands, where repeated petroleum-exploration surveys can alter surface morphology, hydrology, and recovery potential. Although low-impact seismic (LIS) techniques are designed to minimize ground disturbance, the long-term consequences of re-using existing lines remain poorly understood. This study used remotely piloted aircraft system (RPAS)-based LiDAR and optical imagery to examine how peatland microtopography and hydrology evolve following repeated seismic surveys. We quantified four attributes-ground depression, hummock cover, depth to water, and surface water cover-across new seismic lines (cut in 2021), old seismic lines (cut in 1996), and re-disturbance (cut in 1996, re-cut in 2021) LIS lines, as well as adjacent undisturbed peatland, in a boreal fen of northern Alberta. New disturbances were depressed by approximately 10 cm relative to the surrounding peatland and exhibited reduced microtopographic variability. Hummock cover decreased from 21% in the matrix to 6% on new disturbances. Old disturbances showed greater heterogeneity than new disturbances, with hummock cover partially recovering to 14% and surface water increasing from 7% to 27%, reflecting greater spatial heterogeneity in surface conditions. Re-disturbances exhibited microtopographic conditions similar to or more degraded than old disturbances, with hummock cover reduced to 2% and persistently high surface water cover (27%). These patterns suggest that repeated seismic surveys may limit recovery and maintain altered hydrological and microtopographic conditions. Within the context of this case study, even narrow LIS corridors were associated with persistent alterations when re-used, highlighting the importance of considering re-use effects when developing management strategies for peatland ecosystems. RPAS data provide an effective means to quantify these fine-scale changes and inform peatland restoration and seismic line management.

Trails and tracks are the detectable signs of passage of wildlife and off-highway vehicles in natural landscapes. They record valuable information on the presence and movement of animals and humans. However, published works aimed at mapping trails and tracks with remote sensing are nearly absent from the peer-reviewed literature. Here, we demonstrate the capacity of high-density LiDAR (light detection and ranging) and convolutional neural networks to map undifferentiated trails and tracks automatically across a diverse study area in the Canadian boreal forest. We compared maps developed with LiDAR from a drone platform (10 cm digital terrain model) with those from a piloted-aircraft platform (50 cm digital terrain model). We found no significant difference in the accuracy of the two maps. In fact, the piloted-aircraft map (F1 score of 77 ± 9%) performed nominally better than the drone map (F1 score of 74 ± 6%) and demonstrated a better balance among error types. Our maps reveal a 2829 km network of trails and tracks across the 59 km2 study area. These features are especially abundant in peatlands, where the density of detected trails and tracks was 68 km/km2. We found a particular tendency for wildlife and off-highway vehicles to adopt linear industrial disturbances like seismic lines into their movement networks. While linear disturbances covered just 7% of our study area, they contained 27% of all detected trails and tracks. This type of funnelling effect alters the movement patterns of humans and wildlife across the landscape and impedes the recovery of disturbed areas. While our work is a case study, the methods developed have broader applicability, showcasing the potential to map trails and tracks across large areas using remote sensing and convolutional neural networks. This capability can benefit diverse research and management communities.

Seismic lines in western Canada's boreal region are linear disturbances that affect the habitat of threatened woodland caribou (Rangifer tarandus caribou). To hasten the return of forest cover and ultimately to restore caribou habitat, restoration managers use silviculture treatments in wet areas that involve the mechanical mounding of seismic lines to create artificial hummocks. Tree planting then takes place on top of these hummocks. However, the effectiveness of this silviculture technique for restoring seismic lines in peatlands is largely untested. Here we sampled incremental height growth of coniferous seedlings at 41 seismic line plots in poor and rich treed fens in northeast Alberta, Canada. We compared lines that had been treated with inverted mounding (a type of mechanical mounding) and subsequent tree planting, to nearby lines that were left untreated for natural recovery. We used generalized linear mixed models to test the effect of silviculture treatments on 5‐year growth increments of black spruce (Picea mariana) and tamarack (Larix laricina). Overall, we found that mounding and tree planting improved seedling growth compared to seismic lines regenerating naturally. However, growth responses were found to vary by species, ecosite, initial seedling size, and light availability. This suggests that site‐specific prioritization of mounding within peatlands may be more efficient considering the widespread nature of seismic lines. Future experiments testing growth responses to other mounding techniques that are designed to preserve the soil profile. It is also important for managers to weigh restoration goals beyond tree establishment and an accelerated return to forest cover.

