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To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-R2 = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics (R2 = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. [ABSTRACT FROM AUTHOR]

Accurate and efficient registration of unmanned aerial vehicle light detection and ranging (UAV‐lidar) and terrestrial lidar (T‐lidar) data is crucial for forest structure parameter extraction. This study proposes a novel method based on a starburst pattern for the automatic registration of UAV‐lidar and T‐lidar data in forest scenes. It employs density‐based spatial clustering of applications with noise (DBSCAN) for individual tree identification, constructs starburst patterns separately from both lidar sources, and utilises polar coordinate rotation and matching to achieve coarse registration. Fine registration is achieved using the iterative closest point (ICP) algorithm. Experimental results demonstrate that the starburst‐pattern‐based method achieves the desired registration accuracy (average coarse registration error of 0.157 m). Further optimisation with ICP yields slight improvements with an average fine registration error of 0.149 m. Remarkably, the proposed method is insensitive to the individual tree detection number when exceeding 10, and the tree position error has minimal impact on registration accuracy. Furthermore, our proposed method outperforms two existing methods in T‐lidar and UAV‐lidar registration over forest environments. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle light detection and ranging (UAV‐lidar) and unmanned aerial vehicle (UAV) photogrammetry are currently commonly used surface monitoring technologies. Previous studies have used the two technologies interchangeably and ignored their correlation, or only compared them on a single product. However, there are few quantitative assessments of the differences between these two techniques in monitoring surface deformation and prediction of their application prospects. Therefore, the paper compared the differences between the digital elevation model (DEM) and subsidence basins obtained by the two techniques using Gaussian analysis. The results indicate that the surface DEMs obtained by both the techniques exhibit a high degree of similarity. The statistical analysis of the difference values in the z direction between the two DEMs follows a Gaussian distribution with a standard deviation of less than 0.36 m. When comparing the surface subsidence values monitored by the two techniques, it was found that UAV‐lidar was more sensitive to small‐scale deformation, with a difference range of 0.23–0.44 m compared to photogrammetry. The conclusion provides valuable information regarding the utilisation of multisource monitoring data. [ABSTRACT FROM AUTHOR]

To ensure the safety of tunnel construction, it is necessary to accurately and timely measure the deformation of the shotcrete liner during the management of the construction process. The light detection and ranging (LiDAR) technique, known as the reality reproduction technology, provides a new solution for deformation measurement. However, the nonsmooth defects of shotcrete liner and the scattered noise points caused by the nearby dust, as well as a large number of nontunnel structures such as pipes, construction machinery, and vehicles in the obtained tunnel point cloud datasets, hinder the application of point clouds in deformation measurement of shotcrete liner. In this paper, a ring simulation sampling (RSS) method for automatic sampling and deformation measurement of shotcrete liner using the multiepoch LiDAR point clouds is proposed. This method constructs the virtual ring according to shape of the tunnel cross section and emits the rays from the ring to the center of each tunnel cross section. The length difference of the rays arriving at the outer wall of the multiepoch tunnel point clouds is calculated to achieve the analysis of overall and local deformations of the shotcrete liner. The shotcrete liner point clouds generated by the manual modeling and terrestrial laser scanning are tested to validate the effectiveness of the method in extracting the deformation of shotcrete liner. Compared with the conventional total station monitoring, the mean absolute deviation and root mean square error are 1.39 and 1.93 mm, respectively, which indicates that the RSS method could be effectively applied to the deformation measurement of tunnel shotcrete liner. Deformation measurement of tunnel shotcrete liner is crucial to safety of tunnel construction and implementation of dynamic parameter design. This study recommends the light detection and ranging (LiDAR) technique. The main work of this paper is to determine whether the tunnel shotcrete lining has been deformed as a whole or locally by comparing the obtained multiepoch high-precision 3D point clouds. Furthermore, the authors propose a method for automatic sampling and deformation measurement of shotcrete liner using the multiepoch LiDAR point clouds. Compared with conventional deformation measurement methods (i.e., the total station and fiber optics) that capture deformation information from a limited number of monitoring points, LiDAR technology enables the high-precision, omnidirectional, and noncontact acquisition of spatial data within tunnels. This serves as a complementary and enhanced tool to tunnel safety monitoring techniques and methodologies. In addition, the adoption of the method proposed in this paper facilitates the quality check of shotcrete liner, including thickness assessment and smoothness detection. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date information on urban built-up areas is significant for managing urban growth and development. Earth Observation (EO) data are valuable sources for meeting this demand. However, the extraction of urban built-up areas from EO data is challenging due to the limitations of EO data sources. To overcome this challenge, this study follows an approach that assesses the performance of optical (Sentinel-2), radar (Sentinel-1) and fused (Sentinel-1 and Sentinel-2) data to extract urban built-up areas using machine learning algorithms including Random Forest (RF), K-Nearest Neighbors (KNN) and KDTree KNN. The results were statistically analyzed by considering the Overall Accuracy (OA) and kappa coefficient. In addition, 15 cm GSD (Ground Sample Distance) aerial photography of the study area was used to validate the results. According to the results, Sentinel-2 produced better representation and accuracy of urban built-up areas than Sentinel-1 and even the fused image. Regarding to machine learning algorithms classification performance, RF performed better in both OA and Kappa coefficient along all datasets. The research findings can have significant implications for various domains, such as urban planning, land use management and open avenues for further comparisons of different EO data sources and machine learning algorithms for built-up areas extraction. [ABSTRACT FROM AUTHOR]

Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships remains controversial. Here, we mapped the global distribution of forest CSC and revealed the factors influencing its distribution using worldwide light detection and ranging data. We find that forest CSC predominantly demonstrates significant positive relationships with forest ecosystem productivity and stability globally, although substantial variations exist among forest ecoregions. The effects of forest CSC on productivity and stability are the balanced results of biodiversity and resource availability, providing valuable insights for comprehending forest ecosystem functions. Managed forests are found to have lower CSC but more potent enhancing effects of forest CSC on ecosystem productivity and stability than intact forests, highlighting the urgent need to integrate forest CSC into the development of forest management plans for effective climate change mitigation.. [ABSTRACT FROM AUTHOR]

In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics. [ABSTRACT FROM AUTHOR]

