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Land use / land cover (LULC) has changed dramatically in recent years, especially in areas that have experienced severe climate change and population growth. Evaluation of LULC changes is the most accurate method to understand past land uses and predict their future changes. Analysis of multi-temporal optic and radar remotely sensed images is an effective tool for monitoring LULC changes, and fusion of these datasets has provided significant developments in the detection and modeling of land surface changes due to their unique features individually. Time-series image (TSI) analysis provides a history of changes in an area and allows for predicting future changes. This study aims to develop a method for modeling LULC changes and predicting future trends. To this end, a spatiotemporal regression model is proposed based on TSI. In this method, land surface changes are first modeled by considering the spatiotemporal behavioral patterns of cover changes in a given period. Then, future land covers are predicted by a spatiotemporal regression. To evaluate the proposed method, LULC changes of the Maharloo Lake (Fars province, Iran) were modeled using fused optical and radar TSI (12 consecutive image sets) and then predicted for time t = 13. Compared to ground truth data, the different land covers including; water bodies, vegetation cover, bare land, and saline area were predicted by accuracies of 84.9, 98.4, 90.7, and 97.3%, respectively, indicating the remarkable efficiency of the model proposed for predicting LULC. [ABSTRACT FROM AUTHOR]

A four-wheel differential robot was used as experimental platform to obtain environmental information based on single-line liDAR, and the navigation performance of the robot in the center line of the tree and the autonomous line breaking of the robot at the end of the line were investigated. The environmental information was obtained by single laser radar, and the method of distance threshold, the adaptive density clustering algorithm and the improved method of least square algorithm processing laser data were used to obtain the center line of the tree line as robot navigation theory path. The robot and navigation path horizontal offset, heading angle and velocity were selected as input to design the control model of real-time tracking navigation path. The results show that when the robot reaches the end of line, it can automatically sense and execute the turn to the next line. In the inter-row autonomous navigation experiment, when the robot linear velocities are 0. 2, 0. 4 and 0. 6 m/s with the initial lateral offset of robot not more than 0. 8 m and the heading angle not more than 15°, the average lateral deviation between the navigation trajectory and the center line of tree line is 3. 69 cm, and the maximum lateral deviation is 11 cm. In the autonomous line-breaking test, the robot can accurately turn around to the next line and then continue to navigate between the lines. The proposed orchard autonomous navigation system has good operation stability and high navigation accuracy, which can meet the autonomous driving accuracy requirement of orchard robot. [ABSTRACT FROM AUTHOR]

The geological strength index (GSI) plays an important role in the quality evaluation and stability analysis of rock mass. Traditional methods for quantitatively estimating GSI are often subjective, time-consuming, and dangerous. This paper proposed a method for rapid and quantitative GSI estimate using 3D point clouds, which can be generated through non-contact measurement methods such as photogrammetry and Light Detection and Ranging (LiDAR). The overall methodology is as follows: (1) point clouds were acquired using a terrestrial laser scanner; (2) discontinuities were identified through artificial neural networks (ANN) and density-based spatial clustering of applications with noise (DBSCAN); (3) geometric information was extracted for the detected discontinuities; (4) GSI was estimated according to the detection and characterization of discontinuities. The proposed method was used for the Yujiashan road cut to calculate the GSI and the GSI partitioning was performed simultaneously. Three sets of discontinuity were detected in the Yujiashan road cut, and Structure Rating (SR) and Surface Conditions Point Clouds Rating (SCPC) were calculated to be 13.2 and 25, respectively. Correspondingly, the GSI was estimated to be 32, which was consistent with the results of the in-situ evaluation (rating 25–40). Furthermore, the Yujiashan road cut was divided into 17 segments, and the effect of sampling size on the GSI calculation was discussed. The application results show that the GSI of the rock mass can be obtained objectively and efficiently through 3D point clouds, which can be used as a potential alternative to the traditional method for GSI estimation. Article Highlights: A semi-automatic method was developed to determine the rockmass GSI using laser scanning. Discontinuities were detected from the 3D point cloud using a machine-learning algorithm. Four geometric parameters of detected discontinuities were extracted for GSI calculation. The scale effect of GSI calculation was investigated using the proposed partitioning algorithm. GSI estimation obtained from the proposed method matched the traditional manual surveys. [ABSTRACT FROM AUTHOR]

The application of deep learning techniques on point cloud data holds significant promise for efficient data segmentation and classification of traffic signs. This study proposes modifications to the PointNet++ neural network to improve performance on outdoor scenes. In addition, the method leverages the use of local geometric features in the training process. Several models with different combinations of geometric features and proposed changes were trained using labeled data from seven highway segments in Alberta, Canada. The results indicate that the proposed models have improved performance in accuracy and processing times compared to previous studies on sign detection using point cloud data. The overall per sign detection performance shows a 99.2% recall (98% per point) and a 98% F1-score (97% per point). Overall, the inclusion of z-gradient significantly increased sign detection in terms of precision, recall, and F1-score, by 9%, 4.9%, and 7.1%, respectively, allowing the model to yield notable performance improvements for outdoor scene recognition. Ablation tests were performed to validate the performed PointNet++ modifications. The modified PointNet++ was compared with SqueezeSegV2, a state-of-the-art neural network designed for road-object segmentation, and showed improved performance. A comparison was also made with existing sign detection methods on the Paris-Lille-3D benchmark, finding higher recall rates than existing studies. The proposed approach suggests that with adjustments, the PointNet++ neural network architecture can achieve remarkable results on large metric scale scenes for sign extraction using point cloud data. [ABSTRACT FROM AUTHOR]

Transportation assets, including retaining walls, noise barriers, rumble strips, guardrails, guardrail anchors, and central cable barriers, are important roadside safety hardware and geotechnical structures. They need to be inventoried accurately to support asset management and ensure roadway safety. There are detection methods for some of these transportation assets, which however lack flexibility and multiclass abilities. Moreover, the potential of neural networks has not been fully utilized although some detection methods have used deep learning. This paper for the first time proposes a multiclass transportation asset detection and pixel-wise segmentation model on two-dimensional images, based on region-based convolutional neural network (Mask-RCNN) with a feature pyramid network (FPN). The scale diversity and intensive continual appearance of transportation assets are identified as the main challenges, tending to result in numerous false-positive detections. A methodology with self-attention mechanisms based on the generic region of interest extractor (GRoIE) model along with the intersection over the minimum area merging (IoMA-Merging) postprocessing algorithm was then proposed. The evaluation outcomes demonstrated that our proposed methodology, including GRoIE-global context (GRoIE-GC) with IoMA-Merging achieves the best performance with a significant improvement over baseline. The precision increased by 10.0% on detection and 10.7% on segmentation. This proposed methodology will consequently improve the accuracy of asset inventory. This section serves as an illustration on how we could build a unified transportation inventory as a practical application based on the discrete detection results from our methodology. More details about the methodology can be found in the following sections. For simplicity, the demo in this section is only done with detected bounding boxes of a single object. After detection and segmentation, a tracking algorithm can play an important role in associating detection on the same object across different frames. In this way, we could recognize the distinct objects in the video frames with the help of a tracking algorithm. We also find it feasible to estimate the geographical and geometric information of the object of interest. With high-accuracy and high-frequency global positioning system (GPS) information recorded by our sensing vehicle, it is practical to locate the object and estimate the width in the physical world. Height information is also accessible with either camera calibration or combination of image and light detection and ranging (LiDAR) data. [ABSTRACT FROM AUTHOR]

Single‐frequency semiconductor lasers represent a critical role in optical communications, light detection and ranging systems, photonics integrated circuits, etc. Here, combining atom‐like quantum dot (QD) materials and advanced lateral gratings, a high‐power, ultra‐low‐noise 1.3 µm InAs/GaAs QD distributed feedback laser is demonstrated. Stable single‐longitudinal‐mode output power of 100 mW from 25 ℃ to 85 ℃ is achieved with a maximum side mode suppression ratio of 62.6 dB, and the variations of threshold current and slope efficiency over the temperature range are slight, indicting a high temperature stability. A record‐narrow intrinsic linewidth of 1.62 kHz is achieved at 55 ℃ with a white noise level of merely 515 Hz2 Hz–1, and a minimum averaged relative intensity noise of only –166 dB/Hz between 0.1 GHz and 20 GHz is obtained at 25 ℃. Furthermore, a strong tolerance to external optical feedback (〉 –14 dB) is demonstrated in the range from 25 ℃ to 85 ℃, with a maximum value of –8 dB at 85 ℃. This high‐quality single‐frequency laser fabricated with simplified processes and compact size paves the way for its future large‐scale applications such as high‐capacity optical communication, high‐precision optical detection, high‐speed optical interconnections, etc. [ABSTRACT FROM AUTHOR]

