Eric Gurrola

Rapid Damage Mapping for the 2015 Mw 7.8 Gorkha Earthquake Using Synthetic Aperture Radar Data from COSMO–SkyMed and ALOS-2 Satellites

The 25 April 2015 M_w 7.8 Gorkha earthquake caused more than 8000 fatalities and widespread building damage in central Nepal. The Italian Space Agency’s COSMO–SkyMed Synthetic Aperture Radar (SAR) satellite acquired data over Kathmandu area four days after the earthquake and the Japan Aerospace Exploration Agency’s Advanced Land Observing Satellite-2 SAR satellite for larger area nine days after the mainshock. We used these radar observations and rapidly produced damage proxy maps (DPMs) derived from temporal changes in Interferometric SAR coherence. Our DPMs were qualitatively validated through comparison with independent damage analyses by the National Geospatial-Intelligence Agency and the United Nations Institute for Training and Research’s United Nations Operational Satellite Applications Programme, and based on our own visual inspection of DigitalGlobe’s WorldView optical pre- versus postevent imagery. Our maps were quickly released to responding agencies and the public, and used for damage assessment, determining inspection/imaging priorities, and reconnaissance fieldwork.

Techniques and tools for estimating ionospheric effects in interferometric and polarimetric SAR data

The InSAR Scientific Computing Environment (ISCE) is a flexible, extensible software tool designed for the end-to-end processing and analysis of synthetic aperture radar data. ISCE inherits the core of the ROI_PAC interferometric tool, but contains improvements at all levels of the radar processing chain, including a modular and extensible architecture, new focusing approach, better geocoding of the data, handling of multi-polarization data, radiometric calibration, and estimation and correction of ionospheric effects. In this paper we describe the characteristics of ISCE with emphasis on the ionospheric modules. To detect ionospheric anomalies, ISCE implements the Faraday rotation method using quad-polarimetric images, and the split-spectrum technique using interferometric single-, dual- and quad-polarimetric images. The ability to generate co-registered time series of quad-polarimetric images makes ISCE also an ideal tool to be used for polarimetric-interferometric radar applications.

Locating the LCROSS Impact Craters

The Lunar CRater Observations and Sensing Satellite (LCROSS) mission impacted a spent Centaur rocket stage into a permanently shadowed region near the lunar south pole. The Sheperding Spacecraft (SSC) separated ∼9 hours before impact and performed a small braking maneuver in order to observe the Centaur impact plume, looking for evidence of water and other volatiles, before impacting itself.This paper describes the registration of imagery of the LCROSS impact region from the mid- and near-infrared cameras onboard the SSC, as well as from the Goldstone radar. We compare the Centaur impact features, positively identified in the first two, and with a consistent feature in the third, which are interpreted as a 20 m diameter crater surrounded by a 160 m diameter ejecta region. The images are registered to Lunar Reconnaisance Orbiter (LRO) topographical data which allows determination of the impact location. This location is compared with the impact location derived from ground-based tracking and propagation of the spacecraft’s trajectory and with locations derived from two hybrid imagery/trajectory methods. The four methods give a weighted average Centaur impact location of −84.6796°, −48.7093°, with a 1σ uncertainty of 115 m along latitude, and 44 m along longitude, just 146 m from the target impact site. Meanwhile, the trajectory-derived SSC impact location is −84.719°, −49.61°, with a 1σ uncertainty of 3 m along the Earth vector and 75 m orthogonal to that, 766 m from the target location and 2.803 km south-west of the Centaur impact.We also detail the Centaur impact angle and SSC instrument pointing errors. Six high-level LCROSS mission requirements are shown to be met by wide margins. We hope that these results facilitate further analyses of the LCROSS experiment data and follow-up observations of the impact region.

Radar generates high-resolution topographic map of the Moon

The National Aeronautics and Space Administration’s (NASA’s) long-term exploration goals include resuming manned missions to the Moon that will culminate in a permanent manned lunar station. Before embarking on such a mission, a series of unmanned robotic missions are required to ascertain the best locations for manned exploration or a permanent lunar base. The south polar region of the Moon has attracted much recent attention because significant amounts of frozen water may be trapped in permanently shadowed regions in craters there.1, 2 Knowledge concerning the lunar topography is of particular importance in planning survivable landings and locating regions accessible to exploration. Synthetic aperture radar exploits the motion of a radar relative to a surface to create fine-resolution images essentially independent of the distance from the radar to the surface. By creating images from two spatially separated radars, an interferometric image can be derived containing topographic information. JeanLuc Margot and co-authors used the Goldstone Solar System Radar (GSSR) in this configuration to derive topographic maps of the lunar surface at 150m spatial resolution and 50m vertical accuracy.3–5 Subsequently, in 2006, the GSSR system was upgraded to support the collection of finer-resolution imagery of the lunar surface. The topographic map described here was derived from this data.

