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• 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]

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]

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]

TianQin is expected to deploy three spacecraft in Earth orbit at the altitude of about 1 × 108 m to form an equilateral triangular constellation with arm lengths of about 1.7 × 108 m. It is aimed at detecting gravitational waves in the frequency range from 0.1 mHz to 1 Hz by means of high sensitive laser ranging interferometry with pathlength measurement noise below 1 pm/Hz1/2. The long-range propagation of beam in the intersatellite laser ranging interferometry between the three spacecraft results in a non-negligible time delay of 0.58 s. Therefore, there is an angular difference between the optimal outgoing and incoming laser beam of laser ranging interferometry on each spacecraft, which is denoted as the point-ahead angle. Due to the relative motion between the three spacecraft, the point-ahead angle of TianQin constellation is approximately 22.96 μrad with a dynamic variation range of ±24.71 nrad. Point-ahead-angle mechanism is a fine steering mirror used for point-ahead angle compensation to diminish the beam pointing error, thus to maintain the intersatellite laser link as well as to diminish the tilt-to-length coupling noise, which are vital to the gravitational wave detection. Both the static and dynamic point-ahead angle compensation strategies can be used in TianQin and the selection of the two is based on minimizing the total point-ahead-angle-related tilt-to-length coupling noise. In this paper, a criteria for selection of the two strategies are proposed by establishing a model of point-ahead-angle-related tilt-to-length coupling noise as well as a corresponding evaluation function for calculation and comparison. The results indicate that the dynamic compensation strategy is the optimal choice only when the first-order tilt-to-length coupling coefficient of point-ahead-angle mechanism is less than 6.4 × 10−9 m/rad, otherwise the static compensation strategy should be used. The proposed criteria, as well as the model and the requirement for point-ahead-angle mechanism, can serve as a common theoretical basis in laser ranging interferometry applications. • Criteria for selection of point-ahead angle compensation strategies. • Minimize tilt-to-length coupling noise in intersatellite laser interferometry. • Quantitative comparison of tilt-to-length noise in different compensation strategies. • Static compensation strategy is more appropriate in TianQin according to the criteria. [ABSTRACT FROM AUTHOR]

Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based adaptive weighted fusion SLAM (AWF-SLAM) for autonomous robots to achieve accurate and robust SLAM in complex underground spaces. First, a contrast limited adaptive histogram equalization (CLAHE) that combined adaptive gamma correction with weighting distribution (AGCWD) in hue, saturation, and value (HSV) space is proposed to enhance the brightness and contrast of visual images in underground spaces. Then, the performance of each sensor is evaluated using a consistency check based on the Mahalanobis distance to select the optimal configuration for specific conditions. Subsequently, we elaborate an adaptive weighting function model, which leverages the residuals from point cloud matching and the inner point rate of image matching. This model fuses data from light detection and ranging (LiDAR), inertial measurement unit (IMU), and cameras dynamically, enhancing the flexibility of the fusion process. Finally, multiple primitive features are adaptively fused within the factor graph optimization, utilizing a sliding window approach. Extensive experiments were conducted to check the performance of AWF-SLAM using a self-designed mobile robot in underground parking lots, excavated subway tunnels, and complex underground coal mine spaces based on reference trajectories and reconstructions provided by state-of-the-art methods. Satisfactorily, the root mean square error (RMSE) of trajectory translation is only 0.17 m, and the mean relative robustness distance between the point cloud maps reconstructed by AWF-SLAM and the reference point cloud map is lower than 0.09 m. These results indicate a substantial improvement in the accuracy and robustness of SLAM in complex underground spaces. [ABSTRACT FROM AUTHOR]

A technique for estimating the velocity of radio meteors is proposed. The presented method is based on the analysis of the phase of the Fourier spectra of complex amplitudes of signals reflected from meteor trails. The method is very fast compared to the Fresnel transform method, works at low signal-to-noise ratios and is automatic. Approaches for increasing the signal-to-noise ratio and criteria for rejecting the obtained estimates are proposed. An error of 1–10% was achieved, depending on the signal-to-noise ratio (from −5 dB) and the velocity of the meteor. Comparison of meteoroid velocities from Kazan Federal University meteor radar measurements with optical observations showed good agreement of estimates with a slight underestimation of 2–8%, presumably due to the neglect of deceleration. [ABSTRACT FROM AUTHOR]

[Display omitted] Forest disturbances such as wildfires can dramatically alter forest structure and composition, increasing the likelihood of ecosystem changes. Up-to-date and accurate measures of post-disturbance forest recovery in managed forests are critical, particularly for silvicultural planning. Measuring the live and dead vegetation post-fire is challenging because areas impacted by wildfire may be remote, difficult to access, and/or dangerous to survey. The difficulties of post-fire monitoring are compounded by the global increase in the frequency and severity of disturbances, as expansion of disturbed areas also increases the number and size of areas requiring post-disturbance monitoring. Methods that safely, efficiently, and extensively differentiate silviculturally beneficial coniferous growth from barren ground or deciduous shrubs are necessary to inform post-fire forest management. Satellite imagery can detect burn patterns, but monitoring changes in forest structure post fire is challenging due to complex vegetation responses. To overcome this challenge, this study combines post-disturbance spectral trajectory measures from a time series of historical Landsat imagery with field and remotely piloted aircraft (RPA) lidar (light detection and ranging) data to examine vegetation recovery of lodgepole pine (Pinus contorta) dominated sub-boreal forests after high-severity fires in 2006 in central British Columbia, Canada. Distinct spectral recovery trajectories were identified using data-clustering from a combination of seven Landsat spectral indices, with trajectories varying by recovery magnitude and rate. The forest structure associated with each distinct trajectory of spectral recovery was analyzed using 430 ha of spatially explicit forest structure measures (e.g., basal area, stem counts) and composition (e.g., percent coniferous) derived from 26 coincident field plots and high density RPA lidar (>200 points/m2) data. By comparing spectral trajectories to forest structure measures, we found the most spatially abundant cluster of spectral recovery coincided with a basal area of 0.62 m2/ha, high stem densities (>5000 stems/ha) and a high abundance of coniferous trees (>95 % coniferous). Around 10 % of the landscape was associated with relatively high abundance of deciduous vegetation (>20 %) in addition to very high conifer stem densities (>8000 stems/ha). By identifying the structural characteristics associated with unique Landsat spectral trajectories, we highlight the combined value of RPA lidar data and satellite image time series in providing a detailed and spatially explicit characterization of post-fire recovery relevant to managed forests. [ABSTRACT FROM AUTHOR]

