Bilal Mutlu

Interferometric Synthetic Aperture Radar (InSAR) enables surface deformation monitoring over large areas with high spatial and temporal resolution, in addition to traditional long-term in-situ monitoring methods such as inclinometers. Conventional Persistent Scatterer InSAR (PS-InSAR) techniques rely on phase-stable point targets and provide reliable results under low deformation rates and high coherence. However, the use of this method is constrained in environments characterized by rapid deformation or vegetation cover. In such environments, the Small Baseline Subset (SBAS) InSAR approach offers a more robust alternative by utilizing interferogram pairs with small spatial and temporal baselines, allowing distributed scatterers to be included in time-series deformation analysis. The main objective of this study is to evaluate surface deformation dynamics in active urban landslide areas through joint InSAR analysis by integrating both SBAS and InSAR results with in-situ inclinometer measurements and to investigate the temporal relationship between deformation behavior and rainfall conditions. The study area is located in the Büyükçekmece region, in the south-western part of Istanbul, Türkiye, where ongoing landslide activity affects a densely urbanized environment and is characterized by predominantly south-westward movement patterns. Historical inclinometer data acquired between 2014 and 2016 were used to characterize subsurface deformation. These measurements were analyzed with SBAS-InSAR LOS (line-of-sight) displacement time series derived from Sentinel-1 descending-orbit data acquired during the same period. Open-source MintPy software within the ASF OpenSARLab was used as a virtual computing environment to process InSAR data and time-series analyses. Low-coherence pixels were excluded, and standard atmospheric phase corrections were applied. Deformation velocities were analyzed by considering their non-linear temporal behavior, evaluating both average velocity patterns and temporal changes in deformation rates. Rainfall data from nearby meteorological stations were incorporated to assess the correlation between precipitation events and deformation acceleration. The novelty of this study lies in the preference for the SBAS-InSAR approach over PS-InSAR; this choice was driven by the rapid deformation characteristics of the landslide area, where the high phase stability required for PS-InSAR is often compromised. Although InSAR does not provide direct subsurface deformation data, the results demonstrate that when integrated with inclinometer measurements, SBAS-InSAR time-series analysis offers a reliable and efficient framework for continuous surface deformation assessment in active urban landslide environments.

Regular physical activities such as walking, running, cycling, swimming, exercising and others are very important for human health. Today, such physical activities have become one of the priorities of both individuals and public administrations within the scope of preventive medicine. The measurement of many parameters related to the physical activities of people, athletes, sports scientists and coaches is very important for performance analysis and management, training planning and program optimization, reliable monitoring of health and performance goals and implementation of correct exercise practices. Typically, Global Navigation Satellite System (GNSS) and other sensors (including altimeter, compass, gyroscope, accelerometer, thermometer, and so on) in smartwatches are used to obtain mainly position, altitude/height, total distance travelled, instantaneous/average/min-max speeds, distance travelled at different speed ranges, pace, frequency/intensity of acceleration/deceleration, pauses, direction, number of steps, biological information, pulse/heart rate, respiration rate, amount of stress, sleep rhythm and hydration and others. The accuracy and reliability of the information provided by GNSS and other sensors on the smart devices is of paramount importance and, in some cases, even vital, to the users. In this study, the positioning accuracy of an embedded GNSS receiver in the Garmin Fenix 7X Solar Sapphire smartwatch has been tested with realistic field measurements under different satellite configurations and different conditions.

