本研究基于Landsat-8 Band8波段数据，采用基于傅里叶变换的相位相关性计算的方法（COSI-Corr）来计算冰川运动速度，获得了亚洲高山区大型冰川2013年到2024年的月尺度冰川运动速度数据。该数据可为研究亚洲高山区冰川物质平衡以及其如何响应气候的变化提供数据支持。
", "ds_source": "Landsat 8卫星于2013年2月发射升空，其搭载了陆地成像仪（OLI）和热红外传感器（TIRS）两种关键的传感器。这两种传感器保持了与前代Landsat 7相同的空间分辨率、覆盖范围及光谱范围，但在部分波段的宽度上进行了优化，特别是OLI传感器的Band5和Band8。本文使用覆盖亚洲高山区的2014-2021年Landsat 8影像Band 8全色波段的遥感影像，其15 m的空间分辨率让精准测量山地冰川的表面位移成为可能。在Landsat 8数据的选取上，为避免云雾遮挡和冰雪反射率的影响，选取研究区内无云、少雪的影像。本数据在文中使用光学影像互相关算法来提取冰川表面运动速度数据。本研究以单条冰川为单位，通过光学影像反照率去云算法筛选出共计101827景可供提取冰川运动速度的影像，其覆盖了亚洲高山区大于10km2的冰川2014-2021年间冰川区上空的全部无云影像。
", "ds_process_way": "（1）本研究以单条冰川为单位，基于GEE平台通过光学影像反照率去云算法筛选出共计101827景可供提取冰川运动速度的影像，其覆盖了亚洲高山区大于10km2的冰川2014-2021年间冰川区上空的全部无云影像。\n
（2）基于ENVI的COSI-Corr插件计算出冰川的东西向位移、南北向位移以及信噪比。\n
（3）我们计算筛选出了每一幅影像中的无云覆盖区域、无积雪区域以及非冰川地形的裸地像元，并且对每两幅影像裸地像元进行特征匹配，并且通过计算得到了裸地像元的南北向以及东西向位移，将其作为每两幅影像之间的匹配误差，随后于每两幅影像得到的位移结果中消除该误差。\n
（4）在上述的基础上本研究使用了GLAFT来对冰川运动速度的不确定性进行评估，这个方法是基于静态地面的速度测量来计算冰川表面运动速度的不确定性。其方法的中心思想是假设临近的无冰地带是静态的，没有水平以及垂直方向的位移，这意味着任何无冰区的位移都是计算结果的误差。我们在遥感影像中去除冰川区域，积雪区域以及被云覆盖区域，从而保留了裸露的地表。在保留裸露的地表的前提下，GLAFT在计算裸露地表位移误差的情况下区分了裸露地表上正确与不正确的特征匹配点，随后在正确的裸露地表特征匹配点中计算出冰川运动速度的不确定性。理想情况下，在正确的裸露地表上计算的不确定性会更加接近理论上的不确定性。\n
", "ds_quality": "质量良好
", "ds_acq_start_time": "2013-04-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "亚洲高山区", "ds_acq_lon_east": 96.99, "ds_acq_lat_south": 29.01, "ds_acq_lon_west": 73.51, "ds_acq_lat_north": 42.269999999999996, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 103213245, "ds_files_count": 2, "ds_format": ".tif", "ds_space_res": "120m", "ds_time_res": "1月", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "dca8a666-4029-413e-a731-dc9200368bbc.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "09314967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2025-01-08 14:41:41", "last_updated": "2025-01-08 14:47:12", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.GLACIER.DB6725.2025", "license": null, "i18n": { "en": { "title": "Monthly velocity data of large glaciers in High Mountain Asia (2013-2024)", "ds_format": ".tif", "ds_source": "The Landsat 8 satellite was launched in February 2013, carrying two key sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). These sensors maintain the same spatial resolution, coverage, and spectral range as their predecessor, Landsat 7, but feature optimized spectral bandwidths for certain bands, particularly Band 5 and Band 8 of the OLI sensor. This study utilizes Landsat 8 imagery from Band 8 (panchromatic band) covering the High Mountain Asia region during the period 2014–2021. With a spatial resolution of 15 meters, the Band 8 imagery enables precise measurement of surface displacements on mountain glaciers.\nTo minimize the effects of cloud cover and snow reflectance, only cloud-free and low-snow imagery from the study area was selected. Glacier surface velocity data were extracted using an optical image cross-correlation algorithm. For this study, individual glaciers served as the unit of analysis, and cloud-free imagery was identified using an optical image albedo-based cloud removal algorithm. In total, 101,827 images were selected for glacier velocity extraction, covering all cloud-free imagery over glaciers larger than 10 km² in the High Mountain Asia region between 2014 and 2021.
