该数据集中利用 1964 年至 2020 年间的 42 幅中高分辨率遥感图像，完成了对喜马拉雅山脉中部整个塔玛甲子盆地的冰川和冰湖的全面调查并整理为数据集，分别对 1964 年、1980 年、1990 年、2000 年、2010 年、2018 年和 2020 年的冰川和 1964 年、1980 年的冰湖进行了人工划界，并对 1987-2020 年的冰湖进行了自动划界，数据格式为.shp。其中，绘制了2020年整个盆地的 271 个冰川和 196 个冰湖的地图，覆盖面积分别为 329.2 ± 1.9 平方公里和 14.4 ± 0.3 平方公里。经统计，在过去的 56 年中，该流域的冰川总面积缩小了 26.2 ± 3.2 平方公里（0.13% a-1），平均变薄了约20 米，平均速度从 1999-2003 年的 5.3 米 a-1 逐渐减慢到 2013-2015 年的 4.0 米 a-1。冰川湖泊的总面积增加了 9.2 ± 0.4 平方公里（约 180%），其中与冰接触的前冰川湖泊的面积扩大率（约 204%）远高于其他湖泊。", "ds_source": "
本研究使用的遥感图像来自美国地质调查局（USGS，https://earthexplorer.usgs.gov/）、地理空间数据云（http://www.gscloud.cn/）和 PlanetLabs（https://www.planet.com/），时间跨度为 1964 年至 2020 年。共使用了 6 幅 KH-4A 和 KH-9 Keyhole (KH) 图像（2.7-9 米）、36 幅 Landsat (TM/ETM+/OLI) 表面反射率图像（30 米）和 4 幅 PlanetScope (PL) 正交瓦片图像（3.125 米）。图像主要采集于季风后和接近消融季节末期（8 月至 12 月），云量和季节性积雪较少。经正交校正的 PL 产品被用作 KH 图像配准的基础图像。", "ds_process_way": "
\n通过使用 Python API 交互界面，可以比 Javascript API 界面更精确地根据原始图像的屏幕检测结果调整分类阈值。所有 Landsat 和 PlanetScope 图像都是经过大气和地形校正的产品。此外，还估算了 ±0.5 像素的误差，以计算面积的不确定性（即线性误差与周长相乘，例如，Landsat 图像的线性误差为 15 米，KH-4A 图像的线性误差约为 1.35 米）。对于超冰川湖泊，只保留常年蓄水的基底湖泊，而剔除季节变化较大的栖水湖泊。", "ds_quality": "
遥感是快速监测冰川动态和灾害风险评估的重要工具。虽然本研究已经获得了冰川湖面积变化的详细信息，但冰川湖的基本数据（如湖盆地地形、大坝建筑材料类型、冰碛坝内部结构等）仍然缺乏，需要实地调查。", "ds_acq_start_time": "1964-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "喜马拉雅山脉；中尼跨境流域；Tama Koshi (Rongxer) basin；", "ds_acq_lon_east": 95.0586111111111, "ds_acq_lat_south": 29.630833333333335, "ds_acq_lon_west": 74.58999999999999, "ds_acq_lat_north": 35.23916666666667, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 2668791, "ds_files_count": 2, "ds_format": "shp", "ds_space_res": "", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "1177edba-0793-471b-bebd-95f6720f7536.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-01-26 10:20:15", "last_updated": "2026-01-12 17:36:46", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB4166.2024", "i18n": { "en": { "title": "Rapid glacier Shrinkage and Glacial Lake Expansion of a China-Nepal Transboundary Catchment in the Central Himalayas dataset（1964-2020）", "ds_format": "shp", "ds_source": "
The remote sensing images used in this study were obtained from the United States Geological Survey (USGS), https://earthexplorer.usgs.gov/ ）Geospatial data cloud（ http://www.gscloud.cn/ ）And PlanetLabs（ https://www.planet.com/ ）The time span is from 1964 to 2020. A total of 6 KH-4A and KH-9 Keyhole (KH) images (2.7-9 meters), 36 Landsat (TM/ETM+/OLI) surface reflectance images (30 meters), and 4 PlanetScope (PL) orthogonal tile images (3.125 meters) were used. The images are mainly collected after the monsoon and near the end of the melting season (August to December), with less cloud cover and seasonal snow accumulation. The PL product with orthogonal correction is used as the base image for KH image registration.", "ds_quality": "
Remote sensing is an important tool for rapidly monitoring glacier dynamics and assessing disaster risks. Although detailed information on the changes in glacial lake area has been obtained in this study, basic data on glacial lakes (such as lake basin topography, dam building material types, internal structure of glacial moraine dams, etc.) are still lacking and require field investigation.", "ds_ref_way": "", "ds_abstract": "
