青藏高原高寒区是我国矿产资源的重要储备区，木里矿区高强度开挖和大范围尾矿堆积破坏了原有地形、地貌和地质条件，严重影响了高寒矿区内的冻土。本研究利用Landsat影像数据、现场实测地温数据、钻孔编录数据等数据源，基于GEE平台、ArcGIS软件，利用随机森林方法、Stefan方程等方法，生成高寒矿区30m空间分辨率活动层、热稳定性、含冰量图集，具有高精度、大范围、采矿前后对比等特点，该数据可为高寒矿区冻土分布及热稳定性等研究提供基础数据。", "ds_source": "
Landsat系列卫星数据由美国地质调查局（USGS）和国家航空航天局（NASA）联合研制并提供（https://landsat.gsfc.nasa.gov/）。 Landsat计划自1972年启动，迄今已有多颗卫星相继发射，如Landsat 4、5、7、8和9。Landsat数据时间跨度长、连续性好，是研究地表变化的重要遥感数据源。Landsat 8的陆地成像仪（OLI）和热红外传感器（TIRS）提供了多光谱和热红外影像，其空间分辨率分别为30 m（多光谱）、100 m（热红外），重访周期为16天。Landsat数据经过辐射、大气和几何校正，可免费下载获取，用于全球土地覆盖、植被、温度等监测和分析。Landsat数据可通过USGS Earth Explorer网站（https://earthexplorer.usgs.gov/） 获取。木里矿区多年冻土区地貌沉积类型依据1:20万地质资料图进行划分，底图资料是由青海省地质资料馆提供的半矢量化图。植被分类使用184个样本点，地温数据由48个使用中国科学院西北生态环境资源研究院冻土实验室的测温电缆与CR3000数采仪的钻孔测量得出，编录数据由钻孔中进行采样与记录。", "ds_process_way": "
（1）基于GEE平台，对Landsat影像利用随机森林方法进行植被分类。\n
（2）考虑了海拔、经度、纬度、植被、坡向、坡度共六种地理要素，利用48个样本数据建立起研究区年平均地温和影响因子之间的多元统计关系，并使用ArcGIS平台将统计关系扩展到整个研究区从而得到大通河流域的年平均地温。\n
（3）在专题制作的木里矿区植被覆盖图、多年冻土分布图、多年冻土活动层厚度图的基础上，进行地貌类型区划、垂直方向地层分类统计及不同底层含冰量统计，估算多年冻土上限至10m深度范围内的多年冻土地下冰含量。\n
（4）将矿山、渣山区域单独利用新增钻孔数据与活动层数据进行计算，利用数理统计与数值模拟办法确定矿坑、矸石山对多年冻土地温、活动层与地下冰含量的影响，与采矿前图集进行融合，获得采矿后高寒矿区多年冻土图集。", "ds_quality": "
考虑了海拔、经度、纬度、植被、坡向、坡度共六种地理要素，利用48个现场踏勘样本数据利用随机森林方法进行植被分类，使用70余个地温孔长期地温监测数据，结合海拔、经度、纬度、植被、坡向、坡度共六种地理要素建立研究区年均地温；使用祁连山区近百个多年地温钻孔编录数据，确定不同地质地貌条件下地层含冰量，最终获得多年冻土地下冰含量，数据与新钻进的地温孔对比误差小于20%。利用数值模拟对矿山、渣山对多年冻土影响进行模拟，确定采矿对多年冻土影响，对比实地钻孔数据误差小于20%。", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "青藏高原高寒区", "ds_acq_lon_east": 99.7538888888889, "ds_acq_lat_south": 37.82333333333334, "ds_acq_lon_west": 98.85, "ds_acq_lat_north": 38.330000000000005, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 316235526, "ds_files_count": 0, "ds_format": "*.tif", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "64f73663-781b-492c-8efe-b53785929207.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "5b99d600-008a-4069-8fc3-7adb9c3f2f8b", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 0, "publish_time": "2026-06-10 10:03:03", "last_updated": "2026-06-10 10:03:03", "protected": false, "protected_to": "2027-08-20 00:00:00", "lang": "zh", "cstr": null, "i18n": { "en": { "title": "Atlas of the Spatiotemporal Variations of Permafrost in Alpine Mining Areas", "ds_format": "*.tif", "ds_source": "
The Landsat series of satellite data are jointly developed and provided by the United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) ([https://landsat.gsfc.nasa.gov/](https://landsat.gsfc.nasa.gov/)). The Landsat program was initiated in 1972 and has since launched multiple satellites, including Landsat 4, 5, 7, 8, and 9. Due to its long temporal span and continuous coverage, Landsat data serve as a critical remote sensing source for studying surface changes. Landsat 8’s Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) provide multispectral and thermal infrared imagery with spatial resolutions of 30 m (multispectral) and 100 m (thermal infrared), respectively, and a revisit cycle of 16 days. The data undergo radiometric, atmospheric, and geometric corrections, are freely available, and are widely used for global monitoring and analysis of land cover, vegetation, temperature, and other parameters. Landsat data can be accessed through the USGS Earth Explorer website ([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)). The geomorphological and sedimentary types of the permafrost region in the Muli mining area were classified based on 1:200,000 scale geological maps, with base map data provided as semi-vectorized maps by the Qinghai Geological Data Center. Vegetation classification was conducted using 184 sample points. Ground temperature data were obtained from 48 boreholes using thermistor cables manufactured by the Permafrost Laboratory of the Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, combined with CR3000 data loggers. Logging data were acquired through sampling and recording within the boreholes.", "ds_quality": "
