Articles de revues : « Remote Sensing, Landslides, SAR Interferometry »

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Lan, Hengxing, Xiao Liu, Langping Li, Quanwen Li, Naiman Tian et Jianbing Peng. « Remote Sensing Precursors Analysis for Giant Landslides ». Remote Sensing 14, n^o 17 (4 septembre 2022) : 4399. http://dx.doi.org/10.3390/rs14174399.

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Monitoring and early warning systems for landslides are urgently needed worldwide to effectively reduce the losses of life and property caused by these natural disasters. Detecting the precursors of giant landslides constitutes the premise of landslide monitoring and early warning, and remote sensing is a powerful means to achieve this goal. In this work, we aim to summarize the basic types and evolutionary principles of giant landslide precursors, describe the remote sensing methods capable of identifying those precursors, and present typical cases of related sliding. Based on a review of the literature and an analysis of remote sensing imagery, the three main types of remote sensing techniques for capturing the geomorphological, geotechnical, and geoenvironmental precursors of giant landslides are optical, synthetic aperture radar (SAR), and thermal infrared methods, respectively. Time-series optical remote sensing data from medium-resolution satellites can be used to obtain abundant information on geomorphological changes, such as the extension of cracks and erosion ditches, and band algebraic analysis, image enhancement, and segmentation techniques are valuable for focusing on the locations of geomorphological landslide precursors. SAR sensors have the ability to monitor the slight slope deformation caused by unfavorable geological structures and can provide precursor information on imminent failure several days before a landslide; furthermore, persistent scatterer interferometric SAR has significant advantages in large-scale surface displacement monitoring. Thermal infrared imagery can identify landslide precursors by monitoring geoenvironmental information, especially in permafrost regions where glaciers are widely distributed; the reason may be that freeze–thaw cycles and snowmelt caused by increased temperatures affect the stability of the surface. Optical, SAR, and thermal remote sensing all exhibit unique advantages and play an essential role in the identification of giant landslide precursors. The combined application of these three remote sensing technologies to obtain the synthetic geomorphological, geotechnical, and geoenvironmental precursors of giant landslides would greatly promote the development of landslide early warning systems.

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Chaturvedi, Sudhir Kumar. « Landslide Assessment Using Sentinel-I SAR-C Interferometry Technique ». Nature Environment and Pollution Technology 21, n^o 3 (1 septembre 2022) : 1201–7. http://dx.doi.org/10.46488/nept.2022.v21i03.025.

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Landslides might remain unknown or unnoticed for a long time in various remote areas due to the unavailability of optical images caused by cloud persistence, which creates difficulties for civil protection rescue operations, and disaster management as well. Rapid crisis response for humanitarian and reconstruction operations in the affected area after such dangerous landslides is necessary. Thus, a rapid detection map is necessary to detect the affected area with damage grade and level for further investigation and human safety protocols. To detect landslide incidents, the unprecedented availability of Sentinel-1 SAR-C band images provides new solutions and better safety reports. In this study, we performed an efficient evaluation of Sentinel-1 SAR C band images before and after landslide incidents. This study provides a comprehensive evaluation based on the advanced space-borne remote sensing technology aiming at SAR products for rapid damage detection and analysis with respect to the interferometric coherence and intensity correlation. We presented the results of a pilot study on the Rudraprayag Uttarakhand massive landslide incident, which includes the different types, sizes, slope expositions, and human safety aspects. Our study and outcomes represent an updated method, which provides a solution for critical terrain rescue operations and an upgraded geomatics map that provides subsidence data with historical data with topographical statistics. Finally, an outlook into the Sentinel-1 SAR-C analysis demonstrates probable solutions to certain constraints, enabling global applicability of the proposed damage assessment methods with the improved accuracy from 50 to 60 % for the obtained temporal resolution datasets.

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Liu, Xiaojie, Chaoying Zhao, Qin Zhang, Jianbing Peng, Wu Zhu et Zhong Lu. « Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets—A Case Study of Heifangtai Loess Landslides, China ». Remote Sensing 10, n^o 11 (7 novembre 2018) : 1756. http://dx.doi.org/10.3390/rs10111756.

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The Interferometric Synthetic Aperture Radar (InSAR) technique is a well-developed remote sensing tool which has been widely used in the investigation of landslides. Average deformation rates are calculated by weighted averaging (stacking) of the interferograms to detect small-scale loess landslides. Heifangtai loess terrace, Gansu province China, is taken as a test area. Aiming to generate multi-temporal landslide inventory maps and to analyze the landslide evolution features from December 2006 to November 2017, a large number of Synthetic Aperture Radar (SAR) datasets acquired by L-band ascending ALOS/PALSAR, L-band ascending and descending ALOS/PALSAR-2, X-band ascending and descending TerraSAR-X and C-band descending Sentinel-1A/B images covering different evolution stages of Heifangtai terrace are fully exploited. Firstly, the surface deformation of Heifangtai terrace is calculated for independent SAR data using the InSAR technique. Subsequently, InSAR-derived deformation maps, SAR intensity images and a DEM gradient map are jointly used to detect potential loess landslides by setting the appropriate thresholds. More than 40 active loess landslides are identified and mapped. The accuracy of the landslide identification results is verified by comparison with published literatures, the results of geological field surveys and remote sensing images. Furthermore, the spatiotemporal evolution characteristics of the landslides during the last 11 years are revealed for the first time. Finally, strengths and limitations of different wavelength SAR data, and the effects of track direction, geometric distortions of SAR images and the differences in local incidence angle between two adjacent satellite tracks in terms of small-scale loess landslides identification, are analyzed and summarized, and some suggestions are given to guide the future identification of small-scale loess landslides with the InSAR technique.

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Atanasova, Mila, Hristo Nikolov et Lyubka Pashova. « Application of InSAR satellite method for mapping of active landslides in Bulgaria – opportunities and perspectives ». Proceedings of the ICA 4 (3 décembre 2021) : 1–6. http://dx.doi.org/10.5194/ica-proc-4-10-2021.

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Abstract. Landslides are geological phenomena that are spread on Bulgarian territory mainly along the northern Black Sea coast and on the right banks of the Danube in the western part of the country. Mitigation of the negative effects of these destructive geological phenomena is the compilation of inventory maps of their distribution and registers with the main characteristics of the individual landslides. Conventional methods for making such maps are time-consuming and resource-intensive. Modern satellite, air and ground-based remote sensing technologies facilitate the production of landslide maps, reducing the time and resources required to compile and systematically update them. In this paper, we demonstrate the applicability of Differential Sentinel-1A satellite SAR interferometry (DInSAR) to assess the movement activity and use the information for further updating the national landslide inventories in Bulgaria. We perform several analyses based on multi-temporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides. The use of new opportunities for free access to satellite images, which can be applied in conjunction with other methods, greatly facilitates the processes of inventory, mapping and study of landslides.

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Ramirez, Ryan, Seung-Rae Lee et Tae-Hyuk Kwon. « Long-Term Remote Monitoring of Ground Deformation Using Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) : Applications and Insights into Geotechnical Engineering Practices ». Applied Sciences 10, n^o 21 (23 octobre 2020) : 7447. http://dx.doi.org/10.3390/app10217447.

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Development of synthetic aperture radar (SAR) technology and the dedicated suite of processing tools have aided the evolution of remote sensing techniques for various Earth Observation (EO) applications. Interferometric SAR (InSAR) is a relatively new geodetic technique which provides high-speed and reliable geographic, geologic, and hazards information allowing the prognosis of future environmental and urban planning. In this study, we explored the applicability of two differential interferometry techniques, conventional and advanced differential InSAR (A-DInSAR), for topographic mapping and long-term geotechnical monitoring by exploiting satellite data, particularly Sentinel-1 SAR data, which is publicly shared. We specifically used the open-source tools of SeNtinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS) for interferometric data processing to implement A-DInSAR. This study presents various applications, which include generation of a digital elevation model (DEM), mapping of seismically induced displacement and associated damages, and detection and long-term monitoring of tunneling-induced ground deformation and rainfall-induced landslide. Geometric and temporal decorrelations posed challenges and limitations in the successful implementation of Sentinel-1 SAR interferometry specifically in vegetated areas. The presented results proved the validity and reliability of the exploited SAR data and InSAR techniques for addressing geotechnical engineering related problems.

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Sousa, Joaquim J., Guang Liu, Jinghui Fan, Zbigniew Perski, Stefan Steger, Shibiao Bai, Lianhuan Wei et al. « Geohazards Monitoring and Assessment Using Multi-Source Earth Observation Techniques ». Remote Sensing 13, n^o 21 (24 octobre 2021) : 4269. http://dx.doi.org/10.3390/rs13214269.

