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.

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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.

The sentence "The obtained pre-event NDVI map is presented in Figure 2" should be "The obtained pre-event NDVI map is presented in Figure 4".2. The schematic of the procedure and the resulting combined map obtained after masking for the two thresholds (NDVI and coherence) are shown in Figure 5, and not in Figure 3.The sentence "A schematic of the procedure and the resulting combined map obtained after masking for the two thresholds are shown in Figure 3" should be "A schematic of the procedure and the resulting combined map obtained after masking for the two thresholds are shown in Figure 5".The sentence "The combined map (Figure 3) could, therefore, be divided into three major classes" should be "The combined map ( Figure 5) could, therefore, be divided into three major classes".We apologize for any inconvenience caused to the readers by these changes. The changes do not affect the scientific results. The original manuscript [1] will be updated and will remain online on the article webpage, with a reference to this Erratum. Reference 1.Havivi, S.; Schvartzman, I.; Maman, S.; Rotman, S.R.; Blumberg, D.G. Combining TerraSAR-X and Landsat Images for Emergency Response in Urban Environments.

<p>The damage caused by a natural disaster in rural areas differs in nature, extent, landscape and in structure, from the damage in urban environments. Previous and current studies focus mainly on mapping damaged structures in urban areas after catastrophe events such as an earthquake or tsunami. Yet, research focusing on the damage level or its distribution in rural areas is absent. In order to apply an emergency response and for effective disaster management, it is necessary to understand and characterize the nature of the damage in each different environment. </p><p>Havivi et al. (2018), published a damage assessment algorithm that makes use of SAR images combined with optical data, for rapid mapping and compiling a damage assessment map following a natural disaster. The affected areas are analyzed using interferometric SAR (InSAR) coherence. To overcome the loss of coherence caused by changes in vegetation, optical images are used to produce a mask by computing the Normalized Difference Vegetation Index (NDVI) and removing the vegetated area from the scene. Due to the differences in geomorphological settings and landuse\landcover between rural and urban settlements, the above algorithm is modified and adjusted by inserting the Modified Normalized Difference Water Index (MNDWI) to better suit rural environments and their unique response after a disaster. MNDWI is used for detection, identification and extraction of waterbodies (such as irrigation canals, streams, rivers, lakes, etc.), allowing their removal which causes lack of coherence at the post stage of the event. Furthermore, it is used as an indicator for highlighting prone regions that might be severely affected pre disaster event. Thresholds are determined for the co-event coherence map (≤ 0.5), the NDVI (≥ 0.4) and the MNDWI (≥ 0), and the three layers are combined into one. Based on the combined map, a damage assessment map is generated. </p><p>As a case study, this algorithm was applied to the areas affected by multi-hazard event, following the Sulawesi earthquake and subsequent tsunami in Palu, Indonesia, which occurred on September 28th, 2018. High-resolution COSMO-SkyMed images pre and post the event, alongside a Sentinel-2 image pre- event are used as inputs. The output damage assessment map provides a quantitative assessment and spatial distribution of the damage in both the rural and urban environments. The results highlight the applicability of the algorithm for a variety of disaster events and sensors. In addition, the results enhance the contribution of the water component to the analysis pre and post the event in rural areas. Thus, while in urban regions the spatial extent of the damage will occur in its proximity to the coastline or the fault, rural regions, even in significant distance will experience extensive damage due secondary hazards as liquefaction processes.     </p>

The damage caused by natural disasters in rural areas differs in nature extent, landscape, and structure, from the damage caused in urban environments. Previous and current studies have focused mainly on mapping damaged structures in urban areas after catastrophic events such as earthquakes or tsunamis. However, research focusing on the level of damage or its distribution in rural areas is lacking. This study presents a methodology for mapping, characterizing, and assessing the damage in rural environments following natural disasters, both in built-up and vegetation areas, by combining synthetic-aperture radar (SAR) and optical remote sensing data. As a case study, we applied the methodology to characterize the rural areas affected by the Sulawesi earthquake and the subsequent tsunami event in Indonesia that occurred on 28 September 2018. High-resolution COSMO-SkyMed images obtained pre- and post-event, alongside Sentinel-2 images, were used as inputs. This study’s results emphasize that remote sensing data from rural areas must be treated differently from that of urban areas following a disaster. Additionally, the analysis must include the surrounding features, not only the damaged structures. Furthermore, the results highlight the applicability of the methodology for a variety of disaster events, as well as multiple hazards, and can be adapted using a combination of different optical and SAR sensors.