Topography Maps Research Articles

The Effect of Eyelid Laxity on Corneal Mapping and Corneal Topography

Purpose To evaluate the effect of eyelid laxity on the structure of the cornea. Methods This was a retrospective study performed in one institution. Patients with and without upper and lower eyelid laxity, which were determined using the snap-back test (SBT) and the upper eyelid distraction test (LDT), were evaluated for corneal structural changes with Scheimpflug imaging corneal tomography for the keratometric readings, and cor-neal spectral domain optical coherence tomography for the epithelial thickness (ET) measurements. The ET values were obtained using the statistics report for the superior quadrant (SET), the inferior quadrant (IET) within the 2- to 7-mm region, and central thickness (CET) within the 2-mm region. The minimum corneal thickness values within the central 5-mm region (MinCT) were also included in the study. Keratometric readings (in diopters), horizontal white-to-white (HWTW) measurements, pachymetry, and ET measurements were assessed along with the presence or absence of eyelid laxity. Results A total of 170 eyes from 85 patients were evaluated (50.6% with eyelid laxity, and 49.4% without eyelid laxity). There was no significant difference between the mean keratometric readings, HWTW, and the mean IET and CET values ( P > .05); however, the difference was significant for the MinCT and SET ( P < .05). A statistically significant negative correlation was found between the SET values and age, LDT, and SBT measurements ( P < .05). Conclusions A strong association was found between decreased SET and MinCT values and eyelid laxity. The possible reason for these decreases may be inflammation, which plays a role in the pathophysiology of eyelid laxity. [ J Refract Surg . 2024;40(11):e783–e791.]

A Global Evaluation of Radar‐Derived Digital Elevation Models: SRTM, NASADEM, and GLO‐30

AbstractThis study evaluates global radar‐derived digital elevation models (DEMs), namely the Shuttle Radar Topography Mission (SRTM), NASADEM and GLO‐30 DEMs. We evaluate their accuracy over bare‐earth terrain and characterize elevation biases induced by forests using global Lidar measurements from the Ice, Cloud, and Land Elevation Satellite (ICESat)'s Geoscience Laser Altimeter System (GLAS), the Global Ecosystem Dynamics Investigation (GEDI) and the ICESat‐2 Advanced Topographic Laser Altimeter System (ATLAS) instruments collected on locally flat terrain. Our analysis is based on error statistics calculated for each DEM tile, which are then summarized as global error percentiles, providing a regional characterization of DEM quality. We find NASADEM to be a significant improvement upon the SRTM V3. Over bare ground areas, the mean elevation bias and root mean square error (RMSE) improved from 0.68 to 2.50 m respectively to 0.00 and 1.5 m as compared to ICESat/GLAS. GLO‐30 is more accurate with bare ground elevation bias and RMSE were below 0.05 and 0.55 m. Similar improvements were observed when compared to GEDI and ICESat‐2 measurements. The DEM biases associated with the presence of vegetation vary linearly with canopy height, and more closely follow the percentile of Lidar Relative Height (RH50). Other factors such as canopy density, radar frequency and Lidar technology also contribute to observed elevation biases. This global analysis highlights the potential of various technologies for mapping of Earth's topography, and the need for more advanced remote sensing observations that can resolve vegetation structure and sub‐canopy ground elevation.

Wavelet-Entropy Enhanced Clustering: A Comprehensive Analysis of Drought Patterns in the Southern Plains, USA

Abstract Droughts may exhibit spatiotemporal heterogeneity at regional scale. Effective drought assessment and management necessitates identifying homogeneous areas. However, previous studies often simplified clustering analysis by focusing only on a single variable. In this study, we present a novel drought risk map for the Southern Plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatio-temporal patterns of drought-related variables across various resolutions while eliminating redundant information. We considered multiple drought indicators and indices including gridded precipitation (P), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), and Standardized Precipitation Evapotranspiration Index (SPEI) as well as geographical coordinates and topography map. Through evaluating five different combinations of input datasets, we selected the one demonstrating optimal results based on Davies-Bouldin and Calinski-Harabasz criteria. In addition to P, PET, and NDVI, including the coordinates and elevation as secondary variables significantly enhanced the clustering performance. Using these variables, the region was subdivided into 21 clusters. The Pearson’s correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.84 to 0.94. Comparison with an existing cluster map (DRA) for the region revealed that our proposed cluster map showed higher variability between clusters for P, PET, and NDVI, confirming the robustness of the clustering results for drought conditions in the SP. The new clustering framework is expected to provide valuable insights for understanding and addressing drought dynamics in the SP region.

