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.
Rapid damage mapping following a disaster event, especially in an urban environment, is critical to ensure that the emergency response in the affected area is rapid and efficient. This work presents a new method for mapping damage assessment in urban environments. Based on combining SAR and optical data, the method is applicable as support during initial emergency planning and rescue operations. The study focuses on the urban areas affected by the Tohoku earthquake and subsequent tsunami event in Japan that occurred on 11 March 2011. High-resolution TerraSAR-X (TSX) images of before and after the event, and a Landsat 5 image before the event were acquired. The affected areas were analyzed with the SAR data using only one interferometric SAR (InSAR) coherence map. To increase the damage mapping accuracy, the normalized difference vegetation index (NDVI) was applied. The generated map, with a grid size of 50 m, provides a quantitative assessment of the nature and distribution of the damage. The damage mapping shows detailed information about the affected area, with high overall accuracy (89%), and high Kappa coefficient (82%) and, as expected, it shows total destruction along the coastline compared to the inland region.
Commission VIII, WG VIII/9KEY WORDS: Non-linear filter, Unsupervised Automatic Classification, Natural Hazards ABSTRACT:Among the different types of marine pollution, oil spill is a major threat to the sea ecosystems. Remote sensing is used in oil spill response. Synthetic Aperture Radar (SAR) is an active microwave sensor that operates under all weather conditions and provides information about the surface roughness and covers large areas at a high spatial resolution. SAR is widely used to identify and track pollutants in the sea, which may be due to a secondary effect of a large natural disaster or by a man-made one . The detection of oil spill in SAR imagery relies on the decrease of the backscattering from the sea surface, due to the increased viscosity, resulting in a dark formation that contrasts with the brightness of the surrounding area. Most of the use of SAR images for oil spill detection is done by visual interpretation. Trained interpreters scan the image, and mark areas of low backscatter and where shape is a-symmetrical. It is very difficult to apply this method for a wide area. In contrast to visual interpretation, automatic detection algorithms were suggested and are mainly based on scanning dark formations, extracting features, and applying big data analysis. We propose a new algorithm that applies a nonlinear spatial filter that detects dark formations and is not susceptible to noises, such as internal or speckle. The advantages of this algorithm are both in run time and the results retrieved. The algorithm was tested in genesimulations as well as on COSMO-SkyMed images, detecting the Deep Horizon oil spill in the Gulf of Mexico (occurred on 20/4/2010). The simulation results show that even in a noisy environment, oil spill is detected. Applying the algorithm to the Deep Horizon oil spill, the algorithm classified the oil spill better than focusing on dark formation algorithm. Furthermore, the results were validated by the National Oceanic and Atmospheric Administration (NOAA) data.
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