2,052 research outputs found

    Minimizing the residual topography effect on interferograms to improve DInSAR results: estimating land subsidence in Port-Said City, Egypt

    Get PDF
    The accurate detection of land subsidence rates in urban areas is important to identify damage-prone areas and provide decision-makers with useful information. Meanwhile, no precise measurements of land subsidence have been undertaken within the coastal Port-Said City in Egypt to evaluate its hazard in relationship to sea-level rise. In order to address this shortcoming, this work introduces and evaluates a methodology that substantially improves small subsidence rate estimations in an urban setting. Eight ALOS/PALSAR-1 scenes were used to estimate the land subsidence rates in Port-Said City, using the Small BAse line Subset (SBAS) DInSAR technique. A stereo pair of ALOS/PRISM was used to generate an accurate DEM to minimize the residual topography effect on the generated interferograms. A total of 347 well distributed ground control points (GCP) were collected in Port-Said City using the leveling instrument to calibrate the generated DEM. Moreover, the eight PALSAR scenes were co-registered using 50 well-distributed GCPs and used to generate 22 interferogram pairs. These PALSAR interferograms were subsequently filtered and used together with the coherence data to calculate the phase unwrapping. The phase-unwrapped interferogram-pairs were then evaluated to discard four interferograms that were affected by phase jumps and phase ramps. Results confirmed that using an accurate DEM (ALOS/PRISM) was essential for accurately detecting small deformations. The vertical displacement rate during the investigated period (2007โ€“2010) was estimated to be โˆ’28 mm. The results further indicate that the northern area of Port-Said City has been subjected to higher land subsidence rates compared to the southern area. Such land subsidence rates might induce significant environmental changes with respect to sea-level rise

    EMISAR: A Dual-frequency, Polarimetric Airborne SAR

    Get PDF

    Indoor experiments on polarimetric SAR interferometry

    Get PDF
    A coherence optimization method, which makes use of polarimetry to enhance the quality of SAR interferograms, has been experimentally tested under laboratory conditions in an anechoic chamber. By carefully selecting the polarization in both images, the resulting interferogram exhibits an improved coherence above the standard HH or VV channel. This higher coherence produces a lower phase variance, thus estimating the underlying topography more accurately. The potential improvement that this technique provides in the generation of digital elevation models (DEM) of non-vegetated natural surfaces has been observed for the first time on some artificial surfaces created with gravel. An experiment on a true outdoor DEM has not been accomplished yet, but the first laboratory results show that the height error for an almost planar surface can be drastically reduced within a wide range of baselines by using the optimization algorithm. This algorithm leads to three possible interferograms associated with statistically independent scattering mechanisms. The phase difference between those interferograms has been employed for extracting the height of vegetation samples. This retrieval technique has been tested on three different samples: maize, rice, and young fir trees. The inverted heights are compared with ground truth for different frequency bands. The estimates are quite variable with frequency, but their complete physical justification is still in progress. Finally, an alternative simplified scheme for the optimization is proposed. The new approach (called polarization subspace method) yields suboptimum results but is more intuitive and has been used for illustrating the working principle of the original optimization algorithm.Peer Reviewe

    ์‹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€

    Get PDF
    ํ•™์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•

    Asteroid Models from Multiple Data Sources

    Full text link
    In the past decade, hundreds of asteroid shape models have been derived using the lightcurve inversion method. At the same time, a new framework of 3-D shape modeling based on the combined analysis of widely different data sources such as optical lightcurves, disk-resolved images, stellar occultation timings, mid-infrared thermal radiometry, optical interferometry, and radar delay-Doppler data, has been developed. This multi-data approach allows the determination of most of the physical and surface properties of asteroids in a single, coherent inversion, with spectacular results. We review the main results of asteroid lightcurve inversion and also recent advances in multi-data modeling. We show that models based on remote sensing data were confirmed by spacecraft encounters with asteroids, and we discuss how the multiplication of highly detailed 3-D models will help to refine our general knowledge of the asteroid population. The physical and surface properties of asteroids, i.e., their spin, 3-D shape, density, thermal inertia, surface roughness, are among the least known of all asteroid properties. Apart for the albedo and diameter, we have access to the whole picture for only a few hundreds of asteroids. These quantities are nevertheless very important to understand as they affect the non-gravitational Yarkovsky effect responsible for meteorite delivery to Earth, or the bulk composition and internal structure of asteroids.Comment: chapter that will appear in a Space Science Series book Asteroids I

    Integration of high-resolution, Active and Passive Remote Sensing in support to Tsunami Preparedness and Contingency Planning

    Get PDF
    In the aftermath of the Sri Lanka tsunami disaster, a stack of synoptic procedures and remote sensing techniques was chosen for satisfying the urgent mapping needs of the Government. This choice presented the undebated advantage of (a) allowing to start the work immediately (b) without relying upon ground logistics until the onset of the air campaign, (c) minimizing the duration of the work on spot, while (d) covering fast - and at an otherwise unreacheable resolution - large portions of a difficult-to-penetrate territory, (e) keeping the work sustainable and, overall, (f) allowing to carry out the work. This combination of airborne and spaceborne techniques is ready-to-use worldwide, and the techniques for flooding simulation and scenario building can be chosen at whatever level of complexity - choosing preferably robustness. It is also worth noting further that the new generation of metric resolution, X-band Radar satellite constellations (as TerraSAR-X and Cosmo-SkyMED), may allow creating LiDAR-like products avoiding airborne missions. The products of the space-and-air campaign were handed over by the Ambassador of Italy to the Minister for Disaster Management and Humanitarian Affairs on 7th December 2006, Colombo, Sri Lanka

    Spacecraft applications of advanced global positioning system technology

    Get PDF
    The purpose of this study was to evaluate potential uses of Global Positioning System (GPS) in spacecraft applications in the following areas: attitude control and tracking; structural control; traffic control; and time base definition (synchronization). Each of these functions are addressed. Also addressed are the hardware related issues concerning the application of GPS technology and comparisons are provided with alternative instrumentation methods for specific functions required for an advanced low earth orbit spacecraft

    Exploiting satellite SAR for archaeological prospection and heritage site protection

    Get PDF
    Optical and Synthetic Aperture Radar (SAR) remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications, yet further advances are viable through the exploitation of novel sensor data and imaging modes, big data and high-performance computing, advanced and automated analysis methods. This paper showcases the main research avenues in this field, with a focus on archaeological prospection and heritage site protection. Six demonstration use-cases with a wealth of heritage asset types (e.g. excavated and still buried archaeological features, standing monuments, natural reserves, burial mounds, paleo-channels) and respective scientific research objectives are presented: the Ostia-Portus area and the wider Province of Rome (Italy), the city of Wuhan and the Jiuzhaigou National Park (China), and the Siberian โ€œValley of the Kingsโ€ (Russia). Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite (e.g. Copernicus Sentinels and ESA Third Party Missions) and aerial (e.g. Unmanned Aerial Vehicles, UAV) platforms, as well as field-based evidence and ground truth, auxiliary topographic data, Digital Elevation Models (DEM), and monitoring data from geodetic campaigns and networks. The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes, identify threats to cultural heritage assets due to ground instability and urban development in large metropolises, and monitor post-disaster impacts in natural reserves
    • โ€ฆ
    corecore