The Forward Propagation of Integrated System Component Errors within Airborne Lidar Data

Goulden, Tristan; Hopkinson, Chris

doi:10.14358/pers.76.5.589

Cited by 37 publications

(25 citation statements)

References 19 publications

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“…These results suggest that both of the assessed solutions are suitable for use in forest inventory assessment. However, the SfM observations allows the accuracy to improve to a level comparable to that achieved by modern full-scale systems (based on the values reported in [47]). This improvement will allow for direct comparison and integration of the two datasets.…”

Section: Point Cloud Accuracymentioning

confidence: 96%

Development of a UAV-LiDAR System with Application to Forest Inventory

et al. 2012

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We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m 2 ) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points per m 2 , to 0.15 m when the higher density data was used.Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.Remote Sens. 2012, 4 1520

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Section: Point Cloud Accuracymentioning

confidence: 96%

Development of a UAV-LiDAR System with Application to Forest Inventory

et al. 2012

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“…Initial error estimates are based on the individual errors associated with the observations of the hardware sub-systems of the lidar sensor including the GNSS, IMU, laser scanner, laser ranger, and the interaction of the laser pulse with the terrain. The error model used to quantify error due to the sensor hardware is found by applying the general law of propagation of variances (GLOPOV, Wolf and Ghilani 1997) to the lidar direct georeferencing equation as described in Glennie (2007) and Goulden and Hopkinson (2010). The result is a 3 × 3 matrix (C) which describes the variance and covariance of each individual lidar point observation written as…”

Section: Development Of Terrain-based Error Modelling Algorithmmentioning

confidence: 99%

“…Goulden and Hopkinson (2010) and Glennie (2007) have described and validated methodologies for propagating errors based on category one, sensor sub-system errors. Currently no lidar error model simulations exist which consider the terrain morphology and stochastic error characteristics of individual lidar observations, with appropriate validation.…”

Section: Introductionmentioning

confidence: 99%

“…However, the study did not provide quantitative values of error related to a real-world coordinate system; furthermore, quality index values were not validated. This research proposes to extend lidar sensor hardware error models, developed by Goulden and Hopkinson (2010), to further include the laser pulse interaction with the terrain morphology through a novel terrain-based error propagation model. It is hypothesized that the terrain-based error propagation model will provide a better simulation of error magnitude than previous hardware-only error simulation models.…”

Section: Introductionmentioning

confidence: 99%

“…Goulden and C. Hopkinson found in Goulden and Hopkinson (2010). Table 1 indicates stochastic errors of each of the sub-systems in the ALTM 3100 system which can be used in the application of the GLOPOV.…”

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confidence: 99%

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Mapping simulated error due to terrain slope in airborne lidar observations

Goulden

Hopkinson

2014

International Journal of Remote Sensing

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Quantification of geo-location error in light detection and ranging (lidar) observations is typically limited to empirical assessments, commonly quantified as the fundamental vertical accuracy (FVA). Methodological recommendations indicate that validation observations used for quantifying the FVA should not be collected in sloped terrain; however, terrain slope has been shown to contribute to the lidar error budget. Therefore, users of lidar information generally do not have adequate information to characterize error in sloped conditions. This study proposes a novel geometric terrainbased error propagation algorithm for simulating error bounds of individual lidar observations in the presence of terrain slope. A steep, glacierized, alpine test site in the Canadian Rockies was used to evaluate the algorithm. Error simulations were modelled from the terrain-based error propagation algorithm as well as a pre-existing sensor hardware error propagation algorithm and validated with high-accuracy GPS observations. Simulated versus observed errors showed that terrain-based error simulations provided a reasonable 'worst-case scenario' simulation of potential error and were superior to hardware-only simulated errors. Results were separated into three individual flight lines, and terrain-based error simulations were greater than the observed errors in 82%, 89%, and 100% of the tested points in each respective flight line, or 90%, overall. This contrasted hardware-only error simulations, which were greater than observed errors in 32%, 42%, and 84% of tested points in each respective flight line, or 50% overall. This work provides a comprehensive understanding of the distribution of errors within lidar point clouds over complex terrain types and provides a new methodology for propagating lidar observational errors into derived products.

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Sensitivity of watershed attributes to spatial resolution and interpolation method of LiDAR DEMs in three distinct landscapes

Goulden

Hopkinson

Jamieson

et al. 2014

Water Resources Research

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This study investigates scaling relationships of watershed area and stream networks delineated from LiDAR DEMs. The delineations are tested against spatial resolution, including 1, 5, 10, 25, and 50 m, and interpolation method, including Inverse Distance Weighting (IDW), Moving Average (MA), Universal Kriging (UK), Natural Neighbor (NN), and Triangular Irregular Networks (TIN). Study sites include Mosquito Creek, Scotty Creek, and Thomas Brook, representing landscapes with high, low, and moderate change in elevation, respectively. Results show scale-dependent irregularities in watershed area due to spatial resolution at Thomas Brook and Mosquito Creek. The highest sensitivity of watershed area to spatial resolution occurred at Scotty Creek, due to high incidence of LiDAR sensor measurement error and subtle changes in elevation. Length of drainage networks did not show a scaling relationship with spatial resolution, due to algorithmic complications of the stream initiation threshold. Stream lengths of main channels at Thomas Brook and Mosquito Creek displayed systematic increases in length with increasing spatial resolution, described through an average fractal dimension of 1.059. The scaling relationship between stream length and DEM resolution allows estimation of stream lengths from low-resolution DEMs in the absence of highresolution DEMs. Single stream validation at Thomas Brook showed the 1 m DEM produced the lowest length error and highest spatial accuracy, at 3.7% and 71.3%, respectively. Single stream validation at Mosquito Creek showed the 25 m DEM produced the lowest length error, and the 1 m DEM the highest spatial accuracy, at 0.6% and 61.0%, respectively.

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The Forward Propagation of Integrated System Component Errors within Airborne Lidar Data

Cited by 37 publications

References 19 publications

Development of a UAV-LiDAR System with Application to Forest Inventory

Development of a UAV-LiDAR System with Application to Forest Inventory

Mapping simulated error due to terrain slope in airborne lidar observations

Sensitivity of watershed attributes to spatial resolution and interpolation method of LiDAR DEMs in three distinct landscapes

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