Using UAS-Aided Photogrammetry to Monitor and Quantify the Geomorphic Effects of Extreme Weather Events in Tectonically Active Mass Waste-Prone Areas: The Case of Medicane Ianos

Abstract

Extreme weather events can trigger various hydrogeomorphic phenomena and processes including slope failures. These shallow instabilities are difficult to monitor and measure due to the spatial and temporal scales in which they occur. New technologies such as unmanned aerial systems (UAS), photogrammetry and the structure-from-motion (SfM) technique have recently demonstrated capabilities useful in performing accurate terrain observations that have the potential to provide insights into these geomorphic processes. This study explores the use of UAS-aided photogrammetry and change detection, using specialized techniques such as the digital elevation model (DEM) of differences (DoD) and cloud-to-cloud distance (C2C) to monitor and quantify geomorphic changes before and after an extreme medicane event in Myrtos, a highly visited touristic site on Cephalonia Island, Greece. The application demonstrates that the combination of UAS with photogrammetry allows accurate delineation of instabilities, volumetric estimates of morphometric changes, insights into erosion and deposition processes and the delineation of higher-risk areas in a rapid, safe and practical way. Overall, the study illustrates that the combination of tools facilitates continuous monitoring and provides key insights into geomorphic processes that are otherwise difficult to observe. Through this deeper understanding, this approach can be a stepping stone to risk management of this type of highly-visited sites, which in turn is a key ingredient to sustainable development in high-risk areas.

Drone application for landslide hazard assessment on the Western Flank of Lawu Volcano, Central Java, Indonesia

“…At the research site, the type of landslide is creep, which is a slow earth flow composed of very slow and steady downward movement of soil or rock forming a slope [3]. According to Nuryanto's research, Guyon Hamlet, Tengklik Village is one of the villages in Tawangmangu District that has the potential to encounter creep type landslides, which cause the ground to continue to shift, especially during the rainy season [14].…”

“…The termites migrate in a southerly direction, with a crack width of 5-20 cm and a length of 85 meters. Previously, a landslide in early 2007 caused the soil to drop by 20 cm, reaching 260 cm in August 2009 [14], As a result, houses were damaged and roads are cracked. The growth of ground movements at Tengklik Village in February 2023 increased the number of buildings impacted, with 4 houses slightly damaged, 1 mosque significantly damaged, and the connecting road between villages damaged along 85 meters.…”

“…The use of unmanned aerial vehicles (UAVs) for landslide research has several advantages over traditional monitoring approaches, including the ability to capture high-resolution images, access difficult-to-reach regions, and collect data more often and at a cheaper cost [13] [14]. Based on the advantages of UAVs, this research aims to analyze landslide activity in Nglelaka Hamlet, Segorogunung Village, Ngargoyoso District on February 15 2023 and in Guyon Hamlet, Tengklik Village, Tawangmangu District on February 2023, using UAVs, because morphologically these two areas is in a hilly area with a steep slope [6] [15], so using UAVs to monitor landslide activity in these two locations is the best option.…”

Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques

“…Similarly, there is increasing interest in the use of Unmanned Aerial Vehicles (UAV) for landslide studies [10], as UAVs offer several advantages over traditional monitoring methods, including the ability to collect high-resolution imagery, to access difficult-toreach areas, and to collect data frequently and at a lower cost [11,12]. Therefore, it is natural for scholars to adopt and further exploit this technology also for landslide timeseries monitoring [13,14], mapping and characterization [15][16][17] as well as combining their outputs with machine learning [18] and computer vision algorithms [19,20].…”

“…For the estimation of landslide surface changes through UAV flyovers, scholars have traditionally used straightforward techniques, such as measuring the difference between Digital Surface Model (DSM) elevation products from consecutive surveys (see [12,16,21,22]) or the direct difference between point clouds (e.g., [11]), which usually results in the quantification of volumetric changes. Such approaches are usually powerful when the landslide under investigation exhibits significant and abrupt changes, however, for some smaller changes and displacements, they can be less efficient, as most of the time alignment error between two products is introduced [23,24], which makes their estimation more difficult.…”