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P-band radar interferometry is a promising tool for digital terrain modelling in forested areas such as the Amazon due to the ability of P-band to penetrate densely forested areas. The quality of Digital Terrain Models (DTMs) is a primary requirement for many geoscience applications because DTM accuracy is an essential variable for characterizing landform shapes. This article presents a multicriteria evaluation of the overall quality of a digital terrain model produced using P-band SAR interferometric processing. The evaluation considers behaviours related to elevation and slope and analyses the impact of landscape properties such as slope and aspect. The multicriteria accuracy of the study showed promising results about the realism of landforms, according to applied geomorphological criteria, and also in the visual comparison between the produced DTM and the reference. The study provided an estimate of the accuracy of P-band radar DTM in terms of elevation with a standard deviation of 2.36 m and in terms of slope with a mean error of approximately −4°, a standard deviation of 4.74°, and a RMSE (Root Mean Square Error) slope error of 6.14°, using a reference DTM derived from an airborne LiDAR (Light Detection and Ranging) survey. The spatial behaviour of these errors and their sensitivity to slope and aspect were analysed. The investigation of hydrographic inconsistencies in the DTM was conducted based on various criteria, including identifying depressions along watercourses and adhering to Horton's law. Finally, the effects of the acquisition, processing, and resampling processes are revealed through indicators such as the rose histogram. This multicriteria study demonstrates the suitability of airborne P-band radar interferometry for digital elevation modelling in tropical rainforest environments. [ABSTRACT FROM AUTHOR]

Tomographic Synthetic Aperture Radar (SAR) allows the reconstruction of the 3D radar reflectivity of forests from a large(r) number of multi-angular acquisitions. However, in most practical implementations it suffers from limited vertical resolution and/or reconstruction artefacts as the result of non-ideal acquisition setups. Polarisation Coherence Tomography (PCT) offers an alternative to traditional tomographic techniques that allow the reconstruction of the low-frequency 3D radar reflectivity components from a small(er) number of multi-angular SAR acquisitions. PCT formulates the tomographic reconstruction problem as a series expansion on a given function basis. The expansion coefficients are estimated from interferometric coherence measurements between acquisitions. In its original form, PCT uses the Legendre polynomial basis for the reconstruction of the 3D radar reflectivity. This paper investigates the use of new basis functions for the reconstruction of X-band 3D radar reflectivity of forests derived from available lidar waveforms. This approach enables an improved 3D radar reflectivity reconstruction with enhanced vertical resolution, tailored to individual forest conditions. It also allows the translation from sparse lidar waveform vertical reflectivity information into continuous vertical reflectivity estimates when combined with interferometric SAR measurements. This is especially relevant for exploring the synergy of actual missions such as GEDI and TanDEM-X. The quality of the reconstructed 3D radar reflectivity is assessed by comparing simulated InSAR coherences derived from the reconstructed 3D radar reflectivity against measured coherences at different spatial baselines. The assessment is performed and discussed for interferometric TanDEM-X acquisitions performed over two tropical Gabonese rainforest sites: Mondah and Lopé. The results demonstrate that the lidar-derived basis provides more physically realistic vertical reflectivity profiles, which also produce a smaller bias in the simulated coherence validation, compared to the conventional Legendre polynomial basis. [ABSTRACT FROM AUTHOR]

Accurate marine wind detection under all-weather conditions is crucial for maritime activities. The joint detection of lidar and radar is supposed to be a potential way to carry out the all-weather sensing of wind. However, their performance analysis has not been well studied, particularly in the far sea area, where the wind-tracing particles are quite different from those inland. Based on the particle distributions above the sea surface under different weather conditions, this study investigated the scattering and attenuation effects of lidar and radar waves in open sea areas with the Mie theory and T-matrix method. Then, the maximum detection range and velocity accuracies of lidar/radar were comprehensively analyzed based on detection principles to optimize the combination of lidar and radar. According to the simulation results, it was difficult to maintain the detection capability of a single lidar/radar under all-weather conditions, and 1.55 μ m lidar and W-band radar presented a promising joint detection scheme, as they exhibited optimal weather adaptability in clear sky and precipitation conditions, respectively. [ABSTRACT FROM AUTHOR]

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In autonomous vehicles, the LiDAR and radar sensors are indispensable components for measuring distances to objects. While deep-learning-based algorithms for LiDAR sensors have been extensively proposed, the same cannot be said for radar sensors. LiDAR and radar share the commonality of measuring distances, but they are used in different environments. LiDAR tends to produce less noisy data and provides precise distance measurements, but it is highly affected by environmental factors like rain and fog. In contrast, radar is less impacted by environmental conditions but tends to generate noisier data. To reduce noise in radar data and enhance radar data augmentation, we propose a LiDAR-to-Radar translation method with a voxel feature extraction module, leveraging the fact that both sensors acquire data in a point-based manner. Because of the translation of high-quality LiDAR data into radar data, this becomes achievable. We demonstrate the superiority of our proposed method by acquiring and using data from both LiDAR and radar sensors in the same environment for validation. [ABSTRACT FROM AUTHOR]

Clouds are an important component of weather systems and are difficult to effectively characterize using current climate models and estimation of radiative forcing. Due to the limitations in observational capabilities, it remains difficult to obtain high-spatiotemporal-resolution, continuous, and accurate observations of clouds. To overcome this issue, we propose a novel and practical combined retrieval method using millimeter-wave radar and lidar, which enables the microphysical properties of thin liquid water clouds, such as cloud droplet effective radius, number concentration, and liquid water content, to be retrieved. This method was utilized to analyze the clouds observed at the Shanghai World Expo Park and was validated through synchronous observations with a microwave radiometer. Furthermore, the most suitable extinction backscatter ratio was determined through sensitivity analysis. This study provides vertical distributions of cloud microphysical properties with a time resolution of 1 min and a spatial resolution of 30 m, demonstrating the scientific potential of this combined retrieval method. [ABSTRACT FROM AUTHOR]

In this study, we investigate the synergy of elastic backscatter lidar, Ku-band radar, and submillimeter-wave radiometer measurements in the retrieval of ice from satellite observations. The synergy is analyzed through the generation of a large dataset of ice water content (IWC) profiles and simulated lidar, radar and radiometer observations. The characteristics of the instruments (frequencies, sensitivities, etc.) are set based on the expected characteristics of instruments of the Atmosphere Observing System (AOS) mission. A hold-out validation methodology is used to assess the accuracy of the IWC profiles retrieved from various combinations of observations from the three instruments. Specifically, the IWC and associated observations are randomly divided into two datasets, one for training and the other for evaluation. The training dataset is used to train the retrieval algorithm, while the evaluation dataset is used to assess the retrieval performance. The dataset of IWC profiles is derived from CloudSat reflectivity and CALIOP lidar observations. The retrieval of the ice water content IWC profiles from the computed observations is achieved in two steps. In the first step, a class, of 18 potential classes characterized by different vertical distribution of IWC, is estimated from the observations. The 18 classes are predetermined based on the k-means clustering algorithm. In the second step, the IWC profile is estimated using an ensemble Kalman smoother algorithm that uses the estimated class as a priori information. The results of the study show that the synergy of lidar, radar, and radiometer observations is significant in the retrieval of the IWC profiles. Nevertheless, it should be mentioned that this synergy was found under idealized conditions, and additional work might be required to materialize it in practice. The inclusion of the lidar backscatter observations in the retrieval process has a larger impact on the retrieval performance than the inclusion of the radar observations. As ice clouds have a significant impact on atmospheric radiative processes, this work is relevant to ongoing efforts to reduce uncertainties in climate analyses and projections. [ABSTRACT FROM AUTHOR]

