Compressive satellite LiDAR (CS-LiDAR) has been recently introduced as a radically different computational sensing and reconstruction approach for LiDAR sensing of Earth. It is based on NASA’s adaptive wavelength scanning LiDAR (AWSL) system. Unlike conventional 1D LiDAR methods, CS-LiDAR utilizes sparse coded laser illumination across a 2D field-of-view. The aim is to compressively capture Earth from hundreds of kilometers above, enabling computational 3D imagery reconstruction with resolution that is comparable to that attained with data collected from just hundreds of meters. The forward imaging model captures the light propagation phenomena affecting the photon pulses transmitted from the sensor to the Earth’s surface and back. This work enhances CS-LiDAR by integrating imaging spectroscopy into a multimodal system and employing a transformer network for the inverse imaging problem, driven by multimodal attention mechanisms. Emulations enabled by enormous observational LiDAR data of Earth, available from NASA’s G-LiHT imaging observatory, highlight the efficacy of methods developed.

This paper proposes an algorithm to enhance the resolution of satellite lidar data using Generative Adversarial Networks (GANs) under the hyperheight data cube framework. A super-resolution algorithm based on adversarial training is applied to overcome the challenges of long-range satellite lidar systems. The algorithm generates high-resolution super-resolved outputs from low-resolution inputs, improving the quality of several lidar representations such as canopy height models and profiles. This approach not only advances lidar-based models but also facilitates sophisticated lidar data analysis for various fields, such as environmental science, urban planning, and disaster management. The super-resolved lidar data provides a more precise depiction of the Earth's surface, opening up new avenues for research and applications in different domains. The framework's effectiveness was validated in the Florida Everglades National Park, where the resolution was increased from a 3m x 6m grid with 10m footprints to a 3m x 3m grid with 3m footprints, and the vertical resolution was enhanced from 0.5m to 0.25m.

We present the design and performance of a Concurrent Artificially-intelligent Spectrometry and Adaptive Lidar System (CASALS) for 3D imaging from Space. With a single fast wavelength tuning laser, CASALS accomplishes a 1,200 resolvable spots swath mapping by grating dispersion wavelength steering. Any subset of these 1,200 spots can be selected by wavelength switching. The validation operating principle was accomplished and reported in IGASS-2022. With configurable base design, we report the designs and progress of the CASALS airplane campaign with 256 contiguous ground spots. It is accomplished with a single fast tuning lase at 1040-nm, 1.152MHz tuning rate, and pulse modulated 2-ns on each wavelength. Return pulses are mapped to an eight-pixel detector array with single-photon sensitivity. The lidar returns are time-multiplexed to two outputs that are digitized with two 1-GSPS-digitizer. A grating spectrometer rejects solar background noise spatially and spectrally. We are developing a 1040-nm CASALS intended for multiple LEO orbit missions: Earth Venture Mission on ESPA Grande, STV Mission on ESPA Grande SmallSat, STV Mission on spacecraft equivalent to ICESat-2.

Compressive satellite LiDAR (CS-LiDAR) has been recently introduced as a radically different computational sensing and reconstruction approach for LiDAR sensing of Earth. It is based on NASA’s adaptive wavelength scanning LiDAR (AWSL) system. Rather than measuring 1D line footprints over a satellite’s swath path as is the norm today, CS-LiDAR adopts sparse coded laser illumination over a 2D wide field-of-view. The objective is to compressively sense Earth from hundreds of km above Earth to then computationally reconstruct the 3D imagery with resolution and coverage as if the data was collected from just hundreds of meters in height. The forward imaging model captures the light propagation phenomena affecting the photon pulses transmitted from the sensor to the Earth’s surface and back. This paper advances CS-LiDAR on many fronts. First, imaging spectroscopy side-information, often jointly available with LiDARs, is integrated into a multimodal imaging system. Secondly, the inverse imaging problem is cast under a transformer network architecture driven by multimodal attention mechanisms. Finally, by directing the snapshot spectral cameras in front of the LiDAR, the transformer mechanisms autonomously adjust the LiDAR’s beam scanning to focus on specific target locations, thus attaining end-to-end optimal adaptive sampling that can respond to varying observational conditions, surface events, and scientific priorities. Emulations enabled by enormous observational LiDAR data of Earth, available from NASA’s G-LiHT imaging observatory, show the advantages attained by methods developed in this work.

