Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada

Remote Sensing

Tobias Ullmann¹  Andreas Schmitt²  Achim Roth²  Jason Duffe³  Stefan Dech¹  Hans-Wolfgang Hubberten⁴ 

[1] Institute for Geography and Geology, University of Wuerzburg, D-97074 Wuerzburg, Germany; E-Mail:;German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), D-82234 Wessling, Germany; E-Mails:;National Wildlife Research Center (NWRC), Ottawa, ON K1A 0H3, Canada; E-Mail:;Alfred Wegener Institute for Polar and Marine Research (AWI), Research Section Potsdam, Telegrafenberg A43, D-14473 Potsdam, Germany; E-Mail:

关键词: arctic;    tundra;    land cover;    classification;    polarimetry;    radar;    PolSAR;    SAR;    TerraSAR-X;    Radarsat-2;   

DOI  :  10.3390/rs6098565

来源: mdpi

【 摘 要 】

In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques.

【 授权许可】

CC BY   
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