Considering six geographic factors—elevation, longitude, latitude, vegetation, aspect, and slope—vegetation classification was performed using the Random Forest method based on 48 field survey sample points. Long-term ground temperature monitoring data from over 70 boreholes were combined with these six geographic factors to establish the annual mean ground temperature for the study area. Using borehole logging data from nearly one hundred permafrost sites in the Qilian Mountains, ice content in stratigraphic layers under varying geological and geomorphological conditions was determined, yielding estimates of subsurface permafrost ice content with an error margin of less than 20% compared to newly drilled temperature boreholes. Numerical simulations were employed to model the impacts of mining pits and waste rock dumps on permafrost, quantifying mining-induced effects on permafrost with discrepancies less than 20% when compared to field borehole data.", "ds_ref_way": "", "ds_abstract": "
The alpine region of the Qinghai-Tibet Plateau is a significant reserve of mineral resources in China. Intensive excavation and extensive tailings accumulation in the Muli mining area have severely disrupted the original topography, geomorphology, and geological conditions, seriously impacting the permafrost within the alpine mining area. This study integrates Landsat imagery, in-situ ground temperature measurements, and borehole logging data, utilizing the Google Earth Engine (GEE) platform and ArcGIS software. Employing methods such as Random Forest and the Stefan equation, we generated high-precision atlases of active layer thickness, thermal stability, and ice content at a spatial resolution of 30 meters for the alpine mining area. These datasets provide a robust foundation for research on permafrost distribution and thermal stability in alpine mining regions.", "ds_time_res": "", "ds_acq_place": "Qinghai-Tibet Plateau alpine region", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Based on the Google Earth Engine (GEE) platform, vegetation classification was performed on Landsat imagery using the Random Forest method.\r\nConsidering six geographical factors—elevation, longitude, latitude, vegetation, aspect, and slope—a multivariate statistical relationship between annual mean ground temperature and these influencing factors was established using data from 48 sample points. This statistical model was then extended to the entire study area via the ArcGIS platform to derive the annual mean ground temperature for the Datong River Basin.\r\nBased on thematic maps including vegetation cover, permafrost distribution, and active layer thickness in the Muli mining area, geomorphological zoning, vertical stratigraphic classification, and statistics of ice content in different substrata were conducted. Subsequently, the permafrost ice content within the upper 10 meters below the permafrost table was estimated.\r\nUsing additional borehole data and active layer measurements, the mining and waste rock dump areas were separately analyzed. Mathematical statistics and numerical simulation methods were applied to quantify the impacts of mining pits and gangue hills on permafrost ground temperature, active layer thickness, and subsurface ice content. These results were integrated with pre-mining atlases to produce post-mining permafrost maps for the alpine mining region.", "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, "ds_topic_tags": [ "冻土", "热稳定性", "活动层", "含冰量" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原高寒区" ], "ds_time_tags": [ 2000, 2025 ], "ds_contributors": [ { "true_name": "陈继", "email": "chenji@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": "陈书峄", "email": "chenshuyi@nieer.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": "马燊", "email": "2297526573@qq.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈继", "email": "chenji@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": "陈书峄", "email": "chenshuyi@nieer.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": "马燊", "email": "2297526573@qq.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "陈继", "email": "chenji@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "冻土" }