Seismic lines are 1.5 to 10 m wide linear corridors associated with petroleum exploration that fragment the landscape of northern Alberta. After clearing, seismic lines frequently exhibit low seedling recruitment and growth rates, due in part to the altered hydrologic conditions on the line. This research quantifies how seismic lines alter soil hydrophysical properties, hydrologic properties and snow accumulation patterns at two sites south of Fort McMurray, Alberta. During 2021 and 2022, water table levels were measured weekly during the summer months and were paired with measurements of soil properties (bulk density, saturated hydraulic conductivity, porosity and specific yield) on seismic lines and in the adjacent matrix. Bulk density increased on seismic lines by 19% and 113% in lowland and upland ecosystems, respectively. Saturated hydraulic conductivity decreased on seismic lines by one order of magnitude for both upland and lowland ecosystems. Water table variability was increased on seismic lines, but depth to water table was related to external factors such as topographic position. Snow measurements in March 2022 indicated that snow water equivalent was greater on seismic lines that were oriented in the East-West (EW) direction and on lines that were widest compared to SWE within the adjacent matrix. Our results provide information to researchers and industry on the alteration of important ecosystem factors on seismic lines that may limit line recovery trajectories. This information will help to guide restoration planning decisions through an enhanced knowledge of limiting factors for vegetation growth on seismic lines.

AbstractSupraglacial drainage networks play an integral role in both glacier dynamics and run-off timing, and mapping them provides insight into their role in glacial systems. Here we present a reproducible approach for semi-automated mapping of supraglacial hydrologic features, which complements existing work in automated and manual mapping by providing clear definitions for identification of features. This framework uses a digital terrain model (DTM) to identify potential flow routes on the glacier surface, which are then classified using a set of standardized rules based on the DTM and an orthomosaic. We found that the normalized difference water index calculated from digital imagery was influenced by image brightness and introduce a new approach using average RGB values to correct for this. Using this framework we mapped supraglacial drainage networks at Nàłùdäy and Thores Glacier, Canada. The framework was easier to implement with high-resolution (0.5 m) imagery and DTMs, compared to data with lower resolution (10 m), due to the increased detail in topography and feature boundaries at high-resolution. Lower-resolution data captured larger streams (>2 pixels wide), however, indicating that the framework can still be used at this resolution. Mapping supraglacial hydrology using standardized methods opens possibilities for investigating many questions relating to changes in supraglacial hydrology over time.

Terrestrial photographic imagery combined with structure-from-motion (SfM) provides a relatively easy-to-implement method for monitoring environmental systems, even in remote and rough terrain. However, the collection of in-situ positioning data and the identification of control points required for georeferencing in SfM processing is the primary roadblock to using SfM in difficult-to-access locations; it is also the primary bottleneck for using SfM in a time series. We describe a novel, computationally efficient, and semi-automated approach for georeferencing unreferenced point clouds (UPC) derived from terrestrial overlapping photos to a reference dataset (e.g., DEM or aerial point cloud; hereafter RPC) in order to address this problem. The approach utilizes a Discrete Global Grid System (DGGS), which allows us to capitalize on easily collected rough information about camera deployment to coarsely register the UPC using the RPC. The DGGS also provides a hierarchical set of grids which supports a hierarchical modified iterative closest point algorithm with natural correspondence between the UPC and RPC. The approach requires minimal interaction in a user-friendly interface, while allowing for user adjustment of parameters and inspection of results. We illustrate the approach with two case studies: a close-range (<1 km) vertical glacier calving front reconstructed from two cameras at Fountain Glacier, Nunavut and a long-range (>3 km) scene of relatively flat glacier ice reconstructed from four cameras overlooking Nàłùdäy (Lowell Glacier), Yukon, Canada. We assessed the accuracy of the georeferencing by comparing the UPC to the RPC, as well as surveyed control points; the consistency of the registration was assessed using the difference between successive registered surfaces in the time series. The accuracy of the registration is roughly equal to the ground sampling distance and is consistent across time steps. These results demonstrate the promise of the approach for easy-to-implement georeferencing of point clouds from terrestrial imagery with acceptable accuracy, opening the door for new possibilities in remote monitoring for change-detection, such as monitoring calving rates, glacier surges, or other seasonal changes at remote field locations.

