Remotely Piloted Aircraft (RPA) is presently in continuous development at a rapid pace. Unmanned Aerial Vehicles (UAVs) or more extensively Unmanned Aerial Systems (UAS) are platforms considered under the RPAs paradigm. Simultaneously, the development of sensors and instruments to be installed onboard such platforms is growing exponentially. These two factors together have led to the increasing use of these platforms and sensors for remote sensing applications with new potential. Thus, the overall goal of this paper is to provide a panoramic overview about the current status of remote sensing applications based on unmanned aerial platforms equipped with a set of specific sensors and instruments. First, some examples of typical platforms used in remote sensing are provided. Second, a description of sensors and technologies is explored which are onboard instruments specifically intended to capture data for remote sensing applications. Third, multi-UAVs in collaboration, coordination, and cooperation in remote sensing are considered. Finally, a collection of applications in several areas are proposed, where the combination of unmanned platforms and sensors, together with methods, algorithms, and procedures provide the overview in very different remote sensing applications. This paper presents an overview of different areas, each independent from the others, so that the reader does not need to read the full paper when a specific application is of interest

Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs)

Numerical methods for Engineers and Scientists

This article presents an environmental remote sensing application using a UAV that is specifically aimed at reducing the data gap between field scale and satellite scale in soil erosion monitoring in Morocco. A fixed-wing aircraft type Sirius I (MAVinci, Germany) equipped with a digital system camera (Panasonic) is employed. UAV surveys are conducted over different study sites with varying extents and flying heights in order to provide both very high resolution site-specific data and lower-resolution overviews, thus fully exploiting the large potential of the chosen UAV for multi-scale mapping purposes. Depending on the scale and area coverage, two different approaches for georeferencing are used, based on high-precision GCPs or the UAV’s log file with exterior orientation values respectively. The photogrammetric image processing enables the creation of Digital Terrain Models (DTMs) and ortho-image mosaics with very high resolution on a sub-decimetre level. The created data products were used for quantifying gully and badland erosion in 2D and 3D as well as for the analysis of the surrounding areas and landscape development for larger extents.

Unmanned Aerial Vehicle (UAV) for monitoring soil erosion in Morocco

Micro-unmanned aerial vehicles often collect a large amount of images when mapping an area at an ultrahigh resolution. A direct georeferencing technique potentially eliminates the need for ground control points. In this paper, we developed a camera-global positioning system (GPS) module to allow the synchronization of camera exposure with the airframe's position as recorded by a GPS with 10-20-cm accuracy. Lever arm corrections were applied to the camera positions to account for the positional difference between the GPS antenna and the camera center. Image selection algorithms were implemented to eliminate blurry images and images with excessive overlap. This study compared three different software methods (Photoscan, Pix4D web service, and an in-house Bundler method). We evaluated each based on processing time, ease of use, and the spatial accuracy of the final mosaic produced. Photoscan showed the best performance as it was the fastest and the easiest to use and had the best spatial accuracy (average error of 0.11 m with a standard deviation of 0.02 m). This accuracy is limited by the accuracy of the differential GPS unit (10-20 cm) used to record camera position. Pix4D achieved a mean spatial error of 0.24 m with a standard deviation of 0.03 m, while the Bundler method had the worst mean spatial accuracy of 0.76 m with a standard deviation of 0.15 m. The lower performance of the Bundler method was due to its poor performance in estimating camera focal length, which, in turn, introduced large errors in the Z-axis for the translation equations.

Direct Georeferencing of Ultrahigh-Resolution UAV Imagery

Structure-from-motion (SfM) photogrammetry is revolutionising the collection of detailed topographic data, but insight into geomorphological processes is currently restricted by our limited understanding of SfM survey uncertainties. Here, we present an approach that, for the first time, specifically accounts for the spatially variable precision inherent to photo-based surveys, and enables confidence-bounded quantification of 3-D topographic change. The method uses novel 3-D precision maps that describe the 3-D photogrammetric and georeferencing uncertainty, and determines change through an adapted state-of-the-art fully 3-D point-cloud comparison (M3C2; Lague, et al., 2013), which is particularly valuable for complex topography. We introduce this method by: (1) using simulated UAV surveys, processed in photogrammetric software, to illustrate the spatial variability of precision and the relative influences of photogrammetric (e.g. image network geometry, tie point quality) and georeferencing (e.g. control measurement) considerations; (2) we then present a new Monte Carlo procedure for deriving this information using standard SfM software and integrate it into confidence-bounded change detection; before (3) demonstrating geomorphological application in which we use benchmark TLS data for validation and then estimate sediment budgets through differencing annual SfM surveys of an eroding badland. We show how 3-D precision maps enable more probable erosion patterns to be identified than existing analyses, and how a similar overall survey precision could have been achieved with direct survey georeferencing for camera position data with precision half as good as the GCPs’. Where precision is limited by weak georeferencing (e.g. camera positions with multi-metre precision, such as from a consumer UAV), then overall survey precision can scale as n-½ of the control precision (n = number of images). Our method also provides variance-covariance information for all parameters. Thus, we now open the door for SfM practitioners to use the comprehensive analyses that have underpinned rigorous photogrammetric approaches over the last half-century.

