Lidar Research Articles

Multi-Temporal Image Fusion-Based Shallow-Water Bathymetry Inversion Method Using Active and Passive Satellite Remote Sensing Data

In the active–passive fusion-based bathymetry inversion method using single-temporal images, image data often suffer from errors due to inadequate atmospheric correction and interference from neighboring land and water pixels. This results in the generation of noise, making high-quality data difficult to obtain. To address this problem, this paper introduces a multi-temporal image fusion method. First, a median filter is applied to separate land and water pixels, eliminating the influence of adjacent land and water pixels. Next, multiple images captured at different times are fused to remove noise caused by water surface fluctuations and surface vessels. Finally, ICESat-2 laser altimeter data are fused with multi-temporal Sentinel-2 satellite data to construct a machine learning framework for coastal bathymetry. The bathymetric control points are extracted from ICESat-2 ATL03 products rather than from field measurements. A backpropagation (BP) neural network model is then used to incorporate the initial multispectral information of Sentinel-2 data at each bathymetric point and its surrounding area during the training process. Bathymetric maps of the study areas are generated based on the trained model. In the three study areas selected in the South China Sea (SCS), the validation is performed by comparing with the measurement data obtained using shipborne single-beam or multi-beam and airborne laser bathymetry systems. The root mean square errors (RMSEs) of the model using the band information after image fusion and median filter processing are better than 1.82 m, and the mean absolute errors (MAEs) are better than 1.63 m. The results show that the proposed method achieves good performance and can be applied for shallow-water terrain inversion.

Investigating LiDAR Metrics for Old-Growth Beech- and Spruce-Dominated Forest Identification in Central Europe

Old-growth forests are essential for maintaining biodiversity, as they are formed by the complexity of diverse forest structures, such as broad variations in tree height and diameter (DBH) and conditions of living and dead trees, leading to various ecological niches. However, many efforts of old-growth forest mapping from LiDAR have targeted only one specific forest structure (e.g., stand height, basal area, or stand density) by deriving information through a large number of LiDAR metrics. This study introduces a novel approach for identifying old-growth forests by optimizing a set of selected LiDAR standards and structural metrics. These metrics effectively capture the arrangement of multiple forest structures, such as canopy heterogeneity, multilayer canopy profile, and canopy openness. To determine the important LiDAR standard and structural metrics in identifying old-growth forests, multicollinearity analysis using the variance inflation factor (VIF) approach was applied to identify and remove metrics with high collinearity, followed by the random forest algorithm to rank which LiDAR standard and structural metrics are important in old-growth forest classification. The results demonstrate that the LiDAR structural metrics (i.e., advanced LiDAR metrics related to multiple canopy structures) are more important and effective in distinguishing old- and second-growth forests than LiDAR standard metrics (i.e., height- and density-based LiDAR metrics) using the European definition of a 150-year stand age threshold for old-growth forests. These structural metrics were then used as predictors for the final classification of old-growth forests, yielding an overall accuracy of 78%, with a true skill statistic (TSS) of 0.58 for the test dataset. This study demonstrates that using a few structural LiDAR metrics provides more information than a high number of standard LiDAR metrics, particularly for identifying old-growth forests in mixed temperate forests. The findings can aid forest and national park managers in developing a practical and efficient old-growth forest identification and monitoring method using LiDAR.

A review of error analysis and calibration techniques for spherical coordinate 3D laser scanners: Focus on non-instrumental and non-geometric factors

