通过检索World Imagery Wayback历史影像，收集典型流动沙丘区域10年的时序遥感数据。结合高分辨率影像中沙脊线清晰、纹理突变明显等典型地表特征，利用深度学习U-Net语义分割模型提取各时期的沙脊线位置，并结合光流法与EWMA方法及目视解译，对沙脊线进行了速变区域综合判定，以验证时序影像中沙丘形态变化的空间真实性与准确性。最终制备出覆盖典型流动沙丘的时序沙脊线及速变区域数据集，为沙丘移动过程研究与速度变化分析提供可靠数据支撑。", "ds_source": "
World Imagery Wayback影像：通过ArcGIS Wayback Imagery服务访问", "ds_process_way": "
1.沙脊线的语义分割提取：利用深度学习U-Net语义分割模型自动提取各时期沙脊线。依据沙脊线在影像中呈现的纹理突变明显、亮度差异清晰、整体走向连续等典型地貌特征，对模型输出进行质量校验，获取准确刻画沙丘几何形态的时序沙脊线结果，形成数据集的核心标签部分。\n
2.光流法与EWMA检测速变区域及目视验证：在沙脊线提取的基础上，利用光流法结合EWMA控制图检测沙丘移动的速变区域，并通过高分辨率影像开展目视解译，对检测结果进行验证，确保检测的速变区域与实际沙丘速变区域一致。", "ds_quality": "
数据质量验证显示，沙脊线与原始影像形态分布一致，速变区域标注准确可靠，为沙丘移动分析提供坚实基础。", "ds_acq_start_time": "2010-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "腾格里沙漠黄骅-山丹公路附近", "ds_acq_lon_east": 103.94611111111111, "ds_acq_lat_south": 40.077222222222225, "ds_acq_lon_west": 103.93694444444445, "ds_acq_lat_north": 40.09444444444445, "ds_acq_alt_low": 1377.0, "ds_acq_alt_high": 1400.0, "ds_share_type": "apply-access", "ds_total_size": 164559908, "ds_files_count": 19, "ds_format": "tif", "ds_space_res": "0.5m", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "WGS 1984 UTM Zone 50N", "ds_thumbnail": "19272a4d-ebcf-4e67-9f3a-d1e6bd11362a.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "本数据集基于多时相World Imagery Wayback遥感影像及沙脊线分割结果构建，适用于流动沙丘移动过程分析、地表形变速变识别、风沙动力过程研究等相关工作。光流法能够构建沙丘表面的运动速度场，EWMA控制图可对其时间序列进行变化点检测，两者结合能够较为稳定地识别沙脊线快速迁移所对应的速变区域，为复杂背景下的沙脊线动态变化提供有效参考。速变区域标注反映的是沙脊线位置的快速变化特征，并不直接代表沙丘体积变化或三维形变过程，必要时可与DEM、激光点云或实测风场数据结合，以获得更加完整的动力学解释。", "ds_from_station": null, "organization_id": "9de89acc-5714-4927-aba3-ac88067dff8a", "ds_serv_man": "康建芳", "ds_serv_phone": "0931-4967597 ", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2025-12-09 16:59:46", "last_updated": "2025-12-09 16:59:46", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7027.2025", "i18n": { "en": { "title": "Rapid change detection data set of mobile dunes in Tengger Desert (2010-2022)", "ds_format": "", "ds_source": "
World Imagery Wayback imagery: accessed through ArcGIS Wayback Imagery service", "ds_quality": "
Data quality verification shows that the distribution of sand ridge lines is consistent with the original image morphology, and the labeling of rapidly changing areas is accurate and reliable, providing a solid foundation for sand dune movement analysis.", "ds_ref_way": "", "ds_abstract": "
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.
", "ds_time_res": "年", "ds_acq_place": "Near Huanghua Shandan Highway in Tengger Desert", "ds_space_res": "0.5m", "ds_projection": "", "ds_process_way": "1. Semantic segmentation and extraction of sand ridges: Utilizing the deep learning U-Net semantic segmentation model to automatically extract sand ridges at different stages. Based on the typical geomorphic features presented by the sand ridge line in the image, such as obvious texture changes, clear brightness differences, and continuous overall direction, the quality of the model output is verified to obtain accurate temporal sand ridge line results that depict the geometric shape of sand dunes, forming the core label part of the dataset.\n
2. Optical flow method and EWMA detection of rapidly changing areas and visual verification: Based on the extraction of sand ridge lines, optical flow method combined with EWMA control map is used to detect the rapidly changing areas of sand dune movement, and visual interpretation is carried out through high-resolution images to verify the detection results, ensuring that the detected rapidly changing areas are consistent with the actual rapidly changing areas of sand dunes.", "ds_ref_instruction": "This dataset is constructed based on multi temporal World Imagery Wayback remote sensing images and sand ridge segmentation results, and is suitable for analyzing the movement process of mobile sand dunes, identifying surface deformation, studying wind and sand dynamic processes, and other related work. The optical flow method can construct the velocity field of sand dune surface, and the EWMA control map can detect the change points in its time series. The combination of the two can stably identify the speed changing areas corresponding to the rapid migration of sand ridge lines, providing effective reference for the dynamic changes of sand ridge lines in complex backgrounds. The annotation of rapidly changing areas reflects the rapid changes in the position of sand ridges, and does not directly represent the volume changes or three-dimensional deformation processes of sand dunes. If necessary, it can be combined with DEM, laser point clouds, or measured wind field data to obtain a more complete dynamic interpretation." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "ds_topic_tags": [ "沙丘移动", "速变感知", "光流法", "EWMA" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "腾格里沙漠黄骅-山丹公路附近" ], "ds_time_tags": [ 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "周增光", "email": "zhouzg@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "祁涛明", "email": "qitaoming23@mails.ucas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "周增光", "email": "zhouzg@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "周增光", "email": "zhouzg@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "category": "沙漠与荒漠化" }