Daniel Mandl

The Earth Observing One (EO-1) Satellite Mission: Over a Decade in Space

The Earth Observing One (EO-1) satellite was launched in November 2000 as a technology demonstration mission with an estimated 1-year lifespan. It has now successfully completed 12 years of high spatial resolution imaging operations from low Earth orbit. EO-1s two main instruments, Hyperion and the Advanced Land Imager (ALI), have both served as prototypes for new generation satellite missions. ALI, an innovative multispectral instrument, is the forerunner of the Operational Land Imager (OLI) onboard the Landsat Data Continuity Missions (LDCM) Landsat-8 satellite, recently launched in Feb. 2013. Hyperion, a hyperspectral instrument, serves as the heritage orbital spectrometer for future global platforms, including the proposed NASA Hyperspectral Infrared Imager (HyspIRI) and the forthcoming (in 2017) German satellite, EnMAP. This JSTARS Special Issue is dedicated to EO-1. This paper serves as an introduction to the Hyperion and ALI instruments, their capabilities, and the important contributions this mission has made to the science and technology communities. This paper also provides an overview of the EO-1 mission, including the several operational phases which have characterized its lifetime. It also briefly describes calibration and validation activities, and gives an overview of the spin-off technologies, including disaster monitoring and new Web-based tools which can be adapted for use in future missions.

Interoperable Infrastructure for Flood Monitoring: SensorWeb, Grid and Cloud

The paper presents an international multi-disciplinary initiative, a Namibia SensorWeb Pilot Project, that was created as a testbed for evaluating and prototyping key technologies for rapid acquisition and distribution of data products for decision support systems to monitor floods. Those key technologies include SensorWebs, Grids and Computation Clouds. This pilot project aims at developing an operational trans-boundary flood management decision support system for the Southern African region to provide useful flood and water-borne disease forecasting tools for local decision makers. This effort integrates space-based and ground sensor data along with higher level geospatial data products to enable risk assessment and ultimately risk maps related to flood disaster management and water-related disease management. We present an overall architecture of the Pilot along with components and services being developed. Additionally, case-studies and results achieved so far are discussed. The presented work is being carried out within GEO 2009-2011 Work Plan as CEOS WGISS contribution.

Onboard Science Processing Concepts for the HyspIRI Mission

This paper presents the operational concept for onboard processing for the HysIRI mission which is an Earth observing mission that includes both thermal infrared instrument and a hyperspectral visible/shortwave infrared instrument, and that is being considered for launch in the next decade. This article describes the potential application of AI techniques for the HyspIRI mission-for both onboard processing and ground-based, automated mission planning.

Use of the Earth Observing One (EO-1) Satellite for the Namibia SensorWeb Flood Early Warning Pilot

The Earth Observing One (EO-1) satellite was launched in November 2000 as a one year technology demonstration mission for a variety of space technologies. After the first year, it was used as a pathfinder for the creation of SensorWebs. A SensorWeb is the integration of a variety of space, airborne and ground sensors into a loosely coupled collaborative sensor system that automatically provides useful data products. Typically, a SensorWeb is comprised of heterogeneous sensors tied together with an open messaging architecture and web services. SensorWebs provide easier access to sensor data, automated data product production and rapid data product delivery. Disasters are the perfect arena to test SensorWeb functionality since emergency workers and managers need easy and rapid access to satellite, airborne and in-situ sensor data as decision support tools. The Namibia Early Flood Warning SensorWeb pilot project was established to experiment with various aspects of sensor interoperability and SensorWeb functionality. The SensorWeb system features EO-1 data along with other data sets from such satellites as Radarsat, Terra and Aqua. Finally, the SensorWeb team began to examine how to measure economic impact of SensorWeb technology infusion. This paper describes the architecture and software components that were developed along with performance improvements that were experienced. Also, problems and challenges that were encountered are described along with a vision for future enhancements to mitigate some of the problems.

Cloud Implementation of a Full Hyperspectral Unmixing Chain Within the NASA Web Coverage Processing Service for EO-1

The launch of the NASA Earth Observing 1 (EO-1) platform in November 2000 marked the establishment of spaceborne hyperspectral technology for land imaging. The Hyperion sensor onboard EO-1 operates in the 0.4-2.5 micrometer spectral range, with 10 nanometer spectral resolution and 30-meter spatial resolution. Spectral unmixing has been one of the most successful approaches to analyze Hyperion data since its launch. It estimates the abundance of spectrally pure constituents (endmembers) in each observation collected by the sensor. Due to the high spectral dimensionality of Hyperion data, unmixing is a very time-consuming operation. In this paper, we develop a cloud implementation of a full hyperspectral unmixing chain made up of the following steps: 1) dimensionality reduction; 2) automatic endmember identification; and 3) fully constrained abundance estimation. The unmixing chain will be available online within the Web Coverage Processing Service (WCPS), an image processing framework that can run on the cloud, as part of the NASA SensorWeb suite of web services. The proposed implementation has been demonstrated using the EO-1 Hyperion imagery. Our experimental results with a hyperspectral scene collected over the Okavango Basin in Botswana suggest the (present and future) potential of spectral unmixing for improved exploitation of spaceborne hyperspectral data. The integration of the unmixing chain in the WCPS framework as part of the NASA SensorWeb suite of web services is just the start of an international collaboration in which many more processing algorithms will be made available to the community through this service. This paper is not so much focused on the theory and results of unmixing (widely demonstrated in other contributions) but about the process and added value of the proposed contribution for ground processing on the cloud and onboard migration of those algorithms to support the generation of low-latency products for new airborne/spaceborne missions.