The regular acquisition and delivery of high-resolution, accurate elevation data has historically been provided by airborne lidar (light detection and ranging) solutions, which are costly and highly localised. Providing similar data sets globally has notable scientific and commercial applications, but comes with challenges around scale. In this work, an investigation into such a service, from low Earth orbit satellites, is presented. The suitability of different space mission architectures is analysed based on platform size and optics mirror design, with the aim of providing true global, high-resolution (5-30 m sample resolution) lidar data, annually. The technical challenges, cost implications and feasible solution sets are presented, suggesting that a small number of large platforms offers a cost-effective solution, with the optimal design (of those evaluated) being that of a micro-satellite (∼ 150 kg class) constellation with deployed optics capability. Solutions offering relatively low spatial resolution (30 m) are lower cost, with the cost rising as a square law with increasing resolution. As platform size continues to decrease, the number of satellites required to maintain global coverage scales exponentially, demanding prohibitively large constellations to ensure global coverage with smaller satellites. [Display omitted] • Global annual lidar coverage from space is technologically and financially feasible. • Deployable optics significantly increase the value potential of a space lidar mission. • Semiconductor (Diode) lasers are more promising than traditional solid-state lasers. • Fewer, larger platforms generally perform better than many, smaller platforms. • Nano-satellite platforms offer limited value due to their constrained aperture area. [ABSTRACT FROM AUTHOR]

Numerous satellites with electric propulsion perform long duration maneuvers during their orbit acquisition phase. This poses a challenge to space object cataloging activities if no information regarding the maneuver plan is known a priori. Various works have been devoted to maneuver detection and tracking of space objects using radar and optical surveillance data, but the special case of unknown dynamics, analogous to a maneuver spanning several days or months, has received less attention. Herein, we propose a methodology to maintain custody of uncooperative low-thrust spacecraft under the special characteristics of surveillance radar observations. Based on the stochastic hybrid systems framework, a multi-hypothesis algorithm leverages information derived from measurements and the dynamical characteristics of the object. The latter is accomplished in an efficient manner by means of a low-thrust control metric that relies on the Thrust Fourier Coefficients approximation. To improve filter robustness, the maneuver transition density is assumed to be uniform within the set of reachable states, whose outer bound is approximated via the proposed control measure. The algorithm is applied to the orbit acquisition phase of a LEO satellite using synthetic data from a simulated surveillance radar network, and its shown to be capable of maintaining custody throughout the entire three-month transfer. • Custody maintenance of uncooperative low-thrust spacecraft using radar surveillance measurements. • Definition of an efficient control metric tailored to thrust-limited transfers. • Uninformative maneuver transition densities seem to reduce biasedness and improve filter consistency. [ABSTRACT FROM AUTHOR]

One of the most common reasons for poor patient outcomes and revision surgery in spinal fusion is hardware failure. Screw loosening or pullout occurs in up to one-quarter of all cases. It is known that even small screw–rod misalignments can cause significant mechanical overloads during rod fixation, which can result in hardware failure. To address this crucial surgical step, a novel augmented reality-assisted software was developed to generate custom rod templates that are precisely adapted to the individual patient. The novel software, which runs on a tablet, is used in spinal fusion surgery and is based on the use of a specific pedicle screw system, in which the polyaxial screw heads are connected to detachable guides. These guides can be recognized by the tablet camera and a light detection and ranging scanner. This image information is processed to determine the spatial positions of the screw heads and to calculate an ideally fitting rod template. The calculated rod template is displayed in a 1-to-1 scale on the tablet screen. This template is used to cut and bend the rods of the pedicle screw system. Finally, the custom bent rod can be inserted into the screw heads without tension. The augmented reality-assisted software is intended to give surgeons access to patient-specific intraoperative real-time data, helping them in bending rods that are more precisely adapted to the individual patient compared with the freehand technique. [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]

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]

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]

Understanding the main drivers of biodiversity loss in Europe's agricultural landscapes has been a research priority in the last decades. One of the most important factors promoting biodiversity in farmed landscapes is habitat heterogeneity, which has often proved crucial for avian species and communities. Birds are highly sensitive to environmental changes and make use of a broad range of ecological niches, thus being exceptionally sensitive to the loss of habitat heterogeneity. Remote sensing data are particularly suited to quantify habitat heterogeneity at fine scales over relatively large extents, allowing to consider how different measures of heterogeneity may affect biotic communities at a regional scale. Here, we used airborne LiDAR (Light Detection And Ranging) and satellite multispectral data to derive vegetation canopy height and primary productivity for 118 sites in complex agricultural landscapes in a region in the Central Alps. We computed different bird diversity indices and classified bird species into guilds according to specific traits to analyse the relationship between avian communities and different facets of habitat heterogeneity. Results confirmed that habitat heterogeneity is essential in shaping rich and diverse bird communities, and it is particularly important for several farmland birds. By comparing the effects of canopy height, primary productivity, and specific vegetation features (e.g., cover of grassland, shrub, and tree layers), we showed how different habitat characteristics as well as landscape heterogeneity affected bird richness, diversity, functional entropy, and trait patterns. Landscape and height heterogeneity, estimated by NDVI and LiDAR Rao's Q indices, strongly influenced all response variables, for example, high NDVI values promoted species diversity and ground-understory nesters, and shrub layer was important for ground-understory nesters and forest specialists. Finally, we provide recommendations for conservation practices and mitigation measures to improve bird diversity in agricultural landscapes. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR)-derived point cloud has become the standard spatial data for digital terrain model (DTM) construction; however, it suffers from huge data with much redundant information due to the oversampling. This often causes considerable inconvenience in the downstream data processing. To this end, an adaptive coarse-to-fine clustering and terrain feature-aware-based method is proposed to reduce data points in the context of terrain modeling in this paper. Firstly, a coarse-to-fine clustering method with the consideration of terrain complexity is developed to adaptively cluster LiDAR terrain points. Then, according to the geometric properties of terrain breaklines, a terrain feature-aware multi-strategy method is presented to pick representative points in the clusters. Finally, important boundary points including inflection point on the boundary curve and critical point on terrain features are further selected. The proposed method is compared with seven state-of-the-art point cloud simplification methods under six data reduction ratios on six plots with different terrain characteristics. Results indicate that the proposed method obtains a good balance between terrain-feature preservation and uniform distribution of data points. Compared to the state-of-the-art methods, the proposed method reduces the average root mean square errors (absolute errors) of the DTMs by 12.2%-51.7% (7.69%-83.8%) on the six plots. Moreover, the proposed method obtains the mean terrain slope and terrain roughness more reasonably approximate to the references. In short, the newly developed method can be considered as an alternative tool to select representative points from the huge remote-sensing-derived point cloud in the context of DTM production. [ABSTRACT FROM AUTHOR]