With the emergence of autonomous functions in road vehicles, there has been increased use of Advanced Driver Assistance Systems comprising various sensors to perform automated tasks. Light Detection and Ranging (LiDAR) is one of the most important types of optical sensor, detecting the positions of obstacles by representing them as clusters of points in three-dimensional space. LiDAR performance degrades significantly when a vehicle is driving in the rain as raindrops adhere to the outer surface of the sensor assembly. Performance degradation behaviors include missing points and reduced reflectivity of the points. It was found that the extent of degradation is highly dependent on the interface material properties. This subsequently affects the shapes of the adherent droplets, causing different perturbations to the optical rays. A fundamental investigation is performed on the protective polycarbonate cover of a LiDAR assembly coated with four classes of material—hydrophilic, almost-hydrophobic, hydrophobic, and superhydrophobic. Water droplets are controllably dispensed onto the cover to quantify the signal alteration due to the different droplets of various sizes and shapes. To further understand the effects of droplet motion on LiDAR signals, sliding droplet conditions are simulated using numerical analysis. The results are validated with physical optical tests, using a 905 nm laser source and receiver to mimic the LiDAR detection mechanism. Comprehensive explanations of LiDAR performance degradation in rain are presented from both material and optical perspectives. These can aid component selection and the development of signal-enhancing strategies for the integration of LiDARs into vehicle designs to minimize the impact of rain. [ABSTRACT FROM AUTHOR]

Most advances in the remote sensing of floating marine plastic litter have been made using passive remote-sensing techniques in the visible (VIS) to short-wave-infrared (SWIR) parts of the electromagnetic spectrum based on the spectral absorption features of plastic surfaces. In this paper, we present developments of new and emerging remote-sensing technologies of marine plastic litter such as passive techniques: fluid lensing, multi-angle polarimetry, and thermal infrared sensing (TIS); and active techniques: light detection and ranging (LiDAR), multispectral imaging detection and active reflectance (MiDAR), and radio detection and ranging (RADAR). Our review of the detection capabilities and limitations of the different sensing technologies shows that each has their own weaknesses and strengths, and that there is not one single sensing technique that applies to all kinds of marine litter under every different condition in the aquatic environment. Rather, we should focus on the synergy between different technologies to detect marine plastic litter and potentially the use of proxies to estimate its presence. Therefore, in addition to further developing remote-sensing techniques, more research is needed in the composition of marine litter and the relationships between marine plastic litter and their proxies. In this paper, we propose a common vocabulary to help the community to translate concepts among different disciplines and techniques. [ABSTRACT FROM AUTHOR]

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management. [ABSTRACT FROM AUTHOR]

The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%. [ABSTRACT FROM AUTHOR]

Tunable and narrowband light detection provides a means of selectively detecting optical signals at a specific wavelength, enabling a precise tool for object identification, machine vision, spectroscopy, and so on. Simultaneous tunable and narrowband response in shortwave infrared (SWIR) detectors is critical yet still challenging. This work utilizes dual-Fano resonance enhanced sum-frequency generation in a two-layer structure comprising a silicon metasurface and a two-dimensional (2D) nonlinear GaSe layer to realize tunable and narrowband light detection in the SWIR range. The silicon metasurface affords a high-quality-factor dual-Fano resonance in the SWIR regime, which enhances the near-field optical density of the two resonant wavelengths (pump and signal) when passing through the 2D nonlinear layer, leading to drastically enhanced sum-frequency generation. The sum-frequency light at a visible wavelength that contains the information of the SWIR signal light, can then be detected by a low-noise visible detector. The tunability and selectivity in the response spectrum stem from the geometry-dependent dual-Fano resonance in the silicon metasurface, covering the 1200–1550 nm range. The upconversion detector exhibits a sub-nanometer narrowband detection with a full width at half maximum of down to ∼0.1 nm, owing to the high quality factor of the Fano resonances. This SWIR narrowband detection is one of the best performances reported so far, much narrower than commercial filter products. The peak value of the specific detectivity of 1.5 × 1012 Jones at 1256.3 nm is achieved, comparable to broadband commercial InGaAs detectors. The detector designs in this work open up the opportunity of upconversion sensors for delicate spectroscopic applications. [ABSTRACT FROM AUTHOR]

Near‐infrared organic light‐emitting diodes (NIR OLEDs) with emission peak above 900 nm are attractive for many emerging applications, spanning from bioimaging to light detection and ranging. However, the device performance of NIR OLEDs is generally limited by the low quantum efficiency of emitters because of the fast nonradiative transition process imposed by energy‐gap law and aggregation quenching. So far, only a few Pt(II) complexes delivering external quantum efficiency (EQE) over 1% are reported, while there is no comparable electroluminescence in heavy‐metal‐free fluorescent organic emitters. Here, NIR OLEDs centered at 934 nm by blending an acceptor–donor–acceptor type molecule Y11 into a polymer host poly[(9,9‐dioctylfluorenyl‐2,7‐diyl)‐alt‐(4,4′‐(N‐(4‐sec‐butylphenyl)diphenylamine) (TFB) are reported. The OLEDs show a remarkably high EQE of 1.72%. Moreover, owing to a low turn‐on voltage (≈0.9 V), the resultant NIR OLEDs have an electricity‐to‐light power efficiency surpassing 20 mW W−1. The improved device performance can be attributed to enhanced photoluminescence quantum yields (PLQYs) of the blends owing to suppressed aggregation quenching, and favorable light extraction from the emissive layer. Such values are the highest among fluorescent OLEDs with electroluminescence above 900 nm. [ABSTRACT FROM AUTHOR]

Global marine gravity anomaly models are predominantly recovered from along-track radar altimeter data. While remarkable advancements has been achieved in gravity anomaly modelling, the quality of gravity anomaly model remains constrained by the absence of across-track geoid gradients and the reduction of radar altimeter data, particularly in coastal and high-latitudes regions. ICESat-2 laser altimetry operates three-pair laser beams with a small footprint and near-polar orbit, enabling the determination of across-track geoid gradients and providing more valid observations in certain regions. The ICESat-2 altimeter data processing method is presented including the determination of across-track geoid gradients and the combination of along/across-track geoid gradients. A new global marine gravity model, SDUST2022GRA, is recovered from radar and laser altimeter data using different method for determining each altimeter data error. The accuracy and spatial resolution of SDUST2022GRA is assessed by published global gravity anomaly models (DTU17, V32.1, NSOAS22) and available shipborne gravity measurements. The accuracy of SDUST2022GRA is 4.43 mGal on a global scale, which is at least 0.22 mGal better than that of others models. Moreover, in local coastal and high-latitude regions, SDUST2022GRA achieves an accuracy improvement of 0.16–0.24 mGal compared to others models. The spatial resolution of SDUST2022GRA is approximately 20 km in a certain region, slightly better superior others models. These assessments suggests that SDUST2022GRA is a reliable global marine gravity anomaly model. By comparing SDUST2022GRA with incorporating ICESat-2 and SDUST2021GRA without ICESat-2, the percentage contribution of ICESat-2 to the improvement of gravity anomaly model accuracy is 13 % in the global ocean region, and it is increasing with an proportion of ICESat-2 altimeter data in high-latitude and coastal regions. The SDUST2022GRA are freely available at the site of https://doi.org/10.5281/zenodo.8337387 (Li et al., 2023). [ABSTRACT FROM AUTHOR]

