Updating Active Deformation Inventory Maps in Mining Areas by Integrating InSAR and LiDAR Datasets

Slope failures, subsidence, earthworks, consolidation of waste dumps, and erosion are typical active deformation processes that pose a significant hazard in current and abandoned mining areas, given their considerable potential to produce damage and affect the population at large. This work proves the potential of exploiting space-borne InSAR and airborne LiDAR techniques, combined with data inferred through a simple slope stability geotechnical model, to obtain and update inventory maps of active deformations in mining areas. The proposed approach is illustrated by analyzing the region of Sierra de Cartagena-La Union (Murcia), a mountainous mining area in southeast Spain. Firstly, we processed Sentinel-1 InSAR imagery acquired both in ascending and descending orbits covering the period from October 2016 to November 2021. The obtained ascending and descending deformation velocities were then separately post-processed to semi-automatically generate two active deformation areas (ADA) maps by using ADATool. Subsequently, the PS-InSAR LOS displacements of the ascending and descending tracks were decomposed into vertical and east-west components. Complementarily, open-access, and non-customized LiDAR point clouds were used to analyze surface changes and movements. Furthermore, a slope stability safety factor (SF) map was obtained over the study area adopting a simple infinite slope stability model. Finally, the InSAR-derived maps, the LiDAR-derived map, and the SF map were integrated to update a previously published landslides’ inventory map and to perform a preliminary classification of the different active deformation areas with the support of optical images and a geological map. Complementarily, a level of activity index is defined to state the reliability of the detected ADA. A total of 28, 19, 5, and 12 ADAs were identified through ascending, descending, horizontal, and vertical InSAR datasets, respectively, and 58 ADAs from the LiDAR change detection map. The subsequent preliminary classification of the ADA enabled the identification of eight areas of consolidation of waste dumps, 11 zones in which earthworks were performed, three areas affected by erosion processes, 17 landslides, two mining subsidence zone, seven areas affected by compound processes, and 23 possible false positive ADAs. The results highlight the effectiveness of these two remote sensing techniques (i.e., InSAR and LiDAR) in conjunction with simple geotechnical models and with the support of orthophotos and geological information to update inventory maps of active deformation areas in mining zones.This research was funded by the ESA-MOST China DRAGON-5 project (ref. 59339) and funded by a Chinese Scholarship Council studentship awarded to Liuru Hu (Ref. 202004180062)

Analysis of regional large-gradient land subsidence in the Alto Guadalentín Basin (Spain) using open-access aerial LiDAR datasets

Land subsidence associated with groundwater overexploitation in the Alto Guadalentín Basin (Spain) aquifer system has been detected during the last decades. In this work, for the first time, we propose a new point cloud differencing methodology to detect land subsidence at basin scale, based on the multiscale model-to-model cloud comparison (M3C2) algorithm. This method is applied to two open-access airborne LiDAR datasets acquired in 2009 and 2016, respectively. First the internal edge connection errors in the different flight lines were addressed by means of a smoothing point cloud method. LiDAR datasets capture information from ground and non-ground points. Therefore, a method combining gradient filtering and cloth simulation filtering (CSF) algorithms was applied to remove non-ground points. The iterative closest point (ICP) algorithm was used for point cloud registration of both point clouds exhibiting a very stable and robust performance. The results show that vertical deformation rates are up to −14 cm/year in the basin from 2009 to 2016, in agreement with the displacement reported by previous studies. LiDAR results have been compared to the velocity measured by continuous GNSS stations and an InSAR dataset. For the GNSS-LiDAR and InSAR-LiDAR comparison, we computed a common 100 × 100 m grid in order to assess any similarities and discrepancies. The results show a good agreement between the vertical displacements obtained from the three different surveying techniques. Furthermore, LiDAR results were compared with the distribution of compressible soil thickness showing a clear relationship. The study underlines the potential of open-access and non-customized LiDAR to monitor the distribution and magnitude of vertical deformations in areas prone to be affected by groundwater-withdrawal-induced land subsidence.This research was funded by the ESA-MOST China DRAGON-5 project (ref. 59339) and by a Chinese Scholarship Council studentship awarded to Liuru Hu (Ref. 202004180062). María I. Navarro-Hernández and Guadalupe Bru are funded by the PRIMA programme supported by the European Union under grant agreement No 1924, project RESERVOIR

