International audienceMarine conglomerates at high elevation on the flanks of ocean islands are usually interpreted as evidence of mega-tsunamis generated by volcano flank collapses, although their origin is sometimes debated (elevated littorals vs. tsunami). In this review, we introduce case studies of well-documented examples of tsunami conglomerates in Hawaii (Pacific Ocean), the Canary and Cape Verde Islands (Atlantic Ocean), and Mauritius Island (Indian Ocean). Other less-documented marine conglomerates are also presented as tsunami candidates. Then, we build a comprehensive picture of the general characteristics of these conglomerates and the different methods that can be applied to date them. Different perspectives of research are proposed, especially on the use of tsunami conglomerates as proxies for better constraining numerical models of ocean island flank collapses and associated tsunamis. We also discuss the possible links between volcano growth, flank instability, and climate
Desert oases are fragile agrarian areas, very vulnerable to sand encroachment by wind. Ensuring their conservation highly depends on our capacity to identify sand encroachment patterns, e.g. the origin of sand and its spatial distribution in the irrigated plots. Here we show how to tackle this issue using the case study of Erg Chebbi (Morocco), where two oases (Hassilabiad and Merzouga) are surrounded by dunes, Hamada and alluvial sediments from the Wadi Ziz. We combine field interviews with the study of wind dynamics, sediment sampling, Particle Size Distribution (PSD) tests and End-Member Modelling Analysis (EMMA). We observe that the most relevant contributor to sand encroachment is the Wadi Ziz (30%), followed by the Hamada (28%), an undetermined source of dust (25%), and the Erg dunes (16%). These genetically different sediments cluster unevenly in the oases, indicating the existence of areas with contrasting degrees of exposure to sedimentary sources. The results allow to define on solid grounds which sand source areas should be stabilized first in order to obtain the greatest reduction in sand encroachment. Our approach also provides policy-makers with better tools to identify which spots are specially vulnerable to accumulate a specific sediment, thus allowing for a more nuanced management of sand in oasis environments.
Morphologies of highly complex star dunes are the result of aeolian dynamics in past and present times. These dynamics reflect climatic conditions and associated forces like sediment availability and vegetation cover, as well as feedbacks with adjacent environments. However, an understanding of aeolian dynamics on star dune morphometries is still lacking sufficient detail, and their influence on formation and evolution remains unclear. We therefore investigate the dynamics of a complex star dune (Erg Chebbi, Morocco) by analysing wind measurements compared to morphometric changes derived from multitemporal high-accuracy 3D observations during two surveys (October 2018 and February 2020). Using real-time kinematic global navigation satellite system (RTK-GNSS) measurements and terrestrial laser scanning (TLS), the reaction of a star dune surface to an observed constant unimodal sand-moving wind is presented. TLS point clouds are used for morphometric analysis as well as direct surface change analysis, which relates to sand transport. RTK-GNSS measurements enable the assessment of horizontal crest movement. Observed surface changes lead to the identification of an overall shielding effect, resulting in sand accumulation mainly on windward slopes. Our results point to a self-sustained dune growth, whichhas not yet been described in such spatial detail. Steep slopes, often found on star dunes around the globe, seem to partly hinder upslope sand transport. Though a comparatively short observation period, we therefore hypothesize that, besides wind intensity alone, slope angles are more decisive for sand transport than previously assumed. Our methodological approach of combining meteorological data and highresolution multitemporal 3D elevation models can be used for monitoring all dune forms and contributes to a general understanding of dune dynamics and evolution.
In this paper, we describe a strategy for training neural networks for object detection in range images obtained from one type of LiDAR sensor using labeled data from a different type of LiDAR sensor. Additionally, an efficient model for object detection in range images for use in self-driving cars is presented. Currently, the highest performing algorithms for object detection from LiDAR measurements are based on neural networks. Training these networks using supervised learning requires large annotated datasets. Therefore, most research using neural networks for object detection from LiDAR point clouds is conducted on a very small number of publicly available datasets. Consequently, only a small number of sensor types are used. We use an existing annotated dataset to train a neural network that can be used with a LiDAR sensor that has a lower resolution than the one used for recording the annotated dataset. This is done by simulating data from the lower resolution LiDAR sensor based on the higher resolution dataset. Furthermore, improvements to models that use LiDAR range images for object detection are presented. The results are validated using both simulated sensor data and data from an actual lower resolution sensor mounted to a research vehicle. It is shown that the model can detect objects from 360 • range images in real time.
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