Drone Laser Scanning for Modeling Riverscape Topography and Vegetation: Comparison with Traditional Aerial Lidar

Resop, Jonathan P.; Lehmann, Laura; Hession, W. Cully

doi:10.3390/drones3020035

Cited by 60 publications

(97 citation statements)

References 34 publications

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“…A recent study [10] comparing ALS, TLS, and ULS concluded that ULS fills the gap between TLS and ALS by enabling both extensive coverage of a project area that is difficult to achieve with TLS as well as the acquisition of sufficient point density for describing small morphologies missed in ALS, which is vital for landslide monitoring applications. Additionally, Resop et al [4] also compared ULS to ALS in order to show the ability of ULS to capture important topographic features. In the USGS Lidar Base Specification (v1.3), for the highest quality level (QL0), the USGS suggests an aggregate nominal point spacing of ≤ 0.35 m, and an aggregate nominal point density of ≥ 8 points/m 2 [11].…”

Section: Uls Accuracy and Data Qualitymentioning

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: Uls Accuracy and Data Qualitymentioning

confidence: 99%

Evaluation of Uncrewed Aircraft Systems’ Lidar Data Quality

Babbel

Olsen

Che

et al. 2019

IJGI

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“…A combination of terrestrial and airborne LiDAR with high spatial resolution RS-RGB data are crucial RS technologies for monitoring and assessing stream bank conditions [168,174,241]. UAV-based laser scanning in combination with other sensor technologies have also been used increasingly more for monitoring and modeling riverscape morphometric and vegetation traits [242][243][244].…”

Section: Flood Hazardmentioning

confidence: 99%

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Lausch

Schaepman

Skidmore³

et al. 2020

Remote Sensing

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The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

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“…It is also noteworthy to recognise the increasing use of UAV-based LiDAR, which is rapidly becoming a practical alternative to UAV-based imagery in situations where penetration of water of plant canopy is desired in order to image underlying topography [99]. Although drone laser scanning technology (e.g., LiDAR) is currently more commonly employed by non-UAV remote sensing platforms (e.g., manned aircraft and satellites), increasing technological development of these sensors will improve accessibility of this technology as a surveying option and thus greatly improve its utility for UAV application into the future [99].…”

Section: Analysis and Reporting Of Restoration Monitoring Datamentioning

confidence: 99%

Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration

et al. 2019

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The last decade has seen an exponential increase in the application of unmanned aerial vehicles (UAVs) to ecological monitoring research, though with little standardisation or comparability in methodological approaches and research aims. We reviewed the international peer-reviewed literature in order to explore the potential limitations on the feasibility of UAV-use in the monitoring of ecological restoration, and examined how they might be mitigated to maximise the quality, reliability and comparability of UAV-generated data. We found little evidence of translational research applying UAV-based approaches to ecological restoration, with less than 7% of 2133 published UAV monitoring studies centred around ecological restoration. Of the 48 studies, > 65% had been published in the three years preceding this study. Where studies utilised UAVs for rehabilitation or restoration applications, there was a strong propensity for single-sensor monitoring using commercially available RPAs fitted with the modest-resolution RGB sensors available. There was a strong positive correlation between the use of complex and expensive sensors (e.g., LiDAR, thermal cameras, hyperspectral sensors) and the complexity of chosen image classification techniques (e.g., machine learning), suggesting that cost remains a primary constraint to the wide application of multiple or complex sensors in UAV-based research. We propose that if UAV-acquired data are to represent the future of ecological monitoring, research requires a) consistency in the proven application of different platforms and sensors to the monitoring of target landforms, organisms and ecosystems, underpinned by clearly articulated monitoring goals and outcomes; b) optimization of data analysis techniques and the manner in which data are reported, undertaken in cross-disciplinary partnership with fields such as bioinformatics and machine learning; and c) the development of sound, reasonable and multi-laterally homogenous regulatory and policy framework supporting the application of UAVs to the large-scale and potentially trans-disciplinary ecological applications of the future.

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Drone Laser Scanning for Modeling Riverscape Topography and Vegetation: Comparison with Traditional Aerial Lidar

Cited by 60 publications

References 34 publications

Evaluation of Uncrewed Aircraft Systems’ Lidar Data Quality

Evaluation of Uncrewed Aircraft Systems’ Lidar Data Quality

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration

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