LiDAR remote sensing systems are deployed in various platforms including satellites, airplanes, and drones — which, in essence, determines the sampling characteristics of the underlying imaging system. Low-altitude LiDARs provide high photon count and high spatial resolution but only in very localized patches. Satellite LiDARs, on the other hand, provide measurements at a global scale but are limited by low photon count and their samples are sparsely apart along swath line trajectories that are far in between. This paper describes a new class of satellite remote sensing LiDARs, aimed at overcoming the limitations of current satellite imaging systems. It exploits the principles of compressive sensing and machine learning (ML) to compressively sense Earth from hundreds of km above Earth to then reconstruct the 3D imagery with resolution and coverage, as if the data was collected from airborne platforms at just hundreds of meters in height.We introduce a novel representation of waveform altimetry profiles, coined HyperHeight Data Cubes (HHDC), which encompass rich information about the 3D structure of a scene. Canopy height models, digital terrain models, and many other features of a scene that are embedded in HHDC are easily extracted with simple statistical quantiles.We introduce machine learning methods to reconstruct the compressive LiDAR measurements so as to attain high-resolution, dense coverage, and broad field-of-view per swath pass. ML training data is attained from NASA’s G-LiHT imaging missions. Simulations with various types of forests across the US illustrate the power of the new LiDAR imaging systems.

New Observing Systems (NOS) will be NASA’s next generation approach for Earth remote sensing, utilizing many diverse observing capabilities to produce optimized measurements integrated from multiple vantage points and in multiple dimensions. NOS will require strong data fusion foundations to be able to intelligently combine, and retrieve information from, data coming from assets differing in characteristics like instrument type, spectral domain, and spatial and temporal resolution. We are developing an end-to-end data fusion framework employing advanced Artificial Intelligence (AI) Machine Learning (ML) techniques with the primary purpose to drive the design and operation of multi-sensor NOS for Earth sciences and beyond. This work requires building ML-enabled analytic tools and advanced environments to take advantage of high-performance computing systems for the creation of a NOS workflow that utilizes large amounts of diverse airborne and satellite observations along with ancillary information including climate and drought time series and soil properties. We are demonstrating the framework using a forest productivity and degradation use case, but the framework is designed to be applicable to a wide variety of NOS scientific objectives.

Low-altitude airborne lidars deliver high spatial resolution swath mapping using dense laser footprint sampling but only in limited areas, while satellite lidars offer global sampling but are hampered by low resolution due to sparse footprints. This work presents a novel approach to satellite lidar remote sensing designed to address the low spatial resolution by leveraging the principles of compressive sensing and machine learning applied to a highly efficient, adaptive lidar capable of dense footprint sampling. Compressive sensing enables the distribution of footprints across a swath with a density appropriate to recover the features of interest, without unnecessarily oversampling the terrain. Machine learning techniques are employed to reconstruct the compressive lidar measurements, leading to high-resolution, dense coverage, and a broad field-of-view per swath pass. HyperHeight Data Cubes are introduced, which offer a wealth of information about the 3D structure of a scene, including digital surface models, canopy height models and the internal organization of canopies. Training data was obtained from NASA's G-LiHT airborne lidar, and simulations of satellite observations performed on various forest types across the US demonstrate the efficacy of the new lidar imaging approach.

We present the design and performance of an adaptive wavelength scanning lidar (AWSL) for highly efficient mapping from low Earth orbit (LEO). Mapping is accomplished by steering a laser beam across 1,200 resolvable spots using wavelength tuning and grating dispersion. Any subset of these 1,200 spots can be selected by wavelength switching. The design is validated with an 1550-nm prototype using a fast wavelength-tuned (500-kHz) pulsed (2-ns) fiber laser with the beam dispersed by gratings for beam steering. Reflected pulses are detected with an eight-pixel detector array with single-photon sensitivity. Eight lidar returns are time-multiplexed to one output that is digitized with a single 1-GSPS-digitizer, to save power. A grating spectrometer rejects solar background noise spatially and spectrally, and images laser footprints on to the detector array. The gratings retain the fiber laser beam quality. We are developing a 1030-nm AWSL intended for a LEO SmallSat platform.

