{ "created": "2020-12-16 07:25:33", "updated": "2026-05-01 12:54:31", "id": "77d1c8e1-e8b5-4c1e-a061-1b2d987bbe11", "version": 3, "ds_topic": null, "title_cn": "青藏高原热融湖塘编目数据集", "title_en": "Thermokarst lakes on the Qinghai-Tibet Plateau", "ds_abstract": "

  青藏高原是世界上最大的高、低纬度多年冻土带，近几十年来，其多年冻土带迅速退化，其最显著的特征之一就是热融湖塘的形成。这样的湖泊由于能够调节碳循环、水和能量通量而引起了极大的关注。然而，这一地区的热喀斯特湖的分布在很大程度上仍不为人所知，这阻碍了我们对多年冻土的响应及其碳反馈对气候变化的理解。本数据集基于200余景Sentinel-2A影像，结合ArcGIS、NDWI和Google Earth Engine平台，通过GEE自动提取和人工目视解译的方法提提取青藏高原多年冻土区内热融湖塘边界。在2018年热融湖塘数据集中，青藏高原多年冻土区共有121758个热融湖塘，面积为0.00035-0.5 km²，总面积分别为1730 km² 。本次热融湖塘编目数据集为青藏高原水资源评价、多年冻土退化评价、热喀斯特研究提供了基础数据。

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  1.数据集命名

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  QTP_thermokarst_lake_2018.shp

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  2.属性信息

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  TLAKE_ID: 热融湖塘的编码

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  TL_Area: 热融湖塘面积 (m2)

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  TL_Long:热融湖塘中心点经度 (°)

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  TL_Lati: 热融湖塘中心点纬度 (°)

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", "ds_source": "
    \n
  1. Sentinel-2A卫星影像数据\n 从美国地质调查局网站(https://earthexplorer.usgs.gov)下载。
    2. \n
  2. 多年冻土数据\n Zou, D., Lin, Z., Yu, S., Ji, C., Cheng, G., 2017. A New Map of the Permafrost Distribution on the Tibetan Plateau. Chinese Pharmaceutical Affairs 11, 1-28.
    3. \n
  3. SRTM\n 空间分辨率为1″的SRTM DEM数据，下载地址: http://imagico.de/map/demsearch.php。
    4. \n
", "ds_process_way": "
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  1. 遥感数据预处理：\n 陆地卫星图像的假彩色合成
    2. \n
  2. 初步提取湖泊范围:：\n 使用基于中近红外波段(NIR)和绿色波段(green)的NDWI，利用GEE自动提取整个QTP区域的每张图像。\n 公式: NDWI=(B_GREEN-B_NIR)/(B_GREEN+B_NIR ) \n参考文献：Mcfeeters, S. K.: The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, Int. J. Remote Sens., 17(7), 1425-1432, doi: 10.1080/01431169608948714, 1996.
    3. \n
  3. 人工矢量化\n 对青藏高原所有水体人工矢量化去除河流、冰川、冰湖等非热融湖塘水体，并对自动提取的热融湖塘水体进行校对。
    4. \n
  4. 交互检查和精度控制\n 通过人机交互识别热融湖塘;\n 采用无人机影像作为误差检测的重要辅助参考数据源。
    5. \n
", "ds_quality": "

\"数据精度：\n (1) 由于Sentinel-2A数据分辨率为10米且Sentinel-2A数据适用于提取350㎡以上的水体，所以热融湖塘数据集分辨率为10m。
\n (2)误差计算的结果表明,在2018年，热融湖塘编目数据集的相对面积误差均在0-0.5之间。\"

", "ds_acq_start_time": "2018-04-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "青藏高原多年冻土区", "ds_acq_lon_east": 105.0, "ds_acq_lat_south": 26.0, "ds_acq_lon_west": 73.5, "ds_acq_lat_north": 40.0, "ds_acq_alt_low": 1357.0, "ds_acq_alt_high": 6247.0, "ds_share_type": "login-access", "ds_total_size": 115634715, "ds_files_count": 9, "ds_format": "shp", "ds_space_res": "10米", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "77d1c8e1-e8b5-4c1e-a061-1b2d987bbe11.jpg", "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": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170" ], "quality_level": 3, "publish_time": "2021-11-23 10:05:23", "last_updated": "2025-03-19 16:32:31", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.NIEER.2021.1688", "i18n": { "en": { "title": "Thermokarst lakes on the Qinghai-Tibet Plateau", "ds_format": "shp", "ds_source": "

\"1. Sentinel-2A Images \n Download from the websites of the United States Geological Survey (https://www.usgs.gov/).\n2. Permafrost data\n Zou, D., Lin, Z., Yu, S., Ji, C., Cheng, G., 2017. A New Map of the Permafrost Distribution on the Tibetan Plateau. Chinese Pharmaceutical Affairs 11, 1-28.\n3. SRTM\n Shuttle Radar Topography Mission digital elevation model with spatial resolution of 1″download from the website http://imagico.de/map/demsearch.php.\n\"