Both Terrestrial Laser Scanners (TLSs) and Metrological Laser Radars (MLRs) are typical Spherical Coordinate Laser Scanners (SCLSs), which are used for various fields requiring surface 3D scanning. TLSs are popular in historical preservation and archiving, reverse engineering, surveying geographic modeling, and digital cities, etc. MLRs, on the other hand, are primarily applied in large-scale precision measurements in industrial manufacturing due to their high accuracy in scanned 3D points. The 3D point precision of both TLSs and MLRs is affected significantly by geometric errors inside instruments. While the geometric errors of SCLSs have been reviewed in some studies, a systematic review of external non-instrumental errors and internal non-geometric errors for TLSs and MLRs is still lacking. These error sources also affect the measurement accuracy of SCLSs. This paper aims to review the quantitative and qualitative analysis of external non-instrumental errors and internal non-geometric errors in TLSs and MLRs. The external non-instrumental errors are further classified into surface properties, scanning geometry, and working environment in this review. For internal non-geometric errors, current research primarily focuses on laser signal processing but lacks studies on error mechanisms and their impact on accuracy. Most existing studies address Time-of-Flight (ToF)-based TLSs, with limited attention given to MLRs. While external factors such as scan depth and surface reflectance are well-studied, environmental factors like temperature remain underexplored. Additionally, there is insufficient research on error compensation models for external factors. Future research on MLRs may focus on several aspects. These include analyzing external non-instrument error factors qualitatively and quantitatively, developing error compensation models, studying the impact of the working environment, and evaluating uncertainty using large-scale standard objects. This review may be beneficial for understanding the research development about the non-instrumental and non-geometric errors of SCLSs.

Using airborne LiDAR and enhanced-geolocated GEDI metrics to map structural traits over a Mediterranean forest

Using airborne LiDAR and enhanced-geolocated GEDI metrics to map structural traits over a Mediterranean forest

Recent Advances in Tunable External Cavity Diode Lasers

A narrow linewidth tunable laser source is a critical component in various fields, including laser radar, quantum information, coherent communication, and precise measurement. Tunable external cavity diode lasers (ECDLs) demonstrate excellent performance, such as narrow linewidth, wide tunable range, and low threshold current, making them increasingly versatile and widely applicable. This article provides an overview of the fundamental structures and recent advancements in external cavity semiconductor lasers. In particular, we discuss external cavity semiconductor lasers based on quantum well and quantum dot gain chips. The structure of the gain chip significantly influences laser’s performance. External cavity quantum well laser has a narrower linewidth, higher power, and better mode stability. Conversely, external cavity quantum dot laser provides a wider tunable range and a remarkably lower threshold current. Furthermore, dual-wavelength external cavity tunable diode lasers are gaining importance in applications such as optical switching and terahertz radiation generation. With the continuous optimization of chips and external cavity structures, external cavity diode lasers are increasingly recognized as promising light sources with narrow linewidth and wide tunability, opening up broader application prospects.

An Adaptive Denoising Method for Photon-Counting LiDAR Point Clouds: Application in Intertidal Zones

The intertidal zone, as a dynamic ecosystem at the interface of land and sea, plays a critical role in environmental protection and disaster mitigation. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with the Advanced Topographic Laser Altimeter System (ATLAS) with the ability to penetrate the water bodies, enabling its use for bathymetric measurements. However, the complex land cover types and frequent environmental changes in intertidal zones pose significant challenges for precise measurement and dynamic monitoring. In an effort to address the denoising challenges of ICESat-2 photon point cloud data in such complex environments, this study proposes an adaptive photon denoising method that is capable of dynamically adjusting the denoising strategy for different types of photon data. ATL03 data from four typical intertidal zones were selected for denoising experiments. The results indicated that the proposed adaptive denoising method achieved average recall, precision, and F-score values of 0.9885, 0.9927, and 0.9906, respectively, demonstrating excellent denoising performance and stability. This method provides an effective data processing approach for high-precision monitoring of intertidal zone topography.

Applications of Laser Radar (LiDAR) in Geospatial Intelligence and AI-Driven Emergency Management

Applications of Laser Radar (LiDAR) in Geospatial Intelligence and AI-Driven Emergency Management

Impact of atmospheric turbulence on the signal intensity of the laser ranging echo of space debris

Based on the research of laser atmospheric transmission theory, this paper derives the laser radar ranging equation under the influence of turbulence under the condition of slant path transmission, and simulates and analyzes the changes in the number of debris laser ranging (DLR) echo photons under different parameters. The results show that the number of echo photons is inversely proportional to the intensity of atmospheric turbulence and proportional to the relative angular spread. The greater the elevation angle, the more echo photons; among them, under strong, weak atmospheric turbulence, and no turbulence conditions, the ratio of the number of echo photons is approximately between 1/500 and 4/5. The success rate of the DLR system detecting effective echo signals under different turbulence intensities is comparatively analyzed. Finally, based on the comparison of the experimental results of the 700 mm DLR system and the simulation results of the corrected model, the average relative error is found to be 13.43%.

