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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]

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]

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]

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]

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]

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]

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]

Forecasting landslide inundation upon catastrophic failure is crucial for reducing casualties, yet it remains a long‐standing challenge owing to the complex nature of landslides. Recent global studies indicate that catastrophic hillslope failures are commonly preceded by a period of precursory creep, motivating a novel scheme to foresee their hazard. Here, we showcase an approach to hindcast landslide inundation by linking satellite‐captured precursory displacements to modeling of consequent granular‐fluid flows. We present its application to the 2021 Chunchi, Ecuador landslide, which failed catastrophically and evolved into a mobile debris flow after four months of precursory creep, destroying 68 homes along its lengthy flow path. Underpinned by uncertainty quantification and in situ validations, we highlight the feasibility and potential of forecasting landslide inundation damage using observable precursors. This forecast approach is broadly applicable for flow hazards initiated from geomaterial failures. Plain Language Summary: One of the most effective approaches to reduce landslide damage, is somehow getting to know in advance where the target landslide is about to occur and how large the damage area will be when it occurs. Here, we show a possible solution of using satellite‐observed precursory motion to find and quantify the landslide source, and then input this information into a granular‐flow model to estimate its potential damage area when evolving into a debris flow. This seamlessly integrated method could allow to effectively inform hazard reduction, as large catastrophic landslides have been widely observed to manifest precursory destabilization weeks to months before the final failure. As a representative example, we applied this approach to the 2021 Chunchi, Ecuador landslide event and found it highly effective for predicting landslide inundation based on both model uncertainty quantification and field validations. Key Points: Satellite radar and optical observations uncover precursory landslide motion to infer source area and volumeWe propose an approach to forecast landslide inundation through seamless integration of precursory motion and granular‐flow modelingUncertainty quantification and in situ validations corroborate the effectiveness of this forecast approach [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]

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]

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]

With LiDAR (Light Detection and Ranging) time series being used for various applications, the optimal realization of a common geodetic datum over many epochs is a highly important prerequisite with a direct impact on the accuracy and reliability of derived measures. In our work, we develop and define several approaches to the adjustment of multi-temporal LiDAR data in a given software framework. These approaches, ranging from pragmatic to more rigorous solutions, are applied to an 8-year time series with 21 individual epochs. The analysis of the respective results suggests that a sequence of bi-temporal adjustments of each individual epoch and a designated reference epoch brings the best results while being more flexible and computationally viable than the most extensive approach of using all epochs in one single multi-temporal adjustment. With a combination of sparse control patches measured in the field and one selected reference block, the negative impacts of changing surfaces on orientation quality are more effectively avoided than in any other approach. We obtain relative discrepancies in the range of 1–2 cm between epoch-wise DSMs for the complete time series and mean offsets from independent checkpoints in the range of 3–5 cm. Based on our findings, we formulate design criteria for setting up and adjusting future time series with the proposed method. [ABSTRACT FROM AUTHOR]

Spectral confusion among land cover classes is quite common, let alone in a complex and heterogenous system like the semi-arid Mediterranean environment; thus, employing new developments in remote sensing, such as multispectral imagery (MSI) captured by unmanned aerial vehicles (UAVs) and airborne light detection and ranging (LiDAR) techniques, with deep learning (DL) algorithms for land cover classification can help to address this problem. Therefore, we propose an image-based land cover classification methodology based on fusing multispectral and airborne LiDAR data by adopting CNN-based semantic segmentation in a semi-arid Mediterranean area of northeastern Aegean, Greece. The methodology consists of three stages: (i) data pre-processing, (ii) semantic segmentation, and (iii) accuracy assessment. The multispectral bands were stacked with the calculated Normalized Difference Vegetation Index (NDVI) and the LiDAR-based attributes height, intensity, and number of returns converted into two-dimensional (2D) images. Then, a hyper-parameter analysis was performed to investigate the impact on the classification accuracy and training time of the U-Net architecture by varying the input tile size and the patch size for prediction, including the learning rate and algorithm optimizer. Finally, comparative experiments were conducted by altering the input data type to test our hypothesis, and the CNN model performance was analyzed by using accuracy assessment metrics and visually comparing the segmentation maps. The findings of this investigation showed that fusing multispectral and LiDAR data improves the classification accuracy of the U-Net, as it yielded the highest overall accuracy of 79.34% and a kappa coefficient of 0.6966, compared to using multispectral (OA: 76.03%; K: 0.6538) or LiDAR (OA: 37.79%; K: 0.0840) data separately. Although some confusion still exists among the seven land cover classes observed, the U-Net delivered a detailed and quite accurate segmentation map. [ABSTRACT FROM AUTHOR]

