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Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series
(2018)
The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6–12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth’s surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.
The effect of Indian Summer Monsoon rainfall on surface water delta D values in the central Himalaya
(2018)
Stable isotope proxy records, such as speleothems, plant-wax biomarker records, and ice cores, are suitable archives for the reconstruction of regional palaeohydrologic conditions. But the interpretation of these records in the tropics, especially in the Indian Summer Monsoon (ISM) domain, is difficult due to differing moisture and water sources: precipitation from the ISM and Winter Westerlies, as well as snow- and glacial meltwater. In this study, we use interannual differences in ISM strength (2011-2012) to understand the stable isotopic composition of surface water in the Arun River catchment in eastern Nepal. We sampled main stem and tributary water (n = 204) for stable hydrogen and oxygen isotope analysis in the postmonsoon phase of two subsequent years with significantly distinct ISM intensities. In addition to the 2011/2012 sampling campaigns, we collected a 12-month time series of main stem waters (2012/2013, n = 105) in order to better quantify seasonal effects on the variability of surface water delta O-18/delta D. Furthermore, remotely sensed satellite data of rainfall, snow cover, glacial coverage, and evapotranspiration was evaluated. The comparison of datasets from both years revealed that surface waters of the main stem Arun and its tributaries were D-enriched by similar to 15 parts per thousand when ISM rainfall decreased by 20%. This strong response emphasizes the importance of the ISM for surface water run-off in the central Himalaya. However, further spatio-temporal analysis of remote sensing data in combination with stream water d-excess revealed that most high-altitude tributaries and the Tibetan part of the Arun receive high portions of glacial melt water and likely Winter Westerly Disturbances precipitation. We make the following two implications: First, palaeohydrologic archives found in high-altitude tributaries and on the southern Tibetan Plateau record a mixture of past precipitation delta D values and variable amounts of additional water sources. Second, surface water isotope ratios of lower elevated tributaries strongly reflect the isotopic composition of ISM rainfall implying a suitable region for the analysis of potential delta D value proxy records.
Hazards and accessibility
(2018)
The assessment of natural hazards and risk has traditionally been built upon the estimation of threat maps, which are used to depict potential danger posed by a particular hazard throughout a given area. But when a hazard event strikes, infrastructure is a significant factor that can determine if the situation becomes a disaster. The vulnerability of the population in a region does not only depend on the area’s local threat, but also on the geographical accessibility of
the area. This makes threat maps by themselves insufficient for supporting real-time decision-making, especially for those tasks that involve the use of the road network, such as management of relief operations, aid distribution, or planning of evacuation routes, among others. To overcome this problem, this paper proposes a multidisciplinary approach divided in two parts. First, data fusion of satellite-based threat data and open infrastructure data from OpenStreetMap, introducing a threat-based routing service. Second, the visualization of this data through cartographic generalization and schematization. This emphasizes critical areas along roads in a simple way and allows users to visually evaluate the impact natural hazards may have on infrastructure. We develop and illustrate this methodology with a case study of landslide threat for an area in Colombia.
Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series
(2018)
The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6–12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth’s surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.