Hopkinson, Christopher

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https://opus.uleth.ca/handle/10133/4541

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Browsing Hopkinson, Christopher by Title

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    A bi-temporal airborne lidar shrub-to-tree aboveground biomass model for the taiga of western Canada
    (Taylor & Francis, 2024) Flade, Linda; Hopkinson, Christopher; Chasmer, Laura
    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.
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    Aboveground biomass allocation of boreal shrubs and short-stature trees in northwestern Canada
    (MDPI, 2021) Flade, Linda; Hopkinson, Christopher; Chasmer, Laura
    In this follow-on study on aboveground biomass of shrubs and short-stature trees, we provide plant component aboveground biomass (herein ‘AGB’) as well as plant component AGB allometric models for five common boreal shrub and four common boreal short-stature tree genera/species. The analyzed plant components consist of stem, branch, and leaf organs. We found similar ratios of component biomass to total AGB for stems, branches, and leaves amongst shrubs and deciduous tree genera/species across the southern Northwest Territories, while the evergreen Picea genus differed in the biomass allocation to aboveground plant organs compared to the deciduous genera/species. Shrub component AGB allometric models were derived using the three-dimensional variable volume as predictor, determined as the sum of line-intercept cover, upper foliage width, and maximum height above ground. Tree component AGB was modeled using the cross-sectional area of the stem diameter as predictor variable, measured at 0.30 m along the stem length. For shrub component AGB, we achieved better model fits for stem biomass (60.33 g ≤ RMSE ≤ 163.59 g; 0.651 ≤ R2 ≤ 0.885) compared to leaf biomass (12.62 g ≤ RMSE ≤ 35.04 g; 0.380 ≤ R2 ≤ 0.735), as has been reported by others. For short-stature trees, leaf biomass predictions resulted in similar model fits (18.21 g ≤ RMSE ≤ 70.0 g; 0.702 ≤ R2 ≤ 0.882) compared to branch biomass (6.88 g ≤ RMSE ≤ 45.08 g; 0.736 ≤ R2 ≤ 0.923) and only slightly better model fits for stem biomass (30.87 g ≤ RMSE ≤ 11.72 g; 0.887 ≤ R2 ≤ 0.960), which suggests that leaf AGB of short-stature trees (<4.5 m) can be more accurately predicted using cross-sectional area as opposed to diameter at breast height for tall-stature trees. Our multi-species shrub and short-stature tree allometric models showed promising results for predicting plant component AGB, which can be utilized for remote sensing applications where plant functional types cannot always be distinguished. This study provides critical information on plant AGB allocation as well as component AGB modeling, required for understanding boreal AGB and aboveground carbon pools within the dynamic and rapidly changing Taiga Plains and Taiga Shield ecozones. In addition, the structural information and component AGB equations are important for integrating shrubs and short-stature tree AGB into carbon accounting strategies in order to improve our understanding of the rapidly changing boreal ecosystem function.
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    Allometric equations for shrubs and short-stature tree aboveground biomass within boreal ecosystems of northwestern Canada
    (MDPI, 2020) Flade, Linda; Hopkinson, Christopher; Chasmer, Laura
    Aboveground biomass (AGB) of short-stature shrubs and trees contain a substantial part of the total carbon pool within boreal ecosystems. These ecosystems, however, are changing rapidly due to climate-mediated atmospheric changes, with overall observed decline in woody plant AGB in boreal northwestern Canada. Allometric equations provide a means to quantify woody plant AGB and are useful to understand aboveground carbon stocks as well as changes through time in unmanaged boreal ecosystems. In this paper, we provide allometric equations, regression coefficients, and error statistics to quantify total AGB of shrubs and short-stature trees. We provide species- and genus-specific as well as multispecies allometric models for shrub and tree species commonly found in northwestern boreal forest and peatland ecosystems. We found that the three-dimensional field variable (volume) provided the most accurate prediction of shrub multispecies AGB (R2 = 0.79, p < 0.001), as opposed to the commonly used one-dimensional variable (basal diameter) measured on the longest and thickest stem (R2 = 0.23, p < 0.001). Short-stature tree AGB was most accurately predicted by stem diameter measured at 0.3 m along the stem length (R2 = 0.99, p < 0.001) rather than stem length (R2 = 0.29, p < 0.001). Via the two-dimensional variable cross-sectional area, small-stature shrub AGB was combined with small-stature tree AGB within one single allometric model (R2 = 0.78, p < 0.001). The AGB models provided in this paper will improve our understanding of shrub and tree AGB within rapidly changing boreal environments.
