461 research outputs found

    Glacier motion estimation using SAR offset-tracking procedures

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    Two image-to-image patch offset techniques for estimating feature motion between satellite synthetic aperture radar (SAR) images are discussed. Intensity tracking, based on patch intensity cross-correlation optimization, and coherence tracking, based on patch coherence optimization, are used to estimate the movement of glacier surfaces between two SAR images in both slant-range and azimuth direction. The accuracy and application range of the two methods are examined in the case of the surge of Monacobreen in Northern Svalbard between 1992 and 1996. Offset-tracking procedures of SAR images are an alternative to differential SAR interferometry for the estimation of glacier motion when differential SAR interferometry is limited by loss of coherence, i.e., in the case of rapid and incoherent flow and of large acquisition time intervals between the two SAR images. In addition, an offset-tracking procedure in the azimuth direction may be combined with differential SAR interferometry in the slant-range direction in order to retrieve a two-dimensional displacement map when SAR data of only one orbit configuration are available

    Error Analysis for Interferometric SAR Measurements of Ice Sheet Flow

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    ์‹œ๊ณ„์—ด InSAR ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๋น„์ •์ƒ์  ํ•ด์ˆ˜๋ฉด ์ƒ์Šน ๊ธฐ๋ก์„ ๋ณด์ธ ์กฐ์œ„๊ด€์ธก์†Œ์˜ ์ˆ˜์ง์ง€๋ฐ˜๋ณ€์œ„ ํ‰๊ฐ€

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€, 2021.8. ๊น€๋•์ง„.Global sea level rise has been a serious threat to the low-lying coasts and islands around the world. It is important to understand the global and regional sea level changes for preventing the coastal zones. Tide gauges are installed around the world, which directly measures the change in sea level relative to the local datum. Sea level in the past three decades has risen to 1.8 mm/year compared to the sea level rise in the 20th century (3.35 mm/year), estimated by the Intergovernmental Panel on Climate Change (IPCC). However, along with the contributors of sea level rise, vertical land motion (VLM) is indeed an essential component for understanding the regional sea level change; however, its contribution remains still unclear. The VLM is referred to as change in elevation of land at tide gauge due to the regional and local processes by both natural and anthropogenic activities can deteriorate the sea level records and lead to spurious sea level acceleration. Assessing the vertical land motion at tide gauges with the accuracy of sub-millimeters is essential to reconstruct the global and regional sea level rise. Previous studies attempt to observe the vertical land movements at sparse locations through Global