青藏高原（QTP）的永久冻土因其对气候变化的高度敏感性而受到越来越多的关注。对青藏高原进行了大量的空间建模研究，以评估冻土层的现状，预测冻土层未来的变化，并诊断造成冻土层变化的因素。然而，高质量的冻土分布图将是一个很好的选择。现有的 QTP 永久冻土分布图很难达到这一目的。理想的空间建模基准地图应方法合理、足够准确，并基于测绘年的观测数据，而不是几十年的所有历史数据。因此，在本研究中，我们采用一种新颖的永久冻土绘图方法，以卫星得出的地表融化和冻结指数作为输入，以基于勘测的分区域冻土地图作为约束。\n
该数据集包含新的2010年青藏高原永久冻土分布图以及制图中使用的数据。所有数据均以GeoTIFF（.tif）文件的形式提供。空间分辨率为 1 km，地理坐标系为 WGS_1984。\n
1. “permafrost_distribution_map_2010_QTP.tif”是2010年青藏高原上空多年冻土分布图。\n
2. “soil_cluster_QTP.tif”是青藏高原土团的空间分布。\n
3. “DDT_mean_annual.tif”包含2005-2010年QTP平均解冻指数，来自MODIS LST数据。\n
4. “DDF_mean_annual.tif”包含 2005-2010 年 QTP 平均的年度冻结指数，源自 MODIS LST 数据。", "ds_source": "
首先，最新发布的 QTP 永久冻土热状态综合数据集提供了65个钻孔，这些钻孔在10米和20米深处的土壤温度。因此，2010 年前后钻孔位置是否存在永久冻土的判断依据是根据上述两个深度的年平均土壤温度，确定 2010 年前后钻孔位置是否存在永久冻土。这些钻孔被进一步分为三类：永久冻土稳定的钻孔、不稳定冻土层的井孔，以及有季节性霜冻的井孔;其次，从现有文献中收集了7个钻孔，这些钻孔提供了有关黄河源区永久冻土存在情况的信息，黄河源区是 QTP 东部的一个关键区域。这些钻孔的地温是在 2013 年和 2014 年夏季测量的，并假定它们反映了黄河源区的热机制。如果15米深处的土壤温度高于0.5 摄氏度，则钻孔位置被归类为季节性霜冻区。否则被归类为永久冻土。", "ds_process_way": "
将Hu等人开发的FROSTNUM/COP映射方法应用于绘制QTP上永久冻土的分布图。它基于扩展的地表霜冻由卫星温度数据提供的数量（FROSTNUM）模型，它需要永久冻土分布图子区域作为优化约束。此方法考虑本地通过模型参数E的因子，其值已最佳确定对于遵循空间聚类、参数优化和决策树程序的所有空间单元。DDF和DDT是根据11级MOD1A11和MYD1A3计算的产品（版本 6）。由于混浊，导致MODIS LST数据出现缺口，因此,基于这些数据绘制永久冻土图的不确定性。 选择基于太阳-云-卫星的逐步插值方法几何（SCSG）效应插入数据间隙 MODIS LST 数据中的区域。粒子群优化（PSO）算法是用于查找与每个土簇相关的E的最佳值。在这种基于人群的启发式方法，引导候选解 朝向搜索空间中最知名的位置，从而实现非常 快速收敛到最佳值。在之前的研究中，唯一的目标函数是科恩的kappa系数），它量化了模拟地图与 基于调查的次区域永久冻土分布图。尽管有好处 在实验研究领域实现的性能，这个相对简单的目标函数 不可避免地导致在更大的区域（如 QTP）中的等效性。 认识到kappa系数是仿真结果与次区域地图之间的总体一致性，我们保留了 kappa 系数并制作了目标函数通过添加专门定义的边界一致性来更加严格。", "ds_quality": "
使用基于勘测的分区域永久冻土地图（K=0.74）和钻孔记录（总体精度=0.85 和k=0.43）进行的验证显示，与其他两份最新的永久冻土地图相比，该地图的精度更高。鉴于其卓越的准确性，该地图可作为基准地图，用于约束/验证地表模拟的基准图，也可作为历史参考图，在全球背景下预测 QTP 未来的永久冻土变化。在全球变暖的背景下预测 QTP 未来的冻土变化。", "ds_acq_start_time": "2010-01-01 00:00:00", "ds_acq_end_time": "2010-12-31 00:00:00", "ds_acq_place": "青藏高原", "ds_acq_lon_east": 100.0, "ds_acq_lat_south": 25.0, "ds_acq_lon_west": 80.0, "ds_acq_lat_north": 35.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 24450225, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "d887704d-54bc-4594-9a0b-0cb3c2833d51.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "", "doi_value": "", "subject_codes": [ "170.45", "170.50", "170.55" ], "quality_level": 3, "publish_time": "2023-09-05 18:09:15", "last_updated": "2023-09-11 09:19:36", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB3940.2023", "i18n": { "en": { "title": "A new 2010 permafrost distribution map over the Qinghai–Tibet Plateau based on subregion survey maps: a benchmark for regional permafrost modeling", "ds_format": "tif", "ds_source": "
Firstly, the newly released QTP Permafrost Thermal State Comprehensive Dataset provides 65 boreholes with soil temperatures at depths of 10 and 20 meters. Therefore, the determination of whether there is permafrost at the drilling locations before and after 2010 is based on the annual average soil temperature of the two depths mentioned above, to determine whether there is permafrost at the drilling locations before and after 2010. These boreholes are further divided into three categories: boreholes in stable permafrost, boreholes in unstable permafrost, and boreholes with seasonal frost; Secondly, 7 boreholes were collected from existing literature, which provided information on the presence of permafrost in the Yellow River source area, which is a key area in the eastern part of QTP. The ground temperatures of these boreholes were measured in the summer of 2013 and 2014, and it is assumed that they reflect the thermal mechanism of the Yellow River source area. If the soil temperature at a depth of 15 meters is higher than 0.5 degrees Celsius, the drilling location is classified as a seasonal frost zone. Otherwise, it will be classified as permafrost.", "ds_quality": "
Verification using a survey based subarea permafrost map (K=0.74) and borehole records (overall accuracy=0.85 and k=0.43) showed that the accuracy of this map was higher compared to the other two latest permafrost maps. Given its excellent accuracy, this map can serve as a reference map for constraining/validating surface simulations, as well as a historical reference map for predicting future permafrost changes in QTP in a global context. Predicting future permafrost changes in QTP in the context of global warming.", "ds_ref_way": "", "ds_abstract": "
