For the accurate and high-precision measurement of the deformation field in mining areas using different data sources, the probability integral model was used to process deformation data obtained from an Unmanned Aerial Vehicle (UAV), Differential InSAR (DInSAR), and Small Baseline Subset InSAR (SBAS-InSAR) to obtain the complete deformation field. The SBAS-InSAR, DInSAR, and UAV can be used to obtain small-scale, mesoscale, and large-scale deformations, respectively. The three types of data were all superimposed by the Kriging interpolation, and the deformation field was integrated using the probability integral model to obtain the complete high-precision deformation field with complete time series in the study area. The study area was in the WangJiata mine in Western China, where mining was carried out from 12 July 2018 to 25 October 2018, on the 2S201 working face. The first observation was made in June 2018, and steady-state observations were made in April 2019, totaling four UAV observations. During this period, the Canadian Earth Observation Satellite of Radarsat-2 (R2) was used to take 10 SAR images, the surface subsidence mapping was undertaken using DInSAR and SBAS-InSAR techniques, and the complete deformation field of the working face during the 106-day mining period was obtained by using the UAV technique. The results showed that the subsidence basin gradually expanded along the mining direction as the working face advanced. When the mining advance was greater than 1.2–1.4 times the coal seam burial depth, the supercritical conditions were reached, and the maximum subsidence stabilized at the value of 2.780 m. The subsidence rate was basically maintained at 0.25 m/d. Finally, the accuracy of the method was tested by the Global Navigation Satellite System (GNSS) data, and the medium error of the strike was 0.103 m. A new method is reached by the fusion of active and passive remote sensing data to construct efficient, complete and high precision time-series subsidence basins with high precision.

With the exploitation of mineral resources, pollution of the ecological environment in mines has garnered public attention. Particularly,erosion of the surrounding ecological environment re-sulting from heavy metals in tailings pond could be highly concerning. Instead of traditional field sampling and laboratory analysis method, remote sensing can be used to high-precisi es-timation soil heavy metal with less time and effort. soil heavy metal content is generally low, the spectral sensitivities of various heavy metals are insignificant, and the surface landscape is complex, there exist difficulties associated with heavy metal content estimation. Therefore, herein, we propose optimization of the commonly used partial least-square regression (PLS) method. In the optimized method, a variety of remote sensing indices and the modeled heavy metals were added as modeling factors to indirect estimation soil heavy metal. The method was validated via inversion experiments of heavy metals (Ni, Cu, and Zn) in the tailing pond and its surrounding environment,it improve the goodness-of-fit of Ni, Cu, and Zn by 0.0852,0.2291, and 0.2919 compared with traditional PLS. Spatia l analysis was then conducted on the entire studied area using the estimation model of the three heavy metals. It was shown that the results were essentially consistent with the actual heavy metal distribution in the area. Therefore, the indirect PLS model with multiple factors proves effective for the estimation of soil heavy metals. It also provides technical support for treatment and evaluation of ecological environments in mining areas.

Currently, a point cloud extraction method based on geometric features requires the configuration of two essential parameters: the neighborhood radius within the point cloud and the criterion for feature threshold selection. This article addresses the issue of manual selection of feature thresholds and proposes a feature extraction method for 3D point clouds based on the Otsu algorithm. Firstly, the curvature value of each point cloud is calculated based on the r-neighborhood of the point cloud data. Secondly, the Otsu algorithm is improved by taking the curvature values as input for the maximum inter-class variance method. The optimal segmentation threshold is obtained based on the Otsu algorithm to divide the point cloud data into two parts. Point cloud data whose curvature is greater than or equal to the threshold is extracted as feature point data. In order to verify the reliability of the algorithm presented in this paper, a method for accuracy assessment of regular point cloud data is proposed. Additionally, comparative analysis was conducted on data with varying point cloud densities and on data contaminated with Gaussian white noise using multiple methods. Experimental results show that the proposed algorithm achieves good extraction results for data with 90% simplification rate and low noise.