The expansive plains of West Siberia contain globally significant carbon stocks, with Earth's most extensive peatland complex overlying the world's largest-known hydrocarbon basin. Numerous terrestrial methane seeps have recently been discovered on this landscape, located along the floodplains of the Ob and Irtysh Rivers in hotspots covering more than 2500 km(2). We articulated three hypotheses to explain the origin and migration pathways of methane within these seeps: (H1) uplift of Cretaceous-aged methane from deep petroleum reservoirs along faults and fractures, (H2) release of Oligocene-aged methane capped or trapped by degrading permafrost, and (H3) horizontal migration of Holocene-aged methane from surrounding peatlands. We tested these hypotheses using a range of geochemical tools on gas and water samples extracted from seeps, peatlands, and aquifers across the 120,000 km(2) study area. Seep-gas composition, radiocarbon age, and stable isotope fingerprints favor the peatland hypothesis of seep-methane origin (H3). Organic matter in raised bogs is the primary source of seep methane, but observed variability in stable isotope composition and concentration suggest production in two divergent biogeochemical settings that support distinct metabolic pathways of methanogenesis. Comparison of these parameters in raised bogs and seeps indicates that the first is bogs, via CO2 reduction methanogenesis. The second setting is likely groundwater, where dissolved organic carbon from bogs is degraded via chemolithotrophic acetogenesis followed by acetate fermentation methanogenesis. Our findings highlight the importance of methane lateral migration in West Siberia's bog-dominated landscapes via intimate groundwater connections. The same phenomenon could occur in similar landscapes across the boreal-taiga biome, thereby making groundwater-fed rivers and springs potent methane sources.

Mapping and monitoring disturbances in vegetation over large areas demand reliable approaches and accurate end-user maps. Methods and algorithms have been developed to meet satisfactory disturbance map accuracies, and the combination of multiple approaches has shown promise as a reliable alternative to any single method. However, extracting meaningful disturbance information from these combined methods is still challenging. Data variance from environmental conditions and disturbance drivers leads to spatial-temporal heterogeneity in land surfaces over large areas, which results in mapping errors. We evaluated the effectiveness of ensemble classification and data-driven regionalization for mapping vegetation disturbances at a broad scale. Using Google's Earth Engine cloud computing platform, our ensemble approach combines multispectral LandTrendr outputs reflecting preliminary disturbance information in a Random Forest model to map disturbances in Minas Gerais, Brazil. We then applied an unsupervised clustering technique to perform data-driven regionalization of our study area using several sources of environmental and anthropogenic information and analysed gains and losses in map accuracies. Our results indicated gains in accuracy by the ensemble method compared to non-ensemble methods of disturbance mapping, which ranged from 7.3 to 29.9% in overall accuracy at the 5% significance level. Data-driven regionalization addressed complexities arising from variability in vegetation types, local climate, and topography across our study area, identifying climate and seasonal metrics as important variables for reducing uncertainties in vegetation disturbance maps. The integration of these techniques has revealed significant potential for increasing map accuracy and has provided important insights into the development of disturbance mapping methods in heterogeneous environments.

