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TitleSAR backscatter and InSAR coherence for monitoring wetland extent, flood pulse and vegetation: a study of the Amazon lowland
AuthorCanisius, F; Brisco, B; Murnaghan, K; Van Der Kooij, M; Keizer, E
SourceRemote Sensing vol. 11, issue 6, 720, 2019 p. 1-18, Open Access logo Open Access
Alt SeriesNatural Resources Canada, Contribution Series 20190167
PublisherMDPI AG
Mediaon-line; digital
File formatpdf (Adobe® Reader®); html
AreaAmazon River; Santarém City; Tapajós River; Brazil
Lat/Long WENS -55.5000 -54.2500 -1.2500 -2.7500
Subjectshydrogeology; geophysics; Science and Technology; Nature and Environment; wetlands; floods; flood plains; vegetation; remote sensing; satellite imagery; surface waters; water levels; RADARSAT-2; synthetic aperture radar surveys (SAR); Hydrology
Illustrationslocation maps; satellite images; totals; flow diagrams; geoscientific sketch maps; plots; time series; bar graphs
ProgramRemote Sensing Science
Released2019 03 26
AbstractSynthetic aperture radar (SAR) data have been identified as a potential source of information for monitoring surface water, including open water and flooded vegetation, in frequent time intervals, which is very significant for flood mapping applications. The SAR specular reflectance separates open water and land surface, and its canopy penetration capability allows enhanced backscatter from flooded vegetation. Further, under certain conditions, the SAR signal from flooded vegetation may remain coherent between two acquisitions, which can be exploited using the InSAR technique. With these SAR capabilities in mind, this study examines the use of multi-temporal RADARSAT-2 C band SAR intensity and coherence components to monitor wetland extent, inundation and vegetation of a tropical wetland, such as Amazon lowland. For this study, 22 multi-temporal RADARSAT-2 images (21 pairs) were used for InSAR processing and the pairs in the low water stage (November, December) showed high coherence over the wetland areas. The three-year intensity stack was used for assessing wetland boundary, inundation extent, flood pulse, hydroperiod, and wetland vegetation. In addition to the intensity, derived coherence was used for classifying wetland vegetation. Wetland vegetation types were successfully classified with 86% accuracy using the statistical parameters derived from the multi-temporal intensity and coherence data stacks. We have found that in addition to SAR intensity, coherence provided information about wetland vegetation. In the next year, the Canadian RADARSAT Constellation Mission (RCM), will provide more data with frequent revisits, enhancing the application of SAR intensity and coherence for monitoring these types of wetlands at large scales.
Summary(Plain Language Summary, not published)
The main objective of this current study is to evaluate the use of radar satellite information to characterize the wetlands in the Amazon tropical floodplain region throughout the dry and wet seasons. The study showed that Canada's RADARSAT-2 satellite can monitor Amazon wetland extent, inundation and vegetation and the information about the changes is very useful for understanding water cycle, climate change, biodiversity and fish populations and associated fishery yields. The knowledge gained from this study will be used to propose a methodology that could lead to operational use of satellite radar images for wetland monitoring.

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