Abstract
Abstract. Detecting temporal changes is one of the most important applications of satellite sensors. In recent years, the increasing availability of regular time-series of SAR imagery, provided by the Sentinel-1 mission of the European Space Agency (ESA), has drawn increasing attention to these techniques, especially in earth environment monitoring and risk management. Within this paper, a coherent change detection analysis for evaluating the risk due to movements of dunes and sand sheets in desertic areas is proposed. To this purpose, we introduce a novel, coherence-based index, named Temporal Stability Index (TSI), that is suited for characterizing the percentage of stability of a target with time. TSI maps can be generated over areas as wide as hundreds of kilometers, in a short time, and mostly by exploiting available software tools (plus some simple coding). The information provided is complementary to the average of the short-term coherence, here shown. Results of analysis performed on two desertic regions (the United Arab Emirates and Egypt) document the usefulness of TSI for the identification of dune movements and areas subject to sand accumulation, supporting risk mitigation measures.
Highlights
We introduce a novel, coherence-based index, named Temporal Stability Index (TSI), that is suited for characterizing the percentage of stability of a target with time
Change detection represents one of the most relevant applications of remote sensing and it is applied to a broad range of environmental monitoring applications, e.g. change detection over time of forests, water, wetland, and ice (Rignot, van Zyl, 1993; Engeset et al, 2002; Preiss et al, 2006; Dabboor et al, 2015; Durieux et al, 2019), or damage assessment caused by flooding, volcanic ash, earthquake, etc. (Tamura, 2015; Jung et al, 2016; Jung et al, 2017; Monti-Guarnieri et al, 2018)
We propose a novel, coherence-based index, named Temporal Stability Index (TSI), for the evaluation in time and space of SAR-derived scene stability
Summary
(Tamura, 2015; Jung et al, 2016; Jung et al, 2017; Monti-Guarnieri et al, 2018). Within this context, SAR (Synthetic Aperture Radar) observations have a privileged role since, compared to optical observations, they are not affected by time or weather conditions, such as different sunlight illumination and/or cloud cover. The detection of changes based on SAR observations involves the comparison of a pair of coregistered images acquired over the same area at two different times. Incoherent change detection techniques are based on the mean backscatter intensity, including polarimetric observations; this makes them comparable to optical images methods. Incoherent techniques sense changes mainly due to humidity and surface roughness due to vegetation, wind over water, man-made structures
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