中巴经济走廊北起中国新疆喀什，南至巴基斯坦瓜达尔港，经过兴都库什山脉、喀喇昆仑山脉和喜马拉雅山脉地区，全长3000公里，是一条包括公路、铁路、油气和光缆通道在内的贸易走廊。本数据集基于时序InSAR技术对2014—2018年间覆盖中巴经济走廊全部区域的Sentinel 1A数据进行地表形变监测，从而获取形变数据，反映了4年间该地区地表物质迁移运动情况和地质灾害风险水平。本数据集可以作为研究中巴经济走廊建设的参考数据，满足用户对中巴经济走廊自然灾害科学数据的需求，支撑科学研究和技术创新活动，服务中巴经济走廊经济社会可持续发展。", "ds_source": "
1）SRTM 90m分辨率DEM数据。基础影像共19景，将其进行拼接、裁剪和空值填补后建立了覆盖走廊范围的完整DEM，高程范围为986～7770m，空间参考椭球体为WGS_1984。\n
2）欧空局合成孔径雷达（C波段）Sentinel 1A数据。所涉及轨道为相对轨道107（下降轨道）、27（上升轨道）。时间范围从2014年10月到2018年6月。其中107轨道共400景、覆盖74天，27轨道共340景、覆盖85天。虽然107轨道缺少少数日期的数据，但是采样时间分布相对均匀，可以满足图像高精度配准要求。", "ds_process_way": "
采用PSInSAR技术获得地表形变量，其基本原理是利用多景同一地区的SAR影像，通过统计分析时间序列上幅度和相位信息的稳定性，探测不受时间、空间基线去相关影响的稳定点目标。PSInSAR技术通过对主辅影像进行干涉处理，将获取的强度与相位信息进行统计分析，选取在时间上散射特性相对稳定、回波信号较强的永久散射体（Persistent Scatterer，PS）目标点作为观测对象。这些目标点可能是人工建筑物、裸露的岩石、人工布设的角反射器等。由于它们在时间序列SAR影像中几乎不受斑点噪声的影响，经过长时间间隔仍然保持稳定的散射特性，所以被称作PS。构建PS点网络，网络中任意相邻PS点间经过差分处理的方式分离出形变信息，进而求出相邻点间的高程差值和形变速度差值。以高程差和形变速度差作为原始数据，利用相位解缠算法求解出所有PS点的形变、高程参数，再通过最小二乘法等求得研究区内PS点的形变量和形变速度。\n
采用SARProZ软件——SAR影像处理系统，主要面向雷达影像数据PSInSAR处理技术及相关应用，基于并行多线程处理技术，能够快速处理分析海量雷达影像数据，可识别区分建筑物和地面上的PS点，区分建筑物形变和地表形变；精确反演影像采集时间的大气相位图，剔除大气相位残值；对于城市区域或强反射硬目标形变速率监测精度可达3～5mm。
", "ds_quality": "本数据集基础数据选用欧空局合成孔径雷达（C波段）Sentinel 1A数据，它具有良好的基线控制和短暂的重返周期，能够在短暂时间积累大量影像数据，有利于InSAR时序分析，并且提升雷达图像参数反演的精度。在PSInSAR数据处理过程中，通过影像配准、干涉成像、相位解缠、高程解算和地理编码等关键步骤，有效地去除地平、高程相位以及大气效应，获得了理论精度毫米级的形变信息。并且文中采用了107轨道和27轨道两个轨道，分别对研究区进行计算，不同轨道计算结果吻合较好。", "ds_acq_start_time": "2014-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "中巴经济走廊区域", "ds_acq_lon_east": 75.815, "ds_acq_lat_south": 37.44305555555555, "ds_acq_lon_west": 74.76944444444445, "ds_acq_lat_north": 38.70333333333333, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 50287707, "ds_files_count": 2, "ds_format": "KML、ESRI SHAPE FILE", "ds_space_res": "20", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "501cb969-3f61-4d75-920e-fad2c43f1b2c.jpeg", "ds_thumb_from": 0, "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": "lihongxingc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2022-09-29 15:52:44", "last_updated": "2025-04-29 14:58:54", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.nieer.2020.1658", "i18n": { "en": { "title": "A dataset of surface deformation along the China-Pakistan Economic Corridor from 2014 to 2018", "ds_format": "KML,ESRI SHAPE FILE", "ds_source": "
1) SRTM 90m resolution DEM data. A total of 19 basic images are spliced, cropped and filled with null values to establish a complete DEM covering the corridor, with an elevation range of 986~7770m and a spatial reference ellipsoid of WGS_ 1984。
\n2) ESA Synthetic Aperture Radar (C-band) Sentinel 1A data. The orbits involved are relative orbits 107 (descending orbit) and 27 (ascending orbit). The time range is from October 2014 to June 2018. Among them, 107 tracks have 400 views, covering 74 days, and 27 tracks have 340 views, covering 85 days. Although the 107 track lacks a few date data, the sampling time distribution is relatively uniform, which can meet the requirements of high-precision image registration.", "ds_quality": "
The basic data of this data set uses the ESA Synthetic Aperture Radar (C-band) Sentinel 1A data, which has good baseline control and short return period, and can accumulate a large amount of image data in a short time, which is conducive to InSAR time series analysis and improves the accuracy of radar image parameter inversion. In the process of PSInSAR data processing, through key steps such as image registration, interference imaging, phase unwrapping, elevation calculation and geocoding, the horizon, elevation phase and atmospheric effects are effectively removed, and the deformation information with theoretical accuracy of millimeter level is obtained. In addition, 107 and 27 orbits are used to calculate the study area respectively, and the calculated results of different orbits are in good agreement.
