An insight in cloud computing solutions for intensive processing of remote sensing data

The investigation of Earth's surface deformation phenomena provides critical insights into several processes of great interest for science and society, especially from the perspective of further understanding the Earth System and the impact of the human activities. Indeed, the study of ground deformation phenomena can be helpful for the comprehension of the geophysical dynamics dominating natural hazards such as earthquakes, volcanoes and landslide. In this context, the microwave space-borne Earth Observation (EO) techniques represent very powerful instruments for the ground deformation estimation. In particular, Small BAseline Subset (SBAS) is regarded as one of the key techniques, for its ability to investigate surface deformation affecting large areas of the Earth with a centimeter to millimeter accuracy in different scenarios (volcanoes, tectonics, landslides, anthropogenic induced land motions). The current Remote Sensing scenario is characterized by the availability of huge archives of radar data that are going to increase with the advent of Sentinel-1 satellites. The effective exploitation of this large amount of data requires both adequate computing resources as well as advanced algorithms able to properly exploit such facilities. In this work we concentrated on the use of the P-SBAS algorithm (a parallel version of SBAS) within HPC infrastructure, to finally investigate the effectiveness of such technologies for EO applications. In particular we demonstrated that the cloud computing solutions represent a valid alternative for scientific application and a promising research scenario, indeed, from all the experiments that we have conducted and from the results obtained performing Parallel Small Baseline Subset (P-SBAS) processing, the cloud technologies and features result to be absolutely competitive in terms of performance with in-house HPC cluster solution

Evaluation of the SBAS InSAR Service of the European Space Agency’s Geohazard Exploitation Platform (GEP)

The analysis of remote sensing data to assess geohazards is being improved by web-based platforms and collaborative projects, such as the Geohazard Exploitation Platform (GEP) of the European Space Agency (ESA). This paper presents the evaluation of a surface velocity map that is generated by this platform. The map was produced through an unsupervised Multi-temporal InSAR (MTI) analysis applying the Parallel-SBAS (P-SBAS) algorithm to 25 ENVISAT satellite images from the South of Spain that were acquired between 2003 and 2008. This analysis was carried out using a service implemented in the GEP called “SBAS InSAR”. Thanks to the map that was generated by the SBAS InSAR service, we identified processes not documented so far; provided new monitoring data in places affected by known ground instabilities; defined the area affected by these instabilities; and, studied a case where GEP could have been able to help in the forecast of a slope movement reactivation. This amply demonstrates the reliability and usefulness of the GEP, and shows how web-based platforms may enhance the capacity to identify, monitor, and assess hazards that are associated to geological processes.Spanish “Juan de la Cierva” grants support part of the work of Jorge P. Galve. The expenses related to the hired researcher contract of Jorge P. Galve and the field surveying were funded by the project CGL2015-67130-C2-1-R (FEDER and Spanish Ministry of Economy and Competitiveness)

Improving landslide inventories by combining satellite interferometry and landscape analysis: the case of Sierra Nevada (Southern Spain)

An updated and complete landslide inventory is the starting point for an appropriate hazard assessment. This paper presents an improvement for landslide mapping by integrating data from two well-consolidated techniques: Differential Synthetic Aperture Radar (DInSAR) and Landscape Analysis through the normalised channel steepness index (ksn). The southwestern sector of the Sierra Nevada mountain range (Southern Spain) was selected as the case study. We first propose the double normalised steepness (ksnn) index, derived from the ksn index, to remove the active tectonics signal. The obtained ksnn anomalies (or knickzones) along rivers and the unstable ground areas from the DInSAR analysis rapidly highlighted the slopes of interest. Thus, we provided a new inventory of 28 landslides that implies an increase in the area affected by landslides compared with the previous mapping: 33.5% in the present study vs. 14.5% in the Spanish Land Movements Database. The two main typologies of identified landslides are Deep-Seated Gravitational Slope Deformations (DGSDs) and rockslides, with the prevalence of large DGSDs in Sierra Nevada being first revealed in this work. We also demonstrate that the combination of DInSAR and Landscape Analysis could overcome the limitations of each method for landslide detection. They also supported us in dealing with difficulties in recognising this type of landslides due to their poorly defined boundaries, a homogeneous lithology and the imprint of glacial and periglacial processes. Finally, a preliminary hazard perspective of these landslides was outlined.Universidad de Granada/CBUAMarie Curie Actions B-RNM-305-UGR18 A-RNM-508-UGR20 P18-RT-3632ERDF through the project RISKCOAST' of the Interreg SUDOE Programme SOE3/P4/E0868Project MORPHOMED' from the Spanish Ministry of Science (MCIN)/State Research Agency (SRA) PID2019-107138RB-I00Ramon y Cajal' Programme of the Spanish Ministry of Science RYC-2017-23335NoR 6373

