In the last two decades, airborne laser scanning (ALS) has found widespread application and driven fundamental advances in the Earth sciences. With increasing availability and accessibility, ...multi-temporal ALS data have been used to advance key research topics related to dynamic Earth surface processes. This review presents a comprehensive compilation of existing applications of ALS change detection to the Earth sciences. We cover a wide scope of material pertinent to the broad field of Earth sciences to encourage the cross-pollination between sub-disciplines and discuss the outlook of ALS change detection for advancing scientific discovery. While significant progress has been made in applying repeat ALS data to change detection, numerous approaches make fundamental assumptions that limit the full potential of repeat ALS data. The use of such data for 3D change detection is, therefore, in need of novel, scalable, and computationally efficient processing algorithms that transcend the ever-increasing data density and spatial coverage. Quantification of uncertainty in change detection results also requires further attention, as it is vitally important to understand what 3D differences detected between epochs represent actual change as opposed to limitations in data or methodology. Although ALS has become increasingly integral to change detection across the Earth sciences, the existence of pre- and post-event ALS data is still uncommon for many isolated hazard events, such as earthquakes, volcanic eruptions, wildfires, and landslides. Consequently, data availability is still a major limitation for many ALS change detection applications.
Capturing and quantifying the world in three dimensions (x,y,z) using light detection and ranging (lidar) technology drives fundamental advances in the Earth and Ecological Sciences (EES). However, ...additional lidar dimensions offer the possibility to transcend basic 3-D mapping capabilities, including i) the physical time (t) dimension from repeat lidar acquisition and ii) laser return intensity (LRIλ) data dimension based on the brightness of single- or multi-wavelength (λ) laser returns. The additional dimensions thus add to the x,y, and z dimensions to constitute the five dimensions of lidar (x,y,z, t, LRIλ1… λn). This broader spectrum of lidar dimensionality has already revealed new insights across multiple EES topics, and will enable a wide range of new research and applications. Here, we review recent advances based on repeat lidar collections and analysis of LRI data to highlight novel applications of lidar remote sensing beyond 3-D. Our review outlines the potential and current challenges of time and LRI information from lidar sensors to expand the scope of research applications and insights across the full range of EES applications.
•X, y, z, time, and laser return intensity constitute the 5-dimensions of LiDAR.•We review recent advances to highlight novel applications of LiDAR beyond 3D.•Beyond 3D LiDAR has and will enable a wide range of new research and applications.
In this paper we provide a description of airborne mapping LiDAR, also known as airborne laser scanning (ALS), technology and its workflow from mission planning to final data product generation, with ...a specific emphasis on archaeological research. ALS observations are highly customizable, and can be tailored to meet specific research needs. Thus it is important for an archaeologist to fully understand the options available during planning, collection and data product generation before commissioning an ALS survey, to ensure the intended research questions can be answered with the resultant data products. Also this knowledge is of great use for the researcher trying to understand the quality and limitations of existing datasets collected for other purposes. Throughout the paper we use examples from archeological ALS projects to illustrate the key concepts of importance for the archaeology researcher.
Information derived from full-waveform (FW) light detection and ranging (lidar) data has already been shown to be relevant for point cloud analysis tasks. Relevant waveform attributes to populate the ...corresponding point's feature vector are typically provided through a post-processing FW analysis (FWA) technique based on fitting the echo waveform with a parametric function describing the shape and location of the echo pulse in the waveform. Samples of the digitized echo are the primary source for any waveform analysis using parametric functions. On the other hand, for some FW lidar scanning systems, describing the complex system response model using a simple parametric function seems challenging or impractical. Earlier studies have shown the potential of a waveform's digital samples as relevant waveform attributes for point cloud classification. The main goal of this study is to extend earlier experiments on direct exploitation of returned waveform signals collected by a FW terrestrial laser scanning (TLS) system to multireturn waveform signals for point cloud classification in a built environment. Furthermore, the classification performance on feature vectors containing calibrated waveform attributes, derived from a waveform processing approach performed in real-time by the FW TLS system, is evaluated on multiple-echo waveforms and compared with the classification performance derived from the proposed FW data classification technique via deep learning. Classification performance derived through the proposed technique demonstrates high information content of raw digitized waveform samples. Results show that feature vectors containing samples of digitized echoes carry more information about the physical properties of the target than those containing calibrated waveform attributes.