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Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere—both in remote and/or in highly populated areas—and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology—especially satellite-based—can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar) interferometry, SAR tomography, high-resolution image matching and data modelling are used to map out landslides and other geohazards and to also monitor possible hazardous geological activity, addressing different study areas: (i) surface deformation of mountain slopes and glaciers; (ii) land surface displacement; and (iii) subsidence, landslides and ground fissure. Results from both the processing and analysis of a dataset of earth observation (EO) multi-source data support the conclusion that geohazards can be identified, studied and monitored in an effective way using new techniques applied to multi-source EO data. As future work, the aim is threefold: extend this study to sensitive areas located in different countries; monitor structures that have strategic, cultural and/or economical relevance; and resort to artificial intelligence (AI) techniques to be able to analyse the huge amount of data generated by satellite missions and extract useful information in due course.

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Pastor, José Luis, Roberto Tomás, Luca Lettieri, Adrián Riquelme, Miguel Cano, Donato Infante, Massimo Ramondini et Diego Di Martire. « Multi-Source Data Integration to Investigate a Deep-Seated Landslide Affecting a Bridge ». Remote Sensing 11, n^o 16 (12 août 2019) : 1878. http://dx.doi.org/10.3390/rs11161878.

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The integration of data from different sources can be very helpful in understanding the mechanism, the geometry, the kinematic, and the area affected by complex instabilities, especially when the available geotechnical information is limited. In this work, the suitability of different techniques for the study of a deep-seated landslide affecting a bridge in Alcoy (Spain) is evaluated. This infrastructure presents such severe damage that has rendered the bridge unusable, which prevents normal access to an important industrial area. Differential SAR Interferometry (DInSAR) and terrestrial Light Detection and Ranging (LiDAR) remote sensing techniques have been combined with ground displacement monitoring techniques, such as inclinometers and conventional geological and geotechnical investigation, electrical-seismic tomography, damage, and topographic surveys, to determine the boundaries, mechanism, and kinematics of the landslide. The successful case study that is illustrated in this work highlights the potential and the need for integrating multi-source data for the optimal management of complex landslides and the effective design of remedial measurements.

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Meng, Qingkai, Pierluigi Confuorto, Ying Peng, Federico Raspini, Silvia Bianchini, Shuai Han, Haocheng Liu et Nicola Casagli. « Regional Recognition and Classification of Active Loess Landslides Using Two-Dimensional Deformation Derived from Sentinel-1 Interferometric Radar Data ». Remote Sensing 12, n^o 10 (12 mai 2020) : 1541. http://dx.doi.org/10.3390/rs12101541.

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Identification and classification of landslides is a preliminary and crucial work for landslide risk assessment and hazard mitigation. The exploitation of surface deformation velocity derived from satellite synthetic aperture radar interferometry (InSAR) is a consolidated and suitable procedure for the recognition of active landslides over wide areas. However, the calculated displacement velocity from InSAR is one-dimensional motion along the satellite line of sight (LOS), representing a major hurdle for landslide type and failure mechanism classification. In this paper, different velocity datasets derived from both ascending and descending Sentinel-1 data are employed to analyze the surface ground movement of the Huangshui region (Northwestern China). With global warming, precipitation in the Huangshui region, geologically belonging to the loess basin in the eastern edge of Qing-Tibet Plateau, has been increasing, often triggering a large number of landslides, posing a potential threat to local citizens and natural and anthropic environments. After processing both SAR data geometries, the surface motion was decomposed to obtain the two-dimensional displacements (vertical and horizontal E–W). Thus, a classification criterion of the loess landslide types and failure mode is proposed, according to the analysis of deformation direction, velocities, texture, and topographic characteristics. With the support of high-resolution images acquired by remote sensing and unmanned aerial vehicle (UAV), 14 translational slides, seven rotational slides, and 10 loess flows were recognized in the study area. The derived results may provide solid support for stakeholders to comprehend the hazard of unstable slopes and to undertake specific precautions for moderate and slow slope movements.

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Xue, D., X. Yu, S. Jia, F. Chen et X. Li. « STUDY ON LANDSLIDE DISASTER EXTRACTION METHOD BASED ON SPACEBORNE SAR REMOTE SENSING IMAGES – TAKE ALOS PALSAR FOR AN EXAMPLE ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (30 avril 2018) : 2023–27. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2023-2018.

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In this paper, sequence ALOS PALSAR data and airborne SAR data of L-band from June 5, 2008 to September 8, 2015 are used. Based on the research of SAR data preprocessing and core algorithms, such as geocode, registration, filtering, unwrapping and baseline estimation, the improved Goldstein filtering algorithm and the branch-cut path tracking algorithm are used to unwrap the phase. The DEM and surface deformation information of the experimental area were extracted. Combining SAR-specific geometry and differential interferometry, on the basis of composite analysis of multi-source images, a method of detecting landslide disaster combining coherence of SAR image is developed, which makes up for the deficiency of single SAR and optical remote sensing acquisition ability. Especially in bad weather and abnormal climate areas, the speed of disaster emergency and the accuracy of extraction are improved. It is found that the deformation in this area is greatly affected by faults, and there is a tendency of uplift in the southeast plain and western mountainous area, while in the southwest part of the mountain area there is a tendency to sink. This research result provides a basis for decision-making for local disaster prevention and control.

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Balbarani, S., P. A. Euillades, L. D. Euillades, F. Casu et N. C. Riveros. « Atmospheric corrections in interferometric synthetic aperture radar surface deformation – a case study of the city of Mendoza, Argentina ». Advances in Geosciences 35 (4 septembre 2013) : 105–13. http://dx.doi.org/10.5194/adgeo-35-105-2013.

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Abstract. Differential interferometry is a remote sensing technique that allows studying crustal deformation produced by several phenomena like earthquakes, landslides, land subsidence and volcanic eruptions. Advanced techniques, like small baseline subsets (SBAS), exploit series of images acquired by synthetic aperture radar (SAR) sensors during a given time span. Phase propagation delay in the atmosphere is the main systematic error of interferometric SAR measurements. It affects differently images acquired at different days or even at different hours of the same day. So, datasets acquired during the same time span from different sensors (or sensor configuration) often give diverging results. Here we processed two datasets acquired from June 2010 to December 2011 by COSMO-SkyMed satellites. One of them is HH-polarized, and the other one is VV-polarized and acquired on different days. As expected, time series computed from these datasets show differences. We attributed them to non-compensated atmospheric artifacts and tried to correct them by using ERA-Interim global atmospheric model (GAM) data. With this method, we were able to correct less than 50% of the scenes, considering an area where no phase unwrapping errors were detected. We conclude that GAM-based corrections are not enough for explaining differences in computed time series, at least in the processed area of interest. We remark that no direct meteorological data for the GAM-based corrections were employed. Further research is needed in order to understand under what conditions this kind of data can be used.

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Romeo, Saverio, Antonio Cosentino, Francesco Giani, Giandomenico Mastrantoni et Paolo Mazzanti. « Combining Ground Based Remote Sensing Tools for Rockfalls Assessment and Monitoring : The Poggio Baldi Landslide Natural Laboratory ». Sensors 21, n^o 8 (8 avril 2021) : 2632. http://dx.doi.org/10.3390/s21082632.

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Nowadays the use of remote monitoring sensors is a standard practice in landslide characterization and monitoring. In the last decades, technologies such as LiDAR, terrestrial and satellite SAR interferometry (InSAR) and photogrammetry demonstrated a great potential for rock slope assessment while limited studies and applications are still available for ArcSAR Interferometry, Gigapixel imaging and Acoustic sensing. Taking advantage of the facilities located at the Poggio Baldi Landslide Natural Laboratory, an intensive monitoring campaign was carried out on May 2019 using simultaneously the HYDRA-G ArcSAR for radar monitoring, the Gigapan robotic system equipped with a DSLR camera for photo-monitoring purposes and the DUO Smart Noise Monitor for acoustic measurements. The aim of this study was to evaluate the potential of each monitoring sensor and to investigate the ongoing gravitational processes at the Poggio Baldi landslide. Analysis of multi-temporal Gigapixel-images revealed the occurrence of 84 failures of various sizes between 14–17 May 2019. This allowed us to understand the short-term evolution of the rock cliff that is characterized by several impulsive rockfall events and continuous debris production. Radar displacement maps revealed a constant movement of the debris talus at the toe of the main rock scarp, while acoustic records proved the capability of this technique to identify rockfall events as well as their spectral content in a narrow range of frequencies between 200 Hz to 1000 Hz. This work demonstrates the great potential of the combined use of a variety of remote sensors to achieve high spatial and temporal resolution data in the field of landslide characterization and monitoring.

