不同于现状分布图，本数据构建了融合“实测灾害扰动信息”与“环境潜在风险因子”的机器学习综合评价模型，实现了对未调查路段及潜在高风险区域的定量化风险区划。数据以不稳定性指数（0.0–1.0）表征风险程度，揭示了多年冻土热稳定性、地形水文条件与人类工程扰动（公路热岛效应、管道主动热源）之间的空间耦合关系。该成果为寒区线性工程的风险识别、预防性养护及选线规划提供了关键的数据支撑。\n
数据集核心字段为不稳定性指数（0-1.0），按照风险程度划分为五个等级：\n
0–0.20（极高稳定性/低风险）：主要分布于南界（SLLP）以南的季节冻土区。\n
0.20–0.40（高稳定性/较低风险）：属于弱敏感区，多见于地温较低（<-1.5℃）、排水良好的山脊地带。\n
0.40–0.60（中等稳定性/中风险）：关键预警区，主要分布于1970s与1990s多年冻土南界之间的退化过渡带。\n
0.60–0.80（低稳定性/较高风险）：工程不稳定带，紧邻国道与省道主干线，热融灾害多发。\n
0.80–1.00（极低稳定性/极高风险）：重点干预区，高度聚焦于道路（铁路，公路）沿线及中俄原油管道（CRCOP）及北部大片不连续冻土区的交通枢纽。", "ds_source": "
自主产生，融合了多期野外科学考察记录，以及机器学习模型得到预测结果。", "ds_process_way": "
综合利用机器学习算法对地温（LST）、地温（MAGT）、植被覆盖（NDVI）、地形（坡度坡向）及工程热扰动因子进行权重反演。通过融合实测冻害点位进行监督学习，构建连续不稳定性指数模型，实现从点状现状向面状风险潜势的转化。\n
输入参数：整合了东北多年冻土区1970s与1990s南界变迁数据。\n
扰动模拟：针对公路“被动集热”产生的热岛效应与原油管道“主动排热”引发的局部融化筒结构进行物理机制建模。\n
精度评估：通过对漠河枢纽、根河-牙克石退化前缘等典型脆弱区进行实地验证，确保风险等级划分的准确性。", "ds_quality": "
（1）精度与尺度：数据采用1:10万比例尺，空间分辨率为30米，确保了在线性工程缓冲区（公路、铁路、管道）内的精细化表达。\n
（2）模型方法：采用融合“实测冻融灾害扰动信息”与“环境风险潜在因子”的综合评价模型。通过计算不稳定性指数，实现从定性描述向定量评估的转变。\n
（3）标准规范：评价等级严格划分为五个梯度（0–0.2至0.8–1.0），各等级定义明确。例如，极高风险区（0.8–1.0）通过对冲效应机理进行验证，高稳定性区（0.2–0.4）则参考了多年平均地温及土壤排水条件等物理指标。\n
（4）验证与可靠性：风险分布结果与大兴安岭北部大片不连续冻土区、中南部岛状冻土区表现出高度一致性。特别针对G111、G331及中俄原油管道等典型廊道进行了实地扰动模拟验证，确保了预测结果对预防性养护的指导价值。", "ds_acq_start_time": "2023-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "东北多年冻土区", "ds_acq_lon_east": 135.08333333333334, "ds_acq_lat_south": 37.2, "ds_acq_lon_west": 114.25, "ds_acq_lat_north": 53.6, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 2186185962, "ds_files_count": 6, "ds_format": "*.tif", "ds_space_res": "30m", "ds_time_res": "2年", "ds_coordinate": "WGS84", "ds_projection": "WGS_1984_Albers", "ds_thumbnail": "ce899e87-0dcb-435f-8cb7-a70ff209a932.jpg", "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", "170.50" ], "quality_level": 3, "publish_time": "2026-04-03 08:44:09", "last_updated": "2026-05-12 15:24:56", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7264.2026", "i18n": { "en": { "title": "30m freeze-thaw disaster risk assessment map of the permafrost region in Northeast China (2023-2024)", "ds_format": "*.tif", "ds_source": "
Independently generated, integrating multiple field scientific expedition records and machine learning models to obtain prediction results.", "ds_quality": "
(1) Accuracy and Scale: The data is presented at a scale of 1:100000 with a spatial resolution of 30 meters, ensuring precise representation within linear engineering buffer zones (highways, railways, pipelines).\r\n
(2) Model method: A comprehensive evaluation model integrating \"measured freeze-thaw disaster disturbance information\" and \"potential environmental risk factors\" is adopted. By calculating the instability index, the transition from qualitative description to quantitative evaluation can be achieved.\r\n
(3) Standard specification: The evaluation level is strictly divided into five gradients (0-0.2 to 0.8-1.0), with clear definitions for each level. For example, the high-risk area (0.8-1.0) was validated through the mechanism of hedging effects, while the high stability area (0.2-0.4) referred to physical indicators such as multi-year average ground temperature and soil drainage conditions.\r\n
(4) Verification and reliability: The risk distribution results show high consistency with the large discontinuous permafrost areas in the northern part of the Greater Khingan Range and the island shaped permafrost areas in the central and southern parts. We conducted on-site disturbance simulation verification specifically for typical corridors such as G111, G331, and the China Russia crude oil pipeline, ensuring the guiding value of the predicted results for preventive maintenance.", "ds_ref_way": "", "ds_abstract": "
Unlike the current distribution map, this data constructs a machine learning comprehensive evaluation model that integrates \"measured disaster disturbance information\" and \"potential environmental risk factors\", achieving quantitative risk zoning for uninspected road sections and potential high-risk areas. The data is characterized by an instability index (0.0-1.0) to indicate the degree of risk, revealing the spatial coupling relationship between the thermal stability of permafrost, topographical and hydrological conditions, and human engineering disturbances (such as road heat island effect and pipeline active heat source). This achievement provides key data support for risk identification, preventive maintenance, and route planning of linear engineering in cold regions.\r\n
The core field of the dataset is the instability index (0-1.0), which is divided into five levels according to the degree of risk:\r\n
0-0.20 (extremely stable/low-risk): mainly distributed in the seasonally frozen soil area south of the southern boundary (SLLP).\r\n
0.20-0.40 (high stability/low risk): Belongs to weakly sensitive areas, mostly found in mountain ridges with low ground temperature (<-1.5 ℃) and good drainage.\r\n