Multisensor fusion‐based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather‐sensitive, while the frequency‐modulated continuous wave Radar has strong adaptability but suffers from noise and ghost effects. In this paper, we propose a heterogeneous localization method called Radar on LiDAR Map, which aims to enhance localization accuracy without relying on loop closures by mitigating the accumulated error in Radar odometry in real time. To accomplish this, we utilize LiDAR scans and ground truth paths as Teach paths and Radar scans as the trajectories to be estimated, referred to as Repeat paths. By establishing a correlation between the Radar and LiDAR scan data, we can enhance the accuracy of Radar odometry estimation. Our approach involves embedding the data from both Radar and LiDAR sensors into a density map. We calculate the spatial vector similarity with an offset to determine the corresponding place index within the candidate map and estimate the rotation and translation. To refine the alignment, we utilize the Iterative Closest Point algorithm to achieve optimal matching on the LiDAR submap. The estimated bias is subsequently incorporated into the Radar SLAM for optimizing the position map. We conducted extensive experiments on the Mulran Radar Data set, Oxford Radar RobotCar Dataset, and our data set to demonstrate the feasibility and effectiveness of our proposed approach. Our proposed scan projection descriptors achieves homogeneous and heterogeneous place recognition and works much better than existing methods. Its application to the Radar SLAM system also substantially improves the positioning accuracy. All sequences' root mean square error is 2.53 m for positioning and 1.83° for angle. [ABSTRACT FROM AUTHOR]

The Atmospheric InfraRed Sounder (AIRS) aboard Aqua provides essential long‐term data on vertical cloud fraction, particularly valuable in the Arctic region. This study offers a comprehensive assessment of Arctic vertical cloud fraction derived from AIRS through a comparison with independent ground‐ and space‐based radar and lidar observations. In comparison to the measurements at the North Slope Alaska site, results reveal a significant underestimation of low‐level cloud cover by AIRS, especially for near‐surface clouds, while mid‐ and high‐level cloud fractions show better consistency. In comparison to the satellite‐based product from 3S‐GEOPROF‐COMB, the accuracy varies across different underlying surfaces (land vs. sea) and seasons. AIRS shows significant positive biases in mid‐level cloud fraction over sea surfaces with sea ice concentration below 15%, indicating potential limitations in the cloud retrieval algorithm in regions with large sea ice variations. The issue of low‐level clouds identification is primarily caused by the limited penetrating capability of infrared hyperspectral sensing and the accuracy of preceding surface and atmospheric state products, which diminish the accuracy of AIRS low‐level cloud fraction. Plain Language Summary: The Atmospheric InfraRed Sounder (AIRS) instrument on the Aqua satellite provides important information on vertical cloud fraction in the Arctic. This research compared the AIRS cloud cover data with observations from ground‐based and space‐based radar and lidar. Findings are that AIRS underestimates low‐level clouds, especially near the surface, but is more accurate for mid‐ and high‐level clouds. The accuracy also varies over land and sea surfaces, as well as throughout different seasons. In regions with significant sea ice variations, AIRS overestimates clouds at mid‐levels. The study suggests that the ability to accurately detect low‐level clouds from AIRS is limited by the capability of infrared hyperspectral sensing to penetrate the atmosphere. Key Points: Initial evaluation of AIRS vertical cloud fraction product over the Arctic is presented using ground‐ and space‐based radar‐lidar observationsAIRS underestimates low‐level clouds, especially near the surface, more accurately reflects mid‐ and high‐level cloud fractionsAIRS mid‐level cloud fraction demonstrates significant positive biases over regions with sea ice concentration below 15% [ABSTRACT FROM AUTHOR]

The vertical distribution of wildfire smoke aerosols is important in determining its environmental impacts but existing observations of smoke heights generally do not possess the temporal resolution required to fully resolve the diurnal behavior of wildfire smoke injection. We use Weather Surveillance Radar‐1988 Doppler (WSR‐88D) dual polarization data to estimate injection heights of Biomass Burning Debris (BBD) generated by fires. We detect BBD as a surrogate for smoke aerosols, which are often collocated with BBD near the fire but are not within the size range detectable by these radars. Injection heights of BBD are derived for 2–10 August 2019, using WSR‐88D reflectivity (Z ≥ 10 dBZ) and dual polarization correlation coefficients (0.2 < C.C < 0.9) to study the Williams Flats fire. Results show the expected diurnal cycles with maximum injection heights present during the late afternoon period when the fire's intensity and convective mixing are maximized. WSR‐88D and airborne lidar injection height comparisons reveal that this method is sensitive to outliers and generally overpredicts maximum heights by 40%, though mean and median heights are better captured (<20% mean error). WSR‐88D heights between the 75th and 90th percentile seem to accurately represent the maximum heights, with the exception of heights estimated during the occurrence of a pyro‐cumulonimbus. Location specific mapping of WSR‐88D and lidar injection heights reveal that they diverge further away from the fire as expected due to BBD settling. Most importantly, WSR‐88D‐derived injection height estimates provide near continuous smoke height information, allowing for the study of diurnal variability of smoke injections. Plain Language Summary: Wildfire smoke aerosols injected into the atmosphere pose a serious threat to human health and the environment. Once in the atmosphere, these aerosols can be transported downwind, affecting air quality regionally. Aerosols advected downwind travel distances that are strongly correlated with the maximum heights that aerosols can reach near their source, making it important to observe these 'injection heights'. However, existing observations of injection heights are limited temporally, making it difficult to study their diurnal and day‐to‐day variability. Here, we use weather radar data to estimate the injection heights of Biomass Burning Debris (BBD), which is assumed to be collocated with aerosols that are too small to be detected by these radars. Injection heights are estimated for the Williams Flats Fire event in Washington for 2–10 August 2019. Results show that daily maximum injection heights occur in the late afternoon, when the wildfire's intensity is strongest. Further, weather radar‐derived heights are compared to airborne lidar‐derived heights for the same fire, revealing that the maximums are overpredicted but intermediate values like the mean are well represented. Weather radar‐derived injection height estimates allow for near continuous smoke heights, making them relevant for future studies. Key Points: Weather radar estimates of biomass burning debris injection heights are evaluated against aerosol heights from airborne lidarRadar maximum injection heights tend to be overpredicted while mean, median, 75th and 90th percentiles perform betterThe maximum injection height can be predicted generally well by the 75th to 90th percentiles of the radar estimates [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is a vital indicator for studying carbon sinks in forest ecosystems. Semiarid forests harbor substantial carbon storage but received little attention due to the high spatial–temporal heterogeneity that complicates the modeling of AGB in this environment. This study assessed the performance of different data sources (annual monthly time-series radar was Sentinel-1 [S1]; annual monthly time series optical was Sentinel-2 [S2]; and single-temporal airborne light detection and ranging [LiDAR]) and seven prediction approaches to map AGB in the semiarid forests on the border between Gansu and Qinghai Provinces in China. Five experiments were conducted using different data configurations from synthetic aperture radar backscatter, multispectral reflectance, LiDAR point cloud, and their derivatives (polarimetric combination indices, texture information, vegetation indices, biophysical features, and tree height- and canopy-related indices). The results showed that S2 acquired better prediction (coefficient of determination [R2]: 0.62–0.75; root mean square error [RMSE]: 30.08–38.83 Mg/ha) than S1 (R2: 0.24–0.45; RMSE: 47.36–56.51 Mg/ha). However, their integration further improved the results (R2: 0.65–0.78; RMSE: 28.68–35.92 Mg/ha). The addition of single-temporal LiDAR highlighted its structural importance in semiarid forests. The best mapping accuracy was achieved by XGBoost, with the metrics from the S2 and S1 time series and the LiDAR-based canopy height information being combined (R2: 0.87; RMSE: 21.63 Mg/ha; relative RMSE: 14.45%). Images obtained during the dry season were effective for AGB prediction. Tree-based models generally outperformed other models in semiarid forests. Sequential variable importance analysis indicated that the most important S1 metric to estimate AGB was the polarimetric combination indices sum, and the S2 metrics were associated with red-edge spectral regions. Meanwhile, the most important LiDAR metrics were related to height percentiles. Our methodology advocates for an economical, extensive, and precise AGB retrieval tailored for semiarid forests. [ABSTRACT FROM AUTHOR]

