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

• Published in: Remote sensing of environment 2022-10, Vol.280, p.113218, Article 113218
• Main Authors: Hu, Liuru, Navarro-Hernández, María I., Liu, Xiaojie, Tomás, Roberto, Tang, Xinming, Bru, Guadalupe, Ezquerro, Pablo, Zhang, Qingtao
• Format: Article
• Language: English
• Subjects: Alto Guadalentín aquifer; Cross-correlation analysis; InSAR; Land subsidence; LiDAR; M3C2
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2022.113218

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. •A new approach is proposed to measure land subsidence using point cloud datasets.•Some difficulties derived when using wide-spread LiDAR datasets are overcome.•Vertical deformation rates up to −12 cm/year are measured between 2009 and 2016.•The relationship between subsidence and conditioning/triggering factors is studied.•The advantages and drawbacks of using LiDAR against InSAR are discussed.