This study compared 3D point cloud data derived from Structure-from-Motion (SfM) in 2021 and lidar in 2022 acquired using remotely piloted aircraft systems (RPAS). The overall objective was to develop and compare optical and active point cloud methods for deriving vegetation structures commonly measured in the field to quantify wildfire fuel distribution. The outcomes of the modelling framework were then applied to examine the impacts of mountain pine beetle (MPB) on canopy fuel load volumes in Jasper National Park prior to a high intensity wildfire in 2024. Tree species were classified using geographic object-based image analysis (GEOBIA) with an overall accuracy of similar to 90%, with higher performance in relatively open canopies with minimal shadow. Photogrammetric and lidar point clouds resolved accurate individual tree height (R-2 = 0.96; 0.99, respectively) when compared to field measurements. Crown base height derived using a windowed point density approach improved agreement with field data (R-2 = 0.76; 0.91, respectively) and improved relative to previously reported methods. Across sites with varying MPB-induced tree mortality, plots dominated by dead conifers showed a redistribution of canopy fuels towards the ground compared to plots of mostly live conifers. This structural shift suggests increased ladder fuel development, reduced canopy continuity, and a heightened likelihood of surface to crown fire transition. The results demonstrate that RPAS point clouds can effectively characterize tree structure and improve crown base height estimation, supporting more accurate assessment of canopy bulk density. These measurements provide a viable alternative to labour-intensive field surveys and can then be used as calibration and validation data for broad-area forest assessment fuel modelling using airborne and satellite remotely sensed data.

Wildland fire regimes are shifting in western Canada; however, relatively little has been directly synthesized on the state of fuels and their influence on and response to fire activity in recent decades. This article presents a systematic review of the literature from Western Canada between 1985 and 2025 to examine the role of fuels, climate, and weather conditions in shaping fire behaviour. We reviewed 245 articles on fire-related research in British Columbia, Alberta, Saskatchewan, Northwest Territories, and Yukon Territory to assess the literature on the significant feedbacks among climate, fuel conditions, and fire behaviour, and to evaluate how large fires over the last four decades have affected fuel characteristics. Secondarily, we examined trends in publicly available National Burned Area Composite (NBAC) data to assess how large fires (≥95th percentile by size) interact with pre-fire vegetation types and how post-fire severity has changed over time. Our findings indicate shifts in fuels, including abundance, composition, and conditions that interact with changing climate dynamics, while also highlighting substantial limitations in the literature regarding structural arrangement and continuity of fuels. Consistent with recent publications, our results show an increase in the number of extremely large fires (P95) over the last decade. However, increasing severity does not necessarily correspond to increasing fire size. Collectively, findings reveal geographic gaps in fire research coverage, particularly in northern regions, and limitations in current systems for accurately representing ongoing changes in fuels in western Canada. This review highlights important advances in understanding fuel dynamics and fire behaviour while also identifying critical gaps that limit our ability to anticipate how these systems will respond to continued climatic change.

Climatic warming is likely to affect the Canadian Rockies, leading to changes in the land cover (LC) and hydrological cycles. This study estimates climate-induced changes in LC (open water, marsh, wet meadow, and woody/shrub) in the Upper Columbia River Wetlands (UCRW), British Columbia, Canada, from 1984 to 2040. An artificial Neural Network (ANN) approach was used with Landsat series archive data from 1984 to 2022 to project seasonal LC change from 2020s to 2040s. Concurrently, hydroclimatic-based models (using air temperature and precipitation to predict river discharge at the UCRW, 1984–2022) were developed (average Nash Sutcliffe: training 0.75 and validation of 0.70) to predict (1984–2040) river discharge forced by Representative Concentration Pathway (RCP) 4.5 and 8.5. The 1984–2022 regression between river discharge and UCRW open water area was forced by RCP scenario river discharge results, calculating open water area for both scenarios. ANN-predicted LC with a Kappa of 0.85 (average of all seasons) for 2020s reference and projected LC, and 0.82 for reference and projected LC change maps (2000s–2020s). From 2020s to 2040s, the ANN projected a reduction (−5 %) of open water areas during late summer (August to mid-September) in the UCRW, consistent with RCP 4.5 forecasts. The peak of the open water area in the UCRW is projected to shift from summer (late-May to July) to spring (April to mid-May) in both RCP scenarios. The projected changing hydrological conditions reduced the marsh area (−1 % to −12 %) and increased the wet meadow (+1 % to +4 %) mostly in the summer and late summer. Meanwhile, woody and shrubby vegetation on the floodplain increased (3 % to 5 %), indicating that the floodplain is projected to dry out.