In this study, the potential of raw samples of digitized echo waveforms collected by full-waveform (FW) terrestrial laser scanning (TLS) for point cloud classification is investigated. Two different ...TLS systems are employed, both equipped with a waveform digitizer for access to the raw waveform and online waveform processing which assigns calibrated waveform attributes to each point measurement. Point cloud classification based on samples of the raw single-peak echo waveform is compared with point cloud classification based on the calibrated online waveform attributes. A deep convolutional neural network (DCNN) is designed for the supervised classification. Random forest classifier is used as a benchmark to evaluate the performance of the proposed DCNN model. In addition, feature importance and temporal stability of the raw waveform samples versus the calibrated waveform attributes for point cloud classification are reported. Classification results are evaluated at two study sites, a built environment on a university campus and a coastal wetland environment. Results show that direct classification of the raw waveform samples outperforms classification based on the set of waveform attributes at both study sites. Results also show that the contribution of the range, as the only geometric attribute in the raw waveform feature vector, significantly increases the classification performance. Finally, the performance of the DCNN for filtering ground points to generate a digital terrain model (DTM) based on classification of the raw waveform samples is assessed and compared to a DTM generated from a progressive morphological filter and to real-time kinematic (RTK) GNSS survey data.
In this article, we validate Ice, Cloud, and Land Elevation Satellite2 (ICESat-2)-derived interpolated terrain elevations (h_te_interp), top of canopy elevations (h_canopy_abs), and estimated canopy ...heights (h_canopy) in dense tropical forests of Mexico, Belize, Guatemala, and Honduras. Data from close to 30 000 ICESat-2 ATL-08 segments are compared against parameters derived from high-density (>15 pulses/m 2 ) topographic airborne lidar (HDL) data from seven different sites with a variety of forest structure and terrain conditions, totaling 3742 km 2 of validated area. Our results indicate that in these high closure forests the range of errors (within the 5th to 95th percentiles) for these parameters vary widely, but their median and interquartile range (IQR) grow proportionally to the HDL-derived reference canopy height (rCH). The errors in h_te_interp retrieval grow in proportion to the rCH from ±2.5 m for areas with fairly low rCH (5-10 m) all the way to −10 to 24 m for areas with rCH of 40-45 m. The median of h_te_interp errors also grows proportionally to rCH and is consistently overestimated with regards to the reference. With respect to h_canopy_abs, it was observed that the errors also grow with increasing rCH but a much lower rate; the IQR is generally constrained between −5.5 and 6.0 m, while the median remains mostly uniform and independent of the rCH and underestimates the reference by −0.5-−2.0 m. The IQR of the errors in h_canopy normalized to rCH exhibits a mostly uniform behavior across the range of rCH between −33.5% and 7.0%, with the median fluctuating around an underestimation level of -16.5%.
Convolutional neural networks (CNNs) are becoming an increasingly popular approach for classification mapping of large complex regions where manual data collection is too time consuming. Stream ...boundaries in hyper-arid polar regions such as the McMurdo Dry Valleys (MDVs) in Antarctica are difficult to locate because they have little hydraulic flow throughout the short summer months. This paper utilizes a U-Net CNN to map stream boundaries from lidar derived rasters in Taylor Valley located within the MDVs, covering ∼770 km2. The training dataset consists of 217 (300 × 300 m2) well-distributed tiles of manually classified stream boundaries with diverse geometries (straight, sinuous, meandering, and braided) throughout the valley. The U-Net CNN is trained on elevation, slope, lidar intensity returns, and flow accumulation rasters. These features were used for detection of stream boundaries by providing potential topographic cues such as inflection points at stream boundaries and reflective properties of streams such as linear patterns of wetted soil, water, or ice. Various combinations of these features were analyzed based on performance. The test set performance revealed that elevation and slope had the highest performance of the feature combinations. The test set performance analysis revealed that the CNN model trained with elevation independently received a precision, recall, and F1 score of 0.94±0.05, 0.95±0.04, and 0.94±0.04 respectively, while slope received 0.96±0.03, 0.93±0.04, and 0.94±0.04, respectively. The performance of the test set revealed higher stream boundary prediction accuracies along the coast, while inland performance varied. Meandering streams had the highest stream boundary prediction performance on the test set compared to the other stream geometries tested here because meandering streams are further evolved and have more distinguishable breaks in slope, indicating stream boundaries. These methods provide a novel approach for mapping stream boundaries semi-automatically in complex regions such as hyper-arid environments over larger scales than is possible for current methods.
