The examination of landscape surface processes is often accomplished using a digital elevation model interpolated from a lidar point cloud. The resulting DEM often does not include the inherent uncertainties of the individual lidar points, and, even if provided, those uncertainties do not include consideration of the DEM interpolation method used, or the effect of terrain surface roughness and raw point density. Here, we present a single comprehensive model of the vertical terrain uncertainty and the characteristics of the terrain (normalized surface roughness), based on a representative number of datasets from a range of landscapes with various surface roughness and vegetation cover. To show the utility of properly scaled vertical covariance, we then use this modeled relationship to scale per-point estimates of lidar source data uncertainty and consider the estimated point accuracy in a normalized cross-correlation for change detection to show that the result is a more representative measure of change detection uncertainty.

Information derived from full-waveform (FW) data collected by FW laser scanning systems 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 laser scanning systems, describing the complex system response model using a simple parametric function seems challenging or impractical. Earlier studies have shown the potential of 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 in a built environment for point cloud classification, to multi-return waveform signals. 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. 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 physical properties of the target than those containing calibrated waveform attributes.

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.

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.

Nearshore estimates of bathymetry are crucial for understanding coastal processes. However, current passive remote sensing methods for estimating bathymetry require in situ depth measurements to train inversion models, which can be difficult or impossible to obtain in many areas. To address this issue, we investigated the fusion of range measurements from the advanced topographic laser altimeter system (ATLAS) aboard the NASA ICESat-2 satellite, and multispectral satellite imagery from European Space Agency (ESA) Sentinel-2 using two common bathymetric inversion algorithms. The active ranging capability of the ATLAS green (532-nm) laser has been shown to generate returns of up to 38-m depth in optically clear waters, providing depth measurements to constrain passive bathymetric inversion results. Data acquired in November 2018 over the nearshore in Destin, FL, USA, offer a proof of concept for this approach. The results of the bathymetric inversion were quantitatively assessed by comparison with airborne bathymetric LiDAR collected using the U.S. Army Corps Coastal Zone Mapping and Imaging LiDAR (CZMIL) system in October-November 2018. Overall, the results of the bathymetric inversion compared with the CZMIL data have a root mean square error (RMSE) of 0.35 m in waters with similar turbidity and bottom reflectivity, and demonstrate that a combination of ICESat-2 depth observations with Sentinel-2 multispectral imagery can estimate seamless nearshore bathymetry for optically clear coastal waters.

Quantifying measurement uncertainty is an important component of geospatial data analysis, including spatial displacement measurements computed from multi-temporal digital elevation models (DEMs). Uncertainty estimates provide context to the validity of reported measurements and enable hypotheses on the statistical significance of observed spatial motion. Although error propagation is sometimes applied to simple differencing of DEMs to generate uncertainty estimates in the computed vertical change, it is virtually non-existent in automated 2D and 3D change detection methods. We report on the performance of rigorous forward error propagation of source data uncertainty, i.e., Jacobian-based variance propagation, through an image correlation technique applied to DEMs to measure horizontal ground motion. We provide an example application and conclude with a brief outline of future work required to fully validate and automate the method.

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.

Lidar intensity is correlated with illuminated target physical properties, particularly target reflectance, making it a valuable quantity for applications, such as land cover classification, data registration, structural damage detection, and qualitative point cloud interpretation. In contrast to traditional linear-mode lidar (LML) hardware, single photon lidar (SPL) detectors produce a binary response to impinging photons and therefore do not provide an intensity measure for each detected return. This is a significant drawback but can be addressed by computing a measure of local point cloud density for each point. Since the arrival and detection of single photon are governed by statistics such that the observations of brighter surfaces are more probable to generate a detection event than darker surfaces, a local point cloud density metric can be used as a proxy for traditional LML intensity. We define the relationship between target reflectance and photon detection probability and compare the predicted relationship with empirical observations of ground reflectance and an estimate of detection probability generated from local point cloud density. We also examine a pulsewidth measure provided by the SPL sensor used for this letter, as well as the influence of neighborhood radius on variance in the probability estimates and a filtered version of the hardware-supplied pulsewidth.

