# 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
### Compilation of sinkhole Inventories
In an investigation in Spain, all but one of the sinkholes found in a comprehensive inventory could be detected using aerial imagery. It was concluded that historical imagery can be extremely useful to  
Figure 6: (**A**) The process of ERT data collection; and (**B**) Sample ERT profile indicating the presence of anomalies including a potential cavity and know surface cavings (with permission from [59]). detect sinkholes in areas where anthropogenic landscape transformation would later erase surface expression of their presence [22]. The construction of sinkhole inventories using remote sensing data is frequently difficult due to complications in identifying sinkhole-related geometric features if high resolution data at high temporal frequency is unavailable. Specifically, in several cases, erosion, sedimentation and anthropogenic fills may also mask sinkhole-related geometric features soon after they have formed. Such transformation is a big challenge for detecting sinkholes using visual clues only [22; 31]. To overcome the limitation of relying only on visible geomorphic expressions, recent experiments have focused on using infrared data to identify temperature differentials between sinkhole-related features and the surrounding landscape. Using drone-based infra-red sensing, this technique relies on sinkhole depressions being colder at night than the surrounding environment and thereby providing a good dataset for machine learning classifiers [68]. Extensive validation and verification of these approaches remains to be performed. The use of terrain models to extract the characteristic geomorphic expressions associated with sinkholes has also been investigated. Terrain models can be generated from many sources with the simplest being topographic maps generated by surveying techniques. Remote sensing techniques to generate terrain models include terrestrial airborne laser scanning [45; 62; 69], stereo-photogrammetry using optical imagery [70], drone-based Structure from Motion (SfM) [71] and SAR interferometry [72; 73] among others. Global elevation models such as the SRTM and ASTER GDEM, generated through spaceborne SAR interferometry or stereo-photogrammetry respectively, are freely available. However, their use for sinkhole inventories is limited due to their spatial resolution (\(\sim\)30 m) and absolute vertical height accuracy (\(>\)16 m) [74] being lower than typical sinkhole feature dimensions. Sinkhole inventories are therefore typically built using airborne platforms. It should be noted that the higher spatial resolution (12 m) and absolute vertical accuracy (6 m) of the recently released WorldDEM [75] may be suitable for the compilation of sinkhole inventories, especially for large sinkhole events, for example in the cases where widths exceeding 100 m and depths exceeding 30 m have been reported [4; 76]. Studies have found that high quality topographic maps generated using stereogrammetry on orthophotos can be used to detect and delineate surface depressions likely to be related to sinkholes [22]. However, it was found that the technique relies on sinkhole being large enough to map using these depending on the resolution of the data. The limitation of airborne surveys is that the acquisition of high resolution data over extended areas at high temporal frequency is costly. To overcome this limitation UAV-based image acquisition has evolved to be a simple and cost effective alternative. This data can be used in combination with a three-dimensional image stitching technique called Structure from Motion (SfM), similar in concept to stereogrammetry, to extract terrain models [71]. SfM allows for quick and cost effective generation of 3D point clouds over small areas at a fraction of the cost of airborne surveys [63]. In general, SfM models provide vertical accuracy in the cm to metre scale, with reported RMSE's ranging between 0.1 m and 1 m [71]. These accuracies have been found to be accurate enough to map surface topography and to detect all but the smallest sinkhole depressions [71].