Glacial acceleration is a main source of uncertainty in sea-level-change assessment. Measurement of ice-surface heights with a spatial and temporal resolution that not only allows elevation-change calculation, but also captures ice-surface morphology and its changes is required to aid in investigations of the geophysical processes associated with glacial acceleration. The Advanced Topographic Laser Altimeter System aboard NASA's future ICESat-2 Mission (launch 2017) will implement multibeam micropulse photon-counting lidar altimetry aimed at measuring ice-surface heights at 0.7-m along-track spacing. The instrument is designed to resolve spatial and temporal variability of rapidly changing glaciers and ice sheets and the Arctic sea ice. The new technology requires the development of a new mathematical algorithm for the retrieval of height information. We introduce the density-dimension algorithm (DDA) that utilizes the radial basis function to calculate a weighted density as a form of data aggregation in the photon cloud and considers density an additional dimension as an aid in autoadaptive threshold determination. The autoadaptive capability of the algorithm is necessary to separate returns from noise and signal photons under changing environmental conditions. The algorithm is evaluated using data collected with an ICESat-2 simulator instrument, the Slope Imaging Multi-polarization Photon-counting Lidar, over the heavily crevassed Giesecke Brær in Northwestern Greenland in summer 2015. Results demonstrate that ICESat-2 may be expected to provide ice-surface height measurements over crevassed glaciers and other complex ice surfaces. The DDA is generally applicable for the analysis of airborne and spaceborne micropulse photon-counting| lidar data over complex and simple surfaces.

NASA's Ice, Cloud, and land Elevation Satellite (ICESat), which operated between 2003 and 2009, made the first satellite-based global lidar measurement of Earth's ice sheet elevations, sea-ice thickness and vegetation canopy structure. The primary instrument on ICESat was the Geoscience Laser Altimeter System (GLAS), which measured the distance from the spacecraft to Earth's surface via the roundtrip travel time of individual laser pulses. GLAS utilized pulsed lasers and a direct detection receiver consisting of a silicon avalanche photodiode (Si APD) and a waveform digitizer. Early in the mission, the peak power of the received signal from snow and ice surfaces was found to span a wider dynamic range than planned, often exceeding the linear dynamic range of the GLAS 1064-nm detector assembly. The resulting saturation of the receiver distorted the recorded signal and resulted in range biases as large as ~50 cm for ice and snow-covered surfaces. We developed a correction for this "saturation range bias" based on laboratory tests using a spare flight detector, and refined the correction by comparing GLAS elevation estimates to those derived from Global Positioning System (GPS) surveys over the calibration site at the salar de Uyuni, Bolivia. Applying the saturation correction largely eliminated the range bias due to receiver saturation for affected ICESat measurements over Uyuni and significantly reduced the discrepancies at orbit crossovers located on flat regions of the Antarctic ice sheet.

Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone.

Statistical measures of patterns (textures) in surface roughness are used to quantitatively differentiate volcanic deposit facies on the Pumice Plain, on the northern flank of Mount St. Helens (MSH). Surface roughness values are derived from a Light Detection and Ranging (LiDAR) point cloud collected in 2004 from a fixed-wing airborne platform. Patterns in surface roughness are characterized using co-occurrence texture statistics. Pristine-pyroclastic, reworked-pyroclastic, mudflow, boulder beds, eroded lava flows, braided streams, and other units within the Pumice Plain are all found to have significantly distinct roughness textures. The MSH deposits are reasonably accessible, and the textural variations have been verified in the field. Results of this work indicate that by affecting the distribution of large clasts and tens-of-meter scale landforms, modification of pyroclastic deposits by lahars alters the morphology of the surface in detectable quantifiable ways. When a lahar erodes a pyroclastic deposit, surface roughness increases, as does the randomness in the deposit surface. Conversely, when a lahar deposits material, the resulting landforms are less rough but more random than pristine pumice-rich pyroclastic deposits. By mapping these relationships and others, volcanic deposit facies can be differentiated. This new method of mapping, based on roughness texture, has the potential to aid mapping efforts in more remote regions, both on this planet and elsewhere in the solar system.