Quantification of Nearshore Sandbar Seasonal Evolution Based on Drone Pseudo-Bathymetry Time-Lapse Data

Nearshore sandbars are dynamic features that characterize shallow morphobathymetry and vary over a wide range of geometries and temporal lifespans. Nearshore sandbars influence beach geometry by altering the energy of incoming waves; thus, monitoring the evolution of sandbars is a fundamental approach in effective coastal planning. Due to several natural and technical limitations related to shallow seafloor mapping, there is a significant gap in the availability of high-resolution, shallow bathymetric data for monitoring the dynamic behaviour of nearshore sandbars effectively. This study introduces a novel image-processing technique that produces time series of pseudo-bathymetric data by utilizing multi-temporal (monthly) drone imagery, and it provides an assessment of local morphodynamics at a sandy beach in the southeast Mediterranean. The technique is called standardized-ratio bathymetric index (SRBI), and it transforms natural-colour drone imagery to pseudo-bathymetric data by applying an empirical formula used for satellite-derived bathymetry. This technique correlates well with laser altimetry depth measurements; however, it does not require in situ depth data for implementation. The resulting pseudo-bathymetric data allows for extracting cross-shore profiles and delineating the sandbar crest with 4 m horizontal accuracy. Stacking of temporal profiles allowed for the quantification of the sandbar’s crest and trough changes at different alongshore sections. The main findings suggest that the nearshore crescentic sandbar at Episkopi Beach (north Crete) shows strong seasonality regarding net offshore migration that is promoted by enhanced wave action during winter months. In addition, the crescentic sandbar is susceptible to morphology arrestment during prolonged weeks of low wave action. The average migration rate during winter is 10 m.month−1, with some sections exhibiting a maximum of 60 m.month−1. This study (a) offers a novel remote-sensing approach, suitable for nearshore seafloor monitoring with low computational complexity, (b) reveals sandbar geometry and temporal change in superior detail compared to other observational methods, and (c) advances knowledge about nearshore sandbar monitoring in the Mediterranean region.

Central Retinal Thickness Variability as a Predictive Factor for Visual Acuity After Dexamethasone Implant in Retinal Vein Occlusion.

Investigate central retinal thickness (CRT) variability and changes in best-corrected visual acuity (BCVA) after 12 months in patients with retinal vein occlusion (RVO) treated with dexamethasone intravitreal implants. Post hoc analyses of two randomized trials in patients with macular edema associated with branch or central RVO treated with a 0.7-mg dexamethasone implant. Central retinal thickness standard deviation (CRT-SD) and central retinal thickness amplitude (CRT-A) were measures of variability. Analyses included multinomial and simple linear regression. In 400 patients, CRT-SD and CRT-A were significantly associated with central RVO, second dexamethasone implant, and baseline CRT. Baseline BCVA was associated with CRT-A. CRT-SD and CRT-A were significantly correlated with a 12-month change in BCVA (effect sizes of -0.032 and -0.013 letters/µm; P < 0.001). Patients in the highest CRT-SD quartile gained significantly fewer letters (+1.88 letters; 95% CI: -0.46 to 4.23). Greater CRT variability was associated with smaller BCVA improvements in patients with RVO treated with dexamethasone implants. [Ophthalmic Surg Lasers Imaging Retina 2024;55:722-729.].

Atypical Central Retinal Vein Occlusion in an Older Man With Multiple Myeloma: A Case Report.

