Satellite SAR interferometry for monitoring dam deformation in Portugal

The paper offers three examples of satellite SAR interferometry (InSAR) application for monitoring dam deformations: Paradela, Raiva and Alto Ceira, all of them in Portugal. Dam deformations were estimated using several sets of ERS and Envisat C-band SAR data by PS-InSAR method that offers accuracy of a millimeter per year at monitoring man-made tructures. The results show potential of InSAR but also summarize limits of C-band InSAR in these particular cases and can be handful to recognize applicability of new Sentinel-1 data (since 2014) for continuous monitoring of dam deformations. While Alto Ceira dam lies in SAR radar shadow and was represented by only one observable point, and the movement detected (in satellite line-of-sight direction) appears to fit with geodetical measurements. Raiva and Paradela dams were represented by sufficient number of points feasible for PS-InSAR processing. Deformations at slope near to Raiva dam and slow linear movements of the center of Paradela dam were detected

LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor

For the past five years, the 2-satellite Sentinel-1 constellation has provided abundant and useful Synthetic Aperture Radar (SAR) data, which have the potential to reveal global ground surface deformation at high spatial and temporal resolutions. However, for most users, fully exploiting the large amount of associated data is challenging, especially over wide areas. To help address this challenge, we have developed LiCSBAS, an open-source SAR interferometry (InSAR) time series analysis package that integrates with the automated Sentinel-1 InSAR processor (LiCSAR). LiCSBAS utilizes freely available LiCSAR products, and users can save processing time and disk space while obtaining the results of InSAR time series analysis. In the LiCSBAS processing scheme, interferograms with many unwrapping errors are automatically identified by loop closure and removed. Reliable time series and velocities are derived with the aid of masking using several noise indices. The easy implementation of atmospheric corrections to reduce noise is achieved with the Generic Atmospheric Correction Online Service for InSAR (GACOS). Using case studies in southern Tohoku and the Echigo Plain, Japan, we demonstrate that LiCSBAS applied to LiCSAR products can detect both large-scale (>100 km) and localized (~km) relative displacements with an accuracy of <1 cm/epoch and ~2 mm/yr. We detect displacements with different temporal characteristics, including linear, periodic, and episodic, in Niigata, Ojiya, and Sanjo City, respectively. LiCSBAS and LiCSAR products facilitate greater exploitation of globally available and abundant SAR datasets and enhance their applications for scientific research and societal benefit

Application of Sentinel-1 satellite to identify oil palm plantations in Balikpapan Bay

Satellite remote sensing has proved to be efficient for monitoring of canopy changes. In tropical areas, optical or multispectral satellite images are very often negatively affected by cloud cover, on the other hand satellites with polarimetric radars have a great advantage given their ability to penetrate clouds, smoke and atmospheric haze. Copernicus Sentinel-1 radar constellation offers both vertically co-polarized and cross-polarized imagery in a relatively high revisit time and resolution. This work describes an approach to identify selected palm oil plantations in Balikpapan Bay, East Kalimantan (Borneo). It gives an overview about advantages for monitoring temporal changes in the tropic environment using radar imagery but also constraints due to ambiguity of canopy type identification. The paper shows a brief comparison with application of multispectral Copernicus Sentinel-2 data and points a roadmap towards a practical application of the technologies

Large-scale demonstration of machine learning for the detection of volcanic deformation in Sentinel-1 satellite imagery

Radar (SAR) satellites systematically acquire imagery that can be used for volcano monitoring, characterising magmatic systems and potentially forecasting eruptions on a global scale. However, exploiting the large dataset is limited by the need for manual inspection, meaning timely dissemination of information is challenging. Here we automatically process ~ 600,000 images of > 1000 volcanoes acquired by the Sentinel-1 satellite in a 5-year period (2015–2020) and use the dataset to demonstrate the applicability and limitations of machine learning for detecting deformation signals. Of the 16 volcanoes flagged most often, 5 experienced eruptions, 6 showed slow deformation, 2 had non-volcanic deformation and 3 had atmospheric artefacts. The detection threshold for the whole dataset is 5.9 cm, equivalent to a rate of 1.2 cm/year over the 5-year study period. We then use the large testing dataset to explore the effects of atmospheric conditions, land cover and signal characteristics on detectability and find that the performance of the machine learning algorithm is primarily limited by the quality of the available data, with poor coherence and slow signals being particularly challenging. The expanding dataset of systematically acquired, processed and flagged images will enable the quantitative analysis of volcanic monitoring signals on an unprecedented scale, but tailored processing will be needed for routine monitoring applications