This paper presents the analysis and comparative evaluation of three background-rejection techniques implemented in CMOS processes for Single Photon Avalanche Diode (SPAD)-based Light Detection and Ranging (LiDAR) systems, using both Monte Carlo simulations and laboratory measurements. The first technique, known as photon coincidence technique , uses the temporal proximity of multiple detections to reject background light and maximize the detection of photons belonging to the reflected laser pulse. With the second technique, named Auto-Sensitivity (AS) technique , background light is rejected by automatically reducing the SPAD photon-detection probability (PDP), in order to avoid sensor saturation due to the intense environment illumination. The third technique we consider is the last-hit detection, which is able to detect and timestamp the last event impinging on the sensor over the acquisition window rather than the first, maximizing the system performance for long distance targets. The photon coincidence technique and AS technique are first compared to each other, showing the photon coincidence technique to outperform the AS technique. Then, the two techniques are applied together, resulting in an increase of the measurement range. Furthermore, a detailed analysis considering three different implementations of the photon coincidence technique is presented, showing pros and cons of each implementation and how the performance is affected. The last set of results focuses on the last-hit detection, which is compared against the standard detection paradigm (first-hit), showing not only an improvement for long distance targets (as expected), but an overall increase in system performance in terms of both success rate and SNR. • Last-hit detection strategy outperform first-hit over the whole range of distances. • Automatic sensitivity with coincidence detection increases the measurement range. • In-depth analysis of three circuital implementations of photon coincidence detection. [ABSTRACT FROM AUTHOR]

As an effective complement to common laser scanning systems, the portable laser scanning system can acquire point clouds flexibly and quickly. Calibration between Light detection and ranging (LiDAR) sensors and inertial measurement units (IMU) is the prerequisite for laser scanning systems to obtain high-quality point clouds. Related methods have been proposed in the last two decades, where the global navigation satellite system (GNSS) or high-precision calibration fields are commonly used. However, the extrinsic self-calibration of LiDAR-IMU is challenging, due to the large distortion in single-frame point cloud caused by rapid motion and the position errors of IMU integration which drift quickly. At the same time, the highly dynamic motion patterns of portable devices and the changes in the scanned scene structure are not well considered in existing LiDAR odometry methods. To take better advantage of the characteristics of non-repetitive scanning LiDAR sensor, this paper proposes AFLI-Calib, which utilizes adaptive frame length LiDAR odometry to perform the extrinsic self-calibration of LiDAR-IMU. Unlike LiDAR odometry methods with a fixed frame length, the LiDAR frame length is dynamically adjusted according to the motion state of sensors and the matching stability of scenes. The single-frame point cloud is registered to the map through a linear-based continuous-time model, eliminating the motion distortion correction in advance. For further optimization of trajectory and extrinsic parameters, IMU raw measurements and LiDAR observations are involved in the multi-constraint optimization, through tightly-coupled IMU pre-integration constraints, LiDAR point-to-plane constraints, and prior constraints. The method is fully validated using self-collected calibration data of indoor and outdoor scenes and different motion modes. Experiments show that on the test data, the translation parameter accuracy of the method is 0.041 m, which is 56.3% higher than the state-of-the-art method. The standard deviation is significantly reduced, with translation deviation (0.017 m, 0.024 m, 0.022 m) and rotation deviation (0.17°, 0.25°, 0.15°), which verifies the robustness of our method. The average RMSE of distances to the reference point cloud acquired by the terrestrial laser scanning system (TLS) is 0.042 m, showing a high accuracy calibration result. Comparative experiments with the fixed frame length LiDAR odometry method and classical "correction-then-registration" motion distortion model further verify the superiority and effectiveness of the proposed adaptive frame length LiDAR odometry. [ABSTRACT FROM AUTHOR]