In this dataset, 42 medium and high resolution remote sensing images from 1964 to 2020 were used to complete a comprehensive survey of glaciers and ice lakes in the entire Tama sub basin in the middle of the the Himalayas and collate them into a dataset. The glaciers in 1964, 1980, 1990, 2000, 2010, 2018 and 2020 and the ice lakes in 1964 and 1980 were demarcated manually, and the ice lakes in 1987-2020 were demarcated automatically in the data format of. shp. Among them, maps of 271 glaciers and 196 glacial lakes in the entire basin in 2020 were drawn, covering an area of 329.2 ± 1.9 square kilometers and 14.4 ± 0.3 square kilometers, respectively. According to statistics, in the past 56 years, the total area of glaciers in the basin has decreased by 26.2 ± 3.2 square kilometers (0.13% a-1), with an average thinning of about 20 meters. The average speed has gradually slowed down from 5.3 meters a-1from 1999 to 2003 to 4.0 meters a-1from 2013 to 2015. The total area of glacial lakes has increased by 9.2 ± 0.4 square kilometers (about 180%), with the expansion rate of pre glacial lakes in contact with ice (about 204%) being much higher than other lakes.
", "ds_time_res": "年", "ds_acq_place": "The Himalayas; Cross border watershed between China and Nepal; Tama Koshi (Rongxer) basin;", "ds_space_res": "", "ds_projection": "", "ds_process_way": "\nBy using the Python API interactive interface, it is possible to adjust the classification threshold more accurately based on the screen detection results of the original image than the Javascript API interface. All Landsat and PlanetScope images are products that have undergone atmospheric and terrain correction. In addition, an error of ± 0.5 pixels was estimated to calculate the uncertainty of the area (i.e. linear error multiplied by perimeter, for example, the linear error of Landsat image is 15 meters, and the linear error of KH-4A image is about 1.35 meters). For super glacial lakes, only the basal lakes that store water year-round are retained, while the lakes with significant seasonal variations are excluded.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "喜马拉雅山", "冰川快速萎缩", "冰川湖扩张" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "喜马拉雅山中部", "中尼跨境流域" ], "ds_time_tags": [ 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "钟妍", "email": "zhongyan19@mails.ucas.ac.cn", "work_for": " 中国科学院山地灾害与环境研究所", "country": "中国" }, { "true_name": "刘巧", "email": "liuqiao@imde.ac.cn", "work_for": "中国科学院、水利部成都山地灾害与环境研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "钟妍", "email": "zhongyan19@mails.ucas.ac.cn", "work_for": " 中国科学院山地灾害与环境研究所", "country": "中国" }, { "true_name": "刘巧", "email": "liuqiao@imde.ac.cn", "work_for": "中国科学院、水利部成都山地灾害与环境研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "钟妍", "email": "zhongyan19@mails.ucas.ac.cn", "work_for": " 中国科学院山地灾害与环境研究所", "country": "中国" }, { "true_name": "刘巧", "email": "liuqiao@imde.ac.cn", "work_for": "中国科学院、水利部成都山地灾害与环境研究所", "country": "中国" } ], "category": "冰川" }