Accurate methane (CH 4 ) emission estimates from Arctic and boreal wetlands are essential for reducing global budget uncertainties but are hindered by poorly constrained wetland distribution and classification. We assessed how land cover map resolution and thematic detail influence these estimates. Using very high spatial resolution land cover maps (≤2.5 m) with five to seven harmonized classes and 4–50% wetland coverage, we estimated CH 4 emissions across seven Arctic and boreal sites in North America and Eurasia. Resampling to coarser resolutions (up to 5 km) revealed that CH 4 flux estimates remained within 13% error when resolution was ≤25 m pixel size. At resolutions coarser than 1 km, four of seven sites shifted from net CH 4 source to sink, due to misrepresentation of wetland extent in heterogeneous landscapes with small, fragmented wetlands. Thematic detail also proved critical, as fens—high CH 4 emitters—were disproportionately underrepresented in coarse (>1 km) maps relative to other wetland types. We also show that existing global or circumpolar land cover maps tend to misrepresent wetlands, either overlooking smaller features or overestimating coverage in wetland dominated areas. Our findings indicate that coarse-scale land cover datasets are unsuitable for estimating CH 4 budgets in these regions, where high spatial resolution and biogeochemically relevant land cover classes are essential for reliable CH 4 emission upscaling.

Wildland fire fuels within the gray-phase of mountain pine beetle ( Dendroctonus ponderosae Hopkins) outbreak (4–10 years post mortality) are treated as homogenous despite potential variability. To assess if proportion of mortality due to mountain pine beetle in gray-phase stands significantly affects fuel structure, composition, and loading, detailed fuel measurements were collected across a range of severity in Jasper National Park, Alberta, Canada, by coincidence 1–3 years before a major wildfire. These were categorized based on outbreak severity thresholds of Light (<35%), Moderate (36%–65%), and Severe (>65%) beetle-induced pine mortality defined as the proportion of killed trees within a stand. The canopy fuels of both mature and small tree cohorts were significantly reduced with increasing outbreak severity. Severely affected stands showed a 49% reduction in mature tree canopy bulk density from original loading but increased herbaceous vegetation. Moderately affected stands had higher coarse woody debris loading (mean = 3.21 kg m – 2 ; SE = 0.49 kg m –2 ). These severity ratings can be used to quantify canopy fuel loss and signal potential shifts to other surface fuel types, barring fire. This is one of the first studies to showcase variability within gray-phase outbreak fuels, despite their prevalence in western Canada, and discuss the potential influences on fire behaviour.

Wetland ecosystems are sensitive to climate variation, yet tracking vegetation type and structure changes through time remains a challenge. This study examines how Landsat-derived vegetation indices (NDVI and EVI) correspond with lidar-derived canopy height model (CHM) changes from 2000 to 2018 across the wetland landscape of the Peace–Athabasca Delta (PAD), Canada. By comparing CHM change and NDVI and EVI trends across woody and herbaceous land covers, this study fills a gap in understanding long-term vegetation responses in northern wetlands. Findings show that ~35% of the study area experienced canopy growth, while 2% saw a reduction in height. CHM change revealed 11% ecotonal expansion, where shrub and treed swamps encroached on meadow and marsh areas. NDVI and EVI correlated significantly (p < 0.001) with CHM, particularly in shrub swamps (r2 = 0.40, 0.35) and upland forests (NDVI r2 = 0.37). However, EVI trends aligned more strongly with canopy expansion, while NDVI captured mature tree height growth and wetland drying, indicated by rising land surface temperatures (LST). These results highlight the contrasting responses of NDVI and EVI—NDVI being more sensitive to moisture-related changes such as wetland drying, and EVI aligning more closely with canopy structural changes—emphasizing the value of combining lidar and satellite indices to monitor wetland ecosystems in a warming climate.