The firn layer on the Greenland Ice Sheet (GrIS) plays a crucial role in buffering surface meltwater runoff, which is constrained by the available firn pore space and impermeable ice layers that limit deeper meltwater percolation. Understanding these firn properties is essential for predicting current and future meltwater runoff and its contribution to global sea-level rise. While very-high-frequency (VHF) radars have been extensively used for surveying the GrIS, their lower bandwidth restricts direct firn stratigraphy extraction. In this study, we use concurrent VHF airborne radar and laser altimetry data collected as part of Operation IceBridge over the 2011–2019 period to investigate our hypothesis that vertical heterogeneities in firn (i.e. ice layers) cause vertical offsets in the radar surface reflection (dz). Our results, corroborated by modelling and firn core analyses, show that a dz larger than 1 m is strongly related to the vertical heterogeneity of a firn profile and effectively delineates between vertically homogeneous and vertically heterogeneous firn profiles over a depth range of ∼ 4 m. Temporal variations in dz align with climatic events and reveal an expansion of heterogeneous firn between 2011–2013 covering an area of ∼ 350 815 km 2 , followed by firn replenishment over the years 2014–2019 spanning an area of ∼ 667 725 km 2. Our approach reveals the firn evolution of key regions on the Greenland Ice Sheet, providing valuable insights for detecting potential alterations in meltwater runoff patterns. [ABSTRACT FROM AUTHOR]

With the increasing number of vehicles, the growing complexity of traffic environments has led to a rise in traffic pressure. As a critical hub connecting roads between cities, expressway traffic safety cannot be ignored.Merging areas, due to their complex road configuration, have become major accident-prone spots on expressways. Improving driving safety in expressway merging areas is of the utmost importance. Predicting the future trajectory of a vehicle can then be used to avoid traffic conflicts or even crashes by using methods such as future trajectories and active control. Therefore, the emergence of trajectory prediction techniques provides new approaches for intelligent traffic management in these areas. First, this paper takes a global perspective fromroadside light detection and ranging (LiDAR) sensors to construct a vehicle trajectory database in the merging area, relying on object detection and trajectory tracking technologies. Then, a vehicle trajectory prediction model based on a self-supervised mechanism is developed, specifically designed for the complex interactive environment of expressway merging areas. Finally, four models, long short-term memory (LSTM), social LSTM (SL), convolutional social LSTM (CSL), and maneuver-aware pooling (MAP), are compared with the proposed model. The evaluation is based on the root mean square error (RMSE) metric for overall, left-turn, rightturn, straight, and merging trajectory accuracies and the Acc metric for horizontal and vertical intention accuracies. Experimental results demonstrate that the proposed model achieves lower errors and higher prediction accuracy in both trajectory prediction and lateral and longitudinal intention prediction. [ABSTRACT FROM AUTHOR]

In the field of robotics and autonomous driving, dynamic occupancy grid maps (DOGMs) are typically used to represent the position and velocity information of objects. Although three-dimensional light detection and ranging (LiDAR) sensor-based DOGMs have been actively researched, they have limitations, as they cannot classify types of objects. Therefore, in this study, a deep learning-based camera–LiDAR sensor fusion technique is employed as input to DOGMs. Consequently, not only the position and velocity information of objects but also their class information can be updated, expanding the application areas of DOGMs. Moreover, unclassified LiDAR point measurements contribute to the formation of a map of the surrounding environment, improving the reliability of perception by registering objects that were not classified by deep learning. To achieve this, we developed update rules on the basis of the Dempster–Shafer evidence theory, incorporating class information and the uncertainty of objects occupying grid cells. Furthermore, we analyzed the accuracy of the velocity estimation using two update models. One assigns the occupancy probability only to the edges of the oriented bounding box, whereas the other assigns the occupancy probability to the entire area of the box. The performance of the developed perception technique is evaluated using the public nuScenes dataset. The developed DOGM with object class information will help autonomous vehicles to navigate in complex urban driving environments by providing them with rich information, such as the class and velocity of nearby obstacles. [ABSTRACT FROM AUTHOR]

This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-channel coprime-sampling (RIP-OMCS) strategy is established. In the RIP-OMCS strategy, the data collected across multiple channels with very low coprime-sampling rates can record accurate range information on each target. Further, the asynchronous problem caused by channel sampling-time errors is considered. The sampling-time errors are estimated using the cross-correlation function. After canceling the asynchronous problem, the data collected by multiple channels are then merged into non-uniform sampled signals. Using data combination, target-range estimation is converted into an optimization problem of sparse representation consisting of a non-uniform Fourier dictionary. This optimization problem is solved using adaptive hybrid re-weighted constraint (AHRC) l1 minimization. Two constraints are formed from statistical attributes of the targets and clutter. Moreover, as the detailed characteristics of the target, clutter, and noise are unknown before the solution, the two constraints can be adaptively modified, which guarantees that l1 minimization obtains the high-resolution range profile and accurate distance of all targets under a low SNR. Our experiments confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Accurately estimating vegetation biomass in urban forested areas is of great interest to researchers as it is a key indicator of the carbon sequestration capacity necessary for cities to achieve carbon neutrality. The emerging vegetation biomass estimation methods that use AI technologies with remote sensing images often suffer from arge estimating errors due to the diversity of vegetation and the complex three-dimensional terrain environment in urban ares. However, the high resolution of Light Detection and Ranging (i.e., LiDAR) data provides an opportunity to accurately describe the complex 3D scenes of urban forests, thereby improving estimation accuracy. Additionally, deep earning foundation models have widely succeeded in the industry, and show great potential promise to estimate vegetation biomass through processing complex and arge amounts of urban LiDAR data efficiently and accurately. In this study, we propose an efficient and accurate method called 3D-CiLBE (3DCity Long-term Biomass Estimation) to estimate urban vegetation biomass by utilizing advanced deep earning foundation models. In the 3D-CiLBE method, the Segment Anything Model (i.e., SAM) was used to segment single wood information from a arge amount of complex urban LiDAR data. Then, we modified the Contrastive Language–Image Pre-training (i.e., CLIP) model to identify the species of the wood so that the classic anisotropic growth equation can be used to estimate biomass. Finally, we utilized the Informer model to predict the biomass in the ong term. We evaluate it in eight urban areas across the United States. In the task of identifying urban greening areas, the 3D-CiLBE achieves optimal performance with a mean Intersection over Union (i.e., mIoU) of 0.94. Additionally, for vegetation classification, 3D-CiLBE achieves an optimal recognition accuracy of 92.72%. The estimation of urban vegetation biomass using 3D-CiLBE achieves a Mean Square Error of 0.045 kg/m2, reducing the error by up to 8.2% compared to 2D methods. The MSE for biomass prediction by 3D-CiLBE was 0.06kg/m2 smaller on average than the inear regression model. Therefore, the experimental results indicate that the 3D-CiLBE method can accurately estimate urban vegetation biomass and has potential for practical application. [ABSTRACT FROM AUTHOR]

