Abstract
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.
Highlights
The emergence of synthetic aperture radar (SAR)-based Earth Observation (EO) satellites over the last decades has led to the development of powerful new methods for monitoring the Earth’s surface
By constructing a time series of SAR coherence data for a region of interest, we suggest a methodology that focuses on three critical objectives: (1) Deriving a more complete distribution of coherence values through SAR time series analysis for each pixel to decipher more accurately anomalous events; (2) Using this distribution, to identify time periods or seasons most suitable for the detection of anomalous, landscape-changing events; (3) Based on the distribution, to derive different significance levels of coherence thresholds to identify natural hazards
We focus our region of interest around Sarpol-e Zahab, a city of ~45,000 inhabitants, located 50 km south-southwest of the earthquake epicentre, which was significantly damaged by the earthquake
Summary
The emergence of synthetic aperture radar (SAR)-based Earth Observation (EO) satellites over the last decades has led to the development of powerful new methods for monitoring the Earth’s surface This is true in the case of monitoring and assessing the impacts of natural hazards [1,2,3]. SAR satellites observe the Earth’s surface independent of weather conditions and time of day, as the active radar signal does not depend on daylight and penetrates cloud cover with minimal atmospheric interaction [4] This is especially an advantage in assessing natural hazards associated with heavy precipitation, such as flooding and rainfall-triggered landslides, debris flows, and mudflows, when persistent cloud-cover may render optical satellite observations of limited use [2]. Because coherence loss between two images with a similar spatial footprint collected at different times results from changes at the Earth’s surface, it is a useful metric to map where potential damage has occurred following natural hazard or meteorological events [6,7,8,9]
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