Hopkinson, Christopher

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Statistical modelling of the snow depth distribution in open alpine terrain
(Copernicus Publications, 2013) Grunewald, T.; Stotter, J.; Pomeroy, J.W.; Dadic, R.; Banos, I.M.; Marturia, J.; Spross, M.; Hopkinson, Christopher; Burlando, P.; Lehnig, M.
The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions.We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m), that 30 to 91% of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A “global” model, combining all the data from all areas investigated, could only explain 23% of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.
Slope estimation from ICESat/GLAS
(M D P I A G, 2014) Mahoney, Craig; Kljun, Natascha; Los, Sietse O.; Chasmer, Laura; Hacker, Jorg M.; Hopkinson, Christopher; North, Peter R. J.; Rosette, Jacqueline A. B.; van Gorsel, Eva
We present a novel technique to infer ground slope angle from waveform LiDAR, known as the independent slope method (ISM). The technique is applied to large footprint waveforms ( 60 m mean diameter) from the Ice, Cloud and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) to produce a slope dataset of near-global coverage at 0:5 0:5 resolution. ISM slope estimates are compared against high resolution airborne LiDAR slope measurements for nine sites across three continents. ISM slope estimates compare better with the aircraft data (R2 = 0:87 and RMSE = 5:16 ) than the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) inferred slopes (R2 = 0:71 and RMSE = 8:69 ). ISM slope estimates are concurrent with GLAS waveforms and can be used to correct biophysical parameters, such as tree height and biomass. They can also be fused with other DEMs, such as SRTM, to improve slope estimates.
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.
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.
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.
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.
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.
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.
Shrub changes with proximity to anthropogenic disturbance in boreal wetlands determined using bi-temporal airborne lidar in the Oil Sands Region, Alberta, Canada
(Elsevier, 2021) Chasmer, Laura; Lima, E. Moura; Mahoney, Craig; Hopkinson, Christopher; Montgomery, Joshua; Cobbaert, Danielle
In this study, we used bi-temporal airborne lidar data to compare changes in vegetation height proximal to anthropogenic disturbances in the Oil Sands Region of Alberta, Canada. We hypothesize that relatively low-impact disturbances such as seismic lines will increase the fragmentation of wetlands, resulting in shrub growth. Bi-temporal lidar data collected circa 2008 and 2018 were used to identify correspondence between the density of anthropogenic disturbances, wetland shape complexity and changes in vegetation height within >1800 wetlands near Fort McKay, Alberta, Canada. We found that up to 50% of wetlands were disturbed by anthropogenic disturbance in some parts of the region, with the highest proportional disturbance occurring within fens. Areas of dense anthropogenic disturbance in bogs resulted in increased growth and expansion of shrubs, while we found the opposite to occur in fens and swamps during the 10-year period. Up to 30% of bogs had increased shrubification, while shrub changes in fens and swamps varied depending on density of disturbance and did not necessarily correspond with shrub growth. As wetland shapes became increasingly elongated, the prevalence of shrubs declined between the two time periods, which may be associated with hydrological drivers (e.g. elongated may indicate surface and ground-water discharge influences). The results of this study indicate that linear disturbances such as seismic lines, considered to have relatively minimal impacts on ecosystems, can impact proximal wetland shape, fragmentation and vegetation community changes, especially in bogs.
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.
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.
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.
Remote sensing of wetlands in the prairie pothole region of North America
(MDPI, 2021) Montgomery, Joshua; Mahoney, Craig; Brisco, Brian; Boychuk, Lyle; Cobbaert, Danielle; Hopkinson, Christopher
The Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.
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.
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.
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.
Delegate perspectives on transitioning the 41st Canadian Symposium on remote sensing to a virtual event due to the COVID-19 Pandemic
(Taylor & Francis, 2022) Hopkinson, Christopher; Coburn, Craig A.
The 41st Canadian Symposium on Remote Sensing (CSRS) was a unique event, originally planned to be hosted as an in-person event in Yellowknife, Northwest Territories but ultimately delivered 100% online due to the COVID-19 global pandemic. As the 41st CSRS represented an unprecedented departure from the CRSS-SCT’s long history of annual in-person symposia, this note summarizes the transition from an in-person to an online event. In particular, delegate feedback on some of the challenges encountered, as well as positive and negative perceptions of the event delivery. It is important that a record of these collective experiences is preserved and considered for future symposia, and our experience is shared with the global research community.
3D graph-based individual-tree isolation (treeiso) from terrestrial laser scanning point clouds
(MDPI, 2022) Xi, Zhouxin; Hopkinson, Christopher
Using terrestrial laser scanning (TLS) technology, forests can be digitized at the centimeter-level to enable fine-scale forest management. However, there are technical barriers to converting point clouds into individual-tree features or objects aligned with forest inventory standards due to noise, redundancy, and geometric complexity. A practical model treeiso based on the cut-pursuit graph algorithm was proposed to isolate individual-tree points from plot-level TLS scans. The treeiso followed the local-to-global segmentation scheme, which grouped points into small clusters, large segments, and final trees in a hierarchical manner. Seven tree attributes were investigated to understand the underlying determinants of isolation accuracy. Sensitivity analysis based on the PAWN index was performed using 10,000 parameter combinations to understand the treeiso’s parameter importance and model robustness. With sixteen reference TLS plot scans from various species, an average of 86% of all trees were detected. The mean intersection-over-union (mIoU) between isolated trees and reference trees was 0.82, which increased to 0.92 within the detected trees. Sensitivity analysis showed that only three parameters were needed for treeiso optimization, and it was robust against parameter variations. This new treeiso method is operationally simple and addresses the growing need for practical 3D tree segmentation tools.
The Multisource Vegetation Inventory (MVI): a satellite-based forest inventory for the Northwest Territories taiga plains
(MDPI, 2022) Castilla, Guillermo; Hall, Ronald J.; Skakun, Rob; Filiatrault, Michelle; Beaudoin, André; Gartrell, Michael; Smith, Lisa; Groenewegen, Kathleen; Hopkinson, Christopher; van der Sluijs, Jurjen
Sustainable forest management requires information on the spatial distribution, composition, and structure of forests. However, jurisdictions with large tracts of noncommercial forest, such as the Northwest Territories (NWT) of Canada, often lack detailed forest information across their land base. The goal of the Multisource Vegetation Inventory (MVI) project was to create a large area forest inventory (FI) map that could support strategic forest management in the NWT using optical, radar, and light detection and ranging (LiDAR) satellite remote sensing anchored on limited field plots and airborne LiDAR data. A new landcover map based on Landsat imagery was the first step to stratify forestland into broad forest types. A modelling chain linking FI plots to airborne and spaceborne LiDAR was then developed to circumvent the scarcity of field data in the region. The developed models allowed the estimation of forest attributes in thousands of surrogate FI plots corresponding to spaceborne LiDAR footprints distributed across the project area. The surrogate plots were used as a reference dataset for estimating each forest attribute in each 30 m forest cell within the project area. The estimation was based on the k-nearest neighbour (k-NN) algorithm, where the selection of the four most similar surrogate FI plots to each cell was based on satellite, topographic, and climatic data. Wall-to-wall 30 m raster maps of broad forest type, stand height, crown closure, stand volume, total volume, aboveground biomass, and stand age were created for a ~400,000 km2 area, validated with independent data, and generalized into a polygon GIS layer resembling a traditional FI map. The MVI project showed that a reasonably accurate FI map for large, remote, predominantly non-inventoried boreal regions can be obtained at a low cost by combining limited field data with remote sensing data from multiple sources.
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.

Hopkinson, Christopher

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