In-Situ Structure and Topography of AMPA Receptor Scaffolding Complexes Visualized by CryoET.

Most synapses in the brain transmit information by the presynaptic release of vesicular glutamate, driving postsynaptic depolarization through AMPA-type glutamate receptors (AMPARs). The nanometer-scale topography of synaptic AMPARs regulates response amplitude by controlling the number of receptors activated by synaptic vesicle fusion. The mechanisms controlling AMPAR topography and their interactions with postsynaptic scaffolding proteins are unclear, as is the spatial relationship between AMPARs and synaptic vesicles. Here, we used cryo-electron tomography to map the molecular topography of AMPARs and visualize their in-situ structure. Clustered AMPARs form structured complexes with postsynaptic scaffolding proteins resolved by sub-tomogram averaging. Sub-synaptic topography mapping reveals the presence of AMPAR nanoclusters with exclusion zones beneath synaptic vesicles. Our molecular-resolution maps visualize the predominant information transfer path in the nervous system.

Enhancing Marine Topography Mapping: A Geometrically Optimized Algorithm for Multibeam Echosounder Survey Efficiency and Accuracy

This research introduces a new multibeam survey line model that optimizes geometric relationships to improve the efficiency and accuracy of surveys over complex seabed topographies. Since existing multibeam echosounder systems have limitations in handling complex terrains, this study presents an advanced model to enhance data quality and operational efficiency. By strategically designing survey lines and optimizing coverage strategies, this paper achieves the optimal configuration of survey lines for secondary measurements in marine areas, ensuring high precision and reliability of measurement data. Experimental results show that the new model significantly outperforms existing technologies in terms of effective coverage area and measurement accuracy, with an average coverage rate of over 95%, higher than existing models, and a weighted average overlap rate of 3.18%, greatly improving the economic efficiency of measurements by reducing redundant coverage and minimizing operational costs. These findings confirm the advantages of the new model in practical applications and offer valuable technical support for advancing seabed mapping technology.

A functional parcellation of the whole brain in high-functioning individuals with autism spectrum disorder reveals atypical patterns of network organization.

Researchers studying autism spectrum disorder (ASD) lack a comprehensive map of the functional network topography in the ASD brain. We used high-quality resting state functional MRI (rs-fMRI) connectivity data and a robust parcellation routine to provide a whole-brain map of functional networks in a group of seventy high-functioning individuals with ASD and a group of seventy typically developing (TD) individuals. The rs-fMRI data were collected using an imaging sequence optimized to achieve high temporal signal-to-noise ratio (tSNR) across the whole-brain. We identified functional networks using a parcellation routine that intrinsically incorporates internal consistency and repeatability of the networks by keeping only network distinctions that agree across halves of the data over multiple random iterations in each group. The groups were tightly matched on tSNR, in-scanner motion, age, and IQ. We compared the maps from each group and found that functional networks in the ASD group are atypical in three seemingly related ways: (1) whole-brain connectivity patterns are less stable across voxels within multiple functional networks, (2) the cerebellum, subcortex, and hippocampus show weaker differentiation of functional subnetworks, and (3) subcortical structures and the hippocampus are atypically integrated with the neocortex. These results were statistically robust and suggest that patterns of network connectivity between the neocortex and the cerebellum, subcortical structures, and hippocampus are atypical in ASD individuals.

Robust algorithm for automatic surface-based outlier detection in MBES point clouds

Bathymetric multibeam echosounder systems (MBES) provide high-resolution mapping of underwater topography but are highly susceptible to errors due to harsh environmental conditions and the measurement process. Traditionally, manual post-processing is required to ensure data quality, a time-consuming, expensive, and subjective task. To address this issue, we propose a surface-based algorithm for pre-processing and cleaning MBES data that reduces manual intervention and improves consistency. A surface-based algorithm models the underwater topography as a surface instead of processing individual points. By assuming a continuous surface for underwater geometry, the algorithm easily identifies observations that deviate significantly from this model. The method combines a hierarchical B-spline surface with iterative robust estimation to automate data cleaning. Preliminary results on example datasets show a balanced outlier detection accuracy of 0.99, with manual processing time reduced from 2 days to just 30 min.