Contemporary forest-health initiatives require technologies and workflows that can monitor forest degradation and recovery simply and efficiently over large areas. Spectral recovery analysis—the examination of spectral trajectories in satellite time series—can help democratize this process, particularly when performed with cloud computing and open-access satellite archives. We used the Landsat archive and Google Earth Engine (GEE) to track spectral recovery across more than 57,000 forest harvest areas in the Canadian province of Alberta. We analyzed changes in the normalized burn ratio (NBR) to document a variety of recovery metrics, including year of harvest, percent recovery after five years, number of years required to achieve 80% of pre-disturbance NBR, and % recovery the end of our monitoring window (2018). We found harvest areas in Alberta to recover an average of 59.9% of their pre-harvest NBR after five years. The mean number of years required to achieve 80% recovery in the province was 8.7 years. We observed significant variability in pre- and post-harvest spectral recovery both regionally and locally, demonstrating the importance of climate, elevation, and complex local factors on rates of spectral recovery. These findings are comparable to those reported in other studies and demonstrate the potential for our workflow to support broad-scale management and research objectives in a manner that is complimentary to existing information sources. Measures of spectral recovery for all 57,979 harvest areas in our analysis are freely available and browseable via a custom GEE visualization tool, further demonstrating the accessibility of this information to stakeholders and interested members of the public.

Interest in the use of unmanned aerial systems (UAS) to estimate the aboveground biomass (AGB) of vegetation in agricultural and non-agricultural settings is growing rapidly but there is no standardized methodology for planning, collecting and analyzing UAS data for this purpose. We synthesized 46 studies from the peer-reviewed literature to provide the first-ever review on the subject. Our analysis showed that spectral and structural data from UAS imagery can accurately estimate vegetation biomass in a variety of settings, especially when both data types are combined. Vegetation-height metrics are useful for trees, while metrics of variation in structure or volume are better for non-woody vegetation. Multispectral indices using NIR and red-edge wavelengths normally have strong relationships with AGB but RGB-based indices often outperform them in models. Including measures of image texture can improve model accuracy for vegetation with heterogeneous canopies. Vegetation growth structure and phenological stage strongly influence model accuracy and the selection of useful metrics and should be considered carefully. Additional factors related to the study environment, data collection and analytical approach also impact biomass estimation and need to be considered throughout the workflow. Our review shows that UASs provide a capable tool for fine-scale, spatially explicit estimations of vegetation AGB and are an ideal complement to existing ground- and satellite-based approaches. We recommend future studies aimed at emerging UAS technologies and at evaluating the effect of vegetation type and growth stages on AGB estimation.

<p>Recent advances in camera sensors, data storage, and structure-from-motion (SfM) processing are opening new possibilities for monitoring glacier processes through time series imagery. With SfM processing, internal and external camera parameters can be estimated in a bundle adjustment, alleviating problems associated with camera stability in the field. Orienting points in real world coordinates, however, still requires manual intervention in the form of ground control identification in imagery when dealing with two camera systems. We are introducing a new automated method of orienting point clouds from two-camera time lapse set ups to allow for fast processing of large numbers of frames. We accomplish this by leveraging several algorithms developed for computer vision and apply them to an analysis of glacier surface elevation change. Two time lapse systems were installed overlooking N&#249;&#322;&#224;d&#228;y (Lowell Glacier), Yukon, Canada, on July 13, 2019. Each system consisted of a Nikon D5600 and a DigiSnap Pro, recording images at 2-hour intervals. On July 1, 2019 a manned aircraft flight collected imagery of the glacier using a Nikon D850 with a differential GPS collecting high precision locations for each image. The July 1 imagery was processed using Agisoft Photoscan Professional through the Python API to produce a target point cloud for orientation of unregistered time lapse imagery. Using Photoscan Professional&#8217;s 4D capability, a time series of images from each time lapse camera were aligned in a one-step bundle adjustment to produce a series of dense point clouds at each time step. Point clouds from time lapse imagery were coregistered to the target point cloud using a Fast Point Feature Histograms and a color-enhanced point cloud alignment based on Rusu et al. (2009) and Park et al. (2017). The M3C2 algorithm (Lague et al., 2013) was used to difference point clouds in the timeseries and derive a time series of elevation change at N&#249;&#322;&#224;d&#228;y with an uncertainty of 1.5 m².&#160; All steps in the workflow are executed through Python, allowing for easy automated execution of data processing. With streamlined processing it is possible to integrate more time steps into SfM analysis of glacier surface elevation change and integrate the data into modelling efforts of glacier evolution.</p>