The main problems of forest parameter extraction and forest stand volume estimation using unmanned aerial vehicle light detection and ranging (UAV-LiDAR) technology are the lack of precision in individual tree segmentation and the inability to directly obtain the diameter at breast height (DBH) parameter. To address such limitations, the study proposed an improved individual tree segmentation method combined with a DBH prediction model to obtain the tree height (H) and DBH for calculating the volume of trees, thus realizing the accurate estimation of forest stand volume from individual tree segmentation aspect. The method involves the following key steps: (1) The local maximum method with variable window combined with the Gaussian mixture model were used to detect the treetop position using the canopy height model for removing pits. (2) The measured tree DBH and H parameters of the sample trees were used to construct an optimal DBH-H prediction model. (3) The duality standing tree volume model was used to calculate the forest stand volume at the individual tree scale. The results showed that: (1) Individual tree segmentation based on the improved Gaussian mixture model with optimal accuracy, detection rate r, accuracy rate p, and composite score F were 89.10%, 95.21%, and 0.921, respectively. The coefficient of determination R2 of the accuracy of the extracted tree height parameter was 0.88, and the root mean square error RMSE was 0.84 m. (2) The Weibull model had the optimal model fit for DBH-H with predicted DBH parameter accuracy, the R2 and RMSE were 0.84 and 2.28 cm, respectively. (3) Using the correctly detected trees from the individual tree segmentation results combined with the duality standing tree volume model estimated the forest stand volume with an accuracy AE of 90.86%. In conclusion, using UAV-LiDAR technology, based on the individual tree segmentation method and the DBH-H model, it is possible to realize the estimation of forest stand volume at the individual tree scale, which helps to improve the estimation accuracy. [ABSTRACT FROM AUTHOR]

To address the issue of accuracy in Simultaneous Localization and Mapping (SLAM) for forested areas, a novel point cloud adaptive filtering algorithm is proposed in the paper, based on point cloud data obtained by backpack Light Detection and Ranging (LiDAR). The algorithm employs a K-D tree to construct the spatial position information of the 3D point cloud, deriving a linear model that is the guidance information based on both the original and filtered point cloud data. The parameters of the linear model are determined by minimizing the cost function using an optimization strategy, and a guidance point cloud filter is subsequently constructed based on these parameters. The results demonstrate that, comparing the diameter at breast height (DBH) and tree height before and after filtering with the measured true values, the accuracy of SLAM mapping is significantly improved after filtering. The Mean Absolute Error (MAE) of DBH before and after filtering are 2.20 cm and 1.16 cm; the Root Mean Square Error (RMSE) values are 4.78 cm and 1.40 cm; and the relative RMSE values are 29.30% and 8.59%. For tree height, the MAE before and after filtering are 0.76 m and 0.40 m; the RMSE values are 1.01 m and 0.50 m; the relative RMSE values are 7.33% and 3.65%. The experimental results validate that the proposed adaptive point cloud filtering method based on guided information is an effective point cloud preprocessing method for enhancing the accuracy of SLAM mapping in forested areas. [ABSTRACT FROM AUTHOR]

Objectives: With the development of Shenzhen city, China, land renovation is more frequent. At the same time, affected by the subtropical monsoon climate, the area under the jurisdiction has abundant rainfall and dense vegetation coverage, making it difficult to identify the hidden dangers of geological hazards widely distributed on artificial slopes and natural slopes. Therefore, it is necessary to develop a set of hazard evaluation system of geological disaster that can solve the unique terrain and climate conditions in Shenzhen, so as to achieve the purpose of preventing disasters in advance and reducing casualties. Methods:(1) On the basis of highprecision digital elevation model of Shenzhen city obtained by airborne light detection and ranging(LiDAR), about 3 500 slope disaster prone points in Shenzhen are obtained through data collection, remote sensing interpretation and field verification. The sample library expanded 330% after proofreading.(2) Taking 3 major factors (8 factors) of terrain, geological structure and human engineering activities into comprehensive consideration, and based on the rainfall-induced disaster mechanism, a rainfall collection factor is proposed, and the weight of evidence method is used to complete the geological disaster hazard evaluation model under rainfall-induced conditions. (3) The threshold determination method of“key point control”under the actual background of single disaster is proposed, and the classification of the risk assessment model is completed. Results: The area under curve value of receiver operating characteristic curve model reaches 0.903, indicating that the model has a good effect on disaster forecasting. LiDAR technology can improve the identification accuracy of geological hazards in cities under dense vegetation coverage. Conclusions: Based on airborne LiDAR data, through a series of means such as expansion of disaster database, analysis of disaster distribution law, establishment of disaster evaluation factors, and classification of risk levels, it can form a refined evaluation system for the hazard evaluation of the slope in densely vegetated areas under the influence of the subtropical monsoon climate. [ABSTRACT FROM AUTHOR]