A Space-Based Sensor Web for Disaster Management

This paper describes work being performed under a NASA Earth Science Technology Office grant to develop a modular Sensor Web architecture based on Open Geospatial Consortium (OGC) standards, which enables discovery and generic tasking capability for sensors, both space-based and insitu. A series of increasingly complex demonstrations have been developed to prototype this architecture. Recent demonstrations have made use of the Hyperion and Advanced Land Imager instruments on the Earth Observing 1 (EO-1) satellite, the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua, the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) on the Terra satellite and the Wildfire sensor on the Ikhana Unmanned Aerial System (UAS). This Sensor Web was used in the recent Southern California fires during October 2007 to deliver key wildfire imagery to the San Diego county Emergency Operations Center (EOC) to assist emergency workers with situational awareness. Presently the team is in the process of prototyping the use of this sensor web for floods in collaboration with the International Federations of the Red Cross/Red Crescent for better flood disaster management. The paper will also describe a general overview of the modular architecture that has thus far been built and capabilities still needed to realize the full vision.

Combining space-based and in-situ measurements to track flooding in Thailand

We describe efforts to integrate in-situ sensing, space-borne sensing, hydrological modeling, active control of sensing, and automatic data product generation to enhance monitoring and management of flooding. In our approach, broad coverage sensors and missions such as MODIS, TRMM, and weather satellite information and in-situ weather and river gauging information are all inputs to track flooding via river basin and sub-basin hydrological models. While these inputs can provide significant information as to the major flooding, targetable space measurements can provide better spatial resolution measurements of flooding extent. In order to leverage such assets we automatically task observations in response to automated analysis indications of major flooding. These new measurements are automatically processed and assimilated with the other flooding data. We describe our ongoing efforts to deploy this system to track major flooding events in Thailand.

Space-based Sensorweb monitoring of wildfires in Thailand

We describe efforts to apply sensorweb technologies to the monitoring of forest fires in Thailand. In this approach, satellite data and ground reports are assimilated to assess the current state of the forest system in terms of forest fire risk, active fires, and likely progression of fires and smoke plumes. This current and projected assessment can then be used to actively direct sensors and assets to best acquire further information. This process operates continually with new data updating models of fire activity leading to further sensing and updating of models. As the fire activity is tracked, products such as active fire maps, burn scar severity maps, and alerts are automatically delivered to relevant parties. We describe the current state of the Thailand Fire Sensorweb which utilizes the MODIS-based FIRMS system to track active fires and trigger Earth Observing One / Advanced Land Imager to acquire imagery and produce active fire maps, burn scar severity maps, and alerts. We describe ongoing work to integrate additional sensor sources and generate additional products.

Onboard Product Generation on Earth Observing One: A Pathfinder for the Proposed Hyspiri Mission Intelligent Payload Module

The proposed HyspIRI mission is evaluating a X-band Direct Broadcast capability that would enable data to be delivered to ground stations virtually as it is acquired. However the HyspIRI VSWIR and TIR instruments are expected to produce over 800 × 106 bits per second of data while the Direct Broadcast capability is approximately 10 × 106 bits per second for a ~ 80x oversubscription. In order to address this data throughput mismatch a Direct Broadcast concept called the Intelligent Payload Module (IPM) has been developed to determine which data to downlink based on both the type of surface the spacecraft is overlying and onboard processing of the data to detect events. For example, when the spacecraft is overlying polar regions it might downlink a snow/ice product. Additionally the onboard software would search for thermal signatures indicative of a volcanic event or wild fire and downlink summary information (extent, spectra) when detected. Earth Observing One (EO-1) has served as a test bed and pathfinder for this type of onboard product generation. As part of the Autonomous Sciencecraft (ASE), EO-1 implemented in ίight software the ability to analyze and develop products for a limited swath of the Hyperion hyperspectral instrument onboard the spacecraft. In a series of technology demonstrations that became part of the operational EO-1 system over 5000 science products have been generated onboard EO-1 and down linked via engineering S-band contacts, a routine automated process that continues to this day. We describe the onboard products demonstrated in EO-1 operations and show how they have paved the way for the HyspIRI Intelligent Payload Module concept.

Rapid Response to Volcanic Eruptions with an Autonomous Sensor Web: The Nyamulagira Eruption of 2006

Rapid response to alerts of impending or active volcanism is vital in the assessment of volcanic risk and hazard. The JPL model-driven volcano sensor Web (MSW) demonstrated such an autonomous response during a volcanic crisis at Nyamulagira volcano, D. R. Congo, in December 2006, quickly providing vital information to volcanologists in the field. The MSW was developed to enable fast science-driven asset command and control. Alerts of volcanic activity from around the world are used to trigger high resolution observations (both spectral and spatial) by the EO-1 spacecraft. Data are processed onboard EO-1 by advanced software (the autonomous sciencecraft experiment [ASE]). If volcanic thermal emission is detected, ASE retasks EO-1 to obtain more data. A summary of the observation is returned within two hours of data acquisition.