Roughness Spectra Derived from Multi-Scale LiDAR Point Clouds of a Gravel Surface: A Comparison and Sensitivity Analysis

Milenković, Milutin; Ressl, Camillo; Karel, Wilfried; Mandlburger, Gottfried; Pfeifer, Norbert

doi:10.3390/ijgi7020069

Cited by 7 publications

(7 citation statements)

References 37 publications

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“…Highly detailed and accurate digital elevation models (DEMs) can provide valuable insights into important topographic characteristics, such as terrain steepness and roughness [1]. Roughness in particular has proven to be an integral characteristic when discriminating geomorphological features, such as landslides [2].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Evaluation of Uncrewed Aircraft Systems’ Lidar Data Quality

Babbel

Olsen

Che

et al. 2019

IJGI

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“…The difference in the leaf inclination angle distribution between different parts within a tree was observed, and a detailed tree structural analysis was conducted. We found that this method enables accurate and efficient leaf inclination angle distribution.A 3D scanner called lidar (light detection and ranging) provides highly accurate and dense 3D point measurements [15,16]. The lidar is very useful for the retrieval of plant structural parameters.…”

mentioning

confidence: 99%

“…A 3D scanner called lidar (light detection and ranging) provides highly accurate and dense 3D point measurements [15,16]. The lidar is very useful for the retrieval of plant structural parameters.…”

mentioning

confidence: 99%

Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

Itakura

Hosoi

2019

Remote Sensing

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The leaf inclination angle is a fundamental variable for determining the plant profile. In this study, the leaf inclination angle was estimated automatically from voxel-based three-dimensional (3D) images obtained from lidar (light detection and ranging). The distribution of the leaf inclination angle within a tree was then calculated. The 3D images were first converted into voxel coordinates. Then, a plane was fitted to some voxels surrounding the point (voxel) of interest. The inclination angle and azimuth angle were obtained from the normal. The measured leaf inclination angle and its actual value were correlated and indicated a high correlation (R 2 = 0.95). The absolute error of the leaf inclination angle estimation was 2.5 • . Furthermore, the leaf inclination angle can be estimated even when the distance between the lidar and leaves is about 20 m. This suggests that the inclination angle estimation of leaves in a top part is reliable. Then, the leaf inclination angle distribution within a tree was calculated. The difference in the leaf inclination angle distribution between different parts within a tree was observed, and a detailed tree structural analysis was conducted. We found that this method enables accurate and efficient leaf inclination angle distribution.A 3D scanner called lidar (light detection and ranging) provides highly accurate and dense 3D point measurements [15,16]. The lidar is very useful for the retrieval of plant structural parameters. Trunk diameter and plant height can be estimated accurately [17][18][19]. Leaf area and parameters related to leaf area can be calculated from the 3D images of plants obtained by the lidar [20][21][22][23][24]. Other than the parameters above, tree volume and species can be estimated [25][26][27]. The detailed and accurate information from lidar provides a useful tool, for example, in the field of plant phenotyping [28]. Guo et al (2018) developed a high-throughput crop phenotyping platform with lidar that can retrieve various crop structural and physiological relevant parameters efficiently [29]. For more detailed information of the lidar application for plant structural parameters, please refer to the comprehensive reviews in [30,31].Some previous studies on leaf inclination angle estimation with 3D images are available [32][33][34][35][36][37]. In these studies, the points that composed each leaf were fitted to a plane by a least-squares method, and normals to the planes were calculated. Although this method is quite effective for leaf inclination angle estimation, each leaf should be selected manually in the 3D images one by one. Thus, exploring the distribution of the leaf inclination angle is very tedious and time-consuming. Further, owing to the manual operation, the number of leaves that can be examined is limited.To overcome this problem, a previous study [38] converted 3D point cloud images obtained with structure from a motion method into a voxel-based 3D image. In voxel-based 3D models, a geographical space is systematically decomposed into a ...

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“…Just as a flashlight makes a larger circle on a wall from a greater distance, laser scans produce larger footprints when the sensor is farther away from a target reflective surface. The combination of footprint size, distance from the target, and target reflectivity fundamentally constrains the spatial resolving capacity of any laser scanner (Lichti and Jamtsho 2006; Milenković et al 2018). Commercial TLS instruments produce footprint sizes in the 2–5 cm range at 100 m (Disney et al 2018), allowing them to resolve small stems and branches.…”

Section: Technical Considerations For Drone Remote Sensingmentioning

confidence: 99%

New Opportunities for Forest Remote Sensing Through Ultra-High-Density Drone Lidar

Kellner

Armston

Birrer³

et al. 2019

Surv Geophys

111

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Current and planned space missions will produce aboveground biomass density data products at varying spatial resolution. Calibration and validation of these data products is critically dependent on the existence of field estimates of aboveground biomass and coincident remote sensing data from airborne or terrestrial lidar. There are few places that meet these requirements, and they are mostly in the northern hemisphere and temperate zone. Here we summarize the potential for low-altitude drones to produce new observations in support of mission science. We describe technical requirements for producing high-quality measurements from autonomous platforms and highlight differences among commercially available laser scanners and drone aircraft. We then describe a case study using a heavy-lift autonomous helicopter in a temperate mountain forest in the southern Czech Republic in support of calibration and validation activities for the NASA Global Ecosystem Dynamics Investigation. Low-altitude flight using drones enables the collection of ultra-high-density point clouds using wider laser scan angles than have been possible from traditional airborne platforms. These measurements can be precise and accurate and can achieve measurement densities of thousands of points • m −2. Analysis of surface elevation measurements on a heterogeneous target observed 51 days apart indicates that the realized range accuracy is 2.4 cm. The single-date precision is 2.1-4.5 cm. These estimates are net of all processing artifacts and geolocation errors under fully autonomous flight. The 3D model produced by these data can clearly resolve branch and stem structure that is comparable to terrestrial laser scans and can be acquired rapidly over large landscapes at a fraction of the cost of traditional airborne laser scanning.

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Roughness Spectra Derived from Multi-Scale LiDAR Point Clouds of a Gravel Surface: A Comparison and Sensitivity Analysis

Cited by 7 publications

References 37 publications

Evaluation of Uncrewed Aircraft Systems’ Lidar Data Quality

Evaluation of Uncrewed Aircraft Systems’ Lidar Data Quality

Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

New Opportunities for Forest Remote Sensing Through Ultra-High-Density Drone Lidar

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