Capturing subcanopy forest information from airborne laser scanning (ALS) is constrained by signal occlusion. This study demonstrates the potential of close-range terrestrial laser scanner (TLS) scanning to mitigate the constraints of ALS in acquiring stem-level forest attributes. A transformer-based neural network was adapted to classify and segment 3D individual trees from ALS data. A deep neural network combined with a gaussian process layer was proposed to estimate tree diameter-at-breast-height (DBH) from ALS data. The performance of these methods was compared to other benchmarked methods using the same dataset, including a total of seven classifiers, five segmentors, and six attribute regressors. The study was conducted across four ALS sample areas and ten combined TLS/ALS plots, primarily in montane forests. Manual delineation of TLS trees provided a precise validation reference. The proposed methods demonstrated high accuracies, with a mean intersection-over-union (mIoU) of 0.92 for ALS tree classification, 0.70 for tree segmentation, and a RMSE of 4.2 cm or 18.9 % for DBH estimation on average of the ten plots. Tree detection accuracy was strongly associated with the final segmentation accuracy. Factors such as tree height, overlapping, inclination, and neighboring conditions impacted segmentation accuracy. Our segmentation method effectively mitigated accuracy loss for short and occluded trees. Overall, this study presents scalable and cost-effective solutions for TLS calibration of ALS scans over two meso-scale montane valleys. Leveraging deep neural networks enables scaling of stem attributes to landscape scales, thereby linking fine-scale forest inventory with sustainable management of expansive forest resources. Our codes are available at https://github.com/truebelief/artemis_treescaling.

Floodplain wetland ecosystems experience significant seasonal water fluctuation over the year, resulting in a dynamic hydroperiod, with a range of vegetation community responses. This paper assesses trends and changes in land cover and hydroclimatological variables, including air temperature, river discharge, and water level in the Upper Columbia River Wetlands (UCRW), British Columbia, Canada. A land cover classification time series from 1984 to 2022 was generated from the Landsat image archive using a random forest algorithm. Peak river flow timing, duration, and anomalies were examined to evaluate temporal coincidence with observed land cover trends. The land cover classifier used to segment changes in wetland area and open water performed well (kappa of 0.82). Over the last 4 decades, observed river discharge and air temperature have increased, precipitation has decreased, the timing of peak flow is earlier, and the flow duration has been reduced. The frequency of both high-discharge events and dry years have increased, indicating a shift towards more extreme floodplain flow behavior. These hydrometeorological changes are associated with a shift in the timing of snowmelt, from April to mid-May, and with seasonal changes in the vegetative communities over the 39-year period. Thus, woody shrubs (+6 % to +12 %) have expanded as they gradually replaced marsh and wet-meadow land covers with a reduction in open-water area. This suggests that increasing temperatures have already impacted the regional hydrology, wetland hydroperiod, and floodplain land cover in the Upper Columbia River valley. Overall, there is substantial variation in seasonal and annual land cover, reflecting the dynamic nature of floodplain wetlands, but the results show that the wetlands are drying out with increasing areas of woody/shrub habitat and loss of aquatic habitat. The results suggest that floodplain wetlands, particularly marsh and open-water habitats, are vulnerable to climatic and hydrological changes that could further reduce their areal extent in the future.

