English

Global Site Selection for Astronomy

Instrumentation and Methods for Astrophysics 2020-03-04 v2 Earth and Planetary Astrophysics

Abstract

A global site selection for astronomy was performed with 1 km spatial resolution (\sim 1 Giga pixel in size) using long term and up-to-date datasets to classify the entire terrestrial surface on the Earth. Satellite instruments are used to get the following datasets of Geographical Information System (GIS) layers: Cloud Coverage, Digital Elevation Model, Artificial Light, Precipitable Water Vapor, Aerosol Optical Depth, Wind Speed and Land Use -- Land Cover. A Multi Criteria Decision Analysis (MCDA) technique is applied to these datasets creating four different series where each layer will have a specific weight. We introduce for the first time a ``Suitability Index for Astronomical Sites'' namely, SIAS. This index can be used to find suitable locations and to compare different sites or observatories. Mid-western Andes in South America and Tibetan Plateau in west China were found to be the best in all SIAS Series. Considering all the series, less than 3 \% of all terrestrial surfaces are found to be the best regions to establish an astronomical observatory. In addition to this, only approximately 10 \% of all current observatories are located in good locations in all SIAS series. Amateurs, institutions or countries aiming to construct an observatory could create a short-list of potential site locations using layout of SIAS values for each country without spending time and budget.The outcomes and datasets of this study has been made available through a web site, namely ``Astro GIS Database'' on \texttt{\url{www.astrogis.org}}.

Keywords

Cite

@article{arxiv.1912.01911,
  title  = {Global Site Selection for Astronomy},
  author = {N. Aksaker and S. K. Yerli and M. A. Erdoğan and Z. Kurt and K. Kaba and M. Bayazit and C. Yesilyaprak},
  journal= {arXiv preprint arXiv:1912.01911},
  year   = {2020}
}

Comments

19 Pages, 4 Figures, 7 tables, Accepted for publication in MNRAS

R2 v1 2026-06-23T12:35:28.590Z