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TitleImprovement of clustering methods for modelling abrupt land surface changes in satellite image fusions
 
AuthorZhong, DORCID logo; Zhou, F
SourceRemote Sensing vol. 11, issue 15, 1759, 2019 p. 1-20, https://doi.org/10.3390/rs11151759 Open Access logo Open Access
Image
Year2019
Alt SeriesNatural Resources Canada, Contribution Series 20190629
PublisherMDPI
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; Science and Technology; remote sensing; satellite imagery; models; modelling; reflectance; floods; Environmental hazards; Methodology; Forest fires
Illustrationsflow diagrams; satellite images; sketch maps; bar graphs
ProgramCanada Centre for Remote Sensing Divsion
Released2019 07 26
AbstractThe complementarity of the 'scattered' and 'received' wave polarization signatures is demonstrated for enhanced characterization of peatlands and surrounding upland forest scattering. Polarimetric L-band ALOS-PALSAR collected in the Athabasca oil sand exploration region, with peatlands and upland forests partially affected by multiple wildfire, are used. It is shown that the scattered wave polarization signature[1], which represents the explicit variations of the degree of polarization (DoP) and the total scattered intensity R0 with transmitted polarization, permits enhanced discrimination of treed bogs from upland forests, and improved identification of fire damages in peat-lands and surrounding forests. The technique introduced in [2] for scattered wave optimization is used as a convenient method for efficient exploitation of the scattered wave polarization signature. The Touzi decomposition [3] is adopted for the optimization of the received wave polarization signature. The unique potential of the scattering type phase generated with the Touzi decomposition is confirmed for enhanced discrimination of poor fens from bogs. These two important pealand classes cannot be separated with the scattered wave optimization, the conventional multi-polarization (HH-HV-VV) channels, the Freeman and Cloude-Pottier decompositions [4], [5]. Finally, the Touzi decomposition is combined with the extrema of the scattered wave main parameters (DoP and R0) for optimum extraction of polarimetric PALSAR information. The comparison with optical Landsat-TM confirms the valuable added information that long penetrating polarimetric L-band PALSAR can provide for enhanced peatland classification and efficient assessment of peat health in burned peatlands.
Summary(Plain Language Summary, not published)
This publication focuses on improving the characterization of peatlands and upland forests using polarimetric radar data collected by the ALOS-PALSAR satellite in the Athabasca oil sands region. This region has peatlands and forests affected by wildfires. The researchers found that by analyzing the polarization signature of the scattered radar waves, they could better distinguish between treed bogs and upland forests and identify fire damage in these areas.
They used a technique for optimizing the scattered wave polarization signature and the Touzi decomposition method to make the most of the received wave polarization signature. The combination of these methods allowed for improved classification of different types of peatlands, specifically poor fens and bogs. These distinctions were challenging to make using other polarization channels and decomposition methods.
By comparing the radar data with optical data from the Landsat satellite, the study demonstrated the added value of using polarimetric radar for classifying peatlands and assessing their health after wildfires.
The scientific impact of this research lies in its contribution to better understanding and monitoring peatlands, which are vital for carbon storage and play a significant role in climate change mitigation. Improved classification and assessment methods are essential for managing and protecting these ecosystems effectively.
GEOSCAN ID321958

 
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