Advances on the investigation of landslides by space-borne synthetic aperture radar interferometry

Landslides are destructive geohazards to people and infrastructure, resulting in hundreds of deaths and billions of dollars of damage every year. Therefore, mapping the rate of deformation of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated risks. In this paper, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59,339 “Earth observation for seismic hazard assessment and landslide early warning system” are reported. The primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks, detect potential landslides in wide regions, and demonstrate EO-based landslide early warning system over selected landslides. This work only focuses on the landslide hazard content of the project, and thus, in order to achieve these objectives, the following tasks were developed up to now: a) a procedure for phase unwrapping errors and tropospheric delay correction; b) an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements; c) the application of polarimetric SAR interferometry (PolInSAR) to increase the number and quality of monitoring points in landslide-prone areas; d) the semiautomatic mapping and preliminary classification of active displacement areas on wide regions; e) the modeling and identification of landslides in order to identify triggering factors or predict future displacements; and f) the application of an InSAR-based landslide early warning system on a selected site. The achieved results, which mainly focus on specific sensitive regions, provide essential assets for planning present and future scientific activities devoted to identifying, mapping, characterizing, monitoring and predicting landslides, as well as for the implementation of early warning systems.This work was supported by the ESA-MOST China DRAGON-5 project with ref. 59339, by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI), and the European Funds for Regional Development under grant [grant number PID2020-117303GB-C22], by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital in the framework of the project CIAICO/2021/335, by the Natural Science Foundation of China [grant numbers 41874005 and 41929001], the Fundamental Research Funds for the Central University [grant numbers 300102269712 and 300102269303], and China Geological Survey Project [grant numbers DD20190637 and DD20190647]. Xiaojie Liu and Liuru Hu have been funded by Chinese Scholarship Council Grants Ref. [grant number 202006560031] and [grant number 202004180062], respectively

Updating Active Deformation Inventory Maps in Mining Areas by Integrating InSAR and LiDAR Datasets

Slope failures, subsidence, earthworks, consolidation of waste dumps, and erosion are typical active deformation processes that pose a significant hazard in current and abandoned mining areas, given their considerable potential to produce damage and affect the population at large. This work proves the potential of exploiting space-borne InSAR and airborne LiDAR techniques, combined with data inferred through a simple slope stability geotechnical model, to obtain and update inventory maps of active deformations in mining areas. The proposed approach is illustrated by analyzing the region of Sierra de Cartagena-La Union (Murcia), a mountainous mining area in southeast Spain. Firstly, we processed Sentinel-1 InSAR imagery acquired both in ascending and descending orbits covering the period from October 2016 to November 2021. The obtained ascending and descending deformation velocities were then separately post-processed to semi-automatically generate two active deformation areas (ADA) maps by using ADATool. Subsequently, the PS-InSAR LOS displacements of the ascending and descending tracks were decomposed into vertical and east-west components. Complementarily, open-access, and non-customized LiDAR point clouds were used to analyze surface changes and movements. Furthermore, a slope stability safety factor (SF) map was obtained over the study area adopting a simple infinite slope stability model. Finally, the InSAR-derived maps, the LiDAR-derived map, and the SF map were integrated to update a previously published landslides’ inventory map and to perform a preliminary classification of the different active deformation areas with the support of optical images and a geological map. Complementarily, a level of activity index is defined to state the reliability of the detected ADA. A total of 28, 19, 5, and 12 ADAs were identified through ascending, descending, horizontal, and vertical InSAR datasets, respectively, and 58 ADAs from the LiDAR change detection map. The subsequent preliminary classification of the ADA enabled the identification of eight areas of consolidation of waste dumps, 11 zones in which earthworks were performed, three areas affected by erosion processes, 17 landslides, two mining subsidence zone, seven areas affected by compound processes, and 23 possible false positive ADAs. The results highlight the effectiveness of these two remote sensing techniques (i.e., InSAR and LiDAR) in conjunction with simple geotechnical models and with the support of orthophotos and geological information to update inventory maps of active deformation areas in mining zones