Characterizing and correcting phase biases in short-term, multilooked interferograms

Interferometric Synthetic Aperture Radar (InSAR) is widely used to measure deformation of the Earth's surface over large areas and long time periods. A common strategy to overcome coherence loss in long-term interferograms is to use multiple multilooked shorter interferograms, which can cover the same time period but maintain coherence. However, it has recently been shown that using this strategy can introduce a bias (also referred to as a “fading signal”) in the interferometric phase. We isolate the signature of the phase bias by constructing “daisy chain” sums of short-term interferograms of different length covering identical 1-year time intervals. This shows that the shorter interferograms are more affected by this phenomenon and the degree of the effect depends on ground cover types; cropland and forested pixels have significantly larger bias than urban pixels and the bias for cropland mimics subsidence throughout the year, whereas forests mimics subsidence in the spring and heave in the autumn. We, propose a method for correcting the phase bias, based on the assumption, borne out by our observations, that the bias in an interferogram is linearly related to the sum of the bias in shorter interferograms spanning the same time. We tested the algorithm over a study area in western Turkey by comparing average velocities against results from a phase linking approach, which estimates the single primary phases from all the interferometric pairs, and has been shown to be almost insensitive to the phase bias. Our corrected velocities agree well with those from a phase linking approach. Our approach can be applied to global compilations of short-term interferograms and provides accurate long-term velocity estimation without a requirement for coherence in long-term interferograms

Displacements Monitoring over Czechia by IT4S1 System for Automatised Interferometric Measurements Using Sentinel-1 Data

The Sentinel-1 satellite system continuously observes European countries at a relatively high revisit frequency of six days per orbital track. Given the Sentinel-1 configuration, most areas in Czechia are observed every 1–2 days by different tracks in a moderate resolution. This is attractive for various types of analyses by various research groups. The starting point for interferometric (InSAR) processing is an original data provided in a Single Look Complex (SLC) level. This work represents advantages of storing data augmented to a specifically corrected level of data, SLC-C. The presented database contains Czech nationwide Sentinel-1 data stored in burst units that have been pre-processed to the state of a consistent well-coregistered dataset of SLC-C. These are resampled SLC data with their phase values reduced by a topographic phase signature, ready for fast interferometric analyses (an interferogram is generated by a complex conjugate between two stored SLC-C files). The data can be used directly into multitemporal interferometry techniques, e.g., Persistent Scatterers (PS) or Small Baseline (SB) techniques applied here. A further development of the nationwide system utilising SLC-C data would lead into a dynamic state where every new pre-processed burst triggers a processing update to detect unexpected changes from InSAR time series and therefore provides a signal for early warning against a potential dangerous displacement, e.g., a landslide, instability of an engineering structure or a formation of a sinkhole. An update of the processing chain would also allow use of cross-polarised Sentinel-1 data, needed for polarimetric analyses. The current system is running at a national supercomputing centre IT4Innovations in interconnection to the Czech Copernicus Collaborative Ground Segment (CESNET), providing fast on-demand InSAR results over Czech territories. A full nationwide PS processing using data over Czechia was performed in 2017, discovering several areas of land deformation. Its downsampled version and basic findings are demonstrated within the article

Evaluation of forest loss in Balikpapan Bay in the end of 2015 based on Sentinel-1A polarimetric analysis

