Chasmer, Laura

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https://opus.uleth.ca/handle/10133/5615

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Browsing Chasmer, Laura by Author "Cobbaert, Danielle"

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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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    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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    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.
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    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.