Chirality Detection in Scanning Tunneling Microscopy Data Using Artificial Intelligence.

Enantiospecific effects play an uprising role in chemistry and technical applications. Chiral molecular networks formed by self-assembly processes at surfaces can be imaged by scanning probe microscopy (SPM). Low contrast and high noise in the topography map often interfere with the automatic image analysis using classical methods. The long SPM image acquisition times restrain Artificial Intelligence-based methods requiring large training sets, leaving only tedious manual work, inducing human-dependent errors and biased labeling. By generating realistic looking synthetic images, the acquisition of real datasets is avoided. Two state-of-the-art object detection architectures are trained to localize and classify chiral unit-cells in a regular molecular chiral network formed by self-assembly of linear molecular bricks. The comparison of different architectures and datasets demonstrates that the training on purely synthetic data outperforms models trained using augmented datasets. A Faster R-CNN model trained solely on synthetic data achieved an excellent mean average precision of 99% on real data. Hence this approach and the transfer to real data show high success, also highlighting the high robustness against experimental noise and different zoom levels across the full experimentally reasonable parameter range. The generalizability of this idea is demonstrated by achieving equally high performance on a different structure,too.

Surface Roughness Evaluation of AZ31B Magnesium Alloy After Rough Milling Using Tools with Different Geometries

This paper presents the experimental results of a study investigating the impact of machining parameters on 3D surface roughness after rough, dry milling. The following 3D roughness parameters were analysed: Sa (arithmetic mean height), Sq (root mean square height), Sz (maximum height), Sku (kurtosis), Ssk (skewness), Sp (maximum peak height), and Sv (maximum pit height). Roughness measurements were made on the end face of the specimens. Additionally, 3D surface topography maps and Abbot-Firestone material ratio curves were generated. Carbide end mills with variable rake and helix angles were used in the study. Experiments were conducted on AZ31B magnesium alloy specimens using a contact-type profilometer. The machining process was conducted using the parameters of so-called high-speed machining. Three variable technological parameters were analysed: cutting speed vc, feed per tooth fz, and axial depth of cut ap. The results showed that the surface roughness of the rough-milled specimens depended to a great extent on the tool geometry and applied machining parameters. Feed per tooth was found to have the greatest impact on surface roughness parameters. Lower values of the analysed surface roughness parameters (and therefore higher surface quality) were obtained (in most cases) for the tools with a rake angle γ of 5° and a helix angle λs of 50°. The results provided both theoretical and practical knowledge about the achievable surface roughness after rough milling using tools with different tool blade geometry. It was shown that rough milling is an effective and efficient type of machining for the AZ31B alloy.

Soil Fertility Status of the Two Eruptions of Mount Tangkuban Parahu and Mount Tampomas in Tanjungsari District Sumedang Regency

Soil fertility is one of the important factors to optimize plant growth and productivity. This study aims to analyze the status of soil fertility through soil physical analysis (texture) soil chemistry (pH H2O and KCl, C-organic, cation exchange capacity, base saturation, P2O5, and K2O) in several land units of Tanjungsari District, Sumedang Regency. The method used to determine sample points is purposive sampling method by considering several land units, namely soil type map, soil parent material map, topography map (slope), land use map, and rainfall data. Soil fertility status is determined by the matching table method between the results of soil chemical analysis with soil fertility status index that refers to the Soil Research Center (PPT) 1995. The results of the matching table data analysis showed that the research area almost entirely included low fertility status criteria except for the pine forest land unit on a moderately steep slope including medium fertility status criteria. The factors of base saturation, k-potential (K2O), and C-organic are the limiting factors of soil fertility status in the Tanjungsari District of Sumedang Regency, including the low to medium category.

