Show simple item record Chasmer, Laura Mahoney, Craig Millard, Koreen Nelson, Kailyn Peters, Daniel Merchant, Michael Hopkinson, Christopher Brisco, Brian Niemann, Olaf Montgomery, Joshua Devito, Kevin Cobbaert, Danielle 2021-10-13T22:42:48Z 2021-10-13T22:42:48Z 2020
dc.identifier.citation Chasmer, L., Mahoney, C., Millard, K., Nelson, K., Peters, D., Merchant, M., Hopkinson, C., Brisco, B., Niemann, O., Montgomery, J., Devito, K., & Cobbaert, D. (2020). Remote sensing of boreal wetlands 2: Methods for evaluating boreal wetland ecosystem state and drivers of change. Remote Sensing, 12(8), Article 1321. en_US
dc.description Open access article. Creative Commons Attribution 4.0 International License (CC BY 4.0) applies en_US
dc.description.abstract 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. en_US
dc.language.iso en_US en_US
dc.publisher MDPI en_US
dc.subject Object oriented classification en_US
dc.subject Decision-tree en_US
dc.subject Lidar en_US
dc.subject Hyperspectral en_US
dc.subject Monitoring en_US
dc.subject Ecosystem change en_US
dc.subject Boreal wetlands en_US
dc.subject Ramsar Convention en_US
dc.subject.lcsh Machine learning
dc.subject.lcsh Decision trees
dc.subject.lcsh Synthetic aperture radar
dc.subject.lcsh Optical radar
dc.title Remote sensing of boreal wetlands 2: methods for evaluating boreal wetland ecosystem state and drivers of change en_US
dc.type Article en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Chemistry and Biochemistry en_US
dc.description.peer-review Yes en_US
dc.publisher.institution University of Lethbridge en_US
dc.publisher.institution Alberta Environment and Parks en_US
dc.publisher.institution Carleton University en_US
dc.publisher.institution Environment and Climate Change Canada en_US
dc.publisher.institution Ducks Unlimited Canada en_US
dc.publisher.institution Canada Centre for Mapping and Earth Observation en_US
dc.publisher.institution University of Victoria en_US
dc.publisher.institution University of Alberta en_US
dc.publisher.url en_US

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