Positioning System (GPS). However, the VLM observed from the sparse GPS network makes the estimation uncertain. In this study, an alternative approach is proposed in this study to directly measure the relative vertical land motion including spatial and temporal variations through Synthetic Aperture Radar (SAR) data by using time-series SAR interferometric (InSAR) techniques. This work presents a contribution enhancing the estimation of VLM rates with high spatial resolution over large area using time-series InSAR analysis. First, the C-band Interferometric Wide-swath (IW) mode SAR data from the Sentinel-1 A/B satellite was used in this study to estimate the VLM rates of tide gauges. The Sentinel-1 A/B SAR data were obtained during the period between 2014/10 and 2020/12 (~ 6 years). Stanford Method for Persistent Scatterers โ€“ Persistent Scatterer Interferometry (StaMPS-PSI) time-series InSAR algorithm was initially applied to the case study: Pohang tide gauge in the Korean peninsula for monitoring the stability of tide gauge station and its VLM rates during 2014 ~ 2017. For the Pohang tide gauge site, SAR data acquired in both ascending and descending passes and derived the ground movement rates at tide gauge along the line-of-sight direction. The vertical movements from the collocated POHA GPS station were compared with the InSAR derived VLM rates for determining the correlation between the two methods. The VLM rates at the Pohang tide gauge site were -25.5 mm/year during 2014 ~ 2017. This VLM rate at Pohang tide gauge derived by StaMPS-PSI estimates were from the strong dominant scatterers along the coastal regions. Second, for the terrains, with few dominant scatterers and more distributed scatters, a short temporal InSAR pair selection approach was introduced, referred as Sequential StaMPS-Small baselines subset (StaMPS-SBAS) was proposed in this study. Sequential StaMPS-SBAS forms the interferograms of short temporal sequential order (n = 5) to increase the initial pixel candidates on the natural terrains in the vicinity of tide gauges. Sentinel-1 A/B SAR data over ten tide gauges in the Korean peninsula having different terrain conditions were acquired during 2014 ~ 2020; and employed with sequential StaMPS-SBAS to estimate the VLM rates and time-series displacements. The initial pixel density has been doubled and ~ 1.25 times the final coherent pixels identified over the conventional StaMPS-SBAS analysis. Third, the potential for the fully automatic estimation of time-series VLM rates by sequential StaMPS-SBAS analysis was investigated. A fully automatic processing module referred to as โ€˜Seq-TInSARโ€™, was developed which has three modules 1) automatically downloads Sentinel-1 Single look complex (SLC) data, precise orbit files, and Digital Elevation Model (DEM); 2) SLC pre-processor: extract bursts, fine