# The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment
## 4 The role of Earth Observation in Sinkhole Hazard Assessment
### Detecting Precursors to Sinkhole Development
& Incidence Angle: 61\({}^{\circ}\) & descending passes & & \\ \hline  & Event: Structural damage & SAR: EIS/2, Envisat, & Diameter: 20–40 m & \\ Heerlen, & Geology: Old comel mining & Radar-2 & Technique: PSI & Magnitude: 0.08 m & \\ Netherlands, 1992–2011, [34] & cavities Delomite & Band: C & Surface model: LiDAR & Rate: 0.03-0.015 m/year & Diameter: 8 m \\  & Trigger: Cavity migration & Scenes: 160 & Projection: Vertical & Behavioral: Non-linear, & \\  & & & & & periodically increasing \\ \hline  & Event: Subslidence surround 2 & SAR: ERS 1/2, & & Diameter: 9–30 m & \\ Texas, USA, 1992–1998, & Large existing Sinkholes & COSMO-SkyMed & Technique: PSI + SqueeSAR & Magnitude: 0.03-0.040 m & Diameter: 100 m \\
2011–2012, [21] & Trigger: self dissolution and water abstraction & Senes: 54 & & Rate: 0.001 m/year & Depth: 34 m \\ \hline  & Events: Sinkholes and & SAR: ERS-1/2 & & & \\ Ebro Valley, & subsidence Geology: & Band: C & Technique: SSAS & Diameter: 1–100 & \\ Spain, & Evaporite & Resolution:90 m & Surface model: SRTM 90 m & Magnitude: 0.024m & m \\
1995–2000, [7] & Trigger: water ingress and abstraction & Scenes: 27 & & Rate: 0.017 m/year & Depth: 2 m \\ \hline Virginia, USA, & Event: deformation related to & SAR: Cosmo-Skymed & & \\
2011–2016, [105, 112] & Geology: dolemite and & Band: X & Technique: PSI, SqueeSAR & Rate: 0.003-0.002 m/year & N/R \\  & & & & & \\  & & & &The successful detections of sinkhole precursors were achieved using ground-based, airborne as well as spaceborne SAR sensors. In the case of the ground-based InSAR (GB-InSAR) system, approximately 3 mm of deformation could be detected using data acquisitions three months apart, leading to a road closure before the sinkhole formed, mitigating further damages [26]. Importantly, it was found that the deformation basin was approximately three times larger (10 m) than the actual sinkhole event (2.5 m). This is an important consideration since it points to small-scale sinkholes being detectable by precursory basins larger than they are. In this case, the successful detection was also attributed to the presence of a tar road surface that is believed to undergo plastic deformation prior to failure [26]. Although the successful detection of precursory deformation in this instance highlights the potential of GB-InSAR for sinkhole early warning, the investigation also observed some instances of sinkhole development for which no precursory deformation could be detected [26]. The sinkhole in question was 1.5 m in diameter and occurred on the side of a road. Retrospective analysis of interferograms and displacement maps revealed no signs of precursory deformation despite the 1 \(\times\) 1 m resolution provided by the system. In this case, the cause for the non-detection of precursory deformation was attributed to a combination of factors including the small dimensions of the sinkhole as well as the presence of vegetation reducing the reflectivity and visibility of the feature [26]. The short time-period between the detection of precursors and the collapse has also been identified as potentially problematic [26]. Although the investigation proves the potential for GB-InSAR systems for detecting sinkhole precursors, the systems are limited in extent and not frequently deployed, limiting its large scale ability for operational sinkhole precursor detection. The use of airborne SAR data has also been used to demonstrate the ability to detect sinkhole-induced surface deformation. Most notably, one case study proved the ability to detect precursory deformation in a challenging environment using NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data. In this investigation, primarily horizontal movement of 260 mm was detected in forested marsh conditions months before a sinkhole formed [5]. The success of repeat-pass DInSAR in this terrain was attributed to the high resolution provided by UAVSAR, the long wavelength (L-band) of the sensor as well as the presence of tree stems at the site.