• Evolving the Radar System Simulator into the Sensor Network Simulator. • Newly implemented Optical Performance Model was verified and validated with real data. • The SNS tool suite can be used to analyze sensor networks and their performance. The number of human-made objects orbiting the Earth is growing steadily. This trend is further accelerating during recent years due to increasing commercialization in the field of satellite operations and an increasing launch rate. Concerning the area of space surveillance and tracking (SST), this leads to higher demands with respect to object observation capabilities. In order to ensure the safe usability of the different Earth orbits for all spacecraft operators, it is of great importance to continuously observe and catalog as many of the human-made objects as possible. This is especially true for inactive and uncontrollable objects, i.e. space debris. Only by maintaining accurate catalogs of the space debris environment, involuntary collisions can be avoided and risks of losing a spacecraft kept low. While the number of space debris objects can only be expected to further increase, it is not trivial to increase the number of observation sites, such as telescopes and radars, or their capabilities to counter this. One way to increase the observation capabilities without constructing new observation sites is to search for possibilities to improve the efficiency with which the existing sensors observe objects. With the goal to study and analyze single sensors, sensor networks, and how they can be used in the most efficient way, a sensor network simulator tool suite has been developed at the Institute of Space Systems of the Technical University of Braunschweig. Originally, the tool suite was developed as a radar system simulator concentrating on the different radar system configurations, such as reflector antennas or phased arrays that are used in a mono- or bistatic mode. Additionally, the corresponding radar performance model is able to simulate a tracking as well as a scanning mode. Building upon this tool suite, it is now extended to include an optical performance model and the possibility to simulate not only ground-based but also space-based sensors. Within this work, the preliminary results obtained with the newly implemented optical performance model are shown. Using the enhanced version of this sensor network simulator tool suite, thorough analyses can now be made using the radar and optical performance models. Sensor networks consisting of radars and telescopes being both ground- or space-based can be studied in different configurations. Thereby, different aspects, such as observation strategies, sensor tasking, or choosing the right location and sensor to reach a certain goal can be investigated. [ABSTRACT FROM AUTHOR]

Moderate severity disturbances, those that do not result in stand replacement, play an essential role in ecosystem dynamics. Despite the prevalence of moderate severity disturbances and the significant impacts they impose on forest functioning, little is known about their effects on forest canopy structure and how these effects differ over time across a range of disturbance severities and disturbance types.Using longitudinal data from the National Ecological Observatory Network project, we assessed the effects of three moderate severity press disturbances (beech bark disease, hemlock woolly adelgid and emerald ash borer, which are characterized by continuous disturbance and sustained mortality) and three moderate severity pulse disturbances (spring cankerworm moth, spongy moth and ground fire, which are associated with discrete and relatively short mortalities) on temperate forest canopy structure in eastern US. We studied (1) how light detection and ranging (LiDAR)‐derived metrics of canopy structure change in response to disturbance and (2) whether initial canopy complexity offsets impact of disturbances on canopy structure over time. We used a mixed‐effects modelling framework which included a non‐linear term for time to represent changes in canopy structure caused by disturbance, and interactions between time and both disturbance intensity and initial canopy complexity.We discovered that high intensity of both press and pulse disturbances inhibited canopy height growth while low intensity pulse disturbances facilitated it. In addition, high intensity pulse disturbances facilitated increases in the complexity of the canopy over time. Concerning the impact of initial canopy complexity, we found that the initial canopy complexity of disturbed plots altered the effects of moderate disturbances, indicating potential resilience effects.Synthesis. This study used repeated measurements of LiDAR data to examine the effects of moderate disturbances on various dimensions of forest canopy structure, including height, openness, density and complexity. Our study indicates that both press and pulse disturbances can inhibit canopy height growth over time. However, while the impact of press disturbances on other dimensions of canopy structure could not be clearly detected, likely because of compensatory growth, the impact of pulse disturbances over time was more readily apparent using multi‐temporal LiDAR data. Furthermore, our findings suggest that canopy complexity might help to mitigate the impact of moderate disturbances on canopy structures over time. Overall, our research highlights the usefulness of multi‐temporal LiDAR data for assessing the structural changes in forest canopies caused by moderate severity disturbances. [ABSTRACT FROM AUTHOR]

Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. Many hot issues can be investigated in desert ecosystems, including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species Distribution Models (SDMs) can be used to understand these relationships. We used data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light Detection and Ranging) data of one square kilometer in the inner Mongolia region of China to develop SDMs. We evaluated the performance of SDMs developed with a variety of both the parametric and nonparametric algorithms (Bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, Generalized Linear Model, Generalized Additive Model, Random Forest (RF), and Support Vector Machine). The area under the receiver operating characteristic curve was used to evaluate these algorithms. The SDMs developed with RF showed the best performance based on the area under curve (0.7733). We also produced the Nitraria tangutorum Bobr. distribution maps with the best SDM and suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to southern part with 0–20 degree slopes at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on the desert species distribution on a small scale. The presented SDMs can have important applications for predicting species distribution and will be useful for preparing conservation and management strategies for desert ecosystems on a small scale. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) technology is positioning itself as one of the most effective non-destructive methods to collect accurate information on ground crop fields, as the analysis of the three-dimensional models that can be generated with it allows for quickly measuring several key parameters (such as yield estimations, aboveground biomass, vegetation indexes estimation, perform plant phenotyping, and automatic control of agriculture robots or machinery, among others). In this survey, we systematically analyze 53 research papers published between 2005 and 2022 that involve significant use of the LiDAR technology applied to the three-dimensional analysis of ground crops. Different dimensions are identified for classifying the surveyed papers (including application areas, crop species under study, LiDAR scanner technologies, mounting platform technologies, and the use of additional instrumentation and software tools). From our survey, we draw relevant conclusions about the use of LiDAR technologies, such as identifying a hierarchy of different scanning platforms and their frequency of use as well as establishing the trade-off between the economic costs of deploying LiDAR and the agronomically relevant information that effectively can be acquired. We also conclude that none of the approaches under analysis tackles the problem associated with working with multiple species with the same setup and configuration, which shows the need for instrument calibration and algorithmic fine tuning for an effective application of this technology. [ABSTRACT FROM AUTHOR]

Potato holds significant importance as a staple food crop worldwide, particularly in addressing the needs of a growing population. Accurate estimation of the potato Leaf Area Index (LAI) plays a crucial role in predicting crop yield and facilitating precise management practices. Leveraging the capabilities of UAV platforms, we harnessed their efficiency in capturing multi-source, high-resolution remote sensing data. Our study focused on estimating potato LAI utilizing UAV-based digital red–green–blue (RGB) images, Light Detection and Ranging (LiDAR) points, and hyperspectral images (HSI). From these data sources, we computed four sets of indices and employed them as inputs for four different machine-learning regression models: Support Vector Regression (SVR), Random Forest Regression (RFR), Histogram-based Gradient Boosting Regression Tree (HGBR), and Partial Least-Squares Regression (PLSR). We assessed the accuracy of individual features as well as various combinations of feature levels. Among the three sensors, HSI exhibited the most promising results due to its rich spectral information, surpassing the performance of LiDAR and RGB. Notably, the fusion of multiple features outperformed any single component, with the combination of all features of all sensors achieving the highest R2 value of 0.782. HSI, especially when utilized in calculating vegetation indices, emerged as the most critical feature in the combination experiments. LiDAR played a relatively smaller role in potato LAI estimation compared to HSI and RGB. Additionally, we discovered that the RFR excelled at effectively integrating features. [ABSTRACT FROM AUTHOR]

Optimizing the spatial structure of forests is important for improving the quality of forest ecosystems. Light detection and ranging (LiDAR) could accurately extract forest spatial structural parameters, which has significant advantages in spatial optimization and resource monitoring. In this study, we used unmanned aerial vehicle LiDAR (UAV-LiDAR) and backpack-LiDAR to acquire point cloud data of Metasequoia plantation forests from different perspectives. Then the parameters, such as diameter at breast height and tree height, were extracted based on the point cloud data, while the accuracy was verified using ground-truth data. Finally, a single-tree-level thinning tool was developed to optimize the spatial structure of the stand based on multi-objective planning and the Monte Carlo algorithm. The results of the study showed that the accuracy of LiDAR-based extraction was (R2 = 0.96, RMSE = 3.09 cm) for diameter at breast height, and the accuracy of R2 and RMSE for tree height extraction were 0.85 and 0.92 m, respectively. Thinning improved stand objective function value Q by 25.40%, with the most significant improvement in competition index CI and openness K of 17.65% and 22.22%, respectively, compared to the pre-optimization period. The direct effects of each spatial structure parameter on the objective function values were ranked as follows: openness K (1.18) > aggregation index R (0.67) > competition index CI (0.42) > diameter at breast height size ratio U (0.06). Additionally, the indirect effects were ranked as follows: aggregation index R (0.86) > diameter at breast height size ratio U (0.48) > competition index CI (0.33). The study realized the optimization of stand spatial structure based on double LiDAR data, providing a new reference for forest management and structure optimization. [ABSTRACT FROM AUTHOR]