This report aims to describe the clinical presentation of a patient with an atypical central retinal vein occlusion (CRVO) in the setting of multiple myeloma. The patient was a 72-year-old man with an extensive hematologic-oncologic history and cardiovascular risk factors who presented with new-onset blurry vision and visual acuity (VA) of 20/40 in the left eye. The VA worsened to 20/60 two months after the initial presentation. By the 6-month follow-up visit, a series of three intravitreal bevacizumab injections for macular edema improved VA from 20/60 to 20/40. This case demonstrates the importance of fluorescein angiography in diagnosing a CRVO and exemplifies an atypical presentation on exam and imaging studies. [Ophthalmic Surg Lasers Imaging Retina 2024;55:730-733.].

An adaptive continuous threshold wavelet denoising method for LiDAR echo signal

Atmospheric aerosols are the primary contributors to environmental pollution. As such aerosols are micro-to nanosized particles invisible to the naked eye, it is necessary to utilize LiDAR technology for their detection. The laser radar echo signal is vulnerable to background light and electronic thermal noise. While single-photon LiDAR can effectively reduce background light interference, electronic thermal noise remains a significant challenge, especially at long distances and in environments with a low signal-to-noise ratio (SNR). However, conventional denoising methods cannot achieve satisfactory results in this case. In this paper, a novel adaptive continuous threshold wavelet denoising algorithm is proposed to filter out the noise. The algorithm features an adaptive threshold and a continuous threshold function. The adaptive threshold is dynamically adjusted according to the wavelet decomposition level, and the continuous threshold function ensures continuity with lower constant error, thus optimizing the denoising process. Simulation results show that the proposed algorithm has excellent performance in improving SNR and reducing root mean square error (RMSE) compared with other algorithms. Experimental results show that denoising of an actual LiDAR echo signal results in a 4.37 dB improvement in SNR and a 39.5% reduction in RMSE. The proposed method significantly enhances the ability of single-photon LiDAR to detect weak signals.

Airborne lidar intensity correction for mapping snow cover extent and effective grain size in mountainous terrain

ABSTRACT Differentially mapping snow depth in mountain watersheds from airborne laser altimetry is a valuable hydrologic technique that has seen an expanded use in recent years. Additionally, lidar systems also record the strength of the returned light pulse (i.e. intensity), which can be used to characterize snow surface properties. For near-infrared lidar systems, return intensity is relatively high over snow and inversely related to the effective grain size, a primary control on snow albedo. Raw intensity is also sensitive to laser range and incidence angle, however, and requires a correction for snow property retrieval that is especially pertinent in mountainous terrain. Here, we describe a workflow to correct the intensity using the plane trajectory, lidar scan angle, and lidar-derived topography. As a proof of concept for snow retrievals, we apply the workflow to an airborne 1064 nm lidar flight over a snow-covered mountain basin in the Colorado Rockies. Corrected intensity was empirically related to reflectance before delineating snow extent and retrieving grain size. Relative to the traditional snow classification derived from optical imagery, the lidar-derived snow extent covered 5.4% more area due to the fine resolution point cloud and absence of shadows common in optical imagery. The lidar-derived grain size retrievals had a MAE of 32 µm compared to those from field spectroscopy, which translated to a 1% error in snow albedo. We found high incidence angles yielded an overcorrection in intensity that introduced a high bias in the grain size distribution and, therefore, suggest using an incidence angle threshold (40°). Developing methods specifically for quantitative snow surface property retrievals from lidar intensity is timely and relevant as aerial lidar is increasingly being used to map snow depth for hydrologic and cryospheric studies.