Forest land-use planning and restoration requires effective tools for mapping and attributing linear disturbances such as roads, trails, and asset corridors over large areas. Most existing linear-feature databases are generated by heads-up digitizing. While suitable for cartographic purposes, these datasets often lack the fine spatial details and multiple attributes required for more demanding analytical applications. To address this need, we developed the Forest Line Mapper (FLM), a semi-automated software tool for mapping and attributing linear features using LiDAR-derived canopy height models. Accuracy assessments conducted in the boreal forest of Alberta, Canada showed that the FLM reliably predicts both the center line (polyline) and footprint (extent polygons) of a variety of linear-feature types including roads, pipelines, seismic lines, and power lines. Our analysis showed that FLM outputs were consistently more accurate than publicly available datasets produced by human photo-interpreters, and that the tool can be reliably deployed across large application areas. In addition to accurately delineating linear features, the FLM generates a variety of spatial attributes associated with line geometry and vegetation characteristics from input canopy height data. Our statistical evaluation indicates that spatial attributes generated by the FLM may be useful for studying and classifying linear features based on disturbance type and ground conditions. The FLM is open-source and freely available and is aimed to assist researchers and land managers working in forested environments everywhere.

Resource-access road crossings are expected to alter peatland hydrological properties by obstructing surface and sub-surface water flows. We conducted a multi-year study at two boreal peatlands - a forested bog and a shrubby rich fen near Peace River, Alberta - to study the impacts of resource access roads on the hydrology of adjacent peatland. Field measurements (bi-weekly depth to water table and hydraulic head, one-time hydraulic conductivity) during the growing seasons (May-August) of 2016 and 2017 were taken from sampling plots representing: 1) sides of the road (upstream and downstream); 2) distance from the road (obstruction); and 3) distance from culverts. Compared to the growing season average precipitation for the region of 1.8 mm d(-1), the study period had very wet conditions in 2016 (3.7 mm d(-1)) and dry conditions in 2017 (1.1 mm d(-1)). In contrast to our assumptions, resource access road disturbed the surface and sub-surface water flow at the bog, but the effect was minimal at the fen as the road orientation was nearly parallel to the flow direction at the latter. At the bog, the shallowest depth to water table position was observed at upstream areas closer to the road, when culverts were located > 20m distance from transects. In contrast, when culverts were present < 2m from the transects, variation in hydrological conditions between upstream and downstream areas were greatly reduced. Our work shows road effects on peatland hydrology could be minimized by aligning roads parallel to the water flow direction when possible. If water flow is perpendicular to the road, adequate spacing and installation of culverts could help to reduce flow obstruction.