The remote detection of hydrothermally altered grounds in geothermal exploration demands datasets capable of reliably detecting key outcrops with fine spatial resolution. While optical thermal or radar-based datasets have resolution limitations, airborne LiDAR offers point-based detection through its LiDAR return intensity (LRI) values, serving as a proxy for surface reflectivity. Despite this potential, few studies have explored LRI value variations in the context of hydrothermal alteration and their utility in distinguishing altered from unaltered rocks. Although the link between alteration degree and LRI values has been established under laboratory conditions, this relationship has yet to be demonstrated in airborne data. This study investigates the applicability of laboratory results to airborne LRI data for alteration detection. Utilising LRI data from an airborne LiDAR point cloud (wavelength 1064 nm, density 12 points per square metre) acquired over a prospective geothermal area in Bajawa, Indonesia, where rock sampling for a related laboratory study took place, we compare the airborne LRI values within each ground sampling area of a 3 m radius (due to hand-held GPS uncertainty) with laboratory LRI values of corresponding rock samples. Our findings reveal distinguishable differences between strongly altered and unaltered samples, with LRI discrepancies of approximately ~28 for airborne data and ~12 for laboratory data. Furthermore, the relative trends of airborne and laboratory-based LRI data concerning alteration degree exhibit striking similarity. These consistent results for alteration degree in laboratory and airborne data mark a significant step towards LRI-based alteration mapping from airborne platforms. [ABSTRACT FROM AUTHOR]

This article addresses the challenges of measuring the 3D architecture traits, such as height and volume, of fruit tree canopies, constituting information that is essential for assessing tree growth and informing orchard management. The traditional methods are time-consuming, prompting the need for efficient alternatives. Recent advancements in unmanned aerial vehicle (UAV) technology, particularly using Light Detection and Ranging (LiDAR) and RGB cameras, have emerged as promising solutions. LiDAR offers precise 3D data but is costly and computationally intensive. RGB and photogrammetry techniques like Structure from Motion and Multi-View Stereo (SfM-MVS) can be a cost-effective alternative to LiDAR, but the computational demands still exist. This paper introduces an innovative approach using UAV-based single-lens stereoscopic photography to overcome these limitations. This method utilizes color variations in canopies and a dual-image-input network to generate a detailed canopy height map (CHM). Additionally, a block structure similarity method is presented to enhance height estimation accuracy in single-lens UAV photography. As a result, the average rates of growth in canopy height (CH), canopy volume (CV), canopy width (CW), and canopy project area (CPA) were 3.296%, 9.067%, 2.772%, and 5.541%, respectively. The r2 values of CH, CV, CW, and CPA were 0.9039, 0.9081, 0.9228, and 0.9303, respectively. In addition, compared to the commonly used SFM-MVS approach, the proposed method reduces the time cost of canopy reconstruction by 95.2% and of the cost of images needed for canopy reconstruction by 88.2%. This approach allows growers and researchers to utilize UAV-based approaches in actual orchard environments without incurring high computation costs. [ABSTRACT FROM AUTHOR]

Hurricanes Ian and Nicole caused devastating destruction across Florida in September and November 2022, leaving widespread damage in their wakes. This study focuses on the assessment of barrier islands' shorelines, encompassing natural sand dunes and dune vegetation as well as armored dunes with man-made infrastructure such as seawalls. High-resolution satellite imagery from Planet was used to assess the impacts of these hurricanes on the beach shorelines of Volusia, Flagler, and St. Johns Counties on the Florida Central East Coast. Shorefront vegetation was classified into two classes. Normalized Difference Vegetation Index (NDVI) values were calculated before the hurricanes, one month after Hurricane Ian, one month after Hurricane Nicole, and one-year post landfall. LiDAR (Light Detection and Ranging) was incorporated to calculate vertical changes in the shorelines before and after the hurricanes. The results suggest that natural sand dunes were more resilient as they experienced less impact to vegetation and elevation and more substantial recovery than armored dunes. Moreover, the close timeframe of the storm events suggests a compound effect on the weakened dune systems. This study highlights the importance of understanding natural dune resilience to facilitate future adaptive management efforts because armored dunes may have long-term detrimental effects on hurricane-prone barrier islands. [ABSTRACT FROM AUTHOR]

The Brazilian Atlantic Rainforest presents great diversity of flora and stand structures, making it difficult for traditional forest inventories to collect reliable and recurrent information to classify forest succession stages. In recent years, remote sensing data have been explored to save time and effort in classifying successional forest stages. However, there is a need to understand if any of these sensors stand out for this purpose. Here, we evaluate the use of multispectral satellite data from four different platforms (CBERS-4A, Landsat-8/OLI, PlanetScope, and Sentinel-2) and airborne light detection and ranging (LiDAR) to classify three forest succession stages in a subtropical ombrophilous mixed forest located in southern Brazil. Different features extracted from multispectral and LiDAR data, such as spectral bands, vegetation indices, texture features, and the canopy height model (CHM) and LiDAR intensity, were explored using two conventional machine learning methods such as random trees (RT) and support vector machine (SVM). The statistically based maximum likelihood (MLC) algorithm was also compared. The classification accuracy was evaluated by generating a confusion matrix and calculating the kappa index and standard deviation based on field measurements and unmanned aerial vehicle (UAV) data. Our results show that the kappa index ranged from 0.48 to 0.95, depending on the chosen dataset and method. The best result was obtained using the SVM algorithm associated with spectral bands, CHM, LiDAR intensity, and vegetation indices, regardless of the sensor. Datasets with Landsat-8 or Sentinel-2 information performed better results than other optical sensors, which may be due to the higher intraclass variability and less spectral bands in CBERS-4A and PlanetScope data. We found that the height information derived from airborne LiDAR and its intensity combined with the multispectral data increased the classification accuracy. However, the results were also satisfactory when using only multispectral data. These results highlight the potential of using freely available satellite information and open-source software to optimize forest inventories and monitoring, enabling a better understanding of forest structure and potentially supporting forest management initiatives and environmental licensing programs. [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]