", "ds_quality": "good quality
", "ds_ref_way": "", "ds_abstract": "This study utilizes Landsat-8 Band 8 data and applies a phase correlation method based on Fourier transform (COSI-Corr) to calculate glacier velocity. Monthly-scale glacier velocity data for major glaciers in the High Mountain Asia were obtained for the period from 2013 to 2024. This dataset provides essential support for studying the mass balance of glaciers in High Mountain Asia and their responses to climate change.
", "ds_time_res": "1月", "ds_acq_place": "High Mountain Aisa", "ds_space_res": "120m", "ds_projection": "", "ds_process_way": "(1) In this study, individual glaciers were used as units of analysis. A total of 101,827 cloud-free images suitable for extracting glacier velocity were selected using an albedo-based cloud removal algorithm on the Google Earth Engine (GEE) platform. These images covered all cloud-free scenes over glaciers larger than 10 km² in High Mountain Asia from 2014 to 2021. \n
(2) The east-west displacement, north-south displacement, and signal-to-noise ratio (SNR) of glaciers were calculated using the COSI-Corr plugin in ENVI. \n
(3) Cloud-free areas, snow-free areas, and bare ground pixels in non-glacier terrains were identified in each image. Feature matching was conducted for bare ground pixels between paired images, and the north-south and east-west displacements of bare ground pixels were calculated as the matching error between the two images. These errors were subsequently removed from the displacement results derived from each image pair. \n
(4) Based on the above steps, this study employed the GLAFT to assess the uncertainty in glacier velocity. This method estimates glacier surface velocity uncertainty by measuring the displacement of static ground, with the core assumption that adjacent non-glacier areas are static, with no horizontal or vertical displacement. This implies that any displacement observed in non-glacier areas represents measurement error. Glacier, snow-covered, and cloud-covered regions were excluded from the remote sensing imagery, leaving only exposed bare ground. Under the condition of retaining exposed bare ground, GLAFT distinguished between correct and incorrect feature matching points on the bare ground during the calculation of displacement errors. Subsequently, the uncertainty of glacier velocity was estimated from the correct feature matching points on bare ground. Ideally, the uncertainty calculated from the correct bare ground matches would be closer to the theoretical uncertainty.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "冰川运动速度" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "亚洲高山区" ], "ds_time_tags": [ 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024 ], "ds_contributors": [ { "true_name": "田汉强", "email": "thq@mail.ynu.edu.cn", "work_for": "云南大学", "country": "中国" }, { "true_name": "刘时银", "email": "liusy@lzb.ac.cn", "work_for": "云南大学国际河流与生态安全研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "田汉强", "email": "thq@mail.ynu.edu.cn", "work_for": "云南大学", "country": "中国" }, { "true_name": "刘时银", "email": "liusy@lzb.ac.cn", "work_for": "云南大学国际河流与生态安全研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "田汉强", "email": "thq@mail.ynu.edu.cn", "work_for": "云南大学", "country": "中国" }, { "true_name": "刘时银", "email": "liusy@lzb.ac.cn", "work_for": "云南大学国际河流与生态安全研究院", "country": "中国" } ], "category": "冰川" }