Climate change in northern latitudes is increasing the vulnerability of peatlands and the riparian transition zones between peatlands and upland forests (referred to as ecotones) to greater frequency of wildland fires. We examined early post-fire vegetation regeneration following the 2011 Utikuma complex fire (central Alberta, Canada). This study examined 779 peatlands and adjacent ecotones, covering an area of ~182 km 2 . Based on the known regional fire history, peatlands that burned in 2011 were stratified into either long return interval (LRI) fire regimes of >80 years (i.e., no recorded prior fire history) or short fire return interval (SRI) of 55 years (i.e., within the boundary of a documented severe fire in 1956). Data from six multitemporal airborne lidar surveys were used to quantify trajectories of vegetation change for 8 years prior to and 8 years following the 2011 fire. To date, no studies have quantified the impacts of post-fire regeneration following short versus long return interval fires across this broad range of peatlands with variable environmental and post-fire successional trajectories. We found that SRI peatlands demonstrated more rapid vascular and shrub growth rates, especially in peatland centers, than LRI peatlands. Bogs and fens burned in 1956, and with little vascular vegetation (classified as “open peatlands”) prior to the 2011 fire, experienced the greatest changes. These peatlands tended to transition to vascular/shrub forms following the SRI fire, while open LRI peatlands were not significantly different from pre-fire conditions. The results of this study suggest the emergence of a positive feedback, where areas experiencing SRI fires in southern boreal peatlands are expected to transition to forested vegetation forms. Along fen edges and within bog centers, SRI fires are expected to reduce local peatland groundwater moisture-holding capacity and promote favorable conditions for increased fire frequency and severity in the future.

Monitoring aboveground biomass (AGB) is critical for carbon reporting and quantifying ecosystem change. AGB from field data can be scaled to the region using airborne lidar. However, lidar-based AGB products emphasize upland forests, which may not represent the conditions in rapidly changing peatland complexes in the southern Taiga of western Canada. In addition, to ensure that modeled AGB changes do not incorporate systematic error due to differences between older and newer lidar technologies, model transfer tests are required. The aim of this study was to develop one bi-temporal lidar-based AGB model applicable to (1) vegetation structures at varying vertical and horizontal continuity in this region and to (2) data collected with an earlier generation lidar system for which Canada-wide aerial coverage is available. Goodness-of-fit metrics show that AGB can be modeled with moderate (R2 = 48%–58% Taiga Shield, peatlands) to high accuracies (R2 = 83%–89% Taiga Plains, upland/permafrost plateau forests including ecotones) by using the point clouds average height and 90th height percentile within a weighted approach as function of modeled AGB and calibrating the earlier lidar data. These results are important for quantifying climate change effects on forest to peatland ecotones.

Lakes in discontinuous permafrost peatlands are on the front lines of climate change, sensitive to even modest increases in air temperature. The aim of this study was to provide the first limnological characterization of shallow (similar to 1-2 m depth) lakes in the Scotty Creek basin (Northwest Territories, Canada), a field site of circumpolar significance due to the existence of long-term ecohydrological monitoring going back decades. We use this previous work as a foundation to advance our process-based understanding of the potential drivers of lake ecosystem change. Our results showed that dissolved organic carbon (DOC) and lake color were not correlated, a pattern that seems to be an important driver of diatom (siliceous single-celled algae) assemblages in these lakes. Diatoms in the study lakes tended to fall into 1 of 2 assemblage clusters. One cluster, composed of small benthic Fragilariaceae and small Navicula species (sensu lato), was found associated with higher lake color; the second cluster, composed of Encyonopsis and large Navicula species, was found associated with high DOC, lower color, and the presence of a benthic moss mat. From this finding, we suggest that DOC quality is a primary control on lake ecology in this region for its role in controlling light penetration to the lake bottom. Our hypothesis that the prevalence of nearshore fens and collapse scar wetlands would be important drivers of DOC was not supported in the 9 study lakes with available data to map shoreline features.

Seismic lines are the dominant anthropogenic disturbance in the boreal forest of the Canadian province of Alberta, fragmenting over 1900 km2 of peatland areas and accounting for more than 80% of all anthropogenic disturbance in this region. The goal of this study is to determine whether the wildland fires that burn across seismic lines in peatlands result in the regeneration of woody vegetation within the ecotonal areas adjacent to seismic lines. We use a combination of seismic line and vegetation structural characteristics derived from multi-spectral airborne lidar across a post-fire peatland chronosequence. We found an increasing encroachment of shrubs and trees into seismic lines after many years since a fire, especially in fens, relative to unburned peatlands. Fens typically had shorter woody vegetation regeneration (average = 3.3 m ± 0.9 m, standard deviation) adjacent to seismic lines compared to bogs (average = 3.8 m ± 1.0 m, standard deviation), despite enhanced shrubification closer to seismic lines. The incoming solar radiation and seismic line age since the establishment of seismic line(s) were the factors most strongly correlated with enhanced shrubification, suggesting that the increased light and time since a disturbance are driving these vegetation changes. Shrub encroachment closer to seismic lines tends to occur within fens, indicating that these may be more sensitive to drying conditions and vegetation regeneration after several years post-fire/post-seismic line disturbance.

