Space-based geodetic measurement techniques such as Interferometric Synthetic Aperture Radar (InSAR) and Continuous Global Positioning System (CGPS) are now critical elements in the toolset for monitoring earthquake-generating faults, volcanic eruptions, landslides, glacial ablation, reservoir subsidence, and other natural and man-made hazards. Geodetic imaging’s unique ability to capture surface deformation with high spatial and temporal resolution has revolutionized both earthquake science and volcanology. Continuous monitoring of surface deformation and surface change before, during, and after natural hazards allows for better forecasts, increased situational awareness, and more informed recovery. Combining high-spatial-resolution InSAR products with high-temporal-resolution GPS products, and automating this data preparation and processing across global-scale areas of interest, is an untapped science and monitoring opportunity.
The Advanced Rapid Imaging and Analysis for Monitoring Hazards (ARIA-MH) Science Data System (SDS) was developed for hazard monitoring and rapid processing, leveraging NASA-funded algorithms and data system capabilities to enable both science and decision-making communities to monitor areas of interest via seamless data management, processing, discovery, access, and distribution. The science data system:
- Enables high-volume and low-latency automatic generation of NASA Solid Earth science data products (InSAR and GPS) to support hazards monitoring and research.
- Facilitates NASA-USGS collaborations to share NASA InSAR and GPS data products for decision support related to disasters.
- Enables interoperable discovery, access, and sharing of NASA observations and derived actionable products, and between the observation and decision- making communities.
- Employs NASA’s first hybrid cloud science data system supporting on-premise processing of near real-time data streams and bursting out to public cloud resources (e.g. AWS) for processing when demand exceeds local capacity.
- Retires risks of future science data systems for NASA Earth Radar mission(s).
ARIA-MH aims to bring geodetic imaging capabilities to a level that will enable NASA scientists and technologists to support local, national, and international hazard response communities. Through development of a science data system, technological innovations such as hybrid cloud-based computing and distribution, faceted navigation of resources, and faceted-based monitoring and triggering for automated processing have been applied.
This work was done by Hook Hua, Susan E. Owen, Gerald John M. Manipon, Gian Franco Sacco, Piyush S. Agram, Angelyn W. Moore, Sang Ho Yun, Eric J. Fielding, Paul R. Lundgren, Paul A. Rosen, Frank H. Webb, Zhen Liu, Alexander Smith, Brian D. Wilson, Michael D. Starch, and Mark Simons of Caltech; and Michael Poland and Peter Cervelli of the United States Geological Survey for NASA’s Jet Propulsion Laboratory.
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Advanced Rapid Imaging and Analysis
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Overview
The document discusses the advancements in satellite-based Synthetic Aperture Radar (SAR) missions and the integration of rapidly expanding GPS networks to enhance disaster response capabilities. It emphasizes the necessity for automated data systems to efficiently analyze the vast amounts of data generated by these technologies, particularly from NASA's proposed NASA NI-SAR mission. The transition from manual to automated data processing is highlighted as crucial for operational effectiveness in disaster management.
A key focus is on the implementation of hybrid cloud computing as a solution to address the challenges posed by high data volumes and the need for low-latency processing. The document suggests that a hybrid cloud approach can facilitate on-premise processing while allowing for off-site elasticity, enabling systems to handle bursts of data efficiently. This is particularly important given the projected petabyte-scale data volumes that will require careful architectural design of data systems.
The document outlines essential components of a data system, including compute resources, block storage, bucket storage, virtual machines (VMs), and identity management. It also highlights the importance of package management tools, such as Puppet and Chef, to streamline deployment across various cloud environments. Monitoring capabilities are emphasized for both event processing and data product management, ensuring that the system can respond dynamically to changing conditions.
Additionally, the document describes the data access mechanisms that leverage the interoperable WebDAV specification, allowing users to access data products through multiple interfaces, including web, command-line, and mobile phone interfaces. This flexibility is crucial for users needing quick access to data for analysis and decision-making during disaster response scenarios.
Overall, the document underscores the critical role of advanced data management and processing technologies in enhancing the effectiveness of remote sensing for disaster response. It advocates for a systematic approach to translate specialized data analysis into operational capabilities, ultimately aiming to improve hazard monitoring and response efforts through innovative technological solutions.
