Abstract
Aeolian processes are the most dominant geological processes in the current environment of Mars. Studying other planets through terrestrial analogs is an important step in planetary science studies. One of the most enigmatic and mysterious aeolian features on this planet is known as Transverse Aeolian Ridges (TARs). In the present study, I described a terrestrial analog for TARs on Mars in the Lut desert of Iran. TAR-like features in this area have considerable similarities with Martian TARs in morphometry. This area represents a unique site to study the formation and evolution of these enigmatic features with the potential to develop a better understanding of TARs on Mars.
Furthermore, mapping small linear trending features such as TARs has been a restriction in planetary remote sensing. In this study, I present a new framework for mapping these features based on an Artificial Neural Network (ANN) algorithm known as Self Organizing Maps (SOM). Appropriate layer selection and network settings have been defined for mapping features with small footprints on satellite images. Accuracy assessment using different planetary images such as Worldview, Quickbird and HiRISE shows that this is a promising method in remote sensing analysis of these features. Moreover, this method can be applied in other disciplines for different purposes.
The new mapping technique was applied to satellite images in order to evaluate TARs on Earth and Mars. I produced a large sample set of these bedforms through Mars and Earth satellite images. Results from different metrics showed similarities between these features in the Lut desert and TARs on Mars. Interesting results have been acquired by using multi-temporal satellite images and morphometry analysis of TARs on both planets. The features have been classified into three different regions based on their migration rate, direction and location in this area. I define TARs that are unique to Mars from TARs that have the similar analogs on Earth.
