Remote sensing of boreal wetlands 2: methods for evaluating boreal wetland ecosystem state and drivers of change

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dc.contributor.authorChasmer, Laura
dc.contributor.authorMahoney, Craig
dc.contributor.authorMillard, Koreen
dc.contributor.authorNelson, Kailyn
dc.contributor.authorPeters, Daniel
dc.contributor.authorMerchant, Michael
dc.contributor.authorHopkinson, Christopher
dc.contributor.authorBrisco, Brian
dc.contributor.authorNiemann, Olaf
dc.contributor.authorMontgomery, Joshua
dc.contributor.authorDevito, Kevin
dc.contributor.authorCobbaert, Danielle
dc.date.accessioned2021-10-13T22:42:48Z
dc.date.available2021-10-13T22:42:48Z
dc.date.issued2020
dc.descriptionOpen access article. Creative Commons Attribution 4.0 International License (CC BY 4.0) appliesen_US
dc.description.abstractThe 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.description.peer-reviewYesen_US
dc.identifier.citationChasmer, 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. https://doi.org/10.3390/rs12081321en_US
dc.identifier.urihttps://hdl.handle.net/10133/6061
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.publisher.departmentDepartment of Chemistry and Biochemistryen_US
dc.publisher.facultyArts and Scienceen_US
dc.publisher.institutionUniversity of Lethbridgeen_US
dc.publisher.institutionAlberta Environment and Parksen_US
dc.publisher.institutionCarleton Universityen_US
dc.publisher.institutionEnvironment and Climate Change Canadaen_US
dc.publisher.institutionDucks Unlimited Canadaen_US
dc.publisher.institutionCanada Centre for Mapping and Earth Observationen_US
dc.publisher.institutionUniversity of Victoriaen_US
dc.publisher.institutionUniversity of Albertaen_US
dc.publisher.urlhttps://doi.org/10.3390/rs12081321en_US
dc.subjectObject oriented classificationen_US
dc.subjectDecision-treeen_US
dc.subjectLidaren_US
dc.subjectHyperspectralen_US
dc.subjectMonitoringen_US
dc.subjectEcosystem changeen_US
dc.subjectBoreal wetlandsen_US
dc.subjectRamsar Conventionen_US
dc.subject.lcshMachine learning
dc.subject.lcshDecision trees
dc.subject.lcshSynthetic aperture radar
dc.subject.lcshOptical radar
dc.titleRemote sensing of boreal wetlands 2: methods for evaluating boreal wetland ecosystem state and drivers of changeen_US
dc.typeArticleen_US
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