", "ds_ref_way": "", "ds_abstract": "The China Pakistan Economic Corridor starts from Kashgar, Xinjiang, China in the north and ends at Gwadar Port, Pakistan in the south. It passes through the Hindu Kush Mountains, Karakoram Mountains and Himalayan Mountains, with a total length of 3000 kilometers. It is a trade corridor including roads, railways, oil and gas and optical cable channels. Based on time series InSAR technology, this data set monitors the surface deformation of Sentinel 1A data covering all areas of the China Pakistan Economic Corridor from 2014 to 2018, so as to obtain deformation data, which reflects the movement of surface materials and the risk level of geological disasters in this area in the past four years. This data set can be used as reference data to study the construction of the China Pakistan Economic Corridor, meet users' needs for scientific data on natural disasters in the China Pakistan Economic Corridor, support scientific research and technological innovation activities, and serve the sustainable economic and social development of the China Pakistan Economic Corridor.
", "ds_time_res": "日", "ds_acq_place": "China Brazil Economic Corridor Region", "ds_space_res": "20", "ds_projection": "", "ds_process_way": "The basic principle of using PSInSAR technology to obtain surface shape variables is to use SAR images of multiple scenes in the same area to detect stable point targets that are not affected by the decorrelation of temporal and spatial baselines through statistical analysis of the stability of amplitude and phase information in time series. Through the interference processing of the primary and secondary images, the PSInSAR technology will statistically analyze the acquired intensity and phase information, and select the permanent scatterer (PS) target point with relatively stable scattering characteristics in time and strong echo signal as the observation object. These target points may be artificial buildings, exposed rocks, artificially laid corner reflectors, etc. Because they are almost not affected by speckle noise in time series SAR images and remain stable scattering characteristics after a long time interval, they are called PS. The PS point network is constructed, and the deformation information between any adjacent PS points in the network is separated by differential processing, and then the elevation difference and deformation velocity difference between adjacent points are calculated. With the elevation difference and deformation velocity difference as the original data, the phase unwrapping algorithm is used to solve the deformation and elevation parameters of all PS points, and then the least square method is used to obtain the shape variable and deformation velocity of PS points in the study area.
\nThe SARProZ software - SAR image processing system is adopted, which is mainly oriented to the PSInSAR processing technology of radar image data and related applications. Based on parallel multithreading processing technology, it can quickly process and analyze massive radar image data, identify and distinguish the PS points on buildings and the ground, and distinguish building deformation and ground deformation; Accurately invert the atmospheric phase map of image acquisition time, and eliminate the residual value of atmospheric phase; For urban areas or strong reflection hard targets, the monitoring accuracy of deformation rate can reach 3~5mm
", "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": [ "影像变化检测技术", "时序InSAR", "中巴经济走廊", "Sentinel-1A" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中巴经济走廊区域" ], "ds_time_tags": [ 2014, 2015, 2016, 2017, 2018 ], "ds_contributors": [ { "true_name": "康建芳", "email": "kangjf@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": "白艳萍", "email": "", "work_for": "兰州大学资源环境学院", "country": "中国" }, { "true_name": "李萌", "email": "", "work_for": "兰州大学资源环境学院", "country": "中国" }, { "true_name": "韩守富", "email": "", "work_for": "兰州大学资源环境学院", "country": "中国" }, { "true_name": "赵宝强", "email": "", "work_for": "兰州大学资源环境学院", "country": "中国" }, { "true_name": "艾鸣浩", "email": "aimh@lzb.ac.cn", "work_for": "中科院西北研究院", "country": "中国" }, { "true_name": "马金辉", "email": "majh@lzu.edu.cn", "work_for": "兰州大学资源环境学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "马金辉", "email": "majh@lzu.edu.cn", "work_for": "兰州大学资源环境学院", "country": "中国" } ], "ds_managers": [ { "true_name": "白艳萍", "email": "", "work_for": "兰州大学资源环境学院", "country": "中国" } ], "category": "灾害" }