Lidar Research Articles

Global digital elevation model (GDEM) product generation by correcting ASTER GDEM elevation with ICESat-2 altimeter data

Abstract. Advancements in scientific inquiry and practical applications have created a higher demand for the accuracy of global digital elevation models (GDEMs), especially for GDEMs whose main data source is optical imagery. To address this challenge, integrating GDEM and satellite laser altimeter data (global coverage and high-accuracy ranging) is an important research direction, in addition to the technological enhancement of the main data source. In this paper, we describe the datasets and algorithms used to generate a GDEM product (IC2-GDEM) by correcting ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model) elevation data with ICESat-2 altimeter data. The algorithm scheme presents the details of the strategies used for the various challenges, such as the processing of DEM boundaries, the fusion of the different data, and the geographical layout of the satellite laser altimeter data. We used a high-accuracy global elevation control point dataset and multiple high-accuracy local DEMs as the validation data for a comprehensive assessment at the global scale. The results from the validation comparison show that the elevation accuracy of IC2-GDEM is evidently superior to that of the ASTER GDEM product: (1) the RMSE reduction ratio of the corrected GDEM elevation is between 16 % and 82 %, and the average reduction ratio is about 47 %; and (2) from the analysis of the different topographies and land covers, this error reduction is effective even in areas with high topographic relief (>15°) and high vegetation cover (>60 %). ASTER GDEM has been in use for more than a decade, and many historical datasets and models are based on its elevation data. IC2-GDEM facilitates seamless integration with these historical datasets, which is essential for longitudinal studies examining long-term environmental change, land use dynamics, and climate impacts. Meanwhile, IC2-GDEM can serve as a new complementary data source for existing DEMs (such as Copernicus DEM) mainly sourced from synthetic aperture radar (SAR) observation. By cross-validating qualities, filling data gaps, and conducting multi-scale analyses, it can lead to more reliable and comprehensive scientific discoveries, thereby improving the overall quality and reliability of Earth science research. The IC2-GDEM product is openly available at https://doi.org/10.11888/RemoteSen.tpdc.301229 (Xie et al., 2024).

Excavating trajectory planning of electric shovel based on dynamic excavating volume prediction

Mining electric shovels are one of the core equipments for open-pit mining, and are currently moving towards intelligent and unmanned transformation, with intelligent mining instead of traditional manual operation. In the excavation operation process, due to the complexity and changeability of the material surfaces, different excavation strategies should be adopted to achieve the optimal excavation trajectory. It is an important research direction to realize the unmanned excavation of electric shovels by studying a trajectory planning method that is not limited to fixed resting angle surface, can comprehensively consider the type of material surfaces and aim at the minimum excavation energy consumption per unit volume. Therefore, an electric shovel excavation trajectory planning method based on material surface perception is proposed, which firstly obtains the point cloud data of the material surface through laser radar to perceive the excavation environment, and then carries out horizontal calibration and filtering processing on the point cloud data, and adopts Delaunay triangulation rule to realize the calculation of the dynamic excavation volume of the material. Then, the trajectory model of the dipper tooth tip is established by using the 6th polynomial interpolation method, and the minimum excavation energy consumption per unit volume is taken as the optimization objective, and the whale optimization algorithm is used to plan the excavation trajectories for four kinds of complex stacking surfaces (concave, convex, convex–concave and stepped stacking surfaces). Finally, an electric shovel scaled model test bench is built to experimentally verify the trajectory planning results of the simulation. The correlation coefficient R2 values between the test results and the simulation results are both greater than 0.85, and the deviation between the material amount in the bucket and the planned excavation volume is about 5%, which verifies the reliability of the trajectory optimization model and the accuracy of dynamic modeling.

A multi-frequency altimetry snow depth product over Arctic sea ice

Sea ice thickness is an essential variable to understand and forecast the dynamic ice cover and can be estimated by satellite altimetry. Nevertheless, it is affected by uncertainties especially from snow depth, a key parameter to derive it from ice freeboard. We developed a snow depth product based on the differences between CryoSat-2 SAR Ku and IceSat-2 laser altimeters which have different snow penetration capabilities. We compared this product to other existing snow depth products. This comparison indicates correlations all above 0.37 and reaching 0.91 relative to airborne snow radar. We estimate the snow depth uncertainties of our product to be 0.06 m on average. Combining two frequencies to simultaneously measure ice and total freeboard reduces the uncertainties in ice thickness by around a factor of two compared with a single-frequency solution combined with a snow depth climatology. The lack of in situ measurements is a limiting factor to assess the advantages of coincident CryoSat-2 and ICESat-2 acquisitions. Validation data are of major importance for upcoming missions.

Laser Radar System Based on Lightweight Diffractive Lens Receiver System

Laser Radar System Based on Lightweight Diffractive Lens Receiver System

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

Tractional Retinoschisis in Proliferative Diabetic Retinopathy Imaged With Swept-Source Optical Coherence Tomography Angiography.

Tractional retinoschisis (TRS) secondary to proliferative diabetic retinopathy (PDR) may be differentiated from tractional retinal detachment (TRD) by its characteristically nonprogressive course. The purpose of the current study was to describe the use of swept-source optical coherence tomography angiography (SS-OCTA) in the diagnosis and monitoring of TRS secondary to PDR. Retrospective, consecutive case series of patients with TRS secondary to PDR are featured. Clinical notes, fundus photography, and SS-OCTA images are reviewed. The study comprised three eyes of three patients with PDR and TRS. Visual acuity and anatomic features on SS-OCTA were stable in all patients at one year or more of follow-up. Tractional retinoschisis secondary to PDR is typically nonprogressive and may be observed without surgery. SS-OCTA serves as a noninvasive and effective imaging tool to distinguish TRS from TRD and to demonstrate anatomic stability over time. [Ophthalmic Surg Lasers Imaging Retina 2024;55:XX-XX.].