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Papoutsis, Ioannis, Charalampos Kontoes, Stavroula Alatza, Alexis Apostolakis et Constantinos Loupasakis. « InSAR Greece with Parallelized Persistent Scatterer Interferometry : A National Ground Motion Service for Big Copernicus Sentinel-1 Data ». Remote Sensing 12, n^o 19 (1 octobre 2020) : 3207. http://dx.doi.org/10.3390/rs12193207.

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Advances in synthetic aperture radar (SAR) interferometry have enabled the seamless monitoring of the Earth’s crust deformation. The dense archive of the Sentinel-1 Copernicus mission provides unprecedented spatial and temporal coverage; however, time-series analysis of such big data volumes requires high computational efficiency. We present a parallelized-PSI (P-PSI), a novel, parallelized, and end-to-end processing chain for the fully automated assessment of line-of-sight ground velocities through persistent scatterer interferometry (PSI), tailored to scale to the vast multitemporal archive of Sentinel-1 data. P-PSI is designed to transparently access different and complementary Sentinel-1 repositories, and download the appropriate datasets for PSI. To make it efficient for large-scale applications, we re-engineered and parallelized interferogram creation and multitemporal interferometric processing, and introduced distributed implementations to best use computing cores and provide resourceful storage management. We propose a new algorithm to further enhance the processing efficiency, which establishes a non-uniform patch grid considering land use, based on the expected number of persistent scatterers. P-PSI achieves an overall speed-up by a factor of five for a full Sentinel-1 frame for processing in a 20-core server. The processing chain is tested on a large-scale project to calculate and monitor deformation patterns over the entire extent of the Greek territory—our own Interferometric SAR (InSAR) Greece project. Time-series InSAR analysis was performed on volumes of about 12 TB input data corresponding to more than 760 Single Look Complex Sentinel-1A and B images mostly covering mainland Greece in the period of 2015–2019. InSAR Greece provides detailed ground motion information on more than 12 million distinct locations, providing completely new insights into the impact of geophysical and anthropogenic activities at this geographic scale. This new information is critical to enhancing our understanding of the underlying mechanisms, providing valuable input into risk assessment models. We showcase this through the identification of various characteristic geohazard locations in Greece and discuss their criticality. The selected geohazard locations, among a thousand, cover a wide range of catastrophic events including landslides, land subsidence, and structural failures of various scales, ranging from a few hundredths of square meters up to the basin scale. The study enriches the large catalog of geophysical related phenomena maintained by the GeObservatory portal of the Center of Earth Observation Research and Satellite Remote Sensing BEYOND of the National Observatory of Athens for the opening of new knowledge to the wider scientific community.

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Makineci, H. B., et H. Karabörk. « EVALUATION DIGITAL ELEVATION MODEL GENERATED BY SYNTHETIC APERTURE RADAR DATA ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (2 juin 2016) : 57–62. http://dx.doi.org/10.5194/isprs-archives-xli-b1-57-2016.

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Digital elevation model, showing the physical and topographical situation of the earth, is defined a tree-dimensional digital model obtained from the elevation of the surface by using of selected an appropriate interpolation method. DEMs are used in many areas such as management of natural resources, engineering and infrastructure projects, disaster and risk analysis, archaeology, security, aviation, forestry, energy, topographic mapping, landslide and flood analysis, Geographic Information Systems (GIS). Digital elevation models, which are the fundamental components of cartography, is calculated by many methods. Digital elevation models can be obtained terrestrial methods or data obtained by digitization of maps by processing the digital platform in general. Today, Digital elevation model data is generated by the processing of stereo optical satellite images, radar images (radargrammetry, interferometry) and lidar data using remote sensing and photogrammetric techniques with the help of improving technology. One of the fundamental components of remote sensing radar technology is very advanced nowadays. In response to this progress it began to be used more frequently in various fields. Determining the shape of topography and creating digital elevation model comes the beginning topics of these areas. It is aimed in this work , the differences of evaluation of quality between Sentinel-1A SAR image ,which is sent by European Space Agency ESA and Interferometry Wide Swath imaging mode and C band type , and DTED-2 (Digital Terrain Elevation Data) and application between them. The application includes RMS static method for detecting precision of data. Results show us to variance of points make a high decrease from mountain area to plane area.

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Makineci, H. B., et H. Karabörk. « EVALUATION DIGITAL ELEVATION MODEL GENERATED BY SYNTHETIC APERTURE RADAR DATA ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (2 juin 2016) : 57–62. http://dx.doi.org/10.5194/isprsarchives-xli-b1-57-2016.

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Digital elevation model, showing the physical and topographical situation of the earth, is defined a tree-dimensional digital model obtained from the elevation of the surface by using of selected an appropriate interpolation method. DEMs are used in many areas such as management of natural resources, engineering and infrastructure projects, disaster and risk analysis, archaeology, security, aviation, forestry, energy, topographic mapping, landslide and flood analysis, Geographic Information Systems (GIS). Digital elevation models, which are the fundamental components of cartography, is calculated by many methods. Digital elevation models can be obtained terrestrial methods or data obtained by digitization of maps by processing the digital platform in general. Today, Digital elevation model data is generated by the processing of stereo optical satellite images, radar images (radargrammetry, interferometry) and lidar data using remote sensing and photogrammetric techniques with the help of improving technology. One of the fundamental components of remote sensing radar technology is very advanced nowadays. In response to this progress it began to be used more frequently in various fields. Determining the shape of topography and creating digital elevation model comes the beginning topics of these areas. It is aimed in this work , the differences of evaluation of quality between Sentinel-1A SAR image ,which is sent by European Space Agency ESA and Interferometry Wide Swath imaging mode and C band type , and DTED-2 (Digital Terrain Elevation Data) and application between them. The application includes RMS static method for detecting precision of data. Results show us to variance of points make a high decrease from mountain area to plane area.

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Fratarcangeli, F., A. Nascetti, P. Capaldo, A. Mazzoni et M. Crespi. « CENTIMETER COSMO-SKYMED RANGE MEASUREMENTS FOR MONITORING GROUND DISPLACEMENTS ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (22 juin 2016) : 815–20. http://dx.doi.org/10.5194/isprs-archives-xli-b7-815-2016.

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The SAR (Synthetic Aperture Radar) imagery are widely used in order to monitor displacements impacting the Earth surface and infrastructures. The main remote sensing technique to extract sub-centimeter information from SAR imagery is the Differential SAR Interferometry (DInSAR), based on the phase information only. However, it is well known that DInSAR technique may suffer for lack of coherence among the considered stack of images. New Earth observation SAR satellite sensors, as COSMO-SkyMed, TerraSAR-X, and the coming PAZ, can acquire imagery with high amplitude resolutions too, up to few decimeters. Thanks to this feature, and to the on board dual frequency GPS receivers, allowing orbits determination with an accuracy at few centimetres level, the it was proven by different groups that TerraSAR-X imagery offer the capability to achieve, in a global reference frame, 3D positioning accuracies in the decimeter range and even better just exploiting the slant-range measurements coming from the amplitude information, provided proper corrections of all the involved geophysical phenomena are carefully applied. The core of this work is to test this methodology on COSMO-SkyMed data acquired over the Corvara area (Bolzano – Northern Italy), where, currently, a landslide with relevant yearly displacements, up to decimeters, is monitored, using GPS survey and DInSAR technique. The leading idea is to measure the distance between the satellite and a well identifiable natural or artificial Persistent Scatterer (PS), taking in account the signal propagation delays through the troposphere and ionosphere and filtering out the known geophysical effects that induce periodic and secular ground displacements. The preliminary results here presented and discussed indicate that COSMO-SkyMed Himage imagery appear able to guarantee a displacements monitoring with an accuracy of few centimetres using only the amplitude data, provided few (at least one) stable PS’s are available around the monitored area, in order to correct residual biases, likely due to orbit errors.

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Fratarcangeli, F., A. Nascetti, P. Capaldo, A. Mazzoni et M. Crespi. « CENTIMETER COSMO-SKYMED RANGE MEASUREMENTS FOR MONITORING GROUND DISPLACEMENTS ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (22 juin 2016) : 815–20. http://dx.doi.org/10.5194/isprsarchives-xli-b7-815-2016.

