Wind erosion is an important surface process on terrestrial planets but is difficult to quantify particularly on long‐term timescales. Here we use high‐resolution Terrestrial Laser Scanning and cosmogenic Beryllium‐10 (¹⁰Be) exposure dating to investigate the yardangs, a wind‐eroded landform with aerodynamic forms, within the western Qaidam Basin, Central Asia. The results indicate that the yardangs started to form at ~105 ka, coincident with the early phase of the arid last glacial period. We further calibrated the wind erosion rate in the last glacial period to be ~0.26 mm/a, twice as fast as that of the present‐day interglacial period in our study site. Our results highlight the severe wind erosion during glacial periods and emphasize the fundamental control of global glacial‐interglacial climate oscillations on the secular variation in wind erosion in the Qaidam Basin during the late Quaternary period.

Sand waves are a widespread bed-form in the tidal environment. Their formation, migration, and other properties have great significance in the fields of geology and oceanography. Currently, research on sand waves is mainly based on multi-beam bathymetric (MB) data. For large sand wave regions, however, the acquisition ability of MB data becomes limited; instead, interval-line measurement is adopted but with the inherent problem that it cannot achieve the full spatial coverage required. Reconstructed sand wave bathymetry (SWB) using interpolation algorithms cannot reflect the real SWB either. In this article, we propose a new approach in using both satellite imagery and MB data to reconstruct SWB, which takes advantage of characteristic information (ripple and crest) of SWB rendered in satellite imagery and MB data. The new approach is exemplified by a case study of the Taiwan Banks. We use the imagery obtained by the Charge-Coupled Device from the Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting, and the MB data from R2Sonic to reconstruct the digital elevation model. The results show that the root mean square error of the reconstructed water depth is 1.47 m (compared to the MB data not using this approach), suggesting that the new approach is effective in reconstructing SWB. This approach allows a reduction in MB data track density, which may lead to an improvement in efficiency.

The morphological characteristics of yardangs are the direct evidence that reveals the wind and fluvial erosion for lacustrine sediments in arid areas. These features can be critical indicators in reconstructing local wind directions and environment conditions. Thus, the fast and accurate extraction of yardangs is key to studying their regional distribution and evolution process. However, the existing automated methods to characterize yardangs are of limited generalization that may only be feasible for specific types of yardangs in certain areas. Deep learning methods, which are superior in representation learning, provide potential solutions for mapping yardangs with complex and variable features. In this study, we apply Mask region-based convolutional neural networks (Mask R-CNN) to automatically delineate and classify yardangs using very high spatial resolution images from Google Earth. The yardang field in the Qaidam Basin, northwestern China is selected to conduct the experiments and the method yields mean average precisions of 0.869 and 0.671 for intersection of union (IoU) thresholds of 0.5 and 0.75, respectively. The manual validation results on images of additional study sites show an overall detection accuracy of 74%, while more than 90% of the detected yardangs can be correctly classified and delineated. We then conclude that Mask R-CNN is a robust model to characterize multi-scale yardangs of various types and allows for the research of the morphological and evolutionary aspects of aeolian landform.

Geospatial data, especially remote sensing (RS) data, are of significant importance for public services and production activities. Expertise is critical in processing raw data, generating geospatial information, and acquiring domain knowledge and other remote sensing applications. However, existing geospatial service platforms are more oriented towards the professional users in the implementation process and final application. Building appropriate geographic applications for non-professionals remains a challenge. In this study, a geospatial data service architecture is designed that links desktop geographic information system (GIS) software and cloud-based platforms to construct an efficient user collaboration platform. Based on the scalability of the platform, four web apps with different themes are developed. Data in the fields of ecology, oceanography, and geology are uploaded to the platform by the users. In this pilot phase, the gap between non-specialized users and experts is successfully bridged, demonstrating the platform’s powerful interactivity and visualization. The paper finally evaluates the capability of building spatial data infrastructures (SDI) based on GeoNode and discusses the current limitations. The support for three-dimensional data, the improvement of metadata creation and management, and the fostering of an open geo-community are the next steps.