Gregory John McDermid

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.

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. We demonstrate the capacity of high-density LiDAR 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 better balance among error types. Our maps reveal a 2,829-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 funneling 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 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.

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.

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 (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.

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.

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.

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.