", "ds_quality": "

\"Accuracy of Data：\n (1) Since the Sentinel-2A data resolution is 10 meters and the Sentinel-2A data is suitable for extracting water over 350 square meters, the resolution of the hot melt ponds data set is 10m.
\n (2)The error calculation results show that in 2018, the relative area error of the catalog data set of thermokarst lakes is between 0 and 0.5.\"

", "ds_ref_way": "", "ds_abstract": "

The Qinghai-Tibetan Plateau (QTP), the largest high-altitude and low-latitude permafrost zone in the world, has experienced rapid permafrost degradation in recent decades, and one of the most remarkable resulting characteristics is the formation of thermokarst lakes. Such lakes have attracted significant attention because of their ability to regulate carbon cycle, water, and energy fluxes. However, the distribution of thermokarst lakes in this area remains largely unknown, hindering our understanding of the response of permafrost and its carbon feedback to climate change.Based on more than 200 sentinel-2A images and combined with ArcGIS, NDWI and Google Earth Engine platform, this data set extracted the boundary of thermokarst lakes in permafrost regions of the Qinghai-Tibet Plateau through GEE automatic extraction and manual visual interpretation.In 2018, there were 121758 thermokarst lakes in the permafrost area of the Qinghai-Tibet Plateau, covering an area of 0.0004-0.5km2, with a total area of 1,730.34km respectively.The cataloging data set of Thermokarst Lakes provides basic data for water resources evaluation, permafrost degradation evaluation and thermal karst study on the Qinghai-Tibet Plateau.

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  1. Name of Data\n QTP_thermokarst_lake_2018.shp
    2. \n
  2. Data description of attribute items
    \n GLAKE_ID: The coding of thermokarst Lake\n GL_Area: The area of thermokarst lake (m2)\n GL_Long: The longitude of center point of the thermokarst lakes (°)\n GL_Lati: The latitude of center point of the thermokarst lakes (°)
    3. \n
", "ds_time_res": "", "ds_acq_place": "Permafrost area of the Qinghai-Tibet Plateau", "ds_space_res": "10米", "ds_projection": "", "ds_process_way": "
    \n
  1. Pre-processing of Remote Sensing Data：\n False Colour Compositing of Landsat Image
    2. \n
  2. Extracting the preliminary lake extent：\n Using NDWI based on NIR and Green bands, each image of the entire QTP region is automatically extracted by GEE..\n formula: NDWI=(B_GREEN-B_NIR)/(B_GREEN+B_NIR ) \nReference：Mcfeeters, S. K.: The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, Int. J. Remote Sens., 17(7), 1425-1432, doi: 10.1080/01431169608948714, 1996.
    3. \n
  3. Manual Vectorization and Entering of Attribute Data\n The artificial vectorization of all water bodies on the Qinghai-Tibet Plateau has been used to remove non-thermal melting lake water bodies such as rivers, glaciers and glacial lakes, and the self-extracted thermal melting lake water bodies have been checked.
    4. \n
  4. Interactive Checking and Accuracy Control\n Thermokarst lakes were discerned via human–computer interaction;\n The UAV imagery was used as an important auxiliary reference data source for error examination.\"
    5. \n
", "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, "is_paper_in_submitting": false, "ds_topic_tags": [ "气候变化", "多年冻土退化", "青藏高原", "热融湖塘" ], "ds_subject_tags": [ "地球科学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原多年冻土区" ], "ds_time_tags": [ 2018 ], "ds_contributors": [ { "true_name": "陈旭", "email": "xchen2018@lzu.edu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" }, { "true_name": "牟翠翠", "email": "mucc@lzu.edu.cn", "work_for": "兰州大学资源环境学院西部环境教育部重点实验室", "country": "中国" }, { "true_name": "贾麟", "email": "jial14@lzu.edu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" }, { "true_name": "李志龙", "email": "lizhl2019@lzu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" }, { "true_name": "范成彦", "email": "fanchy15@lzu.edu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" }, { "true_name": "母梅", "email": "fanchy15@lzu.edu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" }, { "true_name": "彭小清", "email": "pengxiaoqing1987@gmail.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "" }, { "true_name": "吴晓东", "email": "wxd565@163.com", "work_for": "中国科学院西北生态环境与资源研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈旭", "email": "xchen2018@lzu.edu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" }, { "true_name": "牟翠翠", "email": "mucc@lzu.edu.cn", "work_for": "兰州大学资源环境学院西部环境教育部重点实验室", "country": "中国" } ], "ds_managers": [ { "true_name": "陈旭", "email": "xchen2018@lzu.edu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" } ], "category": "冻土" }