An Improved R-Index Model for Terrain Visibility Analysis for Landslide Monitoring with InSAR

Abstract

The interferometric synthetic aperture radar (InSAR) technique is widely adopted for detecting and monitoring landslides, but its effectiveness is often degraded in mountainous terrains, due to geometric distortions in the synthetic aperture radar (SAR) image input. To evaluate the terrain effect on the applicability of InSAR in landslide monitoring, a variety of visibility evaluation models have been developed, among which the R-index models are quite popular. In consideration of the poor performance of the existing R-index models in the passive layover region, this study presents an improved R-index model, in which a coefficient for improving the visibility evaluation in the far passive layover regions is incorporated. To demonstrate the applicability of the improved R-index model, the terrain visibility of SAR images in Fengjie, a county in the Three Gorges Reservoirs region, China, is studied. The effectiveness of the improved R-index model is demonstrated through comparing the visibility evaluation results with those obtained from the existing R-index models and P-NG method. Further, the effects of the line-of-sight (LOS) parameters of SAR images and the resolution of the digital elevation model (DEM) on the terrain visibility are discussed.

An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

“…In reference to Figure 1, the proposed interpretation approach of ascending-descending SAR data for landslide recognition mainly consists of the following three components. First, the SAR visibility analysis is conducted based on a recently improved R-index model [25], through which the areas with good SAR visibility are identified and a large number of MPs in the areas with good SAR visibility could be obtained from the TS-InSAR analysis. Second, a conversion analysis of the obtained deformation rates is undertaken, through which the ground surface deformation rates of the MPs along the LOS direction of the satellite radar are converted into those along the steepest slope such that the deformation of the concerned slope could be better characterized.…”

“…The other two types of geometric distortion regions in SAR images are the areas with poor terrain visibility, which cannot be monitored by the TS-InSAR technique. Among the various terrain visibility analysis methods, a recently improved R-index model by the authors [25], which is calculated from the orientation parameters of the satellite LOS (in terms of incidence angle and azimuth) and the terrain parameters (in terms of slope and aspect), can be more effective in detecting the layover regions in SAR images. Thus, the recently improved R-index model is adopted in this approach for identifying the areas with good SAR visibility and extracting the related MPs in TS-InSAR analysis results.…”

“…Thus, the recently improved R-index model is adopted in this approach for identifying the areas with good SAR visibility and extracting the related MPs in TS-InSAR analysis results. According to Ren et al [25] the improved R-index, denoted as R-index (im) , is formulated as follows.…”

“…where θ is the incidence angle of the satellite LOS; ϕ is the azimuth angle of the satellite LOS; α is the slope of the terrain; β is the aspect of the terrain; Sh is the shadow coefficient, the value of which is 0 in the shadow region while that in the other region is 1.0; La is the layover coefficient, the value of which is 0 in the active layover and near passive regions while that in the other region is 1.0; and Fa is the far passive layover coefficient, the value of which is 0 in the active layover and passive layover regions while that in the other region is 1.0. The shadow coefficient (Sh), layover coefficient (La), and far passive layover coefficient (Fa) can be calculated using the hillshade model in ArcGIS, and more information about the three coefficients can be found in Notti et al [30] and Ren et al [25]. Note that the calculated value of the improved R-index, R-index (im) , is between 0 and 1.0, based on which the visibility of an area can be evaluated: (1) if R-index (im) is greater than or equal to sin(θ) (i.e., R-index (im) ≥ sin(θ)), the related area is an area with good visibility;…”

“…Despite the wide application of the InSAR technology to landslide identification, there exist some challenges that may degrade its effectiveness in mountainous regions [10,23]. For example, the terrain could cause geometric distortions (e.g., foreshortening, layover, and shadow) of SAR images [24,25], due to the side-view imaging mode of satellite radar. The distribution of geometric distortion regions in the SAR images depends upon the local terrain characteristics and the acquisition direction of the satellite radar.…”