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Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data

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dc.contributor.authorKim, Miae-
dc.contributor.authorKim, Hyun-cheol-
dc.contributor.authorIm, Jungho-
dc.contributor.authorLee, Sanggyun-
dc.contributor.authorHan, Hyangsun-
dc.date.accessioned2021-05-12T08:00:50Z-
dc.date.available2021-05-12T08:00:50Z-
dc.date.issued2020-06-
dc.identifier.urihttps://repository.kopri.re.kr/handle/201206/11980-
dc.description.abstractLandfast sea ice (fast ice) is an important feature prevalent around the Antarctic coast, which is affected by climate change and energy exchanges with the atmosphere and ocean. This study proposed a method for detection of the West Antarctic fast ice using the Advanced Land Observing Satellite Phased Array L-band SAR (ALOS PALSAR) images. The algorithm has combined image segmentation, image correlation analysis, and machine learning techniques (i.e., random forest (RF), extremely randomized trees (ERT), and logistic regression (LR)). We used SAR images with a baseline of 5 days that are not in the same orbit but overlap each other as overlaps between swaths in adjacent orbits are often available in the polar regions. The underlying assumption for the proposed fast ice detection algorithm is that fast ice regions in SAR images with a time interval of 5 days are highly correlated. The object-based approach proposed in this study was well suited to high-resolution SAR images in deriving spatially homogeneous fast ice regions. The image segmentation results using the optimized parameters showed a distinct difference in the backscatter temporal evolution between fast ice and pack ice regions. Correlation and STD of backscattering coefficients were found to be the most significant variables for the object-based fast ice detection from two temporally separated images. In overall, the quantitative and qualitative evaluation demonstrated that the algorithm was an effective approach to detect fast ice with high accuracies. The models well detected various fast ice regions in the West Antarctica but misclassified some objects. The misclassifications occurred toward the edge of fast ice regions with relatively rapid changes in backscattering between both data acquisitions. On the other hand, few fast ice objects were misclassified as uniform backscattering over time occurred by chance on very small objects far from the coast. Very old multi-year fast ice regions with high backscattered signals were also a source for some misclassifications. This may be due to the sensitivity of L-band to snow structure to some extent and a thinner ice over the region with either ice growth (no deformation) or closing (slight deformation) between both images. Heavy snow load on the ice could be another error source for some misclassification as well. The approach allowed for the reliable detection of fast ice regions by using L-band SAR images with a small local incidence angle difference.en_US
dc.languageEnglishen_US
dc.language.isoen_USen_US
dc.subjectEnvironmental Sciences & Ecologyen_US
dc.subjectRemote Sensingen_US
dc.subjectImaging Science & Photographic Technologyen_US
dc.subject.classification해당사항없음en_US
dc.titleObject-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR dataen_US
dc.title.alternativeALOS PALSAR를 이용한 객체기반 서남극 고착빙 탐지en_US
dc.typeArticleen_US
dc.identifier.bibliographicCitationKim, Miae, et al. 2020. "Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data". <em>REMOTE SENSING OF ENVIRONMENT</em>, 242(1): 111782-111782.-
dc.citation.titleREMOTE SENSING OF ENVIRONMENTen_US
dc.citation.volume242en_US
dc.citation.number1en_US
dc.identifier.doihttps://doi.org/10.1016/j.rse.2020.111782-
dc.citation.startPage111782en_US
dc.citation.endPage111782en_US
dc.description.articleClassificationSCI-
dc.description.jcrRateJCR 2018:2.789en_US
dc.subject.keywordLandfast sea iceen_US
dc.subject.keywordL-band SARen_US
dc.subject.keywordALOS PALSARen_US
dc.subject.keywordObject correlation analysisen_US
dc.subject.keywordMachine learningen_US
dc.identifier.localId2020-0038-
dc.identifier.scopusid2-s2.0-85082000537-
dc.identifier.wosid000523965600019-
Appears in Collections  
2020-2020, Study on remote sensing for quantitative analysis of changes in the Arctic cryosphere (20-20) / Kim, Hyun-cheol (PE20080)
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