Global warming is an unanimously accepted phenomenon by the international scientific community, being already highlighted by the analysis of observational data over long periods of time, with an increase in temperature of over 1 °C. Climate change in Romania is part of the global context, taking into account the regional conditions, with an increasing trend of arid summer. While the link between high temperatures, climate change and rainfall has been modeled in detail, the situation is not the same for plant water accessibility. The period of time between 1898 and the present, corresponding to the annual records of precipitation and evapotranspiration, overlapped with important political and administrative changes in the studied area, and with extensive hydro-amelioration works. The aim of the paper is to statistically follow the evolution over time of precipitation, namely of evapotranspiration measured in Western Romania, which would allow the expression of conclusions regarding the improvement directions of the water regime. In order to follow the evolution in time of these data, the interval of 1898–2019 was divided into three periods: 1898–1950, 1951–1989 and 1990–2019, respectively. The increase in temperature, especially during the vegetation period and the large number of years in which evapotranspiration quantitatively exceeds the precipitation, indicating the need for effective measures to regulate the water balance.

Stability in time of major and important objectives is vital and can be achieved by 3D scanners which follow changes in time with construction, respective of the natural or artificial hydrotechnical dams and the obtaining of 3D data in real time with the possibility of evaluating and making quick decisions. This scientific paper approaches a research topic of great importance and actuality in the field of Civil Engineering, Hydrotechnics, and Geomatics using the 3D scanning technologies for the hydrotechnical arrangements (Topolovăţu Mic, Coșteiu and Sânmartinu Maghiar) and hydroameliorative (Cruceni Pumping Station). In Romania, data collection was carried out for the first time using the mobile scanning technology (MMS), “Backpack” type, namely, Leica Pegasus Backpack. Data collection using terrestrial laser scanning technology (Terrestrial Laser Scanning) was carried out with the Leica C10 equipment. The processing of point clouds was carried out using the Inertial Explorer program, and the processing of point clouds was carried out with the Cyclone program. The collection of ground checkpoints used for checking, correcting, and analyzing point clouds was carried out using the GPS Leica GS08 equipment. Compared with traditional methods using classical measuring instruments, precise data was obtained (with an error of 2–4 cm) through 3D laser scanning technology in a short time and with multiple possibilities of processing and visualizing point clouds.

Peri-urban areas in Eastern Europe are undergoing rapid land transformation driven by suburban housing expansion and infrastructure development, yet the processes through which vegetation is progressively degraded and built-up areas intensify remain insufficiently documented. This study analyses vegetation loss and urban expansion in the peri-urban belt of Timișoara, Western Romania, between 2020 and 2025 using Sentinel-2 multispectral imagery, two key spectral indices (NDVI and NDBI), and a Random Forest (RF) classifier. The results reveal a gradual, multi-stage transformation trajectory, where dense vegetation transitions first into sparse vegetation and bare soil before consolidating into built-up surfaces, rather than being replaced abruptly. Substantial vegetation decline is accompanied by notable increases in built-up land, with strong spatial differences between communes depending on development pressure. The integration of RF classification with spectral index analysis allows these transitions to be validated and interpreted more reliably, helping distinguish structural suburbanisation from short-term spectral variability. Overall, the study demonstrates the value of combining NDVI, NDBI and AI-supported land-cover classification to capture nuanced peri-urban transformation dynamics and provides actionable insights for spatial planning and sustainable land management in rapidly growing metropolitan regions.