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.

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<p>The paper focuses on presenting a methodology that can be used to rapidly assess and map kinematics of landslides when these occur in areas with dense vegetation cover. The method is based on using aerial imagery collected with UAV (Unmanned Aerial Vehicle) and their derived products obtained by applying the Structure from Motion (SfM) technique. The landslide occurred on May 3, 2021, and is located in the Livadea village, Curvature Subcarpathians (Romania). It affected several houses from the vicinity, and the people were relocated because of the high probability of landslide reactivation. To mitigate the consequences of this landslide, a preliminary investigation, based on three UAV surveys and field geological-geomorphological surveys, was carried out to delineate active parts of the landslide and to define evacuation measures. Three UAV flights (May 6, May 25 and July 10) were performed using DJI Phantom 4 and Phantom 4 RTK drones. Because it is a heavily forested area, a semi-automated processing of the landslide kinematics and change detection analysis were not possible. The landslide displacement rates and the changes in terrain morphology between flights were assessed by manual interpolating of collected landmarks on all three UAV flights. Tilted trees were used to estimate the landslide direction and evolution. The results show an average displacement of 9.55 m (minimum 1.2 m, maximum 20.6 m) between the first and the second flight and an average of 19.27 m (minimum 1.98 m and maximum 46.3 m) between the second and the third flight, respectively. This approach proved quick and efficient for rapid landslide investigations when fast response and measures are necessary to reduce landslide consequences.</p><p>Acknowledgement</p><p>This work was supported by a grant of the Romanian Ministry of Education and Research, CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED-2019-5152, within PNCDI III (project coordinator Ionuț Șandric, https://slidemap.gmrsg.ro) and by the project PN19450103 / Core Program (project coordinator Viorel Ilinca).</p>

<div> <p><span>The current study is focused on assessing the spatial and temporal patterns of landslide volume displacements using a semiautomated method and </span><span>Unmanned Aerial Vehicle</span><span> </span><span>(</span><span>UAV</span><span>) </span><span>aerial imagery. The case study is located in the Livadea village from Curvature Subcarpathians, Romania, where a landslide was triggered on May 3, 2021. Three separate flights were flown on May 6, May 25, and July 10 using DJI Phantom 4 and Phantom 4 RTK drones. Even though there is a difference in camera resolution, each flight plan was created to correspond to a 4cm/pixel spatial resolution, meaning that the constant height above ground was different between the first flight and the next two flights. For the first flight, because the UAV equipped with the RTK receiver was not available, a graded consumer UAV equipped with a Non-RTK receiver was used. A maximum overlap with the smallest errors possible between all the flights was obtained by orthorectifying the first and the third flights with GCPs collected from the second flight. </span><span>The method is based on using aerial imagery collected with UAV and their derived products obtained by applying the Structure from Motion (SfM) technique. Because it is an area with dense forest, the Visible Atmospherically Resistant Index (VARI) was used to filter out all the pixels with vegetation from the digital surface models (DSM). The gaps were filled by using the Empirical Bayesian Kriging interpolation method, implemented in ArcGIS Pro. The results show volume displacement rates of 0.005 cubic meters/meter for the period between the first and second flights and 0.05 cubic meters/meter for the period between the second and third flights. The overall displaced volume was approximately 406000 cubic meters with approximately 41000 cubic meters for the period between the first and second flights and approximately 365000 cubic meters between the second and the third flight. This approach proved quick and efficient for assessing landslide volume displacement when fast response and measures are necessary to reduce landslide consequences.</span><span> </span></p> </div><div> <p><strong><span>Acknowledgement</span></strong><span> </span></p> </div><div> <p><span>This work was supported by a grant of the Romanian Ministry of Education and Research, CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED-2019-5152, within PNCDI III (project coordinator Ionuț Șandric, </span><span>https://slidemap.gmrsg.ro</span><span>) and by the project PN19450103 / Core Program (project coordinator Viorel Ilinca).</span><span> </span></p> </div>