UAV monitoring and documentation of a large landslide

Lindner, Gerald; Schraml, Klaus; Mansberger, Reinfried; Hübl, Johannes

doi:10.1007/s12518-015-0165-0

Cited by 113 publications

(68 citation statements)

References 16 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: 71%

“…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: 76%

“…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: 86%

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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: 71%

Section: Accuracies Errors and Uncertaintiesmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 86%

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

et al. 2016

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“…This topic has been described by several authors (Dewitte et al, 2008;Turner and Lucieer, 2013;Travelletti et al, 2012;Lucieer et al 2014a;Turner et al, 2015;Marek et al, 2015;Lindner et al, 2016;Peppa et al, 2017). In these works, numerous techniques based on the multi-temporal comparison of RPAS data sets for the definition of the evolution of landslides have been presented and discussed.…”

Section: Landslide Monitoringmentioning

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.

132

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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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“…They have been developed in recent years and have played unparalleled roles in eco-environmental monitoring, environmental protection, urban construction and planning and have been used to provide accurate estimates of agricultural production and real-time data acquisition during natural disaster (e.g., snowstorms and landslides) monitoring periods [39][40][41]. At present, UAVs equipped with digital cameras are critical for acquiring high-resolution remote sensing data, which serve as beneficial supplements to ground (camera) and high-altitude (satellite) observational data [42,43].…”

Section: Introductionmentioning

confidence: 99%

Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau

et al. 2017

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Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression model (LR), a linear spectral mixture analysis model (LSMA) and a back-propagation artificial neural network model (BP-ANN) based on MODIS data (version 006) and unmanned aerial vehicle (UAV) data. The accuracies of the three models were validated against Landsat 8 Operational Land Imager (OLI) snow-cover maps (Landsat 8 FSC) and compared with the MODIS global FSC product (MOD10A1 FSC, version 005) for the purpose of finding the optimal algorithm for FSC extraction for the Tibetan Plateau. The results showed that (1) the overall retrieval results of the LR and BP-ANN models based on MODIS and UAV data were relatively similar to the OLI snow-cover maps; the accuracy and stability were greatly improved, with even some reduction in errors; compared to the Landsat 8 FSC, the correlation coefficients (r) were 0.8222 and 0.8445 respectively and the root-mean-square errors (RMSEs) were 0.2304 and 0.2201, respectively. (2) The accuracy and stability of the fully constrained LSMA model using the pixel purity index (PPI) endmember extraction method based only on MODIS data suffered the worst performance of the three models; r was only 0.7921 and the RMSE was as large as 0.3485. There were some serious omission phenomena in the study area, specifically for the largest mean absolute error (MAE = 0.2755) and positive mean error (PME = 0.3411). (3) The accuracy of the MOD10A1 FSC product was much lower than that of the LR and BP-ANN models, although its accuracy slightly better that of the LSMA based on comprehensive evaluation of six accuracy indices. (4) The optimal model was the BP-ANN model with combined inputs of surface reflectivity data (R1-R7), elevation (DEM) and temperature (LST), which can easily incorporate auxiliary information (DEM and LST) on the basis of (R1-R7) during the relationship training period and can effectively improve the accuracy of snow area monitoring-it is the ideal algorithm for retrieving FSC for the Tibetan Plateau.

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UAV monitoring and documentation of a large landslide

Cited by 113 publications

References 16 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

Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau

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