&lt;p&gt;Recent advances in camera sensors, data storage, and structure-from-motion (SfM) processing are opening new possibilities for monitoring glacier processes through time series imagery. With SfM processing, internal and external camera parameters can be estimated in a bundle adjustment, alleviating problems associated with camera stability in the field. Orienting points in real world coordinates, however, still requires manual intervention in the form of ground control identification in imagery when dealing with two camera systems. We are introducing a new automated method of orienting point clouds from two-camera time lapse set ups to allow for fast processing of large numbers of frames. We accomplish this by leveraging several algorithms developed for computer vision and apply them to an analysis of glacier surface elevation change. Two time lapse systems were installed overlooking N&amp;#249;&amp;#322;&amp;#224;d&amp;#228;y (Lowell Glacier), Yukon, Canada, on July 13, 2019. Each system consisted of a Nikon D5600 and a DigiSnap Pro, recording images at 2-hour intervals. On July 1, 2019 a manned aircraft flight collected imagery of the glacier using a Nikon D850 with a differential GPS collecting high precision locations for each image. The July 1 imagery was processed using Agisoft Photoscan Professional through the Python API to produce a target point cloud for orientation of unregistered time lapse imagery. Using Photoscan Professional&amp;#8217;s 4D capability, a time series of images from each time lapse camera were aligned in a one-step bundle adjustment to produce a series of dense point clouds at each time step. Point clouds from time lapse imagery were coregistered to the target point cloud using a Fast Point Feature Histograms and a color-enhanced point cloud alignment based on Rusu et al. (2009) and Park et al. (2017). The M3C2 algorithm (Lague et al., 2013) was used to difference point clouds in the timeseries and derive a time series of elevation change at N&amp;#249;&amp;#322;&amp;#224;d&amp;#228;y with an uncertainty of 1.5 m&lt;sup&gt;2&lt;/sup&gt;.&amp;#160; All steps in the workflow are executed through Python, allowing for easy automated execution of data processing. With streamlined processing it is possible to integrate more time steps into SfM analysis of glacier surface elevation change and integrate the data into modelling efforts of glacier evolution.&lt;/p&gt;

Coarse woody debris (CWD, parts of dead trees) is an important factor in forest management, given its roles in promoting local biodiversity and unique microhabitats, as well as providing carbon storage and fire fuel. However, parties interested in monitoring CWD abundance lack accurate methods to measure CWD accurately and extensively. Here, we demonstrate a novel strategy for mapping CWD volume (m(3)) across a 4300-hectare study area in the boreal forest of Alberta, Canada using optical imagery and an infra-canopy vegetation-index layer derived from multispectral aerial LiDAR. Our models predicted CWD volume with a coefficient of determination (R2) value of 0.62 compared to field data, and a root-mean square error (RMSE) of 0.224 m(3)/100 m(2). Models using multispectral LiDAR data in addition to image-analysis data performed with up to 12% lower RMSE than models using exclusively image-analysis layers. Site managers and researchers requiring reliable and comprehensive maps of CWD volume may benefit from the presented workflow, which aims to streamline the process of CWD measurement. As multispectral LiDAR radiometric calibration routines are developed and standardized, we expect future studies to benefit increasingly more from such products for CWD detection underneath canopy cover.

Detecting disturbances in native vegetation is a crucial component of many environmental management strategies, and remote sensing-based methods are the most efficient way to collect multi-temporal disturbance data over large areas. Given that there is a large range of datasets for monitoring, analyzing, and detecting disturbances, many methods have been well-studied and successfully implemented. However, factors such as the vegetation type, input data, and change detection method can significantly alter the outcomes of a disturbance-detection study. We evaluated the spatial agreement of disturbance maps provided by the Breaks For Additive Season and Trend (BFAST) algorithm, evaluating seven spectral indices in three distinct vegetation domains in Brazil: Atlantic forest, savanna, and semi-arid woodland, by assessing levels of agreement between the outputs. We computed individual map accuracies based on a reference dataset, then ranked their performance, while also observing their relationships with specific vegetation domains. Our results indicated a low rate of spatial agreement among index-based disturbance maps, which itself was minimally influenced by vegetation domain. Wetness indices produced greater detection accuracies in comparison to greenness-related indices free of saturation. The normalized difference moisture index performed best in the Atlantic forest domains, yet performed poorest in semi-arid woodland, reflecting its specific sensitivity to vegetation and its water content. The normalized difference vegetation index led to high disturbance detection accuracies in the savanna and semi-arid woodland domains. This study offered novel insight into vegetation disturbance maps, their relationship to different ecosystem types, and corresponding accuracies. Distinct input data can produce non-spatially correlated disturbance maps and reflect site-specific sensitivity. Future research should explore algorithm limitations presented in this study, as well as the expansion to other techniques and vegetation domains across the globe.

