Wildfires in the boreal forest of North America are generally stand renewing, with the initial phase of recovery often governing the vegetation trajectory for decades. Here, we investigated post-fire vegetation changes in dry boreal forests of the Northwest Territories, Canada, during the first 5 years following the unusually severe 2014 wildfire season. We sampled post-fire tree regeneration and the understory plant community at 1, 3, and 5 years post-fire across different stand types within fires that burned in 2014. Post-fire trajectories of tree recruitment, understory cover by plant functional types, and plant diversity varied widely among sampled stands, as well as among years post-fire. Tree seedling density reached relative equilibrium by 3-years post-fire, whereas trends in understory plant cover and understory species assemblages suggested an ongoing change that will extend beyond 5 years of observation. In almost half of sampled stands, the composition of recruited trees differed from that of the pre-fire stand, suggesting a change in tree species dominance. An analysis of regional climate revealed a significant, albeit spatially variable, warming and drying trend that will further accelerate forest stand transformation through both climate drivers of plant community composition and indirectly through increasing fire activity. While the 2014 wildfires enhanced the structural and compositional heterogeneity of the region, they also triggered vegetation changes that are likely to be persistent. As such, this study exemplifies the speed and variability that characterizes post-fire stand development in a strongly moisture-limited part of North America.

Seismic lines are narrow, linear corridors cleared through forests for oil and gas exploration. Their inconsistent recovery has led to Alberta’s forests being highly fragmented, resulting in the need for seismic line restoration programs and subsequent monitoring. Light detection and ranging (LiDAR) is becoming an increasingly popular technology for the fast and accurate measurement of forests. Mobile LiDAR scanners (MLS) are emerging as an alternative to traditional aerial LiDAR due to their increased point cloud density. To determine whether MLS could be effective for collecting vegetation data on seismic lines, we sampled 17 seismic lines using the Emesent Hovermap™ in leaf-on and leaf-off conditions. Processing the LiDAR data was conducted with GreenValley International’s LiDAR 360 software, and data derived from the point clouds were compared to physically measured field data. Overall, the tree detection algorithm was unsuccessful at accurately segmenting the point clouds. Complex vegetation environments on seismic lines, including small conifers with obscured stems or extremely dense and tall shrubs with overlapping canopies, posed a challenge for the software’s capacity to differentiate trees As a result, tree densities and diameters were overestimated, while tree heights were underestimated. Exploration of alternative algorithms and software is needed if measuring vegetation data on seismic lines with MLS is to be implemented.

Seismic lines, which are narrow linear clearings used for hydrocarbon exploration, have accumulated throughout Alberta’s forest landscapes for decades. The inconsistent natural recovery of seismic lines over time has led to a fragmented landscape and has incited the need for restoration programs and associated monitoring of forest recovery on seismic lines. In this study, we evaluated a technique where we used satellite imagery to visually assign recovery classifications based on whether the seismic line remained >50% visible (Not Recovered), <50% visible (Fractionally Recovered), or not visible (Recovered) in upland mixedwood forests. We ground validated the recovery classification on 22 seismic lines using the recovery criteria of 2000 stems ha⁻¹ and a mean tree height of 3 m. The categories of Recovered and Fractionally Recovered met the recovery criteria with 100% and 80% accuracy, respectively, while the Not Recovered category identified lines that failed to meet the recovery criteria with 83% accuracy. Based on these findings, visual interpretation of satellite imagery can be used to provide cursory-level recovery information for monitoring forest recovery on upland seismic lines at landscape-level scales.

Surface mining and bitumen extraction in Alberta's oil sands generates various tailings waste streams as by-products. Among these tailings, froth treatment tailings (FTT), originating from the froth treatment process, are particularly complex due to high levels of iron sulfide minerals (i.e. pyrite) with acid mine drainage (AMD) potential. Paraffinic FTT that are passed through a Tailings Solvent Recovery Unit (TSRU) to recover residual solvent are referred to as TSRU tailings. This study evaluated the ability of TSRU tailings to support upland and wetland vegetation under various thin capping designs using peat mineral soil mix (PMM) and coarse sand tailings (CST). A 30 cm PMM cover cap successfully supported upland and wetland vegetation for three years, while a thicker, multi-layer cap of 30 cm PMM above 20 cm of CST further improved plant survival and growth. The cap was found to effectively act as a barrier, protecting the vegetation from the adverse effects of pyrite oxidation in the TSRU tailings. While the TSRU tailings acidified (pH ≤ 2) under all capping treatments, the thickest cap (30 cm PMM/20 cm CST) delayed acidification by one year. TSRU and CST had low microbial biomass and diversity, with bacterial communities mostly composed of sulfur oxidizers and acidophilic taxa. In contrast, the PMM layer maintained a higher microbial biomass, diversity, and stability across all treatments, highlighting its potential to enhance plant-microbe interactions and improve the reclamation success of TSRU tailings.

Monitoring reclaimed wellsites in boreal forest environments requires accurate, scalable, and repeatable methods for assessing vegetation recovery. This study evaluates the use of uncrewed aerial vehicle (UAV)-based light detection and ranging (LiDAR) and multispectral (MS) imagery for individual tree detection, crown delineation, and classification across five reclaimed wellsites in Alberta, Canada. A deep learning workflow using 3D convolutional neural networks was applied to LiDAR and MS data collected in spring, summer, and autumn. Results show that LiDAR alone provided high accuracy for tree segmentation and height estimation, with a mean intersection over union (mIoU) of 0.94 for vegetation filtering and an F1-score of 0.82 for treetop detection. Incorporating MS data improved deciduous/coniferous classification, with the highest accuracy (mIoU = 0.88) achieved using all five spectral bands. Coniferous species were classified more accurately than deciduous species, and classification performance declined for trees shorter than 2 m. Spring conditions yielded the highest classification accuracy (mIoU = 0.93). Comparisons with ground measurements confirmed a strong correlation for tree height estimation (R² = 0.95; root mean square error = 0.40 m). Limitations of this technique included lower performance for short, multi-stemmed trees and deciduous species, particularly willow. This study demonstrates the value of integrating 3D structural and spectral data for monitoring forest recovery and supports the use of UAV remote sensing for scalable post-disturbance vegetation assessment. The trained models used in this study are publicly available through the TreeAIBox plugin to support further research and operational applications.

Remote sensing (RS) technologies, particularly light detection and ranging (LiDAR) and multispectral (MS) imagery, provide broad-scale vegetation monitoring capabilities at varying spatial resolutions. Remotely piloted aircraft systems (RPAS) equipped with LiDAR and MS sensors can enhance vegetation assessments by offering flexible flight schedules and capturing fine-resolution data. Further integration of deep learning (DL) models holds promise for automating the post-processing workflow, which is particularly important for vegetation monitoring applications. This protocol outlines a suite of practical methods for collecting, processing, aligning, and merging RPAS-based LiDAR and MS data for individual 3D tree delineation using an interactive DL plugin. The proprietary DL model effectively detects and segments tree boundaries across various sensors, study sites, and data resolutions within forest ecosystems. Our specific application and motivation for developing this protocol and tool is for monitoring forest recovery on reclaimed oil and gas wellsites. Currently field-based assessment methods are time-consuming, labor-intensive, and spatially limited. As reclamation efforts expand, there is a growing need for more efficient, and scalable approaches to monitor reclamation success and ecosystem recovery. By advancing RPAS-based DL applications, this research supports the monitoring of ecological recovery on reclaimed wellsites and is also applicable to other forested landscapes.