Life in the Atacama: A scoring system for habitability and the robotic exploration for life

Abstract

[1] The science goals of the Life in the Atacama (LITA) robotic field experiment are to understand habitat and seek out life in the Atacama Desert, Chile, as an analog to future missions to Mars. To those ends, we present a new data analysis tool, the LITA Data Scoring System (DSS), which (1) integrates rover and orbital data relevant to environmental habitability and life detection, and (2) provides a standard metric, or ''score'' to evaluate (a) the potential habitability, and (b) the strength of evidence for life at all locales along the rover's traverse. Designed and tested during the 2005 field campaign, first results from the DSS indicate that the three selected sites in the Atacama Desert are generally inhospitable. The strength of evidence for life is positively correlated with potential habitability at two of the three sites. Using factor analysis, we find three factors explain 79.9% of the variance in biological observations and five factors explain 96.2% of the variance in potential habitability across all sites. These factors are used to focus a discussion of scoring variable definitions for future robotic missions in the Atacama and of instrument selection and strategy development for future robotic missions on Earth and Mars. Citation: Hock, A. N., et al. (2007), Life in the Atacama: A scoring system for habitability and the robotic exploration for life,

The Coevolution of Life and Environment on Mars: An Ecosystem Perspective on the Robotic Exploration of Biosignatures

“…Another means of supporting biosignature exploration prior to, and during, a mission is by integrating survey techniques developed in microbial ecology. Past and recent NASA-funded rover field experiments testing microbial habitat and biosignature exploration strategies at the surface and in the subsurface of the Atacama Desert ( e.g., Wettergreen et al, 2005 , Cabrol et al, 2007a ) led to the development of ecological mapping techniques adapted to autonomous rover operations, and to the generation of predictive maps of habitat potential ( e.g., Hock et al, 2007 ; Warren-Rhodes et al, 2007a , 2007b , and unpublished data; Smith et al, 2007 ). This is typically an area where advances in detection algorithms and machine learning can bring new support to biosignature detection, including from orbit through, for example, the segmentation of hyperspectral imagery, correlation of orbital and surface spectra, novelty detection, multiscale adaptive sampling, data segmentation and superpixel clustering, and biohint probability mapping, among others ( e.g., Thompson et al, 2011 ; Candela et al, 2017 ).…”

The Argyre Region as a Prime Target forin situAstrobiological Exploration of Mars

“…Within and around the basin, resurfacing has included physical and chemical erosion, the emplacement of diverse sedimentary deposits, the accumulation and ablation of glacial ice, major etching of the rim materials, and the highly localized flow of gully-associated water. Also, possible fog concentration, including moisture, could have periodically embanked against topographic highs and bottle-necked into valleys and basins in Argyre, providing suitable environmental conditions for fog-dependent ecosystems, similarly to fog-driven ecosystems on Earth (e.g., Hock et al, 2007;Warren-Rhodes et al, 2007;Borthagaray et al, 2010;Azúa-Bustos et al, 2011;Latorre et al, 2011).…”

“…In addition, the protecting layers that formed after the wet eolian sedimentation or icy periods would provide a protected environment for existing life or its remains, as mantling by eolian drifts could have reduced the rates of sublimation. In summary, OSPs, as indicated through investigations of analogous terrestrial settings, could be host to past (and maybe present) heat fluxes, ice, liquid water, and nutrients (e.g., Dohm et al, 2004;Hock et al, 2007;Warren-Rhodes et al, 2007). (4) Sedimentary deposits left behind by glaciers along the basins' margins as evidenced by ridges interpreted to be eskers (e.g., Kargel and Strom, 1992;Kargel, 2004;Banks et al, 2008;Fig.…”

Spatial Spectroscopic Models for Remote Exploration

“…First, they often hinge on prior knowledge of the environment and preexisting remote sensing measurements (Thompson et al, 2015b). Second, human scientists continually reinterpret their measurements with a growing contextual knowledge of the environment; and, contrary to most artificial intelligence applications, real missions involve frequent reformulation of objectives throughout the investigation (Hock et al, 2007). Third, science decisions must consider complementary information from multiple sensing modalities and multiscale spatial relationships (Thompson et al, 2011).…”