Lidar Accuracy Research Articles

Appraisal of seedling stand vegetation with airborne imagery and discrete-return LiDAR – an exploratory analysis

The potential for combined use of airborne discrete-return LiDAR and digital imagery in the classification and measurement of common seedling stand vegetation was examined in southern Finland (61°50’N, 24°20’E). Classification was based on spectral and textural image features in addition to geometric and radiometric features of the LiDAR. The accuracy of leaf-on, LiDAR-based terrain elevation models was tested as well as the accuracy of LiDAR in the measurement of vegetation heights. LiDAR-based canopy height and the range-normalized intensity of the LiDAR were strong explanatory variables in vegetation classification. Interspecies variation was observed in the height measurement accuracy of LiDAR for different tree, shrub and low vegetation canopies. Elevation models derived with 1–15 pulses per m showed an inherent noise of app. 15–25 cm, which restricts the use of LiDAR in regeneration assessment of very young stands. The spatial pattern of the competing vegetation was reproduced in classification-based raster surfaces, which could be useful in deriving meaningful treatment proposals.2

Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations

We used LiDAR topographic data, AVIRIS hyperspectral data, and locally measured tidal fluctuations to characterize patterns of plant distribution within a southern California salt marsh (Carpinteria Salt Marsh (CSM)). LiDAR data required ground truthing and correction before they were suitable for use. Twenty to forty percent of the uncertainty associated with LiDAR was due to variance in the elevation of the target surface, the balance was attributed to error inherent in the LiDAR system. The incidence of LiDAR penetration of plant canopy cover (i.e., registration of ground elevation) was only three percent. The depth of LiDAR penetration into the plant canopy varied according to plant species composition; plant species-specific corrections significantly improved LiDAR accuracy (58% reduction in overall uncertainty) and with the use of ground-based surveys, reduced overall RMSE to an average of 6.3 cm in vegetated areas. A supervised classification of AVIRIS data was used to generate a vegetation map with six classification types; overall classification accuracy averaged 59% with a kappa coefficient of 0.40. The vegetation classification map was overlaid with a LiDAR-based digital elevation model (DEM) to compute elevation distributions and frequencies of tidal inundation. The average elevations of the dominant plant classifications found in CSM (e.g., Salicornia virginica, Jaumea carnosa, and salt-grass mix, a mixture of multiple marsh plant species) occurred within a 17 cm range, a vertical change that resulted in a 7% difference in the period of tidal inundation.

A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods

Tree height is an important variable in forest inventory programs but is typically time-consuming and costly to measure in the field using conventional techniques. Airborne light detection and ranging (lidar) provides individual tree height measurements that are highly correlated with field-derived measurements, but the imprecision of conventional field techniques does not allow for definitive assessments regarding the absolute accuracy of lidar tree height measurements and the relative influence of beam divergence setting (i.e., laser footprint size), species type, and digital terrain model (DTM) error on the accuracy of height measurements. In this study, we developed a methodology for acquiring accurate individual tree height measurements (<2 cm error) using a total station survey and used these measurements to establish the expected accuracy of lidar- and field-derived tree height measurements for two of the most ecologically and commercially significant species in western North America, Douglas-fir (Pseudotsuga menziesii) and ponderosa pine (Pinus ponderosa). Tree height measurements obtained from narrow-beam (0.33 m), high-density (6 points/m2) lidar were more accurate (mean error ± SD = –0.73 ± 0.43 m) than those obtained from wide-beam (0.8 m) lidar (–1.12 ± 0.56 m). Lidar-derived height measurements were more accurate for ponderosa pine (–0.43 ± 0.13 m) than for Douglas-fir (–1.05 ± 0.41 m) at the narrow beam setting. Although tree heights acquired using conventional field techniques (–0.27 ± 0.27 m) were more accurate than those obtained using lidar (–0.73 ± 0.43 m for narrow beam setting), this difference will likely be offset by the wider coverage and cost efficiencies afforded by lidar-based forest survey.