Vegetation cover fraction (fCover) and related quantities are basic yet critical vegetation structure variables in various disciplines and applications. Ground- and aerial-based proximal and remote sensing techniques have been widely adapted across multiple spatial extents. However, the definitions of fCover-related nomenclatures have not yet been fully standardized, leading to confusing terms and making comparing historic measures difficult. With the issues potentially arising from an increasing diversity of fCover and related quantities estimation methods and corresponding uncertainties, there is also a growing need to spread knowledge on the current advances, challenges, and perspectives, especially in the context of no such existing review for ground- and aerial- based estimation. This paper provides the current knowledge mainly concerning passive image-based methods and active light detection and ranging (LiDAR) -based methods. We first harmonized the definitions of fCover and its related quantities (e.g., effective canopy cover, crown cover, stratified vegetation cover, and canopy fraction). Secondly, the typical applications of fCover and related quantities over a range of scales, fields, and ecosystems were summarized. Thirdly yet importantly, we offered a comprehensive review of traditional non-imaging methods, image-based methods (e.g., segmentation, unmixing, and spectral retrieval), point cloud-based methods (e.g., rasterization), and LiDAR return-based methods (e.g., return number index and return intensity retrieval) across different platforms (i.e., ground, unmanned aerial vehicle (UAV) and airplane). Our investigation of fCover and related quantities estimation touches upon various vegetation ecosystems, including agriculture cropland, grassland, wetland, and forest. Finally, the current challenges and future directions were discussed, such as image signal processing under complex heterogeneous surfaces and stratified cover and non-photosynthesis cover retrieval. We, therefore, expect that this review may offer an insight into fCover and related quantities estimation and serve as a reference for remote sensing scientists, agronomists, silviculturists, and ecologists. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) provides rich spectral–spatial information and the light detection and ranging (LiDAR) data reflect the elevation information, which can be jointly exploited for better land-cover classification. However, due to different imaging mechanisms, HSI and LiDAR data always present significant image difference, current pixel-wise feature fusion classification methods relying on concatenation or weighted fusion are not effective. To achieve accurate classification result, it is important to extract and fuse similar high-order semantic information and complementary discriminative information contained in multimodal data. In this paper, we propose a novel coupled adversarial learning based classification (CALC) method for fusion classification of HSI and LiDAR data. In specific, a coupled adversarial feature learning (CAFL) sub-network is first trained, to effectively learn the high-order semantic features from HSI and LiDAR data in an unsupervised manner. On one hand, the proposed CAFL sub-network establishes an adversarial game between dual generators and discriminators, so that the learnt features can preserve detail information in HSI and LiDAR data, respectively. On the other hand, by designing weight-sharing and linear fusion structure in the dual generators, we can simultaneously extract similar high-order semantic information and modal-specific complementary information. Meanwhile, a supervised multi-level feature fusion classification (MFFC) sub-network is trained, to further improve the classification performance via adaptive probability fusion strategy. In brief, the low-level, mid-level and high-level features learnt by the CAFL sub-network lead to multiple class estimation probabilities, which are then adaptively combined to generate a final accurate classification result. Both the CAFL and MFFC sub-networks are collaboratively trained by optimizing a designed joint loss function, which consists of unsupervised adversarial loss and supervised classification loss. Overall, by optimizing the joint loss function, the proposed CALC network is pushed to learn highly discriminative fusion features from multimodal data, leading to higher classification accuracies. Extensive experiments on three well-known HSI and LiDAR data sets demonstrate the superior classification performance by the proposed CALC method than several state-of-the-art methods. The source code of the proposed method will be made publicly available at https://github.com/Ding-Kexin/CALC. • Propose a novel coupled adversarial learning method for multi-modal classification. • Fuse high-order semantic information and multi-modal complementary information. • Explore effective multi-level feature fusion strategy to improve discrimination. • Design an end-to-end deep learning framework for two sub-network joint training. [ABSTRACT FROM AUTHOR]

We studied a newly identified, multiple-kilometer-long rock slope failure in the Aiguilles Rouges massif (Chamonix valley, France). Owing to a high-resolution light detection and ranging (LiDAR) digital elevation model (DEM) and field work, we mapped morphostructures, including scarps, open fractures, and counterscarps. In some places, vertical offsets can reach tens of meters and crevasses can be meters wide. The evidence of gravitational activity (boulder displacements from analyses of archival satellite images) and the sharpness of the scarp outcrops together suggest very recent movements. These observations agree with ground displacement rates of a few millimeters per year estimated by interferometric synthetic aperture radar (InSAR) time series between 2014 and 2018. We sampled two vertical profiles along the top scarps to define the chronology of the slope failure using beryllium-10 (10Be) surface exposure dating. Glacially polished surfaces cut by these gravitational scarps were also sampled to determine glacial retreat timing as well as to constrain the pre-exposure 10Be inheritance. In total, 11 samples were studied. Our results highlight a significant time lag (approximately 15 ka) between the first evidence of nonglacial activity and the initiation of the slope failure that happened 1.3–2.5 ka ago, depending on the inheritance schemes. This suggests that the delayed opening of the crevasse is only one stage of a process that began when the valley was deepened and the glacial debuttressing is not the unique driving factor. This process of progressive failure of an excessively steep slope may continue, and the evolution of this slope failure may constitute a hazard for the upper part of the Chamonix valley. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LIDAR) sensors measure the free wind ahead of the rotor, enabling the use of new feedforward control strategies. However, there exist some sources of error inherent to the measuring process that should be considered during the design of LIDAR-based controllers. Typically, the coherence function is used for that purpose, but it is not compatible with some robust design methodologies. This paper presents an analytic relation between the coherence function and a non-parametric uncertainty model of LIDAR sensors, suitable for the design of controllers via μ -synthesis or Quantitative Feedback Theory. Such a relation is applied to a realistic LIDAR simulator. First, the linear non-parametric uncertainty model is identified using simulation data obtained from the well-known NREL 5 MW wind turbine. Then, it is validated against the coherence model by comparing linear predictions of the simulation outputs. • The squared coherence function is the standard measurement of LIDAR reliability. • Squared coherence is inversely proportional to a nonparametric uncertainty model. • The uncertain model is useful for analysis and robust controller design purposes. • The new model is compatible with design tools such as QFT and H ∞ control toolbox. • The new model has been validated against the FAST nonlinear simulator. [ABSTRACT FROM AUTHOR]

