Abstract
Dunes and sand sheets motion natural hazard affect many desertic areas worldwide and require careful assessment to develop effective mitigation plans to protect populated sites, infrastructure, and human activities. The study explores the suitability of Synthetic Aperture Radar (SAR) coherent methods to detect desert area instabilities and estimate sand accumulations displacements. The SAR methods have been applied to long time series of images provided by Sentinel-1. Moreover, the research introduces a novel robust index, named Temporal Stability Index, able to characterize the percentage of stability of a target with time. The work reports the experiments performed on the United Arab Emirates (UAE) and Egypt desertic areas and proves the usefulness of SAR coherent methods to support sand mitigation measures.
Highlights
Multitemporal InSAR CoherenceIn several desert areas, instabilities due to dunes and sand sheets movements represent a significant threat to transportation, urban areas, and human activities [1,2,3]
We present the Mean Short-Term Coherence (MSTC) and Temporal Stability Index (TSI) indexes obtained from the stacks of preprocessed images (Section 2.2.1)
The 64 coherence images for the United Arab Emirates (UAE) were generated by applying a spatial estimation window of 120 m in range and 170 m in azimuth and geocoded into a 20 m × 20 m geographical grid
Summary
Instabilities due to dunes and sand sheets movements represent a significant threat to transportation, urban areas, and human activities [1,2,3]. Proper planning and implementation of risk mitigation measures require an in-depth knowledge of dunes structure and information about migration rates. Among the existing methods for detecting and tracking dunes movements, wide-coverage satellite images have increasingly become valuable resources that extend investigations, previously based on local-scale surveys, to larger areas [4]. Most of the works in the literature which investigate dunes dynamic evolution are performed on optical satellite data. Beneficial information is derived from multi-temporal optical images through classic visual interpretation and analysis [5,6] or more sophisticated techniques based on imagery cross-correlation [7,8,9]
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