数据基于现场机械/人工钻探与探坑获取的地下冰储量实测样本数据，采用随机森林回归模型，以实测体积含水量(VWC)为因变量，多环境因子为自变量，进行空间预测建模。模型预测结果输出为不同深度层（2m以上、2-5m、5m以下）的栅格数据，最终合成并制作为“大兴安岭东坡塔河地区卡马兰河流域多年冻土地下冰储量图（TIFF格式，空间分辨率30m）。数据时间为2023年7月4日-2024年9月2日。", "ds_source": "
野外采样数据：基于大兴安岭东坡卡马兰河流域现场机械/人工钻探与探坑获取的地下冰储量实测样本数据，采样点经纬度定位精确至小数点后5位，海拔记录精确至米级。\n
环境数据：来源于Google Earth Engine (GEE)平台及权威网站下载的气候、土壤、地形、植被类型等多源空间数据集，作为模型预测变量。", "ds_process_way": "
利用Python和ArcGIS对所有环境因子数据进行预处理，包括格式转换、空间配准（统一至WGS84坐标系）、重采样（至目标分辨率）与归一化。采用随机森林回归模型，以实测体积含水量(VWC)为因变量，多环境因子为自变量，进行空间预测建模。模型预测结果输出为不同深度层（2m以上、2-5m、5m以下）的栅格数据，最终合成并制作为“大兴安岭东坡塔河地区卡马兰河流域多年冻土地下冰储量图”。", "ds_quality": "
本数据采用交叉验证方法评估随机森林模型的预测精度，确保模型可靠。使用ArcGIS对生成的栅格数据进行可视化检查与逻辑分析，确保地下冰储量空间分布符合区域冻土分布规律，无显著异常值。", "ds_acq_start_time": "2023-07-04 00:00:00", "ds_acq_end_time": "2024-09-02 00:00:00", "ds_acq_place": "大兴安岭东坡卡马兰河流域", "ds_acq_lon_east": 123.66222222222223, "ds_acq_lat_south": 51.86611111111111, "ds_acq_lon_west": 122.5275, "ds_acq_lat_north": 52.35888888888889, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 23113717, "ds_files_count": 3, "ds_format": "*.tif", "ds_space_res": "30m", "ds_time_res": "2年", "ds_coordinate": "WGS84", "ds_projection": "WGS_1984_Albers", "ds_thumbnail": "9e739040-987d-44d8-bcf4-263970ba95dd.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "221ebf56-1b0b-4574-972b-1fb6d3cf1be7", "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": "2026-03-31 18:21:12", "last_updated": "2026-05-11 17:55:04", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7241.2026", "i18n": { "en": { "title": "30 meter permafrost underground ice storage map of the Kamalan River basin in the Dongpo Tahe area of the Greater Khingan Range (2023-2024)", "ds_format": "*.tif", "ds_source": "
Field sampling data: Based on on-site mechanical/manual drilling and exploration of underground ice reserves in the Kamaran River Basin on the east slope of the Greater Khingan Range, the latitude and longitude positioning of the sampling points is accurate to 5 decimal places, and the altitude recording is accurate to the meter level.\r\n
Environmental data: sourced from multi-source spatial datasets such as climate, soil, terrain, vegetation types downloaded from the Google Earth Engine (GEE) platform and authoritative websites, used as model predictive variables.", "ds_quality": "
This data uses cross validation method to evaluate the prediction accuracy of the random forest model, ensuring the reliability of the model. Use ArcGIS to visually inspect and logically analyze the generated raster data, ensuring that the spatial distribution of underground ice storage conforms to the regional permafrost distribution pattern and has no significant outliers.", "ds_ref_way": "", "ds_abstract": "
The data is based on the measured sample data of underground ice reserves obtained through on-site mechanical/manual drilling and exploration pits. A random forest regression model is used to conduct spatial prediction modeling with measured volumetric water content (VWC) as the dependent variable and multiple environmental factors as independent variables. The model prediction results are output as raster data of different depth layers (2m above, 2-5m, 5m below), and finally synthesized and produced as the \"Permafrost Underground Ice Storage Map of Kamaran River Basin in Dongpo Tahe Area of Greater Khingan Range (TIFF format, spatial resolution 30m). The data period is from July 4th, 2023 to September 2nd, 2024.", "ds_time_res": "", "ds_acq_place": "Kamaran River Basin on the East Slope of Daxing'an Mountains", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Preprocess all environmental factor data using Python and ArcGIS, including format conversion, spatial registration (unified to WGS84 coordinate system), resampling (to target resolution), and normalization. Using a random forest regression model, with measured volumetric water content (VWC) as the dependent variable and multiple environmental factors as independent variables, spatial prediction modeling is conducted. The predicted results of the model are output as raster data for different depth layers (above 2m, 2-5m, below 5m), and finally synthesized and produced as the \"Permafrost Underground Ice Storage Map of the Kamaran River Basin in the Dongpo Tahe Area of the Greater Khingan Range\".", "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, "ds_topic_tags": [ "地下冰储量", "多年冻土", "卡马兰河流域" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "大兴安岭东坡卡马兰河流域" ], "ds_time_tags": [ 2023, 2024 ], "ds_contributors": [ { "true_name": "臧淑英", "email": "zsy6311@163.com", "work_for": "哈尔滨师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "余江涛", "email": "yujiangtao23@163.com", "work_for": "哈尔滨师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "孙丽", "email": "sunli_wabb@163.com", "work_for": "哈尔滨师范大学", "country": "中国" } ], "category": "冻土" }