Lunar topographic mapping using a new high resolution mode for the GSSR radar

Mapping the Moons topography using Earth based radar interferometric measurements by the Goldstone Solar System Radar (GSSR) has been done several times since the mid 1990s. In 2008 we reported at this conference the generation of lunar topographic maps having approximately 4 m height accuracy at a horizontal posting of 40 m. Since then GSSR radar has been improved to allow 40 MHz bandwidth imaging and consequently obtained images and interferograms with a resolution of about 4 m in range by 5 m in azimuth. The long synthetic aperture times of approximately 90 minutes in duration necessitated a migration from range/Doppler image formation techniques to spotlight mode processing and autofocusing methods. The improved resolution imagery should permit the generation of topographic maps with a factor of two better spatial resolution with about same height accuracy. Coupled the with the recent availability of new lidar topography maps of the lunar surface made by orbiting satellites of Japan and the United States the geodetic control of the radar generated maps products can be improved dramatically. This paper will discuss the hardware and software improvements made to the GSSR and present some of the new high resolution products.

Plant: Polarimetric-interferometric Lab and Analysis Tools for ecosystem and land-cover science and applications

PLANT (Polarimetric-interferometric Lab and Analysis Tools) is a new collection of software tools developed at the Jet Propulsion Laboratory to support processing and analysis of Synthetic Aperture Radar (SAR) data for ecosystem and land-cover/land-use change science and applications. PLANT inherits code components from the Interferometric Scientific Computing Environment (ISCE) to generate high-resolution, coregistered polarimetric-interferometric SLC stacks from Level-0/1 data for a variety of airborne and spaceborne sensors. The goal is to provide the ecosystem and land-cover/land-use change communities with rigorous and efficient tools to perform multi-temporal, polarimetric and tomographic analyses in order to generate calibrated, geocoded and mosaicked Level-2 and Level-3 products (e.g., maps of above-ground biomass and forest disturbance). In this paper we introduce the capabilities of PLANT and report first results obtained with the tools developed up to date.

Recent rapid disaster response products derived from COSMO-Skymed synthetic aperture radar data

The April 25, 2015 M7.8 Gorkha earthquake caused more than 8,000 fatalities and widespread building damage in central Nepal. Four days after the earthquake, the Italian Space Agencys (ASIs) COSMO-SkyMed Synthetic Aperture Radar (SAR) satellite acquired data over Kathmandu area. Nine days after the earthquake, the Japan Aerospace Exploration Agencys (JAXAs) ALOS-2 SAR satellite covered larger area. Using these radar observations, we rapidly produced damage proxy maps derived from temporal changes in Interferometric SAR (InSAR) coherence. These maps were qualitatively validated through comparison with independent damage analyses by National Geospatial-Intelligence Agency (NGA) and the UNITARs (United Nations Institute for Training and Researchs) Operational Satellite Applications Programme (UNOSAT), and based on our own visual inspection of DigitalGlobes WorldView optical pre- vs. post-event imagery. Our maps were quickly released to responding agencies and the public, and used for damage assessment, determining inspection/imaging priorities, and reconnaissance fieldwork.

Distributed computing framework for synthetic aperture radar application

We are developing an extensible software framework, in response to Air Force and NASA needs for distributed computing facilities for a variety of radar applications. The objective of this work is to develop a Python-based software framework that is the framework elements of the middleware that allows developers to control processing flow on a grid in a distributed computing environment. Framework architectures to date allow developers to connect processing functions together as interchangeable objects, thereby allowing a data flow graph to be devised for a specific problem to be solved. The Pyre framework, developed at the California Institute of Technology (Caltech), and now being used as the basis for next-generation radar processing at JPL, is a Python-based software framework. We have extended the Pyre framework to include new facilities to deploy processing components as services, including components that monitor and assess the state of the distributed network for eventual real-time control of grid resources.