The geological environment damage caused by coal mining subsidence has become an important factor affecting the sustainable development of mining areas. Reconstruction of the Coal Mining Subsidence Field (CMSF) is the key to preventing geological disasters, and the needs of CMSF reconstruction cannot be met by solely relying on a single remote sensing technology. The combination of Unmanned Aerial Vehicle (UAV) and Synthetic Aperture Radar (SAR) has complementary advantages; however, the data fusion strategy by refining the SAR deformation field through UAV still needs to be updated constantly. This paper proposed a Prior Weighting (PW) method based on Satellite Aerial (SA) heterogeneous remote sensing. The method can be used to fuse SAR and UAV Light Detection and Ranging (LiDAR) data for ground subsidence parameter inversion. Firstly, the subsidence boundary of Differential Interferometric SAR (DInSAR) combined with the large gradient subsidence of Pixel Offset Tracking (POT) was developed to initialize the SAR preliminary CMSF. Secondly, the SAR preliminary CMSF was refined by UAV LiDAR data; the weights of SAR and UAV LiDAR data are 0.4 and 0.6 iteratively. After the data fusion, the subsidence field was reconstructed. The results showed that the overall CMSF accuracy improved from ±144 mm to ±51 mm. The relative errors of the surface subsidence factor and main influence angle tangent calculated by the physical model and in situ measured data are 1.3% and 1.7%. It shows that the proposed SAR/UAV fusion method has significant advantages in the reconstruction of CMSF, and the PW method contributes to the prevention and control of mining subsidence. [ABSTRACT FROM AUTHOR]

We study the interrelations between salt karst and landscape evolution at the Ze'elim and Hever alluvial fans, Dead Sea (DS), Israel, in an attempt to characterize the ongoing surface and subsurface processes and identify future trends. Using light detection and ranging, interferometric synthetic aperture radar, drone photography, time-lapse cameras, and direct measurements of floodwater levels, we document floodwater recharge through riverbed sinkholes, subsurface salt dissolution, groundwater flow, and brine discharge at shoreline sinkholes during the years 2011–2023. At the Ze'elim fan, most of the surface floodwater drains into streambed sinkholes and discharges at shoreline sinkholes, whereas at the Hever fan, only a small fraction of the floodwater drains into sinkholes, while the majority flows downstream to the DS. This difference is attributed to the low-gradient stream profiles in Ze'elim, which enable water accumulation and recharge in sinkholes and their surrounding depressions, in contrast with the higher-gradient Hever profiles, which yield high-energy floods capable of carrying coarse gravel that eventually fill the sinkholes. The rapid drainage of floodwater into sinkholes also involves slope failure due to pore-pressure drop and cohesion loss within hours after each drainage event. Surface subsidence lineaments detected by InSAR indicate the presence of subsurface dissolution channels between recharge and discharge sites in the two fans and in the nearby Lynch straits. Subsidence and streambed sinkholes occur in most other fans and streams that flow to the DS; however, with the exception of Ze'elim, all other streams show only minor or no recharge along their course. This is due to either the high-gradient profiles, the gravelly sediments, the limited floods, or the lack of conditions for sinkhole development in the other streambeds. Thus, understanding the factors that govern the flood-related karst formation is of great importance for predicting landscape evolution in the DS region and elsewhere and for sinkhole hazard assessment. [ABSTRACT FROM AUTHOR]

Snow provides critical water resources for billions of people, making the remote sensing of snow water equivalent (SWE) a highly prioritized endeavor, particularly given current and projected climate change impacts. Synthetic Aperture Radar (SAR) is a promising method for remote sensing of SWE because radar penetrates snow and SAR interferometry (InSAR) can be used to estimate changes in SWE (ΔSWE) between SAR acquisitions. We calculated ΔSWE retrievals from 10 NASA L-band Uninhabited Aerial Vehicle SAR (UAVSAR) acquisitions in northern Colorado during the winters of 2020 and 2021 and evaluated the retrievals against measurements of SWE from ground-penetrating radar (GPR) and terrestrial lidar scans (TLS) collected as part of the NASA SnowEx 2020 and 2021 Time Series Campaigns. Next, we evaluated the full UAVSAR time series at the northern Colorado sites using SWE measured at seven automated stations and ascertained whether coherence can be used as an accuracy metric for ΔSWE retrievals. For single InSAR pairs, UAVSAR ΔSWE retrievals displayed high correlation with TLS and GPR ΔSWE retrievals (overall r = 0.72–0.79) and yielded an RMSE of 19–22 mm. When compared to SWE at seven automated stations, cumulative SWE from UAVSAR retrievals exhibited poor agreement in 2020, but high agreement in 2021. We found that SWE can be reliably retrieved, even for lower coherences, as RMSE values ranged by <10 mm from coherences of 0.10 to 0.90. The upcoming NASA-ISRO SAR satellite mission, with a 12-day revisit period, offers an exciting opportunity to apply this methodology globally, but further quantification of limitations is necessary, particularly in forested environments and as the snowpack begins to melt. [ABSTRACT FROM AUTHOR]

The photon-counting light laser detection and ranging (LiDAR), especially the Geiger mode avalanche photon diode (Gm-APD) LiDAR, can obtain three-dimensional images of the scene, with the characteristics of single-photon sensitivity, but the background noise limits the imaging quality of the laser radar. In order to solve this problem, a depth image estimation method based on a two-dimensional (2D) Kaniadakis entropy thresholding method is proposed which transforms a weak signal extraction problem into a denoising problem for point cloud data. The characteristics of signal peak aggregation in the data and the spatio-temporal correlation features between target image elements in the point cloud-intensity data are exploited. Through adequate simulations and outdoor target-imaging experiments under different signal-to-background ratios (SBRs), the effectiveness of the method under low signal-to-background ratio conditions is demonstrated. When the SBR is 0.025, the proposed method reaches a target recovery rate of 91.7%, which is better than the existing typical methods, such as the Peak-picking method, Cross-Correlation method, and the sparse Poisson intensity reconstruction algorithm (SPIRAL), which achieve a target recovery rate of 15.7%, 7.0%, and 18.4%, respectively. Additionally, comparing with the SPIRAL, the reconstruction recovery ratio is improved by 73.3%. The proposed method greatly improves the integrity of the target under high-background-noise environments and finally provides a basis for feature extraction and target recognition. [ABSTRACT FROM AUTHOR]

Vegetation information is essential for analyzing aboveground biomass and understanding subsurface characteristics, such as root biomass, soil organic matter, and soil moisture conditions. In this study, we mapped boreal forest canopy height (FCH) and forest species (FS) distributions in the Delta Junction region of interior Alaska, by integrating multi-source remote sensing observations within a machine learning framework based on the extreme gradient boosting technique. Model inputs included multi-frequency (C-/L-/P-band) SAR observations from Sentinel-1, UAVSAR (Uninhabited Aerial Vehicle SAR) and AirMOSS (Airborne Microwave Observatory of Subcanopy and Subsurface), and Sentinel-2 optical reflectance data. LVIS (Land Vegetation and Ice Sensor) LiDAR measurements (RH98) and Tanana Valley State Forest timber inventory data were used as respective canopy height and species ground truth data. The combination of multi-source datasets produced the best model performance (RMSE 1.62 m for FCH, and 84.27% overall FS classification accuracy) over other models developed from single source observations. The resulting FCH and FS maps using multi-source datasets were derived at 30 m spatial resolution and showed favorable agreement with plot level field measurements from the Forest Inventory and Analysis record. The model results also captured characteristic differences in stand structure between dominant species and from post-fire vegetation succession. Our results show the potential of multi-source remote sensing observations, including low frequency microwave sensors, for monitoring boreal forest complexity and changes due to global warming.

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R2 = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available. [ABSTRACT FROM AUTHOR]

Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory estimation, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot be effectively achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range Light Detection and Ranging (LiDAR) data, which enables the measurement of individual tree height in forest plots with high precision. Besides, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and Synthetic Aperture Radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining basal area of individual tree within plots using UAV LiDAR data, this study calculated weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at large spatial scale, we also developed a machine learning-based mixed-effects model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 m and 13.3 m with standard deviations of 2.9 m and 3.3 m, respectively. The accuracy of mapped products was validated utilizing LiDAR and field measurement data. The correlation coefficient (푟) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while RMSE ranged from 2.6 to 4.1 m and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024). [ABSTRACT FROM AUTHOR]

The current mature Simultaneous Localisation And Mapping (SLAM) algorithms, when applied to tunnel scenarios with point cloud degradation and poor lighting conditions, often lead to a sharp increase in the estimated attitude error of the unmanned aerial vehicle (UAV), or even prevent the UAV from moving autonomously due to severe feature degradation. To address the above problems, the authors propose a SLAM algorithm based on factor graph optimisation, Iterative Closest Point and Normal Distributions Transform algorithms. A front‐end point cloud registration module and a back‐end construction algorithm based on filtering and graph optimisation are designed. To verify the effectiveness of the proposed algorithm, experiments are conducted on KITTI dataset and real tunnel scenes, and compared with LiDAR Odometry and Mapping (LOAM) and lightweight and ground optimised (LeGO)‐LOAM algorithms. The results show that the average processing time of the proposed method is about 75 ms, which can meet the real‐time requirements of autonomous aerial vehicles. Compared with LOAM and LeGO‐LOAM in the real tunnel experiment, the proposed method shows the tunnel 3D map construction. [ABSTRACT FROM AUTHOR]

This study aims to enhance the safety and efficiency of port navigation by reducing ship collision accidents, minimizing environmental risks, and optimizing waterways to increase port throughput. Initially, a three-dimensional map of the port's waterway, including data on water depth, rocks, and obstacles, is generated through laser radar scanning. Visual perception technology is adopted to process and identify the data for environmental awareness. Single Shot MultiBox Detector (SSD) is utilized to position ships and obstacles, while point cloud data create a comprehensive three-dimensional map. In order to improve the optimal navigation approach of the Rapidly-Exploring Random Tree (RRT), an artificial potential field method is employed. Additionally, the collision prediction model utilizes K-Means clustering to enhance the Faster R-CNN algorithm for predicting the paths of other ships and obstacles. The results indicate that the RRT enhanced by the artificial potential field method reduces the average path length (from 500 to 430 m), average time consumption (from 30 to 22 s), and maximum collision risk (from 15 to 8%). Moreover, the accuracy, recall rate, and F1 score of the K-Means + Faster R-CNN collision prediction model reach 92%, 88%, and 90%, respectively, outperforming other models. Overall, these findings underscore the substantial advantages of the proposed enhanced algorithm in autonomous navigation and collision prediction in port waterways. [ABSTRACT FROM AUTHOR]

Knowledge about the vertical structure of forests, such as forest height, above-ground biomass (AGB), and the vertical biomass distribution is important for understanding carbon allocation, structural diversity, and succession and degradation dynamics in forest ecosystems. While the use of lidar (light detection and ranging) observations is well established to investigate the vertical structure of forests, the sensitivity of P-band synthetic aperture radar tomography (TomoSAR) observations to biomass and vertical forest structure is not yet well understood. Here we use lidar observations from NASA's Global Ecosystem Dynamics Investigation (GEDI) to analyse the sensitivity of airborne P-band SAR tomography backscatter to forest height and AGB at two tropical forests in Lopé and Mondah, Gabon, Africa. We use GEDI observations to parametrize an empirical model for estimating forest height and we use a random forest model for estimating AGB from TomoSAR profiles. The validation with Land, Vegetation, and Ice Sensor (LVIS) airborne lidar data shows moderate performance for estimating forest height (RMSE = 8.2 m in Lopé and 9.8 m in Mondah) and moderate to good performance for total AGB (RMSE = 115.3 Mg/ha in Lopé and 117.8 Mg/ha in Mondah). We also estimated the vertical distribution of AGB using the corrected TomoSAR backscatter and compared it with AGB profiles derived from field observations in Mondah, which indicates potential to use TomoSAR observations for estimating vertical AGB distribution over tropical forests. However, our results demonstrate the need for targeted field observations of vertical biomass profiles in order to make full use of P-band TomoSAR to map the vertical structure of tropical forests. [ABSTRACT FROM AUTHOR]

The achievements of high driving performance and error minimization of autonomous vehicles (AVs) in harsh weather are the biggest challenges for the society of autonomous research area. AVs are mainly driven by the sensor fusion technology of light detection and ranging (LiDAR), radio detection and ranging (RADAR), and camera sensors. In harsh weather such as rain, storm, law lighting, snowfall, and vapor, the detection performances of all the sensors are obstructed. The camera imaging for object detection systems is highly affected by different types of noise in adverse weather conditions and its performance is very anxious for error-free AV driving. This article proposes the prediction-based adaptive fusion alignment (AFA) algorithm of the robust path and object tracking systems with the deep convolutional neural networking (D-CNN) model for detection accuracy improvement, calculative error reduction, and overall driving error minimization of AVs in harsh weather conditions. RADAR and LiDAR are not deep learning (DL) based yet. The D-CNN model of DL algorithms for camera image processing and the segmentation process of object classification is used for actual object detection and localization. The AV-simulated driving accuracy in harsh weather is significantly increased with the proposed AFA and D-CNN algorithms. [ABSTRACT FROM AUTHOR]

The integration of multi-modal data allows for a more accurate representation of the ground characteristics. For a comprehensive interpretation of remote sensing data, existing multi-modal data fusion research mainly focuses on the joint utilization of 3D Light Detection and Ranging (LiDAR) and 2D Hyperspectral Image (HSI) data. However, existing algorithms do not pay much attention to the interaction of high-level semantic information between different modal data before fusion. This paper proposes a novel multi-modal data fusion deep learning network with the Cross-Modal Self-Attentive Feature Fusion Transformer (SAFFT). The framework employs a multi-head self-attention layer to fuse various attention information from multiple heads, effectively enhancing advanced feature information from different modalities for comprehensive integration. Experimental results on the Houston 2013 dataset demonstrate the effectiveness of the proposed method, which achieves an overall accuracy (OA) of 94.3757% in classifying 15 semantic classes. [ABSTRACT FROM AUTHOR]