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The SAR (Synthetic Aperture Radar) imagery are widely used in order to monitor displacements impacting the Earth surface and infrastructures. The main remote sensing technique to extract sub-centimeter information from SAR imagery is the Differential SAR Interferometry (DInSAR), based on the phase information only. However, it is well known that DInSAR technique may suffer for lack of coherence among the considered stack of images. New Earth observation SAR satellite sensors, as COSMO-SkyMed, TerraSAR-X, and the coming PAZ, can acquire imagery with high amplitude resolutions too, up to few decimeters. Thanks to this feature, and to the on board dual frequency GPS receivers, allowing orbits determination with an accuracy at few centimetres level, the it was proven by different groups that TerraSAR-X imagery offer the capability to achieve, in a global reference frame, 3D positioning accuracies in the decimeter range and even better just exploiting the slant-range measurements coming from the amplitude information, provided proper corrections of all the involved geophysical phenomena are carefully applied. The core of this work is to test this methodology on COSMO-SkyMed data acquired over the Corvara area (Bolzano – Northern Italy), where, currently, a landslide with relevant yearly displacements, up to decimeters, is monitored, using GPS survey and DInSAR technique. The leading idea is to measure the distance between the satellite and a well identifiable natural or artificial Persistent Scatterer (PS), taking in account the signal propagation delays through the troposphere and ionosphere and filtering out the known geophysical effects that induce periodic and secular ground displacements. The preliminary results here presented and discussed indicate that COSMO-SkyMed Himage imagery appear able to guarantee a displacements monitoring with an accuracy of few centimetres using only the amplitude data, provided few (at least one) stable PS’s are available around the monitored area, in order to correct residual biases, likely due to orbit errors.

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Wu, Mingtang, Xiaoyu Yi, Jiawei Dun, Jianyuan Yang, Wei Cai et Guoqiang Zhang. « Understanding the Slow Motion of the Wangjiashan Landslide in the Baihetan Reservoir Area (China) from Space-Borne Radar Observations ». Advances in Civil Engineering 2022 (10 mai 2022) : 1–14. http://dx.doi.org/10.1155/2022/1766038.

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The analysis of landslide evolution using archived optical remote-sensing images is common, but it is often limited by the acquisition frequency, cloud cover, and resolution. With the development of space-borne radar observation technology, small baseline subset interferometric synthetic aperture radar (SAR) technology provides a new technical approach for detecting landslide deformation before disasters. The Sentinel-1A SAR datasets (20170219–20210330) were used to study the time-series deformation characteristics of the Wangjiashan landslide in the Baihetan Reservoir area before its impoundment. The time-series results show that the Wangjiashan landslide was in an initial deformation state in the prior four years, and the deformation first occurred in the middle part and then expanded to the landslide toe and crown retrogressive movement characteristics. Combined with an analysis of field deformation signs, these findings suggest that the upper landslide mass formed a local sliding surface, which caused serious deformation of the road. An analysis of historical rainfall data revealed that the Wangjiashan landslide is sensitive to rainfall, and the deformation is not only significantly correlated with cumulative rainfall but also influenced by concentrated heavy rainfall. The research in this study provides a basis for the monitoring, early warning, and risk management of the Wangjiashan landslide during the impoundment period. This work also provides a useful reference for investigations using space-borne SAR Earth observations in geological disaster prevention and control.

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Kontoes, Charalampos, Constantinos Loupasakis, Ioannis Papoutsis, Stavroula Alatza, Eleftheria Poyiadji, Athanassios Ganas, Christina Psychogyiou, Mariza Kaskara, Sylvia Antoniadi et Natalia Spanou. « Landslide Susceptibility Mapping of Central and Western Greece, Combining NGI and WoE Methods, with Remote Sensing and Ground Truth Data ». Land 10, n^o 4 (12 avril 2021) : 402. http://dx.doi.org/10.3390/land10040402.

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The exploitation of remote sensing techniques has substantially improved pre- and post- disaster landslide management over the last decade. A variety of landslide susceptibility methods exists, with capabilities and limitations related to scale and spatial accuracy issues, as well as data availability. The Interferometric Synthetic Aperture Radar (InSAR) capabilities have significantly contributed to the detection, monitoring, and mapping of landslide phenomena. The present study aims to point out the contribution of InSAR data in landslide detection and to evaluate two different scale landslide models by comparing a heuristic to a statistical method for the rainfall-induced landslide hazard assessment. Aiming to include areas with both high and low landslide occurrence frequencies, the study area covers a large part of the Aetolia–Acarnania and Evritania prefectures, Central and Western Greece. The landslide susceptibility product provided from the weights of evidence (WoE) method proved more accurate, benefitting from the expert opinion and the landslide inventory. On the other hand, the Norwegian Geological Institute (NGI) methodology has the edge on its immediate implementation, with minimum data requirements. Finally, it was proved that using sequential SAR image acquisitions gives the benefit of an updated landslide inventory, resulting in the generation of, on request, updated landslide susceptibility maps.

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Dong, Xiujun, Tao Yin, Keren Dai, Saied Pirasteh, Guanchen Zhuo, Zhiyu Li, Bing Yu et Qiang Xu. « Identifying Potential Landslides on Giant Niexia Slope (China) Based on Integrated Multi-Remote Sensing Technologies ». Remote Sensing 14, n^o 24 (14 décembre 2022) : 6328. http://dx.doi.org/10.3390/rs14246328.

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The Niexia slope, located in Danba County, Sichuan Province, China, with steep slope terrain and dense vegetation coverage, has a height difference of about 3002 m. A traditional manual survey cannot be performed here, and single remote sensing technology is not comprehensive enough to identify potential landslides on such high and steep slopes. In this paper, an integrated approach with multi-remote sensing techniques was proposed to identify potential landslides of the Niexia slope, which combined Interferometry Synthetic Aperture Radar (InSAR), airborne Light Detection and Ranging (LiDAR), and optical remote sensing technologies. InSAR technology was used to monitor the small displacements of the whole slope, and three potential landslides on Niexia slope were identified. The maximum cumulative displacement reached up to 11.9 cm over 1 year. Subsequently, high-resolution optical remote sensing images acquired by remote sensing satellites and a Digital Elevation Model (DEM) without vegetation influence obtained by LiDAR were used to finely interpret the sign of landslide micro-geomorphology and to determine the potential landslide geometry boundaries. As a result, four and nine potential landslides with landslide micro-geomorphic features were identified, respectively. Finally, the identification results of the three techniques were fused and analyzed to assess the potential landslides on the Niexia slope. We compared the results from multi-remote sensing technologies, showing that the three techniques have advantages and disadvantages in terms of monitoring objects, monitoring range, and monitoring accuracy. The integrated use of these three technologies can identify and monitor potential landslides more comprehensively, which could play an important role in the future.

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Delacourt, Christophe, Pascal Allemand, Etienne Berthier, Daniel Raucoules, Bérangère Casson, Philippe Grandjean, Claude Pambrun et Eric Varel. « Remote-sensing techniques for analysing landslide kinematics : a review ». Bulletin de la Société Géologique de France 178, n^o 2 (1 mars 2007) : 89–100. http://dx.doi.org/10.2113/gssgfbull.178.2.89.

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Abstract Surface displacement field of landslides is a key parameter to access to their geometries and mechanical properties. Surface displacements can be calculated using remote-sensing methods such as interferometry for radar data and image correlation for optical data. These methods have been elaborated this last decade and successfully applied on sensors (radar, cameras, terrestrial 3D laser scanner imaging) either attached to space or aerial platforms such as satellites, planes, and unmanned radio-controlled platforms (drones and helicopters) or settled at fixed positions emplaced in the front of landslides. This paper reviews the techniques of image analysis (interferometry and optical data correlation) to measure displacements and examines the performance of each type of platforms. Examples of applications of these techniques in French South Alps are shown. Depending on the landslide characteristics (exposure conditions, size, velocity) as well as the goal of the study (operational or scientific purpose), one or a combination of several techniques and data (characterized by several resolution, accuracy, covered surface, revisiting time) have to be used. Radar satellite data processed with differential interferometric or PS methods are mainly suitable for scientific purposes due to various application limitations in mountainous area. Optical satellite and aerial images can be used for scientific studies at fairly high resolution but are strongly dependant on atmospheric conditions. Platforms and sensors such as drone, fixed camera, fixed radar and Lidar have the advantage of high adaptability. They can be used to obtain very high resolution and precise 3D data (of centimetric accuracy) suitable for both scientific and operational purposes.

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Cascini, L., D. Peduto, G. Pisciotta, L. Arena, S. Ferlisi et G. Fornaro. « The combination of DInSAR and facility damage data for the updating of slow-moving landslide inventory maps at medium scale ». Natural Hazards and Earth System Sciences 13, n^o 6 (18 juin 2013) : 1527–49. http://dx.doi.org/10.5194/nhess-13-1527-2013.

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Abstract. Testing innovative procedures and techniques to update landslide inventory maps is a timely topic widely discussed in the scientific literature. In this regard remote sensing techniques – such as the Synthetic Aperture Radar Differential Interferometry (DInSAR) – can provide a valuable contribution to studies concerning slow-moving landslides in different geological contexts all over the world. In this paper, DInSAR data are firstly analysed via an innovative approach aimed at enhancing both the exploitation and the interpretation of remote sensing information; then, they are complemented with the results of an accurate analysis of survey-recorded damage to facilities due to slow-moving landslides. In particular, after being separately analysed to provide independent landslide movement indicators, the two datasets are combined in a DInSAR-Damage matrix which can be used to update the state of activity of slow-moving landslides. Moreover, together with the information provided by geomorphological maps, the two datasets are proven to be useful in detecting unmapped phenomena. The potentialities of the adopted procedure are tested in an area of southern Italy where slow-moving landslides are widespread and accurately mapped by using geomorphological criteria.