0.40-0.60 (moderate stability/moderate risk): Key warning area, mainly distributed in the degradation transition zone between the southern boundary of permafrost in the 1970s and 1990s.\r\n
0.60-0.80 (low stability/high risk): The project is located in an unstable zone, adjacent to the main roads of national and provincial highways, and prone to thermal and melting disasters.\r\n
0.80-1.00 (extremely low stability/extremely high risk): Key intervention areas, highly focused on transportation hubs along roads (railways, highways), the China Russia crude oil pipeline (CRCOP), and large areas of discontinuous permafrost in the north.", "ds_time_res": "", "ds_acq_place": "Northeast Permafrost Region", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Comprehensively utilizing machine learning algorithms to invert the weights of ground temperature (LST), ground temperature (MAGT), vegetation cover (NDVI), terrain (slope direction), and engineering thermal disturbance factors. By integrating measured freezing damage points for supervised learning, a continuous instability index model is constructed to achieve the transformation from point like status quo to surface level risk potential.\r\n
Input parameters: Integrated data on the transition of the southern boundary between the 1970s and 1990s in the permafrost region of Northeast China.\r\n
Disturbance simulation: Modeling the physical mechanisms of the heat island effect caused by passive heat collection on highways and the local melting cylinder structure caused by active heat dissipation on crude oil pipelines.\r\n
Accuracy assessment: By conducting on-site verification of typical vulnerable areas such as Mohe Hub and Genhe Yakeshi degradation front, the accuracy of risk level classification is ensured.", "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": [ "冻融灾害风险评估", "不稳定性指数", "线性工程", "中俄原油管道", "空间格局" ], "ds_subject_tags": [ "地理学", "地质学" ], "ds_class_tags": [], "ds_locus_tags": [ "东北多年冻土区" ], "ds_time_tags": [ 2023, 2024 ], "ds_contributors": [ { "true_name": "金会军", "email": "hjjin@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "唐建军", "email": "jianjuntang@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "王文辉", "email": "wangwenhui@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "金晓颖", "email": "xyj@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "李善珍", "email": "lsz@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "黄帅", "email": "s_hwang@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "陈敦", "email": "chendun@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": "王宏伟", "email": "wanghw@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": "杨岁桥", "email": "yangsuiqiao@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "李祖旺", "email": "lzwang@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "燕翱翔", "email": "yanaoxiang@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "程耀辉", "email": "yhcheng@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "李景涛", "email": "ljtao@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "张泽", "email": "zez@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "王立峰", "email": "9431629@qq.com", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "张虎", "email": "zhanghu@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "刘萌心", "email": "liumengxin@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "张圣嵘", "email": "zhangshengrong@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "杨雪", "email": "yangx014@nenu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "刘子瑞", "email": "lzr@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "岳子颖", "email": "zyyue@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "吴海彬", "email": "haibinwu@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "邢鲁宁", "email": "18363701763@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "陈思宇", "email": "chensy@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "徐景妍", "email": "JingyanXu@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "何峥雲", "email": "yunhardworking@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "米虹岐", "email": "mimhq07@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "彭文昊", "email": "Pengwh@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "梁峻贺", "email": "liangjh@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "史艳玲", "email": "15247298387@163.com", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "周智熠", "email": "v1ncentharrious@outlook.com", "work_for": "东北林业大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "金会军", "email": "hjjin@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "唐建军", "email": "jianjuntang@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "王文辉", "email": "wangwenhui@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" } ], "ds_managers": [ { "true_name": "金会军", "email": "hjjin@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" } ], "category": "灾害" }