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    Applying remote sensing for large-landscape problems: inventorying and tracking habitat recovery for a broadly distributed Species At Risk
    (Wiley, 2023) Dickie, Melanie; Hricko, Branislav; Hopkinson, Christopher; Tran, Victor; Kohler, Monica; Toni, Sydney; Serrouya, Robert; Kariyeva, Johan
    1. Anthropogenic habitat alteration is leading to the reduction of global biodiversity. Consequently, there is an imminent need to understand the state and trend of habitat alteration across broad areas. In North America, habitat alteration has been linked to the decline of threatened woodland caribou. As such, habitat protection and restoration are critical measures to support recovery of self-sustaining caribou populations. Broad estimates of habitat change through time have set the stage for understanding the status of caribou habitat. However, the lack of updated and detailed data on post-disturbance vegetation recovery is an impediment to recovery planning and monitoring restoration effectiveness. Advances in remote sensing tools to collect high-resolution data at large spatial scales are beginning to enable ecological studies in new ways to support ecosystem-based and species-based management. 2. We used semi-automated and manual methodologies to fuse photogrammetry point clouds (PPC) from high-resolution aerial imagery with wide-area light detection and ranging (LiDAR) data to quantify vegetation structure (height, density, class) on disturbances associated with caribou declines. We also compared vegetation heights estimated from the semi-automated PPC-LiDAR fusion to heights estimated in the field, using stereoscopic interpretation, and using multi-channel TiTAN LiDAR. 3. Vegetation regrowth was occurring on many of the disturbance types, though there was local variability in the type, height and density of vegetation. Heights estimated using PPC-LiDAR fusion were highly correlated (r ≥ 0.87 in all cases) with heights estimated using stereomodels, TiTAN multi-channel LiDAR and field measurements. 4. We demonstrated that PPC-LiDAR fusion can be operationalized over large areas to collect comprehensive and consistent vegetation data across landscape levels, providing opportunities to link fine-resolution remote sensing to landscape-scale ecological studies. Crucially, these data can be used to estimate rates of habitat recovery at resolutions that are not feasible using more commonly used satellite-based sensors, bridging the gap between resolution and extent. Such data are needed to achieve effective and efficient habitat monitoring to support caribou recovery efforts, as well as a myriad of additional forest management needs.
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    Benchmarking tree species classification from proximally sensed laser scanning data: introducing the FOR-species20K dataset
    (Wiley, 2025) Pulti, Stefano; Lines, Emily R.; Mullerova, Jana; Frey, Julian; Schindler, Zoe; Straker, Adrian; Allen, Matthew J.; Winiwarter, Lukas; Rehush, Nataliia; Hristova, Hristina; Murray, Brent; Calders, Kim; Coops, Nicholas; Hofle, Bernhard; Irwin, Liam; Junttila, Samuli; Krucek, Martin; Krok, Grzegorz; Kral, Kamil; Levick, Shaun R.; Luck, Linda; Missarov, Azim; Mokros, Martin; Owen, Harry J. F.; Sterenczak, Krzysztof; Pitkanen, Timo P.; Puletti, Nicola; Saarinen, Ninni; Hopkinson, Christopher; Terryn, Louise; Torresan, Chiara; Tomelleri, Enrico; Weiser, Hannah; Astrup, Rasmus
    1. Proximally sensed laser scanning presents new opportunities for automated forest ecosystem data capture. However, a gap remains in deriving ecologically pertinent information, such as tree species, without additional ground data. Artificial intelligence approaches, particularly deep learning (DL), have shown promise towards automation. Progress has been limited by the lack of large, diverse, and, most importantly, openly available labelled single-tree point cloud datasets. This has hindered both (1) the robustness of the DL models across varying data types (platforms and sensors) and (2) the ability to effectively track progress, thereby slowing the convergence towards best practice for species classification. 2. To address the above limitations, we compiled the FOR-species20K benchmark dataset, consisting of individual tree point clouds captured using proximally sensed laser scanning data from terrestrial (TLS), mobile (MLS) and drone laser scanning (ULS). Compiled collaboratively, the dataset includes data collected in forests mainly across Europe, covering Mediterranean, temperate and boreal biogeographic regions. It includes scattered tree data from other continents, totaling over 20,000 trees of 33 species and covering a wide range of tree sizes and forms. Alongside the release of FOR-species20K, we benchmarked seven leading DL models for individual tree species classification, including both point cloud (PointNet++, MinkNet, MLP-Mixer, DGCNNs) and multi-view 2D-based methods (SimpleView, DetailView, YOLOv5). 3. 2D Image-based models had, on average, higher overall accuracy (0.77) than 3D point cloud-based models (0.72). Notably, the performance was consistently >0.8 across scanning platforms and sensors, offering versatility in deployment. The top-scoring model, DetailView, demonstrated robustness to training data imbalances and effectively generalized across tree sizes. 4. The FOR-species20K dataset represents an important asset for developing and benchmarking DL models for individual tree species classification using proximally sensed laser scanning data. As such, it serves as a crucial foundation for future efforts to classify accurately and map tree species at various scales using laser scanning technology, as it provides the complete code base, dataset, and an initial baseline representative of the current state-of-the-art of point cloud tree species classification methods.
