Mapping landslide cracks is essential in understanding landslide dynamics and evolution across space and time. Each landslide crack’s depth, direction, and width are related to the stress and strain imposed on the landslide body. Moreover, their spatial distribution can indicate areas where the landslide can extend, mainly if located in the upper part of the main landslide scarp. Even though the cracks leave a distinct pattern on the landslide body when fresh or when there is a high contrast between the bare soil and surrounding vegetation, these patterns gradually diminish over time, making their detection difficult. The problem of landslide cracks mapping in various environmental conditions and having different ages was tackled in the current study using very high spatial resolution unmanned aerial vehicle (UAV) aerial imagery and derived products in conjunction with deep learning models. U-Net and DeepLab CNN models were applied using masked and non-masked training samples with different tile sizes. As the tile size decreases, the performance metrics, such as precision, recall, and F1-score, generally decrease. Overall, the lowest accuracy was approximately 0.79 for non-mask samples and tile size of 64 pixels, and reached over 0.93 for masked samples and tile size of 512 pixels.

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.

Detection and classification of crown trees properties and assessment of their health status have raised much interest for the scientists from the forest and environmental sciences due to their essential role in landscape ecology and forestry management. The present study proposes a method based on consumer-based UAVs and Deep Learning techniques for detecting individual orchard trees and assessing key properties characterising their health status. In the proposed scheme, the Mask R-CNN model is used for detecting and mapping each individual tree morphometrical property such as the height and the crown width. Tree's health assessment across is based on the use of vegetation indices such as the Visual Atmospheric Resistance Index (VARI) and Green Leaf Index (GLI), computed from the visible spectrum camera mounted on the UAV platform. The use of the proposed approach is demonstrated at five different orchard tree species, namely plum, apricot, walnut, olive, and almond, located in Romania and Greece, computing a series of statistical metrics. Results returned outstanding ability of the algorithm's performance to map the individual trees and assess their health for four out of the five tree species (plum, walnut, apricot, almond) and satisfactory results for the fifth (olive trees). Overall, the study findings highlighted the promising potential of the proposed methodological framework and its scalable potential for wider applicability as a low-cost, effective solution in mapping individual trees properties and health conditions in the field.

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.