Implementing the European Space Agency’s SentiNel application platform’s open-source Python module for differential synthetic aperture radar interferometry coseismic ground deformation from Sentinel-1 data

Differential SAR Interferometry is a largely exploited technique to study ground deformations. A key application is the detection of the effects promoted by earthquakes, including detailed variations in ground deformations at different scales. In this work, an implemented Python script (Snap2DQuake) based on the “snappy” module by SNAP software 9.0.8 (ESA) for the processing of satellite imagery is proposed. Snap2DQuake is aimed at producing detailed coseismic deformation maps using Sentinel-1 C-band data by the DInSAR technique. With this alternative approach, the processing is simplified, and several issues that may occur using the software are solved. The proposed tool has been tested on two case studies: the Mw 6.4 Petrinja earthquake (Croatia, December 2020) and the Mw 5.7 to Mw 6.3 earthquakes, which occurred near Tyrnavós (Greece, March 2021). The earthquakes, which occurred in two different tectonic contexts, are used to test and verify the validity of Snap2DQuake. Snap2DQuake allows us to provide detailed deformation maps along the vertical and E-W directions in perfect agreement with observations reported in previous works. These maps offer new insights into the deformation pattern linked to earthquakes, demonstrating the reliability of Snap2DQuake as an alternative tool for users working on different applications, even with basic coding skills.Peer ReviewedPostprint (published version

Implementazione dei software Snap e StaMPS per l'elaborazione di immagini SAR con tecnica interferometrica

L’Earth Observation (EO), osservazione della Terra, è una disciplina che negli ultimi 30 anni è stata notevolmente sviluppata ed innovata, per poter soddisfare le sempre crescenti necessità dell’uomo di controllare il nostro pianeta, studiarne i cambiamenti e monitorarne l’evoluzione. Uno dei principali strumenti di remote-sensing in questi anni sempre più utilizzato è il SAR, il Radar ad Apertura Sintetica, soggetto principale del presente studio. Il SAR è uno strumento attivo, che non dipende da altre fonti di energia elettromagnetica, in grado di individuare gli oggetti e stimarne la distanza, con una precisione millimetrica. Sfruttando le microonde il SAR non è ostacolato dalle nubi e può quindi operare durante tutte le condizioni meteorologiche, di giorno e di notte. Le acquisizioni SAR si presentano come immagini, in cui all’interno di ogni pixel sono contenute informazioni legate alla fase del segnale ricevuto e all’ampiezza della risposta energetica generata dal bersaglio colpito al suolo. Scopo del presente studio è stato individuare, analizzare e iniziare a padroneggiare i diversi software necessari ad individuare i Persistent Scatterers, PS: pixel che durante le diverse acquisizioni mantengono una risposta stabile e possono essere per questo utilizzati come riferimento per valutare l’evoluzione dinamica della superficie terrestre. Per il nostro studio è stato scelto di analizzare i software free Snap e StaMPS, effettuando una descrizione esaustiva delle singole operazioni da svolgere, dei parametri necessari e dei prodotti intermedi per individuare i PS. Infine, per apprezzare le notevoli potenzialità del SAR, sono state svolte due elaborazioni di più di 200 acquisizioni effettuate tra il 2016 e il 2021, dai satelliti Sentinel-1, dell’area della provincia di Bologna, utilizzando Snap e StaMPS, per valutare l’evoluzione del suolo in questa importante area della nostra penisola

Morphological Analysis of Anak Krakatau Volcano after 22 December 2018 Eruption using Differential Interferometry Synthetic Aperture Radar (DInSAR)

Anak Krakatau Volcano is an active volcano located in the Krakatau Complex, Sunda Strait, Indonesia. On 22 December 2018, the volcano experienced a major eruption that led to a tsunami that devastated the shores of the islands of Java and Sumatra and killed up to 437 people. The eruption also destroyed the volcano’s body and change its shape drastically and forming a large crater in the southwestern part. After that eruption, the volcano continues to grow up. This research aims to analyze the deformation of the Anak Krakatau Volcano post-2018 eruption by using the differential interferometry SAR method (DInSAR). In order to support the analysis, we additionally compare the DInSAR result with tectonic-volcanic activity. Sentinel 1-A type SLC satellite imagery data from 5 June 2019 to 7 January 2020; consisting of 19 images or 18 pairs as master and slave were used to producing a deformation map. DInSAR result shows the volcano was generally experiencing deflation during the period, ranging from -1.03 to -4.81 cm (-3.01 cm average). However, inflation also occurred ranging from 0 to 5.99 cm, correlating with shallow and deep volcanic activity and followed by eruptions in October 2019 when the highest activities were observed. Furthermore, coherence value should be highly considered along with DInSAR processing, and this research allows that coherence to be acceptable