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    Delineating and reconstructing 3D forest fuel components and volumes with terrestrial laser scanning
    (MDPI, 2023) Xi, Zhouxin; Chasmer, Laura; Hopkinson, Christopher
    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%.
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    Ecological impacts of shortening fire return intervals on boreal peatlands and transition zones using integrated in situ field sampling and lidar approaches
    (Wiley, 2022) Jones, Emily; Chasmer, Laura; Devito, Kevin; Rood, Stewart; Hopkinson, Christopher
    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.
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    Examining drivers of post-fire seismic line ecotone regeneration in a boreal peatland environment
    (MDPI, 2023) Enayetullah, Humaira; Chasmer, Laura; Hopkinson, Christopher; Thompson, Daniel; Cobbaert, Danielle
    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.
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    Filtering stems and branches from terrestrial laser scanning point clouds using deep 3-D fully convolutional networks
    (MDPI, 2018) Xi, Zhouxin; Hopkinson, Christopher; Chasmer, Laura
    Terrestrial laser scanning (TLS) can produce precise and detailed point clouds of forest environment, thus enabling quantitative structure modeling (QSM) for accurate tree morphology and wood volume allocation. Applying QSM to plot-scale wood delineation is highly dependent on wood visibility from forest scans. A common problem is to filter wood point from noisy leafy points in the crowns and understory. This study proposed a deep 3-D fully convolution network (FCN) to filter both stem and branch points from complex plot scans. To train the 3-D FCN, reference stem and branch points were delineated semi-automatically for 14 sampled areas and three common species. Among seven testing areas, agreements between reference and model prediction, measured by intersection over union (IoU) and overall accuracy (OA), were 0.89 (stem IoU), 0.54 (branch IoU), 0.79 (mean IoU), and 0.94 (OA). Wood filtering results were further incorporated to a plot-scale QSM to extract individual tree forms, isolated wood, and understory wood from three plot scans with visual assessment. The wood filtering experiment provides evidence that deep learning is a powerful tool in 3-D point cloud processing and parsing.
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    Fluvial carbon export from a lowland Amazonian rainforest in relation to atmospheric fluxes
    (AGU Publications, 2016) Vihermaa, Leena E.; Waldron, Susan; Domingues, Tomas; Grace, John; Cosio, Eric G.; Limonchi, Fabian; Hopkinson, Christopher; da Rocha, Humberto R.; Gloor, Emanuel
    We constructed a whole carbon budget for a catchment in the Western Amazon Basin, combining drainage water analyses with eddy covariance (EC) measured terrestrial CO2 fluxes. As fluvial C export can represent permanent C export it must be included in assessments of whole site C balance, but it is rarely done. The footprint area of the flux tower is drained by two small streams (~5–7 km2 ) from which we measured the dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), particulate organic carbon (POC) export, and CO2 efflux. The EC measurements showed the site C balance to be +0.7 9.7 Mg C ha 1 yr 1 (a source to the atmosphere) and fluvial export was 0.3 0.04 Mg C ha 1 yr 1 . Of the total fluvial loss 34% was DIC, 37% DOC, and 29% POC. The wet season was most important for fluvial C export. There was a large uncertainty associated with the EC results and with previous biomass plot studies ( 0.5 4.1 Mg C ha 1 yr 1 ); hence, it cannot be concluded with certainty whether the site is C sink or source. The fluvial export corresponds to only 3–7% of the uncertainty related to the site C balance; thus, other factors need to be considered to reduce the uncertainty and refine the estimated C balance. However, stream C export is significant, especially for almost neutral sites where fluvial loss may determine the direction of the site C balance. The fate of C downstream then dictates the overall climate impact of fluvial export.