For airborne repeat-pass synthetic aperture radar interferometry (InSAR), precise trajectory information is needed to compensate for deviations of the platform movement from a linear track. Using the trajectory information, motion compensation (MoCo) can be implemented within SAR data focusing. Due to the inaccuracy of current navigation systems, residual motion errors (RMEs) exist between the real and measured trajectory, causing phase undulations in the final interferograms. Up to now, MoCo and RME estimation have usually been combined in airborne InSAR to estimate ground deformation. Conventional MoCo methods generally involve azimuthal and range resampling and phase correction. Then frequency-domain focusing techniques can be used to generate the SAR images. After focusing SAR images with MoCo, both multisquint and autofocus approaches can be used to estimate RME. In addition to the MoCo-based frequency-domain focusing, the time-domain backprojection (BP) technique can also focus the SAR data obtained from highly nonlinear platform trajectories. In this paper, we present, for the first time, the combination of BP and multisquint techniques for RME estimation. A detailed derivation of the implementation of the multisquint approach using the BP-focusing images is presented. Repeat-pass data from the SlimSAR system over Slumgullion landslide are used to demonstrate the feasibility of RME estimation for both stationary and nonstationary scenes. We conclude that the proposed method can effectively remove the RME.

We present an investigation into different approaches for high-resolution mapping of near-field surface displacement for strike-slip earthquakes. Airborne laser scanning (ALS) and optical imagery are two common sources of earth observation data available to geoscientists for earthquake documentation and studies. Optical image correlation and point cloud differencing techniques are among the most widely used methods for retrieving displacement signals in the near field. We compare the performances of these techniques for estimating near-field deformation using pre and postevent high-resolution ALS and airborne imagery of the August 24, 2014 Mw 6.0 Napa, California earthquake. Estimates of deformation agree with field observations within a decimeter, at the expected accuracy level of the data. We show that the correlation of intensity images from ALS data can unveil the near-field deformation successfully and outperforms optical image correlation in vegetated areas as well as in the absence of geodetic markers (man-made structures). Furthermore, we illustrate that the point clouds generated with structure from motion perform comparably to ALS point clouds for retrieving the displacement signal in unvegetated areas. Overall, we conclude that ALS data are generally better than imagery for estimating near-field deformation regardless of the estimation methodology and that the iterative closest point algorithm was more effective at recovering the displacement signal.

The large amount of noise returns in single photon Lidar (SPL) point clouds represent a significant challenge for the utilization of these new generation laser scanning systems. Numerous filtering methods have been proposed that attempt to effectively remove noise points from the final point cloud model. However, weak signal points have similar characteristics as noise returns, and thus can be incorrectly eliminated with noise points during the filtering process. Herein, a novel voxel-spherical adaptive ellipsoid searching (VS-AES) 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 data set containing multiple flightlines, in which the noise density is unevenly distributed throughout the whole data set. In addition, an improved adaptive ellipsoid searching (AES) method based on hypothesis testing is developed that is able to remove noise points more robustly than the original version. The experimental results show that the proposed method retains 89.1% of the weak signal point returns from power transmission lines, which is a significant improvement over the performance of either the original AES method (25.9%) or a histogram filtering method (13.4%).

In this study, we present a case study of the Slumgullion landslide conducted in July 2015 to demonstrate the feasibility of deformation mapping with an airborne synthetic aperture radar (SAR) system known as ARTEMIS SlimSAR, which is a compact, modular, and multi-frequency radar system. For this study, the L-band SlimSAR was installed on a Cessna 206 aircraft and data were collected on July 3, 7, and 10 of 2015 and processed using the time-domain backprojection algorithm. Airborne light detection and ranging (LiDAR) campaign, GPS surveys and spaceborne InSAR analysis using COSMO-SkyMed images were also conducted to verify the performance of the airborne SAR system. The airborne InSAR results showed satisfying agreement with the GPS and spaceborne InSAR results. A 3-D deformation map over Slumgullion landslide was also generated, which displayed distinct correlation between the landslide motion and topography.

Airborne Light Detection And Ranging (LiDAR) data are widely used for high-resolution land cover mapping. The LiDAR data are typically used as complementary information to passive multispectral or hyperspectral imagery to obtain higher land cover classification accuracy. In this paper, we examine the capabilities of a recently developed multispectral airborne laser scanner, manufactured by Teledyne Optech, for classification of multispectral point clouds into typical land cover classes. This scanner, Titan MW (multi-wavelength), collects point clouds using three different wavelength lasers simultaneously, hence opening the door to new possibilities in land cover classification using only LiDAR data. We show that the recorded intensities of returned laser pulses together with structural characteristics of the features on the Earth surface calculated from the 3D positions of returns are sufficient enough to classify the point cloud into 10 distinct land cover classes. We achieved an overall accuracy of 95.9% with a kappa coefficient of 0.95 using a Support Vector Machine (SVM) classifier to classify single-return points and an overall accuracy of 89.2% and kappa coefficient of 0.82 using a rule-based classifier on multi-return points.