Paleoseismological research for a slowly deforming intraplate fault can provide essential information for understanding not only the spatiotemporal characteristics of past earthquakes but also seismic behavior in the case of long recurrence intervals. To reveal the paleoseismological properties and faulting processes of the intraplate fault, the Geumwang Fault Zone in the central Korean Peninsula, we conducted comprehensive paleoseismological investigations along the fault zone, incorporating geomorphological mapping with airborne light detection and ranging (LiDAR), electrical resistivity tomography (ERT), borehole drilling, trench excavation, optically stimulated luminescence (OSL) dating, and microstructural analysis. Along NE-SW-striking lineaments of the Geumwang Fault Zone, surface deformation is weakly recognized by LiDAR imagery in a damage zone along the northern section of the fault zone (Suha site). Results of ERT and borehole logging at the Suha site suggest a localized zone of low resistivity and unconformity level separation in sedimentary layers, respectively. A trench section excavated along the ERT traverse and borehole sites exposes a fault contact between granite and unconsolidated Quaternary strata comprising boulders (47 ± 3 ka), clayey sand (24 ± 2 ka), pebbly cobbles, coarse sand, and artificial layers from bottom to top. The < 5-cm-wide slip zone is oriented N09°E/85°NW and cuts the granite to the west and the boulder layer to the east. This slip zone that covered by the clayey sand stratum records an apparent vertical offset of ∼1.5 m and has sub-horizontal striations indicating dextral movement. Microstructures at the contact between the granite and the boulder layer support the occurrence of seismic slip propagation along the contact and include injected sedimentary materials, clay-clast aggregates, and fresh, open fractures in quartz and feldspar grains in the boulder layer. The slip zone consists of a < 4.5-cm-wide zone of cataclasite and a < 5-mm-wide principal slip zone (PSZ). Microstructures in the slip zone and sediments near the slip zone include seismic-slip indicators of pressurized gouge materials and fluid injection within PSZ, and deformed sediments. These reveal that the slip zone underwent repeated seismic slip events during uplift to the surface. Our paleoseismological analyses with microstructures show that the boulder layer was cut by strike-slip faulting with a minor vertical component between 47–24 ka, following which the overlying sediments were deposited along the exposed fault scarp as incision fill. The results show that microstructural observations can provide key information on the deformation of unconsolidated sediments and on the nature and timing of seismic faulting. [ABSTRACT FROM AUTHOR]

Advanced technologies such as light detection and ranging (LiDAR) and unmanned aerial vehicles (UAVs) have revolutionized the detection of subtle surface deformation and the generation of high-resolution digital elevation models, overcoming the challenges posed by low tectonic activity and climatic surface erosion on fault-generated landscapes. This study presents a new record of paleoearthquake surface rupture along a section of the central Gongju Fault, transecting the central part of the Korean Peninsula, by analyzing geomorphic, stratigraphic, and structural features. We identified a NE-SW-striking, prominent fault-generated landform derived from LiDAR analysis and surface ruptures showing a vertical offset of < 15 cm by trench excavation. We also constrained the depositional ages to ∼94 ka using optically stimulated luminescence (OSL). Our comprehensive findings suggest that the seismic activity along the main trace of the Gongju Fault resulted in a distributed deformation within the fault zone, likely from multiple seismic events rather than a single occurrence. Structural features of the surface ruptures exposed on the trench wall show systematic changes in slip zone geometry, influenced by the pre-existing fault geometry, the imposed regional tectonic stress, and the physical properties of unconsolidated materials. This study enhances our understanding of seismic activity and fault dynamics in the central part of the Korean Peninsula, highlighting the significant influence of geological and climatic factors over tens of thousands of years in the intraplate regions with similar tectonic and climate settings. [ABSTRACT FROM AUTHOR]

Large mammalian herbivores exert strong top‐down control on plants, which in turn influence most ecological processes. Accordingly, the decline, displacement, or extinction of wild large herbivores in African savannas is expected to alter the physical structure of vegetation, the diversity of plant communities, and downstream ecosystem functions. However, herbivore impacts on vegetation comprise both direct and indirect effects and often depend on herbivore body size and plant type. Understanding how herbivores affect savanna vegetation requires disaggregating the effects of different herbivores and the responses of different plants, as well as accounting for both the structural complexity and composition of plant assemblages. We combined high‐resolution Light Detection and Ranging (LiDAR) with field measurements from size‐selective herbivore exclosures in Kenya to determine how herbivores affect the diversity and physical structure of vegetation, how these impacts vary with body size and plant type, and whether there are predictable associations between plant diversity and structural complexity. Herbivores generally reduced the diversity and abundance of both overstory and understory plants, though the magnitude of these impacts varied substantially as a function of body size and plant type: only megaherbivores (elephants and giraffes) affected tree cover, whereas medium‐ and small‐bodied herbivores had stronger effects on herbaceous diversity and abundance. We also found evidence that herbivores altered the strength and direction of interactions between trees and herbaceous plants, with signatures of facilitation in the presence of herbivores and of competition in their absence. While megaherbivores uniquely affected tree structure, medium‐ and small‐bodied species had stronger (and complementary) effects on metrics of herbaceous vegetation structure. Plant structural responses to herbivore exclusion were species‐specific: of five dominant tree species, just three exhibited significant individual morphological variation across exclosure treatments, and the size class of herbivores responsible for these effects varied across species. Irrespective of exclosure treatment, more species‐rich plant communities were more structurally complex. We conclude that the diversity and architecture of savanna vegetation depend on consumptive and nonconsumptive plant–herbivore interactions; the roles of herbivore diversity, body size, and plant traits in mediating those interactions; and a positive feedback between plant diversity and structural complexity. [ABSTRACT FROM AUTHOR]

Thin film lithium niobate (TFLN) has become a promising material platform for large scale photonic integrated circuits (PICs). As an indispensable component in PICs, on-chip electrically tunable narrow-linewidth lasers have attracted widespread attention in recent years due to their significant applications in high-speed optical communication, coherent detection, precision metrology, laser cooling, coherent transmission systems, light detection and ranging (LiDAR). However, research on electrically driven, high-power, and narrow-linewidth laser sources on TFLN platforms is still in its infancy. This review summarizes the recent progress on the narrow-linewidth compact laser sources boosted by hybrid TFLN/III-V semiconductor integration techniques, which will offer an alternative solution for on-chip high performance lasers for the future TFLN PIC industry and cutting-edge sciences. The review begins with a brief introduction of the current status of compact external cavity semiconductor lasers (ECSLs) and recently developed TFLN photonics. The following section presents various ECSLs based on TFLN photonic chips with different photonic structures to construct external cavity for on-chip optical feedback. Some conclusions and future perspectives are provided. [ABSTRACT FROM AUTHOR]

This paper introduces a semiconductor optical amplifier (SOA) with high power and narrow linewidth broadening achieved through active region mode control. By integrating mode control with broad-spectrum epitaxial material design, the device achieves high gain, high power, and wide band output. At a wavelength of 1550 nm and an ambient temperature of 20 °C, the output power reaches 757 mW when the input power is 25 mW, and the gain is 21.92 dB when the input power is 4 mW. The 3 dB gain bandwidth is 88 nm, and the linewidth expansion of the input laser after amplification through the SOA is only 1.031 times. The device strikes a balance between high gain and high power, offering a new amplifier option for long-range light detection and ranging (LiDAR). [ABSTRACT FROM AUTHOR]

