Jin Woo Kim
Organization:
Southern Methodist University
Email:
Business Address:
Southern Methodist University
3225 Daniel Ave
Suite 207
Dallas, TX 75205
United StatesFirst Author Publications:
- Kim, J. W., et al. (2022), Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia, Remote Sensing of Environment, 281, 113231, doi:10.1016/j.rse.2022.113231.
- Kim, J. W., and Z. Lu (2021), Short Communication Taking the pulse of aging oil wells in the Permian Basin from space Jin Woo Kim, Zhong Lu ⁎ Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX, USA, Science of the Total Environment, 757, 143932, doi:10.1016/j.scitotenv.2020.143932.
- Kim, J. W., and Z. Lu (2021), Sinkholes in Wink, Texas, Observed by Satellite Radar Imagery, Oklahoma Geological Survey Circular, 113, 105-110.
- Kim, J. W., Z. Lu, and J. Kaufmann (2019), Evolution of sinkholes over Wink, Texas, observed by high-resolution optical and SAR imagery, Remote Sensing of Environment, 222, 119-132, doi:10.1016/j.rse.2018.12.028.
- Kim, J. W., and Z. Lu (2018), Association between localized geohazards in West Texas and human activities, recognized by Sentinel-1A/B satellite radar imagery, Scientific Reports, 8, doi:10.1038/s41598-018-23143-6.
Co-Authored Publications:
- Qu, F., et al. (2023), Mapping and characterizing land deformation during 2007–2011 over the Gulf Coast by L-band InSAR, Remote Sensing of Environment, 284, 113342, doi:10.1016/j.rse.2022.113342.
- Xu, Y., et al. (2023), P-band SAR for ground deformation surveying: Advantages and challenges, Remote Sensing of Environment, 287, 113474, doi:10.1016/j.rse.2023.113474.
- Zheng, W., et al. (2023), Bayesian Monte Carlo Inversion of InSAR Time Series Deformation Induced by Wastewater Injection: A Case Study in West Texas, Geophys. Res. Lett., Bayesian Monte Carlo, A case study in West Texas.
- Xu, Y., et al. (2022), Landslides, Technical Note, doi:10.1007/s10346-020-01533-0.
- Lu, Z., and J. W. Kim (2021), GeoHazards Review A Framework for Studying Hydrology-Driven Landslide Hazards in Northwestern US Using Satellite InSAR, Precipitation and Soil Moisture Observations: Early Results and Future Directions, Early Results and Future Directions., 2, 17-40, doi:10.3390/geohazards2020002.
- Molan, Y. E., Z. Lu, and J. W. Kim (2020), Influence of the Statistical Properties of Phase and Intensity on Closure Phase, IEEE Trans. Geosci. Remote Sens., 58, doi:10.1109/TGRS.2020.2982062.
- Qu, F., et al. (2020), Identify and Monitor Growth Faulting Using InSAR over Northern Greater Houston, Texas, USA, Remote Sensing, doi:10.3390/rs11121498.
- Xu, Y., et al. (2020), Twelve‐Year Dynamics and Rainfall Thresholds for Alternating Creep and Rapid Movement of the Hooskanaden Landslide From Integrating InSAR, Pixel Offset Tracking, and Borehole and Hydrological Measurements, J. Geophys. Res., 125, e2020JF005640, doi:10.1029/2020JF005640.
- Xu, Y., et al. (2019), Characterizing Seasonally Rainfall-Driven Movement of a Translational Landslide using SAR Imagery and SMAP Soil Moisture, Remote Sensing, doi:10.3390/rs11202347.
- Zheng, W. Y., et al. (2019), Wastewater leakage in West Texas revealed by satellite radar imagery and numerical modeling, Scientific Reports, 9, 14601, doi:10.1038/s41598-019-51138-4.
- Molan, E. Y., et al. (2018), L-band temporal coherence assessment and modeling over Interior Alaska, Remote Sensing, 10, 150, doi:10.3390/rs10010150.
- Molan, Y. E., et al. (2018), Modeling Wildfire-Induced Permafrost Deformation in an Alaskan Boreal Forest Using InSAR Observations, Remote Sensing, 10, doi:10.3390/rs10030405.
- Zhang, A., J. Lu, and J. W. Kim (2017), Detecting mining-induced ground deformation and associated hazards using spaceborne InSAR techniques, Geomatics Natural Hazards and Risk, doi:10.1080/19475705.2017.1415229.
- Schaefer, L. N., et al. (2016), Three-dimensional displacements of a large volcano flank movement during the May 2010 eruptions at Pacaya Volcano, Guatemala, Geophys. Res. Lett., 44, doi:10.1002/2016GL071402.