Satellite remote sensing has proved to be efficient for forest change monitoring. In tropical areas, polarimetric satellite images have a great potential given their ability to see through clouds, smoke and atmospheric haze. For Balikpapan Bay (Borneo, Indonesia), Sentinel-1A acquired images every 24 days during 2015 in both vertically co-polarized and cross-polarized modes. Using series of polarimetric radar images taken before and after an observed event (in this case a fire), information about changes in native forest can be delivered. In this work we detect and delineate areas burnt or damaged by catastrophic fires in autumn 2015. This work demonstrates a potential of satellite radar imagery using a relatively simple method for identification of forest changes. The whole processing chain as presented has been prepared for using open-source software (mainly ESA SNAP). Presented results were compared to both global services (GLAD and FIRMS databases) and local observation (UAV image over burnt area at Bugis canal)

Concept of an Effective Sentinel-1 Satellite SAR Interferometry System

This brief study introduces a partially working concept being developed at IT4Innovations supercomputer (HPC) facility. This concept consists of several modules that form a whole body of an efficient system for observation of terrain or objects displacements using satellite SAR interferometry (InSAR). A metadata database helps to locate data stored in various storages and to perform basic analyzes. A special database has been designed to describe Sentinel-1 data, on its burst level. Custom Sentinel-1 TOPS processing algorithms allow an injection of coregistered bursts into the database. Once the area of interest is set and basic processing parameters are given, the selected data are merged and processed by the Persistent Scatterers (PS) InSAR method or an optimized Small Baselines (SB) InSAR derivative. Depending on the expected deliverables, the processing results can be post-analyzed using a custom approach, in order to achieve a set of reliable measurement points. Final results can be post-processed and visualized using a custom GIS toolbox, consisting in open-source GIS functionality. The GIS post-processing is enforced by HPC power as well. To demonstrate the practical applicability of the described system, a subsidence area in Konya city, Turkey is used as the study area for Sentinel-1 InSAR evaluation

Investigation of the phase bias in the short term interferograms

Interferometric Synthetic Aperture Radar (InSAR) is a powerful tool for monitoring ground deformation associated with earthquakes, volcanoes, landslides, and different anthropogenic activities. The accuracy of the estimated deformation depends on a number of parameters including tropospheric and ionospheric delays, unwrapping errors, phase decorrelation due to changes in scattering behavior and system noise. However, recently an additional source of phase noise has been identified [1], which is strongest in short-interval multi-looked interferograms and, unlike other sources of noise, leads to biased, non-zero loop closure phases. This is problematic for time-series analysis because short-interval interferograms may be the only ones that maintain coherence for some areas. In this study, we explore the characteristics of this phenomenon and propose a mitigation strategy

Monitorización de infraestructuras críticas expuestas a riesgos naturales y antrópicos mediante interferometría radar de satélite

