Landslides threaten more than before the urbanized areas and are a worldwide growing problem for the already affected communities and the local authorities committed to landslide risk management and mitigation. For this reason, it is essential to analyze landslide dynamics and environmental conditioning factors. Various techniques and instruments exist for landslide investigation and monitoring. Out of these, Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) techniques have been widely used in the last decades. Their capabilities are enhanced by the availability of the active Sentinel-1 mission, whose 6-day revisiting time enables near real-time monitoring of landslides. Interferometric results, coupled with ground measurements or other approaches such as numerical modeling, significantly improve the knowledge of the investigated surface processes. In this work, we processed the C-band SAR images of the available European Space Agency (ESA) satellite missions, using MT-InSAR methods to identify the surface deformations related to landslides affecting the Iași Municipality (Eastern Romania). The results (i.e., velocity maps) point out the most active landslides with velocities of up to 20 mm/year measured along the satellite Line of Sight (LOS). Following, we focused on the most problematic landslide that affects the Țicău neighborhood and is well-known for its significant implications that it had. To better understand its behavior and the sensitivity of the displacements to the environmental factors (i.e., rainfall), we carried out 2D numerical modeling using a finite difference code. The simulated displacement field is consistent with the InSAR displacements and reveals the most active sectors of the landslide and insights about its mechanism.

Land use is paramount to sustainable development, and in the past, important changes happened under the influence of various factors. Revealing these changes in a meaningful manner, not just as total statistics but also as fluxes and at a spatial level, allows us to detect and associate them with the factors involved. We show a study case in Iași County, Romania, using a raster approach to change detection for a land-use-type database that extends to the 1920s. The database was created from topographic, remote sensing, and field data collected between 1920 and 2006, with five intervals between 1960, 1980, 1990, and 2000, starting from CORINE Land Cover data. These periods mark the socio-political and natural changes in the study area. The change detection results are well matched with the identified drivers and their spatial distribution. The fluctuations between land-use types provide a good way to create drivers’ associations. Our analysis can be easily applied to any other concerned areas and could be used as base references for any legislative intention to determine land-use-type changes because it can be learned from past conversions with regard to failures or examples of good practice.

Ditrău Alkaline Massif is one of the few syenitic Massifs in Europe subjected to mining exploration in the past, located in the Eastern Carpathians, Romania. The heterogenous petrography includes acid to ultrabasic rocks such as syenites, hornblendites, and diorites, making it the defining feature of the Massif. In this study, we analyze the river bed sediments of two rivers, Ditrău and Jolotca, draining the Ditrău Alkaline Massif to determine their geochemical composition, with particular interest in Rare Earth Elements. The analysis was carried out with various analytical methods, including Inductive Coupled Plasma Mass Spectrometry, powder X-ray diffractometry, and electronic microscopy for mineralogical analysis to determine the presence of heavy minerals and quantify the concentration of Rare Earth Elements in the river sediment samples. The results indicate the existence of heavy minerals and Rare Earth Elements in bearing minerals such as Monazite and Epidote. High concentration values of Light Rare Earth Elements are identified, with values more than double compared to the Upper Continental Crust in some cases, of which stands out Cerium with 175.47 mg·kg^-1 and Lanthanum with 108.32 mg·kg^-1. Most samples share three main minerals: Quartz, K Feldspar, and Albite, while Diopside is only present in the Jolotca sediment samples, and Plagioclase exists in Ditrău samples. Moreover, many identified trace elements, such as Niobium, Tantalum, and Zirconium, indicate high enrichments, with samples’ mean value of 265.62 mg·kg^-1 for Zirconium and 200.24 mg·kg^-1 for Niobium. The sum of Rare Earth Elements identified in the analyzed river sediments is 385.01 mg·kg^-1 for Ditrău samples and 368.72 mg·kg^-1 for Jolotca, with Cerium being the most significant element. The La/Th and Hf distinction plots suggest a mixed felsic/basic source for the Ditrău area and an acidic source for the Jolotca area.

Access to comprehensive and updated statistical and spatial databases represents a prerequisite of sound risk, hazard, vulnerability and resilience analyses, which have been advancing in terms of complexity and efficiency for the last 50 years, alongside the development of GIS techniques. Without adequate quantitative and qualitative datasets, research is sensitive to inaccurate and imprecise results, failing to meet the requirements for which it was designed and having zero input to the scientific progress.Most developing countries - including Romania, face the problem of incomplete, inaccurate or outdated data in many fields of research, including geology-related fields and statistics about the building stock and transport infrastructure. These types of data are fundamental for vulnerability assessments of urban spaces to seismic or landslide hazards. This paper aims to provide a GIS-based methodology for acquiring datasets of the geological surface deposits and of the building stock at the scale of urban settlements, focusing on Iași City in the NE of Romania, respectively on the Țicău area of this city.The mapping of geological surface deposits relies on automatic image analysis and landforms extraction algorithms that identify and delineate geological deposits based on slope and curvature, using High-resolution DEMs, as well as on cluster analysis. Slope deposits are delineated via watershed segmentation performed by Vision with Generic Algorithms (ViGRA), whereas the Schmidt-Hewitt classification is used to delineate floodplain and ridge deposits. The building stock is extracted from LiDAR point clouds with densities of 4 to 6 points per square meter using various approaches: neural network and deep learning for classification, and bounding rectangles for building boundary extraction. While LiDAR data is not available, high resolution imagery provided by the Copernicus programme can be used in conjunction with classification and edge detection algorithms to delineate building objects. The results are promising and show how the already available tools can be used to fill in the gaps of the “no-data problem” and overcome such a challenging issue. The obtained spatial data, namely surface deposits and building stock, become a major asset for the further vulnerability assessments that integrate geotechnical and physical aspects. This may help identify what local scale elements contribute to disaster resilience or, on the contrary, what fuels vulnerability.

Land use is paramount to sustainable development, and in the past, important changes happened under the influence of various factors. Revealing these changes in a meaningful manner, not just as total statistics but also as fluxes and at a spatial level, allows us to detect and associate them with the factors involved. We show a study case in Iași County, Romania, using a raster approach to change detection for a land-use-type database that extends to the 1920s. The database was created from topographic, remote sensing, and field data collected between 1920 and 2006, with five intervals between 1960, 1980, 1990, and 2000, starting from CORINE Land Cover data. These periods mark the socio-political and natural changes in the study area. The change detection results are well matched with the identified drivers and their spatial distribution. The fluctuations between land-use types provide a good way to create drivers’ associations. Our analysis can be easily applied to any other concerned areas and could be used as base references for any legislative intention to determine land-use-type changes because it can be learned from past conversions with regard to failures or examples of good practice.