Terrestrial hyperspectral light detection and ranging (LiDAR) (HSL), as one novel LiDAR system integrated with more wavelengths, has supplied enhanced possibilities in many fields by delivering the geographic coordinate and the hyperspectral backscattered intensity simultaneously. However, like the traditional single-wavelength LiDAR, it is faced with the distance effect, which restricts the accurate acquisition of the hyperspectral intensities and the quantitative retrieval of characteristics of the targets. The previously reported distance effect correction models mainly concentrated on the distance effect correction for the single-wavelength LiDAR system, and some normalization methods with the standard reflectance panel were also provided to calibrate the data measured by multispectral LiDAR and HSL, producing the reflectance-related value. Nevertheless, it is valuable to systematically explore the distance effect of HSL over long distances and search for the corresponding correction methods. Along these lines, in this study, the homogeneous targets and standard reflectance panels with different reflectance values were employed to participate in the distance experiment to systematically explore and analyze the distance effect phenomenon on terrestrial HSL data. A novel practical piecewise fitting model was proposed to output realistic correction results and it was also compared with the classic inverse distance square law and polynomial fitting model. To further demonstrate the feasibility and good capability, the models were employed in the intensity correction for the various vegetation leaf species. A simple 3-D scene experiment was then carried out to demonstrate the intensity variation before and after the implementation of the distance effect correction. From the acquired result, it suggests that (1) the distance effect on terrestrial HSL data is not related to the wavelength and the scanned target. All the wavelengths share a common distance effect; (2) the concept of the 'distance effect function' could represent well the distance effect for the terrestrial HSL; (3) as far as the correction ability is concerned, the proposed piecewise fitting model achieved better performance than the others because it exhibited the smallest standard deviation (σ), coefficient of variation (CV), and the ratio of the coefficient of variation (ε) for the correction results of the homogeneous targets, and the leaf experiments confirmed it again; and (4) the further 3-D experiment result exhibited the intensity variation. It proves that the distance effect does exist in the 3-D scanning results and the correction is necessary. Besides, for small-scanned scenes, the incidence angle effect dominates and largely affects the intensity data instead of the distance effect. This study enriches the study of the distance effect mechanism on the terrestrial HSL data, which facilitates the development of research on the radiative effect correction of the HSL. At the same time, the proposed model can be adopted for the analysis and correction of the distance effect on terrestrial HSL data in actual scanning tasks. [ABSTRACT FROM AUTHOR]

The booming development of remote sensing images in many visual tasks has led to an increasing demand for obtaining images with more precise details. However, it is impractical to directly supply images that are simultaneously rich in spatial, spectral, and temporal information. One feasible solution is to fuse the information from multiple images. Since deep learning has achieved impressive achievements in image processing recently, this paper aims to provide a comprehensive review of deep learning-based methods for fusing remote sensing images at pixel-level. Specifically, we first introduce some traditional methods with their main limitations. Meanwhile, a brief presentation is made on four basic deep learning models commonly used in the field. On this basis, the research progress of these models in spatial information fusion and spatio-temporal fusion are reviewed. The current status on these models is further discussed with some coarse quantitative comparisons using several image quality metrics. After that, we find that deep learning models have not achieved overwhelming superiority over traditional methods but show great potential, especially the generative adversarial networks with its great capabilities in image generation and unsupervised learning should become a hot topic for future research. The joint use of different models should also be considered to fully extract multi-modal information. In addition, there is a lack of valuable research on pixel-level fusion of radar and optical images, requiring more attention in future work. • Reviewing the deep learning models applied in this area with representative work. • Deep learning does not gain an overwhelming superiority over traditional methods. • GAN shows better results among deep learning models, but still needs further study. • Fusion of optical and radar images have the potential to solve intractable problems. [ABSTRACT FROM AUTHOR]

Optimized PTP (Point to Point) Navigation by customized mobile agent constitutes extensive research domain in Machine Intelligence. Distinct Literature survey confirms the proposal of SLAM (Simultaneous Localization And Mapping) mediated sensor usage for visual data collection in a particular GPS (Global Positioning System) - denied indoor environment. This has been further followed by fused data collection using multi sensor for obtaining long time and high precision localization and obstacle detection in MRN (Mobile Robot Navigation). This paper proposes an idea to incorporate machine learning module for data modeling so that the obstacle detection and identification results in relatively more precise, accurate and efficient. The comparative numerical analysis has been presented to establish the proposed methodology. Along with this, Fusion SLAM algorithm has been created and proposed which combines features of both 2D LiDAR (Light Detection And Ranging) and RGB-D (Red, Green, Blue Depth) SLAM, resulting in visual reference of combined detected periphery and reconstructed 3D depth of on-path obstacles. The aforesaid procedure is further extended with integration of Bio Intelligent path planning approach. Experimental analysis carried out with sample candidate functions with respect to concerned mobile robot and considered indoor environment has been presented which would prove to be important citation for further research in the domain of collision-free optimum MRN. [ABSTRACT FROM AUTHOR]

Accurate and comprehensive knowledge of the atmospheric environment and its evolution within the coastal ocean boundary layer are necessary for understanding the sources, chemical mechanisms, and transport processes of air pollution in land, sea, and atmosphere. We present an overview of coastal ocean boundary layer detection technology and equipment in China and summarize the progress and main achievements in recent years. China has developed a series of coastal ocean boundary layer detection technologies, including Light Detection and Ranging (LIDAR), turbulent exchange analyzer, air-sea flux analyzer, stereoscopic remote sensing of air pollutants, and oceanic aerosol detection equipment to address the technical bottleneck caused by harsh environmental conditions in coastal ocean regions. Advances in these technologies and equipment have provided scientific assistance for addressing air pollution issues and understanding land-sea-atmosphere interactions over coastal ocean regions in China. In the future, routine atmospheric observations should cover the coastal ocean boundary layer of China. [Display omitted] [ABSTRACT FROM AUTHOR]

With the increase in the number of objects orbiting Earth, Space Situational Awareness (SSA) has becoming an important area of research in the space sector. Currently most sensors that contribute to SSA are large dedicated optical or radar stations, such as space fence (Pechkis, et al., 2015). With the increase in low resolution sensors in LEO there is a growing potential to utilize these to augment current SSA efforts. Star trackers are readily available and used in space for attitude determination, with recent work performed to demonstrate the benefit of using spaced-based optical measurements for Resident Space Object (RSO) detection. In this paper, we describe the interpretation of space-based measurement for light curve of an RSOs to estimate the RSOs shape, attitude and optical properties. In this model, two Bidirectional Reflectance Distribution Functions (BDRF's) are compared, namely a defined facet model and an anthropic Phong model. From the initial results an RSOs shape, attitude, optical properties can be estimated with basic a-priory information on the shape of the RSO with both models. [ABSTRACT FROM AUTHOR]