Shape-from-Shading Derived DEMs from ShadowCam Images in Permanently Shadowed Regions at the Lunar South Pole: A First Look

Abstract. The lunar south pole, particularly its permanently shadowed regions (PSRs), has been the focus of future lunar exploration. The solar altitude angle at the lunar south pole is less than ∼ 2°, resulting in the PSRs not being directly illuminated and the possible existence of water ice in the PSRs due to the low temperatures. High-resolution digital elevation models (DEMs) in PSRs are crucial for future exploration missions and scientific research. However, existing image datasets (e.g., LROC NAC images) cannot penetrate the PSRs. Laser altimetry (e.g., LOLA) can measure the PSRs, but the DEMs derived from LOLA suffer from noises and interpolation defects. ShadowCam is a newly deployed camera onboard the Korea Pathfinder Lunar Orbiter, with a high imaging sensitivity. ShadowCam can capture the faint secondary illumination within PSRs, revealing previously indistinguishable topographic features in persistent darkness. Based on our existing Shape-from-Shading (SfS) pipeline for generating high-resolution DEMs from monocular images, we introduce a novel SfS approach that utilizes secondary illumination and reflectance to reconstruct pixel-wise high-resolution DEMs from ShadowCam images in PSRs. The approach consists of three steps: (1) modeling the secondary illumination on the target surfaces in PSRs based on the view factor; (2) optimizing a low-resolution LOLA DEM using the secondary illumination model; (3) refining the DEM to pixel-wise resolution based on SfS using the ShadowCam images. A test area (approximately 1 km × 1 km) within the PSR in the Shackleton crater near the lunar south pole is used for experimental evaluation. The results show that the DEM generated by the proposed approach is highly consistent with the direct LOLA measurements, particularly in small-scale topographic features such as crater floors and rims that are absent from the LOLA DEM. A first look at the results indicates that the proposed approach has great potential for generating high-resolution DEMs from ShadowCam images in PSRs, which can aid future exploration missions targeting the PSRs at the lunar south pole and support related scientific research on the water ice in the PSRs.

Monitoring Activity of the Subglacial Lake Mercer Based on CryoSat-2 and ICESat-2 Altimetry Data from 2011 to 2023

Abstract. Active subglacial lakes are capable of exchanging and transporting water through water flow paths, thus having an important impact on ice sheet movement and even on the regional mass balance. The use of satellite altimetry can indirectly reveal internal subglacial hydrological activity through direct surface elevation observation, which plays an important role in the study of the evolution of active subglacial lakes. This paper monitors and analyzes the activity of the subglacial lake Mercer (SLM) in the Whillans and Mercer Ice Stream (WIS/MIS), using data from the CryoSat-2 satellite radar altimetry and ICESat-2 satellite laser altimetry. We adopt the differential DEM model and the repeat orbit model based on their respective data characteristics. Finally, we reveal the temporal and spatial evolution patterns of SLM during 2011-2023, and construct the elevation anomaly time series. The results show that the central and western parts of SLM were the areas with more significant elevation anomalies, and there were three significant fill-drain cycles during the time periods studied in this research, with the elevation anomaly range reaching about 8 m and 4 m for the first and second cycles, respectively, and the lake is currently in the third significant draining phase. Furthermore, the elevation anomalies obtained during the mission overlap time of CryoSat-2 and ICESat-2 data are in good agreement, which confirms the accuracy of our method.

A hybrid method for delineating homogeneous forest segments from LiDAR data in Catalonia (Spain)

ABSTRACT Laser scanning data are increasingly used to segment forests into homogeneous units, called segments or stand compartments. Region growing and region merging have been traditionally employed for this purpose. Recently, alternative methods such as cellular automata (CA), self-organizing maps (SOM), and combinatorial optimization have emerged, promising more homogeneous stand delineations. However, these newer methods often require fine-tuning due to rugged stand boundaries. To address this, we propose and assess a novel hybrid approach that combines two segmentation algorithms, resulting in smoother boundaries and homogeneous stands. Our hybrid method outperforms traditional fine-tuning techniques, particularly using CA or SOM for initial segmentation and then refining it with another cellular automaton (CA’), adjusted to produce smooth boundaries. The new two-step hybrid method resulted in better segmentation results than the one-step algorithm combined with previously suggested fine-tuning methods. SOM-CA’ showed the highest degree of explained variance (R2) of the LiDAR metrics used in the segmentation. CA-CA’ reached almost the same R2 values with a larger average segment area. When the R2 of the LiDAR metrics was used as the criterion, the segments produced by the hybrid method outperformed the stand delineations of the current Spanish forest map with a clear margin.

