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Готові списки джерел за темами / Multitemporal InSAR

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Добірка наукової літератури з теми "Multitemporal InSAR"

Автор: Grafiati

Опубліковано: 10 березня 2023

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Статті в журналах з теми "Multitemporal InSAR"

1

Shirzaei, Manoochehr. "A seamless multitrack multitemporal InSAR algorithm." Geochemistry, Geophysics, Geosystems 16, no. 5 (May 2015): 1656–69. http://dx.doi.org/10.1002/2015gc005759.

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2

Liang, Hongyu, Lei Zhang, Zhong Lu, and Xin Li. "Nonparametric Estimation of DEM Error in Multitemporal InSAR." IEEE Transactions on Geoscience and Remote Sensing 57, no. 12 (December 2019): 10004–14. http://dx.doi.org/10.1109/tgrs.2019.2930802.

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3

Manzoni, Marco, Monia Elisa Molinari, and Andrea Monti-Guarnieri. "Multitemporal InSAR Coherence Analysis and Methods for Sand Mitigation." Remote Sensing 13, no. 7 (April 2, 2021): 1362. http://dx.doi.org/10.3390/rs13071362.

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Dunes and sand sheets motion natural hazard affect many desertic areas worldwide and require careful assessment to develop effective mitigation plans to protect populated sites, infrastructure, and human activities. The study explores the suitability of Synthetic Aperture Radar (SAR) coherent methods to detect desert area instabilities and estimate sand accumulations displacements. The SAR methods have been applied to long time series of images provided by Sentinel-1. Moreover, the research introduces a novel robust index, named Temporal Stability Index, able to characterize the percentage of stability of a target with time. The work reports the experiments performed on the United Arab Emirates (UAE) and Egypt desertic areas and proves the usefulness of SAR coherent methods to support sand mitigation measures.

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4

Jiang, Mi, Xiaoli Ding, and Zhiwei Li. "Hybrid Approach for Unbiased Coherence Estimation for Multitemporal InSAR." IEEE Transactions on Geoscience and Remote Sensing 52, no. 5 (May 2014): 2459–73. http://dx.doi.org/10.1109/tgrs.2013.2261996.

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5

Hu, Jun, Xiao-Li Ding, Zhi-Wei Li, Jian-Jun Zhu, Qian Sun, and Lei Zhang. "Kalman-Filter-Based Approach for Multisensor, Multitrack, and Multitemporal InSAR." IEEE Transactions on Geoscience and Remote Sensing 51, no. 7 (July 2013): 4226–39. http://dx.doi.org/10.1109/tgrs.2012.2227759.

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6

Engdahl, M. E., and J. M. Hyyppa. "Land-cover classification using multitemporal ERS-1/2 insar data." IEEE Transactions on Geoscience and Remote Sensing 41, no. 7 (July 2003): 1620–28. http://dx.doi.org/10.1109/tgrs.2003.813271.

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7

Esmaeili, Mostafa, Mahdi Motagh, and Andy Hooper. "Application of Dual-Polarimetry SAR Images in Multitemporal InSAR Processing." IEEE Geoscience and Remote Sensing Letters 14, no. 9 (September 2017): 1489–93. http://dx.doi.org/10.1109/lgrs.2017.2717846.

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8

Liang, Hongyu, Lei Zhang, Zhong Lu, and Xin Li. "Correction of spatially varying stratified atmospheric delays in multitemporal InSAR." Remote Sensing of Environment 285 (February 2023): 113382. http://dx.doi.org/10.1016/j.rse.2022.113382.

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9

Liang, Hongyu, Xin Li, and Rou-Fei Chen. "Mapping Surface Deformation Over Tatun Volcano Group, Northern Taiwan Using Multitemporal InSAR." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 2087–95. http://dx.doi.org/10.1109/jstars.2021.3050644.

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10

Hu, Jiyuan, Mahdi Motagh, Jiming Guo, Mahmud Haghshenas Haghighi, Tao Li, Fen Qin, and Wenhao Wu. "Inferring subsidence characteristics in Wuhan (China) through multitemporal InSAR and hydrogeological analysis." Engineering Geology 297 (February 2022): 106530. http://dx.doi.org/10.1016/j.enggeo.2022.106530.