Femtosecond laser-induced fluorescence (FLIF) and femtosecond laser-induced optical breakdown spectroscopy (FIBS) are important tools for remote diagnostics of atmospheric aerosols using LiDAR (Light Identification Detection and Ranging) technology. They are based on light emission excitation in disperse media via multiphoton nonlinear processes in aerosol particles induced by high-power optical pulses. To date, the main challenge restraining the large-scale application of FLIF and FIBS in atmospheric studies is the lack of a valued theory of the stimulated light emission in liquid microparticles with a sufficiently broad range of sizes. In this paper, we fill this gap and present a theoretical model of dye water droplet emission under high intensity laser exposure that adequately simulates the processes of multiphoton excited fluorescence and optical breakdown plasma emission in microparticles and gives quantitative estimates of the angular and power characteristics of nonlinear emission. The model is based on the numerical solution to the inhomogeneous Helmholtz equations for stimulating (primary) and nonlinear (secondary) waves provided by the random nature of molecule emission in particles. We show that droplet fluorescence stimulated by multiphoton absorption generally becomes more intense with increasing particle size. Moreover, far-field plasma emission from liquid particles demonstrates a larger angular diversity when changing the droplet radius in comparison with multiphoton excited fluorescence, which is mainly due to the excitation of the internal optical field resonances in spherical particles. [ABSTRACT FROM AUTHOR]

We tested whether windthrow damage to Nordic conifer forest stands could be reliably detected as canopy height decrease between a pre-storm LiDAR (Light Detection and Ranging) digital surface model (DSM) and a photogrammetric DSM derived from a post-storm WorldView-3 stereo pair. The post-storm ground reference data consisted of field and unmanned aerial vehicle (UAV) observations of windthrow combined with no-damage areas collected by visual interpretation of the available very high resolution (VHR) satellite imagery. We trained and tested a thresholding model using canopy height change as the sole predictor. We undertook a two-step accuracy assessment by (1) running k-fold cross-validation on the ground reference dataset and examining the effect of the potential imperfections in the ground reference data, and (2) conducting rigorous accuracy assessment of the classified map of the study area using an extended set of VHR imagery. The thresholding model produced accurate windthrow maps in dense, productive forest stands with a sensitivity of 96%, specificity of 71%, and Matthews correlation coefficient (MCC) over 0.7. However, in sparse and high elevation stands, the classification accuracy was poor. Despite certain collection challenges during the winter months in the Nordic region, we consider VHR stereo satellite imagery to be a viable source of forest canopy height information and sufficiently accurate to map windthrow disturbance in forest stands of high to moderate density. [ABSTRACT FROM AUTHOR]

Designs of navigation group radio-optical reflectors of circular action have been developed. The results of experimental studies of their scattering characteristics as part of a floating buoy are presented. Comparative estimates of the visibility range of floating buoys equipped with group radio-optical reflectors in the optical and radar wavelength ranges are obtained. Experimental studies were carried out in the Ob basin of inland waterways, on the Ob and Tom rivers. [ABSTRACT FROM AUTHOR]

Soil moisture plays an important role in crop yield estimation, irrigation management, etc. Remote sensing technology has potential for large-scale and high spatial soil moisture mapping. However, offline remote sensing data processing is time-consuming and resource-intensive, and significantly hampers the efficiency and timeliness of soil moisture mapping. Due to the high-speed computing capabilities of remote sensing cloud platforms, a High Spatial Resolution Soil Moisture Estimation Framework (HSRSMEF) based on the Google Earth Engine (GEE) platform was developed in this study. The functions of the HSRSMEF include research area and input datasets customization, radar speckle noise filtering, optical-radar image spatio-temporal matching, soil moisture retrieving, soil moisture visualization and exporting. This paper tested the performance of HSRSMEF by combining Sentinel-1, Sentinel-2 images and in-situ soil moisture data in the central farmland area of Jilin Province, China. Reconstructed Normalized Difference Vegetation Index (NDVI) based on the Savitzky-Golay algorithm conforms to the crop growth cycle, and its correlation with the original NDVI is about 0.99 (P < 0.001). The soil moisture accuracy of the random forest model (R2 = 0.942, RMSE = 0.013 m3/m3) is better than that of the water cloud model (R2 = 0.334, RMSE = 0.091 m3/m3). HSRSMEF transfers time -consuming offline operations to cloud computing platforms, achieving rapid and simplified high spatial resolution soil moisture mapping. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) technology, a cutting-edge advancement in mobile applications, presents a myriad of compelling use cases, including enhancing low-light photography, capturing and sharing 3D images of fascinating objects, and elevating the overall augmented reality (AR) experience. However, its widespread adoption has been hindered by the prohibitive costs and substantial power consumption associated with its implementation in mobile devices. To surmount these obstacles, this paper proposes a low-power, low-cost, single-photon avalanche detector (SPAD)-based system-on-chip (SoC) which packages the microlens arrays (MLAs) and a lightweight RGB-guided sparse depth imaging completion neural network for 3D LiDAR imaging. The proposed SoC integrates an 8 × 8 SPAD macropixel array with time-to-digital converters (TDCs) and a charge pump, fabricated using a 180 nm bipolar-CMOS-DMOS (BCD) process. Initially, the primary function of this SoC was limited to serving as a ranging sensor. A random MLA-based homogenizing diffuser efficiently transforms Gaussian beams into flat-topped beams with a 45° field of view (FOV), enabling flash projection at the transmitter. To further enhance resolution and broaden application possibilities, a lightweight neural network employing RGB-guided sparse depth complementation is proposed, enabling a substantial expansion of image resolution from 8 × 8 to quarter video graphics array level (QVGA; 256 × 256). Experimental results demonstrate the effectiveness and stability of the hardware encompassing the SoC and optical system, as well as the lightweight features and accuracy of the algorithmic neural network. The state-of-the-art SoC-neural network solution offers a promising and inspiring foundation for developing consumer-level 3D imaging applications on mobile devices. [ABSTRACT FROM AUTHOR]

In this work, we introduce a novel approach to model the rain and fog effect on the light detection and ranging (LiDAR) sensor performance for the simulation-based testing of LiDAR systems. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. The time domain analysis permits us to benchmark the virtual LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. In addition, the detection rate (DR), false detection rate (FDR), and distance error  d e r r o r  of the virtual LiDAR sensor due to rain and fog droplets are evaluated on the point cloud level. The mean absolute percentage error (MAPE) is used to quantify the simulation and real measurement results on the time domain and point cloud levels for the rain and fog droplets. The results of the simulation and real measurements match well on the time domain and point cloud levels if the simulated and real rain distributions are the same. The real and virtual LiDAR sensor performance degrades more under the influence of fog droplets than in rain. [ABSTRACT FROM AUTHOR]

The mining rope shovel (MRS) is one of the core pieces of equipment for open-pit mining, and is currently moving towards intelligent and unmanned transformation, replacing traditional manual operations with intelligent mining. Aiming at the demand for online planning of an intelligent shovel excavation trajectory, an MRS excavating trajectory planning method based on material surface perception is proposed here. First, point cloud data of the material stacking surface are obtained through laser radar to perceive the excavation environment and these point cloud data are horizontally calibrated and filtered to reconstruct the surface morphology of the material surface to provide a material surface model for calculation of the mining volume in the subsequent trajectory planning. Second, kinematics and dynamics analysis of the MRS excavation device are carried out using the Product of Exponentials (PoE) and Lagrange equation, providing a theoretical basis for calculating the excavation energy consumption in trajectory planning. Then, the trajectory model of the bucket tooth tip is established by the method of sixth-order polynomial interpolation. The unit mass excavation energy consumption and unit mass excavation time are taken as the objective function, and the motor performance and the geometric size of the MRS are taken as constraints. The grey wolf optimizer is used for iterative optimization to realize efficient and energy-saving excavation trajectory planning of the MRS. Finally, trajectory planning is carried out for material surfaces with four different shapes (typical, convex, concave, and convex–concave). The results of experimental validation show that the actual hoist and crowd forces are essentially consistent with the planned hoist and crowd forces in terms of the peak value and trend variations, verifying the accuracy of the calculation model and confirming that the full bucket rate and various parameters meet the constraints. Therefore, the trajectory planning method based on material surface perception are feasible for application to different excavation conditions. [ABSTRACT FROM AUTHOR]

