Show simple item record Montgomery, Joshua Brisco, Brian Chasmer, Laura Devito, Kevin Cobbaert, Danielle Hopkinson, Christopher 2021-10-20T20:28:02Z 2021-10-20T20:28:02Z 2019
dc.identifier.citation Montgomery, J., Brisco, B., Chasmer, L., Devito, K., Cobbaert, D.,& Hopkinson, C. (2019). SAR and lidar temporal data fusion approaches to boreal wetland ecosystem monitoring. Remote Sensing, 11(2), Article 161. 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 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. en_US
dc.language.iso en_US en_US
dc.publisher MDPI en_US
dc.subject SAR en_US
dc.subject Lidar en_US
dc.subject Boreal wetlands en_US
dc.subject Data fusion en_US
dc.subject Decision-based methodology en_US
dc.subject Time series en_US
dc.subject Ecosystem monitoring
dc.subject.lcsh Synthetic aperture radar
dc.subject.lcsh Optical radar
dc.subject.lcsh Wetlands
dc.subject.lcsh Multisensor data fusion
dc.title SAR and lidar temporal data fusion approaches to boreal wetland ecosystem monitoring en_US
dc.type Article en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Geograpy and Environment en_US
dc.description.peer-review Yes en_US
dc.publisher.institution Alberta Environment and Parks en_US
dc.publisher.institution University of Lethbridge en_US
dc.publisher.institution Natural Resources Canada en_US
dc.publisher.institution University of Alberta en_US
dc.publisher.url en_US

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