Hopkinson, Christopher

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

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Browsing Hopkinson, Christopher by Author "Devito, Kevin"

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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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    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.