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Дисертації з теми "Multitemporal InSAR"

1

Vicente-Guijalba, Fernando. "Teledetección Multitemporal mediante Dinámica de Sistemas." Doctoral thesis, Universidad de Alicante, 2016. http://hdl.handle.net/10045/57626.

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Millin-Chalabi, Gail Rebecca. "Radar multi-temporal and multi-sensor approach to characterise peat moorland burn scars and assess burn scar persistence in the landscape." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/radar-multitemporal-and-multisensor-approach-to-characterise-peat-moorland-burn-scars-and-assess-burn-scar-persistence-in-the-landscape(36288daf-4a05-46e8-9e29-f67c62584fc5).html.

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Peat moorlands represent a nationally significant carbon store. Wildfires in peat moorlands release CO2 into the atmosphere, reducing the carbon store and burn into the seed bank preventing vegetation recovery. Burned areas of bare peat remain, known as ‘burn scars’ which are eroded by freeze thaw and desiccation, then weathered by precipitation and wind to cause discolouration of the water supply. A technique for the systematic monitoring of peat moorland burn scars is essential for informing land management and moorland restoration. Satellite data enables peat moorland burn scars to be monitored at the landscape scale for operational services e.g. European Forest Fire Information System (EFFIS). However, in the UK cloud is highly problematic for optical satellites and thermal data provides only a short window of opportunity for active fire detection. This thesis provides a unique line of enquiry by exploring the potential of Synthetic Aperture Radar (SAR) intensity and Interferometric Synthetic Aperture Radar (InSAR) coherence for burn scar characterisation and persistence, using a multi-temporal and multi-sensor approach for degraded peat moorland. The Peak District National Park (PDNP) was selected because it is a marginal moorland environment, which experiences high rates of peat erosion and will experience more wildfires, based on future projections of increased temperature, due to global warming. Initial SAR intensity results for the Bleaklow 2003 burn scar showed a clear post-fire increase of 7 dB for burned peat bog when acquired under wet conditions. Post-fire, dry − wet InSAR pairs were characterised by vegetation removal caused by combustion within the burn scar area, whereas wet − wet InSAR pairs characterised the burn scar, but also degraded peat moorland caused by previous wildfires blurring the new burn scar perimeter. Intensity differed significantly with slope for the PDNP 2003 wildfires, reducing the effectiveness of the technique for characterising burn scars on slopes facing away from the sensor, although these wildfires showed no significant difference on coherence for the inland bare ground class. When using coherence as a burn scar discriminator, this research found that it is essential to acquire InSAR pairs immediately post-fire with B⊥ < 550 m. Using a combination of intensity and coherence data a multi-difference colour composite was produced and an ISODATA classification applied. Results were reclassified to produce a burned area map with an overall map accuracy of 94% and Kappa Coefficient of 0.69 covering the Bleaklow and Kinder 2003 burn scars. Burn scars < 6 km2 provided a persistently higher burned area intensity signal for up to six months after the wildfire but only 2 − 3 months for coherence. The smaller Edale burn scar (0.10 km2) was characterised by 2 − 3 dB greater intensity for the burned area over a year after the wildfire. The Edale 2008 case study showed that L-band PALSAR data is less sensitive to characterising peat moorland burn scars compared to C-band data. This study therefore strongly recommends C-band data for peat moorland burn scar characterisation and monitoring. Future research will explore the new C-band Sentinel-1 data which offers improved spatial resolution and repeat-pass time.

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3

Shan, Monan. "Permafrost Deformation Monitoring and Interpretation Using InSAR Technique in Northeastern China and Aosta Valley Region, Italy." Doctoral thesis, 2021. http://hdl.handle.net/2158/1247205.

