Affiliation:
1. Departamento de Ingeniería Civil, Escuela Politécnica Superior de Alicante, Universidad de Alicante, 03080 Alicante, Spain
2. Land Satellite Remote Sensing Application Center (LASAC), Ministry of Natural Resources of P.R. China, Beijing 100048, China
3. The First Topographic Surveying Brigade of the Ministry of Natural Resources of the People’s Republic of China, Xi’an 710054, China
4. Geohazards InSAR Laboratory and Modeling Group (InSARlab), Geohazards and Climate Change Department, Geological Survey of Spain (IGME-CSIC), Calle de Ríos Rosas, 23, 28003 Madrid, Spain
5. The School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Abstract
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.
Funder
ESA-MOST China DRAGON-5 project
Chinese Scholarship Council
Subject
General Earth and Planetary Sciences
Reference58 articles.
1. Radar Interferometry Techniques for the Study of Ground Subsidence Phenomena: A Review of Practical Issues through Cases in Spain;Romero;Environ. Earth Sci.,2013
2. Landslide mapping using optical and radar data: A case study from Aminteo, Western Macedonia Greece;Kyriou;Eur. J. Remote Sens.,2020
3. Ground deformation associated with post-mining activity at the French–German border revealed by novel InSAR time series method;Samsonov;Int. J. Appl. Earth Obs. Geoinf.,2013
4. Integration of ground-based radar and satellite InSAR data for the analysis of an unexpected slope failure in an open-pit mine;Farina;Eng. Geol.,2018
5. Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China;Xiaojie;Eng. Geol.,2021
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献