Objectives: The response law of ancient (old) landslides in the reservoir area is an important research topic. Previous research primarily analyzed real-time surface displacement and reservoir water level data. However, professional monitoring conditions are often lacking on most reservoir bank slopes. This complicates tracking the landslide's historical deformation. Satellite and airborne remote sensing platforms enable multi-scale, long-term monitoring of landslide deformation and damage. Methods: This study employs multi-source three-dimensional observation technologies including aerial, space-based, and terrestrial platforms to monitor the deformation and evolution of the Pubugou Hydropower Station's Hongyanzi landslide over approximately 10 years. It utilizes unmanned aerial vehicle photography (optical imaging) and light detection and ranging (LiDAR) for detailed topographic mapping and deformation analysis from 2009 to 2020. Additionally, time-series interferometric synthetic aperture radar (InSAR) technology is used to track long-term surface deformations from October 2014 to July 2020. Field investigations have identified typical deformation and failure characteristics of the landslide, incorporating geological conditions and external factors such as rainfall and reservoir water levels to analyze causal mechanisms and dynamic trends. Results: The irregularly semicircular Hongyanzi landslide spans 20-50 m in thickness, encompasses approximately 15.53 million m³ in volume, and slides at an approximate bearing of 340°. Composed of quaternary pebbled stones, silty sand, and clay, the landslide's bed slopes between 20° and 25°. Its lithology includes Emeishan Formation basalt and Yangxin Formation dolomite. Existing since 2006 or earlier, the landslide features elements like walls and steps. LiDAR imagery from 2009 clearly delineates its boundaries, though it shows no new signs of deformation or failure. Following reservoir impoundment, the reactivated landslide develops new, widening cracks along its rear edge. Post-reactivation, the landslide predominantly undergos uniform deformation, with more significant movement at the trailing edge than the leading edge, without marked acceleration. Heavy rainfall is the most significant control factor, imparting stepwise deformation characteristics to the landslide. Conclusions: A comprehensive analysis of multi-source data reveals that phenomena like the Hongyanzi landslide exhibit typical long-term, gradual, and seasonal movements. Long-term InSAR effectively captures these characteristics. Multi-stage optical remote sensing and surface point cloud data from LiDAR, after vegetation removal, enable more intuitive comparisons of macroscopic deformation across different landslide areas. Integrating this with geological assessments and field investigations allows for detailed engineering analyses to ascertain the causes, patterns, and future trends of landslide activity. [ABSTRACT FROM AUTHOR]

Objectives: Geological hazard points and hidden danger points are the data basis for geological hazard evaluation, while the existing records of geological hazard points have poor timeliness and are incomplete. To solve this problem, the deformation information obtained by multi-temporal interferometric synthetic aperture radar (InSAR) was integrated into the geological hazard evaluation model. And we explore how to make better use of the deformation information. Methods: The greater the deformation level, the greater the possibility of geological hazards. This paper not only takes the deformation points obtained by multi-temporal InSAR as the geological hazard points/hidden danger points, but also integrates the deformation level information obtained by multi-temporal InSAR as an evaluation factor into the susceptibility evaluation model, making full use of the effective deformation information obtained by multi-temporal InSAR. And the evaluation model adopts the coupling model based on information value model and the analytic hierarchy process model to obtain the susceptibility evaluation and zoning of the geological hazards in Baiyin City, Gansu Province,China. Results: Through the verification of the existing geological disaster point data, it is found that the partitions in this paper are in good agreement with the existing geological hazard points distribution.In the designated extremely high-prone areas, there are nearly 8 geological disaster points within 10 km2 , while less than one in the extremely low-prone areas. Conclusion: The multi-temporal InSAR deformation information added to the geological hazard evaluation model greatly improves the timeliness and quantity of records of geological hazard points/hidden points. However, only one kind of synthetic aperture radar data cannot completely identify all geological hazard points/hidden danger points, due to the limitations of incidence angle and microwave wavelength. In the futher work, we will focus on the combination of multiple deformation monitoring technologies to jointly monitor surface deformation, such as multi-sensor and multi-track InSAR technology, airborne light laser detection and ranging and high-resolution optical remote sensing. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of computing a preliminary orbit of a celestial body from one topocentric position vector P 1 and a very short arc (VSA) of optical observations A 2 . Using the conservation laws of the two-body dynamics, we write the problem as a system of 8 polynomial equations in 6 unknowns. We prove that this system is generically consistent, namely, for a generic choice of the data P 1 , A 2 , it always admits solutions in the complex field, even when P 1 , A 2 do not correspond to the same celestial body. The consistency of the system is shown by deriving a univariate polynomial v of degree 8 in the unknown topocentric distance at the mean epoch of the observations of the VSA. Through Gröbner bases theory, we also show that the degree of v is minimum among the degrees of all the univariate polynomials solving this problem. Even though we can find solutions to our problem for a generic choice of P 1 , A 2 , most of these solutions are meaningless. In fact, acceptable solutions must be real and have to fulfill other constraints, including compatibility with Keplerian dynamics. We also propose a way to select or discard solutions taking into account the uncertainty in the data, if present. The proposed orbit determination method is relevant for different purposes, e.g., the computation of a preliminary orbit of an Earth satellite with radar and optical observations, the detection of maneuvres of an Earth satellite, and the recovery of asteroids which are lost due to a planetary close encounter. We conclude by showing some numerical tests in the case of asteroids undergoing a close encounter with the Earth. [ABSTRACT FROM AUTHOR]