The permafrost of the Qinghai Tibet Plateau (QTP) has received increasing attention due to its high sensitivity to climate change. A large amount of spatial modeling research has been conducted on the Qinghai Tibet Plateau to evaluate the current situation of the frozen soil layer, predict future changes in the frozen soil layer, and diagnose the factors that cause changes in the frozen soil layer. However, high-quality permafrost distribution maps would be a good choice. The existing QTP permafrost distribution map is difficult to achieve this goal. The ideal spatial modeling benchmark map should have reasonable and accurate methods, and be based on observation data from surveying years, rather than all historical data from decades. Therefore, in this study, we adopted a novel method for mapping permafrost, using satellite derived surface melting and freezing indices as inputs and surveying based regional permafrost maps as constraints.\n
This dataset contains the new 2010 permafrost distribution map of the Qinghai Tibet Plateau and the data used in the mapping. All data is provided in the form of GeoTIFF (. tif) files. The spatial resolution is 1 km, and the geographic coordinate system is WGS_ 1984.\n
1. \"permafrost_distribution_map_2010_QTP. tif\" is the distribution map of permafrost over the Qinghai Tibet Plateau in 2010.\n
2. 'sour_cluster_QTP. tif' is the spatial distribution of soil masses in the Qinghai Tibet Plateau.\n
3. 'DDT mean annual. tif' contains the QTP average thawing index from 2005 to 2010, derived from MODIS LST data.\n
4. \"DDF_mean_annual. tif\" contains the annual frozen index of the QTP average from 2005 to 2010, derived from MODIS LST data.
", "ds_time_res": "", "ds_acq_place": "Qinghai Tibet Plateau", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Apply the FROSTNUM/COP mapping method developed by Hu et al. to draw the distribution map of permafrost on QTP. It is based on an extended surface frost quantity model provided by satellite temperature data (FROSTNUM), which requires sub regions of the permafrost distribution map as optimization constraints. This method takes into account the factors of the local model parameter E, whose values have been optimally determined for all spatial units following spatial clustering, parameter optimization, and decision tree procedures. DDF and DDT are products calculated based on Level 11 MOD1A11 and MYD1A3 (version 6). Due to turbidity, there is a gap in MODIS LST data, resulting in uncertainty in drawing permafrost maps based on these data. Select the region in the MODIS LST data gap to insert the geometric (SCSG) effect based on the sun cloud satellite stepwise interpolation method. The Particle Swarm Optimization (PSO) algorithm is used to find the optimal value of E related to each soil cluster. In this population-based heuristic method, candidate solutions are guided towards the most well-known position in the search space, achieving very fast convergence to the optimal value. In previous studies, the only objective function was Cohen's Kappa coefficient, which quantified the simulated map and the survey based distribution map of permafrost in the subregion. Despite the performance benefits achieved in the field of experimental research, this relatively simple objective function inevitably leads to equivalence in larger regions such as QTP. Recognizing that the kappa coefficient is the overall consistency between the simulation results and the sub regional map, we retained the kappa coefficient and created an objective function to make it more stringent by adding specially defined boundary consistency.", "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, "ds_topic_tags": [ "青藏高原", "空间聚类", "参数优化", "空间建模", "年度冻结指数", "平均解冻指数" ], "ds_subject_tags": [ "地理学", "地质学", "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原" ], "ds_time_tags": [ 2010 ], "ds_contributors": [ { "true_name": "曹泽涛", "email": "cao.zt@outlook.com", "work_for": "南京师范大学地理科学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "曹泽涛", "email": "cao.zt@outlook.com", "work_for": "南京师范大学地理科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "曹泽涛", "email": "cao.zt@outlook.com", "work_for": "南京师范大学地理科学学院", "country": "中国" } ], "category": "冻土" }