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Miao, Zhao, Panpan Tang et Yong Zhang. « Recognition of Red-Bed Landslides over Eastern Sichuan through Remote Sensing and Field Investigations ». Geofluids 2022 (6 avril 2022) : 1–9. http://dx.doi.org/10.1155/2022/9385352.

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Red-bed soft rock, characterized by the gentle slope, low intensity, and weak weathering resistance, is widely distributed over the Eastern Sichuan Province, China, threatening many lives of people, economy development, and urbanization progress. In this study, Multi-Temporal Synthetic Aperture Radar interferometry (MTInSAR) techniques and detailed field investigations were carried out to detect the potential red-bed landslides. The strategies of small baselines, phase optimization, and atmospheric delay removal were proved to be effective in improving the deformation results. Finally, dozens of slow-moving slopes were found and attributed to frequent human activities, and the applicability of various monitoring tools and the comparisons of their results were discussed. This research is aimed at proving the applicability of remote sensing measures in the monitoring of landslides, increasing the efficiency of this method, and helping the hazard prevention in Southwest China.

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Handwerger, Alexander L., Mong-Han Huang, Shannan Y. Jones, Pukar Amatya, Hannah R. Kerner et Dalia B. Kirschbaum. « Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine ». Natural Hazards and Earth System Sciences 22, n^o 3 (9 mars 2022) : 753–73. http://dx.doi.org/10.5194/nhess-22-753-2022.

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Abstract. Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.

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Mantovani, Matteo, Giulia Bossi, Gianluca Marcato, Luca Schenato, Giacomo Tedesco, Giacomo Titti et Alessandro Pasuto. « New Perspectives in Landslide Displacement Detection Using Sentinel-1 Datasets ». Remote Sensing 11, n^o 18 (13 septembre 2019) : 2135. http://dx.doi.org/10.3390/rs11182135.

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Space-borne radar interferometry is a fundamental tool to detect and measure a variety of ground surface deformations, either human induced or originated by natural processes. Latest development of radar remote sensing imaging techniques and the increasing number of space missions, specifically designed for interferometry analyses, led to the development of new and more effective approaches, commonly referred to as Advanced DInSAR (A-DInSAR) or Time Series Radar Interferometry (TS-InSAR). Nevertheless, even if these methods were proved to be suitable for the study of a large majority of ground surface dynamic phenomena, their application to landslides detection is still problematic. One of the main limiting factors is related to the rate of displacement of the unstable slopes: landslides evolving too fast decorrelate the radar signal making the interferometric phase useless. This is the reason why A-DInSAR techniques have been successfully applied exclusively to measure very slow landslides (few centimetres per year). This study demonstrates how the C-band data collected since 2014 by the Sentinel-1 (S1) mission and properly designed interferometric approaches can pull down this restriction allowing to measure rate of displacements ten times higher than previously done, thus providing new perspectives in landslides detection. The analysis was carried out on a test site located in the Cortina d’Ampezzo valley (Eastern Italian Alps), which is affected by several earth flows characterized by different size and kinematics.

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Hall, D. K., R. S. Williams, J. S. Barton, O. Sigurđsson, L. C. Smith et J. B. Garvin. « Evaluation of remote-sensing techniques to measure decadal-scale changes of Hofsjökull ice cap, Iceland ». Journal of Glaciology 46, n^o 154 (2000) : 375–88. http://dx.doi.org/10.3189/172756500781833061.

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AbstractDynamic surficial changes and changes in the position of the firn line and the areal extent of Hofsjökull ice cap, Iceland, were studied through analysis of a time series (1973–98) of synthetic-aperture radar (SAR) and Landsat data. A digital elevation model of Hofsjökull, which was constructed using SAR interferometry, was used to plot the SAR backscatter coefficient (σ°) vs elevation and air temperature along transects across the ice cap. Seasonal and daily σ° patterns are caused by freezing or thawing of the ice-cap surface, and abrupt changes in σ° are noted when the air temperature ranges from ∼−5° to 0°C. Late-summer 1997 σ° (SAR) and reflectance (Landsat) boundaries agree and appear to be coincident with the firn line and a SAR σ° boundary that can be seen in the January 1998 SAR image. In January 1994 through 1998, the elevation of this σ° boundary on the ice cap was quite stable, ranging from 1000 to 1300 m, while the equilibrium-line altitude, as measured on the ground, varied considerably. Thus the equilibrium line may be obscured by firn from previous years. Techniques are established to measure long-term changes in the elevation of the firn line and changes in the position of the ice margin.

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Ran, Peilian, Shaoda Li, Guanchen Zhuo, Xiao Wang, Mingjie Meng, Liang Liu, Youdong Chen, Huina Huang, Yu Ye et Xiangqi Lei. « Early Identification and Influencing Factors Analysis of Active Landslides in Mountainous Areas of Southwest China Using SBAS−InSAR ». Sustainability 15, n^o 5 (1 mars 2023) : 4366. http://dx.doi.org/10.3390/su15054366.

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Potential landslides in the mountainous areas of southwest China pose a serious threat to the lives and property of local residents. Synthetic aperture radar interferometry (InSAR) technology has the advantages of wide coverage, all weather applicability, and low cost and can quickly and accurately identify large range of active landslides, making it a useful geodetic tool for the early identification and prevention of landslides. This paper employed small baseline subset InSAR (SBAS−InSAR) technology and ascending and descending Sentinel−1 data from January 2019 to December 2021 to early identify active landslides in the Maoxian County to Li County National Highway (G317 and G213). The InSAR deformation results were verified by geometric distortion analysis, optical remote sensing interpretation, and field investigation, and 115 active landslides were successfully determined, among which 23 active landslides were identified by ascending and descending Sentinel−1 data together. In addition, InSAR deformation results show that fault, stratigraphic lithology, and rainfall are the three main factors that accelerate the deformation of active landslides and can trigger new active landslides. This study can provide an important reference for the early identification and prevention of landslides in mountainous areas.

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Dabiri, Z., D. Hölbling, L. Abad et D. Tiede. « ASSESSMENT OF LANDSLIDE-INDUCED MORPHOLOGY CHANGES USING AN OBJECT-BASED IMAGE ANALYSIS APPROACH : A CASE STUDY OF HÍTARDALUR, ICELAND ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (20 août 2019) : 109–14. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-109-2019.

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Abstract. On July 7, 2018, a large landslide occurred at the eastern slope of the Fagraskógarfjall Mountain in Hítardalur valley in West Iceland. The landslide dammed the river, led to the formation of a lake and, consequently, to a change in the river course. The main focus of this research is to develop a knowledge-based expert system using an object-based image analysis (OBIA) approach for identifying morphology changes caused by the Hítardalur landslide. We use synthetic aperture radar (SAR) and optical remote sensing data, in particular from Sentinel-1/2 for detection of the landslide and its effects on the river system. We extracted and classified the landslide area, the landslide-dammed lake, other lakes and the river course using intensity information from S1 and spectral information from S2 in the object-based framework. Future research will focus on further developing this approach to support mapping and monitoring of the spatio-temporal dynamics of surface morphology in an object-based framework by combining SAR and optical data. The results can reveal details on the applicability of different remote sensing data for the spatio-temporal investigation of landslides, landslide-induced river course changes and lake formation and lead to a better understanding of the impact of large landslides on river systems.

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Quincey, D. J., et A. Luckman. « Progress in satellite remote sensing of ice sheets ». Progress in Physical Geography : Earth and Environment 33, n^o 4 (août 2009) : 547–67. http://dx.doi.org/10.1177/0309133309346883.

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Understanding the changing mass balance and surface dynamics of the Earth’s major ice sheets in Greenland and Antarctica is of fundamental importance for accurate predictions of future sea-level rise. In this review, the remote sensing data sources available to ice-sheet studies are considered and the range of information that can be gained from remote sensing is examined. The review demonstrates that the integration of a range of remote sensing data sets can provide information on ice-sheet dynamics and volume changes, melt patterns and formation and drainage of supra- and subglacial lakes. Such data are highly complementary to field investigations by providing a regional-scale, synoptic perspective. The review concludes that emerging remote sensing techniques such as SAR interferometry, feature tracking, scatterometry, altimetry and gravimetry provide vital information without which an understanding of ice sheets would be far less advanced. It also concludes that there remain several key challenges for remote sensing, in particular relating to the observation of rapid dynamical changes that are characteristic of contemporary ice-sheet response to continued climatic warming.