We present a novel technique that allows us to detect, map and quantify changes on the Earth's surface in three-dimensions that occur at time scales of a few seconds. This rapid change detection capability is a byproduct of the development of a multichannel airborne lidar system that scans the target surface at three different times in a single aircraft pass. Results, capabilities, limitations and potential applications are discussed.

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%.

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.

We propose combining a forward model based support vector regression and the semianalytical radiative transfer model to determine shallow water characteristics. The derived water depths were compared to both LiDAR derived water depths and field measured water depths. The bathymetry results show that both LiDAR and hyperspectral imagery are unable to retrieve water depth for deeper water (>7 m) due to the water attenuation. Fusion was also performed with the LiDAR bathymetry as a constraint on the hyperspectral imagery; the constraint varies the estimated water characteristics but we were not able to independently assess the performance because no measurements of water column characteristics were available. The retrieved hyperspectral bathymetry yielded a standard deviation of 20 cm when compared to LiDAR bathymetry.

The new generation of Ice, Cloud, and land Elevation Satellite (ICESat-2) which utilizes photon-counting laser detectors is scheduled for launch in 2017. This upcoming mission will provide data to assess changes of ice sheet elevation and mass, as well as the time-varying volume of sea ice. However, the next-generation ICESat sensor also presents new data processing challenges due to the high number of false returns present in the resultant point cloud that are mainly caused by the high sensitivity of the photon detector to solar returns. In this letter, we propose a novel noise filter for single photon laser altimeter data utilizing a Bayesian decision theory. We applied our algorithm to the Multiple Altimeter Beam Experimental Lidar (MABEL) data sets and compared the filtered estimate of ground to coincident high resolution airborne LiDAR data. The results show that the proposed algorithm differentiates between noise and ground surface returns effectively with 6-m root-mean-square error (RMSE) for the MABEL green channel lasers and 4-m RMSE for the near-infrared channel lasers. The Bayesian approach also outperformed a commonly applied point density-based algorithm, the modified density-based spatial clustering of applications with noise, particularly in areas of steep terrain.

We propose an approach to voxelize bathymetric full-waveform LiDAR (Light Detection and Ranging) to generate orthowaveforms and use them to estimate shallow water bathymetry and turbidity with a nonparametric support vector regression (SVR) method. Two distinct shallow rivers were investigated ranging from clear to turbid water; hyperspectral imagery and traditional full-waveform LiDAR processing were also investigated as a baseline for comparison with the proposed orthowaveform strategy. The orthowaveform showed significant correlation to water depth in both scenarios and outperformed hyperspectral imagery for water depth estimation in more turbid water. The orthowaveforms showed similar performance to full-waveform LiDAR point observations for bathymetry estimation in clear water and outperformed the bathymetry performance of full-waveform processing in turbid water. The orthowaveforms also showed similar performance to hyperspectral imagery for predicting water turbidity in turbid water, with a root mean square error (RMSE) of 1.32 NTU. The fusion of both hyperspectral imagery and orthowaveforms was also investigated and gave superior performance to using either data set alone. The fused data set was able to estimate depth in clear and turbid water with an RMSE of 10 and 21 cm, respectively, and turbidity with an RMSE of 1.16 NTU.

We propose and evaluate an empirical method for water depth determination from hyperspectral imagery when the benthic layer is visible using support vector regression (SVR). The implementation of the empirical method is presented, and its ability to estimate water depths is compared with a more commonly used band ratio method for two distinct fluvial environments. Our analysis shows that SVR outperforms the band ratio method by providing better root-mean-square error (RMSE) agreement and higher R2for both clear and turbid water. We also demonstrate an extension of the nonparametric properties of SVR to provide estimates of water turbidity from hyperspectral imagery and show that the approach is able to estimate turbidity with an RMSE of approximately 1.2 NTU when compared with independent turbidity measurements.

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.

The capability of airborne LiDAR scanners (ALS) to record returns from the ground surface and other targets occluded by forest canopies has been of great value for geosciences and military operations. In this paper we present preliminary results from efforts aimed to characterize different types of forest canopies and to assess the quantity and quality of potential ground returns obtained through different configurations of small footprint airborne mapping LiDAR systems. The final goal of this work is to provide a methodology that allows for the quantification of the “openness” of a forest canopy and procedures to determine the best configuration of ALS systems that ensures maximum detection of ground returns independent of the many different system designs currently available.

