InSAR Greece with Parallelized Persistent Scatterer Interferometry: A National Ground Motion Service for Big Copernicus Sentinel-1 Data

Advances in synthetic aperture radar (SAR) interferometry have enabled the seamless monitoring of the Earth’s crust deformation. The dense archive of the Sentinel-1 Copernicus mission provides unprecedented spatial and temporal coverage; however, time-series analysis of such big data volumes requires high computational efficiency. We present a parallelized-PSI (P-PSI), a novel, parallelized, and end-to-end processing chain for the fully automated assessment of line-of-sight ground velocities through persistent scatterer interferometry (PSI), tailored to scale to the vast multitemporal archive of Sentinel-1 data. P-PSI is designed to transparently access different and complementary Sentinel-1 repositories, and download the appropriate datasets for PSI. To make it efficient for large-scale applications, we re-engineered and parallelized interferogram creation and multitemporal interferometric processing, and introduced distributed implementations to best use computing cores and provide resourceful storage management. We propose a new algorithm to further enhance the processing efficiency, which establishes a non-uniform patch grid considering land use, based on the expected number of persistent scatterers. P-PSI achieves an overall speed-up by a factor of five for a full Sentinel-1 frame for processing in a 20-core server. The processing chain is tested on a large-scale project to calculate and monitor deformation patterns over the entire extent of the Greek territory—our own Interferometric SAR (InSAR) Greece project. Time-series InSAR analysis was performed on volumes of about 12 TB input data corresponding to more than 760 Single Look Complex Sentinel-1A and B images mostly covering mainland Greece in the period of 2015–2019. InSAR Greece provides detailed ground motion information on more than 12 million distinct locations, providing completely new insights into the impact of geophysical and anthropogenic activities at this geographic scale. This new information is critical to enhancing our understanding of the underlying mechanisms, providing valuable input into risk assessment models. We showcase this through the identification of various characteristic geohazard locations in Greece and discuss their criticality. The selected geohazard locations, among a thousand, cover a wide range of catastrophic events including landslides, land subsidence, and structural failures of various scales, ranging from a few hundredths of square meters up to the basin scale. The study enriches the large catalog of geophysical related phenomena maintained by the GeObservatory portal of the Center of Earth Observation Research and Satellite Remote Sensing BEYOND of the National Observatory of Athens for the opening of new knowledge to the wider scientific community

Estimating Next Day’s Forest Fire Risk via a Complete Machine Learning Methodology

Next day wildfire prediction is an open research problem with significant environmental, social, and economic impact since it can produce methods and tools directly exploitable by fire services, assisting, thus, in the prevention of fire occurrences or the mitigation of their effects. It consists in accurately predicting which areas of a territory are at higher risk of fire occurrence each next day, exploiting solely information obtained up until the previous day. The task’s requirements in spatial granularity and scale of predictions, as well as the extreme imbalance of the data distribution render it a rather demanding and difficult to accurately solve the problem. This is reflected in the current literature, where most existing works handle a simplified or limited version of the problem. Taking into account the above problem specificities, in this paper, we present a machine learning methodology that effectively (sensitivity > 90%, specificity > 65%) and efficiently performs next day fire prediction, in rather high spatial granularity and in the scale of a country. The key points of the proposed approach are summarized in: (a) the utilization of an extended set of fire driving factors (features), including topography-related, meteorology-related and Earth Observation (EO)-related features, as well as historical information of areas’ proneness to fire occurrence; (b) the deployment of a set of state-of-the-art classification algorithms that are properly tuned/optimized on the setting; (c) two alternative cross-validation schemes along with custom validation measures that allow the optimal and sound training of classification models, as well as the selection of different models, in relation to the desired trade-off between sensitivity (ratio of correctly identified fire areas) and specificity (ratio of correctly identified non-fire areas). In parallel, we discuss pitfalls, intuitions, best practices, and directions for further investigation derived from our analysis and experimental evaluation

Impact assessment of the catastrophic earthquakes of 6 February 2023 in Turkey and Syria via the exploitation of satellite datasets

Turkey due to its location within the collision zone between the Eurasian, African and Arabian Plates, is a region prone to earthquakes. The country mostly lies on the Anatolian micro-plate, bounded by two major strike-slip fault zones, i.e., the North and the East Anatolian Fault. On 6 February 2023, the activation of a large segment of the East Anatolian Fault generated two earthquakes of 7.8 and 7.5 magnitude, in southern Turkey. The seismic risk is greater along the plate boundaries, however due to the frequency of earthquake occurrence throughout Turkey, detailed seismic risk maps are crucial and need to be continuously updated towards operational purposes, and as the optimal means towards decision making for disaster risk reduction. Extensive Synthetic Aperture Radar (SAR) satellite image analysis was performed to determine ground displacements caused by the seismic sequence in the wider area around the two epicenters. Pre-seismic line of sight displacements, as well as co-seismic deformation, were estimated, providing critical information about the surface rupture and the overall ground deformation in the affected areas. Earthquakes can induce landslides and other ground displacements causing extensive damage to buildings and infrastructure. Therefore, optical (e.g., Sentinel-2, PlanetScope) and SAR (Sentinel-1) imagery were exploited as a useful tool for assessing the impact of earthquakes on the ground. The monitoring and mapping of these changes, in conjunction with SAR analysis, as well as information on building infrastructure and population density, highlight the overall damage assessment in the region, thus, allowing a better understanding of the impact of earthquakes while providing a more effective response and recovery efforts for decision makers and local authorities towards disaster risk reduction