The Digital Zenith Camera Systems (DZCSs) are dedicated astrogeodetic instruments used to obtain highly accurate astrogeodetic vertical deflection (VD) data. The first Turkish DZCS, the Astrogeodetic Camera System (ACSYS), was developed in Istanbul, Turkey in 2015. The ACSYS was capable of determining astrogeodetic VDs with an accuracy of ~0.3 arcseconds. However, it had some limitations in observation duration: because of the semi-automated mechanical design, levelling the system towards zenith was a time-consuming process. Since 2016, the ACSYS has been modernized through system upgrades and new technological components. In this paper, we describe the instrument design of the new DZCS-ACSYS2-observation procedures, evaluation of the test data and calculations of these data. The preliminary ACSYS2 astrogeodetic test observations were conducted at Istanbul Technical University (ITU) test station. The standard deviation results of the repeated observations reveal a VD measurement precision of ~0.3 arcseconds for both the North-South and East-West components. To investigate the accuracy of the system, a lightweight total station based-geodetic system-QDaedalus-was also used at the ITU test station. The comparison of the VDs data between ACSYS2 and QDaedalus system shows that the ACSYS2 can produce reliable VDs data.

Within two hours on 01 July 2023, three earthquakes of Mw 5.8-6.0 hit the SE Fars arc, Iran. In the following months, the region characterized by the collision of the Iranian and the Arabian plate, thrust faulting, and salt diapirism was stroke by more than 120 aftershocks of mL 3.1-5.2, of which two of the largest events occurred within one minute on 23 July 2023 in spatial vicinity to each other. We analyzed both the large mainshocks and aftershocks using different techniques, such as the inversion of seismic and satellite deformation data in a joint process and aftershock relocation. Our results indicate the activation of thrust faults within the lower sedimentary cover of the region along with high aftershock activity in significantly larger depth, supporting the controversial model of a crustal strain decoupling during the collision in the Fars Arc. We resolved a magnitude difference of >0.2 magnitude units between seismic and joint seismic and satellite deformation inversions probably caused by afterslip, thereby allowing to bridge between results from international agencies and earlier studies. We also find evidence for an event doublet and triplet activating the same or adjacent faults within the sedimentary cover and the basement

Deep-seated, slow moving bedrock landslides are significant natural disasters with severe socio-economic repercussions. During the past decades, an acceleration of these hazards has been reported globally due to changes in seasonal freeze-thaw cycles, permafrost thawing, infrastructure development and other anthropogenic sources, changes of precipitation and groundwater levels, and variation in seismic activity. Interferometric Synthetic Aperture Radar(InSAR) is a powerful tool to map landslides movement from space and the Sentinel 1 C-band radar mission provides a high temporal resolution data source to investigate seasonal and intra-annual changes of landslide behaviour.To construct a 2D/3D displacement field, we decompose a combination of different look angles and InSAR ascending and descending tracks of different sensors including Sentinel and ALOS 1 PALSAR data. The ionospheric delay for InSAR observations is estimated with a split range-spectrum technique because significant ionospheric total electron content variation is common in our study area in the Central Andes. Both statistical phase-based and weather model estimation approaches are implemented to minimize the effect of tropospheric signal on InSAR observations.Our observations identify several areas with rapid translational slide movements exceeding 5-10 cm/y. Multi-annual and inter-annual behaviour of deformation is extracted through time series analysis and a hierarchical clustering approach is used to identify geographic areas with similar characteristics and rates. We show the wide-spread spatial distribution of unstable hill slopes in the Eastern Cordillera of the south-central Andes, especially at high elevations where field observations are difficult. We identify driving forces to be a combination of pre-existing geologic structures and climatic parameters.

Slow-moving landslides are an important erosional geomorphic process that shape hillslopes and transport large amounts of sediment material to river channels. They may have potentially catastrophic consequences for infrastructure and human life. Identifying the spatiotemporal pattern of hillslope deformation is essential for understanding the kinematic evolution of hillslope failure and mitigating associated hazards. InSAR (Interferometric Synthetic Aperture Radar) is an effective geodetic method for mapping landslide deformation with high spatiotemporal resolution and precision, especially where direct access to the hillslope areas is difficult.The study area in the south-central Andes is characterized by steep climatic and topographic gradients. The low-elevation eastern foreland areas with dense vegetation cover change to semi-arid and arid, near-vegetation-free high-elevation areas. InSAR phase estimation and landslide mapping in such a complex region can be affected by spatial and temporal variations of soil moisture, vegetation cover, and atmospheric regime. In this study, we extract InSAR time series from the C-band ascending and descending track of Sentinel-1A/B data acquired between 2014 and 2022 and the L-band ascending track of ALOS1 PALSAR data acquired between 2006 and 2011 in the south-central Andes of northwest Argentina. We compare Sentinel deformation time series and maps derived from the linear small baseline subset technique with different numbers of connections in sequential interferogram formation with non-linear phase inversion techniques. We assess the phase bias contribution of short-temporal baseline interferograms for the time series analysis and propose several correction techniques tailored to this study area. Statistical and weather based models are used to reduce the impact of tropospheric delay on the deformation signal, especially during convective events controlled by the South American Monsoon and the large fluctuation of topographic relief effects on the tropospheric phase delay. We investigate the difference between tropospheric correction methods. We further implement a double-difference filter with different local and regional spatial filters to reduce the tropospheric delay on the InSAR time series. After additional filtering steps to remove further ionospheric noise in the time series, we identify the landslide spatial extent and their dynamic through spatial analyses. Our results reveal multiple landslides, including three transitional bodies with downslope velocities of 5-10 cm/yr that demonstrate the importance of carefully filtering InSAR time series for slow-moving landslide detection.