Research Highlights: This is the most comprehensive analysis to date of the accuracy of height estimates for individual conifer seedlings derived from drone-based image point clouds (DIPCs). We provide insights into the effects on accuracy of ground sampling distance (GSD), phenology, ground determination method, seedling size, and more. Background and Objectives: Regeneration success in disturbed forests involves costly ground surveys of tree seedlings exceeding a minimum height. Here we assess the accuracy with which conifer seedling height can be estimated using drones, and how height errors translate into counting errors in stocking surveys. Materials and Methods: We compared height estimates derived from DIPCs of different GSD (0.35 cm, 0.75 cm, and 3 cm), phenological state (leaf-on and leaf-off), and ground determination method (based on either the DIPC itself or an ancillary digital terrain model). Each set of height estimates came from data acquired in up to three linear disturbances in the boreal forest of Alberta, Canada, and included 22 to 189 surveyed seedlings, which were split into two height strata to assess two survey scenarios. Results: The best result (root mean square error (RMSE) = 24 cm; bias = −11 cm; R2 = 0.63; n = 48) was achieved for seedlings >30 cm with 0.35 cm GSD in leaf-off conditions and ground elevation from the DIPC. The second-best result had the same GSD and ground method but was leaf-on and not significantly different from the first. Results for seedlings ≤30 cm were unreliable (nil R2). Height estimates derived from manual softcopy interpretation were similar to the corresponding DIPC results. Height estimation errors hardly affected seedling counting errors (best balance was 8% omission and 6% commission). Accuracy and correlation were stronger at finer GSDs and improved with seedling size. Conclusions: Millimetric (GSD <1 cm) DIPC can be used for estimating the height of individual conifer seedlings taller than 30 cm.

Shrub-dominated ecosystems support biodiversity and play an important storage role in the global carbon cycle. However, it is challenging to characterize biophysical properties of low-stature vegetation like shrubs from conventional ground-based or remotely sensed data. We used spectral and structural variables derived from high-resolution unmanned aerial system (UAS) imagery to estimate the aboveground biomass of shrubs in the Betula and Salix genera in a montane meadow in Banff National Park, Canada using an area-based approach. In single-variable linear regression models, visible light (RGB) indices outperformed multispectral or structural data. A linear model based on the red ratio vegetation index (VI) accumulated over shrub area could model biomass (calibration R2 = 0.888; validation R2 = 0.774) nearly as well as the top multivariate linear regression models (calibration R2 = 0.896; validation R2 > 0.750), which combined an accumulated RGB VI with a multispectral metric. The excellent performance of accumulated RGB VIs represents a novel approach to fine-scale vegetation biomass estimation, fusing spectral and spatial information into a single parsimonious metric that rivals the performance of more complex multivariate models. Methods developed in this study will be relevant to researchers interested in estimating fine-scale shrub aboveground biomass within a range of ecosystems.

Peatlands are globally significant sources of atmospheric methane (CH 4 ). In the northern hemisphere, extensive geologic exploration activities have occurred to map petroleum deposits. In peatlands, these activities result in soil compaction and wetter conditions, changes that are likely to enhance CH 4 emissions. To date, this effect has not been quantified. Here we map petroleum exploration disturbances on peatlands in Alberta, Canada, where peatlands and oil deposits are widespread. We then estimate induced CH 4 emissions. By our calculations, at least 1900 km 2 of peatland have been affected, increasing CH 4 emissions by 4.4–5.1 kt CH 4 yr −1 above undisturbed conditions. Not currently estimated in Canada’s national reporting of greenhouse gas (GHG) emissions, inclusion would increase current emissions from land use, land use change and forestry by 7–8%. However, uncertainty remains large. Research further investigating effects of petroleum exploration on peatland GHG fluxes will allow appropriate consideration of these emissions in future peatland management.