河冰监测对于河流径流过程、区域水循环研究，水资源管理以及冰凌灾害防治有着重要意义。高寒山区河冰所在区域气候严寒，实地观测十分困难，遥感技术已经成为获取河冰信息的重要手段。目前公开发布的高寒山区河冰分布范围数据非常稀少，暂无类似的数据可以作对比。本数据集为三江源区长时间序列的30m河冰分布范围数据。基于Google Earth Engine(GEE)平台提供的长时间序列的Landsat系列卫星地表反射率数据集，将研究区1-4月份河冰发育期云量小于20%的影像逐年合成中值影像，然后使用相对差值河冰提取算法(Relative Difference River Ice, RDRI)和归一化雪指数算法（Normalized Difference Snow Index, NDSI)分别提取河冰范围与冰雪范围，当河冰被较厚积雪覆盖造成遗漏时需要使用NDSI算法提取的冰雪范围进行补充。结合卫星影像、DEM、河道、湖泊范围、冰川范围数据，对获得的河冰分布范围数据进行人工检查，删除误判的冰川、湖冰等其它冰体，对于河冰明显遗漏的区域进行人工修补，最后获得了1990-2023年共34期的三江源区河冰分布范围数据。该数据集以shp文件格式存储，为了方便使用分为黄河源区、长江源区和澜沧江源区三个区域，命名规则为：“area+riverice+sensor+year+Revised.shp”，例如：yellow_basin_riverice_ETM_2012_Revised。本数据集可以为三江源区河冰时空分布特征研究提供直接数据，也可以为其它高寒山区河冰监测与制图提供参考。
", "ds_source": "利用Google Earth Engine(GEE)平台提供的“LANDSAT/LT05/C01/T1_SR”、“LANDSAT/LE07/C01/T1_SR”、“LANDSAT/LC08/C01/T1_SR”、“LANDSAT/LC08/C02/T1_L2”地表反射率数据提取河冰范围，共使用云量小于20%的影像5850景。通过地理空间数据云平台下载的SRTM的DEM数据，利用该数据可以作为参照修正河冰分布范围数据，也可以分析研究区海拔、坡度、坡向等参数。
", "ds_process_way": "将研究区1-4月份河冰发育期云量小于20%的影像合成逐年中值影像，然后使用相对差值河冰提取算法(Relative Difference River Ice, RDRI)和归一化雪指数算法（Normalized Difference Snow Index, NDSI)分别提取河冰范围与冰雪范围，当河冰被较厚积雪覆盖造成遗漏时需要使用NDSI算法提取的冰雪范围进行补充。结合卫星影像、DEM、河道、湖泊范围、冰川范围数据，对河冰分布范围数据进行人工检查，删除误判的冰川、湖冰等其它冰体，对于河冰明显遗漏的区域进行人工修补，最后获得了1990-2023年共34期的三江源区河冰分布范围数据，并以shp文件格式存储。
", "ds_quality": "在整个三江源区数据的总体精度为97.96%，kappa系数为0.8628。分区域的评价结果显示在长江源区、黄河源区和澜沧江源区的河冰分布范围数据总体精度分别为98.28%、98.21%、97.34%，平均kappa系数分别为0.860、0.903、0.824。
", "ds_acq_start_time": "1990-01-01 00:00:00", "ds_acq_end_time": "2023-04-30 00:00:00", "ds_acq_place": "三江源区（青藏高原）", "ds_acq_lon_east": 103.68722222222223, "ds_acq_lat_south": 36.2, "ds_acq_lon_west": 89.16666666666667, "ds_acq_lat_north": 31.016666666666666, "ds_acq_alt_low": 2564.0, "ds_acq_alt_high": 6571.0, "ds_share_type": "login-access", "ds_total_size": 387752880, "ds_files_count": 2, "ds_format": "shp", "ds_space_res": "30米", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "WGS84UTM46N\\WGS84UTM47N", "ds_thumbnail": "ecd863a7-110d-410a-b711-27785921cb15.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确注明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.40", "170.4599", "170.5545" ], "quality_level": 3, "publish_time": "2024-07-02 17:21:11", "last_updated": "2024-08-01 14:39:44", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.RIVER_ICE.DB6532.2024", "i18n": { "en": { "title": "30 m Annual River ice extent dataset in the Three Rivers Source Region (1990-2023)", "ds_format": "shp", "ds_source": "The \"LANDSAT/LT05/C01/T1_SR\", \"LANDSAT/LE07/C01/T1_SR\", \"LANDSAT/LC08/C01/T1_SR\", and \"LANDSAT/LC08/C02/T1_L2\" surface reflectivity datasets were used to extract the river ice extent on Google Earth Engine (GEE) platform, and a total of 5,850 images with less than 20% cloud cover were selected. The DEM data of SRTM downloaded from the Geospatial Data Cloud Platform can be used as a reference to correct the river ice extent data and to analyze the parameters such as elevation, slope, and aspect of the study area.