Multisensor (MS) data fusion is important for improving the stability of vehicle environmental perception systems. MS joint calibration is a prerequisite for the fusion of multimodality sensors. Traditional calibration methods based on calibration boards require the manual extraction of many features and manual registration, resulting in a cumbersome calibration process and significant errors. A joint calibration algorithm for a Light Laser Detection and Ranging (LiDAR) and camera is proposed based on deep learning without the need for other special calibration objects. A network model constructed based on deep learning can automatically capture object features in the environment and complete the calibration by matching and calculating object features. A mathematical model was constructed for joint LiDAR-camera calibration, and the process of sensor joint calibration was analyzed in detail. By constructing a deep-learning-based network model to determine the parameters of the rotation matrix and translation matrix, the relative spatial positions of the two sensors were determined to complete the joint calibration. The network model consists of three parts: a feature extraction module, a feature-matching module, and a feature aggregation module. The feature extraction module extracts the image features of color and depth images, the feature-matching module calculates the correlation between the two, and the feature aggregation module determines the calibration matrix parameters. The proposed algorithm was validated and tested on the KITTI-odometry dataset and compared with other advanced algorithms. The experimental results show that the average translation error of the calibration algorithm is 0.26 cm, and the average rotation error is 0.02°. The calibration error is lower than those of other advanced algorithms. [ABSTRACT FROM AUTHOR]

ABSTRACT Airborne laser scanning (ALS), commonly known as Light Detection and Ranging (LiDAR), is a remote sensing technique that enables transformative archaeological research by providing high‐density 3D representations of landscapes and sites covered by vegetation whose analysis reveals hidden features and structures. ALS can detect targets under trees and grasslands, making it an ideal archaeological survey and mapping tool. ALS instruments are usually mounted on piloted aircraft. However, since the mid‐2010s, smaller laser scanners can be mounted on uncrewed aerial vehicles or drones. In this article, we examined the viability of drone‐based ALS for archaeological applications by utilizing a RIEGL VUX‐UAV22 sensor to capture point clouds with high spatial resolution at the archaeological site of Heloros in Southeastern Sicily, founded by the Greeks in the late eighth century bce. Using this laser scanner, we surveyed over 1.6 km2 of the archaeological landscape, producing datasets that outperformed noncommercial airborne ALS data for the region made available by the Italian government. We produced derivative imagery free of vegetation, which we visualized in GIS using a modified Local Relief Model technique to aid our archaeological analyses. Our findings demonstrate that drone‐based ALS can penetrate the dense Mediterranean canopy of coastal Sicily with sufficient point density to enable more efficient mapping of underlying archaeological features such as stone quarries, cart tracks, defensive towers and fortification walls. Our study proved that drone‐based ALS sensors can be easily transported to remote locations and that in‐house lab staff can safely operate them, which enables multiple on‐demand surveys and opportunistic collections to be conducted on the fly when environmental conditions are ideal. We conclude that these capabilities further increase the benefits of utilizing ALS for surveying the archaeological landscape under the Mediterranean canopy. [ABSTRACT FROM AUTHOR]

Phase‐based light detection and ranging (LiDAR) technology is emerging in the fields of industrial mapping, autonomous driving, and robotics, but the traditional phase‐based ranging technology generally suffers from the problem that the ranging accuracy is inversely proportional to the measurement range under a single measurement frequency, the system structure is complicated, and the performance is unstable, and so forth. In this article, a new type of LiDAR system design based on phase ranging is proposed. The system adopts a 100 + 1 MHz double measuring ruler modulation light source, uses the laser to control the phase difference detection method of the same frequency reference, optimizes the structure of the transceiver optical system, and the design of AD8302 high‐resolution signal phase discriminator circuit, builds a high‐precision laser ranging system, and carries out the experiments on the measurement accuracy of the LiDAR ranging system. The experimental results show that the measurement accuracy of the system is millimeter level, which is simple, practical, and can meet the needs of a wide range of practical applications. This study provides a feasible and innovative solution for LiDAR technology in high‐precision distance measurement. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LIDAR) sensors using a polarization-diverse receiver are able to capture polarimetric information about the target under measurement. We demonstrate this capability using a silicon photonic receiver architecture that enables this on a shot-by-shot basis, enabling polarization analysis nearly instantaneously in the point cloud, and then use this data to train a material classification neural network. Using this classifier, we show an accuracy of 85.4% for classifying plastic, wood, concrete, and coated aluminum. [ABSTRACT FROM AUTHOR]

Partial shading of Photovoltaic (PV) system is commonly observed in outdoor field conditions specially in the Road- Integrated Photovoltaic systems. The non-uniform illumination causes mismatch in the electrical output between the cells, which results in an instantaneous effect on power generated and a long-term effect on reliability, it also causes hotspot issues. The objective of this work is to study the partial shading effect on the Road-Integrated PV (RIPV) cells and the soiling model effect under the active and inactive states of Multilevel Bypass (MLB) diodes. In this work, an electrical Simulation Program with Integrated Circuit Emphasis (Ng-SPICE) simulation model has been developed using irradiance model data. This data was collected using a Digital Elevation Model (DEM) called Light Resolution Light detection and Ranging (LidAR). The model was simulated using the Simple Sky Dome Projector (SSDP) software to analyze the impact of different shading conditions and study the effect of MLB diodes on the partial shading RIPV modules and the effect of MLB diodes in the soiling model. [ABSTRACT FROM AUTHOR]

The distribution and development of rock mass discontinuities in 3D space control the deformation and failure characteristics of the rock mass, which in turn affect the strength, permeability, and stability of rock masses. Therefore, it is essential to accurately and efficiently characterize these discontinuities. Light Detection and Ranging (LiDAR) now allows for fast and precise 3D data collection, which supports the creation of new methods for characterizing rock mass discontinuities. However, uneven density distribution and local surface undulations can limit the accuracy of discontinuity characterization. To address this, we propose a method for characterizing complex rock mass discontinuities based on laser point cloud data. This method is capable of processing datasets with varying densities and can reduce over-segmentation in non-planar areas. The suggested approach involves a five-stage process that includes: (1) adaptive resampling of point cloud data based on density comparison; (2) normal vector calculation using Principal Component Analysis (PCA); (3) identifying non-planar areas using a watershed-like algorithm, and determine the main discontinuity sets using Multi-threshold Mean Shift (MTMS); (4) identify single discontinuity clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN); (5) fitting discontinuity planes with Random Sample Consensus (RANSAC) and determining discontinuity orientations using analytic geometry. This method was applied to three rock slope datasets and compared with previous research results and manual measurement results. The results indicate that this method can effectively reduce over-segmentation and the characterization results have high accuracy. [ABSTRACT FROM AUTHOR]

The Simultaneous Localization and Mapping (SLAM) scanner is an easy and portable Light Detection and Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data were used: the total station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons were tested: cloud-to-cloud, cloud-to-mesh, mesh-to-mesh, and edge detection board assessment. However, the results of the assessments confirmed that the accuracy of indoor SLAM scanner measurements (5 mm) was greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud-to-cloud provided the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it was confirmed that SLAM scanners are effective tools for data acquisition. [ABSTRACT FROM AUTHOR]

