Skip to main content

Vertical Land Motion

Key research question: Can we generate land deformation maps that can account for vertical changes of geospatial infrastructure over time?

NOAA’s National Geodetic Survey includes the time-dependent nature of the Earth’s surface in its new, modernized national geospatial infrastructure, the United States National Spatial Reference System (NSRS). These services will have time-dependent (three-dimensional) coordinates using transformation services of several popular terrestrial reference frames, including mark motion velocity models. Similar to other modeling efforts at NOAA, the NGS land deformation models will provide:

  • Hindcast coordinate model, which is a function of the mark location from today’s coordinates back in time.
  • Forecast coordinate model, which will estimate the mark’s location into the future over common epochs (such as every five or ten years).

Developing these coordinate models requires spatial understanding of the Earth’s deformation mechanism. With lack of geodynamic models that can provide these accuracies, NGS has been using NOAA’s network of permanent stations, Continuously Operating Reference Stations, or CORS, to calculate precise and accurate positions. However, the coordinate functions can be only calculated accurately for the CORS locations. In a recent collaboration with industry partners, NGS has included radar space imagery, known as Interferometric Synthetic Aperture Radar (InSAR), that is used to generate points or maps of surface deformation using differences in the phase of the waves returning to a transmitting radar satellite.

Coverage mage of NOAA’s NGS deformation study with Fugro and TRE-Altamira using SAR imagery

In 2024, NGS has been working with Fugro and TRE-Altamira to create a vertical land motion time series at over 50–100 m resolution in some areas. Currently, this study has focused only on the US Gulf Coast and the US Atlantic Coast using approximately 160 processing areas with different imagery distribution and multiple local reference points. The products only provide discrete locations because of the InSAR sensitivity to land surface characteristics such as vegetation, snow, water, and agricultural activities.

It is important to note that NOAA’s National Geodetic Survey’s focus is on the high accuracy (mm/yr rates of change) control linking vertical land motion observations into the NSRS and the ability to monitor changes in deformation rate. The results of this work can support other efforts within the government that investigate the geological and geophysical processes for land deformation. The climate scale observations of the Earth’s surface can enhance projects, such as sea level rise monitoring.

Peer Review Publications and Conference Presentations

Ferretti, A., A. Fumagalli, F. Novali, C. Prati, F. Rocca, and A. Rucci. 2011. “A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR.” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 9, pp. 3460-3470, Sept. 2011, doi: 10.1109/TGRS.2011.2124465

Ferretti, A., G. Savio, R. Barzaghi, A. Borghi, S. Musazzi, F. Novali, C. Prati, and F. Rocca. 2007. “Submillimeter Accuracy of InSAR Time Series: Experimental Validation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1142-1153., DOI: 10.1109/TGRS.2007.894440

Frumkin, A., S. Pe'eri, and I. Zak, 2021. “Development of banded terrain in an active salt diapir: potential analog to Mars.” Geomorphology 389(3):107824. DOI: 10.1016/j.geomorph.2021.107824

Lagios, E., V. Sakkas, F. Novali, F. Bellotti, A. Ferretti, K. Vlachou, and V. Dietrich. 2013. “SqueeSAR™ and GPS ground deformation monitoring of Santorini Volcano (1992–2012): Tectonic implications,” Tectonophysics, Volume 594, Pages 38-59, http://dx.doi.org/10.1016/j.tecto.2013.03.012

Lattari, F., A. Rucci, and M. Matteucci 2022. “A Deep Learning Approach for Change Points Detection in InSAR Time Series," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022, Art no. 5223916, doi: 10.1109/TGRS.2022.3155969.

Murdaca, G., F. Ricciuti, A. Rucci, B. Le Saux, A., Fumagalli, and C. Prati. 2023. “A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery.” Remote Sensing, 15, 5664. https://doi.org/10.3390/rs15245664

Pe’eri, S., H. A.Zebker, Z. Ben-Avraham, A. Frumkin, and J.K. Hall. 2004. “Spatially-resolved uplift rate of the Mount Sedom (Dead Sea) salt diapir from InSAR observations,“ Israel Journal of Earth Sciences, 53, 99-106.

Perissin, D. and F. Rocca. 2006. “High-Accuracy Urban DEM Using Permanent Scatterers,” IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 11, pp. 3338-3347, Nov. 2006, doi: 10.1109/TGRS.2006.877754.

Tomás, R., J.I.Pagán, J.A. Navarro, M. Cano, J.L. Pastor, A. Riquelme, M. Cuevas-González, M. Crosetto, A. Barra, O. Monserrat, J.M. Lopez-Sanchez, A. Ramón, S. Ivorra, M. Del Soldato, L. Solari, S. Bianchini, F. Raspini, F. Novali, A. Ferretti, M. Costantini, F.Trillo, G. Herrera, and N. Casagli. 2019. “Semi-Automatic Identification and Pre-Screening of Geological–Geotechnical Deformational Processes Using Persistent Scatterer Interferometry Datasets,” Remote Sens. 1(14), 1675; https://doi.org/10.3390/rs11141675