Due to cyclic wetting and drying, the hydro-mechanical behavior of unsaturated soil is impacted significantly. In order to assess the soil strength parameters, knowing the unsaturated behavior is important. Soil moisture content is an important parameter that can define the shear strength of the soil. Most of the highway slopes of Mississippi are built on highly expansive clay. During summer, the evaporation of moisture in the soil leads to shrinkage and the formation of desiccation cracks, while during rainfall, the soil swells due to the infiltration of water. In addition to this, the rainwater gets trapped in these cracks and creates perched conditions, leading to the increased moisture content and reduced shear strength of slope soil. The increased precipitation due to climate change is causing failure conditions on many highway slopes of Mississippi. Vetiver, a perennial grass, can be a transformative solution to reduce the highway slope failure challenges of highly plastic clay. The grass has deep and fibrous roots, which provide additional shear strength to the soil. The root can uptake a significant amount of water from the soil, keeping the moisture balance of the slope. The objective of the current study is to assess the changes in moisture contents of a highway slope in Mississippi after the Vetiver plantation. Monitoring equipment, such as rain gauges and moisture sensors, were installed to monitor the rainfall of the area and the moisture content of the soil. The data showed that the moisture content conditions were improved with the aging of the grass. The light detection and ranging (LiDAR) analysis was performed to validate the field data obtained from different sensors, and it was found that there was no significant slope movement after the Vetiver plantation. The study proves the performance of the Vetiver grass in improving the unsaturated soil behavior and stability of highway slopes built on highly expansive clay. [ABSTRACT FROM AUTHOR]

Ultrasonic anemometers mounted on rotary-wing drones have the potential to provide a cost-efficient alternative to the classical meteorological mast-mounted counterpart for atmospheric boundary layer research. However, the propeller-induced flow may degrade the accuracy of free-stream wind velocity measurements by wind sensors mounted on drones – a fact that needs to be investigated for optimal sensor placement. Computational fluid dynamics (CFD) simulations are an alternative to experiments for studying characteristics of the propeller-induced flow but require validation. Therefore, we performed an experiment using three short-range continuous-wave Doppler lidars (light detection and ranging; DTU WindScanners) to measure the complex and turbulent three-dimensional wind field around a hovering drone at low ambient wind speeds. Good agreement is found between experimental results and those obtained using CFD simulations under similar conditions. Both methods conclude that the disturbance zone (defined as a relative deviation from the mean free-stream velocity by more than 1 %) on a horizontal plane located at 1 D (rotor diameter D of 0.71 m) below the drone extends about 2.8 D upstream from the drone center for the horizontal wind velocity and more than 7 D for the vertical wind velocity. By comparing wind velocities along horizontal lines in the upstream direction, we find that the velocity difference between the two methods is ≤ 0.1 ms-1 (less than a 4 % difference relative to the free-stream velocity) in most cases. Both the plane and line scan results validate the reliability of the simulations. Furthermore, simulations of flow patterns in a vertical plane at the ambient speed of 1.3 ms-1 indicate that it is difficult to accurately measure the vertical wind component with less than a 1 % distortion using drone-mounted sonic anemometers. [ABSTRACT FROM AUTHOR]

Identifying road obstacles hidden from the driver's field of view can ensure road safety in transportation. Current driver assistance systems such as 2D head‐up displays are limited to the projection area on the windshield of the car. An augmented reality holographic point cloud video projection system is developed to display objects aligned with real‐life objects in size and distance within the driver's field of view. Light Detection and Ranging (LiDAR) point cloud data collected with a 3D laser scanner is transformed into layered 3D replay field objects consisting of 400 k points. GPU‐accelerated computing generated real‐time holograms 16.6 times faster than the CPU processing time. The holographic projections are obtained with a Spatial Light Modulator (SLM) (3840×2160 px) and virtual Fresnel lenses, which enlarged the driver's eye box to 25 mm × 36 mm. Real‐time scanned road obstacles from different perspectives provide the driver a full view of risk factors such as generated depth in 3D mode and the ability to project any scanned object from different angles in 360°. The 3D holographic projection technology allows for maintaining the driver's focus on the road instead of the windshield and enables assistance by projecting road obstacles hidden from the driver's field of view. [ABSTRACT FROM AUTHOR]

Single-photon detection and timing has attracted increasing interest in recent years due to their necessity in the field of quantum sensing and the advantages of single-quanta detection in the field of low-level light imaging. While simple bucket detectors are mature enough for commercial applications, more complex imaging detectors are still a field of research comprising mostly prototype-level detectors. A major problem in these detectors is the implementation of in-pixel timing circuitry, especially for two-dimensional imagers. One of the most promising approaches is the use of voltage-controlled ring resonators in every pixel. Each of these runs independently based on a voltage supplied by a global reference. However, this yields the problem that the supply voltage can change across the chip which, in turn, changes the period of the ring resonator. Due to additional parasitic effects, this problem can worsen with increasing measurement time, leading to drift in the timing information. We present here a method to identify and correct such temporal drifts in single-photon detectors based on asynchronous quantum ghost imaging. We also show the effect of this correction on recent quantum ghost imaging (QGI) measurement from our group. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence (AI) and deep learning has revolutionized the field of data analysis in recent years, including signal data acquired by remote sensors. Light Detection and Ranging (LiDAR) technology is widely used in atmospheric research for measuring various atmospheric parameters. Wind measurement using LiDAR data has traditionally relied on the spectral centroid (SC) algorithm. However, this approach has limitations in handling LiDAR data, particularly in low signal-to-noise ratio (SNR) regions. To overcome these limitations, this study leverages the capabilities of customized deep-learning techniques to achieve accurate wind profile reconstruction. The study uses datasets obtained from the European Centre for Medium Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) and the mobile Incoherent Doppler LiDAR (ICDL) system constructed by the University of Science and Technology of China. We present a simulation-based approach for generating wind profiles from the statistical data and the associated theoretical calculations. Whereafter, our team constructed a convolutional neural network (CNN) model based on the U-Net architecture to replace the SC algorithm for LiDAR data post-processing. The CNN-generated results are evaluated and compared with the SC results and the ERA5 data. This study highlights the potential of deep learning-based techniques in atmospheric research and their ability to provide more accurate and reliable results. [ABSTRACT FROM AUTHOR]

Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central Europe. For this purpose, microwave Sentinel-1 and optical multi-temporal Sentinel-2 data, topographic derivatives, and iterative machine learning methods incorporating classifiers random forest (RF), support vector machines (SVMs), and XGBoost (XGB) were used for the identification of thirteen non-forest plant communities (various types of alpine grasslands, shrublands, herbaceous heaths, mountain hay meadows, rocks, and scree communities). Different scenarios were tested to identify the most important variables, retrieval periods, and spectral bands. The overall accuracy results for the individual algorithms reached RF (0.83–0.96), SVM (0.87–0.93), and lower results for XGBoost (0.69–0.82). The best combination, which included a fusion of Sentinel-1, Sentinel-2, and topographic data, achieved F1-scores for classes in the range of 0.73–0.97 (RF) and 0.66–0.95 (SVM). The inclusion of topographic variables resulted in an improvement in F1-scores for Sentinel-2 data by one–four percent points and Sentinel-1 data by 1%–9%. For spectral bands, the Sentinel-2 10 m resolution bands B4, B3, and B2 showed the highest mean decrease accuracy. The final result is the first comprehensive map of non-forest vegetation for the Tatra Mountains area. [ABSTRACT FROM AUTHOR]

Damage detection is essential for the maintenance of transportation infrastructure that experiences high daily traffic levels in potentially extreme environments and changes in use patterns. However, traditional physical inspection is always labor‐intensive, subjective, and biased, lacking the objective perspective required for a comprehensive and reliable assessment. Recently, unmanned aerial vehicles (UAVs) combined with emerging high‐performance sensor(s) have been intensively researched. Here, we present an aerial bridge surface survey method that can be used to assess damage. Existing damage detection methods focus on single types of damage and are limited in locating global damage, whereas our method detects two types of damage on the surface and marks them in a panoramic image. The workflow involves three steps: data acquisition using a meticulous UAV flight strategy that covers the entire surface, data processing using image‐based and point‐cloud models after polynomial rectification, and data output (i.e., damage detected by the combined models). To verify the method, a field test detected damage to two real bridges. A UAV equipped with a camera and light detection and ranging (LiDAR) equipment was employed. Experiments demonstrate the effectiveness of the proposed method, which is capable of producing accurate outputs and detecting damage with an average position error of 13.37 mm and a relative size error of 25.25%. Owing to the data fusion model taking advantage of two‐dimensional (2D) images and 3D LiDAR data, it outputs a high‐resolution 3D model and avoids environmental disturbances. After decision‐making‐level damage fusion, all position and size properties of damage information are computed into a panoramic damage image. This panoramic image showing all detecting damage helps technicians perform maintenance; the image can be zoomed to focus on any issue individually. [ABSTRACT FROM AUTHOR]

A conceptual design for diagnosing erosion of the first wall and divertor plates of a tokamak with reactor technologies TRT is proposed. The principles of constructing a diagnostic complex based on the following systems are developed: laser radar, dual-wavelength digital holographic interferometry and active laser IR thermography. An optical scheme is developed for combining the optical paths to input laser radiation and collect scattered light from diagnostic systems. To view the maximum area of the first wall, a scheme for optical scanning of the surface of the first wall and divertor is proposed. Based on optical simulation, the spatial distribution of the power density and phase of interferometry laser radiation in the illuminated region of the first wall is constructed, and the dimensions of the light fields and power density for IR thermography and laser radar diagnostics are determined. An image formation scheme is proposed and the spatial resolution is determined for interferometry and IR thermography methods. The light scattering function on models of the ITER divertor cladding is studied experimentally. The energy of the collected signal is calculated on the basis on the experimental data for all three diagnostic methods and the requirements for the diagnostic equipment are formulated. [ABSTRACT FROM AUTHOR]

The application of autonomous driving technology in the field of transportation has become a hot research direction, and autonomous vehicles need to accurately detect and track moving targets around. As a kind of sensor widely used in the field of automatic driving, LiDAR has the characteristics of high precision and long distance detection. Therefore, this paper adopts a target detection algorithm based on three-dimensional LiDAR, which can identify moving targets accurately. Then the motion path of the detected target is captured and tracked by optical method, and the motion state of the target is monitored in real time. The experimental results show that the moving target detection algorithm and optical motion acquisition method based on 3D LiDAR can detect and track the moving target effectively, and capture its moving trajectory accurately. The application of this method to autonomous vehicles can improve vehicle perception and driving safety, and also provide a useful reference for other fields of moving object detection and tracking research. [ABSTRACT FROM AUTHOR]

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs. [ABSTRACT FROM AUTHOR]

Recent advances in Deep Learning and aerial Light Detection And Ranging (LiDAR) have offered the possibility of refining the classification and segmentation of 3D point clouds to contribute to the monitoring of complex environments. In this context, the present study focuses on developing an ordinal classification model in forest areas where LiDAR point clouds can be classified into four distinct ordinal classes: ground, low vegetation, medium vegetation, and high vegetation. To do so, an effective soft labeling technique based on a novel proposed generalized exponential function (CE-GE) is applied to the PointNet network architecture. Statistical analyses based on Kolmogorov–Smirnov and Student's t-test reveal that the CE-GE method achieves the best results for all the evaluation metrics compared to other methodologies. Regarding the confusion matrices of the best alternative conceived and the standard categorical cross-entropy method, the smoothed ordinal classification obtains a more consistent classification compared to the nominal approach. Thus, the proposed methodology significantly improves the point-by-point classification of PointNet, reducing the errors in distinguishing between the middle classes (low vegetation and medium vegetation). [ABSTRACT FROM AUTHOR]