Slow-moving landslides pose significant natural hazards to humans and infrastructure.  Analysis of Interferometric Synthetic Aperture Radar (InSAR) time series provide the opportunity to monitor unstable hillslopes in difficult to access terrains at large spatial scales.The geological conditions and land cover of the eastern Central Andes in northwestern Argentina ranges from densely vegetated areas in the low elevation foreland at around 1000 metres to arid, vegetation free conditions at high elevations at about 6000 metres. The land cover has a significant impact on the spatial and temporal InSAR signal decorrelation and deformation estimation. In our study, we extract InSAR time series from Sentinel-1 ascending and descending data acquired between 2014 and 2021 using both linear small baseline technique and non-linear phase inversion techniques to have a better understanding of deformation rate estimation techniques for landslide detection in complex areas. We identified several landslides including three main translational bodies with areas exceeding 1 km2 and downslope deformation rates in excess of 5-10 cm/yr. Our study is influenced by ionospheric total electron content variation for the C band Sentinel-1 ascending phase observations. We applied the split range-spectrum technique to minimize the ionospheric contribution on the phase measurements. The tropospheric signal was estimated using both statistical approaches based on topography and weather models to reduce the effects of atmospheric water vapor during South American Monsoon activity. We explore the impact of topographic relief on tropospheric phase delay. We compared our deformation-rate estimates with a double-differencing time series with local and regional spatial filters to mitigate tropospheric noise and unwrapping problems in the time series. We take advantage of connected component analysis and hierarchical clustering approaches on the mean velocity from the double-difference time series and vertical component derived from the 3D decomposition of InSAR time series to map landslides with similar characteristics. Our results highlight the importance of the several processing parameters during InSAR time-series analysis and their sensitivity toward slow-moving landslide detection.

Slow-moving landslides in high-mountain regions pose a significant natural hazard and are capable of delivering large sediment volumes to the fluvial system. Time series analysis of Interferometric Synthetic Aperture Radar (InSAR) allows us to identify unstable and potentially dangerous areas prone to landsliding, but this technique also helps quantify seasonal dynamics for predicting landslide behavior.Our study in the Eastern Cordillera of the Argentine Andes focuses on enhancing InSAR's reliability for landslide mapping. This region is characterized by moisture changes along the topographic gradient across the orogen and seasonal variability associated with the South American Summer Monsoon. We extract InSAR time series data from Sentinel-1A/B's C-band (2014-2022) and ALOS1 PALSAR's L-band (2006-2011). Tropospheric delay is caused by atmospheric turbulence and vertical stratification changes. These delays can introduce significant errors in deformation measurements, thus impacting the quality of maps portraying landslide deformation rates. To address this problem, we apply various correction techniques, ranging from spatial and temporal filtering to water-vapor estimation from an atmospheric model. Fading signal noise, another challenge caused by multi-looking and short temporal baselines in the Small Baseline Subset (SBAS) technique, additionally compromises InSAR time series accuracy. We investigate the pattern and magnitude of fading signals in landslide areas using Small Baseline Subset (SBAS) with different neighboring connections and non-linear phase inversion methods, such as the Eigenvalue Decomposition-based Maximum Likelihood (EMI), Eigenvalue Decomposition (EVD), and the Phase Triangulation Algorithm (PTA).Our research evaluates both statistical methods and Global Atmospheric Models for correcting tropospheric delays and fading signal noise. We explore statistical methods, such as double-difference filtering and corrections based on phase elevation, for different spatial windows, including individual catchments, moving windows, and adaptive window sizes. The efficiency of these methods varies with the environmental and topographic conditions in the orogen. Both stratified and turbulent components of the troposphere, along with fading signal noise, can significantly influence tropospheric delay and time series quality. In the context of the factors that influence deformation signals and the combined array of methods to obtain robust measurements, we can identify the spatial and temporal characteristics of slow-moving landslides and assess the different impacts on rate changes.