Accuracy Assessment of Lidar Saltmarsh Topographic Data Using RTK GPS

An evaluation was completed to compare the accuracy of lidar (Light Detection and Ranging) against a statistically representative array of Real-Time Kinematic (RTK) GPS data in a low gradient, vegetated Southeastern U.S. salt marsh. In order to discern potential bias, analyses were carried out separately on the platform-only data, the creek-only data and then the combined datasets. Lidar data were found to overestimate the RTK GPS topographic data by an overall average of only 7 cm. Additionally, these data showed little effect from the dominant macrophyte vegetation within the lidar footprint. From this evaluation, 7 cm appears to be an appropriate vertical adjustment factor for using lidar data in low gradient salt marshes. However, local ground control will continue to be crucial in studies of intertidal environments incorporating airborne laser data collection.

Effects of laser beam alignment tolerance on lidar accuracy

One of the major lidar error sources not yet analyzed in the literature is the tolerance of the laser beam alignment with respect to the scanning mirror. In this paper, the problem of quantifying these errors is solved for rotating polygon mirror type lidar systems. An arbitrary deviation of the beam from its design direction–the vector of beam misalignment–can be described by two independent parameters. We choose these as horizontal and vertical components of the misalignment vector in the body frame. Either component affects both, horizontal and vertical lidar accuracy. Horizontal lidar errors appear as scan line distortions—along and across track shifts, rotations and scaling. It is shown that the horizontal component of misalignment results in a scan line first being shifted across the track and then rotated around the vertical at the new center of the scan line. Resulting vertical lidar error, being a linear function of the scan angle, is similar to that produced by a roll bias. The vertical component of the beam misalignment causes scan line scaling and an along track shift. The corresponding vertical error is quadratic with respect to the scan angle. The magnitude of these effects is significant even at tight alignment tolerances and cannot be realistically accounted for in the conventional calibration model, which includes only range, attitude and GPS biases. Therefore, in order to attain better accuracy, this model must be expanded to include the beam misalignment parameters as well. Addition of new parameters into the model raises a question of whether they can be reliably solved for. To give a positive answer to this question, a calibration method must utilize not only ground control information, which is typically very limited, but also the relative accuracy information from the overlapping flight lines.

Estimating relative lidar accuracy information from overlapping flight lines

A pure statistical method for estimating relative lidar accuracy by comparing overlapping lidar datasets is described. It has been designed for commercial, fast turnaround, high-volume data production environment and has the following features. It operates on raw data and no building of grid or TIN is required. The method performs accuracy computations on statistical samples that are orders of magnitude larger than ones used in other methods reported in the literature, thus allowing detailed error analysis. Finally, the method provides a lot of relative accuracy information even when no ground control points are available. Software implementation of the method has been written and extensively tested on almost 100 Gb worth of data. Robust and fully automated, it has become an important quality control tool for data processing at TerraPoint. The method begins with the definition of a relative accuracy estimate, which is generalized from being a measure of point-to-point closeness to a measure of surface-to-surface closeness. Thus, a problem of matching the individual points in the overlapping datasets is eliminated. The generalized accuracy estimate is a function of surface size and flatness. Sampling overlap area by surfaces of different size allows one to discriminate between the random, systematic and locally systematic errors. The relative accuracy in the traditional sense of using only some tie points is a limiting case of the generalized one and can also be computed. The concepts and ideas of the method are illustrated by examples from real-life lidar projects.

Dynamic calibration and compensation of a 3D laser radar scanning system

LIDAR (laser radar) is used to measure three-dimensional (3-D) object positions. It produces a range and an intensity image of the measured object and relies on the range image to determine the 3-D positions of the object. Because the range image is frequently corrupted with noise, a dynamic method to improve the LIDAR accuracy, based on a polynomial calibration model and an autoregressive moving-average calibration model, has been established. Experimental results show that the measurement errors of the LIDAR system have been reduced from 163 counts to 18 counts after compensation using the polynomial calibration model and that the errors have been further reduced to 11 counts with the ARMA model.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Dual-wavelength correlation measurements with an airborne pulsed carbon dioxide lidar system

Dual-wavelength correlation data are presented for ground-level and airborne measurements of topographic and ocean backscatter near the 9.5-microm wavelength. Data were acquired with a pulsed carbon dioxide, dual-laser, dualwavelength lidar system. The best system performance gave a dual-wavelength uncorrelated noise component of 2.8% and 9% standard deviation for ground-based and airborne data, respectively. This sets one limit on lidar measurement accuracy with a single pulse pair.