期刊
REMOTE SENSING
卷 11, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/rs11020161
关键词
SAR; Lidar; boreal wetlands; data fusion; decision-based methodology; time series
类别
资金
- Canada Foundation for Innovation [32436]
- Campus Alberta Innovates Program [32436]
- Government of Alberta (Economic Development and Trade, Environment and Parks), (Alberta Environment and Parks)
- Natural Sciences and Engineering Research Council [RGPIN-2017-04362]
- Government of Alberta Oil Sands Monitoring Program, Wetland Ecosystem Monitoring
- Natural Resources Canada, Canada Centre for Mapping and Earth Observation (CCMEO)
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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