Predictive accuracy in wildland fire behavior is contingent on a thorough understanding of the 3D fuel distribution. However, this task is complicated by the complex nature of fuel forms and the associated constraints in sampling and quantification. In this study, twelve terrestrial laser scanning (TLS) plot scans were sampled within the mountain pine beetle-impacted forests of Jasper National Park, Canada. The TLS point clouds were delineated into eight classes, namely individual-tree stems, branches, foliage, downed woody logs, sapling stems, below-canopy branches, grass layer, and ground-surface points using a transformer-based deep learning classifier. The fine-scale 3D architecture of trees and branches was reconstructed using a quantitative structural model (QSM) based on the multi-class components from the previous step, with volume attributes extracted and analyzed at the branch, tree, and plot levels. The classification accuracy was evaluated by partially validating the results through field measurements of tree height, diameter-at-breast height (DBH), and live crown base height (LCBH). The extraction and reconstruction of 3D wood components enable advanced fuel characterization with high heterogeneity. The existence of ladder trees was found to increase the vertical overlap of volumes between tree branches and below-canopy branches from 8.4% to 10.8%.

Wildland fires and anthropogenic disturbances can cause changes in vegetation species composition and structure in boreal peatlands. These could potentially alter regeneration trajectories following severe fire or through cumulative impacts of climate-mediated drying, fire, and/or anthropogenic disturbance. We used lidar-derived point cloud metrics, and site-specific locational attributes to assess trajectories of post-disturbance vegetation regeneration in boreal peatlands south of Fort McMurray, Alberta, Canada using a space-for-time-chronosequence. The objectives were to (a) develop methods to identify conifer trees vs. deciduous shrubs and trees using multi-spectral lidar data, (b) quantify the proportional coverage of shrubs and trees to determine environmental conditions driving shrub regeneration, and (c) determine the spatial variations in shrub and tree heights as an indicator of cumulative growth since the fire. The results show that the use of lidar-derived structural metrics predicted areas of deciduous shrub establishment (92% accuracy) and classification of deciduous and conifer trees (71% accuracy). Burned bogs and fens were more prone to shrub regeneration up to and including 38 years after the fire. The transition from deciduous to conifer trees occurred approximately 30 years post-fire. These results improve the understanding of environmental conditions that are sensitive to disturbance and impacts of disturbance on northern peatlands within a changing climate.

Northern high latitudes are experiencing some of the greatest increases in air temperatures on Earth. Air temperatures (along with other modulating variables including precipitation and the onset of snowmelt) are influenced by atmospheric-oceanic circulation patterns, some of which are persistent and recurrent. One pattern in particular, the Arctic Dipole (AD) anomaly, is a persistent sea-level pressure teleconnection pattern between the Canadian Archipelago and Barents Sea that has unknown impacts on local climate variability. These patterns may be important, especially in hydro-ecologically sensitive areas such as Northwest Territories (NWT), Canada where permafrost thaw and ecosystem changes are influenced by interannual climate variability. The goal of this research is to determine the impacts of the AD on local climate (air temperature, precipitation, snowmelt) for a 66-year period (1950-2015) spanning both latitudinal and longitudinal gradients across NWT from north to south and foothills to plains. Deviations during strong positive and negative modes of the AD index were calculated in reference to the complete 66-year record. Results showed considerable year-to-year variability in the AD pattern, with more frequent strong negative modes during the 2000s. During 1950-2015, there were 64 and 56 occurrences of strong positive and strong negative AD modes, respectively, across all seasons. Spring and summer strong AD modes led to local air temperature anomalies of greater than 0.8 degrees C compared with the long-term (66 years) mean. Earlier onset of snowmelt, by an average of 3-5 days, was also noted during positive AD modes. Despite strong connectivity between the AD and local air temperature, we found less correspondence between the AD and seasonal precipitation. These findings improve understanding of the impacts of the AD on local weather and climate in NWT and suggest implications for ecosystem change, such as drying and shrubification of northern boreal peatlands and possible connectivity to teleconnection impacts on wildland fire.