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InSAR algorithms have been widely applied in monitoring and mapping for the purposes of engineering geology research and the InSAR derived products have been extensively used in geological risk management by administrative entities and Emergency Management authorities. In this PhD thesis, the research was focusing on the application of InSAR technique, including conventional DInSAR and advanced SqueeSARTM in cold regions, (1) to locate the deformation hotspot caused by permafrost degradation, then to improve the regional permafrost mapping; (2) to analyze the deformation characteristics caused by the degradation of permafrost temporally and spatially. The research was developed on two sites of cold regions of different geomorphologies. The first case study was focusing on the detection of permafrost degradation phenomena using DInSAR and SqueeSARTM in the low-land permafrost distributed hilly regions in northern Heilongjiang Province, northeastern China. The study area is located at the southern margin of Siberia permafrost region, which is the largest area of the permafrost distribution in the northern Hemisphere. Studies have been revealing that the increasing mean annual air temperature in this study area has been causing the degradation of permafrost in decades and have been bringing geological risks to man-made infrastructures. Initially, the SqueeSARTM analyses using C-band Sentinel-1 and L-band ALOS PALSAR were conducted to reveal the overall displacements time series in the extracted permanent and distributed scatterers of the study area. Then the DInSAR analyses using Sentinel-1 and ALOS PALSAR data were completed to map the deformation hotspot of the study area. Lastly, by combination of the results acquired by SqueeSARTM and DInSAR analyses, the possible spatial distribution of permafrost deformation hotspot was mapped. PS and DS in the permafrost deformation hotspot are selected and analyzed to reveal the characteristics of permafrost degradation in the study area. The results indicate that in the permafrost distributed areas, the deformation velocity has been reduced in recent years from 2015 to 2019 than 2007 to 2010, roughly a decade ago. It could be related to the final degradation phase of the permafrost in recent years. In the second case study, the feasibility of DInSAR and SqueeSARTM in the study of active rock glacier in Alpine environments of mountainous geomorphologies of Aosta Valley Region, Italy was demonstrated, and the deformation characteristics corresponding to the seasonality of active rock glacier were discovered. The seasonal dynamic feature of the active rock glaciers was then analyzed using the regional monitoring results of Aosta Valley Region using Sentinel-1 SqueeSARTM technique and the regional active rock glacier investigation dataset. Interestingly, according to the result of the analysis, the displacement time series of active rock glacier in Aosta Valley Region has shown a half-year lapse compared with the change of regional annual air temperature that is contradictive to the conventional understanding of permafrost deformation dynamics. The driving factor of such phenomena was left unfound and open to the future analysis. Second, combined with high-resolution optical remote sensing imagery, the conventional DInSAR analysis using ALOS PALSAR data collected in summer season of 2007 has detected more active rock glaciers than the regional active rock glacier investigation dataset. The results have provided the possibility of further analysis of improving active rock glacier mapping using InSAR method in the future. In conclusion, this research highlights the value of using spaceborne DInSAR and SqueeSARTM methods in mapping and monitoring active periglacial landforms in cold regions at regional scale. Thanks to its short revisiting time and medium to high spatial resolution, Sentinel-1 data can be used for systematic and continuous monitoring of ground deformation, especially slow and very slow periglacial processes due to the changing climate in cold regions but the effectiveness of the usage of Sentinel-1 in dealing with winter snow cover and dense vegetation should be enhanced. On the other hand, L-band ALOS PALSAR data has solved the problem of vegetation coverage, which extensively exists in the cold region that has limited the capability of InSAR monitoring. The methods mentioned in this thesis are intended to be implemented in the regional or local geological hazard management in both study areas.

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Частини книг з теми "Multitemporal InSAR"

1

Alexander Correa-Muñoz, Nixon, and Carol Andrea Murillo-Feo. "The Potential of Remote Sensing to Assess Conditioning Factors for Landslide Detection at a Regional Scale: The Case in Southeastern Colombia." In Slope Engineering [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94251.