Comparison of Retinal Blood Flow Parameters in Sickle Cell Disease at Steady State and Healthy Subjects Using Doppler Optical Coherence Tomography.

Sickle cell disease (SCD) is a vascular disease that may affect the retina. This study aimed to evaluate differences in average velocity (AV, mm/s), blood flow (BF, μL/min) and vessel diameter (VD, μm) from the temporal retinal arcades in SCD compared to healthy eyes using Doppler optical coherence tomography (DOCT). A cross-sectional study was conducted between 2021 and 2023. The DOCT scan was located at the superior and inferior temporal vessel arcades of all subjects. Differences between the two groups were assessed. Thirty-nine healthy eyes and 36 SCD eyes were imaged. The median of AV, BF, and VD were 1.4, 2.5, and 1.3 times higher in SCD eyes compared to healthy eyes in the superotemporal arteries (P = 0.002, P = 5.0 × 10-3, P = 4.0 × 10-6, respectively). Similar findings were observed for superotemporal veins and inferotemporal veins. Eyes with SCD eyes exhibited significantly elevated retinal vascular flow parameters compared to healthy eyes. [Ophthalmic Surg Lasers Imaging Retina 2025;56:XX-XX.].

Outcomes of Intravitreal Aflibercept 8 mg in Eyes With Neovascular Age-Related Macular Degeneration Previously Treated With Faricimab.

To assess the efficacy of intravitreal aflibercept 8 mg in treating neovascular age-related macular degeneration (nAMD) in patients previously treated with faricimab. A retrospective study was conducted on nAMD patients with suboptimal response to faricimab injections who were switched to intravitreal aflibercept 8 mg. Visual acuity (VA) and optical coherence tomography features were evaluated. The study included 135 eyes from 85 patients who received an average of 7.4 (4) faricimab injections with a mean interval of 53 days, which decreased to 48 days by the 5th aflibercept 8 mg injection (P = 0.056). Mean VA at the time of switch was 63.9 (14.4) letters and was 65 (13.5) letters after four aflibercept 8 mg injections (P = 0.726). Mean central foveal thickness (CFT) at switch was 325 (104) µm which decreased to 272 (65) after four aflibercept 8 mg injections (P < 0.001). The incidence of intraretinal fluid and subretinal hemorrhage increased from 31.3% and 8.1% at switch to 52.2% and 21.7% (P = 0.029 and 0.004), respectively. Switching from faricimab to aflibercept 8 mg did not result in interval extension or VA improvement. CFT decreased, but intraretinal fluid and subretinal hemorrhage increased. Further studies are warranted. [Ophthalmic Surg Lasers Imaging Retina 2025;56:xx-xx.].

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.

A Methodological Approach for Assessing the Post-Fire Resilience of Pinus halepensis Mill. Plant Communities Using UAV-LiDAR Data Across a Chronosequence

The assessment of fire effects in Aleppo pine forests is crucial for guiding the recovery of burnt areas. This study presents a methodology using UAV-LiDAR data to quantify malleability and elasticity in four burnt areas (1970, 1995, 2008 and 2015) through the statistical analysis of different metrics related to height structure and diversity (Height mean, 99th percentile and Coefficient of Variation), coverage, relative shape and distribution strata (Canopy Cover, Canopy Relief Ratio and Strata Percent Coverage), and canopy complexity (Profile Area and Profile Area Change). In general terms, malleability decreases over time in forest ecosystems that have been affected by wildfires, whereas elasticity is higher than what has been determined in previous studies. However, a particular specificity has been detected from the 1995 fire, so we can assume that there are other situational factors that may be affecting ecosystem resilience. LiDAR metrics and uni-temporal sampling between burnt sectors and control aids are used to understand community resilience and to identify the different recovery stages in P. halepensis forests.

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.].

PRODEM: an annual series of summer DEMs (2019 through 2022) of the marginal areas of the Greenland Ice Sheet

Abstract. Surface topography across the marginal zone of the Greenland Ice Sheet is constantly evolving in response to changing weather, season, climate, and ice dynamics. However, current digital elevation models (DEMs) for the ice sheet are usually based on data from a multi-year period, thus obscuring these changes over time. Here we present four 500 m resolution summer DEMs (PRODEMs) of the Greenland Ice Sheet marginal zone for 2019 through 2022. The PRODEMs cover the marginal zone from the ice edge to 50 km inland, hence capturing all Greenland outlet glaciers. Each PRODEM is based on data fusion of CryoSat-2 radar altimetry and ICESat-2 laser altimetry using regionally varying kriging of elevation anomalies relative to ArcticDEM. The PRODEMs are validated using leave-one-out cross-validation, and PRODEM19 is further validated against an external data set, showcasing their ability to correctly represent surface elevations within the associated spatially varying prediction uncertainties. We observe a general lowering of surface elevations during the 4-year PRODEM period, but the spatial pattern of change is highly complex and with annual changes superimposed. The PRODEMs enable detailed studies of the marginal ice sheet elevation changes. With their high spatio-temporal resolution, the PRODEMs will be of value to a wide range of researchers and users studying ice sheet dynamics and monitoring how the ice sheet responds to changing environmental conditions. PRODEMs from summer 2019 through 2022 are available at https://doi.org/10.22008/FK2/52WWHG (Winstrup, 2024), and we plan to annually update the product henceforth.