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林玉儂 / Lin, Yunung Nina
Associate Research Fellow
林玉儂 / Lin, Yunung Nina
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林玉儂 / Lin, Yunung Nina
Associate Research Fellow
林玉儂 / Lin, Yunung Nina
Research Fields：Interferometric Synthetic Aperture Radar (InSAR) Processing, Time-series Analysis, Reflection Seismology, Velocity Model Building
+886-2-2783-9910 ext. 1426(office), 1207(lab)
Curriculum Vitae
ninalin@earth.sinica.edu.tw
Personal Website
Research interest

From a City to a Country: Multi-scale SAR Processing Techniques and Applications

 

Courses

1. Fundamentals of Active and Passive Remote Sensing (TIGP-ESS, NTU)
Offered every other year in the first semester; next round will be Fall 2024
※ Pre-requirement (optional):
Linear Algebra

2. Synthetic Aperture Radar Integrated Data Analysis (NTU only)
Offered every other year in the second semester; next round will be Spring 2025
※ Pre-requirement (mandatory): 
Fundamentals of Active and Passive Remote Sensing (or equivalent)
Linear Algebra
Programming

3. Special Topics on Synthetic Aperture Radar (TIGP-ESS only)

4. SAR Workshop (public workshop)
One-week workshop offered annually in winter (January or February)

 

Videos

1. 2022中研院地球所院區開放影片欣賞-穿雲透雨雷達現：透視地表祕密的合成孔徑雷達 (in Chinese)
https://www.youtube.com/watch?v=wZGdlenFhvE

2. Performance Study of Landslide Detection Using Multi-Temporal SAR Images
https://www.youtube.com/watch?v=zu6LtISTkig



1. Deformation Monitoring and Modeling with InSAR

My team implements multiple InSAR processing flows on the High Performance Computing (HPC) environment within the Institute of Earth Sciences, Academia Sinica. The processing flows established include small baseline subsets approach, persistent scatterer approach and phase linking approach. We use these methods to monitor and model different types of natural hazards and tectonic deformation. We also open up the HPC to students and collaborators from various universities/institutes. 

Selected publication:

  • Wang, C.-Y., Y.N. Lin*, C. Huang, C K Shum (2024) Observing glacial isostatic adjustment by PSInSAR in southern Hudson Bay, Remote Sensing of Environment, 304, 114023, https://doi.org/10.1016/j.rse.2024.114023
  • Tang, C.-H., Y.N. Lin*, H. Tung, Y. Wang, S.-J. Lee, Y.-J. Hsu, J.B.H. Shyu, Y.-T. Kuo, H.-Y. Chen (2023) Nearby fault interaction within the double-vergence suture in eastern Taiwan during the 2022 Chihshang earthquake sequence, Communications Earth & Environment, 4(1), 333, https://doi.org/10.1038/s43247-023-00994-0
  • Nguyen, M., Y.N. Lin, Q.C. Tran, C.-F. Ni, Y.-C. Chan, K.-H. Tseng, C.-P. Chang (2022) Assessment of long-term ground subsidence and groundwater depletion in Hanoi, Vietnam. Engineering Geology, 299, 106555, https://doi.org/10.1016/j.enggeo.2022.106555
  • Y.N. Lin, E. Park, Y. Wang, Y.P. Quek, J. Lim, E. Alcantara, H. Ho (2021) Multi-sensor data integration for slope failure investigation: A case study of the 2020 Hpakant Jade Mine incident, Myanmar, ISPRS Journal of Photogrammetry and Remote Sensing, 177, 291-305, https://doi.org/10.1016/j.isprsjprs.2021.05.015



2. SAR-based Change Detection

We develop the Growing Split-Based Approach (GSBA) algorithm for automatic change detection based on multi-temporal Synthetic Aperture Radar (SAR) imagery. The detection kernel is the Bayesian probability generalized to a three-class problem rather than the conventional two-class problem. GSBA is statistically robust and computationally efficient, with a user-friendly driver file for operational applications. It can also be utilized for other scenarios, such as changes associated with landslides.

Related publication:

  • Lin, Y.N., Chen, Y.-C., Kuo, Y.-T., Chao, W.-A. (2022) Performance Study of Landslide Detection Using Multi-Temporal SAR Images. Remote Sensing, 1-14,  https://doi.org/10.3390/rs14102444
  • Y.N. Lin, H.-S. Yun, A. Bhardwaj and Emma M. Hill (2019), Urban flood detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A case study for Hurricane Matthew, Remote Sens. 2019, 11, 1778; https://doi.org/10.3390/rs11151778



3. Mobile Shales in Active Orogeny

There are many active mud volcanoes onshore and offshore southwestern Taiwan, yet the source area at depth is usually not clearly imaged. We utilize the industry-level seismic processing software, Geovation, to reprocess long-offset seismic data in order to figure out the geometry and physical properties associated with the patches of mobile shales at depth. The goal is to unravel the role mobile shales play in an active orogenic belt.

Related publication:

  • Wang, Y., Y. N. Lin, Y. Ota, L.-H. Chung, J. B. H. Shyu, H.-W. Chiang, Y.-G. Chen, H.-H. Hsu, and C.-C. Shen (2022), Mud Diapir or fault-related fold? On the development of an active mud-cored anticline offshore southwestern Taiwan, Tectonics, e2022TC007234, https://doi.org/10.1029/2022TC007234
  • Lai, S.-Y., Y.N. Lin, H.-H. Hsu (2022) Efficient 2D multiple attenuation using SRME with curvelet-domain subtraction. Marine Geophysical Research, 43, 1, https://doi.org/10.1007/s11001-021-09464-8
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