Stereoscopic Monitoring Methods for Flood Disasters Based on ICESat-2 and Sentinel-2 Data

Climate change has led to an increased frequency of extreme precipitation events, resulting in increased damage from rainstorms and floods. Rapid and efficient flood forecasting is crucial. However, traditional hydrological simulation methods that rely on site distribution are limited by the limited availability of data and cannot provide fast and accurate flood monitoring information. Therefore, this study took the flood event in Huoqiu County in 2020 as an example and proposes a three-dimensional flood monitoring method based on active and passive satellites, which provides effective information support for disaster prevention and mitigation. The experimental results indicated the following: (1) the flood-inundated area was 704.1 km2, with the Jiangtang Lake section of the Huaihe River and the southern part of Chengdong Lake being the largest affected areas; (2) water levels in the study area ranged from 15.36 m to 17.11 m, which is 4–6 m higher than the original water level. The highest flood water level areas were the Jiangtang Lake section and the flat area in the south of Chengdong Lake, with Chengdong Lake and the north of Chengxi Lake having the greatest water level increase; (3) the flood water depth was primarily between 4 m and 7 m, with a total flood storage capacity of 2833.47 million m3, with Jiangtang Lake having the largest flood storage capacity; and (4) the rainstorm and flood disaster caused a direct economic loss of approximately CNY 7.5 billion and affected a population of approximately 91 thousand people. Three-dimensional monitoring of floods comprehensively reflects the inundation status of floods and can provide valuable information for flood prediction and management

On-Orbit Geometric Calibration and Accuracy Validation for Laser Footprint Cameras of GF-7 Satellite

The Gaofen-7 (GF-7) satellite uses a two-beam laser altimetry system in which each beam is equipped with a laser footprint camera (LFC) to provide geometric processing of the laser footprint images that assist in optical image stereo mapping. Because of the violent vibrations during launch and the difference in the environment before and after entering orbit, the key parameters for geometric processing of the laser footprint images may change, which will cause large geolocation errors. Therefore, it is essential to carry out on-orbit calibration and validation for the laser footprint cameras. This study first constructs a rigorous geometric positioning model for the LFC of the GF-7 satellite and analyses various error sources that affect the geometric positioning accuracy of laser footprint images. Then, a comprehensive calibration method, which effectively eliminates the distortion of the LFC optical system, and the positioning error caused by the long-period jitter of the satellite platform, is proposed based on the multi-scene images combined with image simulation. The proposed method can effectively eliminate various errors that affect the geometric positioning accuracy of the GF-7 laser footprint image. The internal geometric positioning accuracy of the calibrated LFC is better than 0.7 pixels, and the absolute geometric positioning accuracy is within 6.0 m after using precise post-processing orbital and attitude data. Our study will contribute to the processing and application of laser altimetry data from the GF-7 satellite

Registration and Combined Adjustment for the Laser Altimetry Data and High-Resolution Optical Stereo Images of the GF-7 Satellite

The GF-7 satellite is China’s first civil sub-meter resolution stereo mapping satellite, aiming at 1:10,000-scale mapping. To achieve this goal, apart from the stereo optical cameras that reach sub-meter resolution, the GF-7 satellite is equipped with a laser altimetry system capable of obtaining three-dimensional laser points (LPs) with high elevation accuracy. However, the combination of laser altimetry data and optical stereo images has not been thoroughly studied. In this paper, we exploit the images recorded by the highly integrated laser footprint cameras and propose a hierarchical phase correlation method based on a geographic pyramid for the registration of laser altimetry data and high-resolution optical stereo images, which lays a solid foundation for the following combined adjustment. Experiments show that the proposed registration method can automatically locate the LPs on high-resolution stereo images and meet the requirements of bundle adjustment. A series of bundle adjustment experiments were carried out, showing that laser altimetry data can significantly enhance the vertical accuracy of optical image stereo mapping and that elevation accuracy can reach roughly 1.0 m (RSME) without ground control points. Therefore, this study could be a good guide for global high-precision DSM acquisition with the GF-7 satellite