Cloud cover effects make it difficult to evaluate the mangrove ecosystem in tropical locations using solely optical satellite data. Therefore, it is essential to conduct a more precise evaluation using data from several sources and appropriate models in order to manage the mangrove ecosystem as effectively as feasible. In this study, the status of the mangrove ecosystem and its potential contribution to coastal resilience were evaluated using the Google Earth Engine (GEE) and the InVEST model. The GEE was used to map changes in mangrove and other land cover types for the years 2009 and 2019 by integrating both optical and radar data. The quantity allocation disagreement index (QADI) was used to assess the classification accuracy. Mangrove height and aboveground biomass density were estimated using GEE by extracting their values from radar image clipped with a digital elevation model and mangrove vector file. A universal allometric equation that relates canopy height to aboveground biomass was applied. The InVEST model was used to calculate a hazard index of every 250 m of the shoreline with and without mangrove ecosystem. Our result showed that about 16.9% and 21% of mangrove and other vegetation cover were lost between 2009 and 2019. However, water body and bare land/built‐up areas increased by 7% and 45%, respectively. The overall accuracy of 2009 and 2019 classifications was 99.6% (QADI = 0.00794) and 99.1% (QADI = 0.00529), respectively. Mangrove height and aboveground biomass generally decreased from 12.7 to 6.3 m and from 105 to 88 Mg/ha on average. The vulnerability index showed that 23%, 51% and 26% of the coastal segment in the presence of mangrove fall under very low/low, moderate and high risks, respectively. Whereas in the absence of mangrove, 8%, 38%, 39% and 15% fall under low, moderate, high and very high‐risk zones, respectively. This study will among other things help the stakeholders in coastal management and marine spatial planning to identify the need to focus on conservation practices. [ABSTRACT FROM AUTHOR]

Photon-counting light detection and ranging (LiDAR) emits and receives weak photon signals, which are easily mixed with background noise caused by the sun, the atmosphere, etc., and is thus difficult to distinguish. Therefore, point-cloud denoising is a key step in point-cloud data processing of photon-counting LiDAR. To explore the adaptability of different denoising algorithms for photon-counting LiDAR data in different times and spaces, in this paper, NASA's official differential, regressive and Gaussian adaptive nearest neighbor (DRAGANN) algorithm; Herzfeld's radial basis function (RBF) denoising algorithm; and the density-based spatial clustering of applications with noise (DBSCAN) algorithm based on density clustering are used to denoise the ICESat-2 ATL03 photon point-cloud data. Airborne LiDAR data are used to verify the denoising accuracy, and then the adaptability of the three algorithms is discussed. The results show that the DRAGANN algorithm is suitable for data with moderate Fraction Vegetation Coverage (FVC) (45–75%) at night and is less affected by slope; therefore, it is not limited to terrain slope. The denoising accuracy of the RBF algorithm decreases with increasing FVC and decreases with increasing slope. It is suitable for data with low terrain slope (0~55°) and low FVC (0~220°), which is less affected by observation time; therefore, it is suitable for all-day data. The DBSCAN algorithm is suitable for data with moderate FVC (45~75%) at night, regardless of terrain slope. Unlike the DRAGANN algorithm, the DBSCAN algorithm is greatly affected by solar noise photons, but at night, its denoising accuracy is higher than that of the DRAGANN algorithm. The research results have certain reference significance for the subsequent processing and application of ICESat-2 data. [ABSTRACT FROM AUTHOR]

The use of light detection and ranging (LiDAR) for investigating forest parameters has gained attention in recent years. However, the occlusion of trees in natural forests makes it difficult for LiDAR on a single platform to capture complete point clouds of trees. In order to solve this problem, it is crucial to combine multi-platform LiDAR data. Because of the complexity of natural forests and the small difference between the geometric characteristics of trees, current multi-platform LiDAR data fusion remains an ongoing challenge in natural forests. In this paper, an automatic registration framework for multi-platform point cloud data in natural forests based on tree distribution pattern was proposed. It consists of five steps, namely segmenting trees, generating feature descriptors, matching trees and registering coarsely and finely. The proposed registration framework can determine the same and accurate location information of matching trees from multi-platform LiDAR data, and a large number of correct matching trees can be obtained through two rounds of a single tree matching process. The proposed framework was validated by fusing airborne laser scanner (ALS) and backpack laser scanner (BLS) data in natural forest. According to experimental results, the proposed framework has a high registration accuracy (root-mean-square error (RMSE) = 0.133 m, mean absolute error (MAE) = 0.126 m). In addition, when the single tree segmentation accuracy exceeds 0.85, the proposed framework is less affected by segmentation errors. In natural forests, the proposed framework can effectively improve the accuracy and efficiency of multi-platform LiDAR data registration. [ABSTRACT FROM AUTHOR]

Cascaded metasurfaces can exhibit powerful dynamic light manipulation by mechanically tuning the far‐field interactions in the layers. However, in most current designs, the metasurfaces are separated by gaps smaller than a wavelength to form a total phase profile, representing the direct accumulation of the phase profiles of each layer. Such small gap sizes may not only conflict with the far‐field conditions but also pose great difficulties for practical implementations. To overcome this limitation, a design paradigm taking advantage of a ray‐tracing scheme that allows the cascaded metasurfaces to operate optimally at easily achievable gap sizes is proposed. Enabled by the relative lateral translation of two cascaded metasurfaces, a continuous two‐dimensional (2D) beam‐steering device for 1064 nm light is designed as a proof of concept. Simulation results demonstrate tuning ranges of ±45° for biaxial deflection angles within ±3.5 mm biaxial translations, while keeping the divergence of deflected light less than 0.007°. The experimental results agree well with theoretical predictions, and a uniform optical efficiency is observed. The generializeddesign paradigm can pave a way towards myriad tunable cascaded metasurface devices for various applications, including but not limited to light detection and ranging (LiDAR) and free space optical communication. [ABSTRACT FROM AUTHOR]

Objectives: In the process of digital elevation model (DEM) modeling, the existing interpolation methods do not take into account the local topographic characteristics near the breaklines, which makes the elevation of the local area of the breakline smoothed, thus leading to topographic distortion. A weight function with respect to considering the characteristics of breaklines is constructed, and a weight radial basis function (RBF) method is proposed. The proposed method makes full use of the gradient and direction information of the sampling points near the fracture line. Methods: First, the distance between the sampling point and the point to be sought is calculated adaptively by capturing the structure tensor of each sampling point, and then the distance is used to assign a suitable weight to each sampling point, finally the DEM modeling is realized by using weighted interpolation. Results: Examples on 10 public data and 1 private dataset of DEM construction with airborne light detection and ranging (LiDAR) point clouds indicates that the calculation results of each interpolation method gradually decrease with the decrease of sampling points. Compared to RBF and the classical interpolation methods including inverse distance weighting, ordinary Kriging and constrained triangulated irregular network, our proposed method has a better ability to maintain terriain features in the breakline area. Regardless of sample density, our proposed method is always more accurate than the other methods. Conclusions: Overall, the proposed method with the merit of terrain feature preservation is helpful for the construction of high-accurate DEMs, which play an important role in some geoscience applications with data quality as the most important factor. [ABSTRACT FROM AUTHOR]

Accurately orientated and matched images and light detection and ranging (LiDAR) point clouds are becoming the new standard for many applications in geomatics. Large data volumes and different observation types make scalability and efficient optimization challenging, particularly for matching based on variants of the iterative closest point algorithm. Here, we present a method to address this by temporally segmenting the pre-processed global navigation satellite system/inertial navigation system solution and representing LiDAR data as voxels with metadata. The efficiency, accuracy, and scalability of different voxel sizes have been assessed in this study. Hybrid adjustment of image tie-points and voxels from LiDAR data optimized the trajectory and sensor corrections for both LiDAR scanners and cameras in the presented experiment. A little over one billion LiDAR points and ∼ 3000 images were matched in < 1. 5 h and showed high accuracy in the range of a few millimeters. This accuracy is comparable to the existing methods for hybrid image and LiDAR adjustment. [ABSTRACT FROM AUTHOR]

