Interannual oscillations and trend of snow occurrence in the Andes region since 1885

Using documentary sources, a series of the annual number of snow days in the Mendoza area of Argentina has been constructed. Analysis of the series using Singular Spectrum Analysis (SSA) and the Maximum Entropy Method (MEM) showed that the number of snow days exhibits interdecadal and interannual oscillations with periods of about 28 and five years. Furthermore a positive trend was detected. This temporal pattern is consistent with studies of the variability of global surface temperature, indicating a strong relationship between temperature and snow occurrence in the climate system and the potential for using snow occurrence as an indicator of climate change

Learning 3D structure from 2D images using LBP features

An automatic machine learning strategy for computing the 3D structure of monocular images from a single image query using Local Binary Patterns is presented. The 3D structure is inferred through a training set composed by a repository of color and depth images, assuming that images with similar structure present similar depth maps. Local Binary Patterns are used to characterize the structure of the color images. The depth maps of those color images with a similar structure to the query image are adaptively combined and filtered to estimate the final depth map. Using public databases, promising results have been obtained outperforming other state-of-the-art algorithms and with a computational cost similar to the most efficient 2D-to-3D algorithms

Fast 2D to 3D conversion using a clustering-based hierarchical search in a machine learning framework

Automatic 2D-to-3D conversion is an important application for filling the gap between the increasing number of 3D displays and the still scant 3D content. However, existing approaches have an excessive computational cost that complicates its practical application. In this paper, a fast automatic 2D-to-3D conversion technique is proposed, which uses a machine learning framework to infer the 3D structure of a query color image from a training database with color and depth images. Assuming that photometrically similar images have analogous 3D structures, a depth map is estimated by searching the most similar color images in the database, and fusing the corresponding depth maps. Large databases are desirable to achieve better results, but the computational cost also increases. A clustering-based hierarchical search using compact SURF descriptors to characterize images is proposed to drastically reduce search times. A significant computational time improvement has been obtained regarding other state-of-the-art approaches, maintaining the quality results

Improved 2D-to-3D video conversion by fusing optical flow analysis and scene depth learning

Abstract: Automatic 2D-to-3D conversion aims to reduce the existing gap between the scarce 3D content and the incremental amount of displays that can reproduce this 3D content. Here, we present an automatic 2D-to-3D conversion algorithm that extends the functionality of the most of the existing machine learning based conversion approaches to deal with moving objects in the scene, and not only with static backgrounds. Under the assumption that images with a high similarity in color have likely a similar 3D structure, the depth of a query video sequence is inferred from a color + depth training database. First, a depth estimation for the background of each image of the query video is computed adaptively by combining the depths of the most similar images to the query ones. Then, the use of optical flow enhances the depth estimation of the different moving objects in the foreground. Promising results have been obtained in a public and widely used database

Enhanced automatic 2D-3D conversion using retinex in machine learning framework

In this paper, we present an approach for automatically convert images from 2D to 3D. The algorithm uses a color + depth dataset to estimate a depth map of a query color image by searching structurally similar images in the dataset and fusing them. Our experimental results indicate that the inclusion of a retinex based stage for the query image and the dataset images improves the performance of the system on commonly-used databases and for different image descriptors

Subsidence damage assessment of a Gothic church using differential interferometry and field data

The Santas Justa and Rufina Gothic church (fourteenth century) has suffered several physical, mechanical, chemical, and biochemical types of pathologies along its history: rock alveolization, efflorescence, biological activity, and capillary ascent of groundwater. However, during the last two decades, a new phenomenon has seriously affected the church: ground subsidence caused by aquifer overexploitation. Subsidence is a process that affects the whole Vega Baja of the Segura River basin and consists of gradual sinking in the ground surface caused by soil consolidation due to a pore pressure decrease. This phenomenon has been studied by differential synthetic aperture radar interferometry techniques, which illustrate settlements up to 100 mm for the 1993–2009 period for the whole Orihuela city. Although no differential synthetic aperture radar interferometry information is available for the church due to the loss of interferometric coherence, the spatial analysis of nearby deformation combined with fieldwork has advanced the current understanding on the mechanisms that affect the Santas Justa and Rufina church. These results show the potential interest and the limitations of using this remote sensing technique as a complementary tool for the forensic analysis of building structures.Roberto Tomás is supported by a Generalitat Valenciana fellowship BEST-2011/225. The European Space Agency (ESA) Terrafirma project has funded all the SAR data processing with the SPN technique. Additionally, this study has been partially financed by the projects: TEC-2008-06764, TEC2011-28201-C02-02 ACOMP/2010/082, VIGROB-157, and 15224/PI/10