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3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry: precision maps for ground control and directly georeferenced surveys

This study analyses the factors controlling variations in short-term, seasonal and multiyear deformation velocity of an alpine rock glacier from data obtained over periods of 1–20 years The Ritigraben rock glacier, in the western Swiss Alps, was monitored using tacheometry, terrestrial laser scanning, an in situ global positioning system and borehole deformation measurements Rock glacier stratigraphy and ground temperature data were obtained from boreholes, and long-term meteorological data (temperature, precipitation, snow water equivalent) from nearby weather stations Shearing within a distinct water-bearing layer represents the major component of the displacement Short-term accelerations and seasonal velocity patterns of the rock glacier deformation appear to have been triggered by water supply to this layer A long-term acceleration of the rock glacier was probably also caused by increased water supply Permafrost temperature in the rock glacier has increased slightly since 2002, yet no direct causality could be established between this limited warming and rock glacier acceleration Copyright © 2017 John Wiley & Sons, Ltd

Factors Controlling Velocity Variations at Short‐Term, Seasonal and Multiyear Time Scales, Ritigraben Rock Glacier, Western Swiss Alps

In sport science, Global Navigation Satellite Systems (GNSS) are frequently applied to capture athletes' position, velocity and acceleration Application of GNSS includes a large range of different GNSS technologies and methods To date no study has comprehensively compared the different GNSS methods applied Therefore, the aim of the current study was to investigate the effect of differential and non-differential solutions, different satellite systems and different GNSS signal frequencies on position accuracy Twelve alpine ski racers were equipped with high-end GNSS devices while performing runs on a giant slalom course The skiers' GNSS antenna positions were calculated in three satellite signal obstruction conditions using five different GNSS methods The GNSS antenna positions were compared to a video-based photogrammetric reference system over one turn and against the most valid GNSS method over the entire run Furthermore, the time for acquisitioning differential GNSS solutions was assessed for four differential methods The only GNSS method that consistently yielded sub-decimetre position accuracy in typical alpine skiing conditions was a differential method using American (GPS) and Russian (GLONASS) satellite systems and the satellite signal frequencies L1 and L2 Under conditions of minimal satellite signal obstruction, valid results were also achieved when either the satellite system GLONASS or the frequency L2 was dropped from the best configuration All other methods failed to fulfill the accuracy requirements needed to detect relevant differences in the kinematics of alpine skiers, even in conditions favorable for GNSS measurements The methods with good positioning accuracy had also the shortest times to compute differential solutions This paper highlights the importance to choose appropriate methods to meet the accuracy requirements for sport applications

https://www.mdpi.com/1424-8220/14/10/18433/pdf

The Effect of Different Global Navigation Satellite System Methods on Positioning Accuracy in Elite Alpine Skiing

In the sport of alpine skiing, knowledge about the centre of mass (CoM) kinematics (i.e. position, velocity and acceleration) is essential to better understand both performance and injury. This study proposes a global navigation satellite system (GNSS)-based method to measure CoM kinematics without restriction of capture volume and with reasonable set-up and processing requirements. It combines the GNSS antenna position, terrain data and the accelerations acting on the skier in order to approximate the CoM location, velocity and acceleration. The validity of the method was assessed against a reference system (video-based 3D kinematics) over 12 turn cycles on a giant slalom skiing course. The mean (± s) position, velocity and acceleration differences between the CoM obtained from the GNSS and the reference system were 9 ± 12 cm, 0.08 ± 0.19 m · s-1 and 0.22 ± 1.28 m · s-2, respectively. The velocity and acceleration differences obtained were smaller than typical differences between the measures of ...

Determination of the centre of mass kinematics in alpine skiing using differential global navigation satellite systems

UAV systems have become an attractive data acquisition platform in emerging applications. As measuring instrument they extend the lineup of possible surveying methods in the field of geomatics. However, most of UAVs are equipped with low-cost navigation sensors such as GPS or INS, allowing a positioning accuracy of 3 to 5 m. As a result the acquired position- and orientation data fea- tures a low accuracy which implicates that it cannot be used in applications that require high precision data on cm-level (e.g. direct georeferencing). In this paper we will analyze the potential of differential post-processing of GPS data from UAV in order to im- prove the positioning accuracy for applications basing on direct georeferencing. Subsequently, the obtained results are compared and verified with a track of the octocopter carried out with a total station simultaneously to the GPS data acquisition. The results show that the differential post-processing essentially improved the accuracy of the Falcon position data. Thereby the average offset be- tween the data sets (GPS data, track) and the corresponding standard deviation is 0.82 m and 0.45 m, respectively. However, under ideal conditions it is even possible to improve this positioning accuracy to the cm-range. Furthermore, there are still several sources of error such as the offset between the GPS antenna of the Falcon 8 and the prism which is used for the track. Considering this fact there is further room for improvement regarding the here discussed positioning method.

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Philippe Limpach

Papers

Factors Controlling Velocity Variations at Short‐Term, Seasonal and Multiyear Time Scales, Ritigraben Rock Glacier, Western Swiss Alps

The Effect of Different Global Navigation Satellite System Methods on Positioning Accuracy in Elite Alpine Skiing

Determination of the centre of mass kinematics in alpine skiing using differential global navigation satellite systems

Direct georeferencing of uavs

Kinematic investigations on the Furggwanghorn Rock Glacier, Switzerland