%0 Dataset
%T Rapid change detection data set of mobile dunes in Tengger Desert (2010-2022)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/19272a4d-ebcf-4e67-9f3a-d1e6bd11362a
%W NCDC
%R 10.12072/ncdc.nieer.db7027.2025
%A Zhou Zengguang
%A Qi Taoming
%K Sand dune movement;speed perception;optical flow method;EWMA
%X Collect 10-year time-series remote sensing data of typical mobile sand dune areas by searching historical images of World Imagery Wayback. Based on typical surface features such as clear sand ridges and obvious texture changes in high-resolution images, the U-Net semantic segmentation model of deep learning was used to extract the position of sand ridges in each period. Combined with optical flow method, EWMA method, and visual interpretation, the rapid change area of sand ridges was comprehensively determined to verify the spatial authenticity and accuracy of sand dune morphology changes in temporal images. Finally, a time-series sand ridge line and velocity variation area dataset covering typical mobile sand dunes were prepared, providing reliable data support for the study of sand dune movement processes and velocity variation analysis.