3 research outputs found
MT-InSAR and Dam Modeling for the Comprehensive Monitoring of an Earth-Fill Dam: The Case of the BenÃnar Dam (AlmerÃa, Spain)
The BenÃnar Dam, located in Southeastern Spain, is an earth-fill dam that has experienced filtration issues since its construction in 1985. Despite the installation of various monitoring systems, the data collected are sparse and inadequate for the dam’s lifetime. The present research integrates Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and dam modeling to validate the monitoring of this dam, opening the way to enhanced integrated monitoring systems. MT-InSAR was proved to be a reliable and continuous monitoring system for dam deformation, surpassing previously installed systems in terms of precision. MT-InSAR allowed the almost-continuous monitoring of this dam since 1992, combining ERS, Envisat, and Sentinel-1A/B data. Line-of-sight (LOS) velocities of settlement in the crest of the dam evolved from maximums of −6 mm/year (1992–2000), −4 mm/year (2002–2010), and −2 mm/year (2015–2021) with median values of −2.6 and −3.0 mm/year in the first periods (ERS and Envisat) and −1.3 mm/year in the Sentinel 1-A/B period. These results are consistent with the maximum admissible modeled deformation from construction, confirming that settlement was more intense in the dam’s early stages and decreased over time. MT-InSAR was also used to integrate the monitoring of the dam basin, including critical slopes, quarries, and infrastructures, such as roads, tracks, and spillways. This study allows us to conclude that MT-InSAR and dam modeling are important elements for the integrated monitoring systems of embankment dams. This conclusion supports the complete integration of MT-InSAR and 3D modeling into the monitoring systems of embankment dams, as they are a key complement to traditional geotechnical monitoring and can overcome the main limitations of topographical monitoring
Evaluation of the potential of InSAR time series to study the spatio-temporal evolution of piezometric levels in the Madrid aquifer
The Tertiary detritic aquifer of Madrid (TDAM), with an average thickness of
1500 m and a heterogeneous, anisotropic structure, supplies water to
Madrid, the most populated city of Spain (3.2 million inhabitants in the
metropolitan area). Besides its complex structure, a previous work focused in
the north-northwest of Madrid city showed that the aquifer behaves quasi
elastically trough extraction/recovery cycles and ground uplifting during
recovery periods compensates most of the ground subsidence measured during
previous extraction periods (Ezquerro et al., 2014). Therefore, the
relationship between ground deformation and groundwater level through time
can be simulated using simple elastic models. In this work, we model the
temporal evolution of the piezometric level in 19 wells of the TDAM in the
period 1997–2010. Using InSAR and piezometric time series spanning the
studied period, we first estimate the elastic storage coefficient
(Ske) for every well. Both, the Ske of each well and the
average Ske of all wells, are used to predict hydraulic heads at
the different well locations during the study period and compared against the
measured hydraulic heads, leading to very similar errors when using the
Ske of each well and the average Ske of all wells: 14 and
16 % on average respectively. This result suggests that an average
Ske can be used to estimate piezometric level variations in all the
points where ground deformation has been measured by InSAR, thus allowing
production of piezometric level maps for the different extraction/recovery
cycles in the TDAM
HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS
We have studied the spectral features of reflectance and emissivity in the pattern recognition of urban materials in several single
hyperspectral scenes through a comparative analysis of anomaly detection methods and their relationship with city surfaces with the
aim to improve information extraction processes. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR)
and thermal infrared (TIR) from hyperspectral data cubes of AHS sensor and HyMAP and MASTER of two cities, Alcalá de Henares
(Spain) and San José (Costa Rica) respectively, have been used.
In this research it is assumed no prior knowledge of the targets, thus, the pixels are automatically separated according to their
spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by image
segmentation. Several experiments on urban scenarios and semi-urban have been designed, analyzing the behaviour of the standard
RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection
methods. A new technique for anomaly detection in hyperspectral data called DATB (Detector of Anomalies from Thermal
Background) based on dimensionality reduction by projecting targets with unknown spectral signatures to a background calculated
from thermal spectrum wavelengths is presented. First results and their consequences in non-supervised classification and extraction
information processes are discussed