Rapid characterisation of the extremely large landslide threatening the Rules Reservoir (Southern Spain)

When an active landslide is first identified in an artificial reservoir, a comprehensive study has to be quickly conducted to analyse the possible hazard that it may represent to such a critical infrastructure. This paper presents the case of the El Arrecife Landslide, located in a slope of the Rules Reservoir (Southern Spain), as an example of geological and motion data integration for elaborating a preliminary hazard assessment. For this purpose, a field survey was carried out to define the kinematics of the landslide: translational in favour of a specific foliation set, and rotational at the foot of the landslide. A possible failure surface has been proposed, as well as an estimation of the volume of the landslide: 14.7 million m3. At the same time, remote sensing and geophysical techniques were applied to obtain historical displacement rates. A mean subsidence rate of the landslide around 2 cm/year was obtained by means of synthetic aperture radar interferometry (InSAR) and ground-penetrating radar (GPR) data, during the last 5 and 22 years, respectively. The structure-from-motion (SfM) technique provided a rate up to 26 cm/year during the last 14 years of a slag heap located within the foot of the landslide, due to compaction of the anthropical deposits. All of this collected information will be valuable to optimise the planning of future monitoring surveys (i.e. differential global positioning systems, inclinometers, ground drilling, and InSAR) that should be applied in order to prevent further damage on the reservoir and related infrastructures.This work was mainly supported by the European Regional Development Fund (ERDF) through the project “RISKCOAST” (SOE3/P4/E0868) of the Interreg SUDOE Programme. The work of J.P.G., M.M-S., P.R. and J.M.A. was also supported by the “Ramón y Cajal” Programme (RYC-2017–23335) of the Spanish Ministry of Science, the project “MORPHOMED”—PID2019-107138RB-I00 / SRA (State Research Agency / https://doi.org/10.13039/501100011033) and the project “RADANDALUS” (P18-RT-3632) and B-RNM-305-UGR1818 of the FEDER / Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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Volcanic Processes Monitoring and Hazard Assessment Using Integration of Remote Sensing and Ground-Based Techniques

The monitoring of active volcanoes is a complex task based on multidisciplinary and integrated analyses that use ground, drones and satellite monitoring devices. Over time, and with the development of new technologies and increasing frequency of acquisition, the use of remote sensing to accomplish this important task has grown enormously. This is especially so with the use of drones and satellites for classifying eruptive events and detecting the opening of new vents, the spreading of lava flows on the surface or ash plumes in the atmosphere, the fallout of tephra on the ground, the intrusion of new magma within the volcano edifice, and the deformation preceding impending eruptions, and many other factors. The main challenge in using remote sensing techniques is to develop automated and reliable systems that may assist the decision maker in volcano monitoring, hazard assessment and risk reduction. The integration with ground-based techniques represents a valuable additional aspect that makes the proposed methods more robust and reinforces the results obtained. This collection of papers is focused on several active volcanoes, such as Stromboli, Etna, and Volcano in Italy; the Long Valley caldera and Kilauea volcano in the USA; and Cotopaxi in Ecuador

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium