4.7 Article

First delimitation of areas affected by ground deformations in the Guadalfeo River Valley and Granada metropolitan area (Spain) using the DInSAR technique

期刊

ENGINEERING GEOLOGY
卷 105, 期 1-2, 页码 84-101

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.enggeo.2008.12.005

关键词

Differential SAR Interferometry; Terrain motion; Landslides; Subsidence; Granada (Spain)

向作者/读者索取更多资源

A Differential Interferometric SAR (DInSAR) analysis of terrain displacements in the province of Granada (southern Spain) is presented here for two different study zones. The first zone concerns the Guadalfeo River Basin, where abundant landslides and unstable slopes were previously identified and inventoried on a GIS application. However, no instrumental quantification of landslide activity was available. Considering morphological criteria and field observations, these deep-seated landslides were considered to be dormant or moving extremely slowly. The second study zone corresponds to the Granada metropolitan area, where no previous information was available on any vertical movement. The analysis was based on ERS1 and ERS2 images, covering the period from 1993 to 2000. By using DInSAR, we made an initial qualitative assessment of movements on landsliding slopes in the Guadalfeo, River Basin and also vertical land movements in the alluvial Quaternary sediments to the west and south of the Granada metropolitan area. The terrain instability assessment resulting from this research shows maximum annual velocities along the SAR line of sight (LOS) of 6 mm/yr for the Tablones landslide, and up to 13 mm/yr for the Albunuelas village; where progressive differential settlement processes were established as the origin of the movement. In the case of the Granada metropolitan area, two sites were detected with subsidence affecting two villages: Santa Fe, located in the western part of the area, with an estimated average velocity of 8 mm/yr and Otura in the south of the area, with maximum velocities of 12 mm/yr. A description of geological features of the different sites, showing LOS ground displacement, is presented with a discussion of the application of the rates to landslide-activity assessment found, local ground conditions, or exploitation of subterranean water in urban areas. (C) 2008 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Geography, Physical

Classification of ground deformation using sentinel-1 persistent scatterer interferometry time series

S. Mohammad Mirmazloumi, Yismaw Wassie, Jose Antonio Navarro, Riccardo Palama, Vrinda Krishnakumar, Anna Barra, Maria Cuevas-Gonzalez, Michele Crosetto, Oriol Monserrat

Summary: This study aims to investigate the temporal behavior of ground deformation time series and proposes a modified automatic classification workflow to classify ground deformations into seven main trends. The approach shows potential in accurately identifying ground movement types, detecting anomalies, and correctly recognizing stable targets, with an overall classification accuracy of 77.8%.

GISCIENCE & REMOTE SENSING (2022)

Article Geochemistry & Geophysics

Improving Landslide Detection on SAR Data Through Deep Learning

Lorenzo Nava, Oriol Monserrat, Filippo Catani

Summary: In this study, deep learning convolutional neural networks (CNNs) were used to compare the mapping and classification performances of optical images and synthetic aperture radar (SAR) images in landslide detection. The results showed that CNNs based on optical images achieved an overall accuracy of 98.96% in landslide detection, while CNNs based on SAR data reached accuracies beyond 95% in ground range detection.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Article Environmental Sciences

The importance of investigating causative factors and training data selection for accurate landslide susceptibility assessment: the case of Ain Lahcen commune (Tetouan, Northern Morocco)

Ali Bounab, Kamal Agharroud, Younes El Kharim, Rachid El Hamdouni, Layachi Faghloumi

Summary: This study investigated the effects of training data and computation technique selection on the accuracy and performance of landslide susceptibility maps (LSMs). The results showed that including relict landslides in the training data decreases the accuracy of LSMs, while the choice of different predictive variables combinations has a less significant impact. Models produced using frequency ratio and logistic regression tend to perform better, and artificial neural networks seem to outperform other models.

GEOCARTO INTERNATIONAL (2022)

Article Environmental Sciences

Spatio-Temporal Quality Indicators for Differential Interferometric Synthetic Aperture Radar Data

Yismaw Wassie, S. Mohammad Mirmazloumi, Michele Crosetto, Riccardo Palama, Oriol Monserrat, Bruno Crippa

Summary: Satellite-based interferometric synthetic aperture radar (InSAR) is a valuable technique for detecting and monitoring changes on the earth's surface. The multi-temporal differential InSAR (DInSAR) methods estimate the spatio-temporal deformation, but face challenges in resolving the inherent ambiguities of interferometric phases. Quality indices are proposed as important tools for achieving ultimate processing outcomes in DInSAR data processing.

REMOTE SENSING (2022)

Article Environmental Sciences

Rapid Mapping of Landslides on SAR Data by Attention U-Net

Lorenzo Nava, Kushanav Bhuyan, Sansar Raj Meena, Oriol Monserrat, Filippo Catani

Summary: Multiple landslide events are common worldwide and can cause significant damage. This study explores the potential of SAR data combined with other data sources to map landslides, even under cloud cover. The findings demonstrate that the combination of SAR data and DL algorithms can help quickly map landslides, even during storms and under deep cloud cover.

REMOTE SENSING (2022)

Article Environmental Sciences

A Multi-Temporal Small Baseline Interferometry Procedure Applied to Mining-Induced Deformation Monitoring

Riccardo Palama, Michele Crosetto, Jacek Rapinski, Anna Barra, Maria Cuevas-Gonzalez, Oriol Monserrat, Bruno Crippa, Natalia Kotulak, Marek Mroz, Magdalena Mleczko

Summary: This work presents a methodology using interferometric synthetic aperture radar (InSAR) to analyze and monitor ground motion caused by underground mining activities in the Legnica-Glogow copper district, Poland. The technique utilizes a stack of Sentinel-1 synthetic aperture radar images and a small baseline multitemporal approach. The estimated displacement maps and time series are validated using global navigation satellite system (GNSS) measurements. The method is also used to analyze seismic tremors triggered by underground mining activities.

REMOTE SENSING (2022)

Article Environmental Sciences

Supervised Machine Learning Algorithms for Ground Motion Time Series Classification from InSAR Data

S. Mohammad Mirmazloumi, Angel Fernandez Gambin, Riccardo Palama, Michele Crosetto, Yismaw Wassie, Jose A. Navarro, Anna Barra, Oriol Monserrat

Summary: In this study, machine learning models were used to classify DInSAR time series, and their performance was evaluated. The study found that customized features significantly improved the accuracy of classification. The importance of different features in classification was also analyzed, and the reliability of the models was validated.

REMOTE SENSING (2022)

Article Environmental Sciences

ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine

S. Mohammad Mirmazloumi, Mohammad Kakooei, Farzane Mohseni, Arsalan Ghorbanian, Meisam Amani, Michele Crosetto, Oriol Monserrat

Summary: This study proposes a workflow to generate a high-resolution LULC map of Europe using satellite images and survey data. By employing object-based segmentation algorithm, Artificial Neural Network, and rule-based post-processing steps, the generated map exhibits high accuracy in classification and identification of LULC classes.

REMOTE SENSING (2022)

Article Remote Sensing

A low-cost active reflector and a passive corner reflector network for assisting landslide monitoring using multi-temporal InSAR

Guido Luzi, Anna Barra, Qi Gao, Pedro F. Espin-Lopez, Riccardo Palama, Oriol Monserrat, Michele Crosetto, Xavier Colell

Summary: A C-band low-cost active reflector was tested in an experimental campaign for monitoring a landslide-threatened area. With a network of passive corner reflectors and one active reflector installed along a forested slope, interferograms were processed to evaluate area stability. Despite temperature sensitivity, the active reflector operated with acceptable stability for deformation retrieval in monitoring purposes.

REMOTE SENSING LETTERS (2022)

Article Geochemistry & Geophysics

Measuring Glacier Elevation Change by Tracking Shadows on Satellite Monoscopic Optical Images

Niccolo Dematteis, Daniele Giordan, Bruno Crippa, Oriol Monserrat

Summary: Measuring glacier elevation change is crucial for various purposes, such as estimating glacier mass balance, calibrating climate models, and assessing the impact of global warming. In this study, we explored the potential of clinometry as a technique to quantify glacier elevation changes. By analyzing shadow positions in monoscopic optical images, we were able to measure a glacier thinning rate of -1.9 +/- 1.7 ma(-1) between 2017 and 2021 for the Aletsch Glacier (Switzerland), consistent with previous observations.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2023)

Article Geochemistry & Geophysics

Radargrammetry DEM Generation Using High-Resolution SAR Imagery Over La Palma During the 2021 Cumbre Vieja Volcanic Eruption

Riccardo Palama, Oriol Monserrat, Bruno Crippa, Michele Crosetto, Guadalupe Bru, Pablo Ezquerro, Marta Bejar-Pizarro

Summary: This study investigates the potential of high-resolution synthetic aperture radar (SAR) images in generating digital elevation models (DEMs) using the radargrammetry technique. Two SAR images recorded by Capella Space X-band satellite radar sensor over La Palma during the Cumbre Vieja volcanic eruption are processed. An iterative point aggregation algorithm is adopted to identify matching pixels and height estimation is performed using distance minimization routine. The resultant radargrammetric DEM is validated against a lidar-based DEM, showing good agreement in less affected areas. Lava thickness estimation is conducted and compared with photogrammetry estimates from Pleiades mission data.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2023)

Review Chemistry, Multidisciplinary

Assessment of the Socio-Economic Impacts of Extreme Weather Events on the Coast of Southwest Europe during the Period 2009-2020

Rosa Maria Mateos, Roberto Sarro, Andres Diez-Herrero, Cristina Reyes-Carmona, Juan Lopez-Vinielles, Pablo Ezquerro, Monica Martinez-Corbella, Guadalupe Bru, Juan Antonio Luque, Anna Barra, Pedro Martin, Agustin Millares, Miguel Ortega, Alejandro Lopez, Jorge Pedro Galve, Jose Miguel Azanon, Susana Pereira, Pedro Pinto Santos, Jose Luis Zezere, Eusebio Reis, Ricardo A. C. Garcia, Sergio Cruz Oliveira, Arnaud Villatte, Anne Chanal, Muriel Gasc-Barbier, Oriol Monserrat

Summary: Coastal regions in Southwest Europe have undergone significant changes due to tourism-related urban development, making them highly vulnerable to the impacts of climate change. This study analyzed extreme weather events along the Atlantic and Mediterranean coasts of Southwest Europe from 2009 to 2020, quantifying their impacts on fatalities, injuries, and economic losses. The results showed an upward trend in the number of events, as well as human losses and damages. The Mediterranean coast, especially the Spanish Mediterranean coast, had a higher exposure due to rapid tourism growth and inadequate preparation for marine storms.

APPLIED SCIENCES-BASEL (2023)

Article Environmental Sciences

Characterization and Analysis of Landslide Evolution in Intramountain Areas in Loja (Ecuador) Using RPAS Photogrammetric Products

Belizario A. Zarate, Rachid El Hamdouni, Tomas Fernandez del Castillo

Summary: This case study focuses on the monitoring and characterization of landslides in El Plateado, Ecuador. Aerial images captured by a remotely piloted aerial vehicle (RPAS) were processed to generate high-resolution digital elevation models (DEMs) and orthoimages for analysis. The study found that the landslide has high slope and roughness, with notable changes in the main scarp and toe tip. The presence of fissures and rainfall infiltration contribute to instability. The study provides insights into landslide measurement accuracy, identification of elements, morphometric analysis, and the relationship with geotechnical factors.

REMOTE SENSING (2023)

Proceedings Paper Geosciences, Multidisciplinary

GENERATION OF A DIGITAL ELEVATION MODEL USING CAPELLA HIGH-RESOLUTION SAR DATA: FIRST RESULTS OVER LA PALMA ISLAND

Riccardo Palama, Oriol Monserrat, Bruno Crippa, Michele Crosetto, Guadalupe Bru, Pablo Ezquerro, Marta Bejar-Pizarro

Summary: In this study, the potential of generating precise DEMs using SAR data is investigated. The adopted method involves radargrammetry on consecutive SAR images, and the resultant DEM is compared with a Lidar-based DEM to evaluate its accuracy.

2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) (2022)

Article Environmental Sciences

The Suitability of UAV-Derived DSMs and the Impact of DEM Resolutions on Rockfall Numerical Simulations: A Case Study of the Bouanane Active Scarp, Tetouan, Northern Morocco

Ali Bounab, Younes El Kharim, Rachid El Hamdouni

Summary: This study assesses the impact of different resolution DEMs on the accuracy of rockfall simulations, finding that low to medium resolution DEMs result in large errors in the simulated trajectories and runout distances, while the 1m UAV-derived model produces more accurate results.

REMOTE SENSING (2022)

暂无数据