目的目的为充分发挥不同尺度遥感监测在识别高强度煤炭开采地表形变方面的应用价值,方法方法利用卫星影像偏移量追踪技术和无人机激光雷达技术分别获取矿区尺度和工作面尺度的典型沉陷区及其参数,并结合地面观测站数据论证这两种不同尺度遥感监测的差异性。结果以神东矿区两景合成孔径雷达影像为例,利用偏移量追踪技术得到研究时段内矿区多处于地表沉陷中心,沉陷值为−3.8~0m。无人机激光雷达技术有效识别出工作面尺度上的沉陷范围和量级,二次和三次采动区的地表沉陷量高于一次采动区的,有井下煤柱存在的区域沉陷量较小,沉陷量为−1.17~0m。对比分析工作面两处典型区域无人机激光雷达差分数据与卫星偏移量追踪结果的差别,沉陷量较小的区域,偏移量追踪技术精度较高,沉陷量较大的区域,无人机激光雷达技术精度较高,两者得到下沉系数分别为0.61和0.72,说明无人机激光雷达获取的下沉系数更接近地面实测下沉系数。结论结论研究结果可为大范围和高效开采沉陷监测提供参考。

Objectives In order to give full play to the application value of remote sensing monitoring at different scale in identifying surface deformation of high-intensity coal mining, Methods satellite image offset tracking and UAV LiDAR (Light Detection and Ranging) technology are used to obtain two typical subsidence areas and their parameters of the working face scale. The differences of these two remote sensing monitoring methods between these two different scales are demonstrated by combining the ground observation station data. Re⁃sults Taking the two-scene SAR images of Shendong mining area as an example, during the research period, the mining area was mostly located in the center of surface subsidence, with subsidence values ranging from −3.8 to 0 m by using the offset tracking technology. The UAV LiDAR technology can effectively identify the subsidence range and magnitude on the scale of the working face. The surface subsidence in the secondary and tertiary mining areas was higher than that in the primary mining areas, and the subsidence in the areas with underground coal pillars was relatively small, ranging from −1.17~0 m. Comparing and analyzing the difference between the differential data of unmanned aerial UAV LiDAR and satellite offset tracking results in two typical areas of the working face, the accuracy of the latter was higher in areas with smaller subsidence, while the accuracy of former was higher in areas with larger subsidence. The two subsidence coefficients ob‑tained were 0.61 and 0.72, respectively, indicating that the subsidence coefficient obtained by UAV Li‑DAR was closer to the measured subsidence coefficient on the ground. The subsidence coefficient obtained by the UAV LiDAR is closer to the subsidence coefficient measured on the ground. Conclusions This re‑search results could provide reference for large-scale and efficient mining subsidence monitoring.