The problem of sea-level rise, mainly caused by global warming, requires ongoing and comprehensive monitoring through various observation methods. Satellite-based and ground-based surveying techniques are commonly used to monitor sea-level changes. The Global Navigation Satellite System (GNSS) serves as a valuable tool in this context. Besides providing accurate positioning data, GNSS supports both direct and indirect monitoring of environmental conditions through signal propagation in the atmosphere, which aids in modeling ionospheric and tropospheric dynamics. Moreover, analyzing multipath data—often considered as an error source—can reveal valuable insights into the surroundings of GNSS receivers. Recently, GNSS-Reflectometry (GNSS-R) has emerged, utilizing multipath effects to gather data on the Earth’s surface’s physical properties. This study compares the GNSS-R performance of both geodetic-grade and low-cost GNSS receivers by utilizing a ComNav SinoGNSS N2 as geodetic-grade receiver and antenna and a Septentrio Mosaic-x5 low-cost receiver with ArduSimple AS-ANT2B low-cost antenna, both established near above the pond located in ITU Ayazaga Campus. In order to simulate potential variations in water-levels, including tidal fluctuations, the elevations of the antennas were systematically adjusted using a height displacement tool. Subsequently, it was investigated whether the changes in the antenna height were detected by both the geodetic receiver and the low-cost receiver, and the GNSS-R antenna height determination performance of the low-cost receiver/antenna system was revealed. As a conclusion, the discrepancies in antenna height change measurements ranged from 1.4 cm to 3.7 cm for the geodetic receiver, and from 0.7 cm to 5.0 cm for the low-cost setup. Overall, the average height difference for the geodetic-grade receiver was 2.5 cm, while the average height difference for the low-cost receiver was 3.3 cm.

This study evaluates the performance of multi-constellation GNSS Precise Point Positioning (PPP) for offshore kinematic observations in the challenging environment of Antarctica using the data collected on a moving ship during the 6th Turkish Antarctic Expedition in 2022. The analysis involved two open-source PPP software solutions, PRIDE PPP-AR, and Ginan, which were used to process GNSS data from both GPS-only and multi-GNSS (GPS, GLONASS, Galileo, BeiDou) constellations. The results indicate that PRIDE PPP-AR generally provided better horizontal and vertical positional accuracy than Ginan software, achieving improvements of up to approximately 33% in height component root mean square error (RMSE) for both constellation solutions. While the mean value of horizontal position differences for PRIDE PPP-AR in multi-GNSS solution was 4 cm, while 5 cm for Ginan. The anticipated advantages of multi-GNSS over GPS-only solutions were not consistently observed for Ginan software in 2D position and height components. However, PRIDE PPP-AR demonstrated improved internal consistency with multi-GNSS solution, achieving an RMSE value of 5 cm for horizontal positioning compared to 6 cm for Ginan. The general results of the study reveal that the PPP technique, which does not require any reference station’s GNSS data, can achieve almost the same accuracy as the differential positioning technique. With this superiority, the PPP technique is an ideal positioning technique, especially in remote marine environments and polar regions, where geodetic infrastructure is inadequate and environmental conditions are difficult

Road networks are crucial to urban infrastructure and significantly affect transportation, traffic management, and emergency response. Besides, accurate mapping is essential for detecting road networks effectively, but traditional methods like manual digitization and field surveys often struggle in fast-changing urban environments. Remote sensing and deep learning techniques have emerged as effective alternatives, although initial road segmentation faced challenges such as limited image resolution. Recent advances in satellite technology have alleviated these issues by providing ultra-high-resolution (sub-meter) imagery, which is vital for accurately representing road networks. Deep learning models like U-Net have enhanced road segmentation by accurately capturing complex features. This research examines the effectiveness of multi-resolution satellite imagery for road segmentation. This study aims to analyze the accuracy assessment of road segmentation using Sentinel-2 imagery (10 m resolution) and ultra-high-resolution Pléiades Neo imagery (sub-meter resolution). Ground truth data from the Google Maps API were used for validation. Among the tested resolutions, Pléiades Neo at 30 cm achieved the highest accuracy, with an F-score of 0.87. Pléiades Neo at 15 cm resolution followed closely with an F-score of about 0.85. Pléiades Neo at 1 m resolution (upscaled data) showed a moderate decline (F-score of 0.82), while Sentinel-2 had the lowest performance (F-score of 0.78). Overall, Pléiades Neo at 30 cm resolution offers the best balance of accuracy and data efficiency for road segmentation.