", "ds_quality": "The overall accuracy of the dataset in the entire Three Rivers Source Region was 97.96%, with a kappa coefficient of 0.8628. Sub-regional evaluations showed that the overall accuracy of the dataset on the distribution extent of river ice in the Yellow River Source Region, the Yangzi River Source Region, and the Lancang River Source Region was 98.28%, 98.21%, and 97.34%, respectively, and the average kappa coefficients were 0.860, 0.903, and 0.824, respectively.
", "ds_ref_way": "", "ds_abstract": "River ice monitoring is of great significance to the study of river runoff process, regional water cycle, water resources management, and ice flood disaster prevention and control. Remote sensing technology has become an important means of obtaining river ice information in high and cold mountain regions where the climate is very cold and field observation is very difficult. At present, the published data on the distribution extent of river ice in the high and cold mountain regions are very rare, and no similar data can be compared. This data set is a long time series of 30 m river ice distribution extent data in the Three Rivers Source Region. The median image was synthesized year by year from images with less than 20% cloud cover during the river ice development period from January to April in the study area based on the long time-series Landsat series surface reflectivity dataset provided by the Google Earth Engine (GEE) platform. The river ice extent and snow extent were extracted using Relative Difference River Ice (RDRI) and Normalized Difference Snow Index (NDSI) algorithms, respectively, and the snow extent extracted by NDSI algorithms was used to supplement when the river ice was missed due to thicker snow cover. Combined with satellite images, DEM, river, lake extent, and glacier extent data, the obtained river ice extent data were manually inspected, misjudged glaciers, lake ice, and other ice bodies were deleted, and areas with obvious omissions of river ice were manually patched, and finally, a total of 34 periods of river ice extent data of the Three Rivers Source Region were obtained for the period of 1990-2023. The dataset was stored in shp file format, which was divided into three areas of Yellow River Source Region, Yangzi River Source Region, and Lancang River Source Region for convenience of use, and the naming rule was: \"area+riverice+sensor+year+Revised.shp\", for example: yellow_basin_riverice_ETM_2012_Revised. This dataset can provide direct data for the study of the spatial and temporal distribution characteristics of river ice in the Three Rivers Source Region, and can also be used as a reference for the monitoring and mapping of river ice in other high and cold mountain regions.
", "ds_time_res": "年", "ds_acq_place": "Three Rivers Source Region", "ds_space_res": "30米", "ds_projection": "", "ds_process_way": "The images with less than 20% cloud cover during the river ice development period from January to April were synthesized into yearly median images, and then the relative difference river ice (RDRI) and normalized difference snow index (NDSI) algorithms were used to extract the river ice extent and snow extent, respectively. When the river ice is covered by thicker snow, it is necessary to use the snow extent extracted by the NDSI algorithm to supplement. Combined with satellite images, DEM, river, lake extent, and glacier extent data, the river ice extent data were manually checked, misjudged glaciers, lake ice, and other ice bodies were deleted, and areas with obvious river ice omissions were manually patched, and finally the river ice extent data of the Three Rivers Source Region for a total of 34 periods from 1990-2023 were obtained and stored in the shp file format.
", "ds_ref_instruction": "" } }, "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": [ "河冰监测", "河冰分布范围" ], "ds_subject_tags": [ "地图学", "地理学其他学科", "河流学与河口水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原", "青海三江源" ], "ds_time_tags": [ 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, 2021, 2022, 2023 ], "ds_contributors": [ { "true_name": "李浩杰", "email": "lihaojiehao@qq.com", "work_for": "西北师范大学", "country": "中国" }, { "true_name": "邵东航", "email": "shaodonghang@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王禹中", "email": "1402044771@qq.com", "work_for": "西北师范大学", "country": "中国" }, { "true_name": "李红星", "email": "lihongxing@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "张敬", "email": "2023222964@nwnu.edu.cn", "work_for": "西北师范大学", "country": "中国" }, { "true_name": "刘振", "email": "1327618757@qq.com", "work_for": "西北师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李浩杰", "email": "lihaojiehao@qq.com", "work_for": "西北师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "李浩杰", "email": "lihaojiehao@qq.com", "work_for": "西北师范大学", "country": "中国" } ], "category": "积雪" }