# The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment
## 5 Discussion and Perspectives--Towards Operational Sinkhole Early Warning
A fundamental limitation to remote sensing approaches for compiling sinkhole inventories is that the geometric signature of sinkholes may be masked by the transformation of the terrain due to erosion, sedimentation or anthropogenic fills or construction [22; 31]. In such cases geophysical investigations, interviews with inhabitants or historical reports is required to complete sinkhole inventories [22]. It is furthermore recognized that classifying sinkhole features (on imagery or elevation models) is typically done manually, and is an intensive process with accuracy and reproducibility limitations [63; 65; 71; 77]. There is therefore a need to investigate automated extraction techniques, with one of the promising growing fields of research being artificial neural networks [78]. The detection of precursors to sinkhole development mainly relies on the use of SAR interferometry approaches. However, the success of these approaches are dependent on both SAR system and processing parameters as well as surface characteristics [20]. From a SAR system and processing perspective, the resolution of the SAR system is particularly important due to the small-scale of sinkhole events compared to other geological hazards [76] making deformation signals indistinguishable from system noise [20; 76]. For this reason, most successful studies on precursor detection have focused on sinkholes in evaporite terrain which tend to be larger in scale than sinkholes in dolomitic terrain [5; 6]. Although deformation of single point targets can be measured using interferogram stacking approaches, the absence of coherent targets in vegetated regions may be problematic [16; 20; 40; 107; 120; 121; 124]. However, in cases it has been argued that the rural and vegetated areas are generally associated with limited human population and infrastructure and therefore is not deemed to be a priority for analysis [105]. Despite the relative success achieved in the monitoring of sinkhole-related deformation using interferometric techniques, a large portion of known active sinkholes and subsidence features remained unobserved. The non-detections and main limitations of InSAR approaches were attributed to factors including: (1) the resolution of the sensor being too low relative to the size of the feature [20; 97], (2) subsidence rates were either too high or too low for the detection by the sensor employed [20; 97], (3) water accumulation in shallow depressions inducing soil moisture changes leading to a loss of coherence [97], (4) decorrelation in vegetated areas, including irrigated agriculture [16; 97] and 5) the revisit time of some satellites have been identified as potentially problematic depending on the time-interval between the expression of the precursor and the ultimate collapse [16; 26]. The considerations of the geophysical characteristics of the sinkhole and the land surface places limitations due to the nature of the precursors themselves. It was noted that, in some instances, precursors may not be present at all [4; 20]. This may be due to competent roof strata or landcover that is resistant to gradual deformation and prone to sudden failure. In fact, it has been proposed that the surface above a void can be stable for a considerable amount of time since it can be supported by near-surface competent materials such as ferric, or man-made materials (concrete, asphalt etc.) [8]. In these cases, the presence of such materials may affect the potential for precursory deformation to be expressed [20]. However, in another case, it was thought that a tar road acted like a plastic surface that promoted the detection of deformation in the order of a couple of cm prior to failure [26]. These examples highlight the effect of surface cover on the ability to detect sinkhole-induced surface deformation. The land surface conditions also place considerations on the SAR line-of-sight (LOS).