Accurate and timely information on winter wheat spatial distribution is essential for food security and environmental sustainability. However, high-quality nation-wide winter wheat products at high resolutions are still scarce around the world, and the approaches for winter wheat mapping are generally constrained by the lack of sufficient and representative training data. In this study, a knowledge-based approach based on spectral and polarization information from critical stages of winter wheat, was proposed to extract high-quality training data of winter wheat, thereby supporting winter wheat mapping with machine learning classifiers. Additionally, classification model trained by the generated training data was transferred across years to achieve the in-season mapping of winter wheat. Two-year classification scenarios based on the automated training data generation (ATDG) or model transfer (MT) were designed to evaluate the quality of automatically generated training data, the performance of model transfer, the contribution of optical and radar data, and the earliest timing for winter wheat mapping over China. With the ATDG and MT, the first 10-m resolution maps of winter wheat over China (ChinaWheat10) were produced for three consecutive years (2020 & 2021 by ATDG; 2021 & 2022 by MT). For ATDG and MT, the combined features of Sentinel-1 and Sentinel-2 yielded the highest overall accuracies with the random forest classifier. Specifically, winter wheat mapping with the ATDG achieved the highest F1-score of 0.94 for both 2020 and 2021. The MT reached a comparable F1-score of 0.94 and 0.93 for 2021 and 2022, and winter wheat maps with the F1-score of 0.93 and 0.92 could be produced as early as April (two months ahead of harvesting). Besides well-delineated winter wheat parcels, the estimated areas of ChinaWheat10 aligned well with the agricultural census data at the provincial (R2 ≥ 0.95) and municipal (R2 ≥ 0.91) levels. These findings suggest the proposed approaches have a great potential for accurate, cost-effective and high-resolution in-season mapping of winter wheat over large regions. [ABSTRACT FROM AUTHOR]

Accurate measurements of ocean waves underpin efficient offshore operations and optimal offshore structure design, helping to ensure the offshore industry can operate both safely and economically. Popular instruments used by the offshore industry are the Rosemount WaveRadar (Radar) and the Waverider Buoy. The Optech Laser has been used at some locations for specific studies. Recent reports indicate systematic differences of order 10% among the wave measurements made by these instruments. This paper examines the relative performance of these instruments based upon various time-domain comparisons, including results from a quality control (QC) procedure, capabilities of measuring the wave surface profile (skewness), and crest heights for varying wind sea and swell conditions. The QC check provides good-quality data that can be further investigated with an assurance of error-free data, suggesting that the Waverider produces the bestquality data with the lowest failure rate compared to the Laser and Radar. A significant number of theWaverider surface elevation records have negative skewness, particularly at higher sea states, affecting its crest height measurements, which are lower than those from the Laser and Radar. Additionally, the significant wave height (H1/3) estimates of the Radar are lower than the Laser and Waverider, but its zero-crossing wave periods (TZ), on average, are longer than the Laser and theWaverider. The significant heights (H1/3) of Laser and Waverider are in good agreement for all three datasets, but the Waverider's zero-crossing wave period (TZ) estimates are significantly longer than the Laser. [ABSTRACT FROM AUTHOR]

Research on the inversion of forest aboveground biomass based on airborne light detection and ranging (LiDAR) data focuses on finding the relationship between the two, such as established linear or nonlinear models. However, these models may have poorer estimation accuracy for tree-components biomass and cannot guarantee the additivity of each component. Therefore, we aimed to develop an error-in-variable biomass model system that ensures both the compatibility of the individual tree component biomass with the diameter at breast height and the additivity of component biomass. The system we developed used the airborne LiDAR data and field-measured data of principis-rupprechtii (Larix gmelinii var.) trees, collected from north China. Our model system not only ensured the additivity of nonlinear biomass models, it also accounted for the impact of measurement errors. We first selected the airborne LiDAR-derived variable with the highest contribution to the biomass of each component and then developed an inversion model system with that variable as an independent variable and with the biomass of each component as the dependent variable using allometric functions. Moreover, two model estimation methods, two-stage error (TSEM) and nonlinear seemingly unrelated regression (NSUR) with one-step, two-step, and summation methods, were also applied, and their performances were compared. The results showed that both NSUR-one-step and TSEM-one-step led to similar parameter estimates and performance for a system, and the fitting accuracy of a model system was not very attractive. The variance function included in a model system reduced the heteroscedasticity effectively and improved the model accuracy. Overall, this study successfully combined the error-in-variable modeling with the airborne LiDAR data, proposed methods that can be used for the extension of component biomass from an individual tree to a stand and that might improve the estimation accuracy of carbon storage. A compatible model system can be further improved if various sources of error in the variables are identified, and their impacts on the system are effectively accounted for. [ABSTRACT FROM AUTHOR]

Airborne light detection and ranging (LiDAR) technology has been widely utilized for collecting three-dimensional (3D) point cloud data on forest scenes, enabling the generation of high-accuracy digital elevation models (DEMs) for the efficient investigation and management of forest resources. Point cloud filtering serves as the crucial initial step in DEM generation, directly influencing the accuracy of the resulting DEM. However, forest filtering presents challenges in dealing with sparse point clouds and selecting appropriate initial ground points. The introduction of full-waveform LiDAR data offers a potential solution to the problem of sparse point clouds. Additionally, advancements in multi-source data integration and machine learning algorithms have created new avenues that can address the issue of initial ground point selection. To tackle these challenges, this paper proposes a novel filtering method for forest scenes utilizing full-waveform LiDAR data and hyperspectral image data. The proposed method consists of two main steps. Firstly, we employ the improved dynamic graph convolutional neural network (IDGCNN) to extract initial ground points. In this step, we utilize three types of low-correlation features: LiDAR features, waveform features, and spectral features. To enhance its accuracy and adaptability, a self-attention module was incorporated into the DGCNN algorithm. Comparative experiments were conducted to evaluate the effectiveness of the algorithm, demonstrating that the IDGCNN algorithm achieves the highest classification accuracy with an overall accuracy (OA) value of 99.38% and a kappa coefficient of 95.95%. The second-best performer was the RandLA-net algorithm, achieving an OA value of 98.73% and a kappa coefficient of 91.68%. The second step involves refining the initial ground points using the cloth simulation filter (CSF) algorithm. By employing the CSF algorithm, non-ground points present in the initial ground points are effectively filtered out. To validate the efficacy of the proposed filtering method, we generated a DEM with a resolution of 0.5 using the ground points extracted in the first step, the refined ground points obtained with the combination of the first and second steps, and the ground points obtained directly using the CSF algorithm. A comparative analysis with 23 reference control points revealed the effectiveness of our proposed method, as evidenced by the median error of 0.41 m, maximum error of 0.75 m, and average error of 0.33 m. [ABSTRACT FROM AUTHOR]

The prediction of crop yield plays a crucial role in national economic development, encompassing grain storage, processing, and grain price trends. Employing multiple sensors to acquire remote sensing data and utilizing machine learning algorithms can enable accurate, fast, and nondestructive yield prediction for maize crops. However, current research heavily relies on single-type remote sensing data and traditional machine learning methods, resulting in the limited robustness of yield prediction models. To address these limitations, this study introduces a field-scale maize yield prediction model named the convolutional neural network–attention–long short-term memory network (CNN-attention-LSTM) model, which utilizes multimodal remote sensing data collected by multispectral and light detection and ranging (LIDAR) sensors mounted on unmanned aerial vehicles (UAVs). The model incorporates meteorological data throughout the crop reproductive stages and employs the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) for the initial part of the vegetative stage (initial part of the V period), the later part of the vegetative stage (later part of the V period), the reproductive stage (R period), and the maturity stage (M period), along with LIDAR data for Point75–100 in the later part of the V period, Point80–100 in the R period, and Point50–100 in the M period, complemented by corresponding meteorological data as inputs. The resulting yield estimation demonstrates exceptional performance, with an R2 value of 0.78 and an rRMSE of 8.27%. These results surpass previous research and validate the effectiveness of multimodal data in enhancing yield prediction models. Furthermore, to assess the superiority of the proposed model, four machine learning algorithms—multiple linear regression (MLR), random forest regression (RF), support vector machine (SVM), and backpropagation (BP)—are compared to the CNN-attention-LSTM model through experimental analysis. The outcomes indicate that all alternative models exhibit inferior prediction accuracy compared to the CNN-attention-LSTM model. Across the test dataset within the study area, the R2 values for various nitrogen fertilizer levels consistently exceed 0.75, illustrating the robustness of the proposed model. This study introduces a novel approach for assessing maize crop yield and provides valuable insights for estimating the yield of other crops. [ABSTRACT FROM AUTHOR]

