Unmanned Aerial Vehicle Laser Scanning for Erosion Monitoring in Alpine Grassland

Mayr, Andreas; Bremer, Magnus; Rutzinger, Martin; Geitner, Clemens

doi:10.5194/isprs-annals-iv-2-w5-405-2019

Cited by 13 publications

(15 citation statements)

References 30 publications

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“…One of the earliest examples comes from Wallace et al [5], who developed a low-cost ULS system on an OktoKopter platform for forest management applications. Additional examples include canopy height modeling (CHM) and diameter at breast height (DBH) [6], and erosion monitoring on Alpine slopes [7]. For instance, Brede et al [6] compared canopy height models derived from ULS-based lidar point clouds to TLS-based lidar point clouds.…”

Section: Uls Applicationsmentioning

confidence: 99%

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Evaluation of Uncrewed Aircraft Systems’ Lidar Data Quality

Babbel

Olsen

Che

et al. 2019

IJGI

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Uncrewed aircraft systems (UASs) with integrated light detection and ranging (lidar) technology are becoming an increasingly popular and efficient remote sensing method for mapping. Due to its quick deployment and comparatively inexpensive cost, uncrewed laser scanning (ULS) can be a desirable solution to conduct topographic surveys for areas sized on the order of square kilometers compared to the more prevalent and mature method of airborne laser scanning (ALS) used to map larger areas. This paper rigorously assesses the accuracy and quality of a ULS system with comparisons to terrestrial laser scanning (TLS) data, total station (TS) measurements, and Global Navigation Satellite System (GNSS) check points. Both the TLS and TS technologies are ideal for this assessment due to their high accuracy and precision. Data for this analysis were collected over a period of two days to map a landslide complex in Mulino, Oregon. Results show that the digital elevation model (DEM) produced from the ULS had overall vertical accuracies of approximately 6 and 13 cm at 95% confidence when compared to the TS cross-sections for the road surface only and road and vegetated surfaces, respectively. When compared to the TLS data, overall biases of −2.4, 1.1, and −2.7 cm were observed in X, Y, and Z with a 3D RMS difference of 8.8 cm. Additional qualitative and quantitative assessments discussed in this paper show that ULS can provide highly accurate topographic data, which can be used for a wide variety of applications. However, further research could improve the overall accuracy and efficiency of the cloud-to-cloud swath adjustment and calibration processes for georeferencing the ULS point cloud.

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Section: Uls Applicationsmentioning

confidence: 99%

“…A somewhat similar study was performed by Salach et al [9], who compared ULS to both UAS SfM and ALS. The work by Mayr et al [7] provides an example of the capabilities of ULS to successfully monitor erosion through the use of multi-temporal datasets. Part of their work also included performing a comparative analysis of ULS and TLS.…”

Section: Uls Applicationsmentioning

confidence: 99%

Evaluation of Uncrewed Aircraft Systems’ Lidar Data Quality

Babbel

Olsen

Che

et al. 2019

IJGI

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“…Shallow eroded areas are monitored by laser scanning for morphological characterisation and enhanced insights to process dynamics (Mayr et al, 2019a), for assessing the spatial distribution of affected areas (Đomlija et al, 2019), for analysing their spatio-temporal occurrence (Zieher et al, 2016), and to estimate volumetric process magnitudes (Mayr et al, 2019b). Furthermore, laser scanning data and derivatives are inputs to erosion models (Schindewolf et al, 2016;Zieher et al, 2017).…”

Section: Erosion Studies Using Laser Scanningmentioning

confidence: 99%

“…low internal distortions; see e.g. Mayr et al 2019b). In many cases, a reasonable co-registration of multitemporal point clouds can be achieved via stable surface patches, which are either userdefined or automatically detected (e.g.…”

Section: Unmanned Aerial Vehicle Laser Scanningmentioning

confidence: 99%

3d Point Errors and Change Detection Accuracy of Unmanned Aerial Vehicle Laser Scanning Data

Mayr

Bremer

Rutzinger

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Unmanned aerial vehicle laser scanning (ULS) has recently become available for operational mapping and monitoring (e.g. for forestry applications or erosion studies). It combines advantages of terrestrial and airborne laser scanning, but there is still little proof of ULS accuracy. For the detection and monitoring of small-magnitude surfaces changes with multitemporal point clouds, an estimate of the level of detection (LOD) is required. The LOD is a threshold applied on distance measurements to separate real surface change (e.g. due to erosion or deposition by geomorphic processes) from errors. This paper investigates key components of the error budget for two ULS point clouds acquired for erosion monitoring at a grassland site in the Alps. In addition to the registration error and effects of the local surface roughness, we assess the positional uncertainties of each point that result from laser footprint effects, which are a function of the scanning geometry (including range, incidence angle and beam divergence). By removing erroneous points with an increasingly stricter point error criterion, we illustrate that the positional point errors strongly affect the LOD and discuss how this type of error can be mitigated. Moreover, our experimental results with three different surface classes (bare earth and rock, buildings and grassland) show that the level of detection tends to be slightly better for areas with bare earth and rock than for grass-covered areas (due to their roughness). For all these surface types reliable distance measurements are possible with sub-decimetre levels of detection.

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“…Surveillance of larger areas or all potential risk areas in the Alps is more or less impossible in that way. Using cameras allows handing over the job of data acquisition to non-professionals (Urban et al, 2019;Romeo et al, 2019;Mayr et al, 2019;Peppa et al, 2019;Kromer et al, 2019;Esposito et al, 2017;Hendrickx et al, 2019;Warrick et al, 2016;Piermattei et al, 2016;Eltner et al, 2016;Stumpf et al, 2015;Kääb et al, 2014;Westoby et al, 2012). This contribution focuses on the assessment of possible accuracies in 3D reconstruction and change detection based on photogrammetric image sets.…”

Section: Introductionmentioning

confidence: 99%

Change Detection in Photogrammetric Point Clouds for Monitoring of Alpine, Gravitational Mass Movements

Dinkel

Hoegner

Emmert

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This contribution discusses the accuracy and the applicability of Photogrammetric point clouds based on dense image matching for the monitoring of gravitational mass movements caused by crevices. Four terrestrial image sequences for three different time epochs have been recorded and oriented using ground control point in a local reference frame. For the first epoch, two sequences are recorded, one in the morning and one in the afternoon to evaluate the noise level within the point clouds for a static geometry and changing light conditions. The second epoch is recorded a few months after the first epoch where also no significant change has occurred in between. The third epoch is recorded after one year with changes detected. As all point clouds are given in the same local coordinate frame and thus are co-registered via the ground control points, change detection is based on calculating the Multiscale-Model-to-Model-Cloud distances (M3C2) of the point clouds. Results show no movements for the first year, but identify significant movements comparing the third epoch taken in the second year. Besides the noise level estimation, the quality checks include the accuracy of the camera orientations based on ground control points, the covariances of the bundle adjustment, and a comparison the Geodetic measurements of additional control points and a laser scanning point cloud of a part of the crevice. Additionally, geological measurements of the movements have been performed using extensometers.

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Unmanned Aerial Vehicle Laser Scanning for Erosion Monitoring in Alpine Grassland

Cited by 13 publications

References 30 publications

Evaluation of Uncrewed Aircraft Systems’ Lidar Data Quality

Evaluation of Uncrewed Aircraft Systems’ Lidar Data Quality

3d Point Errors and Change Detection Accuracy of Unmanned Aerial Vehicle Laser Scanning Data

Change Detection in Photogrammetric Point Clouds for Monitoring of Alpine, Gravitational Mass Movements

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