Surveying a Landslide in a Road Embankment Using Unmanned Aerial Vehicle Photogrammetry

Carvajal, F.; Agüera, Francisco Orgaz; García, Manuel Pérez

doi:10.5194/isprsarchives-xxxviii-1-c22-201-2011

Cited by 42 publications

(27 citation statements)

References 6 publications

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“…In the last cases, the propagation errors are also taken into account showing values lower than 0.05 m. These errors are of the same order of magnitude than the image resolution, as it has been described in previous comparable studies [26,32,34,38,42,44,45] with similar properties (equipment, flight altitude and resolution).…”

Section: Accuracies Errors and Uncertaintiesmentioning

confidence: 50%

“…These studies can be carried out by means of error analysis of the GCPs (error propagation, distribution of points, field GNSS measurements, etc.) as well as the analysis of the resultant products such as DSM/DTMs or orthophotographs [26,28,31,32,34,41,42,45]. Generally, satisfactory results can be achieved, similar to those calculated with conventional aerial photogrammetry [29,44].…”

Section: Introductionmentioning

confidence: 56%

“…RMS errors also show values of the same order of magnitude of those calculated in this work at GNSS-based and transferred check points, and those found in the aforementioned studies with similar properties. In these studies, the RMS calculated in GNSS-based check points are in the range of 0.05-0.10 m [32,38,45] or even higher [34]. In a lower resolution study [31], a value of 0.50 m is found in the comparison of UAV and LiDAR DSMs, where DSM check points are used in the same way as in this study.…”

Section: Accuracies Errors and Uncertaintiesmentioning

confidence: 94%

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Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques

et al. 2016

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This paper deals with the application of Unmanned Aerial Vehicles (UAV) techniques and high resolution photogrammetry to study the evolution of a landslide affecting olive groves. The last decade has seen an extensive use of UAV, a technology in clear progression in many environmental applications like landslide research. The methodology starts with the execution of UAV flights to acquire very high resolution images, which are oriented and georeferenced by means of aerial triangulation, bundle block adjustment and Structure from Motion (SfM) techniques, using ground control points (GCPs) as well as points transferred between flights. After Digital Surface Models (DSMs) and orthophotographs were obtained, both differential models and displacements at DSM check points between campaigns were calculated. Vertical and horizontal displacements in the range of a few decimeters to several meters were respectively measured. Finally, as the landslide occurred in an olive grove which presents a regular pattern, a semi-automatic approach to identifying and determining horizontal displacements between olive tree centroids was also developed. In conclusion, the study shows that landslide monitoring can be carried out with the required accuracy-in the order of 0.10 to 0.15 m-by means of the combination of non-invasive techniques such as UAV, photogrammetry and geographic information system (GIS).

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Section: Accuracies Errors and Uncertaintiesmentioning

confidence: 50%

Section: Introductionmentioning

confidence: 56%

Section: Accuracies Errors and Uncertaintiesmentioning

confidence: 94%

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Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques

et al. 2016

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“…Landslide event mapping is a wellknown activity obtained through field surveys (Yoon et al, 2012;Santangelo et al, 2010), visual interpretation of aerial or satellite images (Brardinoni et al, 2003;Ardizzone et al, 2013) and combined analysis of lidar DTM and images (Van Den Eeckhaut et al, 2007;Haneberg et al, 2008;Giordan et al, 2013;Razak et al, 2013;Niculiţa, 2016). The use of RPASs for the identification and mapping of a landslide has been described by several authors (Niethammer et al, , 2010(Niethammer et al, , 2011Rau et al, 2011;Carvajal et al, 2011;Travelletti et al, 2012;Torrero et al, 2015;Casagli et al, 2017). Niethammer et al (2009) and Liu et al (2015) showed how RPASs could be considered a good solution for the acquisition of ultra-high-resolution images with low-cost systems.…”

Section: Landslide Recognitionmentioning

confidence: 99%

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Giordan

Hayakawa

Nex

et al. 2018

Nat. Hazards Earth Syst. Sci.

145

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Abstract. The number of scientific studies that consider possible applications of remotely piloted aircraft systems (RPASs) for the management of natural hazards effects and the identification of occurred damages strongly increased in the last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of the literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPASs can acquire on-demand ultra-high-resolution images that can be used for the identification of active processes such as landslides or volcanic activities but can also define the effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologies developed for the study and monitoring of natural hazards. IntroductionIn the last three decades, the number of natural disasters showed a positive trend with an increase in the number of affected populations. Disasters not only affected the poor and characteristically more vulnerable countries but also those thought to be better protected. The Annual Disaster Statistical Review describes recent impacts of natural disasters on the population and reports 342 naturally triggered disasters in 2016 (Guha-Sapir et al., 2017). This is less than the annual average disaster frequency observed from 2006 to 2015 (376.4 events). However, natural disasters are still responsible for a high number of casualties (8733 death). In the period 2006-2015, the average number of causalities caused annually by natural disasters is 69 827. In 2016, hydrological disasters (177) had the largest share in natural disaster occurrence (51.8 %), followed by meteorological disasters (96; 28.1 %), climatological disasters (38; 11.1 %) and geophysical disasters (31; 9.1 %) (Guha-Sapir et al., 2017). To face these disasters, one of the most important solutions is the use of systems able to provide an adequate level of information for correctly understanding these events and their evolution. In this context, surveying and monitoring natural hazards gained importance. In particular, during the emergency phase it is very important to evaluate and control the phenomenon of evolution, preferably operating in near real time or real time, and consequently, use this information for a better risk assessment scenario. The available acquired data must be processed rapidly to support the emergency services and decision makers.Recently, the use of remote sensing (satellite and airborne platform) in the field of natural hazards and disasters has become common, also supported by the increase in geospatial technologies and the ability to provide and process up-to-date imagery (Joyce et al., 2009;Tarolli, 2014). Remotely sensed data play an integral role in predicting hazard events such as floods and landslides, subsidence events and other ground instabilities. Because of their acquisition mode and capabilPublished by Copern...

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“…Current applications of UAS in geomatics can be categorized briefly as follows: agricultural and environmental applications, surveillance, aerial monitoring in engineering, cultural heritage, surveying, and cadastral applications [3]. Particularly, in ground monitoring as a subfield of aerial monitoring in engineering, UAS-based photogrammetry has begun to play an important role in landslide monitoring and detection [4,5].…”

Section: Introductionmentioning

confidence: 99%

Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

Akçay

2015

IJGI

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Unmanned Aerial Systems (UAS) are now capable of gathering high-resolution data, therefore, landslides can be explored in detail at larger scales. In this research, 132 aerial photographs were captured, and 85,456 features were detected and matched automatically using UAS photogrammetry. The root mean square (RMS) values of the image coordinates of the Ground Control Points (GPCs) varied from 0.521 to 2.293 pixels, whereas maximum RMS values of automatically matched features was calculated as 2.921 pixels. Using the 3D point cloud, which was acquired by aerial photogrammetry, the raster datasets of the aspect, slope, and maximally stable extremal regions (MSER) detecting visual uniformity, were defined as three variables, in order to reason fissure structures on the landslide surface. In this research, an Adaptive Neuro Fuzzy Inference System (ANFIS) and a Logistic Regression (LR) were implemented using training datasets to infer fissure data appropriately. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC) curves and by calculating the area under the ROC curve (AUC). The experiments exposed that high-resolution imagery is an indispensable data source to model and validate landslide fissures appropriately.

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Surveying a Landslide in a Road Embankment Using Unmanned Aerial Vehicle Photogrammetry

Cited by 42 publications

References 6 publications

Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques

Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

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