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Solari, Lorenzo, Matteo Del Soldato, Federico Raspini, Anna Barra, Silvia Bianchini, Pierluigi Confuorto, Nicola Casagli et Michele Crosetto. « Review of Satellite Interferometry for Landslide Detection in Italy ». Remote Sensing 12, n^o 8 (24 avril 2020) : 1351. http://dx.doi.org/10.3390/rs12081351.

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Landslides recurrently impact the Italian territory, producing huge economic losses and casualties. Because of this, there is a large demand for monitoring tools to support landslide management strategies. Among the variety of remote sensing techniques, Interferometric Synthetic Aperture Radar (InSAR) has become one of the most widely applied for landslide studies. This work reviews a variety of InSAR-related applications for landslide studies in Italy. More than 250 papers were analyzed in this review. The first application dates back to 1999. The average production of InSAR-related papers for landslide studies is around 12 per year, with a peak of 37 papers in 2015. Almost 70% of the papers are written by authors in academia. InSAR is used (i) for landslide back analysis (3% of the papers); (ii) for landslide characterization (40% of the papers); (iii) as input for landslide models (7% of the papers); (iv) to update landslide inventories (15% of the papers); (v) for landslide mapping (32% of the papers), and (vi) for monitoring (3% of the papers). Sixty-eight percent of the authors validated the satellite results with ground information or other remote sensing data. Although well-known limitations exist, this bibliographic overview confirms that InSAR is a consolidated tool for many landslide-related applications.

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Nhu, Viet-Ha, Ayub Mohammadi, Himan Shahabi, Baharin Bin Ahmad, Nadhir Al-Ansari, Ataollah Shirzadi, John J. Clague, Abolfazl Jaafari, Wei Chen et Hoang Nguyen. « Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment ». International Journal of Environmental Research and Public Health 17, n^o 14 (8 juillet 2020) : 4933. http://dx.doi.org/10.3390/ijerph17144933.

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We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.

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Zhang, L., B. Duan et B. Zou. « RESEARCH ON INVERSION MODELS FOR FOREST HEIGHT ESTIMATION USING POLARIMETRIC SAR INTERFEROMETRY ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (13 septembre 2017) : 659–63. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-659-2017.

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The forest height is an important forest resource information parameter and usually used in biomass estimation. Forest height extraction with PolInSAR is a hot research field of imaging SAR remote sensing. SAR interferometry is a well-established SAR technique to estimate the vertical location of the effective scattering center in each resolution cell through the phase difference in images acquired from spatially separated antennas. The manipulation of PolInSAR has applications ranging from climate monitoring to disaster detection especially when used in forest area, is of particular interest because it is quite sensitive to the location and vertical distribution of vegetation structure components. However, some of the existing methods can’t estimate forest height accurately. Here we introduce several available inversion models and compare the precision of some classical inversion approaches using simulated data. By comparing the advantages and disadvantages of these inversion methods, researchers can find better solutions conveniently based on these inversion methods.

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Hou, X. X., G. M. Huang et Z. Zhao. « Extracting DEM from airborne X-band data based on PolInSAR ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (26 juin 2015) : 35–39. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-35-2015.

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Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) is a new trend of SAR remote sensing technology which combined polarized multichannel information and Interferometric information. It is of great significance for extracting DEM in some regions with low precision of DEM such as vegetation coverage area and building concentrated area. In this paper we describe our experiments with high-resolution X-band full Polarimetric SAR data acquired by a dual-baseline interferometric airborne SAR system over an area of Danling in southern China. Pauli algorithm is used to generate the double polarimetric interferometry data, Singular Value Decomposition (SVD), Numerical Radius (NR) and Phase diversity (PD) methods are used to generate the full polarimetric interferometry data. Then we can make use of the polarimetric interferometric information to extract DEM with processing of pre filtering , image registration, image resampling, coherence optimization, multilook processing, flat-earth removal, interferogram filtering, phase unwrapping, parameter calibration, height derivation and geo-coding. The processing system named SARPlore has been exploited based on VC++ led by Chinese Academy of Surveying and Mapping. Finally compared optimization results with the single polarimetric interferometry, it has been observed that optimization ways can reduce the interferometric noise and the phase unwrapping residuals, and improve the precision of DEM. The result of full polarimetric interferometry is better than double polarimetric interferometry. Meanwhile, in different terrain, the result of full polarimetric interferometry will have a different degree of increase.

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Julzarika, Atriyon, et Kuncoro Teguh Setiawan. « UTILIZATION OF SAR AND EARTH GRAVITY DATA FOR SUB BITUMINOUS COAL DETECTION ». International Journal of Remote Sensing and Earth Sciences (IJReSES) 11, n^o 2 (12 avril 2017) : 143. http://dx.doi.org/10.30536/j.ijreses.2014.v11.a2612.

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Remote sensing data can be used for geological and mining applications, such as coal detection. Coal consists of five classes of Anthracite, Bituminous, Sub-Bituminous, Lignite coal and Peat coal. In this study, the type of coal that is discussed is Sub bituminous, Lignite coal, and peat coal. This study aims to detect potential sub bituminous using Synthetic Aperture Radar (SAR) data, and earth gravity. One type of remote sensing data to detect potential sub bituminous, lignite coal and peat coal are SAR data and satellite data Geodesy. SAR data used in this study is ALOS PALSAR. SAR data is used to predict the boundary between Lignite coal with Peat coal. The method used is backscattering. In addition to the SAR data is also used to make height model. The method used is interferometry. Geodetic satellite data is used to extract the value of the earth gravity and geodynamics. The method used is physical geodesy. Potential sub-bituminous coal can be known after the correlation between the predicted limits lignite coal-peat coal by the earth gravity, geodynamics, and height model. Volume predictions of potential sub bituminous can be known by calculating the volume using height model and transverse profile test. The results of this study useful for preliminary survey of geological in mining exploration activities.

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Murdzek, Radosław, Hubert Malik et Andrzej Leśniak. « The use of the DInSAR method in the monitoring of road damage caused by mining activities ». E3S Web of Conferences 36 (2018) : 02005. http://dx.doi.org/10.1051/e3sconf/20183602005.

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This paper reviews existing remote sensing methods of road damage detection and demonstrates the possibility of using DInSAR (Differential Interferometry SAR) method to identify endangered road sections. In this study two radar images collected by Sentinel-1 satellite have been used. Images were acquired with 24 days interval in 2015. The analysis allowed to estimate the scale of the post-mining deformation that occurred in Upper Silesia and to indicate areas where road infrastructure is particularly vulnerable to damage.

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Muñoz, E. N., O. D. Sánchez et F. L. Hernandez. « METHODOLOGY FOR ESTIMATING LANDSLIDES SUSCEPTIBILITY USING ARTIFICIAL NEURAL NETWORKS ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (6 novembre 2020) : 533–38. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-533-2020.

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Abstract. In this study, the susceptibility to landslides at Sevilla township, Valle del Cauca, located at southwest of Colombia was evaluated. The conditioning factors that involve the generation of landslides were evaluated using Geographic Information Systems (GIS) and Remote Sensing (RS) techniques. For the estimating susceptibility, an Artificial Neural Network (ANN) was implemented by applying the “Backpropagation” method to extract the synoptic weights of the conditioning variables (slopes, flow length, curvature, geology, fracture density, and land cover) on an automatic way with a data training module. The data for the analysis of the conditioning factors were carried out through a Digital Elevation Model (DEM) obtained through Radar Interferometry techniques, with Sentinel-1B satellite images for the year 2018. The results showed that Sevilla’s township has areas with high susceptibility, high slopes, and that it’s crossed by an active geological fault which implies that the earth's dynamics will condition the terrain stability.

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Kuras, Przemysław, Łukasz Ortyl, Tomasz Owerko, Marek Salamak et Piotr Łaziński. « GB-SAR in the Diagnosis of Critical City Infrastructure—A Case Study of a Load Test on the Long Tram Extradosed Bridge ». Remote Sensing 12, n^o 20 (15 octobre 2020) : 3361. http://dx.doi.org/10.3390/rs12203361.

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This article describes a case of using remote sensing during a static load test of a large bridge, which, because of its location, belongs to a critical city infrastructure. The bridge in question is the longest tram flyover in Poland. This is an extradosed-type concrete structure. It conducts a long tram line over 21 other active lines of an important railway station in the center of Cracow. The diagnostic of such bridges involving the load test method is difficult. Traditional, contact measurements of span displacements are not enough anymore. In such cases, remote sensing becomes an indispensable solution. This publication presents an example of using the close-range radar remote sensing technique of ground-based radar interferometry. However, the cross-sections of the huge bridge were observed using several methods. The aim was to confirm the conditions and efficiency of radar displacement measurements. They were therefore traditional contact measurements using mechanic sensors conducted, if possible, to the bottom of the span, for precise leveling and measurement using electronic total station. Comparing the results as well as the discussion held demonstrated the fundamental advantages of remote sensing methods over the other more traditional techniques.