Experimental Verification of Laser Radar Cross Section Model for Complex Targets with Gaussian Beam Irradiation

The reflection of a Gaussian laser beam from a flat Lambert disk is considered theoretically. It was found that the results of experimental measurements of the reflected beam power as a function of the disk radius at various distances from the photodetector to the target are in good agreement with the theoretical model. It was shown that when the radius of the laser beam is greater than the dimensions of the probed complex target this target can be replaced by an equivalent Lambert disk with the same laser radar cross section.

Inversion of the planetary boundary layer height from lidar by combining UNet++ and coordinate attention mechanism

The Arctic plays a significant role in global climate, and the planetary boundary layer height (PBLH) is one of the important parameters for studying Arctic climate. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) atmospheric observatory is an important location for studying the Arctic. However, the weather at the NSA site is complicated and varied. Arctic Haze frequently appears in this region from late autumn to early summer, while low clouds are prone to occur in summer. Meanwhile, due to the consistently low temperatures on the Arctic surface, the frequency of stable boundary layer occurrence is much higher than that in mid-latitude regions. All of these will increase the difficulty of PBLH detection. To address these challenges, we propose a PBLH inversion method based on deep-learning called Coord-UNet++. This method is based on UNet++ and introduces coordinate attention mechanism which can gather features in both horizontal and vertical directions, so it can more effectively capture spatial information in images to cope with complex weather conditions. The training set for the algorithm comes from the micropulse lidar at the NSA site, and the PBLH is labeled by using the microwave radiation profiler at the same site. This algorithm can achieve accurate inversion of the PBLH in complex weather conditions such as cloudy, haze and aerosol layer interference, R2 reaches 0.87, and it performs well in long-term inversion, with much higher stability and accuracy than traditional methods.

High-precision urban rail map construction based on multi-sensor fusion

The construction of high-precision urban rail maps is crucial for the safe and efficient operation of railway transportation systems. However, the repetitive features and sparse textures in urban rail environments pose challenges for map construction with high-precision. Motivated by this, this paper proposes a high-precision urban rail map construction algorithm based on multi-sensor fusion. The algorithm integrates laser radar and Inertial Measurement Unit (IMU) data to construct the geometric structure map of the urban rail. It utilizes image point-line features and color information to improve map accuracy by minimizing photometric errors and incorporating color information, thus generating high-precision maps. Experimental results on a real urban rail dataset demonstrate that the proposed algorithm achieves root mean square errors of 0.345 and 1.033m for ground and tunnel scenes, respectively, representing a 19.31% and 56.80% improvement compared to state-of-the-art methods.

Comparison of Neovascularization Detection in Proliferative Diabetic Retinopathy Using Widefield Swept-Source Optical Coherence Tomography Angiography and Fluorescein Angiography Among Ophthalmology Residents at a Single Institution.

This study compares the ability of resident ophthalmologists to identify neovascularization (NV) in patients with proliferative diabetic retinopathy (PDR) using widefield swept-source optical coherence tomography angiography (SS-OCTA) and fluorescein angiography (FA). Fluorescein angiography and SS-OCTA images were scrambled to create a grading set consisting of 1) early and late phase FA images, 2) B-scan videos, and 3) vitreoretinal interface (VRI) slab. Participants were asked to identify NV. Twelve resident physicians participated in the study. Resident physicians correctly identified 75.6% of NV using FA, 65.3% of NV using SS-OCTA B-scans, and 90.7% of NV using the SSOCTA VRI slab. There was no statistically significant difference in participants' ability to detect NV across imaging modalities (P = 0.08). Detection rates of NV using SS-OCTA were comparable to that of using FA. Results suggest that SS-OCTA may be an appropriate imaging modality for detection of NV in PDR patients. [Ophthalmic Surg Lasers Imaging Retina 2024;55:XX-XX.].