Hazard monitoring from Space: Next generation InSAR time series analysis
Lead Research Organisation:
University of Leeds
Department Name: School of Earth and Environment
Abstract
Radar Interferometry (InSAR) is a technique that provides measurements of surface displacement from Space, potentially with millimetric accuracy. These measurements are used in the natural hazards community for earthquake analysis and monitoring of volcanoes and landslides, as well as for monitoring anthropogenic activities such as oil and gas extraction, and drawdown of underground water storage.
Figure 1. Interferogram spanning the first ~48 hr of volcanic unrest in March 2017 at Cerro Azul Volcano, Gala'pagos Islands, Ecuador (Bagnardi and Hooper, 2018) Each color fringe corresponds to 2.8 cm of displacement towards or away from the satellite. The area beneath Cerro Azul is subsiding as magma is withdrawnfrom beneath and injected beneath the area to the south, causing uplift there.
A single "interferogram" (Figure 1) provides a map of the surface displacement between two image acquisition dates, but also includes noise terms due to variable propagation delays through the atmosphere, changes of scattering properties of the surface, and data processing issues. Time series analysis techniques reduce these error terms to some extent by processing multiple interferograms together (Hooper et al, 2012). However, these techniques were designed to deal with sequences of 10's of acquisitions rather than the 100's of acquisitions that are possible with modern sensors, due to their short revisit times. In addition, hazard monitoring in close-to-real time requires rapid ingestion of new images without complete reanalysis of the time series. Progress in this direction has been made (Spaans and Hooper, 2016) but challenges still remain.
Other issues include: 1) Decorrelation events; InSAR only works when the ground scattering properties do not change significantly, so new construction and farming practices can lead to a complete loss of measurement for some areas. 2) Changes in the scattering properties of the ground due to changes in moisture content and vegetation (De Zan et al, 2015); this effect was previously assumed to average out over time, but it has been shown recently that this noise source can accumulate systematically when long times series are built from interferograms of shorter length.
In this project the student will work with leading scientists at Leeds, NASA and SatSense Ltd to develop a novel time series analysis algorithm that:
Ingests new acquisitions rapidly
Works at a variety of resolutions
Handles variation in soil moisture and vegetation
Handles decorrelation events
The student will apply the new method to selected natural hazards, e.g deforming volcanoes and landslides, and also to a case study of anthropogenic deformation supplied by SatSense Ltd.
Figure 1. Interferogram spanning the first ~48 hr of volcanic unrest in March 2017 at Cerro Azul Volcano, Gala'pagos Islands, Ecuador (Bagnardi and Hooper, 2018) Each color fringe corresponds to 2.8 cm of displacement towards or away from the satellite. The area beneath Cerro Azul is subsiding as magma is withdrawnfrom beneath and injected beneath the area to the south, causing uplift there.
A single "interferogram" (Figure 1) provides a map of the surface displacement between two image acquisition dates, but also includes noise terms due to variable propagation delays through the atmosphere, changes of scattering properties of the surface, and data processing issues. Time series analysis techniques reduce these error terms to some extent by processing multiple interferograms together (Hooper et al, 2012). However, these techniques were designed to deal with sequences of 10's of acquisitions rather than the 100's of acquisitions that are possible with modern sensors, due to their short revisit times. In addition, hazard monitoring in close-to-real time requires rapid ingestion of new images without complete reanalysis of the time series. Progress in this direction has been made (Spaans and Hooper, 2016) but challenges still remain.
Other issues include: 1) Decorrelation events; InSAR only works when the ground scattering properties do not change significantly, so new construction and farming practices can lead to a complete loss of measurement for some areas. 2) Changes in the scattering properties of the ground due to changes in moisture content and vegetation (De Zan et al, 2015); this effect was previously assumed to average out over time, but it has been shown recently that this noise source can accumulate systematically when long times series are built from interferograms of shorter length.
In this project the student will work with leading scientists at Leeds, NASA and SatSense Ltd to develop a novel time series analysis algorithm that:
Ingests new acquisitions rapidly
Works at a variety of resolutions
Handles variation in soil moisture and vegetation
Handles decorrelation events
The student will apply the new method to selected natural hazards, e.g deforming volcanoes and landslides, and also to a case study of anthropogenic deformation supplied by SatSense Ltd.
Organisations
People |
ORCID iD |
| Andrew Hooper (Primary Supervisor) | |
| Jacob Connolly (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| NE/T00939X/1 | 01/10/2020 | 30/09/2027 | |||
| 2443089 | Studentship | NE/T00939X/1 | 01/10/2020 | 30/06/2024 | Jacob Connolly |