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Wang, Jili, Weidong Yu, Yunkai Deng, Robert Wang, Yingjie Wang, Heng Zhang et Mingjie Zheng. « Demonstration of Time-Series InSAR Processing in Beijing Using a Small Stack of Gaofen-3 Differential Interferograms ». Journal of Sensors 2019 (8 avril 2019) : 1–13. http://dx.doi.org/10.1155/2019/4204580.

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More and more synthetic aperture radar (SAR) satellites in orbit provide abundant data for remote sensing applications. In August 2016, China launched a new Earth observation SAR satellite, Gaofen-3 (GF-3). In this paper, we utilize a small stack of GF-3 differential interferograms to map land subsidence in Beijing (China) using the time-series SAR interferometry (InSAR) technique. The small stack of differential interferograms is generated with 5 GF-3 SAR images from March 2017 to January 2018. Orbit errors are carefully addressed and removed during differential InSAR (DInSAR) processing. Truncated singular-value decomposition (TSVD) is applied to strengthen the robustness of deformation rate estimation. To validate the results of GF-3 data, an additional deformation measurement using 26 Sentinel-1B images from March 2017 to February 2018 is carried out using the persistent scatterer interferometry (PSI) technique. By implementing a cross-comparison, we find that the retrieved results from GF-3 images and Sentinel-1 images are spatially consistent. The standard deviation of vertical deformation rate differences between two data stacks is 11.24 mm/y in the study area. The results shown in this paper demonstrate the reasonable potential of GF-3 SAR images to monitor land subsidence.

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Zhu, Yifei, Xin Yao, Leihua Yao et Chuangchuang Yao. « Detection and characterization of active landslides with multisource SAR data and remote sensing in western Guizhou, China ». Natural Hazards 111, n^o 1 (6 janvier 2022) : 973–94. http://dx.doi.org/10.1007/s11069-021-05087-9.

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Nhu, Viet-Ha, Ayub Mohammadi, Himan Shahabi, Baharin Bin Ahmad, Nadhir Al-Ansari, Ataollah Shirzadi, Marten Geertsema et al. « Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia) : A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms ». Forests 11, n^o 8 (30 juillet 2020) : 830. http://dx.doi.org/10.3390/f11080830.

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We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total slide locations, 80% (122 landslides) were utilized for training the selected algorithms, and the remaining 20% (30 landslides) were applied for validation purposes. We employed 17 conditioning factors, including slope angle, aspect, elevation, curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), lithology, soil type, land cover, normalized difference vegetation index (NDVI), distance to river, distance to fault, distance to road, river density, fault density, and road density, which were produced from satellite imageries, geological map, soil maps, and a digital elevation model (DEM). We used these factors to produce landslide susceptibility maps using logistic regression (LR), logistic model tree (LMT), and random forest (RF) models. To assess prediction accuracy of the models we employed the following statistical measures: negative predictive value (NPV), sensitivity, positive predictive value (PPV), specificity, root-mean-squared error (RMSE), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC). Our results indicated that the AUC was 92%, 90%, and 88% for the LMT, LR, and RF algorithms, respectively. To assess model performance, we also applied non-parametric statistical tests of Friedman and Wilcoxon, where the results revealed that there were no practical differences among the used models in the study area. While landslide mapping in tropical environment such as Cameron Highlands remains difficult, the remote sensing (RS) along with machine learning techniques, such as the LMT model, show promise for landslide susceptibility mapping in the study area.

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Bondur, Valery, Tumen Chimitdorzhiev et Aleksey Dmitriev. « A Step-Wise Workflow for SAR Remote Sensing of Perennial Heaving Mound/Crater on the Yamal Peninsula, Western Siberia ». Remote Sensing 15, n^o 1 (3 janvier 2023) : 281. http://dx.doi.org/10.3390/rs15010281.

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Climate change in the Arctic region is more significant than in other parts of our planet. One of the manifestations of these changes is crater creation with blowouts of a gas, ice and frozen soil mixture. In this context, dynamics studies of long-term heaving mounds that turn into craters as a result are relevant. A workflow for detecting and assessing anomalous dynamics of heaving mounds in the Arctic regions is proposed. Areas with anomalous increase of ALOS-2 PALSAR-2 synthetic aperture radar (SAR) backscattering intensity are detected in the first stage. These increases take place due to sudden changes in local terrain slopes when the scattering surface (mound slope) turns toward the radar. Radar backscattering intensity also rises due to depolarization at newly formed frost cracks. Validation of the detected anomaly is carried out at the second stage through a comparison of multi-temporal digital elevation models obtained from bistatic radar interferometry TerraSAR-X/TanDEM-X data. At the final stage, the deformations are assessed within the detected areas using differential SAR interferometry (DInSAR) technique by ALOS-2 PALSAR-2 data. The magnitude of the heaving along the line of sight (LOS) was 22–24 cm in the period from January 2019 to January 2020. In general, effectiveness for detecting the perennial heaving mounds and the rate assessment of their increase were demonstrated in the suggested workflow.

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Park et Lee. « On the Use of Single-, Dual-, and Quad-Polarimetric SAR Observation for Landslide Detection ». ISPRS International Journal of Geo-Information 8, n^o 9 (2 septembre 2019) : 384. http://dx.doi.org/10.3390/ijgi8090384.

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Remote sensing technologies, particularly with Synthetic Aperture Radar (SAR) system, can provide timely and critical information to assess landslide distributions over large areas. Most space-borne SAR systems have been operating in different polarimetric modes to meet various operational requirements. This study aims to discuss how much detectability can be expected in the landslide map produced from the single-, dual-, and quad-polarization modes of observation. The experimental analysis of the characteristic changes of PALSAR-2 signals showed that quad-polarization parameters indicating signal depolarization properties revealed noticeable landslide-induced temporal changes for all local incidence angle ranges. To produce a landslide map, a simple change detection method based on characteristic scattering properties of landslide areas was proposed. The accuracy assessment results showed that the depolarization parameters, such as the co-pol coherence and polarizing contribution, can identify areas affected by landslides with a detection rate of 60%, and a false-alarm rate of 5%. On the other hand, the single- or dual-pol parameters can only be expected to provide half the accuracy with significant false-alarms in areas with temporal variations independent of landslides.

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Bianchini Ciampoli, Luca, Valerio Gagliardi, Chiara Ferrante, Alessandro Calvi, Fabrizio D’Amico et Fabio Tosti. « Displacement Monitoring in Airport Runways by Persistent Scatterers SAR Interferometry ». Remote Sensing 12, n^o 21 (30 octobre 2020) : 3564. http://dx.doi.org/10.3390/rs12213564.

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Deformations monitoring in airport runways and the surrounding areas is crucial, especially in cases of low-bearing capacity subgrades, such as the clayey subgrade soils. An effective monitoring of the infrastructure asset allows to secure the highest necessary standards in terms of the operational and safety requirements. Amongst the emerging remote sensing techniques for transport infrastructures monitoring, the Persistent Scatterers Interferometry (PSI) technique has proven effective for the evaluation of the ground deformations. However, its use for certain demanding applications, such as the assessment of millimetric differential deformations in airport runways, is still considered as an open issue for future developments. In this study, a time-series analysis of COSMO–SkyMed satellite images acquired from January 2015 to April 2019 is carried out by employing the PSI technique. The aim is to retrieve the mean deformation velocity and time series of the surface deformations occurring in airport runways. The technique is applied to Runway 3 at the “Leonardo da Vinci” International Airport in Rome, Italy. The proposed PSI technique is then validated by way of comparison with the deformation outcomes obtained on the runway by traditional topographic levelling over the same time span. The results of this study clearly demonstrate the efficiency and the accuracy of the applied PSI technique for the assessment of deformations in airport runways.

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Piroton, Valentine, Romy Schlögel, Christian Barbier et Hans-Balder Havenith. « Monitoring the Recent Activity of Landslides in the Mailuu-Suu Valley (Kyrgyzstan) Using Radar and Optical Remote Sensing Techniques ». Geosciences 10, n^o 5 (1 mai 2020) : 164. http://dx.doi.org/10.3390/geosciences10050164.