[EN] Synthetic Aperture Radar Interferometry (InSAR) is a remote sensing technique very effective for the measure of smalldisplacements of the Earth’s surface over large areas at a very low cost as compared with conventional geodetictechniques. Advanced InSAR time series algorithms for monitoring and investigating surface displacement on Earth arebased on conventional radar interferometry. These techniques allow us to measure deformation with uncertainties of 1mm/year, interpreting time series of interferometric phases at coherent point scatterers (PS) without the need for humanor special equipment presence on the site. By applying InSAR processing techniques to a series of radar images over thesame region, it is possible to detect line-of-sight (LOS) displacements of infrastructures on the ground and therefore identifyabnormal or excessive movement indicating potential problems requiring detailed ground investigation. A major advantageof this technology is that a single radar image can cover a major area of up to 100 km by 100 km or more as, for example,Sentinel-1 C-band satellites data cover a 250 km wide swath. Therefore, all engineering infrastructures in the area, suchas dams, dikes, bridges, ports, etc. subject to terrain deformation by volcanos, landslides, subsidence due to groundwater,gas, or oil withdrawal could be monitored, reducing operating costs effectively. In this sense, the free and open accessCopernicus Sentinel-1 data with currently up to 6-days revisit time open new opportunities for a near real-time landmonitoring. In addition, the new generation of high-resolution radar imagery acquired by SAR sensors such as TerraSARX,COSMO-SkyMed, and PAZ, and the development of multi-interferogram techniques has enhanced our capabilities inrecent years in using InSAR as deformation monitoring tool. In this paper, we address the applicability of using spaceborneSAR sensors for monitoring infrastructures in geomatics engineering and present several cases studies carried out by ourgroup related to anthropogenic and natural hazards, as well as monitoring of critical infrastructures.[ES] La interferometría radar de apertura sintética (InSAR) es una técnica de teledetección muy eficaz para medir pequeños desplazamientos de la superficie terrestre en grandes áreas a un coste muy pequeño en comparación con las técnicas geodésicas convencionales. Los algoritmos avanzados de series temporales InSAR para monitorizar e investigar el desplazamiento de la superficie terrestre se basan en la interferometría radar convencional. Estas técnicas nos permiten medir la deformación con incertidumbres de un milímetro por año, interpretando series temporales de fases interferométricas en retrodispersores puntuales coherentes (PS) sin necesidad de presencia humana o de equipos especiales en el sitio. Al aplicar técnicas de procesamiento InSAR a una serie de imágenes radar de la misma región, es posible detectar desplazamientos de infraestructuras proyectados en la línea de vista del satélite (line-of-sight o LOS) y, por lo tanto, identificar movimientos anormales o excesivos que indiquen problemas potenciales que requieran una investigación detallada del terreno. Una de las principales ventajas de esta tecnología es que una sola imagen radar puede cubrir un área importante de hasta 100 km por 100 km o más, ya que, por ejemplo, los datos de los satélites de banda C Sentinel-1 cubren una franja de 250 km de ancho. Por lo tanto, todas las infraestructuras civiles de la zona, como presas, diques, puentes, puertos, etc., sujetas a deformaciones del terreno por actividad volcánica, deslizamientos de tierra, hundimientos por extracción de agua subterránea, gas o petróleo, podrían ser monitorizados, reduciendo los costes operativos de manera efectiva. En este sentido, los datos Sentinel-1 de Copernicus, de acceso abierto, con hasta 6 días de tiempo de revisión actual abren nuevas oportunidades para una monitorización terrestre casi en tiempo real. Además, la nueva generación de imágenes radar de alta resolución adquiridas por sensores SAR como TerraSAR-X, COSMOSkyMed y PAZ, y el desarrollo de técnicas multi-interferograma ha mejorado nuestras capacidades en los últimos años en el uso del InSAR como herramienta para el control de deformaciones. En este trabajo se aborda la aplicabilidad del uso de sensores SAR espaciales para la monitorización de infraestructuras civiles en ingeniería geomática y presentamos varios casos de estudio realizados por nuestro grupo relacionados con riesgos naturales y antrópicos, así como de monitorización de infraestructura crítica.ERS-1/2 and Envisat datasets were provided by the European Space Agency (ESA). Sentinel-1A/B data were freely provided by ESA through Copernicus Programme. Data have been processed by DORIS (TUDelft), StaMPS (Andy Hooper), SARPROZ (Copyright (c) 2009-2020 Daniele Perissin), and SNAP (ESA). The satellite orbits are from TUDelft and ESA, as well as from the ESA Quality Control Group of Sentinel-1. Research was supported by [ESA Research and Service Support] for providing hardware resources employed in this work; [Spanish Ministry of Economy, Industry and Competitiveness] under ReMoDams project ESP2017-89344-R (AEI/FEDER, UE); [University of Jaén (Spain)] under PAIUJA-2021/2022 and CEACTEMA; [Junta de Andalucía (Spain)] under RNM-282 research group; [ERDF through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme] within project «POCI-01-0145-FEDER006961»; [National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology)] as part of project UID/EEA/50014/2013; [The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II)] under project «IT4Innovations excellence in science - LQ1602» (Czech Republic); and [Slovak Grant Agency VEGA] under projects No. 2/0100/20Ruiz-Armenteros, A.; Delgado-Blasco, J.; Bakon, M.; Lazecky, M.; Marchamalo-Sacristán, M.; Lamas-Fernández, F.; Ruiz-Constán, A.... (2021). Monitoring critical infrastructure exposed to anthropogenic and natural hazards using satellite radar interferometry. En Proceedings 3rd Congress in Geomatics Engineering. Editorial Universitat Politècnica de València. 137-146. https://doi.org/10.4995/CiGeo2021.2021.12736OCS13714