本数据集整合了藏东南地区雪崩形成的孕灾环境因子及其易发性评估结果。数据包括地形、气象、积雪及植被等多源环境因子，并统一至30 m空间分辨率。在此基础上，基于支持向量机（SVM）模型构建雪崩易发性评价模型，生成2021–2022雪季（12月至次年5月）的逐月雪崩易发性空间分布数据。该数据集可用于雪崩形成机制分析、易发性评价及区域灾害风险研究。", "ds_source": "
地形因子来源于ALOS DEM数据；植被因子NDVI来源于Landsat-8影像；气象因子（降水、气温、风速）来源于TPMFD逐月数据集；积雪因子包括MODIS积雪覆盖产品及中国雪深长时间序列数据集。", "ds_process_way": "
（1）对多源数据进行统一投影与空间分辨率重采样（30 m）；（2）按月组织气象与积雪等动态因子数据；（3）构建包含地形、气象、积雪和植被的孕灾环境因子体系；（4）基于支持向量机（SVM，RBF核）构建雪崩易发性模型；（5）以历史雪崩隐患点为正样本，非雪崩区域为负样本，按7:3划分训练集与验证集，生成逐月易发性结果。", "ds_quality": "
雪崩易发性模型采用AUC指标进行精度评价，各月模型AUC均大于0.93，表明模型具有较高的判别能力。环境因子数据经过统一分辨率处理，保证了数据间的一致性与可比性。", "ds_acq_start_time": "2021-12-01 00:00:00", "ds_acq_end_time": "2022-05-31 00:00:00", "ds_acq_place": "藏东南地区", "ds_acq_lon_east": 98.72, "ds_acq_lat_south": 27.110000000000003, "ds_acq_lon_west": 92.21000000000001, "ds_acq_lat_north": 31.16, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 47930401726, "ds_files_count": 54, "ds_format": "*.tif", "ds_space_res": "30m", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "UTM Zone 48N", "ds_thumbnail": "b398edf9-4882-446d-947a-cb3ea6b2aeb0.png", "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": [ "170.4510" ], "quality_level": 3, "publish_time": "2026-03-27 09:46:14", "last_updated": "2026-04-14 12:51:22", "protected": false, "protected_to": "2028-03-23 00:00:00", "lang": "zh", "cstr": "11738.11.NCDC.SNOW.DB7232.2026", "i18n": { "en": { "title": "Environmental factors and avalanche susceptibility dataset in southeastern Tibet", "ds_format": "*.tif", "ds_source": "
Topographic factors were derived from ALOS DEM. Vegetation index (NDVI) was obtained from Landsat-8 imagery. Meteorological factors (precipitation, temperature, wind speed) were sourced from the TPMFD monthly dataset. Snow-related variables include MODIS snow cover products and long-term snow depth datasets in China.", "ds_quality": "
Model performance was evaluated using the AUC metric, with all monthly results exceeding 0.93, indicating high predictive accuracy. Environmental variables were standardized to a uniform spatial resolution, ensuring consistency and comparability across datasets.", "ds_ref_way": "", "ds_abstract": "
This dataset integrates environmental factors related to avalanche formation and corresponding susceptibility results in southeastern Tibet. The dataset includes multi-source environmental variables such as topography, meteorology, snow conditions, and vegetation, all resampled to a spatial resolution of 30 m. Based on these factors, a Support Vector Machine (SVM) model was employed to generate monthly avalanche susceptibility maps for the 2021–2022 snow season (December to May). This dataset can support studies on avalanche formation mechanisms, susceptibility assessment, and regional hazard analysis.", "ds_time_res": "月", "ds_acq_place": "Southeast Tibet", "ds_space_res": "30m", "ds_projection": "", "ds_process_way": "
(1) Multi-source data were reprojected and resampled to a spatial resolution of 30 m;
(2) Dynamic factors such as meteorological and snow variables were organized on a monthly basis;
(3) A comprehensive environmental factor system including topography, meteorology, snow, and vegetation was constructed;
(4) A Support Vector Machine (SVM) with RBF kernel was applied for susceptibility modeling;
(5) Historical avalanche hazard points were used as positive samples, and non-avalanche areas as negative samples, with a 7:3 split for training and validation to produce monthly susceptibility maps.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 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": [ "雪崩", "易发性", "环境因子", "支持向量机" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国", "藏东南" ], "ds_time_tags": [ 2021, 2022 ], "ds_contributors": [ { "true_name": "付晓茜", "email": "fuxiaoqian24@mails.ucas.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" }, { "true_name": "郝建盛", "email": "haojainsheng14@mails.ucas.ac.cn", "work_for": "1.中国科学院新疆生态与地理研究所 2.中国科学院地理科学与资源研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "付晓茜", "email": "fuxiaoqian24@mails.ucas.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "付晓茜", "email": "fuxiaoqian24@mails.ucas.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "category": "灾害" }