NASA's ICESat-2 (Ice, Cloud and land Elevation Satellite-2) satellite launched in 2018, carrying a single instrument, the Advanced Topographic Laser Altimeter System (ATLAS). The Level 1 science objectives of the mission focus primarily on the cryosphere, with specific interest in monitoring changes in polar ice sheets, glaciers and sea ice. However, in addition to planned observations and data products for polar, land, vegetation, ocean and the atmosphere, ATLAS's photon-counting, green-wavelength instrumentation enables impressive bathymetric measurement capability. Most of the ICESat-2 along-track data products were developed during pre-launch studies, without a dedicated effort focused on bathymetry. The absence of a dedicated bathymetry product has required the scientific community to develop independent, individual algorithms for bathymetric signal extraction, most often tailored to local or regional studies. No existing approaches have been proven applicable to global application. Over the last 3 years, the ICESat-2 Project Science Office has sought to address the need for coastal and nearshore bathymetry through the development of a Level 3a, along-track data product for global shallow-water bathymetry (ATL24). The ATL24 workflow embraces several independent signal extraction algorithms in a machine learning ensemble to provide robust signal extraction of the sea floor and sea surface heights in variable environmental conditions and water quality. This paper explains the approach to the algorithms and an assessment of the algorithm performance to evaluate the usefulness for high-priority science and application use cases.

Although designed primarily for cryospheric science objectives, NASA's ICESat-2 satellite, which launched in 2018, quickly became recognized as the world's first spaceborne bathymetric lidar. Initially, there was no dedicated bathymetric data product for the mission, so researchers interested in using ICESat-2 bathymetry developed their own custom bathymetric signal extraction and correction algorithms. To address this product lack, in 2022, our study team began work on a new, dedicated ICESat-2 coastal and nearshore bathymetric data product, ATL24. Following the development and testing of the algorithms, ICESat-2 bathymetry was processed for the entire globe from the start of the mission in 2018 through present day, through a 13-day processing run. The goals of this study were twofold. First, we sought to leverage the global, full-mission processing run to compute and analyze metrics that provide insight into the global data set, ICESat-2's overall bathymetric mapping capabilities, and parameters that impact bathymetric measurement performance. Second, we assessed the accuracy of the ATL24 data product through comparison to higher-accuracy independent reference data at eight different sites with widely ranging seafloor morphologies and depth ranges. Collectively, the results of these two study phases are anticipated to assist researchers and practitioners in understanding ICESat-2's bathymetric mapping capabilities and in evaluating the utility of ATL24 for specific science and application use cases. We highlight this analysis through consideration of two example use cases. Finally, we conclude with a look ahead to anticipated future ATL24 enhancements and recommendations for ongoing science.

After over a half‐century of development, bathymetric lidar is a mature and widely used technology for mapping the littoral zone in support of nautical charting, benthic habitat assessment, inundation modeling and other applications. In 2018, bathymetric lidar transitioned from a purely airborne technology to also a spaceborne capability with the launch of NASA's ICESat‐2 satellite. An important aspect of obtaining accurate seafloor elevations and horizontal coordinates in bathymetric lidar is refraction correction, which corrects for the change in the speed and direction of the laser at the air‐water interface. Unfortunately, data on the refractive index of seawater needed for correction are largely lacking, especially over global extents, which are required for ICESat‐2 bathymetry. This study developed and evaluated a new global refractive index of water data layer. A two‐phased sensitivity analysis was conducted to investigate how systematic and random uncertainties in the refractive index layers impact bathymetric lidar uncertainty. We then developed the global refractive index of water layer using global marine data sets and evaluated it using a combination of Argo Float data and in situ refractometer measurements. The results provide a strong indication of the usefulness of the global refractive index layer, which is currently being implanted into the workflow for generating a new ICESat‐2 bathymetric data set (ATL24). To benefit other studies, the global refractive index layer is publicly available. Future improvements are possible, leveraging crowd‐sourced data collection to continually improve the spatial resolution and nearshore accuracy of the refractive index data set.

Recent decades have seen rapid growth in algorithms and workflows for generating bathymetry from multispectral satellite imagery, with the output typically referred to as satellite derived bathymetry (SDB). An inherent challenge is that, while SDB algorithms generally output a value for every pixel in each input scene, the value of any particular raster cell may not represent a valid depth. Typically, large portions of a satellite scene will correspond to optically deep waters, where SDB depth retrieval is impossible. Boats, boat wakes, breaking waves, clouds, and land can also cause erroneous depth estimates. Compounding this challenge is the fact that SDB algorithms tend to be poor at self-diagnosing invalid retrievals. As a result, SDB grids often require substantial manual editing to generate reliable bathymetric digital elevation models (DEMs). This study investigates the ability to automatically classify valid and erroneous bathymetry from SDB grids using random forest and surface features generated from the SDB grids, spectral bands values, and band indices. The trained models achieved mean intersection over union accuracies of 73–91% at five geographically-diverse test sites of differing sizes, seafloor morphologies, substrates, and wind and wave climates. These methods can be used to rapidly assess satellite image-based bathymetry grids and are currently being adapted for operational use within the National Oceanic and Atmospheric Administration’s National Ocean Service.