Global forests are increasingly lost to climate change, disturbance, and human management. Evaluating forests' capacities to regenerate and colonize new habitats has to start with the seed production of individual trees and how it depends on nutrient access. Studies on the linkage between reproduction and foliar nutrients are limited to a few locations and few species, due to the large investment needed for field measurements on both variables. We synthesized tree fecundity estimates from the Masting Inference and Forecasting (MASTIF) network with foliar nutrient concentrations from hyperspectral remote sensing at the National Ecological Observatory Network (NEON) across the contiguous United States. We evaluated the relationships between seed production and foliar nutrients for 56,544 tree‐years from 26 species at individual and community scales. We found a prevalent association between high foliar phosphorous (P) concentration and low individual seed production (ISP) across the continent. Within‐species coefficients to nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) are related to species differences in nutrient demand, with distinct biogeographic patterns. Community seed production (CSP) decreased four orders of magnitude from the lowest to the highest foliar P. This first continental‐scale study sheds light on the relationship between seed production and foliar nutrients, highlighting the potential of using combined Light Detection And Ranging (LiDAR) and hyperspectral remote sensing to evaluate forest regeneration. The fact that both ISP and CSP decline in the presence of high foliar P levels has immediate application in improving forest demographic and regeneration models by providing more realistic nutrient effects at multiple scales. [ABSTRACT FROM AUTHOR]

This paper advances an in situ method to measure the spectral linewidth directly from the currently generated wavelength-modulated signals in frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) systems, diverging from traditional methods that focus on the linewidth of the original unmodulated laser source. Our approach, employing a self-heterodyne technique with a short-delay line, specifically targets the modulated signal's linewidth in real-time, which is vital for the operational fidelity of FMCW LiDAR systems. Crucially, our method leverages the unique capabilities of an optical hybrid for accurate phase noise and linewidth measurements, distinguishing it from conventional beat frequency extraction techniques. For the evaluation of the spectral linewidth measurement, a frequency-modulated laser source based on an optical phase-locked loop configuration was first described where the laser achieves linear optical frequency modulation by controlling the injection current of an external cavity diode laser (ECDL). The phase error measured from a Mach–Zehnder interferometer signal is used to detect the frequency deviation error from the target value, which is then fed back to the driving current of the ECDL to compensate it. Utilizing the proposed method, the laser's linewidth for the fabricated FMCW LiDAR was measured to be 287 kHz, exhibiting a clear Lorentzian spectrum shape, where the spectral modulation bandwidth and sweep time were 2.91 GHz and 50 µs, respectively. The results clearly demonstrate that the proposed in situ spectral linewidth measurement provides an efficient method for performance monitoring of FMCW LiDAR. [ABSTRACT FROM AUTHOR]

Gap formations in the forest canopy have natural causes, such as bad weather, and anthropic ones, such as sustainable selective extraction of trees and illegal logging, which can already be detected through orbital remote sensing. However, the Amazon region is under frequent cloud cover, which makes it challenging to detect gaps using passive sensors. This study aimed to identify and delimit gaps in the Amazon forest canopy through airborne LiDAR (Light Detection and Ranging) sensor application while testing six different return densities. LiDAR and forest inventory data were obtained over an Amazon rainforest region, defining the minimum area as a forest canopy gap. The point cloud was processed to obtain six return densities with the generation of their respective CHM (Canopy Height Model), which were applied for segmentation and subsequent identification of gap areas and roads. The minimum gap area found was 34 m², and the Kruskal Wallis test showed no significant difference among the six densities in gap detection; however, road identification decreased as the return density decreased. We concluded that LiDAR data proved promising as point clouds with low return density can be used without impairing gap identification. However, reducing the return density for road identification is not recommended. [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]

Three-dimensional (3D) mapping is important to achieve early warning for construction safety and support the long-term safety maintenance of tunnels. However, generating 3D point cloud maps of excavation tunnels that tend to be deficient in features, have rough lining structures, and suffer from dynamic construction interference, can be a challenging task. In this paper, we propose a novel legged robot-aided 3D tunnel mapping method to address the influence of point clouds in the mapping phase. First, a method of kinematic model construction that integrates information from both the robot's motors and the inertial measurement unit (IMU) is proposed to correct the motion distortion of point clouds. Then, a residual compensation model for unreliable regions (abbreviated as the URC model) is proposed to eliminate the inherent alignment errors in the 3D structures. The structural regions of a tunnel are divided into different reliabilities using the K-means method, and an inherent alignment metric is compensated based on region residual estimation. The compensated alignment metric is then incorporated into a rotation-guided anomaly consistency detection (RAD) model. An isolation forest-based anomaly consistency indicator is designed to remove anomalous light detection and ranging (LiDAR) points and reduce sensor noise caused by ultralong distances. To verify the proposed method, we conduct numerous experiments in three tunnels, namely, a drilling and blasting tunnel, a TBM tunnel, and an underground pedestrian tunnel. According to the experimental results, the proposed method achieves 0.84 ‰, 0.40 ‰, and 0.31 ‰ closure errors (CEs) for the three tunnels, respectively, and the absolute map error (AME) and relative map error (RME) are approximately 1.45 cm and 0.57 %, respectively. The trajectory estimation and mapping errors of our method are smaller than those of existing methods, such as FAST-LIO2, Faster-LIO and LiLi-OM. In addition, ablation tests are conducted to further reveal the roles of the different models used in our method for legged robot-aided 3D mapping in tunnels. [ABSTRACT FROM AUTHOR]

Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture's National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar digital terrain models (DTMs). A survey of NAIP point cloud classification and related ground point-generated DTMs has not yet been undertaken. This study applies a Support Vector Machine (SVM) to classifying ground and nonground points from NAIP point clouds for test sites in Wyoming and Arizona, USA. Light detection and ranging (lidar) data from the U.S. Geological Survey 3D Elevation Program (3DEP) are used to validate the classified NAIP ground points and their corresponding DTMs. Comparing height differences between filtered NAIP ground points and 3DEP ground points, the SVM classifier's results show that the vertical root mean square error value is 1.87 m and 1.69 m for the Wyoming and Arizona sites, respectively. If NAIP point clouds were continuously measured, the resulting availability of medium-resolution DTMs would benefit the application of multitemporal forest health monitoring and DTM generation. [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]

Silicon nitride (Si3N4) photonic integrated circuits (PICs) are of great interest due to their extremely low propagation loss and higher integration capabilities. The number of applications based on the silicon nitride integrated photonics platform continues to grow, including the Internet of Things (IoT), artificial intelligence (AI), light detection and ranging (LiDAR), hybrid neuromorphic and quantum computing. It's potential for CMOS compatibility, as well as advances in heterogeneous integration with silicon‐on‐insulator, indium phosphate, and lithium niobate on insulator platforms, are leading to an advanced hybrid large‐scale PICs. Here, they review key trends in Si3N4 photonic integrated circuit technology and fill an information gap in the field of state‐of‐the‐art devices operating from the visible to the mid‐infrared spectrum. A comprehensive overview of its microfabrication process details (deposition, lithography, etching, etc.) is introduced. Finally, the limitations and challenges of silicon nitride photonics performance are pointed out in an ultra‐wideband, providing routes and prospects for its future scaling and optimization. [ABSTRACT FROM AUTHOR]

Full-Stokes polarimetric imaging light detection and ranging (LiDAR) provides rich information about distance, materials, texture, surface orientations, and profiles of objects, and it is an important remote-sensing technology. One major challenge to reach a long distance is to efficiently collect and detect the echo photons, as for long-range LiDAR, echo photons may become sparse. Here, we demonstrate a full-Stokes polarimetric imaging LiDAR, working at the eye-safe, telecommunication wavelength of 1560 nm, that can reach a range of 4 km. The key enabling technology is a four-channel system with multimode-fiber-coupled, large-area fractal superconducting nanowire single-photon detectors. Furthermore, we also explore faster imaging (e.g., pixel-dwell time of 1 ms) of the objects at a shorter distance, approximately 1 km. Our demonstration has significantly extended the working range of full-Stokes polarimetric imaging LiDAR and represents an important step toward practical systems that may enable many applications in remote sensing and the detection and recognition of targets. [ABSTRACT FROM AUTHOR]

This study proposes an innovative method for achieving autonomous flight to inspect overhead transmission facilities. The proposed method not only integrates multimodal information from novel sensors but also addresses three essential aspects to overcome the existing limitations in autonomous flights of an unmanned aerial vehicle (UAV). First, a novel deep neural network architecture titled the rotational bounding box with a multi‐level feature pyramid transformer is introduced for accurate object detection. Second, a safe autonomous method for the transmission tower approach is proposed by using multimodal information from an optical camera and 3D light detection and ranging. Third, a simple yet accurate control strategy is proposed for tracking transmission lines without necessitating gimbal control because it keeps the UAV's altitude in sync with that of the transmission lines. Systematic analyses conducted in both virtual and real‐world environments confirm the effectiveness of the proposed method. The proposed method not only enhances the performance of autonomous flight but also provides a safe operating platform for inspection personnel. [ABSTRACT FROM AUTHOR]

