Autonomous detection of cryospheric change with hyperion on-board Earth Observing-1
Introduction
This paper describes the development, testing and application of science-event detection with ASE, in this case, a data classifier and triggering algorithms designed to identify changes in the cryosphere which the spacecraft can then autonomously respond to. The structure of the paper is as follows: after a description of the rationale behind ASE, and the desired goal of autonomously monitoring and reacting to changes in the cryosphere, we describe the operational environment for ASE with Hyperion on EO-1, the development of the cryosphere classifier and triggering algorithms suited for this specific application, testing and verification of this approach, with classification assessment done on the ground prior to upload, a field validation study conducted at Lake Mendota, Wisconsin and an assessment of each on-board test conducted, as well as discussion of future applications.
The cryosphere is the component of a planetary body composed of ice in the form of snow, permafrost, floating ice, and glaciers, which dynamically interacts with atmospheres, climates, lithospheres and hydrospheres. Ices, broadly defined as the solid form of substances that are moderately volatile under ambient conditions, are found throughout the solar system. These include the H2O ice caps on Earth, the H2O and CO2 ice caps on Mars (Thomas et al., 1992), indications of ice in the permanently shadowed polar craters of Mercury (Slade et al., 1992) and the Moon (Feldman et al., 1998), and under the broadest definition of ice, the proposed metallic frosts in high altitudes on Venus (Brackett et al., 1995) and hydrocarbons in the Saturn system (Sagan et al., 1984). In addition, ice is a major component of moons in the outer solar system and undergoes dynamic behavior such as cryotectonism (e.g., Nimmo & Gaidos, 2002) and cryovolcanism (Geissler, 2000).
On-board detection of cryospheric change and autonomous spacecraft response was conducted as part of the Autonomous Sciencecraft Experiment (ASE) (Chien et al., 2003), operating on Earth Observing-1 (EO-1) (Ungar et al., 2003) with the Hyperion hyper-spectral visible/infrared spectrometer (Pearlman et al., 2003). This capability would enable future interplanetary missions to search for dynamic cryopsheric events, make optimal use of limited downlink resources, and autonomously task the spacecraft to collect further data with a quicker response time than possible by ground control from Earth.
Section snippets
Mission design
ASE (Chien et al., 2004) is software flying on EO-1. This software enables the spacecraft to autonomously detect and respond to dynamic events occurring on Earth, such as volcanic eruptions (Davies et al., 2006-this issue), flooding (Ip et al., 2006-this issue) and cryospheric changes. The ASE on-board flight software consists of several components, including execution management software using the Spacecraft Command Language (SCL), the Continuous Activity Scheduling Planning Execution and
Classifier algorithm
For the purpose of cryospheric observations with ASE, classifiers were developed that operate within the constraints imposed by the mission architecture. The first classifier, a manually derived decision tree algorithm, was used from March 2004 through mid-January 2005, and will be discussed in this paper. The second classifier was developed using Support Vector Machine (SVM) machine learning techniques (Castano et al., 2005), and has been used from January 2005 onwards. The results of these
Field validation
As a separate test to validate the ASE cryosphere algorithm, field-based mapping was conducted of snow and ice cover of lakes in the vicinity of Madison, Wisconsin. Visible and thermal satellite remote sensing has previously been used for monitoring lake ice (e.g., Warta, 1997, Wiesnet, 1979) based on manual interpretation of data, along with work to classify ice types based on spectral features (e.g., Leshkevich, 1981). In addition to navigation and safety applications, the monitoring of year
On-board tests
Twenty-four on-board tests (Table 7) were conducted with the initial cryosphere classifier, until it was replaced with the SVM cryosphere classifier in mid-January 2005. During operations with ASE Releases 1 and 2, the full classifier output was down linked, permitting direct comparison between the classification and the image data. New downlink restrictions after ASE Release 3 decreased the returned data on the classifier run to a summary within the spacecraft telemetry of the number of pixels
Discussion and conclusions
During initial ASE operations from March 2004 through January 2005, on-board detection of sea ice and its subsequent break-up, thawed lakes and incipient freezing, alpine snow and cloud cover have been demonstrated. The main shortcoming of these initial operations was reliance on a cloud classifier, which although developed for on-board use, was designed to work with fully processed data not available on-board. In addition to the novel constraints in data processing from on-board operations,
Acknowledgements
This study was supported by a contract from the Jet Propulsion Laboratory to Arizona State University. We gratefully acknowledge the technical support of Michael Bentley and Susan Selkirk, the role of the EO-1 team at NASA's Goddard Space Flight Center in getting ASE “flying”, and our collaboration with the University of Wisconsin's Center for Limnology, in particular Ted Cummings, David Balsiger and Tim Kratz.
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