Abstract
InSAR (Interferometric Synthetic Aperture Radar) cloud computing and the subtraction of LiDAR (Light Detection and Ranging) DEMs (Digital Elevation Models) are innovative approaches to detect subsidence in karst areas. InSAR cloud computing allows for analyzing C-band Envisat and Sentinel S1 SAR images through web platforms to produce displacement maps of the Earth’s surface in an easy manner. The subtraction of serial LiDAR DEMs results in the same product but with a different level of accuracy and precision than InSAR maps. Here, we analyze the capability of these products to detect active sinkholes in the mantled evaporite karst of the Ebro Valley (NE Spain). We found that the capability of the displacement maps produced with open access, high-resolution airborne LiDAR DEMs was up to four times higher than InSAR displacement maps generated by the Geohazard Exploitation Platform (GEP). Differential LiDAR maps provide accurate information about the location, active sectors, maximum subsidence rate and growing trend of the most rapid and damaging sinkholes. Unfortunately, artifacts and the subsidence detection limit established at −4 cm/yr entailed important limitations in the precise mapping of the sinkhole edges and the detection of slow-moving sinkholes and small collapses. Although InSAR maps provided by GEP show a worse performance when identifying active sinkholes, in some cases they can serve as a complementary technique to overcome LiDAR limitations in urban areas.
Highlights
The monitoring of ground deformation processes using remote sensing and image processing techniques has experienced an exponential increase in the last decade
The criteria to consider a sinkhole as an active subsidence depression rather than an artifact in the Differencing of digital elevation model (DEM) (DoD) mainly depend on the genetic type of the sinkhole
The FASTVEL maps provided the best detection rate around Zaragoza city (30% of all the active sinkholes) and was the only InSAR approach able to capture displacement within some of the most damaging sinkholes of the analyzed area. These results indicate that the FASTVEL approach yielded the best performance among the Geohazard Exploitation Platform (GEP) web-based InSAR tools, but it was still low compared with conventional sinkhole mapping and differential LiDAR
Summary
The monitoring of ground deformation processes using remote sensing and image processing techniques has experienced an exponential increase in the last decade. An important advantage of this remote-sensing technique includes the possibility of filtering the vegetation to produce accurate and high-resolution bare-ground digital elevation models (DEMs) [6,7]. These models can be used to: (a) generate 3D representations of the terrain (e.g., hillshades, red relief image maps) that facilitate the delineation of the sinkholes [8], (b) extract automatically morphometric parameters [9], and (c) apply automated routines for the extraction of closed depressions [10]. Recent processing algorithms and filters favor the automated detection and morphometric characterization of sinkholes, significantly improving the speed and the efficiency of the sinkhole mapping process using
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