The National Geodetic Survey (NGS) is modernizing the National Spatial Reference System (NSRS) in the United States. The modernization involves significant updates to the official reference frames and vertical datum used across the country, affecting the entire geospatial industry. Key benefits of the Modernized NSRS will include improved accuracies and enhanced interoperability and sustainability of geospatial data and systems. The ASPRS NSRS Modernization Working Group produced this paper to help prepare the geospatial industry for the upcoming changes. It serves as a guide for industry professionals to understand the implications of the Modernized NSRS and recommendations to begin preparing for it. It emphasizes the importance of proactive measures to ensure a smooth transition to the new reference frames and vertical datum.

A major advance in global bathymetric observation occurred in 2018 with the launch of NASA’s ICESat-2 satellite, carrying a green-wavelength, photon-counting lidar, the Advanced Topographic Laser Altimeter System (ATLAS). Although bathymetric measurement was not initially a design goal for the mission, pre- and post-launch studies revealed ATLAS’s notable bathymetric mapping capability. ICESat-2 bathymetry has been used to support a wide range of coastal and nearshore science objectives. However, analysis of ICESat-2 bathymetry in numerous locations around the world revealed instances of missing or clipped bathymetry in areas where bathymetric measurement should be feasible. These missing data were due to the ATLAS receiver algorithms not being optimized for bathymetry capture. To address this, two updates have been made to ICESat-2’s receiver algorithm parameters with the goal of increasing the area for which ICESat-2 can provide bathymetry. This paper details the parameter changes and presents the results of a two-phased study designed to investigate ICESat-2’s bathymetry enhancements at both local and global scales. The results of both phases confirm that the new parameters achieved the intended goal of increasing the amount of bathymetry provided by ICESat-2. The site-specific phase demonstrates the ability to fill critical bathymetric data gaps in open ocean and coastal settings. The global analysis shows that the area of potential bathymetry approximately doubled, with 6.1 million km 2 of new area in which bathymetric measurements may be feasible. These enhancements are anticipated to facilitate a range of science objectives and close the gap between ICESat-2 bathymetry and offshore sonar data.

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.

Over the past two decades, a major advance that enabled airborne bathymetric lidar to benefit a much wider range of marine science applications was the development of procedures for creating seafloor reflectance mosaics from recorded intensity data. It was recognized that intensity data, derived from the amplitudes of laser returns from the seafloor, contained information related to seafloor albedo and composition. However, the raw intensity data were also found to be related to a number of nuisance parameters, such that, when grided, they exhibited discontinuities, seamlines and other artifacts, hindering their use in benthic habitat mapping. These realizations led to the development of tools and workflows for correcting lidar intensity data to produce seamless seafloor reflectance mosaics. At present, an opportunity exists for another major advance in airborne bathymetric lidar by utilizing not only intensity data, but a large suite of waveform features that describe the shape of the return signal from the seafloor, to characterize benthic habitats and perform ecological assessments. However, similar to raw intensity data, other waveform features exhibit salient discontinuities, seamlines, and other artifacts, if uncorrected. Furthermore, in contrast to the case of intensity data, little work has been done on correction of an entire suite of waveform features to create a set of seamless seafloor mosaics. This study aims to address this need through a novel normalization method that integrates two image blending techniques: Gaussian weighted color matching and Laplacian pyramid blending. The proposed approach, Simultaneous Invariant Normalization of Waveform Features (SINWav), is designed to be invariant to the type of input waveform features, such that feature-specific tuning is unnecessary. To handle vast amounts of data efficiently, we developed a memory-efficient sparse matrix representation. The methods were applied to bathymetric lidar data from Saipan containing 16 different waveform features. Both visual assessments and quantitative analyses using quality metrics indicated that the proposed approach outperforms results derived from raw data and conventional linear transform.