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This landslide detection research applied remote sensing techniques. Morphometry to derive both DEM terrain parameters and land use variables. SAR interferometry (InSAR) for showing that InSAR coherence and InSAR displacement obtained with SRTM DEM 30 m resolution were strongly related to landslides. InSAR coherence values from 0.43 to 0.66 had a high association with landslides. PS-InSAR allowed to estimate terrain velocities in the satellite line-of-sight (LOS) in the range − 10 to 10 mm/year concerning extremely slow landslide displacement rates. SAR polarimetry (PolSAR) was used over L-band UAVSAR quad-pol data, obtaining the scattering mechanism of volume and surface retrodispersion more associated with landslides. The optical remote sensing with a multitemporal approach for change detection by multi-year Landsat (5, 7 and 8)-NDVI, showed that NDVI related to landslides had values between 0.42 and 0.72. All the information was combined into a multidimensional grid product and crossed with training data containing a Colombian Geologic Service (CGS) landslide inventory. A detection model was implemented using the Random Forest supervised method relating the training sample of landslides with multidimensional explanatory variables. A test sample with a proportion of 70:30 allowed to find the accuracy of detection of about 70.8% for slides type.

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2

Askne, Jan, and Maurizio Santoro. "Experiences in Boreal Forest Stem Volume Estimation from Multitemporal C-Band InSAR." In Recent Interferometry Applications in Topography and Astronomy. InTech, 2012. http://dx.doi.org/10.5772/35102.

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Тези доповідей конференцій з теми "Multitemporal InSAR"

1

Huang, Quihuan, and Xiufeng He. "Optimum selection of common master image for multitemporal InSAR." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Bruce Hirsch, Zhiguo Cao, and Hanqing Lu. SPIE, 2009. http://dx.doi.org/10.1117/12.833677.

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2

Refice, A., L. Partipilo, F. Bovenga, F. P. Lovergine, R. Nutricato, D. O. Nitti, and D. Capolongo. "Remotely Sensed Detection of Badland Erosion Using Multitemporal InSAR." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883555.

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3

Le, Thu Trang, Jean-Luc Froger, Alexis Hrysiewicz, and Ha Thai Pham. "Multitemporal InSAR Coherence Change Analysis: Application to Volcanic Eruption Monitoring." In 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). IEEE, 2019. http://dx.doi.org/10.1109/multi-temp.2019.8866922.

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4

Jiang, Liming, Mingsheng Liao, Hui Lin, and An Zhao. "Urban change detection using multitemporal ERS-1/2 InSAR data." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2005. http://dx.doi.org/10.1117/12.626983.

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5

Engdahl, Marcus E., and Juha M. Hyyppa. "Unsupervised land-cover classification using multitemporal ERS-1/2 tandem INSAR data." In International Symposium on Remote Sensing, edited by Manfred Ehlers. SPIE, 2002. http://dx.doi.org/10.1117/12.453682.

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6

GKARTZOU, E., I. Parcharidis, E. Karymbalis, and MARIA LOUIZA Drakatou. "Multitemporal SAR Interferometry in the Messolonghi-Etoliko Natura 2000 Overlapping Deltas Area." In Fringe2015: Advances in the Science and Applications of SAR Interferometry and Sentinel-1 InSAR Workshop. European Space Agency, 2015. http://dx.doi.org/10.5270/fringe2015.pp218.

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7

Amitrano, Donato, Francesca Cecinati, Gerardo Di Martino, Antonio Iodice, Deniele Riccio, and Giuseppe Ruello. "Urban Areas Extraction from Multitemporal SAR RGB Images Using Interferometric Coherence and Textural Information." In Fringe2015: Advances in the Science and Applications of SAR Interferometry and Sentinel-1 InSAR Workshop. European Space Agency, 2015. http://dx.doi.org/10.5270/fringe2015.pp114.

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Wang, Yan, Mingsheng Liao, Deren Li, and Hui Lin. "Subsidence monitoring in urban area using multitemporal InSAR data: a case study in China." In Remote Sensing, edited by Manfred Ehlers, Francesco Posa, Hermann J. Kaufmann, Ulrich Michel, and Giacomo De Carolis. SPIE, 2004. http://dx.doi.org/10.1117/12.565518.

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Bruno, Maria F., Matteo G. Molfetta, Michele Mossa, Alberto Morea, Maria Teresa Chiaradia, Raffaele Nutricato, Davide O. Nitti, Luciano Guerriero, and Alessandro Coletta. "Integration of multitemporal SAR/InSAR techniques and NWM for coastal structures monitoring: Outline of the software system and of an operational service with COSMO-SkyMed data." In 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS). IEEE, 2016. http://dx.doi.org/10.1109/eesms.2016.7504837.

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