Dedication This Special Issue is dedicated to the memory of our friend and colleague, Dr. Martin Isenburg. Martin made valuable and colorful contributions to our symposium by hosting a workshop and giving a video presentation from his home and ’laser’ chicken farm in Costa Rica. The creator of the widely popular LAStools software, and an avid traveler and trainer in the international lidar community, he fell victim to the global pandemic in 2021. He will be sadly missed by all whose lives he touched.

Aridity associated with rising air temperatures in northern latitudes is expected to contribute to increased frequency of wildland fires. Here, we examined regenerating vegetation following short return interval (SRI) fire (56 years post-fire) compared to long return interval (LRI) fire (>80 years post-fire) in boreal peatlands and their adjacent transitional areas. The objectives of this study were to quantify if differences exist between (1) peatland and transitional soil characteristics in LRI versus SRI areas and (2) regenerating vegetation species, structural characteristics and diversity. We also determined if patterns of vegetation structural characteristics observed using field data also occur across the broader landscape using airborne lidar data. The Utikuma Region Study Area (URSA) is located in central Alberta, Canada. Here, 19 peatlands were sampled, coincident with an airborne lidar survey of the broader region, where 120 peatlands in short and long fire return intervals were identified. We found that SRI transitional areas had significantly deeper organic soil deposits than those found in LRI (p < 0.0001). Proportions of regenerating species differed significantly between peatlands and transitional areas in SRI versus LRI, where greater proportion of coniferous species were observed in LRI. Deciduous transitional-upland species and taller post-fire vegetation heights were more commonly found SRI peatlands compared with LRI. This suggest that fires with SRIs in this region may result in enhanced deciduous succession, which may transition boreal peatlands into ecosystems that have some characteristics of transitional and upland forests.

Open‐canopy forested systems are found across a range of terrestrial biomes. Forest structure and organization in open‐canopy systems exhibit substantial controls on system process dynamics such as evapotranspiration (ET). The energy reaching sub‐canopy forest layers is greater in open‐canopy systems compared to closed canopy systems, with high spatiotemporal variability in the distribution of energy that both drives ET and controls sub canopy species composition and organization. Yet the impact of their structural complexity and organization on whole system ET dynamics is poorly understood. Using the BETA+ model and measured eddy covariance‐based ET fluxes from a boreal treed peatland, we critically evaluate how stand compositional and organizational complexity influences ET dynamics. Model simulations iteratively increase complexity from a simple ‘big‐leaf’ model to a model representing spatial complexity of all system layers, demonstrating the effect of each complex system component on stand ET dynamics. We show that including forest stand complexity and associated canopy and radiation variability increases ET model estimates by ~26%. In addition to changes in the ET estimates, the inclusion of this spatial complexity is shown to induce temporal variations in the simulated ET that improves model performance by reducing unexplained variance between modelled and measured ET by 10% and reducing hysteresis in model results. These results have clear implications for flux modelling of forest systems and for larger scale climate models where open canopy systems such as this dominate the landscape. Demonstrating that whilst big leaf simulation can approximate ET fluxes, the inclusion of forest‐stand complexity and its influence on spatiotemporal radiation fluxes and ecohydrological processes are necessary to effectively represent ET dynamics within open canopies.