Road markings are essential features to convey important information to various roadway users such as pedestrians, bicyclists, and motorists. Although mobile laser scanning (MLS) technology provides dense and spatially accurate data, automated identification of road markings remains a challenge. Although approaches have been developed to detect road markings from point clouds, prior studies have not thoroughly investigated road discretization and road marking clustering, which are essential for effective instance-level identification and management of road markings. To improve road discretization, our approach uses line segmentation to divide the trajectory of MLS data and fit circles to individual segments. By calculating the intersection between the road points and the circle's center for each trajectory segment, this method ensures no overlap occurs when discretizing roads with sharp turns. To improve road marking clustering, the discretized points are rasterized onto a two-dimensional (2D) image and clustered using connected component labeling. Individual markings are skeletonized to detect junctions and corners, which enables the separation of underclustered road markings. Overclustered road markings are then merged using a rule-based approach optimized with a traffic line manual. The proposed approach was evaluated through extensive experiments using 28 MLS data sets containing 2,340 road marking clusters with complex geometry and wear. Out of these, only 64 instances (2.7%) were falsely clustered, achieving a precision rate of 97.5% and a recall rate of 99.7%. Efficient road maintenance and improved road safety rely heavily on the clear visibility of road markings, which guide diverse users, such as pedestrians, cyclists, and drivers. Traditional methods for inspecting these markings are often time-consuming and prone to human error. In this study, we use MLS technology to collect comprehensive and precise data on roadways. We have developed an automated technique that significantly enhances the identification and management of road markings. This method accurately rasterizes and segments roadway lidar data-even in areas with sharp turns-to apply advanced image processing for the recognition of individual markings, including those that are substantially worn or have complex geometries. This simplifies the geometric data in a GIS database such that each line object is more representative of how someone would manually digitize the marking. Tested on numerous data sets, our approach achieved an accuracy rate of over 97% in clustering individual road markings. The developed approach not only streamlines roadway maintenance but also offers potential benefits for intelligent transportation systems. It contributes to safer autonomous vehicle navigation and supports informed decision-making regarding road infrastructure, ultimately improving all road users' experience.

Since the 2018 launch of NASA's ICESat-2 satellite, numerous studies have documented the bathymetric measurement capabilities of the space-based laser altimeter. However, a commonly identified limitation of ICESat-2 bathymetric point clouds is that they lack accompanying spectral reflectance attributes, or even intensity values, which have been found useful for benthic habitat mapping with airborne bathymetric lidar. We present a novel method for extracting bathymetry from ICESat-2 data and automatically adding spectral reflectance values from Sentinel-2 imagery to each detected bathymetric point. This method, which leverages the cloud computing systems Google Earth Engine and NASA's SlideRule Earth, is ideally suited for "big data" projects with ICESat-2 data products. To demonstrate the scalability of our workflow, we collected 3,500 ICESat-2 segments containing approximately 1.4 million spectrally-attributed bathymetric points. We then used this data set to facilitate training of a deep recurrent neural network for classifying benthic habitats at the ICESat-2 photon level. We trained two identical models, one with and one without the spectral attributes, to investigate the benefits of fusing ICESat-2 photons with Sentinel-2. The results show an improvement in model performance of 18 percentage points, based on F1 score. The procedures and source code are publicly available and will enhance the value of the new ICESat-2 bathymetry data product, ATL24, which is scheduled for release in Fall 2024. These procedures may also be applicable to data from NASA's upcoming CASALS mission.

Bathymetric data, comprising elevations of submerged surfaces (e. g., seafloor or lake bed), constitute a critical need for a wide range of science and application focus areas, such as safety of marine navi- gation, benthic habitat mapping, flood inundation modeling, and coastal engineering. Over the past decade, the availability of near- shore bathymetric data has increased dramatically due to advances in satellite-derived bathymetry (SDB). One notable advance occurred with the 2018 launch of NASA's Ice, Cloud, and land Elevation Satellite 2 (ICESat-2), carrying the Advanced Topographic Laser Altimeter System (ATLAS). However, much like other Earth observing satellites, ATLAS is often hampered by obstructions, such as clouds, which block the sensor's view of the Earth's surface. In this study, we introduce the Deep Occlusion Recovery of Satellite Lidar From ICESat-2 Network (DORSL-FIN) to recover partially occluded bathymetric profiles. We show that DORSL-FIN is able to accurately recover occluded bathymetry and outperforms other methods of interpolation.

