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

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/135827
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Title: Updating Active Deformation Inventory Maps in Mining Areas by Integrating InSAR and LiDAR Datasets
Authors: Hu, Liuru | Tomás, Roberto | Tang, Xinming | López-Vinielles, Juan | Herrera García, Gerardo | Li, Tao | Liu, Zhiwei
Research Group/s: Ingeniería del Terreno y sus Estructuras (InTerEs)
Center, Department or Service: Universidad de Alicante. Departamento de Ingeniería Civil
Keywords: Active deformation area | InSAR | LiDAR | Stability model | Mining area | Landslide | Earthworks | Consolidation of waste dumps | South-eastern Spain
Issue Date: 10-Feb-2023
Publisher: MDPI
Citation: Hu L, Tomás R, Tang X, López Vinielles J, Herrera G, Li T, Liu Z. Updating Active Deformation Inventory Maps in Mining Areas by Integrating InSAR and LiDAR Datasets. Remote Sensing. 2023; 15(4):996. https://doi.org/10.3390/rs15040996
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.
Sponsor: 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).
URI: http://hdl.handle.net/10045/135827
ISSN: 2072-4292
DOI: 10.3390/rs15040996
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: https://doi.org/10.3390/rs15040996
Appears in Collections:INV - INTERES - Artículos de Revistas

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