冰川长度是决定冰川几何形状的关键因素，也是冰川清查和建模的重要参数；冰川中心线是冰川主要流向的沿线，因此是许多冰川学应用的重要输入。在这项研究中，根据最新的全球冰川清单数据、数字高程模型（DEM）和欧洲分配理论，使用自行设计的自动提取算法提取了全球冰川的中心线和最大长度。通过随机目测评估以及与伦道夫冰川清单（RGI）6.0 版的比较，对数据集的准确性进行了评估。RGI 中共有 8.25% 的轮廓被排除，包括10 764 个错误的冰川多边形、7174 个冰帽和 419 个名义冰川。共生成了 198 137 条冰川中心线，占输入冰川的 99.74%。冰川中心线的准确率为 89.68%。该数据集与之前数据集的比较表明，大多数冰川中心线比 RGI v6.0 中的冰川中心线略长，这意味着过去可能低估了一些冰川的最大长度。构建的数据集由17个子数据集组成，包括全球冰川中心线、最大长度和 DEM。", "ds_source": "
数据源于伦道夫冰川清单6.0版(https://www.glims.org/RGI/)。", "ds_process_way": "
这项研究依赖于以下两个关键输入数据集：全球冰川清单和全球冰川高程汇编。主要步骤有 (1) 设计一种算法，检查所有冰川轮廓，排除有缺陷的冰川多边形；(2) 对冰川进行缓冲，生成包含全球冰川及其缓冲区的掩膜；(3) 根据步骤 2 中的掩膜对编制的全球 DEM 进行镶嵌，以准备全球冰川高程数据(4) 在每个区域反复测试，确定全球冰川中心线的自动提取参数， (5) 将全球 DEM、冰川轮廓数据集和所有参数输入设计的自动提取软件 (6) 与现有中心线结果进行验证和比较，以评估新数据集的准确性。", "ds_quality": "
数据质量良好。", "ds_acq_start_time": "2022-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 9399088569, "ds_files_count": 2, "ds_format": "shp,py", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "405e3d5f-318f-48d3-a62a-8ba657b50bac.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "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": "2023-10-23 17:37:33", "last_updated": "2026-01-14 10:54:54", "protected": false, "protected_to": null, "lang": "zh", "cstr": "https://cstr.cn/31253.11.sciencedb.01643", "i18n": { "en": { "title": "A new global dataset of mountain glacier centerline and length", "ds_format": "shp,py", "ds_source": "
Data sourced from Randolph Glacier Inventory version 6.0（ https://www.glims.org/RGI/ ）.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
The centerlines and maximum lengths of global mountain glaciers were extracted using an automatic extraction algorithm based on the global glacier inventory data, digital elevation model (DEM) data, and European allocation theory. In total, 198,137 glacier centerlines were generated, accounting for 99.74% of the total input glaciers and 91.52% of the RGI v6.0. The accuracy of glacier centerlines was 89.68%. A dataset containing 17 sub-datasets was generated through the above work, including two basic input datasets (Input glacier outlines and global glacier DEMs), two key result datasets (Global glacier centerlines and global glacier maximum lengths), four process datasets, six derived result datasets, and three supplementary datasets.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "This study relies on two key input datasets: the Global Glacier Inventory and the Global Glacier Elevation Compilation. The main steps include (1) designing an algorithm to check all glacier contours and eliminate defective glacier polygons; (2) Buffering glaciers to generate masks containing global glaciers and their buffer zones; (3) According to the mask in step 2, embed the global DEM to prepare global glacier elevation data. (4) Repeat testing in each region to determine the automatic extraction parameters of the global glacier centerline. (5) Input the global DEM, glacier contour dataset, and all parameters into the designed automatic extraction software. (6) Verify and compare with the existing centerline results to evaluate the accuracy of the new dataset.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "冰川中心线", "冰川最大长度", "全球冰川DEM", "冰川轮廓" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2023 ], "ds_contributors": [ { "true_name": "张世强", "email": "zhangsq@lzb.ac.cn", "work_for": "西北大学城市与环境科学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张世强", "email": "zhangsq@lzb.ac.cn", "work_for": "西北大学城市与环境科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "张世强", "email": "zhangsq@lzb.ac.cn", "work_for": "西北大学城市与环境科学学院", "country": "中国" } ], "category": "冰川" }