The paper presents a methodology to rapidly assess and map the landslide kinematics in areas with dense vegetation cover. The method uses aerial imagery collected with UAVs (Unmanned Aerial Vehicles) and their derived products obtained from the structure from motion technique. The landslide analysed in the current paper occurred in the spring of 2021 and is located in Livadea village from Curvature Subcarpathians, Romania. This landslide affected the houses in the vicinity, and people were relocated because of the risk of landslide reactivation. To mitigate the landslide consequences, a preliminary investigation based on UAV imagery and geological-geomorphological field surveys was carried out to map the active parts of the landslide and establish evacuation measures. Three UAV flights were performed between 6 May and 10 June using DJI Phantom 4 and Phantom 4 RTK UAVs (Real-Time Kinematic Unmanned Aerial Vehicles). Because it is a densely forested area, semi-automated analyses of the landslide kinematics and change detection analysis were not possible. Instead, the landslide displacement rates and the changes in terrain morphology were assessed by manually interpolating the landmarks, mostly tilted trees, collected from all three UAV flights. The results showed an average displacement of approximately 20 m across the landslides, with maximum values reaching 45 m in the transport area and minimum values below 1 m in the toe area. This approach proved quick and efficient for rapid landslide investigations in a densely forested area when fast response and measures are necessary to reduce the landslide consequences.

In recent decades, water scarcity has become a frequent and widespread phenomenon. Intensification of water scarcity will have economic impact on the main water-using sectors. The highest pressure on the water resources is exerted by agriculture. Irrigation is the largest consumer of the agriculture sector and the efficient use of water is of utmost importance. The aim of this study is to explore the capability of an innovative platform that combines Earth Observation data, weather forecasts and numerical simulations to plan more precisely water allocation in space and time in the irrigated agriculture. This platform, created in the framework of MOSES, provides in Romania short-term irrigation forecasts adapted to the hydrological behavior of alluvial soils specific to the Lower Danube Floodplain. The short-term irrigation forecasts have been tested with applied water volumes and in situ water resources measurements in order to assess the water allocation in the irrigated agriculture. Although irrigation forecast was run operationally only one crop year (2017–2018), in the framework of MOSES Project, the comparison revealed that the irrigation scheduling in this area is more influenced by the infrastructure characteristics and less by the spatial distribution of crop water needs and availability of water resources. Our results show that short-term irrigation forecasts accompanied by real time monitoring of water resources could be successfully used in the irrigation scheduling activity for improving water allocation in space and time in the irrigated agriculture.

This paper presents a methodology for evaluating the uncertainties caused by the misalignment between two digital elevation models in estimating landslide kinematics. The study focuses on the earthflow near the town of Chirlești, located in the Bend Subcarpathians, Buzău County, Romania, which poses a high risk of blocking the DN10 national road. Four flights were conducted between 2018 and 2022 using a DJI Phantom 4 UAV using the same flight plan. Monte Carlo simulations were used to model uncertainty propagation of the DEM misalignments in the landslide kinematics analysis. The simulations were applied to the accuracy values of the structure from a motion process used to generate the digital elevation models. The degree of uncertainty was assessed using the displaced material’s total amount in conjunction with the spatial correlation of the displaced material between two consecutive flights. The results revealed that the increase in the RMS values did not determine an increase in the displaced earth between two UAV flights. Instead, combining the RMS values and the correlation coefficient clearly indicated the correspondence between the spatial distribution of the displaced earth material and the overall changes reported between the two UAV flights. An RMS value of up to 1 unit associated with a correlation coefficient of 0.95 could be considered the maximum allowable error for estimating landslide kinematics across space and time. The current methodology is reliable when studying slow-movement landslides and when using short intervals between UAV flights. For rapid movements or significant terrain changes, such as translational and rotational landslides, careful analysis of the correlation coefficient in conjunction with the RMS values is recommended.