• Computer vision-based displacement measurement system is prone to errors induced by the camera ego-motion. • This study proposes a novel method for measuring structural displacement in full-scale civil structures, allowing for the repositioning of the camera using RGB and LiDAR cameras. • Displacement is measured with an RGB camera and compensated for errors induced by camera motion using a LiDAR camera. • The proposed method is validated in semi-outdoor tests by comparing it with the conventional computer vision-based method. Displacement measurements based on computer vision techniques have shown significant potential in structural health monitoring. However, as the inevitable camera ego-motion results in a drift in the measured displacement, compensation for such errors must be performed carefully. Although an additional RGB camera is considered in conventional methods for error compensation, the need for the sub-target limits its application. This study presents a dual-camera system, consisting of RGB and light detection and ranging (LiDAR) cameras, that enables accurate camera ego-motion compensation for long-term structural displacement measurements. In particular, the LiDAR camera of the proposed system can measure the six-degrees-of-freedom (6-DOF) ego-motion of the camera body regardless of the sub-target and its elaborate design. The accuracy of the proposed method is evaluated by comparing it with that of a conventional RGB camera. The resulting displacement can be employed to monitor the long-term behavior of full-scale bridges. [ABSTRACT FROM AUTHOR]

The joint classification of hyperspectral image (HSI) and Light Detection and Ranging (LiDAR) data can provide complementary information for each other, which has become a prominent topic in the field of remote sensing. Nevertheless, the common CNN-based fusion techniques still suffer from the following drawbacks. (1) Most of these models omit the correlation and complementarity between different data sources and always fail to model the long-distance dependencies of spectral information well. (2) Simply splicing the multi-source feature embeddings overlooks the deep semantic relationships among them. To tackle these issues, we propose a novel graph-attention based multimodal fusion network (GAMF). Specifically, it employs three major components, including an HSI–LiDAR feature extractor, a graph-attention based fusion module and a classification module. In the feature extraction module, we consider the correlation and complementarity between multi-sensor data by parameter sharing and employ Gaussian tokenization for feature transformation additionally. To address the problem of long-distance dependencies, the deep fusion module utilizes modality-specific tokens to construct an undirected weighted graph, which is essentially a heterogeneous graph. And the deep semantic relationships between them are exploited utilizing a graph-attention based fusion framework. At the end, two fully connected layers classify the fused embeddings. Experiment evaluations on several benchmark HSI–LiDAR datasets (Trento, University of Houston 2013 and MUUFL) show that GAMF achieves more accurate prediction results than some state-of-the-art baselines. The code is available at https://github.com/tyust-dayu/GAMF. • A novel graph-attention based multimodal fusion network is designed. • A triple feature extraction backbone is designed for the joint HSI–LiDAR dataset. • An undirected weighted graph is constructed, then fused by attention strategy. [ABSTRACT FROM AUTHOR]

As an emerging subject of the implication on revolutionizing many fields from botany to life science, plant intelligence (PI) has been actively studied but also trapped in debate. Inspired by those earlier botanists such as Darwin conceiving this concept when observing plants outdoors, we propose to track Darwin's footprints – go again to the wild where plants show higher-fold adapting performance than in labs for arousing a re-cognition of PI. However, this plan must face a basic challenge on in-situ plant phenotyping, especially in structure, which serves as the three-dimensional (3D) phenomenological display of varying PI behaviors. Aiming at this core bottleneck, we suggest to go but with 3D remote/proximal sensing (R/PS) devices such as Light Detection and Ranging (LiDAR) – a state-of-the-art technology of fully but fine mapping plants, for starting a 3D cognition of PI. Further, to decode the mechanism of PI occurring, we preview the next-generation (e.g., hyperspectral, fluorescence, and polarization) LiDAR with the latent capacity on all-round phenotyping of plants. Their derived 3D biochemical, physiological, and biophysical functional traits can arouse a beyond-3D cognition of PI. Overall, this theoretical prospect, with the available R/PS technology traced for upgrading PI from conceptual debating to mechanistic understanding, can advance the PI field into its 3D and even beyond-3D times and bring the PI and PI-relevant sciences such as sustainability cognition to breathe new life. • The emerging but controversial field of plant intelligence (PI) now needs innovation. • Tracking Darwin's footprints in the wild is proposed to boot up a re-cognition of PI. • Going to the wild again but with LiDAR is suggested to promote a 3D cognition of PI. • Next-generation LiDAR is previewed to further initiate a beyond-3D cognition of PI. • These discipline-level perspectives can drive PI and its sciences to breath new life. [ABSTRACT FROM AUTHOR]

Photosynthesis is the material basis for crop growth and yield formation. Rapid and real-time monitoring of photosynthetic parameters is essential for crop stress monitoring, light use efficiency assessment, and yield prediction. Methods based on passive optical remote sensing can accurately monitor the photosynthetic traits of crops. However, they are limited in capturing one- or two-dimensional information, and cannot obtain three-dimensional (3D) photosynthetic traits in crops. To address this issue, this study quantifies the 3D photosynthetic trait of wheat based on a digital surface model which fused a light detection and ranging (LiDAR) point cloud with multispectral imagery, both of which were acquired using an unmanned aerial vehicle (UAV). First, we constructed a model to estimate the key photosynthetic parameters (canopy chlorophyll content, fraction of absorbed photosynthetically active radiation, and canopy net photosynthetic rate) based on selected multi-spectral indices in wheat. Second, we applied the model to a point cloud that had been fused with spectral reflectance imagery to generate the 3D spatial and vertical distributions of the above three photosynthetic parameters, and then estimated the 3D photosynthetic traits. Third, we analyzed the 3D photosynthetic traits of wheat in terms of cultivars, growth stages, and cultivation management, and assessed the ability of the derived metrics based on the 3D photosynthetic traits to predict yield. The results showed that the LiDAR point cloud fused with multispectral images could accurately characterize the 3D spatial distribution of the photosynthetic parameters of wheat under different conditions. The photosynthetic traits of the different horizontal layers (upper, middle, and lower) of the canopy were statistically different. In addition, two new developed 3D metrics (CPn_ P 75th and CCC_ P 75th), which were derived from the 3D photosynthetic parameters based on the fusion of point cloud and multispectral data, could predict yield with a higher accuracy and greater robustness than traditional methodology of agronomic traits or remote sensing. This study demonstrate that the fusion of LiDAR point cloud and multispectral imagery is feasible for the accurate quantification of 3D photosynthetic traits and the screening of high-yielding genetic cultivars in crops. In addition, we found that multispectral point clouds generated from UAV multispectral imagery using structure from motion (SfM) combined with multi-view stereo (MVS) algorithms (SfM-MVS) can also be used to estimate the 3D spatial distributions of photosynthetic parameters in crops. Despite the fact that the multispectral point cloud by SfM-MVS is sparse and incomplete, this method has potential application in the study of 3D spatial distribution of crop parameters. • The fusion of LiDAR point clouds and multispectral imagery was implemented for wheat. • Wheat 3D photosynthetic phenotype data were quantified and visualized. • Wheat 3D photosynthetic traits showed significant vertical distribution patterns. • CCC_ P 75th and CPn_ P 75th were correlated with structural and physiological traits. • CCC_ P 75th and CPn_ P 75th outperform the traditional metrics in predicting wheat yield. [ABSTRACT FROM AUTHOR]