Coregistration and stacking and, 3) Sequential StaMPS-SBAS processor: estimates the VLM rates and VLM time-series. Finally, the Seq-TInSAR module is applied to the 100 tide gauges that exhibit abnormal sea level trend with par global mean sea level average. For each tide gauge site, 60 ~ 70 Sentinel-1 A/B SLC scenes were acquired and 300 ~ 350 sequential interferograms were processed to estimate the VLM at tide gauge stations. The final quantitative VLM rates and time-series VLM are estimated for the selected tide gauges stations. Based on the VLM rates, the tide gauges investigated in this study are categorized into different VLM ranges. The in-situ GPS observations available at 12 tide gauge stations were compared with InSAR VLM rates and found strong agreement, which suggests the proposed approach more reliable in measuring the spatial and temporal variations of VLM at tide gauges.์ „ ์„ธ๊ณ„์ ์œผ๋กœ ๋ฐœ์ƒํ•˜๋Š” ํ•ด์ˆ˜๋ฉด ์ƒ์Šน์€ ์ €์ง€๋Œ€ ํ•ด์•ˆ๊ณผ ๋„์„œ ์ง€์—ญ์— ์‹ฌ๊ฐํ•œ ์œ„ํ˜‘์œผ๋กœ ์ž‘์šฉํ•œ๋‹ค. ํ•ด์•ˆ ์ง€์—ญ์„ ๋ณดํ˜ธํ•˜๊ธฐ ์œ„ํ•ด ์ „ ์ง€๊ตฌ ๋ฐ ํ•ด๋‹น ์ง€์—ญ์˜ ํ•ด์ˆ˜๋ฉด ๋ณ€ํ™”๋ฅผ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์€ ๋Œ€๋‹จํžˆ ์ค‘์š”ํ•˜๋‹ค. ์กฐ์œ„ ๊ด€์ธก์†Œ๋Š” ์ „ ์„ธ๊ณ„์— ์„ค์น˜๋˜์–ด ํ•ด๋‹น ์ง€์—ญ ๊ธฐ์ค€์— ๋”ฐ๋ฅธ ํ•ด์ˆ˜๋ฉด ๋ณ€ํ™”๋ฅผ ์ง์ ‘ ์ธก์ •ํ•œ๋‹ค. ์ง€๋‚œ 30 ๋…„๊ฐ„ ํ•ด์ˆ˜๋ฉด์€ IPCC (์ •๋ถ€ ๊ฐ„ ๊ธฐํ›„ ๋ณ€ํ™” ํŒจ๋„)๊ฐ€ ์ถ”์ •ํ•œ 20 ์„ธ๊ธฐ์˜ ํ•ด์ˆ˜๋ฉด ์ƒ์Šน (3.35mm / ๋…„)๋Œ€๋น„ 1.8mm / ๋…„ ๊ฐ€๊นŒ์ด ์ƒ์Šนํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ•ด์ˆ˜๋ฉด ์ƒ์Šน์˜ ์›์ธ๊ณผ ํ•จ๊ป˜ ์—ฐ์ง ์ง€๋ฐ˜ ์šด๋™ (VLM)์€ ์ง€์—ญ ํ•ด์ˆ˜๋ฉด ๋ณ€ํ™”๋ฅผ ์ดํ•ดํ•˜๋Š” ๋ฐ ํ•„์ˆ˜์ ์ธ ์š”์†Œ์ด์ง€๋งŒ ๊ทธ ๊ธฐ์—ฌ๋„๋Š” ์—ฌ์ „ํžˆ ๋ถˆ๋ถ„๋ช…ํ•˜๋‹ค. VLM์€ ์ž์—ฐ ํ™œ๋™๊ณผ ์ธ๊ฐ„ ํ™œ๋™ ๋ชจ๋‘์— ์˜ํ•œ ์ง€์—ญ์  ๋ณ€ํ™”๋กœ ์ธํ•ด ์กฐ์œ„ ๊ด€์ธก์†Œ์—์„œ ์ง€๋ฐ˜์˜ ๊ณ ๋„ ๋ณ€ํ™”๋กœ ์ •์˜๋˜๋ฉฐ ํ•ด์ˆ˜๋ฉด ๋ณ€ํ™” ์ •ํ™•๋„์„ ์•…ํ™”์‹œํ‚ค๊ณ  ์œ ์‚ฌ ํ•ด์ˆ˜๋ฉด ๋ณ€ํ™”์˜ ๊ฐ€์†์„ ์ดˆ๋ž˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ „ ์„ธ๊ณ„ ๋ฐ ์ง€์—ญ ํ•ด์ˆ˜๋ฉด ์ƒ์Šน์„ ์žฌ๊ตฌ์„ฑํ•˜๋ ค๋ฉด 1 ๋ฐ€๋ฆฌ๋ฏธํ„ฐ ๋ฏธ๋งŒ์˜ ์ •ํ™•๋„๋กœ ์กฐ์œ„ ๊ด€์ธก์†Œ์—์„œ VLM์„ ํ‰๊ฐ€ํ•˜๋Š” ๊ฒƒ์ด ํ•„์ˆ˜์ ์ด๋‹ค. ์ด์ „ ์—ฐ๊ตฌ๋Š” GPS (Global Positioning System)๋ฅผ ํ†ตํ•ด ์ œํ•œ๋œ ์œ„์น˜์—์„œ VLM ์„ ๊ด€์ธกํ•˜๋ ค๊ณ  ์‹œ๋„ํ•˜์˜€์œผ๋‚˜ ๊ตญ์†Œ์ ์ธ GPS ์‹ ํ˜ธ๋“ค๋กœ๋ถ€ํ„ฐ ๊ด€์ธก๋œ VLM์œผ๋กœ๋Š” ๊ทธ ์ถ”์ •์ด ๋ถˆํ™•์‹คํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‹œ๊ณ„์—ด SAR ๊ฐ„์„ญ๊ณ„ (InSAR) ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ SAR (Synthetic Aperture Radar) ๋ฐ์ดํ„ฐ๋ฅผ ํ†ตํ•ด ๊ณต๊ฐ„์ , ์‹œ๊ฐ„์  ๋ณ€ํ™”๋ฅผ ํฌํ•จํ•œ ์ƒ๋Œ€์  VLM์„ ์ง์ ‘ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•œ ๋Œ€์•ˆ์  ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ์ž‘์—…์€ ์‹œ๊ณ„์—ด InSAR ๋ถ„์„์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ด‘๋Œ€์—ญ์— ๊ฑธ์ณ ๋†’์€ ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋กœ VLM ์†๋„์˜ ์ถ”์ •์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐ ๊ธฐ์—ฌํ•œ๋‹ค. ์ฒซ์งธ๋กœ, Sentinel-1 A / B ์œ„์„ฑ์˜ C-band Interferometric Wide-swath (IW) ๋ชจ๋“œ SAR ์˜์ƒ์ด ๋ณธ ์—ฐ๊ตฌ์—์„œ ์กฐ์œ„ ๊ด€์ธก์†Œ์˜ VLM ์†๋„๋ฅผ ์ถ”์ •ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. Sentinel-1 A / B SAR ์˜์ƒ์€ 2014 ๋…„ 10 ์›”๋ถ€ํ„ฐ 2020 ๋…„ 12 ์›”๊นŒ์ง€ (~ 6 ๋…„) ๊ธฐ๊ฐ„ ๋™์•ˆ ์ˆ˜์ง‘๋˜์—ˆ๋‹ค. ๊ณ ์ • ์‚ฐ๋ž€์ฒด๋ฅผ ์œ„ํ•œ ์Šคํƒ ํฌ๋“œ ๊ธฐ๋ฒ• โ€“ ๊ณ ์ • ์‚ฐ๋ž€ ๊ฐ„์„ญ๊ณ„ (StaMPS-PSI) ์‹œ๊ณ„์—ด InSAR ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ํ•œ๋ฐ˜๋„ ํฌํ•ญ ์กฐ์œ„ ๊ด€์ธก์†Œ์˜ 2014 ~ 2017 ๋…„ ๋™์•ˆ์˜ ์กฐ์œ„ ๊ด€์ธก์†Œ์˜ ์•ˆ์ •์„ฑ๊ณผ VLM ์†๋„๋ฅผ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๊ธฐ ์œ„ํ•ด ์ ์šฉ๋˜์—ˆ๋‹ค. ํฌํ•ญ ์กฐ์œ„ ๊ด€์ธก์†Œ ๋ถ€์ง€์˜ ๊ฒฝ์šฐ, ์œ„์„ฑ๊ถค๋„์˜ ์ƒ์Šน ๋ฐ ํ•˜๊ฐ• ๊ฒฝ๋กœ๋กœ ํš๋“ํ•œ SAR ์˜์ƒ์„ ํ†ตํ•ด ์‹œ์„  ๋ฐฉํ–ฅ์„ ๋”ฐ๋ผ ์กฐ์œ„ ๊ด€์ธก์†Œ์—์„œ์˜ ์ง€๋ฉด ์ด๋™ ์†๋„๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ํฌํ•ญ GPS ๊ด€์ธก์†Œ์˜ ์—ฐ์ง ์ด๋™์€ ๋‘ ๊ธฐ๋ฒ• ๊ฐ„์˜ ์ƒ๊ด€์„ฑ๋ฅผ ํŒ๋‹จํ•˜๊ธฐ ์œ„ํ•ด InSAR๊ธฐ๋ฒ•์œผ๋กœ๋ถ€ํ„ฐ ์ถ”์ •๋œ VLM ์†๋„์™€ ๋น„๊ต๋˜์—ˆ๋‹ค. ํฌํ•ญ ์กฐ์œ„ ๊ด€์ธก์†Œ์˜ VLM ์†๋„๋Š” 2014 ~ 2017 ๋…„์˜ ๊ธฐ๊ฐ„ ๋™์•ˆ -25.5mm / ๋…„์œผ๋กœ ๊ด€์ธก๋˜์—ˆ๋‹ค. StaMPS-PSI ์ถ”์ •์— ์˜ํ•ด ๋„์ถœ ๋œ ํฌํ•ญ ์กฐ์œ„ ๊ด€์ธก์†Œ์˜ VLM ์†๋„์€ ํ•ด์•ˆ ์ง€์—ญ์˜ ๊ฐ•ํ•œ ์‚ฐ๋ž€ ์ฒด์—์„œ ๊ธฐ์ธํ•œ๋‹ค. ๋‘˜์งธ๋กœ, ๊ฐ•ํ•œ ์‚ฐ๋ž€์ฒด๊ฐ€ ์ˆ˜๊ฐ€ ์ ๊ณ  ๋ถ„์‚ฐ๋œ ์‚ฐ๋ž€์ฒด๊ฐ€ ๋” ๋งŽ์€ ์ง€ํ˜•์˜ ๊ฒฝ์šฐ, ๋ณธ ์—ฐ๊ตฌ์—์„œ Sequential StaMPS-Small baselines (StaMPS-SBAS)์ด๋ผ๋Š” ํ•˜๋Š” ๋‹จ๊ธฐ InSAR ์Œ์˜ ์„ ํƒ์— ์˜ํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. Sequential StaMPS-SBAS๋Š” ์งง์€ ์‹œ๊ฐ„ ๋ฒ”์œ„(n = 5)์˜ ๊ฐ„์„ญ๊ณ„ ์˜์ƒ์„ ํ˜•์„ฑํ•˜์—ฌ ์กฐ์œ„ ๊ด€์ธก์†Œ ๋ถ€๊ทผ์˜ ์ž์—ฐ ์ง€ํ˜•์—์„œ ๋ณ€ํ™”๊ฐ€ ์ ์€ ํ™”์†Œ ์„ ํƒ์„ ์ฆ๊ฐ€์‹œํ‚จ๋‹ค. Sentinel-1 A / B SAR ์˜์ƒ์€ 2014 ๋…„ ~ 2020 ๋…„ ์‚ฌ์ด์— ์„œ๋กœ ๋‹ค๋ฅธ ์ง€ํ˜• ์กฐ๊ฑด์„ ๊ฐ€์ง„ ํ•œ๋ฐ˜๋„์˜ 10 ๊ฐœ ์กฐ์œ„ ๊ด€์ธก์†Œ์—์„œ ์ˆ˜์ง‘๋˜์—ˆ์œผ๋ฉฐ, VLM ์†๋„ ๋ฐ ์‹œ๊ณ„์—ด ๋ณ€์œ„๋ฅผ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•ด Sequential StaMPS-SBAS์™€ ํ•จ๊ป˜ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์ดˆ๊ธฐ ํ™”์†Œ ๋ฐ€๋„๋Š” ๊ธฐ์กด StaMPS-SBAS ๋ถ„์„์„ ํ†ตํ•ด ํ™•์ธ ๋œ ์ตœ์ข…์ ์ธ ๋ถˆ๋ณ€ํ™”์†Œ ๋ฐ€๋„์˜ ์•ฝ 1.25 ๋ฐฐ์™€ ๋‘ ๋ฐฐ๋กœ ๋„์ถœ๋˜์—ˆ๋‹ค. ์…‹์งธ๋กœ, Sequential StaMPS-SBAS ๋ถ„์„์— ์˜ํ•œ ์‹œ๊ณ„์—ด VLM ๋น„์œจ์˜ ์™„์ „ํ•œ ์ž๋™ ์ถ”์ • ๊ฐ€๋Šฅ์„ฑ์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. Seq-TInSAR๋ผ๊ณ ํ•˜๋Š” ์™„์ „ํ•œ ์ž๋™ ์ฒ˜๋ฆฌ ๋ชจ๋“ˆ์ด ๊ฐœ๋ฐœ๋˜์—ˆ์œผ๋ฉฐ, 3 ๊ฐœ์˜ ํ•˜์œ„ ๋ชจ๋“ˆ๋กœ ๊ตฌ์„ฑ๋˜์–ด์žˆ๋‹ค. 1) Sentinel-1 SLC (Single Look Complex) ์˜์ƒ, ์ •๋ฐ€ํ•œ ๊ถค๋„ ์ •๋ณด ๋ฐ DEM (Digital Elevation Model)์˜ ์ž๋™ ๋‹ค์šด๋กœ๋“œ 2) SLC ์ „ ์ฒ˜๋ฆฌ๊ธฐ : ์˜์ƒ ๋ณ„ Burst ์ถ”์ถœ, ์ •๋ฐ€ํ•œ ํ†ตํ•ฉ ๋ฐ Stacking, 3) Sequential StaMPS-SBAS ํ”„๋กœ์„ธ์„œ : VLM ์†๋„ ๋ฐ VLM ์‹œ๊ณ„์—ด ๋ณ€์œ„์˜ ์ถ”์ • ๋งˆ์ง€๋ง‰์œผ๋กœ, Seq-TInSAR ๋ชจ๋“ˆ์€ ๋™์œ„ ํ‰๊ท  ํ•ด์ˆ˜๋ฉด ํ‰๊ท ์œผ๋กœ ๋น„์ •์ƒ์ ์ธ ํ•ด์ˆ˜๋ฉด ์ถ”์„ธ๋ฅผ ๋ณด์ด๋Š” 100 ๊ฐœ์˜ ์กฐ์œ„ ๊ด€์ธก์†Œ์— ์ ์šฉ๋œ๋‹ค. ์กฐ์œ„ ๊ด€์ธก์†Œ ์ง€์ ๋ณ„๋กœ 60 ~ 70 ๊ฐœ์˜ Sentinel-1 A / B SLC ์˜์ƒ์„ ํš๋“ํ•˜๊ณ  300 ~ 350 ๊ฐœ์˜ ์‹œ๊ณ„์—ด ๊ฐ„์„ญ๊ณ„ ์˜์ƒ์„ ์ฒ˜๋ฆฌํ•˜์—ฌ ์กฐ์œ„ ๊ด€์ธก์†Œ์—์„œ VLM์„ ์ถ”์ •ํ•˜์˜€๋‹ค. ์ •๋Ÿ‰์ ์ธ VLM ์†๋„์™€ ์‹œ๊ณ„์—ด VLM์€ ์„ ์ •ํ•œ ์กฐ์œ„ ๊ด€์ธก์†Œ์— ๋Œ€ํ•ด ์ถ”์ •ํ•˜์˜€๋‹ค. VLM ์†๋„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋„์ถœํ•œ ์กฐ์œ„ ๊ด€์ธก์†Œ๋Š” ๋‹ค์–‘ํ•œ VLM ๋ฒ”์œ„๋กœ ๋ถ„๋ฅ˜๋œ๋‹ค. 12 ๊ฐœ์˜ ์กฐ์œ„ ๊ด€์ธก์†Œ์—์„œ ์ทจ๋“ํ•œ ํ˜„์žฅ GPS ๊ด€์ธก ์ž๋ฃŒ๋ฅผ InSAR๋กœ๋ถ€ํ„ฐ ์ถ”์ •ํ•œ VLM ๋น„์œจ๊ณผ ๋น„๊ตํ•˜์—ฌ ๊ฐ•๋ ฅํ•œ ์ƒ๊ด€์„ฑ์„ ์ฐพ์•˜๊ณ , ์ด๋Š” ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์ด ์กฐ์œ„ ๊ด€์ธก์†Œ์—์„œ VLM์˜ ๊ณต๊ฐ„์  ๋ฐ ์‹œ๊ฐ„์  ๋ณ€ํ™”๋ฅผ ์ธก์ •ํ•˜๋Š”๋ฐ ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ์ž๋ฃŒ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค.Chapter 1. Introduction 1 1.1. Brief overview of sea-level rise 1 1.2. Motivations 4 1.3. Purpose of Research 9 1.4. Outline 12 Chapter 2. Sea Level variations and Estimation of Vertical land motion 14 2.1. Sea level variations 14 2.2. Sea level observations 14 2.3. Long term sea level estimation 19 2.4. Factors contributing tide gauge records: Vertical Land Motion 19 2.5. Brief overview of InSAR and Time-series SAR Interferometry 24 Chapter 3. Vertical Land Motion estimation at Tide gauge using Time-series PS-InSAR technique: A case study for Pohang tide gauge 36 3.1. Background 36 3.2. VLM estimation at Pohang tide gauge using StaMPS-PSI analysis 38 3.3. Development of StaMPS-SBAS InSAR using Sequential InSAR pair selection suitable for coastal environments 55 3.4. Discussion 80 Chapter 4. Application of time-series Sequential-SBAS InSAR for Vertical Land Motion estimation at selected tide gauges around the world using Sentinel-1 SAR data 85 4.1. Description of PSMSL tide gauge data 87 4.2. Sentinel-1 A/B SAR data acquisitions 92 4.3. Automatic Time-series InSAR processing module โ€Seq-TInSARโ€ 93 4.4. Results: Estimation of vertical land motions at selected tide gauges 97 4.5. Comparison of InSAR results with GNSS observations 112 4.6. Discussion 125 Chapter 5. Conclusions and Future Perspectives 128 Abstract in Korean 133 Appendix โ€“ A 136 Appendix โ€“ B 146 Bibliography 151๋ฐ•

    Correction of atmospheric delay effects in radar interferometry using a nested mesoscale atmospheric model

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    The temporal variability of the atmosphere through which radio waves pass in the technique of differential radar interferometry can seriously limit the accuracy with which the method can measure surface motion. A forward, nested mesoscale model of the atmosphere can be used to simulate the variable water content along the radar path and the resultant phase delays. Using this approach we demonstrate how to correct an interferogram of Mount Etna in Sicily associated with an eruption in 2004-5. The