Underground coal mining inevitably causes ground fissures, especially permanent cracks that cannot be closed at the boundary of the working face. Studying the underground three-dimensional morphology of the permanent cracks allows one to accurately constrain the formation and development of the ground fissures. This information will contribute to reducing mine disasters and is also a prerequisites to avoid environmental pollution. We selected the Zhangjiamao coal mine (China), which is situated in a collapsible loess area, as a case study for deciphering the formation of permanent cracks. After injecting gypsum slurry into the mine, a three-dimensional model of the ground fissures is obtained by three-dimensional (3D) laser scanner technology that records the 3D underground morphology. Integrating the geological context of a collapsible loess area, the characteristics and main processes of the ground fissure development are constrained: (1) The width of the ground fissure decreases to 0 with increasing depth and is strongly affected by the soil composition. (2) Along the vertical extension direction, the ground fissures are generally inclined to the inner-side of the working face, but the direction remains uncertain at different depths. (3) The transverse propagation direction of the ground fissure becomes more complex with increasing depth. (4) Under the influence of soil texture and water, loose soil fills the bottom of the ground fissure, thus affecting the underground 3D morphology.

Affected by geological factors, the scale of surface deformation in a hilly semi-desertification mining area varies. Meanwhile, there is certain dense vegetation on the ground, so it is difficult to construct a high-precision and complete image of a subsidence basin by using a single monitoring method, and hence the laws of the deformation and inversion of mining parameters cannot be known. Therefore, we firstly propose conducting collaborative monitoring by using InSAR (Interferometric Synthetic Aperture Radar), UAV (unmanned aerial vehicle), and 3DTLS (three-dimensional terrestrial laser scanning). The time-series complete surface subsidence basin is constructed by fusing heterogeneous data. In this paper, SBAS-InSAR (Small Baseline Subset) technology, which has the characteristics of reducing the time and space discorrelation, is used to obtain the small-scale deformation of the subsidence basin, oblique photogrammetry and 3D-TLS with strong penetrating power are used to obtain the anomaly and large-scale deformation, and the local polynomial interpolation based on the weight of heterogeneous data is used to construct a complete and high-precision subsidence basin. Compared with GNSS (Global Navigation Satellite System) monitoring data, the mean square errors of 1.442 m, 0.090 m, 0.072 m are obtained. The root mean square error of the high-precision image of the subsidence basin data is 0.040 m, accounting for 1.4% of the maximum subsidence value. The high-precision image of complete subsidence basin data can provide reliable support for the study of surface subsidence law and mining parameter inversion.

Precipitable water vapor (PWV) is an important meteorological factor for predicting extreme weather events such as tropical cyclones, which can be obtained from zenith tropospheric delay (ZTD) by using a conversion. A time difference of ZTD arrival (TDOZA) model was proposed to monitor the movement of tropical cyclones, and the fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ERA5)-derived ZTD (ERA5-ZTD) was used to estimate the movement of tropical cyclones based on the model. The global navigation satellite system-derived ZTD and radiosonde data-derived PWV (RS-PWV) were used to test the accuracy of the ERA5-ZTD and analyze the correlation between ZTD and PWV, respectively. The statistics showed that the mean Bias, RMS and STD of the ERA5-ZTD were 6.4 mm, 17.1 mm and 16.5 mm, respectively, and the mean correlation coefficient of the ERA5-ZTD and RS-PWV was 0.951, which indicates that the ZTD can be used to predict weather events instead of PWV. Then, spatiao-temporal characteristics of ZTD during the four tropical cyclone (i.e., Merbok, ROKE, Neast and Hato) periods in 2017 were analyzed, and the result showed that the moving directions of ZTD and the tropical cyclones were consistent. Thus, the ZTD time series over the ERA5 grids around the tropical cyclones’ paths were used to estimate the velocity of the tropical cyclones based on the TDOZA model, when the tropical cyclones are approaching or leaving. Compared with the result from the China Meteorological Administration, the mean absolute and relative deviations of the TDOZA model-derived velocity were 2.55 km/h and 10.0%, respectively. These results suggest that ZTD can be used as a new supplementary meteorological parameter for monitoring tropical cyclone events.