The Eco3D campaign was conducted in the Summer of 2011. As part of the campaign three unique and innovative NASA Goddard Space Flight Center airborne sensors were flown simultaneously: The Digital Beamforming Synthetic Aperture Radar (DBSAR), the Slope Imaging Multi-polarization Photon-counting Lidar (SIMPL) and the Cloud Absorption Radiometer (CAR). The campaign covered sites from Quebec to Southern Florida and thereby acquired data over forests ranging from Boreal to tropical wetlands. This paper describes the instruments and sites covered and presents the first images resulting from the campaign.

The ICESat-2 mission will continue NASA's spaceflight laser altimeter measurements of ice sheets, sea ice and vegetation using a new measurement approach: micropulse, single photon ranging at 532 nm. Differential penetration of green laser energy into snow, ice and water could introduce errors in sea ice freeboard determination used for estimation of ice thickness. Laser pulse scattering from these surface types, and resulting range biasing due to pulse broadening, is assessed using SIMPL airborne data acquired over ice-covered Lake Erie. SIMPL acquires Polarimetrie lidar measurements at 1064 and 532 nm using the micropulse, single photon ranging measurement approach.

We report on progress in developing a new multi-beam non- scanning, swath mapping laser altimeter measurement approach for future spaceflight missions using a high repetition rate, short-pulse laser transmitter. The instrument contains multi pixel photon counting detectors, high bandwidth, sixteen-channel 8-bit digitizer and a high- throughput data system.

Geodetic control is extremely important in the production and quality control of topographic data sets, enabling elevation results to be referenced to an absolute vertical datum. Global topographic data with improved geodetic accuracy achieved using global Ground Control Point (GCP) databases enable more accurate characterization of land topography and its change related to solid Earth processes, natural hazards and climate change. The multiple-beam lidar instrument that will be part of the NASA Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission will provide a comprehensive, global data set that can be used for geodetic control purposes. Here we illustrate that potential using data acquired by NASA's Ice, Cloud and land Elevation Satellite (ICEsat) that has acquired single-beam, globally distributed laser altimeter profiles (± 86°) since February of 2003. The profiles provide a consistently referenced elevation data set with unprecedented accuracy and quantified measurement errors that can be used to generate GCPs with sub-decimeter vertical accuracy and better than 10 m horizontal accuracy. Like the planned capability for DESDynI, ICESat records a waveform that is the elevation distribution of energy reflected within the laser footprint from vegetation, where present, and the ground where illuminated through gaps in any vegetation cover. The waveform enables assessment of Digital Elevation Models (DEMs) with respect to the highest, centroid, and lowest elevations observed by ICESat and in some cases with respect to the ground identified beneath vegetation cover. Using the ICESat altimetry data we are developing a comprehensive database of consistent, global, geodetic ground control that will enhance the quality of a variety of regional to global DEMs. Here we illustrate the accuracy assessment of the Shuttle Radar Topography Mission (SRTM) DEM produced for Australia, documenting spatially varying elevation biases of several meters in magnitude.

The Slope Imaging Multi-polarization Photon-counting Lidar is an airborne instrument developed to demonstrate laser altimetry measurement methods that will enable more efficient observations of topography and surface properties from space. The instrument was developed through the NASA Earth Science Technology Office Instrument Incubator Program with a focus on cryosphere remote sensing. The SIMPL transmitter is an 11 KHz, 1064 nm, plane-polarized micropulse laser transmitter that is frequency doubled to 532 nm and split into four push-broom beams. The receiver employs single-photon, polarimetric ranging at 532 and 1064 nm using Single Photon Counting Modules in order to achieve simultaneous sampling of surface elevation, slope, roughness and depolarizing scattering properties, the latter used to differentiate surface types. Data acquired over ice-covered Lake Erie in February, 2009 are documenting SIMPL's measurement performance and capabilities, demonstrating differentiation of open water and several ice cover types. ICESat-2 will employ several of the technologies advanced by SIMPL, including micropulse, single photon ranging in a multi-beam, push-broom configuration operating at 532 nm.