regional mesoscale model (Unified Model) used to simulate the atmosphere at higher resolutions consists of four nested domains increasing in resolution (12, 4, 1, 0.3 km), sitting within the analysis version of a global numerical model that is used to initiate the simulation. Using the high resolution 3D model output we compute the surface pressure, temperature and the water vapour, liquid and solid water contents, enabling the dominant hydrostatic and wet delays to be calculated at specific times corresponding to the acquisition of the radar data. We can also simulate the second-order delay effects due to liquid water and ice

    INSAR Principles B

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    reserved5A. Ferretti; A. Monti Guarnieri; C. Prati; F. Rocca; D. MassonnetFerretti, Alessandro; MONTI-GUARNIERI, ANDREA VIRGILIO; Prati, CLAUDIO MARIA; Rocca, Fabio; D., Massonne

    Monitoring von Hangbewegungen mit InSAR Techniken im Gebiet Ciloto, Indonesien

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    In this doctoral thesis, the InSAR techniques are applied to detect the ground movement phenomenon and to assess the InSAR result geometrically in the Ciloto area, Indonesia. Mainly, one of those techniques, the SB-SDFP algorithm, overcomes the limitations of conventional InSAR in monitoring rural and agricultural areas and can observe extremely slow landslides. The InSAR strategy is positively known as a promising option to detect and quantify the kinematics of active landslides on a large areal scale. To minimize the bias of the InSAR displacement result, the correction of the tropospheric phase delay was carried out in a first step. This procedure is demonstrated in experiments both in the small study area in Ciloto and in a larger area. The latter is an area located in Northern Baja California, Mexico and is dominated by tectonic activity as well as groundwater-induced subsidence. A detailed investigation of the slope movement's behavior in the Ciloto district was conducted utilizing multi-temporal and multi-band SAR data from ERS1/2 (1996-1999), ALOS PALSAR (2007-2009) and Sentinel-1 (2014-2018) satellites. The region was successfully identified as a permanent active landslide prone area, especially in the vicinity of the Puncak Pass and Puncak Highway. The full 3D velocity field and the displacement time series were estimated using the inversion model. The velocity rate was classified from extremely slow to slow movement. To comprehend the landslide's behavior, a further examination of the relationship between InSAR results and physical characteristics of the area was carried out. For the long period of a slow-moving landslide, the relationship between precipitation and displacement trend shows a weak correlation. It is concluded that the extremely slow to slow deformation is not directly