Global warming phenomena lead to melting glaciers, rising sea levels, droughts, and irregular seasonal patterns, especially in polar regions. Besides, water vapor plays a significant role in these processes, contributing to about 60% of the natural greenhouse effect. Increasing temperature raises the atmosphere's capacity for water vapor, creating a positive feedback loop that aggravates global warming and extreme weather events. In polar regions, global warming is causing increased annual rainfall. Despite low overall precipitation, this phenomenon accelerates the melting of snow and ice, impacting local ecosystems. Future projections indicate that precipitation along Antarctica's coastline is expected to increase over the next 80 years. This increase may enhance surface melting through various processes. Consequently, monitoring atmospheric water vapor is crucial for understanding global climate dynamics and weather patterns. However, due to the harsh conditions in the polar regions, there is a shortage of conventional measurements, which makes global atmospheric reanalysis models crucial. The specific humidity and air pressure from the reanalysis models can be used to calculate Precipitable Water Vapor (PWV) (measured in meters), which is one of the most commonly used parameters for measuring atmospheric water vapor. Nevertheless, biases and discrepancies in the models may influence the data, particularly in polar regions where observations are scarce. In addition, the estimation of meteorological parameters can be acquired not only based on meteorological station data but also with the help of geodetic satellite data. Global Navigation Satellite Systems (GNSS) signals are subject to tropospheric refraction as they pass through the Earth's atmosphere, and the resulting zenith delays are divided into two components: hydrostatic (ZHD) and wet delay (ZWD). Moreover, the ZWD can be utilized to compute the PWV by multiplying a conversion factor. PWV can also be obtained by using air temperature and dew point temperature data from radiosonde observations at specific pressure levels. In this study, it is aimed to investigate and compare PWV values produced from GNSS-based, radiosonde-based, and global meteorological reanalysis models. Within the scope of the study, International GNSS Service (IGS) stations, which are located in the Antarctica continent, were used to calculate GNSS-based PWV. Besides, the radiosonde dataset retrieved from the Integrated Global Radiosonde Archive version 2.2 (IGRA 2.2) was used to obtain radiosonde-based PWV. As reanalysis datasets, the most recent reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF), and the National Aeronautics and Space Administration (NASA) were used. The fifth-generation reanalysis product from the ECMWF called ERA-5 and the second-generation version of NASA’s Modern-Era Retrospective analysis for Research and Applications called MERRA-2 data were used to obtain reanalysis based PWV. As a result of the study, root mean square errors (RMSE) and correlation values of GNSS-based PWV compared to radiosonde-based and reanalysis-based PWV were investigated for each IGS station. Besides, it was evaluated whether the GNSS technique could be used as an alternative to other methods in studies related to the troposphere and meteorology in the Antarctic continent.

In this study, the static and kinematic positioning performance of the Garmin GPSMAP 66sr handheld GNSS receiver has been tested. For the static test, GNSS data was collected for 24 h and divided into shorter sessions of 1, 2, and 4 h to assess the performance of the receiver as a function of occupation time. The whole and subgroup data were processed by the relative method for different satellite constellations using three reference stations, to form a very short (45 m), short (5.1 km), and relatively long (73.2 km) baselines. For the kinematic test, the data was collected for approximately 1 h and processed with the relative method. Additionally, the whole and subgroup static and kinematic GNSS data of the Garmin receiver were also processed with the Canadian Spatial Reference System-Precise Point Positioning (CSRS-PPP) online service. All Garmin static and kinematic solutions (both relative and PPP) were compared with those calculated by the geodetic receiver. The overall static results show that the Garmin GPSMAP 66sr handheld receiver provides accuracy in a few centimeters with the relative method when integer ambiguities were correctly fixed and in the decimeter-to-meter level using the PPP technique. For the kinematic scenario, the results were relatively poor within the level of decimeters with the relative method while the level of meters with the PPP technique.