The Taiga Plains ecozone in northwestern Canada is characterized by vast peat landscapes consisting of both mostly tree-less, permafrost-free and forested, permafrost-affected peat landscapes. In response to warming due to ongoing climate change, more frequent and severe wildfires and rapid permafrost thaw affect landscape composition, structure and functioning, whereas more and more ice-rich permafrost peat plateaus transform into water-saturated thermokarst wetlands or lakes. Collectively, these three agents of change, namely warming, wildfire, and thermokarst, could turn these boreal peat landscape from atmospheric carbon and nitrogen sinks into sources with potentially positive climate system feedbacks. We studied net ecosystem exchange (NEE) and its two component fluxes, i.e., gross primary productivity (GPP) and ecosystem respiration (ER), from three sites with five eddy covariance towers near the southern limit of permafrost in western Canada. Around the southernmost site Lutose, both footprint areas around the two towers have completely burned in wildfires in 2007 and 2019, respectively. We hypothesized that these two subsites would act as net CO2 sources, because of the recent disturbance history. This has been confirmed by preliminary results. The two other sites mainly differed in permafrost extent, ranging from sporadic (Scotty Creek) to discontinuous (Smith Creek), and in peat plateau-to-wetland ratio and corresponding forest cover (Scotty Creek < Smith Creek). Between the two sites Scotty Creek and Smith Creek, we hypothesized that the overall landscape GPP and ER will be higher at Scotty Creek compared to the northernmost site Smith Creek, due to both more abundant thermokarst wetlands and higher GPP and ER of the peat plateau areas at this more southern site. We further hypothesized that the effects of warming on GPP are greater than on ER and thus that the warmer Scotty Creek site is a greater net CO2 sink. Contrary to expectations, preliminary results have shown that there is no difference in NEE between Scotty Creek and Smith Creek, whereas both, the overall landscape GPP and ER, are actually higher at Smith Creek. To identify differences in the NEE, GPP, and ER between their peat plateaus and thermokarst wetlands, respectively, we move forward by applying footprint analyses for Smith Creek and Scotty Creek. Through these analyses we will be able to shed light on how each of the drivers, i.e., warming, wildfire, and thermokarst, alters magnitude and direction in greenhouse gas fluxes from rapidly thawing boreal peat landscapes.

Pre- and post-fire airborne lidar data provide an opportunity to determine peat combustion/loss across broad spatial extents. However, lidar measurements of ground surface elevation are prone to uncertainties. Errors may be introduced in several ways, particularly associated with the timing of data collection and the classification of ground points. Ground elevation data must be accurate and precise when estimating relatively small elevation changes due to combustion and subsequent carbon losses. This study identifies the impact of post-fire vegetation regeneration on ground classification parameterizations for optimal accuracy using TerraScan and LAStools with airborne lidar data collected in three wavelengths: 532 nm, 1064 nm, and 1550 nm in low relief boreal peatland environments. While the focus of the study is on elevation accuracy and losses from fire, the research is also highly pertinent to hydrological modelling, forestry, geomorphological change, etc. The study area includes burned and unburned boreal peatlands south of Fort McMurray, Alberta. Lidar and field validation data were collected in July 2018, following the 2016 Horse River Wildfire. An iterative ground classification analysis was conducted whereby validation points were compared with lidar ground-classified data in five environments: road, unburned, burned with shorter vegetative regeneration (SR), burned with taller vegetative regeneration (TR), and cumulative burned (both SR and TR areas) in each of the three laser emission wavelengths individually, as well as combinations of 1550 nm and 1064 nm and 1550 nm, 1064 nm, and 532 nm. We find an optimal average elevational offset of ~0.00 m in SR areas with a range (RMSE) of ~0.09 m using 532 nm data. Average accuracy remains the same in cumulative burned and TR areas, but RMSE increased to ~0.13 m and ~0.16 m, respectively, using 1550 nm and 1064 nm combined data. Finally, data averages ~0.01 m above the field-measured ground surface in unburned boreal peatland and transition areas (RMSE of ~0.19 m) using all wavelengths combined. We conclude that the ‘best’ offset for depth of burn within boreal peatlands is expected to be ~0.01 m, with single point measurement uncertainties upwards of ~0.25 m (RMSE) in areas of tall, dense vegetation regeneration. The importance of classification parameterization identified in this study also highlights the need for more intelligent adaptative classification routines, which can be used in other environments.