Pulsed Light Detection And Ranging (LiDAR) intensity has commonly been used as a measure of relative reflectance of materials to aid in both point classification and object identification. However, as LiDAR systems use a single light wavelength, the intensity has had little value for advanced material classification. With the advent of multispectral LiDAR systems, it may be possible to use the LiDAR intensity in multiple spectral bands to assist in automated target recognition. Towards this end, we present a comparison between LiDAR intensity images and passive reflectance from a hyperspectral imaging system in the same spectral bands. Although qualitatively the LiDAR intensity and hyperspectral imagery show good agreement, a quantitative analysis shows there are significant deviations between their respective reflectance measurements, particularly for complex features such as trees.

Full waveform bathymetric LiDAR allows a detailed examination of laser backscatter from the water surface, water column and benthic layer. The presence of seagrass on the ben-thic layer would also be expected to influence the backscat-tered radiation encapsulated in the full waveform observations. The combination of conventional geometric features, radiometric features and derived geometric features from the waveform analysis may make it possible to identify the presence of seagrass or even classify the different types of sea-grass. An analysis of the correlation of seagrass location with different full waveform LiDAR parameters is presented. Overall, it is found that a high degree of correlation exists between the presence of seagrass and LiDAR intensity, benthic elevation and benthic return pulse width. Surface roughness, curvature and vertical uncertainty are found to have weak correlation with the presence of seagrass.

Differential LiDAR (Light Detection and Ranging) from repeated surveys has recently emerged as an effective tool to measure three-dimensional (3D) change for applications, such as quantifying slip and spatially distributed warping associated with earthquake ruptures, and examining the spatial distribution of beach erosion after hurricane impact. Currently, the primary method for determining 3D change from LiDAR is through the use of the iterative closest point (ICP) algorithm and its variants. However, all current studies using ICP have assumed that all LiDAR points in the compared point clouds have uniform accuracy. This assumption is simplistic given that the error for each LiDAR point is variable, and dependent upon time varying factors such as target range, angle of incidence, and aircraft trajectory accuracy. Therefore, to rigorously determine spatial change, it would be ideal to model the random error for every LiDAR observation in the differential point cloud, and use these error estimates as apriori weights in the ICP algorithm. To test this approach, we implemented a rigorous LiDAR observation error propagation method to generate estimated random error for each point in a LiDAR point cloud, and then determine 3D displacements between two point clouds using an anisotropic weighted ICP (A-ICP) algorithm. The algorithm was evaluated by qualitatively and quantitatively comparing point clouds with synthetic fault ruptures between a uniform weight and anistropically weighted ICP algorithm. Then post-earthquake slip is estimated for the 2010 El Mayor-Cucapah Earthquake (EMC), using pre- and post-event LiDAR. Based on the analysis, Moving Window A-ICP is able to better estimate the synthetic surface ruptures, and provides a smoother estimate of actual displacement for the EMC earthquake.

Current research into the fusion of Hyperspectral Imagery (HI) and full waveform LiDAR (Light detection and ranging) has relied on first processing the full waveform LiDAR (FWL) data to a set of discrete returns before combining. However, more information about target properties can potentially be recovered if the raw waveform is preserved in the fusion with HI. This paper proposes a voxelization method to fuse raw FWL data with HI by dividing the waveform data into voxels, and then synthesizing all waveforms which intersect a voxel into one 3D superposition waveform. The efficacy of this method is evaluated by comparing the synthesized waveform with an actual nadir LiDAR waveform from the voxel of interest. Results show that this method of voxelizing and fusion of FWL data can preserve raw waveform characteristics while effectively representing the FWL data on a 3D raster basis that can be directly co-registered with the HI.

Over the last two decades airborne terrestrial and bathymetric LiDAR systems have experienced exponential development. However, the design criteria for either terrestrial or bathymetric systems have created a gap where these systems are not operational. This gap is in the interface and overlap of land surface and very shallow water (<; 5 m) bodies such as lakes, lagoons, rivers, intracoastal waterways, and the surf zone. These areas are of high interest for geoscientists and engineers, and mapping this topography at high resolution in a single pass with a single system until now has been unachievable. In this paper we present a new LiDAR system that integrates design characteristics of terrestrial and bathymetric systems which enables the high resolution topographic mapping of land and shallow water bathymetry in a single pass. We will also present early results from test flights of the system under different conditions and outline future work.