Spectrally derived bathymetry (SDB) algorithms are rapidly gaining in acceptance and widespread use for nearshore bathymetric mapping. In the past, refraction correction could generally be ignored in SDB, due to the relatively small fields of view (FOVs) of satellite sensors, and the fact that such corrections were typically small in relation to the uncertainties in the output bathymetry. However, the validity of ignoring refraction correction in SDB is now called into question, due to the ever-improving accuracies of SDB, the desire to use the data in nautical charting workflows, and the application of SDB algorithms to airborne cameras with wide FOVs. This study tests the hypothesis that refraction correction leads to a statistically significant improvement in the accuracy of SDB using uncrewed aircraft system (UAS) imagery. A straightforward procedure for SDB refraction correction, implemented as a modification to the well-known Stumpf algorithm, is presented and applied to imagery collected from a commercially available UAS in two study sites in the Florida Keys, U.S.A. The results show that the refraction correction produces a statistically significant improvement in accuracy, with a reduction in bias of 46–75%, a reduction in RMSE of 3–11 cm, and error distributions closer to Gaussian.

Since the 2018 launch of NASA's ICESat-2 mission, one capability of its Advanced Topographic Laser Altimeter System (ATLAS) that has far exceeded expectations is bathymetric measurement. Although ICESat-2 was designed to generate surface-specific along- track and gridded data products, bathymetry was not a pre-launch science requirement of the mission. However, since launch, ATLAS has proven capable of bathymetric measurement to >40 m in very clear waters [1], and ICESat-2 bathymetry is being used in a growing number of science disciplines. Post-launch efforts have focused on bathymetric signal classification (sea surface, water column and seafloor) and correction for refraction at the air-water interface. Because ATLAS provides bathymetry only along discrete tracks, another area of focus is on integration of ATLAS data with relative bathymetry from multispectral satellite imagery-often referred to as satellite-derived bathymetry (SDB)-to obtain spatially-contiguous 2D bathymetric coverage. This paper synthesizes the latest algorithms, techniques and uses of ICESat-2 bathymetry, including collaborative efforts of the Bathymetry Working Group of the ICESat-2 Science Team, and recommends topics for future investigation.

Coastal regions are undergoing rapid change, due to natural and anthropogenic forcings. A current constraint in understanding and modeling these changes is the lack of multi-temporal bathymetric data, or recursive observations. Often, it is difficult to obtain the repeat observations needed to quantify bathymetric change over time or events. However, the recent availability of ICESat-2 bathymetric lidar creates the option to map coastal bathymetry from solely space-based measurements via satellite-derived bathymetry with multispectral imagery (IS-2/SDB). This compositional space-based bathymetric mapping technique can assess temporal change along the coasts without other remote sensing or in situ data. However, questions exist as to the accuracy of the technique relative to both quantitative uncertainties and the ability to resolve the spatial patterns of erosion and deposition in the nearshore environment, indicative of geomorphologic change. This paper addresses the concept using data from the Florida panhandle (Northern Gulf of Mexico) collected by Sentinel-2 and ICESat-2 at two epochs to assess the feasibility of using IS-2/SDB for bathymetric change detection at scientifically relevant scales, spatial resolutions and accuracies. The comparison of the satellite-only result is compared to airborne data collected at similar epochs to reveal both quantitatively and qualitatively the utility of this technique.

In 1967, as directed by the Oregon Beach Bill, the Oregon State Highway Department undertook a survey of the entire coast to delineate the shore zone boundary. Survey control points and photo control were established for an aerial survey that same year, but only hardcopy records currently exist. If the 1967 survey coordinates can be accurately updated, the aerial imagery can be processed in modern photogrammetric software to produce digital elevation models of the entire coast, which will enable volumetric coastal change analysis over a period of half a century. The goal of this study was to develop and test a procedure for combining historic and current horizontal traverse survey data to update the 2D control survey coordinates (with the intent to add leveling data and extend the procedures to obtain 3D coordinates in a later study). First, a custom workflow and algorithms were developed to convert the historical survey records to machine-readable format. GNSS data—both static postprocessed and real-time network (RTN)—were then acquired for recoverable marks. An adjustment of the traverse data constrained to RTN coordinates was compared against an independent adjustment of the static GNSS data performed in the National Geodetic Survey OPUS-Projects software. The results show that the methods can produce updated horizontal coordinates for the 1967 survey accurate to within two centimeters.

A current hindrance to the scientific use of available bathymetric lidar point clouds is the frequent lack of accurate and thorough segmentation of seafloor points. Furthermore, scientific end-users typically lack access to waveforms, trajectories, and other upstream data, and also do not have the time or expertise to perform extensive manual point cloud editing. To address these needs, this study seeks to develop and test a novel clustering approach to seafloor segmentation that solely uses georeferenced point clouds. The proposed approach does not make any assumptions regarding the statistical distribution of points in the input point cloud. Instead, the approach organizes the point cloud into an inverse histogram and finds a gap that best separates the seafloor using the proposed peak-detection method. The proposed approach is evaluated with datasets acquired in Florida with a Riegl VQ-880-G bathymetric LiDAR system. The parameters are optimized through a sensitivity analysis with a point-wise comparison between the extracted seafloor and ground truth. With optimized parameters, the proposed approach achieved F1-scores of 98.14–98.77%, which outperforms three popular existing methods. Further, we compared seafloor points with Reson 8125 MBES hydrographic survey data. The results indicate that seafloor points were detected successfully with vertical errors of −0.190 ± 0.132 m and −0.185 ± 0.119 m (μ ± σ) for two test datasets.