Enhancing the Range and Reliability of the Spacer Layer Imaging Method

The spacer layer imaging method (SLIM) is widely used to measure the thickness of additive and lubricant films, in lubricant development and evaluation, and for fundamental research into elastohydrodynamic lubrication and tribofilm formation mechanisms. The film thickness measurement, as implemented on several popular tribometers, provides powerful, non-destructive in-situ mapping of film topography with nanometre-scale height sensitivity. However, the results can be highly sensitive to experimental procedure, machine condition, and image analysis, in some cases reporting unphysical film thickness trends. The prevailing image analysis techniques make it challenging to interrogate these errors, often hiding their multivariate nonlinear behaviour from the user by spatial averaging. Herein, several common ‘silent errors’ in the SLIM measurement, including colour matching to incorrect fringe orders, and colour drift due to the optical properties of the system or film itself, are discussed, with examples. A robust suite of novel a priori and a posteriori methods to address these issues, and to improve the accuracy and reliability of the measurement, are also presented, including a novel, computationally inexpensive circle-finding algorithm for automated image processing. In combination, these methods allow reliable mapping of films up to at least 800 nm in thickness, representing a significant milestone for the utility of SLIM applied to elastohydrodynamic contact.Graphical abstract

InSAR-DEM Block Adjustment Model for Upcoming BIOMASS Mission: Considering Atmospheric Effects

The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m.

Spatial targeted delivery of riboflavin with a controlled corneal iontophoresis delivery system in theranostic-guided UV-A light photo-therapy.

Seven human donor eye globes underwent corneal cross-linking using theranostic UV-A device with accessory corneal iontophoresis system for patterned delivery of a 0.22% riboflavin solution. Theranostic-guided UV-A light illumination assessed riboflavin distribution and treated corneas at 10 mW/cm2 for 9 min with a 5.0-mm beam size. Corneal topography maps were taken at baseline and 2-h post-treatment. Analysis utilized corneal topography elevation data, with results showing controlled riboflavin delivery led to a consistent gradient, with 40% higher levels centrally (248 ± 79 μg/cm3) than peripherally (180 ± 72 μg/cm3 at ±2.5 mm from the center). Theranostic-guided UV-A light irradiation resulted in significant changes in corneal topography, with a decrease in best-fit sphere value (-0.7 ± 0.2 D; p < 0.001) and consistent downward shift in corneal elevation map (-11.7 ± 3.7 μm). The coefficient of variation was 2.5%, indicating high procedure performance in achieving significant and reliable corneal flattening.

Contribution of Gasdermin D Pore Formation to NLRP3 Inflammasome Product Secretion and Pyroptosis in Podocytes during Obesity-Induced Glomerulopathy

NLRP3 inflammasome activation in podocytes has been reported to play an important role in the development of glomerular inflammation and sclerosis during obesity. Although, the molecular mechanism mediating the secretion of NLRP3 inflammasome products to induce local inflammatory response remains poorly understood. Given that gasdermin D (GSDMD) has been found to form oligomeric pores on plasma membrane for release of inflammasome product and execution of pyroptosis, the present study tested whether GSDMD pore contributes to NLRP3 inflammasome product secretion and pyroptosis in podocytes and thereby initiates glomerular inflammation and sclerosis in obesity-induced glomerulopathy (ORG). By Western blot analysis, we demonstrated that palmitic acid (PA) as an obesity-related danger factor dose-dependently stimulated the reduction of total GSDMD and elevation of cleaved GSDMD (GSDMD-NT) in podocytes. In vivo, glomerular decrease in total GSDMD and increase in GSDMD-NT were remarkable in wild type (WT/WT) mice on high-fat diet (HFD) compared to control mice. Urinary excretion of NLRP3 inflammasome products was also significantly increased by HFD treatment. Moreover, we found that pre-treatment with amitriptyline or MCC950 blocked NLRP3 inflammasome activation as well as GSDMD pore formation on plasma membrane of podocytes, while disulfiram prevented GSDMD pore formation without affecting NLRP3 inflammasome activation. Since acid sphingomyelinase (ASM)-ceramide signaling pathway plays a vital role in the development of ORG, we examined whether ASM activity determines GSDMD pore formation in podocytes. By atom force microscopy, a 3D topography map of a single podocyte was generated, and pore-like structures were observed in the membrane of PA-treated WT/WT podocytes. Podocytes lacking Smpd1 (gene code of ASM) gene did not show pore formation, but Smpd1 gene overexpression amplified PA-induced pore formation in podocytes. Moreover, it was found that HFD-induced GSDMD cleavage and pyroptosis in glomeruli were blocked by Smpd1 gene deletion in Smpd1−/− mice. On the contrary, podocyte-specific Smpd1 gene overexpression amplified GSDMD-NT production and pyroptosis in glomeruli. Correspondingly, HFD-induced podocyte foot process effacement and glomerular sclerosis in mice were inhibited by Smpd1 gene knockout but exaggerated by podocyte-specific Smpd1 gene overexpression. Taken together, our findings suggest that ASM-ceramide signaling pathway may control GSDMD pore formation in podocytes to determine glomerular inflammation and sclerosis in ORG. This study was supported by NIH grants DK054927 and DK120491. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Extracting focus variation data from coherence scanning interferometric measurements

