本数据集包含喜马拉雅中段地区 1990、2000、2010、2015、2020、2022 年六期冰湖编目数据，存储格式为ESRI Shapefile，可用于表征研究区冰湖时空演变特征。1990、2000、2010、2015、2020 年冰湖边界基于Landsat遥感影像，依托Google Earth Engine（GEE）平台，采用改进的归一化水体指数（Enhanced Normalized Difference Water Index，ENDWI）耦合 Otsu 动态阈值法自动提取；2022年冰湖数据基于Sentinel-2影像提取生成。数据投影采用Albers等积投影(Asia_North_Albers_Equal_Area_Conic，参数Central_Meridian: 95.00000000；Standard_Parallel_1: 15.00000000；Standard_Parallel_2: 65.00000000；Latitude_Of_Origin: 30.00000000)。本编目参考 RGI 冰川编目，1990、2000、2010、2010、2015/2020年分为3类冰面湖、冰川接触湖、非冰川接触湖；2022年将冰湖划分为4类：冰面湖、冰川接触湖、非冰川接触湖、非冰川补给湖。所有时相的提取结果均经过人工目视解译与矢量编辑，保障数据精度。这套多时序冰湖编目可为区域冰川变化监测、冰湖溃决洪水（GLOF）灾害风险评估及冰冻圈相关研究提供基础地理数据支撑。", "ds_source": "
Landsat陆地卫星影像数据 从美国地质调查局网站http://earthexplorer.usgs.gov/)。\n
Sentinel 2 从哥白尼官网https://dataspace.copernicus.eu/下载。\n
RGI7数据从www.glims.org下载。", "ds_process_way": "
依托 Google Earth Engine（GEE）平台，采用改进的归一化水体指数（Enhanced Normalized Difference Water Index，ENDWI）耦合 Otsu 动态阈值法自动提取。", "ds_quality": "
冰湖数据集提取误差具有显著尺度效应，小型冰湖面积相对不确定性更高，随冰湖面积增大相对误差随之降低。冰湖矢量边界平面精度可达±0.5像素（±15m和±5m）。人工目视解译校正后的冰湖边界的相对面积百分比误差在0%~22%之间。与Zhang等(2023)冰湖数据集局部交叉验证结果显示(R2=0.99)，误差集中分布于0附近，无明显系统性偏差。山体阴影、湖冰与积雪覆盖、影像空间分辨率差异是主要误差来源，10 m分辨率Sentinel\u001e2影像相比30 m Landsat影像能够有效降低冰湖边界提取不确定性。", "ds_acq_start_time": "1990-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "喜马拉雅中段地区", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 7632325, "ds_files_count": 0, "ds_format": "*.shp", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "Albers等积投影", "ds_thumbnail": "c1e47559-a138-4c18-b9a7-c2bcccefa4c0.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "27c4cf3e-64f6-4925-95e2-8a868a4a9346", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 0, "publish_time": "2026-09-28 10:25:50", "last_updated": "2026-09-28 10:42:53", "protected": false, "protected_to": "2027-03-27 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.db7810.2026", "i18n": { "en": { "title": "Inventory of Glacial Lakes in the Central Himalayas Derived from Multi-source Satellite Imagery (1990-2022)", "ds_format": "*.shp", "ds_source": "
Landsat Landsat image data from the U.S. Geological Survey website http://earthexplorer.usgs.gov/).\r\n
Sentinel 2 is downloaded from the Copernicus website https://dataspace.copernicus.eu/.\r\n
RGI7 data is downloaded from www. glims. org.", "ds_quality": "
The extraction error of ice lake data sets has a significant scale effect. The relative uncertainty of the area of small ice lakes is higher, and the relative error decreases as the area of ice lakes increases. The ice lake vector boundary plane accuracy can reach ±0.5 pixels (±15m and ±5m). The percentage error of the relative area of the ice lake boundary corrected by manual visual interpretation is between 0% and 22%. The local cross-validation results with Zhang et al.(2023) ice lake dataset showed (R2=0.99) that the errors were concentrated around 0, and there was no obvious systematic deviation. Mountain shadows, lake ice and snow cover, and differences in image spatial resolution are the main sources of error. 10 m resolution Sentinel\u001e2 Compared with 30m Landsat images, the image can effectively reduce the uncertainty of glacial lake boundary extraction.", "ds_ref_way": "", "ds_abstract": "
This dataset contains six periods of ice lake catalog data in the central Himalayan region in 1990, 2000, 2010, 2015, 2020, and 2022. The storage format is ESRI Shapefile, which can be used to characterize the spatio-temporal evolution characteristics of ice lakes in the study area. The ice lake boundary in 1990, 2000, 2010, 2015, and 2020 is based on Landsat remote sensing images, relying on the Google Earth Engine (GEE) platform, and automatically extracted using the Improved Normalized Difference Water Index (ENDWI) coupled with the Otsu dynamic threshold method; the ice lake data in 2022 is extracted and generated based on Sentinel-2 images. Data projection uses Albers equal area projection (Asia_North_Albers_Equal_Area_Conic, parameters Central_Meridian: 95.000000;Standard_Parallel_1: 15.00000000;Standard_Parallel_2: 65.00000000;Latitude_Of_Origin: 30.00000000)。This catalog refers to the RGI Glacier Catalog. In 1990, 2000, 2010, 2010, and 2015/2020, it is divided into three types: ice lakes, glacier contact lakes, and non-glacier contact lakes; in 2022, ice lakes will be divided into four types: ice lakes, glacier contact lakes, non-glacier contact lakes, and non-glacier supply lakes. The extraction results of all time phases are subjected to manual visual interpretation and vector editing to ensure data accuracy. This multi-time series glacial lake catalog can provide basic geographical data support for regional glacier change monitoring, glacial lake burst flood (GLOF) disaster risk assessment, and cryosphere related research.", "ds_time_res": "", "ds_acq_place": "Central Himalayan region", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Relying on the Google Earth Engine (GEE) platform, the improved normalized Difference water index (ENDWI) coupled with Otsu dynamic threshold method is used to automatically extract it.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "allow_update_data": false, "created_from": "admin", "ds_topic_tags": [ "冰湖", "ENDWI", "Otsu 动态阈值法" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "喜马拉雅中段地区" ], "ds_time_tags": [ 1990, 2000, 2010, 2015, 2020, 2022 ], "ds_contributors": [ { "true_name": "程小强", "email": "2024026003@chd.edu.cn", "work_for": "长安大学", "country": "中国" }, { "true_name": "上官冬辉", "email": "dhguan@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "" }, { "true_name": "李旺平", "email": "lwp_136@lut.edu.cn", "work_for": "兰州理工大学", "country": "中国" }, { "true_name": "周兆叶", "email": "zhou_zy@lzu.edu.cn", "work_for": "兰州理工大学", "country": "中国" }, { "true_name": "杨成生", "email": "yangchengsheng@chd.edu.cn", "work_for": "长安大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "程小强", "email": "2024026003@chd.edu.cn", "work_for": "长安大学", "country": "中国" }, { "true_name": "上官冬辉", "email": "dhguan@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "" }, { "true_name": "李旺平", "email": "lwp_136@lut.edu.cn", "work_for": "兰州理工大学", "country": "中国" }, { "true_name": "周兆叶", "email": "zhou_zy@lzu.edu.cn", "work_for": "兰州理工大学", "country": "中国" }, { "true_name": "杨成生", "email": "yangchengsheng@chd.edu.cn", "work_for": "长安大学", "country": "中国" } ], "ds_managers": [ { "true_name": "上官冬辉", "email": "dhguan@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "" } ], "category": "冰川" }