In airborne surveying, light detection and ranging (LiDAR) strip adjustment and image bundle adjustment are customarily performed as separate processes. The bundle adjustment is usually conducted from frame images, while using linear pushbroom (LP) images in the bundle adjustment has been historically challenging due to the limited number of observations available to estimate the exterior image orientations. However, data from these three sensors conceptually provide information to estimate the same trajectory corrections, which is favorable for solving the problems of image depth estimation or the planimetric correction of LiDAR point clouds. Thus, our purpose with the presented study is to jointly estimate corrections to the trajectory and interior sensor states in a scalable hybrid adjustment between 3D LiDAR point clouds, 2D frame images, and 1D LP images. Trajectory preprocessing is performed before the low-frequency corrections are estimated for certain time steps in the following adjustment using cubic spline interpolation. Furthermore, the voxelization of the LiDAR data is used to robustly and efficiently form LiDAR observations and hybrid observations between the image tie-points and the LiDAR point cloud to be used in the adjustment. The method is successfully demonstrated with an experiment, showing the joint adjustment of data from the three different sensors using the same trajectory correction model with spline interpolation of the trajectory corrections. The results show that the choice of the trajectory segmentation time step is not critical. Furthermore, photogrammetric sub-pixel planimetric accuracy is achieved, and height accuracy on the order of mm is achieved for the LiDAR point cloud. This is the first time these three types of sensors with fundamentally different acquisition techniques have been integrated. The suggested methodology presents a joint adjustment of all sensor observations and lays the foundation for including additional sensors for kinematic mapping in the future. [ABSTRACT FROM AUTHOR]

• Palaeolithic art is an unknown and often difficult to see cultural manifestation. • The conditions for viewing the rock art have been improved through augmented reality. • The light variation of the decorated panel has been overcome with LiDAR technology. • The LiDAR project has been successful implemented in caves and open-air stations. • The app democratises rock art and enhances the on-site experience. By developing new recording methodologies, current rock art studies generate a large amount of graphic information about sites (tracings, photographs, three-dimensional reproductions) providing visibility of this fragile and little-known heritage, whose accessibility is often difficult or impossible for the general public. In addition, many rock art depictions are challenging to observe, due to the very nature of the artistic entities (fine engravings or faded paintings in karst environments or open-air sites with poor or changing light conditions), or to conservation problems derived from natural factors such as erosion and geological and biological processes, as well as from anthropic factors. These conditions make rock art depictions nearly indistinguishable in many places and on many objects today, except for experts. This difficulty of accessing and visualising rock art heritage, located in fragile environments and often challenging places such as caves or difficult-to-reach open-air sites, makes the information and knowledge generated by investigation of this heritage asset difficult to transfer to society in general, which is frequently unaware of the priceless value of this heritage. The present study proposes generating several mechanisms to transfer the results of research, restitution and documentation of rock art to society in general. An AR (Augmented Reality) application has been developed using LiDAR (Light Detection and Ranging) technology to address current challenges in implementing AR technologies in low-light environments. So far, this app has been developed in a Proof-of-Concept project at Spanish archaeological sites such as Hornos de la Peña (Cantabria), Domingo García (Segovia) and La Salud (Salamanca). This application will be particularly interesting for sites currently visited with or without a guide, allowing user interactivity and real-time reconstruction, for example, of the visibility of graphic motifs. [ABSTRACT FROM AUTHOR]

Cotton is one of the crops that requires the most time and labor. Precision agriculture technology is required for efficient management of cotton, and the identification of cotton attribute information in the field is a necessary and crucial step towards implementing precision agriculture. Unmanned aerial vehicles (UAVs) and Light Detection and Ranging (LiDAR) have evolved into essential instruments for plant phenotyping research. In this study, in order to address the demand for cotton attribute identification over wide areas in the field, an airborne LiDAR system was built based on LiDAR detection technology. This work acquired a dual-view point cloud of a cotton field in order to address the high density and low accuracy of the cotton point cloud attributes. Following pre-processing of the data, the point cloud was first coarsely regenerated using a combination of Fast Point Feature Histograms (FPFH) and Intrinsic Shape Signatures (ISS) techniques. The dual-view point cloud registration was then refined and finished using an Iterative Closest Point (ICP) algorithm. The height of the cotton plant was determined using the reconstructed point cloud of the cotton canopy, and a method combining Graham's algorithm and the Alpha-Shape algorithm was suggested to determine the porosity of the cotton layers. The findings revealed that the root mean square errors (RMSE) between calculated and measured values of cotton plant height and stratified porosity were, respectively, 3.98 cm and 5.21%, and that their mean absolute percentage errors (MAPE) were 4.39% and 9.31%, with correlation coefficients (${R^2}$ R 2 ) of 0.951 and 0.762, respectively. On the whole, our study has demonstrated the effectiveness of the proposed method in terms of providing accurate and reliable cotton parameters in agriculture. [ABSTRACT FROM AUTHOR]

Kinematic laser scanning is a widely-used surveying technique based on light detection and ranging (LiDAR) that enables efficient data acquisition by mounting the laser scanner on a moving platform. In order to obtain a georeferenced point cloud, the trajectory of the moving platform must be accurately known. To this end, most commercial laser scanning systems comprise an inertial measurement unit (IMU) and a global navigation satellite system (GNSS) receiver and antenna. Trajectory estimation is then the task of determining the platform's position and orientation by integrating measurements from the IMU, GNSS, and possibly the laser scanner itself. Here, we present a comprehensive approach to trajectory estimation for kinematic laser scanning, based on batch least-squares adjustment incorporating pre-processed GNSS positions, raw IMU data and plane-based LiDAR correspondences in a single estimation procedure. In comparison to the classic workflow of Kalman filtering followed by strip adjustment, this is a holistic approach with tight coupling of IMU and LiDAR. For the latter, we extend the data-derived stochastic model for the LiDAR plane observations with prior knowledge of the LiDAR measurement process. The proposed trajectory estimation approach is flexible and allows different system configurations as well as joint registration of multiple independent kinematic datasets. This is demonstrated using as a practical example a combined dataset consisting of two independent data acquisitions from crewed aircraft and uncrewed aerial vehicle. All measurements from both datasets are jointly adjusted in order to obtain a single high-quality point cloud, without the need for ground control. The performance of this approach is evaluated in terms of point cloud consistency, precision, and accuracy. The latter is done by comparison to terrestrially surveyed reference data on the ground. The results show improved consistency, accuracy, and precision compared to a standard workflow, with the RMSE reduced from 7.43 cm to 3.85 cm w.r.t. the reference data surfaces, and the point-to-plane standard deviation on the surfaces reduced from 3.01 cm to 2.44 cm. Although a direct comparison to the state-of-the-art can only be made with caution, we can state that the suggested method performs better in terms of point cloud consistency and precision, while at the same time achieving better absolute accuracy. [ABSTRACT FROM AUTHOR]

As LiDAR technology progressively advances, the capability of radar in detecting targets has become increasingly vital across diverse domains, including industrial, military, and automotive sectors. Frequency-modulated continuous-wave (FMCW) LiDAR in particular has garnered substantial interest due to its efficient direct velocity measurement and excellent anti-interference characteristics. It is widely recognized for its significant potential within radar technology. This study begins by elucidating the operational mechanism of FMCW LiDAR and delves into its basic principles. It discuss, in depth, the influence of various parameters on FMCW LiDAR's performance and reviews the latest progress in the field. This paper proposes that future studies should focus on the synergistic optimization of key parameters to promote the miniaturization, weight reduction, cost-effectiveness, and longevity of FMCW LiDAR systems. This approach aims at the comprehensive development of FMCW LiDAR, striving for significant improvements in system performance. By optimizing these key parameters, the goal is to promote FMCW LiDAR technology, ensuring more reliable and accurate applications in automated driving and environmental sensing. [ABSTRACT FROM AUTHOR]