Coherence scanning interferometry (CSI), based on the principle of interference, can achieve sub-nanometer precision for height measurements. On the other hand, focus variation microscopy (FVM), combining the small depth of field of the objective, is a widely used surface topography measurement method suited to surface topography that is mostly optically rough. In this paper, we propose a method to simultaneously obtain the interferometric fringe data and focus variation FVM image stack, from a single vertical scanning process, using a CSI instrument without any hardware modifications. Using a 3D Fourier transform, the FVM signal, looks takes the form of a “bowtie” and the CSI signal resembles two “umbrellas” that are separated in 3D K-space. The signal is recovered using a 3D inverse Fourier transform and the surface topography can be determined by fusing the CSI and FVM signals. Since both signals come from the same instrument and scanning process, there is no need for coordinate registration and data interpolation during the data fusion process. Our method combines the features of CSI and FVM measurement, thereby improving the robustness and data coverage of the measurement. An all-in-focus surface topography map can also be generated using this method. This focusing feature has the potential to significantly improve the defect detection and quality control ability of CSI instruments.

Photophysical and Physiochemical Study of New Pyrenyl Chalcone Derivatives as Sensitizers for Organic Solar Cells

Three pyrenyl chalcone derivatives namely, PCH1, PCH2, and PCH3 were synthesized by utilizing the Claisen-Schmidt condensation technique. Compound PCH1 was successfully recrystallized via the slow evaporation technique. To determine and refine the crystal structure, the single crystal is subjected to X-ray diffraction (XRD) analysis. Crystal packing reveals that intermolecular C-H···O interactions bind molecules in compound PCH1 in a head-to-head arrangement. Intramolecular charge transfer (ICT) between molecules has improved as a result of the compound’s C-H···O contacts. The compounds were then characterized using UV-Vis spectroscopy where all compounds have an energy gap that is within the low range, ranging from 2.89 to 2.94 nm. Furthermore, the HOMO-LUMO energy levels of the pyrenyl chalcones were examined by conducting CV analysis. The energy levels of the compounds essentially lie between the conduction band of TiO2 and the redox potential ( I − / I 3 − ) of the electrolyte. FESEM and EDX analyses were utilized in solar cell performance investigations to examine the surface topography and elemental mapping of the pyrenyl compound on the TiO2 layer, correspondingly. Compound PCH2 emerges as the most effective dye-sensitizer in DSSC applications due to the anchoring of strong substituent groups in the compound, even though all three compounds demonstrated suitability as dye-sensitizer materials.

The foundations of the Patagonian icefields

The two vast Patagonian icefields are a global hotspot for ice-loss. However, not much is known about the total ice volume they store - let alone its spatial distribution. One reason is that the abundant record of direct thickness measurements has never been systematically exploited. Here, this record is combined with remotely-sensed information on past ice thickness mapped from glacier retreat. Both datasets are incorporated in a state-of-the-art, mass-conservation approach to produce a well-informed map of the basal topography beneath the icefields. Its major asset is the reliability increase of thicknesses values along the many marine- and lake-terminating glaciers. For these, frontal ice-discharge is notably lower than previously reported. This finding implies that direct climatic control was more influential for past ice loss. We redact a total volume for both icefields in 2000 of 5351 km3. Despite the wealth of observations used in this assessment, relative volume uncertainties remain elevated.