influenced by the rainfall intensity, yet it effectuates the subsurface and the groundwater flow. The run-off process with rainfall exceeding a soil's infiltration capacity was suspected as the main driver of the slow ground movement phenomenon. However, when analyzing rapid and extremely rapid landslide events at Puncak Pass, a significant increase in the correlation coefficient between precipitation and displacement rate could be observed.In dieser Doktorarbeit wird die Anwendung von erweiterten Verarbeitungsstrategien von InSAR Daten zur Erkennung und geometrischen Bewertung der Bodenbewegungen im Ciloto - Indonesien dargestellt. Dieser Ansatz รผberwindet die Beschrรคnkungen konventioneller SAR-Interferometrie und ermรถglicht sowohl ein kontinuierliches Monitoring dieses landwirtschaftich geprรคgten Gebietes als auch die Erfassung extrem langsamer Hangrutschungen. Um eine Verzerrung der InSAR Deformationsergebnisse zu minimieren, wurde zunรคchst eine Korrektur der troposphรคrischen Phase durchgefรผhrt. Diese neuartige Strategie wird sowohl im Forschungsgebiet Ciloto als auch an einem grรถรŸeren Gebiet demonstriert. Bei letzterem handelt es sich um einen Kรผstenstreifen im nรถrdlichen Niederkalifornien, Mexiko, welcher durch hohe tektonische Aktivitรคt und grundwasserinduzierte Landsetzungen charakterisiert ist. Die detaillierte Untersuchung des Verhaltens von Hangrutschungen im Ciloto erfolgte durch die Verarbeitung multi-temporaler SAR-Daten unter Nutzung verschiedener Frequenzbรคnder, darunter ESR1/2 (1996-1999), ALOS PALSAR (2007-2009) und Sentinel-1 (2014-2018) Daten. Die Region konnte erfolgreich als permanent aktives Hangrutschungsgebiet identifiziert werden, wobei der Puncak Pass und der Puncak Highway ein erhรถhtes Gefahrenpotential aufweisen. Ein 3D- Geschwindig-keitsfeld der Deformation und die zugehรถrigen Zeitreihen wurden mit dem Inversionsmodell berechnet. Die Geschwindigkeitsrate wurde als langsam bis extrem langsam klassifiziert. Um das dynamische Verhalten der Hangrutschung zu verstehen wurde, in einer weiteren Untersuchung die Beziehung zwischen dem InSAR-Ergebnis und den physikalischen Begebenheiten im Forschungsgebiet analysiert. Es wird der Schluss gezogen, dass die langsame bis extrem langsame Verformung nicht direkt von der Niederschlagsintensitรคt beeinflusst wird, diese sich aber auf den Untergrund und die Grundwasserstrรถmung auswirkt. Es wird vermutet, dass der Oberflรคchenablauf, welcher die Infiltrationskapazitรคt des Bodens รผbersteigt, ausschlaggebend fรผr das Phรคnomen der langsamen Bodenbewegung ist. Fรผr die schnellen und extrem schnellen Hangrutschungen jedoch konnte eine signifikante Erhรถhung des Korrelationskoeffizienten zwischen Niederschlag und Verschiebungsrate bei Untersuchungen der Hangrutschung am Puncak-Pass nachgewiesen werden
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