We evaluate the performance of full waveform LiDAR decomposition algorithms with a high-resolution single band airborne LiDAR bathymetry system in shallow rivers. A continuous wavelet transformation (CWT) is proposed and applied in two fluvial environments, and the results are compared to existing echo retrieval methods. LiDAR water depths are also compared to independent field measurements. In both clear and turbid water, the CWT algorithm outperforms the other methods if only green LiDAR observations are available. However, both the definition of the water surface, and the turbidity of the water significantly influence the performance of the LiDAR bathymetry observations. The results suggest that there is no single best full waveform processing algorithm for all bathymetric situations. Overall, the optimal processing strategies resulted in a determination of water depths with a 6 cm mean at 14 cm standard deviation for clear water, and a 16 cm mean and 27 cm standard deviation in more turbid water.

We compared hyperspectral imagery and single-wavelength airborne bathymetric light detection and ranging (lidar) for shallow water (<2 m) bathymetry and seagrass mapping. Both the bathymetric results from hyperspectral imagery and airborne bathymetric lidar reveal that the presence of a strongly reflecting benthic layer under seagrass affects the elevation estimates towards the bottom depth instead of the top of seagrass canopy. Full waveform lidar was also investigated for bathymetry and similar performance to discrete lidar was observed. A provisional classification was performed with limited ground reference samples and four supervised classifiers were applied in the study to investigate the capability of airborne bathymetric lidar and hyperspectral imagery to identify seagrass genera. The overall classification accuracy is highly variable and strongly dependent on the classification strategy used. Features from bathymetric lidar alone are not sufficient for substrate classification, while hyperspectral imagery alone showed significant capability for substrate classification with over 95% overall accuracy. The fusion of hyperspectral imagery and bathymetric lidar only marginally improved the overall accuracy of seagrass classification.

Recent light detection and ranging (lidar) systems using photon-counting technology are able to collect data with significantly higher efficiency compared with the current commercially available linear-mode lidar systems. However, the high quantum sensitivity of single-photon lidar (SPL) systems results in noisy point clouds due to the influence of solar noise and dark count returns. Therefore, an effective noise removal algorithm is required to interpret SPL data. The uneven distribution of noise returns and the removal of noise close to signal returns are two significant challenges for SPL filtering. In this letter, a novel adaptive ellipsoid searching (AES) method is proposed. The AES uses a spherical noise density estimation model and a morphing ellipsoid determined by local principal components. The proposed method was tested on Sigma Space high-resolution quantum lidar system (HRQLS) SPL data sets and the results were compared with voxel-based filtering of the same data. Independent comparisons of each filtered result with coincident linear-mode airborne lidar data were also undertaken. We find that the root mean square error of the AES results on solid planes is 0.09 versus 0.11 m for voxel-based, 0.12 versus 0.14 m for bare ground, and 2.07 versus 2.55 m for vegetation canopy. We also used manually selected solid planar surfaces as a reference and find that the proposed method successfully removed twice as many noise points as the voxel-based method.

Over the past decade, boresight angle calibration of airborne laser scanning (ALS) systems has evolved from ad hoc methods often based on qualitative assessments of point cloud fidelity to rigorous self-calibration algorithms that optimize multiple sensor parameters by minimizing the spatial discrepancies between common features. Although the calibration of linear-mode ALS systems employing oscillating or rotating mirrors has been well developed, little work has addressed the calibration of emergent single-photon lidar (SPL) sensors with circular scan patterns. We adapt a least-squares algorithm employing planar-surface matching to accommodate a spinning wedge prism, employ a synthetic dynamic wedge angle by way of a trigonometric polynomial (TP) to model imperfections in the circular scanning mechanism, and address unique characteristics of SPL data within the stochastic model. Planar fit residuals are reduced by 40% with a boresight and wedge angle adjustment and a further 40% with the introduction of the synthetic wedge angle TP. The addition of the TP also improves the median vertical discrepancy between point clouds generated from fore and aft look angles by over 75%.

Single photon lidar (SPL) systems have great potential to be an effective tool for mapping due to their high data collection efficiency. However, the large number of false returns in SPL point clouds represents a huge challenge for the extraction of weak signal targets with low reflectivity or small cross sections. Numerous filtering methods have been proposed that attempt to effectively remove these noise points from the final point cloud model. However, weak signal points have similar characteristics to noise returns, and thus can be incorrectly eliminated as noise points during the filtering process. Herein, a novel voxel-spherical adaptive ellipsoid searching (VSAES) method is proposed, by which weak signal returns can be successfully retained while still removing a majority of the noise points. By employing this voxel-spherical (VS) model, our proposed method can simultaneously process a combined SPL dataset containing multiple flightlines, in which the noise density is unevenly distributed throughout the whole dataset. In addition, an improved adaptive ellipsoid searching (AES) method based on hypothesis testing is able to remove noise points more robustly than the originally described version. The experimental results show that the proposed method retains 89.1% of the weak signal point returns from electric power lines, which is a significant improvement over the performance of either to the original AES method (25.9%) or a histogram filtering based method (13.4%).