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

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

Deformation monitoring of dam infrastructures via spaceborne MT-InSAR. The case of La Viñuela (Málaga, southern Spain)

Dams require continuous security and monitoring programs, integrated with visual inspection and testing in dam surveillance programs. New approaches for dam monitoring focus on multi-sensor integration, taking into account emerging technologies such as GNSS, optic fiber, TLS, InSAR techniques, GBInSAR, GPR, that can be used as complementary data in dam monitoring, eliciting causes of dam deformation that cannot be assessed with traditional techniques. This paper presents a Multi-temporal InSAR (MT-InSAR) monitoring of La Viñuela dam (Málaga, Spain), a 96 m height earth-fill dam built from 1982 to 1989. The presented MT-InSAR monitoring system comprises three C-band radar (~5,7 cm wavelength) datasets from the European satellites ERS-1/2 (1992-2000), Envisat (2003-2008), and Sentinel-1A/B (2014-2018). ERS-1/2 and Envisat datasets were processed using StaMPS. In the case of Sentinel-1A/B, two different algorithms were applied, SARPROZ and ISCE-SALSIT, allowing the comparison of the estimated LOS velocity pattern. The obtained results confirm that LaViñuela dam is deforming since its construction, as an earth-fill dam. Maximum deformation rates were measured in the initial period (1992-2000), being around -7 mm/yr (LOS direction) on the coronation of the dam. In the period covered by the Envisat dataset (2003-2008), the average deforming pattern was lower, of the order of -4 mm/yr. Sentinel-1A/B monitoring confirms that the deformation is still active in the period 2014-2018 in the central-upper part of the dam, with maximums of velocity reaching -6 mm/yr. SARPROZ and ISCE-SALSIT algorithms provide similar results. It was concluded that MT-InSAR techniques can support the development of new and more effective means of monitoring and analyzing the health of dams complementing actual dam surveillance systems

MT-InSAR and Dam Modeling for the Comprehensive Monitoring of an Earth-Fill Dam: The Case of the Benínar Dam (Almería, Spain)

The Benínar Dam, located in Southeastern Spain, is an earth-fill dam that has experienced filtration issues since its construction in 1985. Despite the installation of various monitoring systems, the data collected are sparse and inadequate for the dam’s lifetime. The present research integrates Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and dam modeling to validate the monitoring of this dam, opening the way to enhanced integrated monitoring systems. MT-InSAR was proved to be a reliable and continuous monitoring system for dam deformation, surpassing previously installed systems in terms of precision. MT-InSAR allowed the almost-continuous monitoring of this dam since 1992, combining ERS, Envisat, and Sentinel-1A/B data. Line-of-sight (LOS) velocities of settlement in the crest of the dam evolved from maximums of −6 mm/year (1992–2000), −4 mm/year (2002–2010), and −2 mm/year (2015–2021) with median values of −2.6 and −3.0 mm/year in the first periods (ERS and Envisat) and −1.3 mm/year in the Sentinel 1-A/B period. These results are consistent with the maximum admissible modeled deformation from construction, confirming that settlement was more intense in the dam’s early stages and decreased over time. MT-InSAR was also used to integrate the monitoring of the dam basin, including critical slopes, quarries, and infrastructures, such as roads, tracks, and spillways. This study allows us to conclude that MT-InSAR and dam modeling are important elements for the integrated monitoring systems of embankment dams. This conclusion supports the complete integration of MT-InSAR and 3D modeling into the monitoring systems of embankment dams, as they are a key complement to traditional geotechnical monitoring and can overcome the main limitations of topographical monitoring

Strategies for improving the communication of satellite-derived InSAR data for geohazards through the analysis of Twitter and online data portals

Satellite-based earth observation sensors are increasingly able to monitor geophysical signals related to natural hazards, and many groups are working on rapid data acquisition, processing, and dissemination to data users with a wide range of expertise and goals. A particular challenge in the meaningful dissemination of Interferometric Synthetic Aperture Radar (InSAR) data to non-expert users is its unique differential data structure and sometimes low signal-to-noise ratio. In this study, we evaluate the online dissemination of ground deformation measurements from InSAR through Twitter, alongside the provision of open-access InSAR data from the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) processing system. Our aim is to evaluate (1) who interacts with disseminated InSAR data, (2) how the data are used, and (3) to discuss strategies for meaningful communication and dissemination of open InSAR data. We found that the InSAR Twitter community was primarily composed of non-scientists (62 %), although this grouping included earth observation experts in applications such as commercial industries. Twitter activity was primarily associated with natural hazard response, specifically following earthquakes and volcanic activity, where users disseminated InSAR measurements of ground deformation, often using wrapped and unwrapped interferograms. For earthquake events, Sentinel-1 data were acquired, processed, and tweeted within 4.7±2.8 d (the shortest was 1 d). Open-access Sentinel-1 data dominated the InSAR tweets and were applied to volcanic and earthquake events in the most engaged-with (retweeted) content. Open-access InSAR data provided by LiCSAR were widely accessed, including automatically processed and tweeted interferograms and interactive event pages revealing ground deformation following earthquake events. The further work required to integrate dissemination of InSAR data into longer-term disaster risk-reduction strategies is highly specific, to both hazard type and international community of practice, as well as to local political setting and civil protection mandates. Notably, communication of uncertainties and processing methodologies are still lacking. We conclude by outlining the future direction of COMET LiCSAR products to maximize their useability.</p

MULTI BAND INSAR ANALYSIS OF SUBSIDENCE DEVELOPMENT BASED ON THE LONG PERIOD TIME SERIES

The SAR Interferometry (InSAR) application has shown great potential in monitoring of land terrain changes and in detection of land deformations such as subsidence. Longer time analysis can lead to understand longer trends and changes. Using different bands of SAR satellite (C- from ERS 1-2 and Envisat, L- from ALOS) over the study area, we achieve knowledge of movements in long-term and evaluation of its dynamic changes within observed period of time. Results from InSAR processing fit with the position changes in vertical direction based on GPS network established over the basin as an effective geodetic network. Time series (StaMPS PS+SB) of several points over Çumra County in eastern part of Konya City show a general trend of the deformation that is expected to be approximately between -13 to -17 mm/year. Northern part of Karaman is affected by faster subsidence, borders of the subsidence trough were identified from Envisat. Presenting InSAR results together with GIS information about locations and time of occurrence of sudden subsidence, urban/industrial growth in time and climate changes helps in better understanding of the situation. This way, the impact of natural and man-made changes will be shown for urban planning thanks to InSAR and GIS comparisons with hydrogeological modeling. In this study we present results of differential and multitemporal InSAR series using different bands and GIS conjunction associated with seasonal and temporal groundwater level changes in Konya Closed Basin