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Central Asian mountain regions are prone to multiple types of natural hazards, often causing damage due to the impact of mass movements. In spring 2017, Kyrgyzstan suffered significant losses from a massive landslide activation event, during which also two of the largest deep-seated mass movements of the former mining area of Mailuu-Suu—the Koytash and Tektonik landslides—were reactivated. This study consists of the use of optical and radar satellite data to highlight deformation zones and identify displacements prior to the collapse of Koytash and to the more superficial deformation on Tektonik. Especially for the first one, the comparison of Digital Elevation Models of 2011 and 2017 (respectively, satellite and unmanned aerial vehicle (UAV) imagery-based) highlights areas of depletion and accumulation, in the scarp and near the toe, respectively. The Differential Synthetic Aperture Radar Interferometry analysis identified slow displacements during the months preceding the reactivation in April 2017, indicating the long-term sliding activity of Koytash and Tektonik. This was confirmed by the computation of deformation time series, showing a positive velocity anomaly on the upper part of both landslides. Furthermore, the analysis of the Normalized Difference Vegetation Index revealed land cover changes associated with the sliding process between June 2016 and October 2017. In addition, in situ data from a local meteorological station highlighted the important contribution of precipitation as a trigger of the collapse. The multidirectional approach used in this study demonstrated the efficiency of applying multiple remote sensing techniques, combined with a meteorological analysis, to identify triggering factors and monitor the activity of landslides.

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Adriano, Bruno, Naoto Yokoya, Hiroyuki Miura, Masashi Matsuoka et Shunichi Koshimura. « A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images ». Remote Sensing 12, n^o 3 (8 février 2020) : 561. http://dx.doi.org/10.3390/rs12030561.

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The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively.

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Arbad, Arliandy Pratama, Wataru Takeuchi, Yosuke Yosuke, Mutiara Jamilah et Achmad Ardy. « TIME-SERIES SAR INTERFEROMETRY ANALYSIS OF SURFACE DEFORMATION AT MT. BROMO INDONESIA ». Seminar Nasional Geomatika 3 (15 février 2019) : 771. http://dx.doi.org/10.24895/sng.2018.3-0.956.

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One of the most active volcanoes in Indonesia is Mt. Bromo, volcanic activities at Mt. Bromo has been recorded in 1775. We observe the surface deformation of the Mt. Bromo which located at eastern Java Indonesia area that includes neighborhood volcanic system on TNBTS (Taman Nasional Bukit Tengger Semeru). Recently, remote sensing has played as an important role to observe volcano behavior. We apply the SAR Interferometry (InSAR) algorithm referred to as Small Baseline Subset (SBAS) approach that allows us to generate mean deformation velocity maps and displacement time series for the studied area. The common SBAS technique, the set of interferometric phase observations writes as a linear combination of individual SAR scene phase values for each pixel independently. Particularly, the proposed analysis is based on 22 SAR data acquired by the ALOS/PALSAR sensors during the 2007–2017 time interval. A fewer studies have been able to show capability of InSAR analysis for investigating cycle of volcano especially of Mt. Bromo which characterized eruption stratovolcano in ranging one to five years. The results expected in this work represent an advancement of previous InSAR studies of the area that are mostly focused on the deformation affecting the caldera. According to the result, we expected this study could implement on risk management or infrastructure management.

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Kelly, R. E. J. « Estimation of the ELA on Hardangerjøkulen, Norway, during the 1995/96 winter season using repeat-pass SAR coherence ». Annals of Glaciology 34 (2002) : 349–54. http://dx.doi.org/10.3189/172756402781817518.

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AbstractThis paper demonstrates the utility of European Remote-sensing Satellite (ERS) synthetic aperture radar (SAR) interferometry for monitoring the transient snow-line (TSL) on Hardangerjøkulen ice cap, southern Norway, during the 1995/96 winter. The study shows how coherence information (an interferometry product) over the ice cap can be used to locate the TSL after the summer melt season. Spatial variations in coherence over the ice cap between successive ERS tandem-phase passes from summer to winter are related to surface and volume snow stability and surface ice stability. Temporal differences of coherence images between winter and summer are investigated using histogram analysis. A histogram threshold is found for the 1995/96 winter that can be used to identify the location of the TSL and then estimate the equilibrium-line altitude (ELA). The result shows good agreement (0.5%) with the field-estimated ELA from the Norwegian Water and Energy Administration. Themethod appears to be straightforward for this ice cap and it is envisaged that it could be a useful complementary method on other ice caps where repeat-pass SAR data are available.

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Sabbà, Maria Francesca, Michela Lerna, Mariella Diaferio et Dora Foti. « Satellite Data for Structural Monitoring of Historical Building : The Temple of Minerva Medica in Rome ». WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 17 (31 décembre 2021) : 1284–89. http://dx.doi.org/10.37394/232015.2021.17.117.

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The Differential Synthetic Aperture Radar Interferometry is a remote sensing technique to acquire deformation velocity and displacement time-series of large territorial areas. The aim of this work is to develop operational methodologies that allow to assess the structures conservation state by integrating information from traditional monitoring systems with the remote sensing application, in order to monitor permanently structures and infrastructure with a historical relevance and developing specific maintenance programs. It is verified that this processing technique is an adequate tool, even in real-time, to monitor any damage or potential critical issues in the case of exceptional events such as earthquakes or landslides. The case study is the Temple of Minerva Medica in Rome, a masonry building characterized by an important historical-artistic value. The data-analysis shows as the use of satellite monitoring can be a valid tool for the structural safety, allowing to identify a vulnerability map of archaeological sites and historical buildings. The data interferometric processing was carried out using a Graphic Information System (GIS) software.

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Crosetto, M., N. Devanthéry, M. Cuevas-González, O. Monserrat et B. Crippa. « Exploitation of the full potential of PSI data for subsidence monitoring ». Proceedings of the International Association of Hydrological Sciences 372 (12 novembre 2015) : 311–14. http://dx.doi.org/10.5194/piahs-372-311-2015.

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Abstract. Persistent Scatterer Interferometry (PSI) is a remote sensing technique used to measure and monitor land deformation from a stack of interferometric SAR images. The main products that can be derived using the PSI technique are the deformation maps and the time series of deformation. In this paper, an approach to apply the PSI technique to a stack of Sentinel-1 images is described. Moreover, the problems encountered during the processing are detailed and an explanation of how they were dealt with is provided. Finally, Sentinel-1 deformation maps and time series obtained over the metropolitan area of Mexico DF are shown.

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Gao, Jay, et Yansui Liu. « Applications of remote sensing, GIS and GPS in glaciology : a review ». Progress in Physical Geography : Earth and Environment 25, n^o 4 (décembre 2001) : 520–40. http://dx.doi.org/10.1177/030913330102500404.

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Remote sensing has served as an efficient method of gathering data about glaciers since its emergence. The recent advent of Geographic Information Systems (GIS) and Global Positioning Systems (GPS) has created an effective means by which the acquired data are analysed for the effective monitoring and mapping of temporal dynamics of glaciers. A large number of researchers have taken advantage of remote sensing, GIS and GPS in their studies of glaciers. These applications are comprehensively reviewed in this paper. This review shows that glacial features identifiable from aerial photographs and satellite imagery include spatial extent, transient snowline, equilibrium line elevation, accumulation and ablation zones, and differentiation of ice/snow. Digital image processing (e.g., image enhancement, spectral ratioing and automatic classification) improves the ease and accuracy of mapping these parameters. The traditional visible light/infrared remote sensing of two-dimensional glacier distribution has been extended to three-dimensional volume estimation and dynamic monitoring using radar imagery and GPS. Longitudinal variations in glacial extent have been detected from multi-temporal images in GIS. However, the detected variations have neither been explored nor modelled from environmental and topographic variables. GPS has been utilized independent of remote sensing and GIS to determine glacier ice velocity and to obtain information about glacier surfaces. Therefore, the potential afforded by the integration of nonconventional remote sensing (e.g., SAR interferometry) with GIS and GPS still remains to be realized in glaciology. The emergence of new satellite images will make remote sensing of glaciology more predictive, more global and towards longer terms.

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Ho, Dinh Tong Dinh, Cuong Quoc Tran, Anh Duc Nguyen et Thuy Le-Toan. « Measuring ground subsidence in Hanoi city by radar interferometry ». Science and Technology Development Journal 19, n^o 2 (30 juin 2016) : 122–29. http://dx.doi.org/10.32508/stdj.v19i2.676.

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The rapidly developing urbanization since the last decade of the 20th century leads to the strong groundwater extraction, resulting in the subsidence phenomena in the Hanoi, Vietnam. Recent advances in the multi-temporal spaceborne SAR interferometry, especially with Persistent Scatters Interferometry (PSI) approach, is the robust remote sensing technique for measuring ground subsidence in large scale with millimetric accuracy. This work has presented an advanced PSI analysis, to provide unprecedented spatial extent and continuous temporal coverage of the subsidence in Hanoi City. The correlation between the reference leveling velocity and the estimated PSI result is R2 = 0.86, and the root mean square error is 4.0 (mm/year), confirming their good agreement. The study shows that subsidence is most severe in the Haihung silt loam areas in the south of the city. The groundwater extraction resulting from urbanization and urban growth is mainly responsible for the subsidence.

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