The Curiosity rover crossed a large aeolian bedform at the location named "Dingo Gap" in late 2013 during its drive toward the Bagnold dune field. Before the arrival of Curiosity, some reports labeled the Dingo Gap bedform as a sand dune (e.g., the High Resolution Imaging Science Experiment [HiRISE] web site image caption for ESP_027834_1755). We propose that the feature crossed by Curiosity at Dingo Gap was a small example of what has been called a Transverse Aeolian Ridge (TAR), a bright-toned, wind-generated bedform (Bourke et al., 2003; Wilson & Zimbelman, 2004). Before going further, clarification is needed regarding the terminology for the bedforms to be discussed. The term TAR was introduced above, but there is not uniform agreement in the literature for what this term means, let alone the plethora of terms in use for a variety of aeolian bedforms on Mars and Earth. Below we summarize some of the many terms used in the aeolian literature, indicating which terms will be used throughout the remainder of this manuscript. Most readers may visualize a "typical" ripple as what one commonly observes on the surface of sand dunes or on a sandy beach. These ripples result from the interaction of saltating sand grains and a monodispersive sand surface; here we refer to these as "sand ripples." Sand ripples develop from even smaller ripples formed by the splashing of sand grains when a saltating grain impacts a sand surface. The splashed grains move in what is called "reptation" rather than saltation, forming ripples approximately 1/6th the wavelength of sand ripples (Anderson, 1987; Anderson & Haff, 1988); these early stage wind ripples smaller than normal sand ripples have also been called "impact ripples." Continuum analytical models utilizing both saltation and reptation of windblown sand have been developed to study aeolian sand deposits on Earth (
Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research.
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