Tropical forests are the world's most structurally complex ecosystems, providing key functions like biomass accumulation, which is linked to this complexity. Tropical forests are also exposed to chronic, non-severe winds, yet their effect on forest structural complexity is understudied. Here we examine drivers of forest structural complexity in Puerto Rico with a particular focus on chronic wind exposure. We used airborne light detection and ranging data collected in 2016 to quantify canopy height and rugosity (variation in height) in ~ 20,000, 0.28 ha forested sites stratified by forest age. We used random forest models to analyze variation in canopy height and rugosity as a function of chronic wind exposure, forest age, mean annual precipitation, elevation, slope (in degrees), soil type, soil available water storage, and exposure to a previous hurricane. Canopy height was driven by precipitation, forest age, and chronic wind exposure, decreasing by 2.12 m (16%) on average in wind-exposed forests across all forest ages. Canopy height increased by 4.0 m (41%) on average in forests aged 25–66 years, and by 4.0 m between sites with 1000 and 2000 mm y−1 precipitation. Canopy rugosity was driven by canopy height, precipitation, forest age, and elevation, increasing log-linearly with canopy height and precipitation, decreasing with elevation, and was highest in younger forests. Chronic wind exposure did not drive variation in canopy rugosity. Our results suggest that chronic wind exposure plays an integral role in limiting canopy height, potentially reducing aboveground carbon accumulation in older tropical forests. [ABSTRACT FROM AUTHOR]

In the summer of 2020, ESA changed the orbit of CryoSat‐2 to align periodically with NASA's ICESat‐2 mission, a campaign known as CRYO2ICE, which allows for near‐coincident CryoSat‐2 and ICESat‐2 observations in space and time over the Arctic until summer 2022, where the CRYO2ICE Antarctic campaign was initiated. This study investigates the Arctic CRYO2ICE radar and laser freeboards acquired by CryoSat‐2 and ICESat‐2, respectively, during the winter seasons of 2020–2022, and derives snow depths from their differences along the orbits. Along‐track snow depth observations can provide high‐resolution snow depth distributions which are vital for air‐ice‐ocean heat and momentum transfer, understanding light transmission, and snow‐ice‐interactions. Generally, ICESat‐2 is backscattered at a surface above the elevation of the CryoSat‐2 signal. CRYO2ICE snow depths are thinner than the daily model‐ or passive‐microwave‐based snow depth composites used for comparison, with differences being most pronounced in the Atlantic and Pacific Arctic. Satellite‐derived and model‐based snow estimates show similar seasonal accumulation over first‐year ice, but CRYO2ICE has limited seasonal accumulation over multi‐year ice which is linked to a slow increase in ICESat‐2, and to some extent CryoSat‐2, freeboards. We present a first estimation of spaceborne along‐track snow depth estimates with average uncertainty of 10–11 ± 2–3 cm for 7‐km segments, with random and systematic contributions of 7 and 4 cm. These observations show the potential for along‐track dual‐frequency observations of snow depth from the future Copernicus mission CRISTAL; but they also highlight uncertainties in radar penetration and the correlation length scales of snow topography that still require further research. Plain Language Summary: Estimates of snow depth on sea ice are currently either outdated or limited in resolution, thus a need to derive high‐resolution snow depth on sea ice is crucial. Former studies have computed snow depth on sea ice using the difference in penetration between radar (Ku‐band) and laser (or Ka‐band radar) altimeters from different satellite missions, assuming the Ku‐band radar and laser/Ka‐band are reflected at the bottom and top of the snow pack, respectively. Those studies have resulted in monthly composites of snow depth due to a limited overlap of individual tracks between the satellite missions. Since the CRYO2ICE (CryoSat‐2/ICESat‐2 Resonance) campaign was initiated in July 2020, we have for the first time the possibility of investigating near‐coincident observations of spaceborne radar and laser altimeters. These results are important to initiate relevant discussions on snow depth retrieval in preparation for the future dual‐frequency altimeter mission, CRISTAL (Copernicus Polar Ice and Snow Topography Altimeter), expected to launch in 2028. Key Points: CRYO2ICE (C2I) orbits are investigated over sea ice for the first time and snow depth is computed along the orbitsC2I snow depth has an uncertainty of 10–11 cm along 7 km segments with 5–95% confidence interval of 7–16 cm for both winter seasonsAlong‐track snow depth is compared with modeled and passive‐microwave estimates and C2I is thinner for the Pacific and Atlantic Arctic [ABSTRACT FROM AUTHOR]

As cities grow, demand for urban materials is set to rise. Meeting sustainability targets will require transformative changes to how cities are constructed. Yet, accurate information on embodied building materials and their environmental impacts at the city scale is still lacking. We use Light Detection and Ranging data, building archetype information, and statistical models to estimate the embodied materials in buildings in Canberra, Australia, and their energy, carbon, and water footprint. In 2015, 57 million tonnes (Mt) of materials were embodied in 140,805 buildings. By weight, concrete was the most used material (44%), followed by sand and stone (32%), and ceramics (11%). Current population growth and building construction trends indicate a need for 2.4 times the building materials stock of 2015 by 2060. Producing such materials would require 1.6 thousand TJ of energy and 793 thousand megaliters of water and emit 48 Mt of CO2e—an environmental footprint 1.6 times the one in 2015. If the additional population were to live only in new single houses, material demand would be 4% higher than under current trends and the environmental footprint 5% higher. Housing new residents in low‐rise apartments would reduce from current trends the material demand by 5% and the environmental footprint by 12%. Using only apartments of four or more stories would reduce material demand by 28% and the environmental footprint by 14%. This research can inform circular economy efforts to improve building materials management by helping estimate the implications of alternative configurations of the urban built environment. [ABSTRACT FROM AUTHOR]

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called "pixels weighted differential gradient", to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. [ABSTRACT FROM AUTHOR]

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA's Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%). [ABSTRACT FROM AUTHOR]

Appropriate environmental sensing methods and visualization representations are crucial foundations for the in situ exploration of planets. In this paper, we developed specialized visualization methods to facilitate the rover's interaction and decision-making processes, as well as to address the path-planning and obstacle-avoidance requirements for lunar polar region exploration and Mars exploration. To achieve this goal, we utilize simulated lunar polar regions and Martian environments. Among them, the lunar rover operating in the permanently shadowed region (PSR) of the simulated crater primarily utilizes light detection and ranging (LiDAR) for environmental sensing; then, we reconstruct a mesh using the Poisson surface reconstruction method. After that, the lunar rover's traveling environment is represented as a red-green-blue (RGB) image, a slope coloration image, and a theoretical water content coloration image, based on different interaction needs and scientific objectives. For the rocky environment where the Mars rover is traveling, this paper enhances the display of the rocks on the Martian surface. It does so by utilizing depth information of the rock instances to highlight their significance for the rover's path-planning and obstacle-avoidance decisions. Such an environmental sensing and enhanced visualization approach facilitates rover path-planning and remote–interactive operations, thereby enabling further exploration activities in the lunar PSR and Mars, in addition to facilitating the study and communication of specific planetary science objectives, and the production and display of basemaps and thematic maps. [ABSTRACT FROM AUTHOR]

TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data. [ABSTRACT FROM AUTHOR]

Road cracks are a major concern for administrators. Visual inspection is labor‐intensive. The accuracy of previous algorithms for detecting cracks in images requires improvement. Further, the length and thickness of cracks must be estimated. Light detection and ranging (Lidar), a standard smartphone feature is used to develop a method for the completely automatic, accurate, and quantitative evaluation of road cracks. The two contributions of this study are as follows. To achieve the highest segmentation accuracy, U‐Net is combined with data augmentation and morphology transform. To calculate the crack length and thickness, crack images are registered into Lidar color data. The proposed algorithm was validated using a public database of road cracks and those measured by the authors. The algorithm was 95% accurate in determining crack length. The coefficient of determination for thickness estimation accuracy was 0.98 addressing various crack shapes and asphalt pavement patterns. [ABSTRACT FROM AUTHOR]

The digitization of the construction site environment has progressed rapidly. In this study, the operations of hydraulic excavators—which are machines widely used in the construction industry—were advanced to enable automation and unmanned operation. To achieve this, it is necessary to determine the environment of the machines at the sites, and a real-time measurement and visualization methodology that can be installed at common construction sites is required. In this study, we propose a measurement system for reconstructing a wide range of surrounding environments using machine-borne sensors mounted on a hydraulic excavator. The proposed system measures the entire surrounding environment using a sensor unit composed of a laser imaging detection and ranging (LiDAR) and a wide-angle camera. Furthermore, methods of time-series integration for wide-range and dynamic measurements during work for occlusion-robust visualization are proposed. In an experiment using actual machines on an earth-moving site, we validated the performance of our proposed system by quantitative evaluation and confirmed that the system provides an effective solution for the digitization of construction sites. [ABSTRACT FROM AUTHOR]

With the wide application of ultra-wideband (UWB) ranging technology in industry, production and aerospace, how to improve the accuracy of UWB ranging has become a research hotspot. If the UWB ranging data is treated as sequence data, it is possible to improve the ranging performance by sequence analysis, which has low computation complexity compared to direct UWB signal processing. However, as the UWB ranging data has its inherent properties, existing sequence analysis methods may not achieve good performance on UWB ranging data. In this paper, a two-path deep-learning framework to process, namely the Multiple Network model with Second order motion (MNS), is proposed the improve UWB ranging performance via ranging sequence analysis. The proposed method fuses the target motion prediction via Newton's laws of motion and error estimation via GRU, LSTM and Bayes. To evaluate our algorithm, we also proposed a method to collect both UWB ranging data and the accurate answer via laser ranging. The collected dataset D A T A _ T S , the proposed MNS algorithm, and the trained model are all open sourced to the community to help researchers for further research,please visit(https://github.com/xiaojiuwotongxue/data-store.git). Experiments on D A T A _ T S shows our proposed method outperforms traditional regresion methods significantly. [ABSTRACT FROM AUTHOR]

Light reflection and diffusion mechanism in the crystalline phase-controlled BSS-HNPs. [Display omitted] • This is the first study on black hollow TiO 2 nanoparticles that can be applied as a painting pigment to correlate the color and the change in the crystal system in the LiDAR application field. • Various crystalline-phase black hollow nanoparticles were synthesized using a simple sol–gel method, followed by calcination at different temperatures, NaBH 4 reduction, and etching. The formation of nanomaterials was analyzed using various methods, and the change in bandgap energy following the crystalline phase was determined using the DFT calculation method.. • Examining the conversion of crystalline phases from anatase to rutile on TiO 2 and exploring the connection between bandgap energy, refractive index, and NIR reflectance have resulted in significant enhancements in NIR reflectance. • True blackness and the change in the refractive index for NIR reflectance were thoroughly explained by the light reflection mechanism, light interference effect, and bond length of the crystal system. The development of highly NIR reflective black single-shell hollow nanoparticles (BSS-HNPs) can overcome the Light Detection and Ranging (LiDAR) sensor limitations of dark-tone materials. The crystalline phase of TiO 2 and the refractive index can be controlled by calcination temperature. The formation of hollow structure and the refractive index is expected to simultaneously increase the light reflection and LiDAR detectability. The BSS-HNPs are synthesized using the sol–gel method, calcination, NaBH 4 reduction, and etching to form a hollow structure with true blackness. The computational bandgap calculation is conducted to determine the bandgap energy (E g) of the white and black TiO 2 with different crystalline structures. The blackness of the as-synthesized materials is determined by the Commission on Illumination (CIE) L * a * b * color system. The hydrophilic nature of BSS-HNPs enables the formulation of hydrophilic paints, allowing the mono-layer coating. With the synergistic effects of hollow structure and the refractive index, BSS-HNPs manifested superb NIR reflectance at LiDAR detection wavelengths. The high detectability, blackness, and hollow structure of BSS-HNPs can expand the variety of LiDAR-detectable dark-tone materials. [ABSTRACT FROM AUTHOR]

Due to the progress in light detection and ranging (LiDAR) technology, the collection of road point cloud data containing depth information and spatial coordinates has become more accessible. Consequently, utilizing point cloud data for pavement distress detection and quantification emerges as a crucial approach to improving the precision and reliability of road maintenance procedures. This paper aims to automatically detect and visualize pavement distress using LiDAR, deep learning-based 3D object detection method, and building information modeling (BIM). A pavement distress data set is first established using the point cloud data obtained from LiDAR. Then, the 3D object detection network, namely PointPillar, is employed for pavement distress detection, and the detection results will be quantified at a region-level. Finally, pavement BIM model integrating parametrically modeled distress families is built to visually manage the detected distress. After training and validating the model with the pavement distress data set, a detection performance index of recall is 78.5%, mean average precision (mAP) is 62.7%, which is better than other compared point cloud-based methods though the detection performance can be further improved. In addition, a newly untrained section of road is applied for the experiment. The detected distress is integrated in BIM environment for a visual management, providing a better maintenance guidance. [ABSTRACT FROM AUTHOR]