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    Identifying conifer tree vs. deciduous shrub and tree regeneration trajectories in a space-for-time boreal peatland fire chronosequence using multispectral lidar
    (MDPI, 2022) Enayetullah, Humaira; Chasmer, Laura; Hopkinson, Christopher; Thompson, Dan; Cobbaert, Danielle
    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.
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    Montane seasonal and elevational precipitation gradients in the southern Rockies of Alberta, Canada
    (Wiley, 2025) Barnes, Celeste; MacDonald, Ryan J.; Hopkinson, Chris
    Modelling precipitation inputs in mountainous terrain is challenging for water resource managers given sparse monitoring sites and complex physical hydroclimatic processes. Government of Alberta weather station uncorrected and bias-corrected precipitation datasets were used to examine elevational precipitation gradients (EPGs) and seasonality of EPGs for six South-Saskatchewan River headwater sites (alpine, sub-alpine, valley). January EPG from valley to alpine sites (730 m elevation difference) using uncorrected precipitation was 19 mm/100 m. Corrected EPG was approximately three times greater (61 mm/100 m). The valley received more precipitation than the alpine (inverse EPG) in late spring and summer. A seasonal signal was present whereby all sites demonstrated 50%–70% lower summertime precipitation relative to winter months, with the greatest seasonal variance at the alpine site. Winter watershed-level spatialized precipitation volume was compared to modelled snow water equivalent (SWE) associated with two late-winter airborne lidar surveys. Uncorrected volumes (2020: 64.0 × 106m3, 2021: 63.2 × 106m3) were slightly higher than modelled mean SWE (2020: 51.6 × 106m3, 2021: 44.2 × 106m3) whereas bias-corrected (2020: 120.5 × 106m3, 2021: 119.7 × 106m3) almost doubled the estimate. Corrected precipitation is assumed closer to the true value. Cumulative sublimation, evaporation and snowmelt losses result in ground-level snowpack yield that deviates from total atmospheric precipitation in an increasingly negative manner. The 2020/2021 simulations suggest wintertime atmospheric precipitation exceeds late-winter snowpack accumulation by up to 57% and 63%, respectively. A loss of 16 × 106m3 (7%) watershed SWE from the alpine zone was partially attributed to redistribution downslope to the treeline-ecotone. Physical snowpack losses from sublimation and melt, or modelling uncertainty due to precipitation correction and alpine snow-density uncertainties can also contribute to observed discrepancies between in situ SWE and cumulative precipitation. Ignoring bias-correction in headwater precipitation estimates can greatly impact headwater precipitation volume estimates and ignoring EPG seasonality is likely to result in under-estimated winter and over-estimated summer yields.
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    Multi-decadal floodplain classification and trend analysis in the Upper Columbia River valley, British Columbia
    (Copernicus Publications, 2024) Rodrigues, Italo Sampaio; Hopkinson, Christopher; Chasmer, Laura; MacDonald, Ryan J.; Bayley, Suzanne E.; Brisco, Brian
    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.
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    Partitioning carbon losses from fire combustion in a montane valley, Alberta Canada
    (Elsevier, 2021) Gerrand, S.; Aspinall, Jesse; Jensen, T.; Hopkinson, Christopher; Collingwood, A.; Chasmer, Laura
    Direct carbon (C) emissions from wildland fires have been difficult to quantify, especially in montane environments where sites are difficult to access. Here we examined pre-fire C partitioning and losses in a southern Canadian montane valley ecosystem, in Waterton Lakes National Park, Alberta Canada. The objectives of this study were to: (a) quantify the C loss due to combustion at a moist riparian site compared with a dry undulating upland site and (b) compare C loss observations to an active multi-spectral lidar remote sensing index. C losses from wildfire were consistently greater at the wet riparian site compared with the dry valley site. Average soil C losses were 92.92 Mg C ha −1 (st. dev. ± 48.60 Mg C ha −1) and 58.05 Mg C ha −1 (st. dev. ± 37.19 Mg C ha −1). Average tree C losses were 114.0 Mg C ha −1 (std.dev. ± 9.9 Mg C ha −1) and 86.9 Mg C ha −1 (std.dev. ± 13.5 Mg C ha −1) respectively. C losses from trees were greater than soils, where trees lost 55% (moist riparian ecosystem) and about 60% (drier valley site) of C during combustion. Using post-fire multi-spectral airborne lidar data, we found that increased proportion of charred soils were significantly related to enhanced reflectivity in SWIR, resulted in more negative active normalised burn ratio (aNBR) results, indicating enhanced burn severity. Increased proportional cover of regenerating vegetation resulted in less negative aNBR both at the drier site, though no significant relationships between aNBR and charred vs. vegetated results were observed at the moist riparian site. No significant relationship was observed between depth of burn/soil C loss and aNBR derived from lidar data, indicating potential limitations when using burn indices for below canopy burn severity. The use of multi-spectral lidar may improve understanding of below canopy fire fuels and C losses in optical imagery, which often occludes these important components of fire ecology. The results of this research improve understanding of C losses associated with wildland fire in montane ecosystems that have undergone fire suppression and management by Euro-American colonizers for over 100 years.