Processing measurement data is an essential part of surveying engineering. One can list several methods in such a context: least squares estimation, M-estimation, R-estimation, etc. Some methods were developed by surveyors, e.g., the Danish method, IGG scheme, or Msplit estimation. The last method is, in fact, a class of estimation procedures dedicated to different problems. As a new approach to processing data, Msplit estimation is still being developed and improved. That paper concerns the local robustness of Msplit estimation and introduces a new Msplit estimation variant that is less sensitive to local outliers. Such a property seems important, especially in big data processing, such as observations from Light Detection and Ranging systems. The new variant modifies the squared Msplit estimation (SMS estimation) by implementing the adapted Tukey weight function, hence its acronym SMSTL estimation. The basic theoretical and empirical analyses, which were performed for the univariate model using, among others, the appropriate measures of robustness, confirmed the expected property of the method. The further tests, based on simulated as well as real data, show that the new method might overperform other Msplit estimation variants and classical methods for the chosen types of observation sets. [ABSTRACT FROM AUTHOR]

We apply a machine learning approach to automatically infer two key attributes – the location of fault or shear zone structures and the thickness of the overburden – in an 18 km2 study area within and surrounding the Archean Fenelon gold deposit in Quebec, Canada. Our approach involves the inversion of carefully curated borehole lithological and structural observations truncated at 480 m below the surface, combined with magnetic and Light Detection and Ranging survey data. We take a computationally low‐cost approach in which no underlying model for geological consistency is imposed. We investigated three contrasting approaches: (1) an inferred fault model, in which the borehole observations represent a direct evaluation of the presence of fault or shear zones; (2) an inferred overburden model, using borehole observations on the overburden‐bedrock contact; (3) a model with three classes – overburden, faulted bedrock and unfaulted bedrock, which combines aspects of (1) and (2). In every case, we applied all 32 standard machine learning algorithms. We found that Bagged Trees, fine K‐nearest neighbours and weighted K‐nearest neighbour were the most successful, producing similar accuracy, sensitivity and specificity metrics. The Bagged Trees algorithm predicted fault locations with approximately 80% accuracy, 70% sensitivity and 73% specificity. Overburden thickness was predicted with 99% accuracy, 77% sensitivity and 93% specificity. Qualitatively, fault location predictions compared well to independently construct geological interpretations. Similar methods might be applicable in other areas with good borehole coverage, providing that criteria used in borehole logging are closely followed in devising classifications for the machine learning training set and might be usefully supplemented with a variety of geophysical survey data types. [ABSTRACT FROM AUTHOR]

Time of flight is promising technology in machine vision and sensing, with an emerging need for low power consumption, a high image resolution, and reliable operation in high ambient light conditions. Therefore, we propose a novel direct time-of-flight pixel using the single-photon avalanche diode (SPAD) sensor, with an in-pixel averaging method to suppress ambient light and detect the laser pulse arrival time. The system utilizes two orthogonal sinusoidal signals applied to the pixel as inputs, which are synchronized with a pulsed laser source. The detected signal phase indicates the arrival time. To evaluate the proposed system's potential, we developed analytical and statistical models for assessing the phase error and precision of the arrival time under varying ambient light levels. The pixel simulation showed that the phase precision is less than 1% of the detection range when the ambient-to-signal ratio is 120. A proof-of-concept pixel array prototype was fabricated and characterized to validate the system's performance. The pixel consumed, on average, 40 μ W of power in operation with ambient light. The results demonstrate that the system can operate effectively under varying ambient light conditions and its potential for customization based on specific application requirements. This paper concludes by discussing the system's performance relative to the existing direct time-of-flight technologies, identifying their strengths and limitations. [ABSTRACT FROM AUTHOR]

Grain is a common bulk cargo. To ensure optimal utilization of transportation space and prevent overflow accidents, it is necessary to observe the grain's shape and determine the loading status during the loading process. Traditional methods often rely on manual judgment, which results in high labor intensity, poor safety, and low loading efficiency. Therefore, this paper proposes a method for recognizing the bulk grain-loading status based on Light Detection and Ranging (LiDAR). This method uses LiDAR to obtain point cloud data and constructs a deep learning network to perform target recognition and component segmentation on loading vehicles, extract vehicle positions and grain shapes, and recognize and make known the bulk grain-loading status. Based on the measured point cloud data of bulk grain loading, in the point cloud-classification task, the overall accuracy is 97.9% and the mean accuracy is 98.1%. In the vehicle component-segmentation task, the overall accuracy is 99.1% and the Mean Intersection over Union is 96.6%. The results indicate that the method has reliable performance in the research tasks of extracting vehicle positions, detecting grain shapes, and recognizing loading status. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of flood-induced hydrodynamic forces and high discharge on the masonry arch "Hannibal Bridge" (called "Ponte di Annibale" in Italy) using the Hydraulic Engineering Center's River Analysis Simulation (HEC-RAS) v6.5.0. hydraulic numerical method, incorporating Unmanned Aerial Vehicle (UAV) photogrammetry and aerial Light Detection and Ranging (LIDAR) data for visual analysis. The research highlights the highly transient behavior of fast flood flows, particularly when carrying debris, and their effect on bridge superstructures. Utilizing a Digital Elevation Model to extract cross-sectional and elevation data, the research examined 23 profiles over 800 m of the river. The results indicate that the maximum allowable water depth in front of the bridge is 4.73 m, with a Manning's coefficient of 0.03 and a longitudinal slope of 9 m per kilometer. Therefore, a novel method to identify the risks through HEC-RAS modeling significantly improves the conservation of masonry bridges by providing precise topographical and hydrological data for accurate simulations. Moreover, the detailed information obtained from LIDAR and UAV photogrammetry about the bridge's materials and structures can be incorporated into the conservation models. This comprehensive approach ensures that preservation efforts are not only addressing the immediate hydrodynamic threats but are also informed by a thorough understanding of the bridge's structural and material conditions. Understanding rating curves is essential for water management and flood forecasting, with the study confirming a Manning roughness coefficient of 0.03 as suitable for smooth open-channel flows and emphasizing the importance of geomorphological conditions in hydraulic simulation. [ABSTRACT FROM AUTHOR]

The integration of autonomous vehicles in industrial settings necessitates advanced positioning and navigation systems to ensure operational safety and efficiency. This study rigorously evaluates the application of Ultra-Wideband (UWB) technology in autonomous industrial trucks and compares its effectiveness with conventional systems such as Light Detection and Ranging (LiDAR), Global Positioning System (GPS), and cameras. Through comprehensive experiments conducted in a real factory environment, this study meticulously assesses the accuracy and reliability of UWB technology across various reference distances and under diverse environmental conditions. The findings reveal that UWB technology consistently achieves positioning accuracy within 0.2 cm 99% of the time, significantly surpassing the 10 cm and 5 cm accuracies of GPS and LiDAR, respectively. The exceptional performance of UWB, especially in environments afflicted by high metallic interference and non-line-of-sight conditions—where GPS and LiDAR's efficacy decreased by 40% and 25%, respectively—highlights its potential to revolutionize the operational capabilities of autonomous trucks in industrial applications. This study underscores the robustness of UWB in maintaining high accuracy even in adverse conditions and illustrates its low power consumption and efficiency in multi-user scenarios without signal interference. This study not only confirms the superior capabilities of UWB technology but also contributes to the broader field of autonomous vehicle technology by highlighting the practical benefits and integration potential of UWB systems in complex and dynamic environments. [ABSTRACT FROM AUTHOR]