Land subsidence affects many areas of the world, posing a serious threat to human structures and infrastructures. It can be effectively monitored using ground-based and remote sensing techniques, such as the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). GNSS provides high precision measurements, but in a limited number of points, and is time-consuming, while InSAR allows one to obtain a very large number of measurement points, but only in areas characterized by a high and constant reflectivity of the signal. The aim of this work is to propose an approach to combine the two techniques, overcoming the limits of each of them. The approach was applied in the Po River Delta (PRD), an area located in Northern Italy and historically affected by land subsidence. Ground-based GNSS data from three continuous stations (CGNSS) and 46 non-permanent sites (NPS) measured in 2016, 2018, and 2020, and Sentinel-1 and COSMO-SkyMed SAR data acquired from 2016 to 2020, were considered. In the first phase of the method, InSAR processing was calibrated and verified through CGNSS measurements; subsequently, the calibrated interferometric data were used to validate the GNSS measurements of the NPS. In the second phase, the datasets were integrated to provide an efficient monitoring system, extracting high-resolution deformation maps. The results showed a good agreement between the different sources of data, a high correlation between the displacement rate and the age of the emerged surfaces composed of unconsolidated fine sediments, and high land subsidence rates along the coastal area (up to 16–18 mm/year), where the most recent deposits outcrop. The proposed approach makes it possible to overcome the disadvantages of each technique by providing more complete and detailed information for a better understanding of the ongoing phenomenon.

Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependencies, uncertainties, and intricate interactions. However, the complexity of modelling and computations associated with Bayesian spatiotemporal models vary across different diseases. Presently, there is a limited comprehensive overview of Bayesian spatiotemporal models and their applications in epidemiology. This article aims to address this gap through a thorough review. The review commences by delving into the historical development of Bayesian spatiotemporal models concerning disease mapping, prediction, and regression analysis. Subsequently, the article compares these models in terms of spatiotemporal data distribution, general spatiotemporal data models, environmental covariates, parameter estimation methods, and model fitting standards. Following this, essential preparatory processes are outlined, encompassing data acquisition, data preprocessing, and available statistical software. The article further categorizes and summarizes the application of Bayesian spatiotemporal models in spatial epidemiology. Lastly, a critical examination of the advantages and disadvantages of these models, along with considerations for their application, is provided. This comprehensive review aims to enhance comprehension of the dynamic spatiotemporal distribution and prediction of epidemics. By facilitating effective disease scrutiny, especially in the context of the global COVID-19 pandemic, the review holds significant academic merit and practical value. It also aims to contribute to the development of improved ecological and epidemiological prevention and control strategies.

Time series interferometric synthetic aperture radar (TS-InSAR) has been a powerful tool for monitoring land surface deformation over the last two decades. Atmospheric effects cause large-scale delays in InSAR observations, which is one of the difficulties facing deformation calculations from differential InSAR and time-series InSAR. Currently, the atmospheric delay is derived mainly from auxiliary data from sources such as the global navigation satellite system (GNSS) and moderate-resolution imaging spectroradiometry (MODIS), but GNSS data are limited by the sparse distribution of observation stations. MODIS data may also not temporally match SAR image acquisition, which leads to low accuracy in atmospheric phase correction. This article presents a decomposition method to remove the atmospheric delay. We consider the atmospheric phase to be caused by the combined changes in spatial position and elevation. Therefore, quadtree segmentation is applied to divide the topographic units, and we improve the drift function of universal kriging by adding an elevation component. We then interpolate the whole atmospheric phase space from reliable sampling points estimated by the coherence coefficient. Using Sentinel-1 data, we test the proposed method in discriminating and monitoring a mining subsidence area in Shanxi Province and compare the results with the results from interferometric point target analysis and the network-based variance-covariance estimation method. The results demonstrate that the proposed method is superior to existing methods for the detection of deformation inverted from TS-InSAR.

Mass movements represent a serious threat to the stability of human structures and infrastructures, and cause loss of lives and severe damages to human properties every year worldwide. Built structures located on potentially unstable slopes are susceptible to deformations due to the displacement of the ground that at worst can lead to total destruction. Synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites and processed by multi-temporal interferometric SAR (MT-InSAR) techniques can measure centimeter to millimeter-level displacement with weekly to monthly updates, characterizing long-term large-scale behavior of the buildings and slopes. However, the spatial resolution and short wavelength weaken the performance of Sentinel-1 in recognizing features (i.e., single buildings) inside image pixels and maintaining the coherence in mountainous vegetated areas. We have proposed and applied a methodology that combines Sentinel-1 interferometry with ground-based geomatics techniques, i.e., global navigation satellite system (GNSS), terrestrial laser scanning (TLS) and terrestrial structure from motion photogrammetry (SfM), for fully assessing building deformations on a slope located in the north-eastern Italian pre-Alps. GNSS allows verifying the ground deformation estimated by MT-InSAR and provides a reference system for the TLS and SfM measurements, while TLS and SfM allow the behavior of buildings located in the investigated slope to be monitored in great detail. The obtained results show that damaged buildings are located in the most unstable sectors of the slope, but there is no direct relationship between the rate of ground deformation of these sectors and the temporal evolution of damages to a single building, indicating that mass movements cause the displacement of blocks of buildings and each of them reacts differently according to its structural properties. This work shows the capability of MT-InSAR, GNSS, TLS and SfM in monitoring both buildings and geological processes that affect their stability, which plays a key role in geohazard analysis and assessment.

The seismic sequence of November 2019 in Albania culminating with the Mw = 6.4 event of 26 November 2019 was examined from the geodetic (InSAR and GNSS), structural, and historical viewpoints, with some ideas on possible areas of greater hazard. We present accurate estimates of the coseismic displacements using permanent GNSS stations active before and after the sequence, as well as SAR interferograms with Sentinel-1 in ascending and descending mode. When compared with the displacements predicted by a dislocation model on an elastic half space using the moment tensor information of a reverse fault mechanism, the InSAR and GNSS data fit at the mm level provided the hypocentral depth is set to 8 ± 2 km. Next, we examined the elastic stress generated by the Mw = 7.2 Montenegro earthquake of 1979, with the Albania 2019 event as receiver fault, to conclude that the Coulomb stress transfer, at least for the elastic component, was too small to have influenced the 2019 Albania event. A somewhat different picture emerges from the combined elastic deformation resulting after the two (1979 and 2019) events: we investigated the fault geometries where the Coulomb stress is maximized and concluded that the geometry with highest induced Coulomb stress, of the order of ca. 2–3 bar (0.2–0.3 MPa), is that of a vertical, dextral strike slip fault, striking SW to NE. This optimal receiver fault is located between the faults activated in 1979 and 2019, and very closely resembles the Lezhe fault, which marks the transition between the Dinarides and the Albanides.