# The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment
## 6 Concluding Remarks  
Due to the consequences of sinkhole development, there is a need for the quantitative determination of sinkhole probability, hazard, risk and vulnerability in sinkhole-prone regions worldwide [7; 112]. Sinkhole hazard assessments specifically aim to predict the spatial and temporal distribution of future sinkholes [37]. This probability of occurrence is usually informed by the analysis of past events, recorded in a sinkhole inventory, and the local conditions affecting sinkhole formation and collapse. The accuracy of the inventory on which sinkhole hazard assessments are based governs the accuracy of the derived hazard maps. Previous and ongoing research show that remote sensing is actively being used to improve and accelerate the process of sinkhole inventory compilation. However, there is a need to standardise the recordings of specific event datasets after every sinkhole occurrence. The information should include event occurrence date, morphometric properties (size, depth), land surface conditions and triggering factors. This information is critical for area-specific sinkhole hazard assessments and the quantitative assessment of sinkhole risk.  
Furthermore, recent research is providing evidence that detecting precursory deformation prior to sinkhole formation could contribute to sinkhole hazard assessment by providing frequent ground stability reports for sinkhole early warnings. There has been very little focus on the geotechnical evidence of precursors, despite their value as early warning signs [24; 35]. The consequent lack of knowledge regarding the physical nature of sinkhole precursors prevents the generation of an accurate model of their spatial and temporal evolution [21; 125]. Prior knowledge about precursory deformation that can be expected under local conditions, particularly the rate, magnitude and scale, would be valuable for positively identifying precursors in DInSAR data sets. This information will be especially valuable for time series processing techniques which often require assumptions of deformation behaviour [126]. Despite this knowledge gap, the work on InSAR data for mapping precursory deformation prior to sinkhole development shows that DInSAR is a valuable tool in improving our understanding of sinkhole precursors. The work further suggests that DInSAR data collected operationally could, in cases, identify sinkhole development before surface collapse, thereby decreasing the risk to infrastructure and human lives and reduce the cost of maintenance by enabling proactive remedial work [5; 6; 20; 34; 105].  
J.E. conceived the paper. A.T. and J.E. wrote the paper. This research was funded by the CSIR Meraka Institute project number KC8 ISLI. The authors would like to thank the editors and the 4 anonymous reviewers for their valuable feedback and constructive comments. We also thank Michael van Schoor from the CSIR for providing the GPRand ERT sample datasets. The research was supported by the Council for Scientific and Industrial Research (CSIR), Meraka Institute.  
The authors declare no conflict of interest.