Deciphering aspect-related hillslope asymmetry can enhance our understanding of the influence of climate on Earth's surface morphology and the linkage between topographic morphology and erosion processes. Although hillslope asymmetry is documented worldwide, the role of microclimatic factors in the evolution of dryland cliffs has received little attention. Here, we address this gap by quantifying aspect-dependent bedrock weathering, slope-rill morphology, and subcliff clast transport rates in the hyperarid Negev desert, Israel, based on light detection and ranging (LiDAR)-derived topography, clast-size measurements, and cosmogenic 10Be concentrations. Cliff retreat rates were evaluated using extrapolated profiles from dated talus flatirons and 10Be measurements from the cliff face and sub-cliff sediments. We document systematic, aspect-dependent patterns of south-facing slopes being less steep and finer-grained relative to east- and north-facing aspects. In addition, cliff retreat and clast transport rates on slopes of the south-facing aspect are faster compared to the other aspects. Our data demonstrate that bedrock weathering of the cliff face and the corresponding grain size of cliff-derived clasts delivered to the slopes constitute a first-order control on cliff retreat and sediment transport rates. We demonstrate that the morphology of the cliff and the pattern of bedrock weathering co-vary with the solar radiation flux and hence that cliff evolution in hyperarid regions is modulated by aspect-dependent solar radiation. These results help to better understand interactions between climate and dryland surface processes. [ABSTRACT FROM AUTHOR]

Dams and reservoirs are human-made infrastructures that have attracted increasing attention because of their societal and environmental significance. Towards better management and conservation of reservoirs, a dataset of reservoir-catchment characteristics is needed, considering that the amount of water and material flowing into and out of reservoirs depends on their locations on the river network and the properties of the upstream catchment. To date, no dataset exists for reservoir-catchment characteristics. The aim of this study is to develop the first database featuring reservoir-catchment characteristics for 3254 reservoirs with storage capacity totaling 682 595 km3 (73.2 % of reservoir water storage capacity in China) to support the management and conservation of reservoirs in the context of catchment level. To ensure a more representative and accurate mapping of local variables of large reservoirs, reservoir catchments are delineated into full catchments (their full upstream contributing areas) and intermediate catchments (subtracting the area contributed by upstream reservoirs from the full upstream part of the current reservoir). Using both full catchments and intermediate catchments, characteristics of reservoir catchments were extracted, with a total of 512 attributes in six categories (i.e., reservoir and catchment body characteristics, topography, climate, soil and geology, land cover and use, and anthropogenic activity characteristics). Besides these static attributes, time series of 15 meteorological variables of catchments were extracted to support hydrological simulations for a better understanding of drivers of reservoir environment change. Moreover, we provide a comprehensive and extensive reservoir dataset on water level (data available for 20 % of 3254 reservoirs), water surface area (99 %), storage anomaly (92 %), and evaporation (98 %) from multisource satellites such as radar and laser altimeters and images from Landsat and Sentinel satellites. These products significantly enhance spatial and temporal coverage in comparison to existing similar products (e.g., 67 % increase in spatial resolution of water level and 225 % increase in storage anomaly) and contribute to our understanding of reservoir properties and functions within the Earth system by incorporated national or global hydrological modeling. In situ data of 138 reservoirs are employed in this study as a valuable reference for evaluation, thus enhancing our confidence in the data quality and enhancing our understanding of the accuracy of current satellite datasets. Along with its extensive attributes, the Reservoir dataset in China (Res-CN) can support a broad range of applications such as water resources, hydrologic/hydrodynamic modeling, and energy planning. Res-CN is on Zenodo through 10.5281/zenodo.7664489 (Shen et al., 2022c). [ABSTRACT FROM AUTHOR]

Mercury is a serious neurotoxic agent, and the control and monitoring of emissions are important. Optical spectroscopy is a powerful technique for measurement of mercury, which in the atmosphere predominantly appears in atomic form. The mercury resonance line close to 254 nm can be utilized in long-path absorption measurements of average concentrations or in light detection and ranging (lidar) studies, where range-resolved concentration values can be obtained. In addition, point monitors often use optical detection, frequently after pre-concentration, as well as for mercury compounds in conjunction with denuders, which transfer the compounds into atomic mercury. The present review discusses mercury measurement methods with respect to merits and sensitivity to interference. The main focus is on remote-sensing techniques, and many examples from industrial and mining monitoring are given. Further, mercury emissions related to the extraction of geothermal energy are discussed. Finally, an example from archaeology—the Qin tomb in Xi'an—is presented. Advanced measurement techniques can help in shaping an environment largely free from mercury contamination. Further, the aspect of mercury being an important geophysical tracer gas can also be exploited. [ABSTRACT FROM AUTHOR]

Based on the extended Huygens–Fresnel principle, we derive the analytical expression of the crossspectral density matrix of a partially coherent Laguerre pulsed vector vortex beam (PCLPVVB) propagating through isotropic and anisotropic atmospheric turbulence. Our outcomes reveal that the atmospheric turbulence affects the evolution of spectral intensity distribution of PCLPVVB, and the beam quickly degenerates on propagation in the strong turbulence. We also can find that PCLPVVB with a larger topological charge has a stronger ability to resist the degeneration caused by atmospheric turbulence in comparison with the non-vortex beam. In addition, increasing the initial coherence length and mode order can increase the anti-turbulence ability of PCLPVVB, and the pulse duration significantly affects the spectral intensity of PCLPVVB in turbulence. Our research results are important for some applications in laser radar detection, remote sensing, and free-space optical communication. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) rely on advanced sensory systems, such as Light Detection and Ranging (LiDAR), to function seamlessly in intricate and dynamic environments. LiDAR produces highly accurate 3D point clouds, which are vital for the detection, classification, and tracking of multiple targets. A systematic review and classification of various clustering and Multi-Target Tracking (MTT) techniques are necessary due to the inherent challenges posed by LiDAR data, such as density, noise, and varying sampling rates. As part of this study, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was employed to examine the challenges and advancements in MTT techniques and clustering for LiDAR point clouds within the context of autonomous driving. Searches were conducted in major databases such as IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and Google Scholar, utilizing customized search strategies. We identified and critically reviewed 76 relevant studies based on rigorous screening and evaluation processes, assessing their methodological quality, data handling adequacy, and reporting compliance. As a result of this comprehensive review and classification, we were able to provide a detailed overview of current challenges, research gaps, and advancements in clustering and MTT techniques for LiDAR point clouds, thus contributing to the field of autonomous driving. Researchers and practitioners working in the field of autonomous driving will benefit from this study, which was characterized by transparency and reproducibility on a systematic basis. [ABSTRACT FROM AUTHOR]

This study proposes a novel hybrid simulation technique for analyzing structural deformation and stress using light detection and ranging (LiDAR)-scanned point cloud data (PCD) and polynomial regression processing. The method estimates the edge and corner points of the deformed structure from the PCD. It transforms into a Dirichlet boundary condition for the numerical simulation using the particle difference method (PDM), which utilizes nodes only based on the strong formulation, and it is advantageous for handling essential boundaries and nodal rearrangement, including node generation and deletion between analysis steps. Unlike previous studies, which relied on digital images with attached targets, this research uses PCD acquired through LiDAR scanning during the loading process without any target. Essential boundary condition implementation naturally builds a boundary value problem for the PDM simulation. The developed hybrid simulation technique was validated through an elastic beam problem and a three-point bending test on a rubber beam. The results were compared with those of ANSYS analysis, showing that the technique accurately approximates the deformed edge shape leading to accurate stress calculations. The accuracy improved when using a linear strain model and increasing the number of PDM model nodes. Additionally, the error that occurred during PCD processing and edge point extraction was affected by the order of polynomial regression equation. The simulation technique offers advantages in cases where linking numerical analysis with digital images is challenging and when direct mechanical gauge measurement is difficult. In addition, it has potential applications in structural health monitoring and smart construction involving machine leading techniques. [ABSTRACT FROM AUTHOR]

Recent advancements in vehicle automation and driver-assistance systems that detect pavement markings has increased the importance of the detectability of pavement markings through various sensor modalities across weather and road conditions. Among the sensing techniques, light detection and ranging (LiDAR) sensors have become popular for vehicle-automation applications. This study used low-cost mobile multi-beam LiDAR to assess the performance of several types of pavement marking materials installed on a limited-access highway in various conditions, and quantified the degradation in detection performance over three years. Four marking materials, HPS-8, polyurea, cold plastic, and sprayable thermoplastic, were analyzed in the current study. LiDAR reflectivity data extracted from a total of 210 passes through the test sections were analyzed. A new detectability score based on LiDAR intensity data was proposed to quantify the marking detectability. The results showed that the pavement marking detectability varied across the material types over the years. The results provide guidance for selecting materials and developing maintenance schedules when marking detectability by LiDAR is a concern. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m2. Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (Rmatch) of tree tops is up to 0.92, the coefficient of determination (R2) of tree height estimation is up to.94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping. [ABSTRACT FROM AUTHOR]