MONITORING THE 2018 ERUPTION OF KĪLAUEA VOLCANO USING VARIOUS REMOTE SENSING TECHNIQUES

Monitoring the regions that are prone to natural hazards is essential in disaster management to provide early warnings. Airborne and space-borne remote sensing techniques are cost-effective in accomplishing this task. Interferometric Synthetic Aperture Radar (InSAR) is an advanced remote sensing technique used to detect and measure the changes in the Earth’s topography over time. Spaceborne InSAR is a precise (~mm accuracy) way to measure the land surface altitudinal changes. Persistent Scatterer Interferometry (PSI) is a powerful method of differential SAR interferometry that processes the InSAR data by automatically selecting the persistent scatterers in the region. In this thesis, I developed a new algorithm to estimate the areal coverage and volume of newly erupted lava by integrating the space-borne InSAR, thermal infrared, Light Detection and Ranging (LiDAR), and Normalized Difference Vegetation Index (NDVI) techniques. I applied this algorithm to the eruption of the East Rift Zone (ERZ) of the Kīlauea volcano that took place between May and August 2018 as a case study, and estimated the areal coverage and volume of lava erupted. I compared the results of InSAR to those derived from airborne LiDAR. I found that although air-borne LiDAR provides data with higher resolution and accuracy, InSAR is almost as good as LiDAR in monitoring deformed areas and has larger spatial and temporal coverage. I also performed the PSI analysis using the Stanford Method for Persistent Scatterers (StaMPS) algorithm, and determined the Line-of-Sight (LOS) deformations prior, during, and after the 2018 eruption of the Kīlauea volcano. Results from the PSI processing show regional subsidence on the Big Island, indicating the deflation of the southern and western part of the Big Island during the eruption at the East Rift Zone. Keywords: Kilauea

Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions

Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification

Opportunistic radar imaging using a multichannel receiver

Bistatic Synthetic Aperture Radars have a physically separated transmitter and receiver where one or both are moving. Besides the advantages of reduced procurement and maintenance costs, the receiving system can sense passively while remaining covert which offers obvious tactical advantages. In this work, spaceborne monostatic SARs are used as emitters of opportunity with a stationary ground-based receiver. The imaging mode of SAR systems over land is usually a wide-swath mode such as ScanSAR or TOPSAR in which the antenna scans the area of interest in range to image a larger swath at the expense of degraded cross-range resolution compared to the conventional stripmap mode. In the bistatic geometry considered here, the signals from the sidelobes of the scanning beams illuminating the adjacent sub-swath are exploited to produce images with high cross-range resolution from data obtained from a SAR system operating in wide-swath mode. To achieve this, the SAR inverse problem is rigorously formulated and solved using a Maximum A Posteriori estimation method providing enhanced cross-range resolution compared to that obtained by classical burst-mode SAR processing. This dramatically increases the number of useful images that can be produced using emitters of opportunity. Signals from any radar satellite in the receiving band of the receiver can be used, thus further decreasing the revisit time of the area of interest. As a comparison, a compressive sensing-based method is critically analysed and proves more sensitive to off-grid targets and only suited to sparse scene. The novel SAR imaging method is demonstrated using simulated data and real measurements from C-band satellites such as RADARSAT-2 and ESA’s satellites ERS-2, ENVISAT and Sentinel-1A. In addition, this thesis analyses the main technological issues in bistatic SAR such as the azimuth-variant characteristic of bistatic data and the effect of imperfect synchronisation between the non-cooperative transmitter and the receiver

Assessment of Landslide-Induced Geomorphological Changes in Hítardalur Valley, Iceland, Using Sentinel-1 and Sentinel-2 Data

Publisher's version (útgefin grein)Landslide mapping and analysis are essential aspects of hazard and risk analysis. Landslides can block rivers and create landslide-dammed lakes, which pose a significant risk for downstream areas. In this research, we used an object-based image analysis approach to map geomorphological features and related changes and assess the applicability of Sentinel-1 data for the fast creation of post-event digital elevation models (DEMs) for landslide volume estimation. We investigated the Hítardalur landslide, which occurred on the 7 July 2018 in western Iceland, along with the geomorphological changes induced by this landslide, using optical and synthetic aperture radar data from Sentinel-2 and Sentinel-1. The results show that there were no considerable changes in the landslide area between 2018 and 2019. However, the landslide-dammed lake area shrunk between 2018 and 2019. Moreover, the Hítará river diverted its course as a result of the landslide. The DEMs, generated by ascending and descending flight directions and three orbits, and the subsequent volume estimation revealed that-without further post-processing-the results need to be interpreted with care since several factors influence the DEM generation from Sentinel-1 imagery.This research has been supported by the Austrian Science Fund (FWF) through the project MORPH (Mapping, monitoring and modelling the spatio-temporal dynamics of land surface morphology; FWF-P29461-N29) and the Doctoral Collage GIScience (DKW1237-N23), as well as by the Austrian Academy of Sciences (?AW) through the project RiCoLa (Detection and analysis of landslide-induced river course changes and lake formation).Peer Reviewe