• An UAV-mounted LiDAR system was constructed to acquire cotton canopy parameters. • Crop canopy morphology was incorporated into UAV spraying decisions. • A prescription map generation technique was developed for UAV spraying. • UAV precise spraying control model with geolocation information was proposed. • Multivariate UAV spraying system based on cotton canopy volume was designed. Implementation of precision agriculture aerial applications using unmanned aerial vehicles (UAVs) represents a pivotal technological aspect in precision agriculture. However, current UAV applications are still far from realizing true precision variable spraying. The use of UAVs to analyze and model crop growth, pest and disease conditions, and other pertinent information to enable targeted precision spraying poses a formidable challenge. Herein, a UAV variable-rate spray system with adaptive flow control based on crop canopy morphology was developed using a UAV equipped with a light detection and ranging (LiDAR) sensor to support precision spraying of cotton. Point cloud data captured by the LiDAR sensor initially undergo processing using the simple morphological filter (SMRF) algorithm and feature extraction function. Subsequently, cotton canopy volumes measured by the alpha shape algorithm, convex hull by slices shape algorithm, and voxel-based method were compared with manual measurements with coefficient of determination R 2 values of 0.89, 0.78, and 0.82, respectively. The results of the validation experiments denote that the cotton canopy volume obtained using the alpha shape algorithm is in good agreement with that obtained using manual measurements; thus, this algorithm is employed for volume calculations in this design. Models were constructed to relate UAV flight parameters, cotton canopy volume, and spraying parameters to the duty cycle of variable control signals, which enabled the UAV to adjust the flow rate in real time to match the cotton canopy structure. In addition, a polygonal prescription map decoding algorithm based on the cotton canopy morphology was introduced. This algorithm is used for real-time decoding of prescription maps and extraction of flow, latitude, and longitude information. Finally, field experiments were conducted, and the results indicate that the UAV variable-rate spray system designed in this study exhibits biological efficacy (effective application thresholds provided by the pesticide manufacturer) comparable to that of the constant-rate spray system at a lower application rate. Furthermore, it exhibited superior spray deposition efficiency and coverage compared with the constant-rate spray system. Notably, the use of the variable-rate spray system in cotton crops resulted in a 43.37% reduction in spray volume while maintaining high application quality, providing an opportunity for pesticide and labor cost savings. [ABSTRACT FROM AUTHOR]

[Display omitted] • Research studies on proximal ground crop phenotyping increased over the past two decades. • Efficient autonomous phenotyping platforms replace labor-based platforms. • Multi-source sensor fusion technologies provide better navigation. • Large-scale model would benefit phenotyping data processing. • Current developments indicate possibilities of future fully autonomous phenotyping. Current crop phenotyping mainly relies on manual measurements and visual inspection for data collection and crop assessment, which is labor-intensive, subjective, and inefficient. Hence, modern methods depend primarily on using sensors for phenotypic data collection to replace labor vision, developing algorithms for decision-making to replace human domain knowledge, and integrating autonomous phenotyping systems to improve efficiencies in the past decades. Despite the research progress in phenotyping, there is a lack of extensive review on this topic that will be useful to various stakeholders interested in this field. Therefore, this study was conducted to perform a comprehensive review of multiple methodologies and techniques used in high-throughput ground crop phenotyping systems. A Web of Science literature search was conducted with appropriate keywords for the recent past, and the research trends in this field were captured. The current review categorizes the progress of technology in terms of phenotyping platform, sensing, data processing, and system integration. Platforms have evolved from manual-based to autonomous. Manual-based platforms require workers for data collection, while autonomous platforms involve new technologies for navigation and data collection. Different sensing techniques are used for phenotyping data collection. This study mainly discusses the mainstream sensors, including RGB, multi/hyperspectral, thermal, stereo, and light detection and ranging, and concludes that multi-source sensors could provide more accurate phenotypic information. Algorithms are applied to collected data to extract useful phenotyping information at different scales (organ, individual plant, and community). Both machine learning (ML) and deep learning (DL) have been used for phenotyping information extraction, and the DL is gradually replacing ML due to its superior performance. A case study of integrated high-throughput proximal phenotyping robot was presented, showing how different sensors and navigation systems come together to achieve on-site and real-time measurements. Advancements in high-throughput proximal ground phenotyping systems through new information, communication, sensing, and autonomous technologies in agriculture are anticipated to be more integrated and efficient phenotyping. It is anticipated that autonomous robots would finally replace workers from laborious phenotyping work. [ABSTRACT FROM AUTHOR]

