Mars Terraforming: A Geographic Information Systems Framework.


Journal

Life sciences in space research
ISSN: 2214-5532
Titre abrégé: Life Sci Space Res (Amst)
Pays: Netherlands
ID NLM: 101632373

Informations de publication

Date de publication:
Feb 2020
Historique:
received: 13 07 2019
revised: 03 12 2019
accepted: 22 12 2019
entrez: 29 1 2020
pubmed: 29 1 2020
medline: 22 1 2021
Statut: ppublish

Résumé

This study has developed a GIS framework that uses spatial environmental and climate data to better understand areas on Earth that share the most environmental similarities to Mars. The purpose of developing this framework is to determine which vegetation is most likely to survive in closed bioregenerative life support systems on Mars, using as many in-situ materials and environmental elements as possible. Using remotely sensed climate data, digital elevation models, and vegetation occurrence data sourced from the Global Biodiversity Information Facility, three Mars-like study areas on Earth were analysed (the Antarctic Peninsula, Ellesmere Island, and Devon Island). This study found that plants that are part of the Bryophyte and Tracheophyta phyla are worthy of further research in regard to possible vegetation candidates that could be brought to Mars. In addition, the most promising candidate of the entire study is the genus Poa, which is found in the phylum Tracheophyta.

Identifiants

pubmed: 31987480
pii: S2214-5524(19)30141-5
doi: 10.1016/j.lssr.2019.12.001
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

50-63

Informations de copyright

Copyright © 2019 The Committee on Space Research (COSPAR). Published by Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare no conflict of interest.

Auteurs

Eric Vaz (E)

Department of Geography and Environmental Studies, Ryerson University, Toronto, ON, Canada. Electronic address: evaz@ryerson.ca.

Elissa Penfound (E)

Yeates School of Graduate Studies, Ryerson University, Toronto, ON, Canada.

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Classifications MeSH