Satellite and aircraft observations have revealed that remarkable changes in the Earth's polar ice cover have occurred in the last decade. The impacts of these changes, which include dramatic ice loss from ice sheets and rapid declines in Arctic sea ice, could be quite large in terms of sea level rise and global climate. NASA's Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), currently planned for launch in 2015, is specifically intended to quantify the amount of change in ice sheets and sea ice and provide key insights into their behavior. It will achieve these objectives through the use of precise laser measurements of surface elevation, building on the groundbreaking capabilities of its predecessor, the Ice Cloud and Land Elevation Satellite (ICESat). In particular, ICESat-2 will measure the temporal and spatial character of ice sheet elevation change to enable assessment of ice sheet mass balance and examination of the underlying mechanisms that control it. The precision of ICESat-2's elevation measurement will also allow for accurate measurements of sea ice freeboard height, from which sea ice thickness and its temporal changes can be estimated. ICESat-2 will provide important information on other components of the Earth System as well, most notably large-scale vegetation biomass estimates through the measurement of vegetation canopy height. When combined with the original ICESat observations, ICESat-2 will provide ice change measurements across more than a 15-year time span. Its significantly improved laser system will also provide observations with much greater spatial resolution, temporal resolution, and accuracy than has ever been possible before.

Government and commercial airborne light detection and ranging (lidar) systems have enabled extensive measurements of the Earth's surface and land cover over the past decade. There is much interest, however, in employing smaller lidar systems that require less power to enable sensing from small unmanned aerial vehicles or satellites. Technological advances in the performance of small microlasers and photodetector sensitivity have recently enabled the development of experimental airborne lidar systems with low signal-to-noise ratios (LSNRs). Recent government and academic prototypes have indicated that LSNR airborne lidars could significantly increase the fidelity of terrain reconstruction over what is possible with existing conventional lidars. Thus, there is a need to build up a modeling capability for such systems in order to aid in future system and mission design. A numerical sensor simulator has been developed to model the expected returns from LSNR microlaser altimeter systems and predict their performance. Both optical and signal processing system components are considered, along with other factors, including atmospheric effects and surface conditions. Topographic (solid Earth) and bathymetric (littoral zone) measurement scenarios are considered. The analysis of topographic simulation data focuses on the effect of solar noise on SNR and elevation accuracy while bathymetric performance is evaluated with regard to water depth and scan angle for different water clarities. The mission conditions chiefly responsible for limiting the performance of LSNR lidar are discussed in detail, along with suggestions for further algorithm development and system performance evaluation.

Analysis of the sensitivity of laser ranging errors to surface conditions indicates that predicted single-shot range errors are primarily dependent on surface slope. Range errors are less sensitive to variations in surface roughness or reflectivity. Values of total surface slope and roughness for nine terrestrial landforms, derived from digital elevation data at a 186 m length scale, vary from 2/spl deg/ to 40/spl deg/ and 0.8 to 15 m, respectively, at a 90% frequency of occurrence. This range of surface morphologies yields a variation in single shot laser ranging error from 0.4 to 8 m, assuming system parameters for the proposed Topographic Mapping Laser Altimeter (TMLA) and a nominal 30% surface reflectivity. The total elevation accuracy of data obtained via satellite laser altimetry, although dominated by the range error, is also a function of additional error sources, including orbit ephemeris, atmospheric, and calibration errors. Averaging of multiple laser measurements improves the vertical accuracy of the elevation data by statistical reduction of random errors. During a three-year mission, two to three laser measurements will be acquired, on average, for each 200-m footprint at low to moderate latitudes, accounting for the latitudinal variation of ground track spacing and cloud cover. For high-latitude regions, the narrow spacing of satellite ground tracks in a polar orbit will provide frequent repeat observations yielding, on average, 4 to 25 measurements of each footprint over the Antarctic and Greenland ice sheets. Averaging of these multiple repeat observations at high latitude will yield an improvement in vertical accuracy by a factor of two to five.>