Subsidence activity maps derived from DInSAR data: Orihuela case study

A new methodology is proposed to produce subsidence activity maps based on the geostatistical analysis of persistent scatterer interferometry (PSI) data. PSI displacement measurements are interpolated based on conditional Sequential Gaussian Simulation (SGS) to calculate multiple equiprobable realizations of subsidence. The result from this process is a series of interpolated subsidence values, with an estimation of the spatial variability and a confidence level on the interpolation. These maps complement the PSI displacement map, improving the identification of wide subsiding areas at a regional scale. At a local scale, they can be used to identify buildings susceptible to suffer subsidence related damages. In order to do so, it is necessary to calculate the maximum differential settlement and the maximum angular distortion for each building of the study area. Based on PSI-derived parameters those buildings in which the serviceability limit state has been exceeded, and where in situ forensic analysis should be made, can be automatically identified. This methodology has been tested in the city of Orihuela (SE Spain) for the study of historical buildings damaged during the last two decades by subsidence due to aquifer overexploitation. The qualitative evaluation of the results from the methodology carried out in buildings where damages have been reported shows a success rate of 100%.The European Space Agency (ESA) Terrafirma project has funded all the SAR data processing with the SPN technique. Additionally, this work has been partially financed by DORIS project (Ground deformation risk scenarios: an advanced assessment service) funded by the EC-GMES-FP7 initiative (grant agreement no. 242212), and the Spanish Geological and Mining Institute (IGME). This work has been also supported by the Spanish Ministry of Science and Research (MICINN) under project TEC2011-28201-C02-02 and EU FEDER

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)

A quasi-elastic aquifer deformational behavior: Madrid aquifer case study

The purpose of this paper is to analyze the quasi-elastic deformational behavior that has been induced by groundwater withdrawal of the Tertiary detrital aquifer of Madrid (Spain). The spatial and temporal evolution of ground surface displacement was estimated by processing two datasets of radar satellite images (SAR) using Persistent Scatterer Interferometry (PSI). The first SAR dataset was acquired between April 1992 and November 2000 by ERS-1 and ERS-2 satellites, and the second one by the ENVISAT satellite between August 2002 and September 2010. The spatial distribution of PSI measurements reveals that the magnitude of the displacement increases gradually towards the center of the well field area, where approximately 80 mm of maximum cumulated displacement is registered. The correlation analysis made between displacement and piezometric time series provides a correlation coefficient greater than 85% for all the wells. The elastic and inelastic components of measured displacements were separated, observing that the elastic component is, on average, more than 4 times the inelastic component for the studied period. Moreover, the hysteresis loops on the stress–strain plots indicate that the response is in the elastic range. These results demonstrate the quasi-elastic behavior of the aquifer. During the aquifer recovery phase ground surface uplift almost recovers from the subsidence experienced during the preceding extraction phase. Taking into account this unique aquifer system, a one dimensional elastic model was calibrated in the period 1997–2000. Subsequently, the model was used to predict the ground surface movements during the period 1992–2010. Modeled displacements were validated with PSI displacement measurements, exhibiting an error of 13% on average, related with the inelastic component of deformation occurring as a long-term trend in low permeability fine-grained units. This result further demonstrates the quasi-elastic deformational behavior of this unique aquifer system.This work was developed during Pablo Ezquerro research stay within the Geohazards InSAR laboratory and Modeling group of the Instituto Geológico y Minero de España in the framework of DORIS project (Ground Deformation Risk Scenarios: an Advanced Assessment Service) funded by the EC-GMES-FP7 initiative (Grant Agreement nº 242212). This work has been also supported by the Spanish Ministry of Science and Research (MICINN) under project TEC2011-28201-C02-02 and EU FEDER. Additional funding was obtained from Spanish Research Program through the project ESP2013-47780-C2-2-R

Deformational behaviours of alluvial units detected by advanced radar interferometry in the Vega Media of the Segura River, southeast Spain

It is widely known that differential land subsidence in a valley significantly controls its fluvial dynamics. Nevertheless, major uncertainty exists about the way in which alluvial forms respond to this process. In this study, morphological and lithostratigraphic data have been combined with advanced differential interferometry (A-DInSAR) to detect changes in alluvial landform elevations and to verify the existence of a differential subsidence pattern influenced by active sedimentary dynamics. For this purpose, the middle reach of the Segura River valley (Vega Media of the Segura River), in southeast Spain, was chosen as the study area. The Vega Media of the Segura River is an alluvial area affected by subsidence processes in close conjunction with depositional conditions, ground-water withdrawals and faults. A high scale mapping of the main younger sedimentary units was carried out by combining multi-temporal aerial photographs, high-resolution digital elevation models derived from LIDAR data, global navigation satellite system data and fieldwork. In addition, lithostratigraphic descriptions were obtained from geotechnical drilling and trenching. Finally, ground surface displacements, measured using A-DInSAR for the periods 1995–2005 and 2004–2008, allowed the determination of elevation rates and ground deformation associated with the different alluvial units. The results from this analysis revealed four typical deformational behaviours: non-deformational units (cemented alluvial fans and upper fluvial terraces); slightly deformable units (lower terraces and old abandoned meanders); moderately deformable units (lateral accretion zones and abandoned meanders before channelisation in 1981); and highly deformable areas (recently active meanders associated with artificial cutoffs by channelisation, non-active floodplains and spilling zones).This work has been supported by project 15224/PI/10 (Dynamics and recent morphological adjustments in the Lower Segura River, Middle Valley) from the Fundación SENECA of the Regional Agency of Science, Murcia, Spain, and the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER, under Projects TEC2011-28201-C02-02, TIN2014-55413-C2-2-P, ESP2013-47780-C2-2-R and PRX14/00100. The European Space Agency’s (ESA) Terrafirma project has provided all the SAR data processed with the SPN technique and the processing itself was funded by this project