# The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment
## 5 Discussion and Perspectives--Towards Operational Sinkhole Early Warning
\begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline \multicolumn{1}{c}{In situ surveying and} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & Measurements are point-based and limited in spatial extent: & Identify high risk areas by use of remote sensing techniques to direct field measurement campaigns \\ \hline High resolution optical and \(\$\$\)MM & Precursor detection through digital elevation model & Cost effective monitoring at high revisit frequencies, derivation of surface models to map sinkhole dimensions and detection of larger scale movement & Surface model differencing prone to error propagation, vertical accuracy not sufficient for monitoring small-scale precursors & Monitoring areas of rapid deformation only \\ \hline LiDAR & Precursor detection through digital elevation model & High resolution surface models can be generated, derivation of surface models to map sinkhole dimensions and detection of larger scale movement & Surface model differencing prone to error propagation, high accuracy requires instrument close to ground, limiting aerial coverage; vertical accuracy not sufficient for monitoring small-scale precursors & Monitoring areas of rapid deformation only \\ \hline \hline \multirow{4}{*}{SAR Interferometry} & Precursor detection through digital elevation model & High resolution surface models can be generated, derivation of surface models to map sinkhole dimensions and detection of larger scale movement & Surface model differencing prone to error propagation, high accuracy requires instrument close to ground, limiting aerial coverage; vertical accuracy not sufficient for monitoring small-scale precursors & Monitoring areas of rapid deformation only \\ \hline \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Sensitive to very small-scale surface movements; early detection of precursors prior to collapse; High revisit frequency, in cases, implying operational monitoring of high risk areas. & Signal decorrelation in rural and vegetated areas; Atmospheric artefacts; Lack of coherent targets in stacking approaches; Very fast or slow movements cause a degradation. & High spatial and temporal resolution is desirable \\ \cline{1-1} \cline{3-3}  & \multirow{2}{*}{Precursor detection} & Ability to analyse impacts of specific events on deformation rates; Early warning potential; low cost per measurement area & Water accumulation in depressions cause decorrelation; Long revisit time; Long revisit time; Potential blind-spots caused by Surface features in the sensor line-of-sight \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._Remote sensing techniques used to build sinkhole inventories are typically based on either image analysis or topographic model generation. Image analysis is often used successfully but the quality and resolution of the data are important considerations [33]. Aerial imagery is therefore often used, particularly since historical imagery is often available allowing the dating of sinkholes that occurred up to decades ago in some cases [22]. LiDAR is the most accurate remote sensing tool for generating topographic models, and is therefore widely used to detect sinkhole features [45; 62]. The main limitation associated with LiDAR for geomorphological analysis is the cost of acquisitions as well as the time required for the acquisition and processing of the data for large areas [123]. The use of SfM algorithms to derive topographic models using UAVs as data acquisition platforms are low cost alternatives. However, their small footprints limits their use to site specific investigations and their use would be limited for regional or large scale investigations. To overcome this limitation, the recently released high-resolution WorldDEM DEM should be considered [75].