Quantifying near-field displacements can help enable a better understanding of earthquake physics and hazards. To date, established remote sensing techniques have failed to recover ...subcentimeter-level near-field displacements at the scale and resolution required for shallow fault physical investigations. In this paper, methods are developed to rapidly extract planar parameters, using fast parallel approaches and an alternative registration approach is employed to automatically match the planes extracted from pairwise temporally spaced mobile laser scanning (MLS) and Airborne laser scanning (ALS) data sets along the Napa fault. The features extracted from two temporally spaced point clouds are then used to calculate rigid-body transformation parameters. The production of robust and accurate deformation maps requires the selection of appropriate planar feature extraction and feature mapping criteria. Rigorously propagated point accuracy estimates are employed to produce realistic estimated errors for the transformation parameters. Displacements of each aggregate study area are computed separately from left and right sides of the fault and compared to be within 3 mm of alinement array displacements. Local differential displacements show distinct patterns which, compared to alinement array measurements, were found to agree within the confidence bounds. The findings demonstrate the ability to accurately estimate near-field deformations from repeated MLS or ALS scans of earthquake-prone urban areas. ALS is also used in conjunction with the MLS data sets, illustrating the algorithm's ability to accommodate different LiDAR collection modalities at subcentimeter-level accuracy. The automated planar extraction and registration is an important contribution to the study of near-field earthquake dynamics and can be used as input observations for future geological inversion models.
Airborne light detection and ranging (LiDAR) has been widely applied to terrain modeling, but a gridded digital elevation model (DEM) is usually adopted for most applications. The LiDAR point cloud ...is transformed to grids by interpolation methods, with triangulated irregular network (TIN) linear interpolation most widely used. Both horizontal and vertical uncertainties exist in a point cloud dataset and should therefore be propagated to grid points during spatial interpolation. Studies in the literature have either considered the vertical component only or both components separately. This letter proposes to apply the general law of propagation of variances (GLOPOVs) to estimate vertical uncertainties at grid points for TIN linear interpolation considering both horizontal and vertical uncertainties of the point cloud simultaneously. The experimental results with an airborne LiDAR dataset indicate that underestimation of grid point vertical uncertainties may be derived if only vertical uncertainties of the point cloud are considered; the amount of underestimation depends on the terrain slope. This letter suggests that both horizontal and vertical uncertainties of point cloud should be considered during TIN linear spatial interpolation. The effect of correlated errors between LiDAR points is also examined. It is shown that if significant correlation between points is ignored, the resulting propagated TIN error is underestimated by a factor of almost 2.
The parametric models used in Light Detection And Ranging (LiDAR) waveform decomposition routines are inherently estimates of the sensor's system response to backscattered laser pulse power. This ...estimation can be improved with an empirical system response model, yielding reduced waveform decomposition residuals and more precise echo ranging. We develop an empirical system response model for a Riegl VZ-400 terrestrial laser scanner, from a series of observations to calibrated reflectance targets, and present a numerical least squares method for decomposing waveforms with the model. The target observations are also used to create an empirical radiometric calibration model that accommodates a nonlinear relationship between received optical power and echo peak amplitude, and to examine the temporal stability of the instrument. We find that the least squares waveform decomposition based on the empirical system response model decreases decomposition fitting errors by an order of magnitude for high-amplitude returns and reduces range estimation errors on planar surfaces by 17% over a Gaussian model. The empirical radiometric calibration produces reflectance values self-consistent to within 5% for several materials observed at multiple ranges, and analysis of multiple calibration data sets collected over a one-year period indicates that echo peak amplitude values are stable to within ±3% for target ranges up to 125 m.
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