本数据集基于中国第二次冰川编目数据和RGI 6.0冰川数据集，利用冰川中流线提取软件提取青藏高原范围内冰川中流线矢量数据，共获得76982条冰川最大中流线，同时给出了各冰川中流线最大长度和平均长度。本数据集为开展青藏高原冰川变化、冰川冰储量估算及冰川一维建模等研究提供了基础数据。\n
数据属性信息如下：\n
GLIMS_ID: 冰川GLIMS编码\n
MaxL: 冰川中流线最大长度(m)\n
MeanL: 冰川中流线平均长度(m)\n
AccL: 冰川积累区中流线长度(m) \n
AblationL: 冰川消融区中流线长度(m)\n
ELA: 冰川物质平衡线高度 (m)", "ds_source": "
1. 冰川编目数据\n
中国第二次冰川编目数据(http://westdc.westgis.ac.cn)和RGI 6.0 (https://www.glims.org/RGI/rgi60_dl.html)。\n
2. SRTM\n
空间分辨率为1″的SRTM DEM数据，下载地址:http://imagico.de/map/demsearch.php。", "ds_process_way": "
数据加工采用自研发的冰川中流线提取软件，输入数据为冰川矢量轮廓及冰川区数字高程模型数据，输出结果包括冰川最大中流线及各支中流线，模型出处见文献：\nZhang D, Yao X, Duan H, et al. A new automatic approach for extracting glacier centerlines based on Euclidean allocation. The Cryosphere, 2021, 15: 1955-1973.", "ds_quality": "
数据精度依赖于冰川矢量轮廓的准确性和数字高程模型自身精度。", "ds_acq_start_time": "1998-08-13 00:00:00", "ds_acq_end_time": "2013-09-13 00:00:00", "ds_acq_place": "青藏高原冰川区", "ds_acq_lon_east": 103.9, "ds_acq_lat_south": 27.08, "ds_acq_lon_west": 68.78, "ds_acq_lat_north": 39.63, "ds_acq_alt_low": 3000.0, "ds_acq_alt_high": 8848.86, "ds_share_type": "apply-access", "ds_total_size": 377351483, "ds_files_count": 9, "ds_format": "shp", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "507813ef-e0c4-411f-81d3-4f12102fba04.png", "ds_thumb_from": 2, "ds_ref_way": "姚晓军, 张大弘, 郭万钦, 刘时银, 孙美平, 张聪, 段红玉, 靳惠安, 周苏刚, 张圆，青藏高原冰川中流线数据集，国家冰川冻土沙漠科学数据中心(www.ncdc.ac.cn)，2023，doi：10.12072/ncdc.nieer.db2881.2023", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "c6ac5c3c-eb4f-4119-86fb-c4534a9bd7fb", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.nieer.db2881.2023", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2023-06-19 10:16:49", "last_updated": "2023-06-21 11:54:31", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB2881.2023", "i18n": { "en": { "title": "Glacier centerlines over the Qinghai-Tibet Plateau", "ds_format": "", "ds_source": "
<p> 1. Glacier Inventory\nAcquired from the Second Chinese Glacier Inventory (http://westdc.westgis.ac.cn) and RGI 6.0 (https://www.glims.org/RGI/rgi60_dl.html).\n
2. SRTM\n
Shuttle Radar Topography Mission digital elevation model with spatial resolution of 1″download from the website http://imagico.de/map/demsearch.php.", "ds_quality": "
<p> The accuracy of data depends on the accuracy of glacier outline and digital elevation model data adopted.\n <p> Based on the Second Chinese Glacier Inventory and RGI .0 Glacier dataset, the glacier centerlines over the Qinghai-Tibet Plateau were built by using the glacier centerline extracting software. This dataset includes 76892 glacier centerlines and their maximum length and mean length. The dataset provides fundamental data for glacier change, glacier volume estimation and one-dimension glacier modelling over the Qinghai-Tibet Plateau.\nData description of attribute items \n
GLIMS_ID: The GLIMS coding of glacier\n
MaxL: The maximum length of glacier centerline (m)\n
MeanL: The mean length of glacier centerline (m)\n
AccL: The centerline length in the accumulation part of glacier (m)\n
AblationL: The centerline length in the ablation part of glacier (m)\n
ELA: The equilibrium-line altitude of glacier (m)
", "ds_time_res": "", "ds_acq_place": "Qinghai Tibet Plateau Glacier Region", "ds_space_res": "", "ds_projection": "", "ds_process_way": " <p> Data process uses the self-developed glacier centerline extracting software. The input data needs the vectorized boundary of glaciers and the digital elevation model data. The output result includes the maximum and mean centerlines of glaciers. The model is as follows:\n Zhang D, Yao X, Duan H, et al. A new automatic approach for extracting glacier centerlines based on Euclidean allocation. The Cryosphere, 2021, 15: 1955-1973. \"\n