Machine learning approach for satellite-based subfield canola yield prediction using floral phenology metrics and soil parameters

Early monitoring of within-field yield variability and forecasting yield potential is critical for farmers and other key stakeholders such as policymakers. Remote sensing techniques are progressively being used in yield prediction studies due to easy access and affordability. Despite the increasing use of remote sensing techniques for yield prediction in agriculture, there is still a need for medium-resolution satellite imagery when predicting canola yield using a combination of crop and soil information. In this study, we investigated the utility of remotely sensed flowering information from PlanetScope (at 4 m) satellite imagery combined with derived soil and topography parameters to predict canola yield. Our yield prediction model was trained and validated using data from 21 fields managed under variable rate seed and fertilizer application, including cleaned harvester yield maps, soil, and topography maps. To quantify the flowering intensity of canola, 9 vegetation indices (VIs) were calculated using spectral bands from PlanetScope imagery acquired for the reproductive stages of canola. We created five random forest regression models using different subsets of covariates, including VIs, soil, and topography features, to predict canola yield within the season. Using a random forest regression algorithm, we recorded accuracies ranging from poor to best performing using coefficient of determination and root mean squared error (R2: 0.47 to 0.66, RMSE: 325 to 399 kg ha−1). The optimal subset of covariates identified electrical conductivity (EC), Normalized Difference Yellowness Index, and Canola Index as the key variables explaining within-spatial variability in canola yield. Our final model exhibited a validation R2 of 0.46 (RMSE = 730 kg ha−1), demonstrating the potential of medium-resolution satellite imagery during the flowering stage to detect and quantify sub-field spatial and temporal floral phenology changes when predicting canola yield.

Ice volume and thickness of all Scandinavian glaciers and ice caps

Abstract We present a new map of bed topography and ice thickness together with a corresponding ice volume estimate representative of the years ~2010 for all Scandinavian ice caps and glaciers. Starting from surface observations, we invert for ice thickness by iteratively running an innovative ice dynamics model on a distributed grid and updating bed topography until modelled and observed glacier dynamics as represented by their rate of surface elevation change (dh/dt) fields align. The ice flow model used is the instructed glacier model (Jouvet and Cordonnier, 2023, Journal of Glaciology 1–15), a generic physics-informed deep-learning emulator that models higher-order ice flow with high-computational efficiency. We calibrate the modelled thicknesses against &gt;11 000 ice thickness observations, resulting in a final ice volume estimate of 302.7 km3 for Norway, 18.4 km3 for Sweden and 321.1 km3 for the whole of Scandinavia with an error estimate of ~ $\pm 11\%$ . The validation statistics computed indicate good agreement between modelled and observed thicknesses (RMSE = 55 m, Pearson's r = 0.87, bias = 0.8 m), outperforming all other ice thickness maps available for the region. The modelled bed shapes thus provide unprecedented detail in the subglacial topography, especially for ice caps where we produce the first maps that show ice-dynamically realistic flow features.

A high-precision fusion bathymetry of multi-channel waveform curvature for bathymetric LiDAR systems

Airborne LiDAR bathymetry (ALB) system is an attractive and efficient method for nearshore bathymetry and underwater topography mapping. To ensure and improve the measurement accuracy and reliability in various water environments, different receivers using a segmented field of view (FOV) have been designed and are implemented in ALB. These are used to obtain various echo data of multiple channels and perform bathymetry at various water depths. However, in echo waveform processing, detailed information about the water body described by the different echo waveforms is lacking, making it difficult to overcome severe waveform superposition in extremely shallow areas or to separate the weak echo of the water bottom from the noise in extremely deep areas. Therefore, we employed a novel method of multi-channel waveform fusion bathymetry (MWFB) to achieve information complementarity, gain multi-channel waveforms, and detect the robustness of water and bottom echo signals. This method uses an adaptive signal waveform extraction of a multichannel fusion mechanism to eliminate systematic and random noise, and the waveform curvature is used to reconstruct the energy–waveform curves. Iterative decomposition based on waveform curvature and energy curves was conducted to improve the correctness of waveform decomposition and the reliability of waveform components. Furthermore, the water surface and bottom peaks were detected based on the multichannel waveform curvature, which enabled acquisition of the accurate temporal positions of the peaks and achieve high-accuracy bathymetry. According to the experimental results and a comparison with reference datasets, the highest bathymetric accuracy of the MWFB method reached an RMSE of 0.22. For the different study areas, the bathymetric accuracy of the slope reached 95%, 92%, and 97% at Ganquan Island, Lingyang Reef, and Bei Island, respectively. Furthermore, the bathymetric point numbers were effectively increased and bathymetric accuracy was also improved in shallow and deep water, which illustrates the superior bathymetric performance of the MWFB method and its ability to provide a high-efficiency waveform dataset through multi-channel data fusion. The novel LiDAR bathymetric method proposed in this study can effectively achieve high-accuracy nearshore bathymetry and seafloor topographical mapping.