The Kahramanmaraş earthquakes of February 6, 2023 occurred in the tectonically active eastern Mediterranean region near the Kahramanmaraş triple junction, where the Anatolian, Arabian, and African plates converge. This structurally complex boundary is characterized by significant strain accumulation, which frequently generates large-magnitude earthquakes. The significant ground shaking experienced in southeastern Turkey and surrounding areas implies the importance of high-resolution studies aimed at monitoring detailed seismotectonic processes in this region.Here, we present a kinematic rupture model of these destructive events derived from a joint inversion of high-rate Global Navigation Satellite System (GNSS) and strong ground motion (SGM) data. We combine GNSS-derived displacement time series from continuously operating stations with displacement waveforms extracted from SGM records. We assess the correlation between these two datasets by comparing stations in close proximity, thereby evaluating the consistency and precision of the derived ground motions. In addition, the earthquake hypocenter and the surface fault trace are taken into account by classifying and grouping the data based on source-to-station geometry. We employ different filtering approaches for near-field and far-field observations to extract the displacements appropriately. We also use the Precise Point Positioning (PPP) technique to obtain the coseismic displacement of GNSS observations. Instead of traditional differential techniques, we tested kinematic PPP while still preserving the effect of ambiguity resolution. Kinematic PPP solutions are derived from raw GNSS phase and pseudorange observations, yielding precise station positions at each epoch during each earthquake separately.To address the spatiotemporal evolution of fault slip, we apply finite fault modeling to the combined dataset and obtain a detailed kinematic rupture model for the earthquake sequence. We compare this model with previously published rupture models for the February 6, 2023 Kahramanmaraş earthquakes, highlighting both similarities in overall fault geometry and slip patterns, and differences in rupture extent and timing. By integrating high-frequency strong ground motion observations with GNSS displacements, we emphasize the importance of combining multiple data sources to gain a more comprehensive understanding of earthquake source processes.

The international service that ensures access to data and products of global navigation satellite systems (GNSSs), known as the IGS, runs a real-time service (RTS) project to support users who need real-time access to precise products. Thanks to the RTS project, it is now possible to obtain real-time precise point positioning (RT-PPP) solutions. RT-PPP can be used in many real-time positioning applications that require a high level of accuracy, efficiency, and flexibility, including earth sciences, atmosphere sciences, marine sciences, natural hazards, and many more. In this study, we tested the impact of different worldwide RTS products and satellite configurations on the performance of RT-PPP accuracy, as well as convergence time, in the Antarctic’s challenging environment and extreme atmospheric conditions. We applied RT-PPP solutions using real-time precise products (satellite orbit/clock corrections, and biases to conduct the real-time PPP) provided by the IGS and NAVCAST (a real-time PPP positioning service) based on different GNSS constellations: GPS-only, Galileo-only, and a combination of GPS and Galileo. In this way, the performance of two different real-time (RT) services was compared with each other. At the same time, the effectiveness of the Galileo global navigation satellite system for RT-PPP was also tested, and the Galileo system’s contribution to the GPS-only RT-PPP solution was investigated. The PPP-WIZARD software was used to process the corrections and GNSS data from a reference station in the Antarctic region. Although GPS-only, Galileo-only, and multi-GNSS solutions obtained from both RT services were found to have very close accuracy to each other, the combination of the GPS and Galileo systems produced better accuracy than when using the GPS system alone. According to the numerical results of this study, it was concluded that the real-time PPP technique gave promising results in such a challenging environment of the Antarctic region. However, we also observe that the RT-PPP technique requires a stable and robust internet connection, which might limit its usefulness in remote regions. Overall, we found the RT-PPP technique to be a viable alternative to conventional relative GNSS positioning techniques, especially in areas where continuously operating reference networks or similar networks are lacking.