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    Peatland-fire interactions: a review of wildland fire feedbacks and interactions in Canadian boreal peatlands
    (Elsevier, 2021) Nelson, Kailyn; Thompson, Dan; Hopkinson, Christopher; Petrone, R.; Chasmer, Laura
    Boreal peatlands store a disproportionately large quantity of soil carbon (C) and play a critical role within the global C-climate system; however, with climatic warming, these C stores are at risk. Increased wildfire frequency and severity are expected to increase C loss from boreal peatlands, contributing to a shift from C sink to source. Here, we provide a comprehensive review of pre- and post-fire hydrological and ecological interactions that affect the likelihood of peatland burning, address the connections between peatland fires and the C-climate cycle, and provide a conceptual model of peatland processes as they relate to wildland fire, hydro-climate, and ecosystem change. Despite negative ecohydrological feedback mechanisms that may compensate for increased C loss initially, the cumulative effects of climatic warming, anthropogenic peatland fragmentation, and subsequent peatland drying will increase C loss to the atmosphere, driving a positive C feedback cycle. However, the extent to which negative and positive feedbacks will compensate for one another and the timelines for each remains unclear. We suggest that a multi-disciplinary approach of combining process knowledge with remotely sensed data and ecohydrological and wildland fire models is essential for better understanding the role of boreal peatlands and wildland fire in the global climate system.
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    Quality control impacts on total precipitation gauge records for montane valley and ridge sites in SW Alberta, Canada
    (MDPI, 2022) Barnes, Celeste; Hopkinson, Christopher
    This paper presents adjustment routines for Geonor totalizing precipitation gauge data collected from the headwaters of the Oldman River, within the southwestern Alberta Canadian Rockies. The gauges are situated at mountain valley and alpine ridge locations with varying degrees of canopy cover. These data are prone to sensor noise and environment-induced measurement errors requiring an ordered set of quality control (QC) corrections using nearby weather station data. Sensor noise at valley sites with single-vibrating wire gauges accounted for the removal of 5% to 8% (49–76 mm) of annual precipitation. This was compensated for by an increase of 6% to 8% (50–76 mm) from under-catch. A three-wire ridge gauge did not experience significant sensor noise; however, the under-catch of snow resulted in 42% to 52% (784–1342 mm) increased precipitation. When all QC corrections were applied, the annual cumulative precipitation at the ridge demonstrated increases of 39% to 49% (731–1269 mm), while the valley gauge adjustments were −4% to 1% (−39 mm to 13 mm). Public sector totalizing precipitation gauge records often undergo minimal QC. Care must be exercised to check the corrections applied to such records when used to estimate watershed water balance or precipitation orographic enhancement. Systematic errors at open high-elevation sites may exceed nearby valley or forest sites.
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    Quantifying lidar elevation accuracy: parameterization and wavelength selection for optimal ground classifications based on time since fire/disturbance
    (MDPI, 2022) Nelson, Kailyn; Chasmer, Laura; Hopkinson, Christopher
    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.