The Mobile Mapping System (MMS) plays a crucial role in generating accurate 3D maps for a wide range of applications. However, traditional MMS that utilizes tilted LiDAR (light detection and ranging) faces limitations in capturing comprehensive environmental data. We propose the "PVL-Cartographer" SLAM (Simultaneous Localization And Mapping) approach for MMS to address these limitations. This proposed system incorporates multiple sensors to yield dependable and precise mapping and localization. It consists of two subsystems: early fusion and intermediate fusion. In early fusion, range maps are created from LiDAR points within a panoramic image space, simplifying the integration of visual features. The SLAM system accommodates both visual features with and without augmented ranges. In intermediate fusion, camera and LiDAR nodes are merged using a pose graph, with constraints between nodes derived from IMU (Inertial Measurement Unit) data. Comprehensive testing in challenging outdoor settings demonstrates that the proposed SLAM system can generate trustworthy outcomes even in feature-scarce environments. Ultimately, our suggested PVL-Cartographer system effectively and accurately addresses the MMS localization and mapping challenge. [ABSTRACT FROM AUTHOR]

The development of autonomous navigation systems requires digital building models at the LoD3 level. Buildings with atypically shaped features, such as turrets, domes, and chimneys, should be selected as landmark objects in these systems. The aim of this study was to develop a method that automatically transforms segmented LiDAR (Light Detection And Ranging) point cloud to create such landmark building models. A detailed solution was developed for selected buildings that are solids of revolution. The algorithm relies on new methods for determining building axes and cross-sections. To handle the gaps in vertical cross-sections due to the absence of continuous measurement data, a new strategy for filling these gaps was proposed based on their automatic interpretation. In addition, potential points associated with building ornaments were used to improve the model. The results were presented in different stages of the modeling process in graphic models and in a matrix recording. Our work demonstrates that complicated buildings can be represented with a light and regular data structure. Further investigations are needed to estimate the constructed building model with vectorial models. [ABSTRACT FROM AUTHOR]

Monitoring vessel activity is an important part of managing marine protected areas (MPAs), but small-scale fishing and recreational vessels that do not participate in cooperative vessel traffic systems require additional monitoring strategies. Marine Monitor (M2) is a shore-based, multi-sensor platform that integrates commercially available hardware, primarily X-band marine radar and optical cameras, with custom software to autonomously track and report on vessel activity regardless of participation in other tracking systems. By utilizing established commercial hardware, the radar system is appropriate for supporting the management of coastal, small-scale MPAs. Data collected in the field are transferred to the cloud to provide a continuous record of activity and identify prohibited activities in real-time using behavior characteristics. To support the needs of MPA managers, both hardware and software improvements have been made over time, including ruggedizing equipment for the marine environment and powering systems in remote locations. Case studies are presented comparing data collection by both radar and the Automatic Identification System (AIS) in urban and remote locations. At the South La Jolla State Marine Reserve near San Diego, CA, USA, 93% of vessel activity (defined as the cumulative time vessels spent in the MPA) was identified exclusively by radar from November 2022 through January 2023. At the Caye Bokel Conservation Area, within the Turneffe Atoll Marine Reserve offshore of Belize, 98% was identified exclusively by radar from April through October 2022. Spatial and temporal patterns of radar-detected and AIS activity also differed at both sites. These case study site results together demonstrate the common and persistent presence of small-scale vessel activity near coastal MPAs that is not documented by cooperative systems. Therefore, an integrated radar system can be a useful tool for independent monitoring, supporting a comprehensive understanding of vessel activity in a variety of areas. [ABSTRACT FROM AUTHOR]

Although telehealth, and in particular RPM, have demonstrated to drive many benefits, such as reduction in cost and hospital-acquired infections, previous research has shown many usability challenges when patients operate a medical device without supervision of a medical professional. To combat this issue, the Pi-CON methodology is applied to develop a novel sensor with the objective to continuously acquire a patient's vital signs from a distance, without the need to attach any markers or sensors to the patient, and with limited user interaction required. Pi-CON stands for passive, continuous and non-contact, and describes a way to improve the user experience for patients or caregivers that have a need to perform a vital signs measurement themselves, without the presence of a medical professional. The developed sensor utilises radar and optical sensing technologies and transmits acquired data to a cloud-based service where it can be viewed in near real-time by the patient or family members from anywhere via an intuitive user interface. This user interface, as well as the sensor itself were designed based on design needs and requirements to adhere to the user-centered design process. The development of the sensor, including utilised technologies, components, and the user interface are presented, including inspirations for future work. [ABSTRACT FROM AUTHOR]

Due to the increasingly large amount of data acquired into point clouds, from LiDAR (Light Detection and Ranging) sensors and 2D/3D sensors, massive point clouds processing has become a topic with high interest for several fields. Current client-server applications usually use multiresolution out-of-core proposals; nevertheless, the construction of the data structures required is very time-consuming. Furthermore, these multiresolution approaches present problems regarding point density changes between different levels of detail and artifacts due to the rendering of elements entering and leaving the field of view. We present an autotuning multiresolution out-of-core strategy to avoid these problems. Other objectives are reducing loading times while maintaining low memory requirements, high visualization quality and achieving interactive visualization of massive point clouds. This strategy identifies certain parameters, called performance parameters, and defines a set of premises to obtain the goals mentioned above. The optimal parameter values depend on the number of points per cell in the multiresolution structure. We test our proposal in our web-based visualization software designed to work with the structures and storage format used and display massive point clouds achieving interactive visualization of point clouds with more than 27 billion points. [ABSTRACT FROM AUTHOR]

On June 17, 2020, Aniangzhai landslide, an ancient landslide located in Danba County, southwest China, was reactivated by Meilonggou debris flow. The front edge of the slope collapsed, mobilizing a soil mass of about 2.35 × 106 m3. Evaluating the stability of the whole slope is of great importance to avoid further landslides and mitigate the damage for Aniangzhai villagers living on this slope. This paper focuses on the inaccessible upper slope of Aniangzhai landslide (no attention paid before) that exhibits a relative elevation difference of more than 1000 m. Multi-source remote sensing, including unmanned aerial vehicle (UAV) photogrammetry, light detection and ranging (LiDAR), and satellite-based interferometric synthetic aperture radar (InSAR) techniques, was used in this research to identify and evaluate this high mountain upper slope potential hazard in Aniangzhai landslide. Considering the huge height difference and the steep slope of Aniangzhai landslide, an iterative route planning method was proposed and adopted to obtain a 3D model with 0.02 m resolution and a DEM with 0.25 m resolution by using UAV and LiDAR close-in flight method, respectively. Meter-level huge cracks were clearly identified by the high-resolution UAV 3D model and LiDAR data, which confirm that the location of these cracks is related to the morphological structure of this ancient landslide. Time series InSAR analysis reveals the activity of this high-altitude area, with a maximum LOS displacement rate of 15 cm/a. The combination of the above remote sensing technologies confirms and reveals the high potential risk and the reactivated condition of the upper slope of Aniangzhai landslide. Through this finding, we show that the evolution of Aniangzhai landslide happened through four stages with a cascading effect. This paper proves the usefulness of an integrated method to successfully identify and evaluate the high-altitude upper slope potential hazard and compares the technical features of them, providing a reference for future works that aimed at mitigating the potential damage of the upper slope. [ABSTRACT FROM AUTHOR]

Dense three-dimensional (3D) point clouds collected from rapidly evolving data acquisition techniques such as light detection and ranging (lidar) and structure from motion (SfM) multiview stereo (MVS) photogrammetry contain detailed geometric information of a scene suitable for a wide variety of applications. Among the many processes within a typical point cloud processing workflow, segmentation is often a crucial step to group points with similar attributes to support more advanced modeling and analysis. Segmenting large point cloud data sets (i.e., hundreds of millions to billions of points) can be extremely time consuming and tedious with current tools, which primarily rely on significant manual effort. While many automated methods have been proposed, the practicality, scalability, and versatility of these approaches remain a bottleneck stifling processing of large data sets. To overcome these challenges, this paper introduces a novel, generalized segmentation framework called Vo-Norvana, which incorporates a new voxelization technique, a normal variation analysis considering the positioning uncertainty of the point cloud, and a custom region growing process for clustering. The proposed framework was tested with several large-volume data sets collected in diverse scene types using several data acquisition platforms including terrestrial lidar, mobile lidar, airborne lidar, and drone-based SfM-MVS photogrammetry. In evaluating the accuracy of models generated from Vo-Norvana against manual segmentation, the average error of the position, orientation, and dimensions are 2.7 mm, 0.083°, and 0.9 mm, respectively. Over 0.2 million points per second and 36 thousand voxels per second can be achieved when segmenting an airborne lidar data set containing over 639 million points to about 1 million segments. [ABSTRACT FROM AUTHOR]

