# 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
Rapid advances are being made in commercial UAV technology and processing algorithms and SfM is expected to become a valuable tool for rapid surface model generation at a low cost [63]. Airborne laser scanning is also commonly used for the extraction of accurate terrain models and several investigations used these models for the mapping of sinkhole-related geomorphological features [33; 45; 62; 64]. Airborne laser scanning has been used for the detection of geomorphic features associated with land degradation and sinkhole events in the Dead Sea region [64]. Data analysis focused on identifying areas characterised by a sharp drop in the surface topography in addition to features of circular to oval shapes. Furthermore, sinkholes in the area were associated with conical collapse structures and concentric cracks outside of the edges. The laser scanning data was noted for its ability to detect the cracks at an early stage, pointing to the potential of laser scanning data being used for early warning of impending sinkhole formation [64]. However, challenges in the use of airborne laser scanning for the compilation of sinkhole inventories have also been identified [33]. The problems include: (1) the under-estimation of the area of the depressions depending on the resolution of the data and the size and depth of the depression, (2) the presence of vegetation that can mask the sinkhole boundaries leading to underestimation of sinkhole extents depending on the vegetation density, (3) in urban areas the presence of buildings and structures mask the presence of depressions, and (4) the inability to distinguish between man-made depressions and sinkholes [33]. Irrespective of the choice of data, the visual inspections of imagery or terrain models in an effort to identify sinkholes are time consuming, labour intensive and highly dependent on operator expertise which may lead to incomplete, unreproducible or biased results [63; 65; 71; 77; 78]. Automated feature extraction algorithms can therefore play a critical role in sinkhole inventory collection. Various techniques, drawn from a wide range of fields, have been used for automated feature extraction from surface models. In general, pixel-based techniques have been found to be inappropriate for successful feature extraction and most research is focussed on object-, rule- or case-based analysis and artificial neural networks [77]. Terrain analysis-based techniques include sink-filling, whereby a depression-less model is subtracted from the original model to locate depressions, and active contour and image filtering approaches [65]. Spatial models, based on parameters estimated for the feature of interest, can also be used to find matching patterns to expected features in the data [21]. Transformations used in computer vision, such as the Hough transform with its ability to extract ellipses, is also very useful on contour data [79]. Other techniques drawing on graph theory are also useful, particularly for their ability to work on sparse graphs since surface models are often not complete continuous datasets due to noise and artefacts [65; 80]. Although the earth observation approaches can identify historical sinkhole events based on the topographic expression or visual clues, their use is limited in areas where natural processes (like erosion and sedimentation) or anthropogenic activities mask the normal topographic expression of these events [22; 31]. Furthermore, mapping sinkholes are very dependent on the specific local geology and land cover as all techniques are not equally effective in different areas. The process of sinkhole inventory creation in general have been described as arduous with complications due to (1) complications in identifying sinkhole morphology in flat areas, (2) the obliteration of sinkhole features due to anthropogenic fills, and (3) the non-disclosure of sinkhole events by land owners due to the fear of depreciation of the value of their property [39].