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    Remote sensing of boreal wetlands 1: data use for policy and mangement
    (MDPI, 2020) Chasmer, Laura; Cobbaert, Danielle; Mahoney, Craig; Millard, Koreen; Peters, Daniel; Devito, Kevin; Brisco, Brian; Hopkinson, Christopher; Merchant, Michael; Montgomery, Joshua; Nelson, Kailyn; Niemann, Olaf
    Wetlands have and continue to undergo rapid environmental and anthropogenic modification and change to their extent, condition, and therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use of remote sensing technologies for the ‘wise use of wetlands’, following Ramsar Convention protocols. The objectives of this review are to provide: (1) a synthesis of the history of remote sensing of wetlands, (2) a feasibility study to quantify the accuracy of remotely sensed data products when compared with field data based on 286 comparisons found in the literature from 209 articles, (3) recommendations for best approaches based on case studies, and (4) a decision tree to assist users and policymakers at numerous governmental levels and industrial agencies to identify optimal remote sensing approaches based on needs, feasibility, and cost. We argue that in order for remote sensing approaches to be adopted by wetland scientists, land-use managers, and policymakers, there is a need for greater understanding of the use of remote sensing for wetland inventory, condition, and underlying processes at scales relevant for management and policy decisions. The literature review focuses on boreal wetlands primarily from a Canadian perspective, but the results are broadly applicable to policymakers and wetland scientists globally, providing knowledge on how to best incorporate remotely sensed data into their monitoring and measurement procedures. This is the first review quantifying the accuracy and feasibility of remotely sensed data and data combinations needed for monitoring and assessment. These include, baseline classification for wetland inventory, monitoring through time, and prediction of ecosystem processes from individual wetlands to a national scale.
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    Remote sensing of boreal wetlands 2: methods for evaluating boreal wetland ecosystem state and drivers of change
    (MDPI, 2020) Chasmer, Laura; Mahoney, Craig; Millard, Koreen; Nelson, Kailyn; Peters, Daniel; Merchant, Michael; Hopkinson, Christopher; Brisco, Brian; Niemann, Olaf; Montgomery, Joshua; Devito, Kevin; Cobbaert, Danielle
    The following review is the second part of a two part series on the use of remotely sensed data for quantifying wetland extent and inferring or measuring condition for monitoring drivers of change on wetland environments. In the first part, we introduce policy makers and non-users of remotely sensed data with an effective feasibility guide on how data can be used. In the current review, we explore the more technical aspects of remotely sensed data processing and analysis using case studies within the literature. Here we describe: (a) current technologies used for wetland assessment and monitoring; (b) the latest algorithmic developments for wetland assessment; (c) new technologies; and (d) a framework for wetland sampling in support of remotely sensed data collection. Results illustrate that high or fine spatial resolution pixels (≤10 m) are critical for identifying wetland boundaries and extent, and wetland class, form and type, but are not required for all wetland sizes. Average accuracies can be up to 11% better (on average) than medium resolution (11–30 m) data pixels when compared with field validation. Wetland size is also a critical factor such that large wetlands may be almost as accurately classified using medium-resolution data (average = 76% accuracy, stdev = 21%). Decision-tree and machine learning algorithms provide the most accurate wetland classification methods currently available, however, these also require sampling of all permutations of variability. Hydroperiod accuracy, which is dependent on instantaneous water extent for single time period datasets does not vary greatly with pixel resolution when compared with field data (average = 87%, 86%) for high and medium resolution pixels, respectively. The results of this review provide users with a guideline for optimal use of remotely sensed data and suggested field methods for boreal and global wetland studies.
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    SAR and lidar temporal data fusion approaches to boreal wetland ecosystem monitoring
    (MDPI, 2019) Montgomery, Joshua; Brisco, Brian; Chasmer, Laura; Devito, Kevin; Cobbaert, Danielle; Hopkinson, Christopher
    The objective of this study was to develop a decision-based methodology, focused on data fusion for wetland classification based on surface water hydroperiod and associated riparian (transitional area between aquatic and upland zones) vegetation community attributes. Multi-temporal, multi-mode data were examined from airborne Lidar (Teledyne Optech, Inc., Toronto, ON, Canada, Titan), synthetic aperture radar (Radarsat-2, single and quad polarization), and optical (SPOT) sensors with near-coincident acquisition dates. Results were compared with 31 field measurement points for six wetlands at riparian transition zones and surface water extents in the Utikuma Regional Study Area (URSA). The methodology was repeated in the Peace-Athabasca Delta (PAD) to determine the transferability of the methods to other boreal environments. Water mask frequency analysis showed accuracies of 93% to 97%, and kappa values of 0.8–0.9 when compared to optical data. Concordance results comparing the semi-permanent/permanent hydroperiod between 2015 and 2016 were found to be 98% similar, suggesting little change in wetland surface water extent between these two years. The results illustrate that the decision-based methodology and data fusion could be applied to a wide range of boreal wetland types and, so far, is not geographically limited. This provides a platform for land use permitting, reclamation monitoring, and wetland regulation in a region of rapid development and uncertainty due to climate change. The methodology offers an innovative time series-based boreal wetland classification approach using data fusion of multiple remote sensing data sources.