Correction Methods for Non-Stationary Noise Floor in Sentinel-1 Images Using Convex Optimization

Synthetic aperture radar (SAR) is a method of creating images of the surface of the Earth by emitting and receiving radar waves. Sentinel-1 is a SAR platform made by the European Space Agency (ESA) that provides a source of SAR images open to the public through the operation of two satellites. Due to the non-uniform radiation pattern projected from the satellite's antenna, there are significant non-stationary noise floor intensity patterns that distract from the desired measurements, which are particularly significant in certain types of image modes, namely Extra Wide and Interferometric Wide modes. While ESA provides a default noise floor estimate with each Sentinel-1 product, with the intention that it be subtracted from the original image so the result is homogeneous, there is clear evidence that it is miscalibrated. This Masters thesis presents two novel methods for estimating the noise floor patterns in the images that are demonstrated to be improvements over the default noise floor. The first method presents a way to dynamically construct and apply linear rescaling to the default noise floor estimate over different sections of the images, called subswaths, by use of least squares optimization. While the method is successful in improving image quality, it is not totally effective because the default noise floor is mis-fit in a non-linear manner. The second method constructs a new noise floor as a power function of the radiation pattern power by using linear programming and least squares optimization. This successfully compensates for the non-linear mis-fit, resulting in an overall increase in image quality, albeit with greater parametric complexity. These methods greatly improve the intrinsic value of Sentinel-1 images in scenarios where the noise floor dominates, such as in cross-polarized images and images where the physical materials result in lower backscatter intensity

PSINSAR COHERENCE BASED DISPLACEMENT ANALYSIS OF KRISHNA DELTA USING SENTINEL-1 INTERFEROMETRIC WIDE SWATH DATA

The problem of decorrelation leading to loss of coherence has been a major source of concern to identify the various problems of erosion and deposition in delta. In this study, Permanent Scatter Interferometric SAR (PsInSAR) technique was used to identify the Permanent Scatter Candidates (PSCs) to explore its potential in identifying displacement based on coherence of various features in delta during the dry and wet periods. PSCs are coherent over interferograms acquired during different time periods. The study was conducted using Sentinel-1 C-band Interferometric Wide (IW) swath datasets acquired from 25th October 2016 to 10th June 2017 over Krishna Delta. The datasets were deramped and stitched prior to co-registering the master and slave images. Interferograms were generated, phase unwrapped and filtered after which the PSCs were identified based on Amplitude Stability index. The problem of tropospheric phase delay causing decorrelation was removed based on the difference in the phase residual of the connected PSCs. Ps coherence map was generated showing coherence as low as 0.28 to 0.38 in mangroves due to volume decorrelation and 0.5 to 0.85 in village areas. A prominent feature, vernal pool exhibited high variation in coherence (0.28 to 0.45) depending on monsoon or summer season. An integrated cumulative displacement map was generated indicating the areas where erosion and deposition has taken place and these depositional values of certain deltaic features were in conjunction with coherence

Automatic Detection of Low-Backscatter Targets in the Arctic Using Wide Swath Sentinel-1 Imagery

Low backscatter signatures in synthetic aperture radar (SAR) imagery are characteristic to surfaces that are highly smooth and specular reflective of microwave radiation. In the Arctic, these typically represent newly formed sea ice, oil spills, and localized weather phenomena such as low wind or rain cells. The operational monitoring of low backscatter targets can benefit from a stronger integration of freely available SAR imagery from Sentinel-1. We, therefore, propose a detection method applicable to Sentinel-1 extra wide-swath (EW) SAR scenes. Using intensity values coupled with incidence angle and noise-equivalent sigma zero (NESZ) information, the image segmentation method is able to detect the low backscatter targets as one segment across subswaths. We use the Barents Sea as a test site due to the abundant presence of low backscatter targets with different origins, and of long-term operational monitoring services that help cross-validate our observations. Utilizing a large set of scenes acquired in the Barents Sea during the freezing season (November–April), we demonstrate the potential of performing large-scale operational monitoring of local phenomena with low backscatter signatures