Rapid, reliable, and continuous information is an essential component in disaster monitoring and management. Remote sensing data could be a solution, but often cannot provide continuous data due to an absence of global coverage and weather and daylight dependency. To overcome these challenges, this study makes use of weather and day/light independent Sentinel-1 data with a global coverage to monitor localized effects of different types of disasters using the Coherence Change-Detection (CCD) technique. Coherence maps were generated from Synthetic Aperture Radar (SAR) images and used to classify areas of change and no change in six study areas. These sites are located in Syria, Puerto Rico, California, and Iran. The study areas were divided into street blocks, and the standard deviation was calculated for the coherence images for each street block over entire image stacks. The study areas were classified by land-use type to reveal the spatial variation in coherence loss after a disaster. While temporal decorrelation exhibits a general loss in coherence over time, disaster occurrence, however, indicates a significant loss in coherence after an event. The variations of each street block from the average coherence for the entire image stack, as measured by a high standard deviation after a particular disaster, is an indication of disaster induced building damage.

Constantine city, Algeria, and its surroundings have always been affected by natural and human-induced slope instability and subsidence. Neogene clay-conglomeratic formations, which form the largest part of Constantine city, are extremely sensitive to the presence of water, which makes them susceptible to landslides. Fast and accurate identification and monitoring of the main areas facing existing or potential hazardous risks at a regional scale, as well as measuring the amount of displacement is essential for the conservation and sustainable development of Constantine. In the last three decades, the application of radar interferometry techniques for the measurement of millimeter-level terrain motions has become one of the most powerful tools for ground deformation monitoring due to its large coverage and low costs. Persistent scatterer interferometry (PS-InSAR) has a demonstrated potential for monitoring a range of hazard event scenarios and tracking their spatiotemporal evolution. We demonstrate the efficiency of Sentinel-1 data for deformation monitoring in Constantine located in the northeast of Algeria, and how an array of information such as geological maps and ground-measurements are integrated for deformation mapping. We conclude this article with a discussion of the potential of advanced differential radar interferometry approaches and their applicability for structural and ground deformation monitoring, including the advantages and challenges of these approaches in the north of Algeria.

Abstract. Arsenic contamination of groundwater resources threatens the health of millions of people worldwide, particularly in the densely populated river deltas of Southeast Asia. Arsenic causes health concerns due to its significant toxicity and worldwide presence in portable water. The major sources of arsenic pollution may be natural process such as dissolution of arsenic containing minerals and anthropogenic activities. Lahore is groundwater dependent city, arsenic contamination is a major issue of portable water and has recently been most environmental health management issue especially in the plain region, where population density is very high. GIS was used in this study for visualizing distribution of arsenic groundwater concentration through geostatistics analysis technique, and exposure risk zones for two years (2010 and 2012). Town's data was compared and concentration variation evaluated. ANOVA test was also applied to compare concentration between cities and years. Arsenic concentrations widely range 7.3–67.8 and 5.2–69.3 μg L−1 in 2010 and 2012, respectively. Over 71% area is represented arsenic concentration range from 20 to 30 μg L−1 in both analyzed years. However, in 2012 arsenic concentration over 40 μg L−1 has covered 7.6% area of Data Gunjbuksh and 8.1% of Ravi Town, while over 90% area of Allama Iqbal, Aziz Bhatti and Samanabad Town contain arsenic concentration between 20–30 μg L−1. ANOVA test depicts concentration probability less than 0.05, while differences were detected among towns. In light of current results, it needs urgent step to ensure groundwater protection and preservation for future.