Marine ecosystems serve as vital indicators of biodiversity, providing habitats for diverse flora and fauna. Canada's extensive coastal regions encompass a rich range of marine habitats, necessitating accurate mapping techniques utilizing advanced technologies, such as remote sensing (RS). This study focused on a study area in Pistolet Bay in Newfoundland and Labrador (NL), Canada, with an area of approximately 170 km2 and depths varying between 0 and −28 m. Considering the relatively large coverage and shallow depths of water of the study area, it was decided to use airborne bathymetric Light Detection and Ranging (LiDAR) data, which used green laser pulses, to map the marine habitats in this region. Along with this LiDAR data, Remotely Operated Vehicle (ROV) footage, high-resolution multispectral drone imagery, true color Google Earth (GE) imagery, and shoreline survey data were also collected. These datasets were preprocessed and categorized into five classes of Eelgrass, Rockweed, Kelp, Other vegetation, and Non-Vegetation. A marine habitat map of the study area was generated using the features extracted from LiDAR data, such as intensity, depth, slope, and canopy height, using an object-based Random Forest (RF) algorithm. Despite multiple challenges, the resulting habitat map exhibited a commendable classification accuracy of 89%. This underscores the efficacy of the developed Artificial Intelligence (AI) model for future marine habitat mapping endeavors across the country. [ABSTRACT FROM AUTHOR]

The short-rotation coppice (SRC) culture of trees provides a sustainable form of renewable biomass energy, while simultaneously sequestering carbon and contributing to the regional carbon feedstock balance. To understand the role of SRC in carbon feedstock balances, field inventories with selective destructive tree sampling are commonly used to estimate aboveground biomass (AGB) and canopy structure dynamics. However, these methods are resource intensive and spatially limited. To address these constraints, we examined the utility of publicly available airborne Light Detection and Ranging (LiDAR) data and easily accessible imagery from Unmanned Aerial Systems (UASs) to estimate the AGB and canopy structure of an American sycamore SRC in the piedmont region of North Carolina, USA. We compared LiDAR-derived AGB estimates to field estimates from 2015, and UAS-derived AGB estimates to field estimates from 2022 across four planting densities (10,000, 5000, 2500, and 1250 trees per hectare (tph)). The results showed significant effects of planting density treatments on LIDAR- and UAS-derived canopy metrics and significant relationships between these canopy metrics and AGB. In the 10,000 tph, the field-estimated AGB in 2015 (7.00 ± 1.56 Mg ha−1) and LiDAR-derived AGB (7.19 ± 0.13 Mg ha−1) were comparable. On the other hand, the UAS-derived AGB was overestimated in the 10,000 tph planting density and underestimated in the 1250 tph compared to the 2022 field-estimated AGB. This study demonstrates that the remote sensing-derived estimates are within an acceptable level of error for biomass estimation when compared to precise field estimates, thereby showing the potential for increasing the use of accessible remote-sensing technology to estimate AGB of SRC plantations. [ABSTRACT FROM AUTHOR]

Monitoring mangrove forest biomass is vital for assessing their carbon sequestration potential. This study uses airborne LiDAR data to estimate the aboveground biomass (AGB) of an old‐growth mangrove forest in the Katunggan It Ibajay Ecopark (KII Ecopark) on Panay Island, Philippines. To establish a relationship between the LiDAR canopy height profile with the field observed AGB at the plot level, we tested 20 LiDAR derived relative height (RH) metrics. First, we tested a relationship between field observed Lorey's mean canopy height (Hm) and RH metrics, which were then used to estimate AGB by applying a previously established allometric model. Second, we tested the direct relationship between RH metrics and observed AGB. Among RH metrics, RH95 showed the best correspondence with the Hm (R2 = 0.79) and when it was applied to the previously developed allometric for AGB estimation, the results showed a large underestimation of AGB (R2 = 0.46) for plots with higher canopy heights. Conversely, the direct method using a power regression model with RH95 and observed AGB provided a better estimate (R2 = 0.58). However, both models still underestimated AGB at the KII Ecopark. We conclude that, LiDAR‐based AGB estimation using Hm as a single variable can result in considerable underestimation, especially in old‐growth mangrove forests such as KII Ecopark. Further studies are necessary to develop accurate models for estimating AGB in such special types of mangroves which is important for mangrove monitoring, reporting and verification (MRV). [ABSTRACT FROM AUTHOR]

In recent years, advancements in Light Detection and Ranging (LiDAR) technology have made 3D LiDAR sensors more compact, lightweight, and affordable. This progress has spurred interest in integrating LiDAR with sensors such as Inertial Measurement Units (IMUs) and cameras for Simultaneous Localization and Mapping (SLAM) research. Public datasets covering different scenarios, platforms, and viewpoints are crucial for multi-sensor fusion SLAM studies, yet most focus on handheld or vehicle-mounted devices with front or 360-degree views. Data from aerial vehicles with downward-looking views is scarce, existing relevant datasets usually feature low altitudes and are mostly limited to small campus environments. To fill this gap, we introduce the Multi-sensor Aerial Robots SLAM dataset (MARS-LVIG dataset), providing unique aerial downward-looking LiDAR-Visual-Inertial-GNSS data with viewpoints from altitudes between 80 m and 130 m. The dataset not only offers new aspects to test and evaluate existing SLAM algorithms, but also brings new challenges which can facilitate researches and developments of more advanced SLAM algorithms. The MARS-LVIG dataset contains 21 sequences, acquired across diversified large-area environments including an aero-model airfield, an island, a rural town, and a valley. Within these sequences, the UAV has speeds varying from 3 m/s to 12 m/s, a scanning area reaching up to 577,000 m2, and the max path length of 7.148 km in a single flight. This dataset encapsulates data collected by a lightweight, hardware-synchronized sensor package that includes a solid-state 3D LiDAR, a global-shutter RGB camera, IMUs, and a raw message receiver of the Global Navigation Satellite System (GNSS). For algorithm evaluation, this dataset releases ground truth of both localization and mapping, which are acquired by on-board Real-time Kinematic (RTK) and DJI L1 (post-processed by its supporting software DJI Terra), respectively. The dataset can be downloaded from: https://mars.hku.hk/dataset.html. [ABSTRACT FROM AUTHOR]