We assess the accuracy of Structure-from-Motion/Multiview stereo (SM) terrain models acquired ad hoc or without high-resolution ground control to analyze their usage as a base for inexpensive 3D bedrock geologic mapping. Our focus is on techniques that can be utilized in field projects without the use of heavy and/or expensive equipment or the placement of ground control in logistically challenging sites (e.g., steep cliff faces or remote settings). We use a Terrestrial Light Detection and Ranging (LiDAR) survey as a basis for the comparison of two types of SM models: (1) models developed from images acquired in a chartered airplane flight with ground control referenced by natural objects located on Google Earth scenes; and (2) drone flights with a georeference established solely from camera positions located by conventional, differentially corrected Global Navigation Satellite systems (GNSS). We find that all our SM models are indistinguishable in scale from the LiDAR reference model. The SM models do, however, show rigid body translations and rotations, with translations generally within the 1–5 m size of the natural objects used for ground control, the resolution of the GNSS receivers, or both. The rigid body rotations can be attributed to a poor imaging plan, which can be avoided with survey planning. Analyses of point densities in various models show a limitation of Terrestrial LiDAR point clouds as a mapping base due to the rapid falloff of resolution with distance. In contrast, SM models are characterized by relatively uniform point densities controlled by camera optics, the numbers of images, and the distance from the target. This uniform density is the product of the Multiview stereo step in SM processing that fills areas between key points and is important for bedrock geologic mapping because it affords direct interpretation on a point cloud at a relatively uniform scale throughout a model. Our results indicate that these simple methods allow SM model construction to be accurate to the range of conventional GNSS with resolutions to the submeter, even cm, scale depending on data acquisition parameters. Thus, SM models can, and should, serve as a base for high-resolution geologic mapping, particularly in a steep terrain where conventional techniques fail. Our SM models appear to provide accurate visualizations of geologic features over km scales that allow detailed geologic mapping in 3D with a relative accuracy to the decimeter or centimeter level and absolute positioning in the 2–5 m precision of GNSS; a geometric precision that will allow unprecedented new studies of any geologic system where geometry is the fundamental data. [ABSTRACT FROM AUTHOR]

Optical metasurfaces are becoming ubiquitous optical components to control light properties. However, most of these devices are passive and cannot be arbitrarily reconfigured according to the change in the surrounding environment. Here the authors propose an innovative design strategy relying on the position of topological singularities to address full phase modulation of light with almost unity efficiency. The active metasurface unit cells consist of asymmetric Gires–Tournois resonators filled with either silicon or hetero‐structured materials to leverage on the thermo‐optical or electro‐optical effects, respectively. In both cases, a full phase modulation associated with 100% reflection amplitude is observed even when dealing with extremely low refractive index change, on the order of 0.01. Improving the deflection efficiencies for each deflection angle is performed by optimizing the refractive index modulation profile in the extended unit cell using an advanced statistical learning optimization methodology. Consequently, active beam steering designs for active thermo‐optical effect with ultimate performance exceeding 90% have been optimized. Furthermore, active wavefront splitting using electro‐optics materials is optimized to reach ultimate modulation performances with nearly 92% efficiency. The realization of highly efficient active beam‐forming operating at high frequencies would open important applications in imaging microscopy, and 3D light detection and ranging (LiDAR). [ABSTRACT FROM AUTHOR]

Visibility (viewshed) plays a significant and diverse role in animals' behaviour and fitness. Understanding how visibility influences animal behaviour requires the measurement of habitat visibility at spatial scales commensurate to individual animal choices. However, measuring habitat visibility at a fine spatial scale over a landscape is a challenge, particularly in highly heterogeneous landscapes (e.g. forests). As a result, our ability to model the influence of fine‐scale visibility on animal behaviour has been impeded or limited.In this study, we demonstrate the application of the concept of three‐dimensional (3D) cumulative viewshed in the study of animal spatial behaviour at a landscape level. Specifically, we employed a newly described approach that combines terrestrial and airborne light detection and ranging (LiDAR) to measure fine‐scale habitat visibility (3D cumulative viewshed) on a continuous scale in forested landscapes. We applied the LiDAR‐derived visibility to investigate how visibility in forests affects the summer habitat selection and the movement of 20 GPS‐collared female red deer Cervus elaphus in a temperate forest in Germany. We used integrated step selection analysis to determine whether red deer show any preference for fine‐scale habitat visibility and whether visibility is related to the rate of movement of red deer.We found that red deer selected intermediate habitat visibility. Their preferred level of visibility during the day was substantially lower than that of night and twilight, whereas the preference was not significantly different between night and twilight. In addition, red deer moved faster in high‐visibility areas, possibly mainly to avoid predation and anthropogenic risk. Furthermore, red deer moved most rapidly between locations in the twilight.For the first time, the preference for intermediate habitat visibility and the adaption of movement rate to fine‐scale visibility by a forest‐dwelling ungulate species at a landscape scale was revealed. The LiDAR technique used in this study offers fine‐scale habitat visibility at the landscape level in forest ecosystems, which would be of broader interest in the fields of animal ecology and behaviour. [ABSTRACT FROM AUTHOR]

Early naturalists such as Humboldt observed that changes in topography and anthropogenic disturbances influenced vegetation structure and the composition of animal communities. This holistic view of community assembly continues to shape conservation and restoration strategies in an era of landscape degradation and biodiversity loss. Today, remote sensing affords ecologists the tools for obtaining rapid and precise measures of topography, disturbance history and vegetation structure. Nonetheless, the capacity of such measures to predict the structure of diverse and functionally important insect communities has not been fully explored.We sampled ground‐dwelling ant assemblages with pitfall traps along a successional gradient (15 grasslands, 21 shrublands and 44 forests) in subtropical Asia, and measured the taxonomic (TD) and functional diversity (FD). We used airborne Light Detection and Ranging (LiDAR) and aerial photography—to measure topography, anthropogenic‐fire history and vegetation structure at each site. Using structural equation models, we tested the hypothesis that vegetation structure mediated the effects of topography and anthropogenic‐fire history on ant assemblage TD and FD, with stronger effects on the latter.We found that low elevation and anthropogenic‐fire history promoted ant TD, and by mediating vegetation structure, these factors further controlled ant FD. Specifically, assemblages of ant species occupying more similar niches—as indicated by their lower FD—were found in secondary forests that had more structurally homogeneous vegetation. These sites also had low insolation and high water moisture content, and were not recently burned as revealed by LiDAR‐derived metrics and aerial images. Furthermore, remotely sensed vegetation structures were closely associated with individual ant traits, such as body size and eye length, which reflect species' preferences for habitat structure.Synthesis. Our study uncovers the interactive effects of topography, disturbance history and vegetation structure in determining the TD and FD of ant assemblages in subtropical landscapes. Moreover, it demonstrates that remote sensed data can be leveraged to efficiently elucidate the complex effects of environmental change and disturbances on vegetation structure and consequently insect biodiversity, representing ecological proxies to refine ground investigation plans and support appropriate conservation and restoration measures for degraded landscapes. [ABSTRACT FROM AUTHOR]

Vegetation structural complexity (VSC)—the three‐dimensional distribution of plants within an ecosystem—is an important ecological trait. To date, research has focused primarily on the effects of VSC on ecological patterns and processes, but comparatively little is known about what drives variation in VSC.Recent advances in active remote sensing technology, particularly light detection and ranging and radio detection and ranging, have allowed the measurement of VSC at unprecedented spatial scales and resolutions. Out of this and earlier work has emerged evidence that VSC is typically associated with greater ecosystem functioning (especially microclimate regulation, productivity, faunal diversity and habitat provisioning), making restoration of vegetation complexity a potentially powerful restoration tool.Recent studies of VSC across natural and experimental gradients of plant diversity have also revealed that more diverse plant communities tend to be more structurally complex. However, the shape and generality of this relationship—and the mechanism(s) by which phytodiversity might contribute to structural complexity—remain poorly understood.Here, we review how active remote sensing has facilitated recent VSC research and shaped our understanding of the relationship between vegetation complexity and ecosystem function. We then present a theoretical framework for the relationship between phytodiversity and VSC based on classic biodiversity‐ecosystem functioning principles. Finally, we evaluate the evidence for the notion that diverse plant assemblages tend to be more structurally complex and explore the shape of the relationship between phytodiversity and VSC using data from 13 recent remote sensing studies.Synthesis. The relationship between phytodiversity and VSC appears to be almost universally positive. Preliminary evidence further suggests that the most common relationships between phytodiversity and VSC are linear or saturating, indicating that the extent of functional redundancy between species varies across plant communities and ecosystems. In contrast, we find little evidence for exponential or negative relationships between plant diversity and VSC, suggesting that even modest increases in plant diversity could markedly increase structural complexity. Additional investigations of phytodiversity‐VSC relationships are necessary to establish whether the observed positive relationships are causal (and, if so, in which direction) and to clarify the potential impact of plant community restoration on structural complexity and broader ecosystem function. [ABSTRACT FROM AUTHOR]