Bathymetry retrievals from 2D, multispectral imagery, referred to as Satellite‐Derived Bathymetry (SDB), afford the potential to obtain global, nearshore bathymetric data in optically clear waters. However, accurate SDB depth retrievals are limited in the absence of “seed depths.” The Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) space‐based altimeter has proven capable of accurate bathymetry, but methods of employing ICESat‐2 bathymetry for SDB retrievals over broad spatial extents are immature. This research aims to establish and test a baseline methodology for generating bathymetric surface models using SDB with ICESat‐2. The workflow is operationally efficient (17–37 min processing time) and capable of producing bathymetry of sufficient vertical accuracy for many coastal science applications, with RMSEs of 0.96 and 1.54 m when using Sentinel‐2 and Landsat 8, respectively. The highest priorities for further automation have also been identified, supporting the long‐range goal of global coral reef habitat change analysis using ICESat‐2‐aided SDB.

This study investigates a new method for measuring water turbidity—specifically, the diffuse attenuation coefficient of downwelling irradiance Kd —using data from a spaceborne, green-wavelength lidar aboard the National Aeronautics and Space Administration's ICESat-2 satellite. The method enables us to fill nearshore data voids in existing Kd data sets and provides a more direct measurement approach than methods based on passive multispectral satellite imagery. Furthermore, in contrast to other lidar-based methods, it does not rely on extensive signal processing or the availability of the system impulse response function, and it is designed to be applied globally rather than at a specific geographic location. The model was tested using Kd measurements from the National Oceanic and Atmospheric Administration's Visible Infrared Imaging Radiometer Suite sensor at 94 coastal sites spanning the globe, with Kd values ranging from 0.05 to 3.6 m –1 . The results demonstrate the efficacy of the approach and serve as a benchmark for future machine-learning regression studies of turbidity using ICESat-2.

Vegetation health monitoring is key to identifying early signs of water stress, pollutant-induced toxicity, and plant diseases in green urban stormwater facilities. However, rigorous monitoring to collect accurate quantitative data is an expensive and time-consuming process. This paper examines the feasibility of using uninhabited aircraft systems (UAS), in comparison to standard ground-based methods, for monitoring biomass and primary production in two bioswale cells at an urban stormwater facility. Implementation of the UAS-based approach involved flight planning in an urban area to meet resolution requirements of bioswale imagery obtained from near-infrared and red-green-blue cameras. The resulting normalized difference vegetation index (NDVI) estimated from UAS data was tracked over a 2-month period during the transition from spring to summer, showing the spatial distribution of NDVI and the change in vegetation coverage areas over time. In comparison, ground-based measurements of the fraction of intercepted photosynthetically active radiation (PAR) presented multiple practical challenges during implementation in the field, leading to over- and underestimates of intercepted PAR. Overall, UAS-derived NDVI was found to be a valuable reflectance-based, vegetation health-monitoring methodology that can be used by utilities and cities for practical, cost-effective, and rapid assessment of vegetation stress and for long-term maintenance in green stormwater facilities.

There is a growing demand for point cloud data that can produce reliable single-tree measurements. The most common platforms for obtaining such data are unmanned aircraft systems with passive sensors (UAS), unmanned aircraft equipped with aerial lidar scanners (ALS), and mobile lidar scanners (MLS). Our objectives were to compare the capabilities of the UAS, ALS, and MLS to locate treetops and stems and to estimate tree lean. The platforms were used to produce overlapping point clouds of a mature Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) stand, from which 273 trees were manually identified. Control trees were used to test tree detection accuracy of four algorithms and the number of stems detectable using each platform. Tree lean was calculated in two ways: using the stem location near the canopy and using the treetop. The treetops were detected more accurately from ALS and UAS clouds than from MLS, but the MLS outperformed ALS and UAS in stem detection. The platform influenced treetop detection accuracy, whereas the algorithms did not. The height estimates from the ALS and MLS were correlated (R2 = 0.96), but the MLS height estimates were unreliable, especially as distance from the scanner increased. The lean estimates using the stem locations or treetop locations produced analogous distributions for all three platforms.