Studies have shown that vehicle trajectory data are effective for calibrating microsimulation models. Light Detection and Ranging (LiDAR) technology offers high-resolution 3D data, allowing for detailed mapping of the surrounding environment, including road geometry, roadside infrastructures, and moving objects such as vehicles, cyclists, and pedestrians. Unlike other traditional methods of trajectory data collection, LiDAR's high-speed data processing, fine angular resolution, high measurement accuracy, and high performance in adverse weather and low-light conditions make it well suited for applications requiring real-time response, such as autonomous vehicles. This research presents a comprehensive framework for integrating LiDAR sensor data into simulation models and their accurate calibration strategies for proactive safety analysis. Vehicle trajectory data were extracted from LiDAR point clouds collected at six urban signalized intersections in Lubbock, Texas, in the USA. Each study intersection was modeled with PTV VISSIM and calibrated to replicate the observed field scenarios. The Directed Brute Force method was used to calibrate two car-following and two lane-change parameters of the Wiedemann 1999 model in VISSIM, resulting in an average accuracy of 92.7%. Rear-end conflicts extracted from the calibrated models combined with a ten-year historical crash dataset were fitted into a Negative Binomial (NB) model to estimate the model's parameters. In all the six intersections, rear-end conflict count is a statistically significant predictor (p-value < 0.05) of observed rear-end crash frequency. The outcome of this study provides a framework for the combined use of LiDAR-based vehicle trajectory data, microsimulation, and surrogate safety assessment tools to transportation professionals. This integration allows for more accurate and proactive safety evaluations, which are essential for designing safer transportation systems, effective traffic control strategies, and predicting future congestion problems. [ABSTRACT FROM AUTHOR]

Geohazards that have developed in densely vegetated alpine gorges exhibit characteristics such as remote occurrence, high concealment, and cascading effects. Utilizing a single remote sensing datum for their identification has limitations, while utilizing multiple remote sensing data obtained based on different sensors can allow comprehensive and accurate identification of geohazards in such areas. This study takes the Latudi River valley, a tributary of the Nujiang River in the Hengduan Mountains, as the research area, and comprehensively uses three techniques of remote sensing: unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR), Small Baseline Subset interferometric synthetic aperture radar (SBAS-InSAR), and UAV optical remote sensing. These techniques are applied to comprehensively identify and analyze landslides, rockfalls, and debris flows in the valley. The results show that a total of 32 geohazards were identified, including 18 landslides, 8 rockfalls, and 6 debris flows. These hazards are distributed along the banks of the Latudi River, significantly influenced by rainfall and distribution of water systems, with deformation variables fluctuating with rainfall. The three types of geohazards cause cascading disasters, and exhibit different characteristics in the 0.5 m resolution hillshade map extracted from LiDAR data. UAV LiDAR has advantages in densely vegetated alpine gorges: after the selection of suitable filtering algorithms and parameters of the point cloud, it can obtain detailed terrain and geomorphological information on geohazards. The different remote sensing technologies used in this study can mutually confirm and complement each other, enhancing the capability to identify geohazards and their associated hazard cascades in densely vegetated alpine gorges, thereby providing valuable references for government departments in disaster prevention and reduction work. [ABSTRACT FROM AUTHOR]

Wood turtles (Glyptemys insculpta) are a species of conservation concern throughout their geographic distribution. Several studies have investigated individual‐level habitat selection of wood turtles in the Upper Midwest in the United States, but the effects of habitat characteristics on abundance are poorly understood. This information is needed to improve landscape‐level habitat management and conservation initiatives for the species. Our study aimed to identify important aquatic and terrestrial habitat characteristics and quantify their influence on abundance dynamics of adult wood turtles in the Laurentian Mixed Forest Province ecoregion of Wisconsin and Minnesota, USA. We collected standardized population survey data at 57 sites within the ecoregion between 2016 and 2022. We used N‐mixture models with a multi‐stage model selection procedure to assess the influence of aquatic and terrestrial predictors on abundance, including several 3‐dimensional forest structure metrics derived from airborne Light Detection and Ranging (LiDAR) data. Several aquatic and terrestrial habitat characteristics influenced local abundance patterns of adult wood turtles. The most influential aquatic predictors were stream velocity and stream width, and the most influential terrestrial predictors were mean return height and vertical coefficient of variation of height. Abundance was high at sites containing comparatively narrow streams with moderate velocities. The most supported terrestrial predictors were derived from LiDAR and indicate that complex forest structures support larger wood turtle populations. Our results can be used in forest management strategies to improve habitat quality for wood turtles, such as selective tree harvesting to increase structural diversity, and potentially identify robust populations in under‐surveyed areas. [ABSTRACT FROM AUTHOR]

