Mapping dune dynamics by InSAR coherence

Abstract

It is generally held that subtle changes in sandy environments are very difficult to detect in imagery. Nonetheless, this study demonstrates how synthetic aperture radar (SAR) interferometric decorrelation can be used to identify changes in individual sand dunes. The use of coherence maps over time facilitates the analysis of dune dynamics, both temporally and spatially. The Ashdod‐Nizzanim coastal dunes, along the southern coastal plain of Israel, were chosen as an illustrative example of the analysis of dune dynamics. High‐resolution TerraSAR‐X (TSX) radar images covering the entire research area were acquired for the period February to July 2012, together with meteorology data (wind and rain) for the area. The coherence results enabled the stability of individual dunes to be described as a function of time. It was found that the dune crests were more stable than the windward slopes and that the degree of stability was dependent on the distance of the dune from the sea. The results of this study show the potential of using interferometric synthetic aperture radar (InSAR) decorrelation for aeolian studies, even in areas characterized by low coherence. Copyright © 2017 John Wiley & Sons, Ltd.

Combining TerraSAR-X and Landsat Images for Emergency Response in Urban Environments

“…Observations of the X-band are the most strongly affected [30], as that wavelength penetrates the upper part of the vegetation cover, such as the canopy and branches [30], and causes volumetric scattering. Furthermore, even slight, wind-generated movement of vegetation causes a loss of coherence, as shown previously by [27].…”

“…The SAR images were co-registered, according to the 20 October 2010 image, which was selected as the reference image ('master') for both periods (before and after the event). The error RMS was up to 0.1 pixel, and an eight-point truncated sinc kernel was used for the interpolation [26,27]. Lastly, the Landsat5 image was georeferenced and resampled (to the TSX spatial resolution) according to the reference image.…”

“…Lower NDVI thresholds overestimated the vegetated area by displaying some of the built areas as vegetation. A threshold value of 0.5 for the co-event coherence map was determined according to [27], which showed it to be the lowest coherence value in an urban area for the same time interval (144 days) depicted between two images. By dividing the results into two classes, a binary map was generated in which ≤0.5 was used for pixels that experienced change and >0.5 was used for those that experienced slight or no change.…”

“…In regions that have experienced major natural disasters, data collected during the period prior to the event contributes valuable information toward understanding the pre-event nature of the land cover. The inland regions of interest that display low coherence are suspected to have exceptionally high vegetation cover, the presence of which markedly influences the results, causing an apparent loss of coherence [27][28][29]. Observations of the X-band are the most strongly affected [30], as that wavelength penetrates the upper part of the vegetation cover, such as the canopy and branches [30], and causes volumetric scattering.…”

Using InSAR Coherence for Investigating the Interplay of Fluvial and Aeolian Features in Arid Lands: Implications for Groundwater Potential in Egypt

“…The coherence products of the InSAR have been recently applied to the investigation of the fluvial and aeolian morphodynamics as this method is one of the most sensitive and powerful for mapping the surface changes [36][37][38][39][40][41]. Ullman et al [40] investigated the use of coherence change detection (CCD) for the identification of surface disturbances, mass movements, and fluvial channel activity around the Damghan Playa in Iran, while Havivi et al [41] have used the CCD method to analyze the dune dynamics along the southern coastal plain of Israel in both spatial and temporal domains. The CCD technique is based on the radar intensity and phase differences between two temporally different SAR images [42][43][44][45][46][47].…”