Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques
The grounding line marks the transition between ice grounded at the bedrock and the floating ice shelf. Its location is required for estimating ice sheet mass balance, modelling of ice sheet dynamics and glaciers and for evaluating ice shelf stability, which merits its long-term monitoring. The line...
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ftdlr:oai:elib.dlr.de:189234 2023-05-15T13:55:04+02:00 Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques Ramanath Tarekere, Sindhu 2022 application/pdf https://elib.dlr.de/189234/ https://elib.dlr.de/189234/1/Thesis_Ramanath_Sindhu_2022.pdf en eng https://elib.dlr.de/189234/1/Thesis_Ramanath_Sindhu_2022.pdf Ramanath Tarekere, Sindhu (2022) Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques. Masterarbeit, Technische Universität München. cc_by_nc SAR-Signalverarbeitung Hochschulschrift NonPeerReviewed 2022 ftdlr 2023-03-20T00:16:29Z The grounding line marks the transition between ice grounded at the bedrock and the floating ice shelf. Its location is required for estimating ice sheet mass balance, modelling of ice sheet dynamics and glaciers and for evaluating ice shelf stability, which merits its long-term monitoring. The line migrates both due to short term influences such as ocean tides and atmospheric pressure, and long-term effects such as changes of ice thickness, slope of bedrock and variations in sea level. Of the numerous in-situ and remote sensing methods currently in use to map the grounding line, Differential Interferometric Synthetic Aperture Radar (DInSAR) is, by far, the most accurate technique which produces spatially dense delineations. Tidal deformation at the ice sheet-ice shelf boundary is visible as a dense fringe belt in DInSAR interferograms and its landward limit is taken as a good approximation of the grounding line location (GLL). The GLL is usually manually digitized on the interferograms by human operators. This is both time consuming and introduces inconsistencies due to subjective interpretation especially in low coherence interferograms. On a large scale and with increasing data availability a key challenge is the automation of the delineation procedure. So far, a limited amount of studies were published regarding the delineation processes of typical features on the ice sheets using deep neural networks (DNNs). The objectives of this thesis were to further explore the feasibility of using machine learning for mapping the interferometric grounding line, as well as exploring the contributions of complementary features such as coherence estimated from phase, Digital Elevation Model, ice velocity, tidal displacement and atmospheric pressure, in addition to DInSAR interferograms. A dataset composed of manually delineated GLLs generated within ESA’s Antarctic Ice Sheet Climate Change Initiative project and corresponding DInSAR interferograms from ERS-1/2, Sentinel-1 and TerraSAR-X missions over Antarctica together ... Thesis Antarc* Antarctic Antarctica Ice Sheet Ice Shelf German Aerospace Center: elib - DLR electronic library Antarctic |
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German Aerospace Center: elib - DLR electronic library |
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English |
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SAR-Signalverarbeitung |
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SAR-Signalverarbeitung Ramanath Tarekere, Sindhu Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques |
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SAR-Signalverarbeitung |
description |
The grounding line marks the transition between ice grounded at the bedrock and the floating ice shelf. Its location is required for estimating ice sheet mass balance, modelling of ice sheet dynamics and glaciers and for evaluating ice shelf stability, which merits its long-term monitoring. The line migrates both due to short term influences such as ocean tides and atmospheric pressure, and long-term effects such as changes of ice thickness, slope of bedrock and variations in sea level. Of the numerous in-situ and remote sensing methods currently in use to map the grounding line, Differential Interferometric Synthetic Aperture Radar (DInSAR) is, by far, the most accurate technique which produces spatially dense delineations. Tidal deformation at the ice sheet-ice shelf boundary is visible as a dense fringe belt in DInSAR interferograms and its landward limit is taken as a good approximation of the grounding line location (GLL). The GLL is usually manually digitized on the interferograms by human operators. This is both time consuming and introduces inconsistencies due to subjective interpretation especially in low coherence interferograms. On a large scale and with increasing data availability a key challenge is the automation of the delineation procedure. So far, a limited amount of studies were published regarding the delineation processes of typical features on the ice sheets using deep neural networks (DNNs). The objectives of this thesis were to further explore the feasibility of using machine learning for mapping the interferometric grounding line, as well as exploring the contributions of complementary features such as coherence estimated from phase, Digital Elevation Model, ice velocity, tidal displacement and atmospheric pressure, in addition to DInSAR interferograms. A dataset composed of manually delineated GLLs generated within ESA’s Antarctic Ice Sheet Climate Change Initiative project and corresponding DInSAR interferograms from ERS-1/2, Sentinel-1 and TerraSAR-X missions over Antarctica together ... |
format |
Thesis |
author |
Ramanath Tarekere, Sindhu |
author_facet |
Ramanath Tarekere, Sindhu |
author_sort |
Ramanath Tarekere, Sindhu |
title |
Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques |
title_short |
Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques |
title_full |
Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques |
title_fullStr |
Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques |
title_full_unstemmed |
Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques |
title_sort |
mapping the grounding line of antarctica in sar interferograms with machine learning techniques |
publishDate |
2022 |
url |
https://elib.dlr.de/189234/ https://elib.dlr.de/189234/1/Thesis_Ramanath_Sindhu_2022.pdf |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic Antarctica Ice Sheet Ice Shelf |
genre_facet |
Antarc* Antarctic Antarctica Ice Sheet Ice Shelf |
op_relation |
https://elib.dlr.de/189234/1/Thesis_Ramanath_Sindhu_2022.pdf Ramanath Tarekere, Sindhu (2022) Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques. Masterarbeit, Technische Universität München. |
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cc_by_nc |
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1766261307458191360 |