The current deformation and stable state of slopes with historical shatter signs is a concern for engineering construction. Suspected landslide scarps were discovered at the rear edge of the Genie slope on the Tibetan Plateau during a field investigation. To qualitatively determine the current status of the surface deformation of this slope, this study used high-resolution optical remote sensing, airborne light detection and ranging (LiDAR), and interferometric synthetic aperture radar (InSAR) technologies for comprehensive analysis. The interpretation of high-resolution optical and airborne LiDAR data revealed that the rear edge of the slope exhibits three levels of scarps. However, no deformation was detected with differential InSAR (D-InSAR) analysis of ALOS-1 radar images from 2007 to 2008 or with Stacking-InSAR and small baseline subset InSAR (SBAS-InSAR) processing of Sentinel-1A radar images from 2017 to 2020. This study verified the credibility of the InSAR results using the standard deviation of the phase residuals, as well as in-borehole displacement monitoring data. A conceptual model of the slope was developed by combining field investigation, borehole coring, and horizontal exploratory tunnel data, and the results indicated that the slope is composed of steep anti-dip layered dolomite limestone and that the scarps at the trailing edges of the slope were caused by historical shallow toppling. Unlike previous remote sensing studies of deformed landslides, this paper argues that remote sensing results with reliable accuracy are also applicable to the study of undeformed slopes and can help make preliminary judgments about the stability of unexplored slopes. The study demonstrates that the long-term consistency of InSAR results in integrated remote sensing can serve as an indicator for assessing slope stability. [ABSTRACT FROM AUTHOR]

This study proposes the use of light detection and ranging (LiDAR) remote sensing (RS) to support morphometric research for estimating the aerodynamic roughness length ( z 0 ) of building placement on various scales. A LiDAR three-dimensional point cloud (3DPC) data processing graphical user interface (GUI) was developed to explore the z 0 and related urban canopy parameters (UCPs) in the Incheon metropolitan area in South Korea. The results show that multi-scale urban aerodynamic roughness exploration is viable and can address differences in urban building data at various spatial resolutions. Although validating morphological multi-scale UCPs using dense tall towers is challenging, emerging low-cost and efficient methods can serve as substitutes. However, further efforts are required to link the measured z 0 to building form regulations, such as floor area ratio, and expand RS research to obtain more quantitative and qualitative knowledge. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEMs) have a wide range of applications and play a crucial role in many studies. Numerous public DEMs, frequently acquired using radar and optical satellite imagery, are currently available; however, DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, compromising the accuracy of models in representing terrain elevation. In this study, we developed a digital terrain model for South America using a novel methodology to remove vegetation bias in the Copernicus DEM GLO-30 (COPDEM) model using machine learning, Global Ecosystem Dynamics Investigation (GEDI) elevation data, and multispectral remote sensing products. Our results indicate considerable improvements compared to COPDEM in representing terrain elevation, reducing average errors (BIAS) from 9.6 m to 1.5 m. Furthermore, we evaluated our product (ANADEM) by comparison with other global DEMs, obtaining more accurate results for different conditions of vegetation fraction cover and land use. As a publicly available and open-source dataset, ANADEM will play a crucial role in advancing studies that demand accurate terrain elevation representations at large scales. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM. [ABSTRACT FROM AUTHOR]

Submerged aquatic vegetation, also referred to as submerged macrophytes, provides important habitats and serves as a significant ecological indicator for assessing the condition of water bodies and for gaining insights into the impacts of climate change. In this study, we introduce a novel approach for the classification of submerged vegetation captured with bathymetric LiDAR (Light Detection And Ranging) as a basis for monitoring their state and change, and we validated the results against established monitoring techniques. Employing full-waveform airborne laser scanning, which is routinely used for topographic mapping and forestry applications on dry land, we extended its application to the detection of underwater vegetation in Lake Constance. The primary focus of this research lies in the automatic classification of bathymetric 3D LiDAR point clouds using a decision-based approach, distinguishing the three vegetation classes, (i) Low Vegetation, (ii) High Vegetation, and (iii) Vegetation Canopy, based on their height and other properties like local point density. The results reveal detailed 3D representations of submerged vegetation, enabling the identification of vegetation structures and the inference of vegetation types with reference to pre-existing knowledge. While the results within the training areas demonstrate high precision and alignment with the comparison data, the findings in independent test areas exhibit certain deficiencies that are likely addressable through corrective measures in the future. [ABSTRACT FROM AUTHOR]