Accurate digital elevation models represent the basic tool for a large spectrum of applications, including geological, architectonic, archaeological, and urbanistic studies. However, aggressive urbanization may significantly alter the morphology of areas of interest. Such is the case of the active Campi Flegrei caldera in southern Italy, where all the buildings, facilities, infrastructures, quarries, and landfills altered the original volcanic morphology. To avoid these limitations, we analyzed a set of vintage aerial photographs acquired in 1943 by Italy's Military Geographic Institute (IGM). We reconstructed the study area's topography before the deep anthropic modification from the 1960s onwards by applying Structure-from-Motion photogrammetric processing. On the reconstructed orthomosaic and a historical-Digital Surface Model (hDMS), we conducted geomorphic analyses along 18 longitudinal stream profiles outside, across the border, and inside the caldera, underlining that the rectilinear sections with preferred orientations and increased incision values suggest a strong structural underpinning on valley incision and stream paths. The analysis of the spatial distribution of sinuosity, SL index, Chi (χ) value and knickpoint maps suggests the presence of tectonic lineaments that influence the stream network from the pre-caldera (>40 ka) to recent times, producing articulated caldera and craters geometry as well as affecting the localization of volcanic vents. The comparison between the 1943 hDSM and 2013 LiDAR (Light Detection and Ranging) DSM allowed us to spotlight the areas that show the most significant differences due to anthropic intervention that obliterated critical features, thus supporting our motivation to use this base. Overall, the results indicate that long-lived volcano-tectonic and tectonic structures control the orientation of drainage patterns and their re-arrangements during volcano-tectonic deformation phases. Finally, a comprehensive structural map, based on the merged 1943 hDSM and the bathymetric DEM, is presented together with a conceptual evolutionary model of the stream network across the caldera border. Under the proper acquisition conditions, vintage aerial photographs can provide a useful tool for morphological analysis and can be applied to several topics in the geosciences. • The original topography of Campi Flegrei is restored using historical aerial photographs. • Several geomorphic indices highlight the activity of volcano-tectonic structures. • Rectilinear and deeply incised valleys occur in different sectors. • NE–SW and NW–SE oriented tectonic features are overprinted by E–W oriented features. • Tectonic lineaments control the occurrence of volcanism through time. [ABSTRACT FROM AUTHOR]

Multi-modal remote sensing (RS) involves the fusion of data from multiple sensors, such as RGB, Multispectral, Hyperspectral, Light Detection and Ranging, Synthetic Aperture Radar, etc., each capturing unique information across different regions of the electromagnetic spectrum. The fusion of different modalities can provide complementary information, allowing for a comprehensive understanding of the Earth's surface. Multi-modal RS image segmentation leverages various RS modalities to achieve pixel-level semantics classification. While deep learning has demonstrated promise in this domain, the limited availability of labeled multi-modal data poses a constraint on leveraging data-intensive techniques like deep learning to their full potential. To address this gap, we present Ticino, a novel multi-modal remote sensing dataset tailored for semantic segmentation. Ticino includes five modalities, including RGB, Digital Terrain Model, Panchromatic, and Hyperspectral images within the visual-near and short-wave infrared spectrum. Specifically annotated for Land Cover and Soil Agricultural Use, the dataset serves as a valuable resource for researchers in the field. Additionally, we conduct a comparative analysis, comparing single-modality with multi-modality deep learning techniques and evaluating the effectiveness of early fusion versus middle fusion approaches. This work aims to facilitate future research efforts in the domain by providing a robust benchmark dataset and insights into the effectiveness of various segmentation approaches. • A multi-modal Remote Sensing (RS) dataset for semantic segmentation. • The dataset includes RGB, Panchromatic, Hyperspectral, and Digital Terrain Model. • The dataset includes labeling for Land Cover and Soil Agricultural Use. • A comparison of single- vs multi-modality and early vs middle fusion DL techniques. • A validation of the multi-modality superiority in RS semantic segmentation. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date habitat maps at national or regional levels are critical for informing conservation and restoration actions. Land cover maps generated from Earth Observation (EO) data can, to varying degrees, indicate the extent of some habitat categories but these are often insufficient in terms of the level of detail required. This study introduces a hierarchical-modular framework based on the globally applicable Food and Agriculture Organization (FAO) Land Cover Classification System (LCCS) whereby the land cover classes can be systematically translated to habitat categories and further differentiated by referencing contextual information and expert knowledge. Application is showcased for Wales (United Kingdom) where 10 m spatial resolution land cover maps were first constructed nationally from time-series of Sentinel-1C-band radar and Sentinel-2 optical data (2018–2020). The contextual datasets derived from non-EO sources were utilised to associate dominant vegetation types to detailed habitat classes. The accuracy assessment done using field survey data highlights that habitat classes directly derived from EO, including cultivated/managed and forest types, demonstrated higher mapping accuracies (>70% User's and Producer's Accuracies) compared to those that were more heavily dependent on non-EO-derived contextual information. These included neutral grasslands and fen/marsh/swamp that demonstrated lower accuracies (<20%) but also other detailed wetland classes (user's and producer's accuracies ranging from 25 to 60%). These reduced accuracies were largely associated with discrepancies in the contextual datasets used for their differentiation and mapping, and those integrated within field validation datasets. Dependency on contextual datasets diminished as detailed habitat maps were generalized into broader categories. The study proposes that, for enhanced habitat mapping, efforts should focus not only on EO data but also on maximising the accuracy and minimizing inconsistencies in contextual datasets as well as taxonomical systems. The flexible structure of the FAO LCCS hierarchical framework is also highlighted, emphasizing its adaptability for future improvements in habitat mapping by incorporating contextual datasets and more advanced algorithms that can improve EO-derived land cover descriptions. • A hierarchical-modular habitat mapping framework has been developed. • It allows systematic integration of remote sensing & contextual data. • It involves combinations of data- and knowledge-driven approaches. • The framework uncovers the significance of contextual data in habitat mapping. • Taxonomical differences between maps and reference data compromise accuracies. [ABSTRACT FROM AUTHOR]

Accurate canopy cover estimation is essential for mature and early-stage young forests, as it guides forest management and silvicultural activities necessary for their growth and regeneration. However, obtaining precise measurements of canopy cover in the field is time-consuming and challenging, especially at the regional and landscape levels. Remote sensing techniques offer a promising alternative to traditional field-based measurements for estimating forest canopy cover. In this study, our objective is to estimate forest canopy cover using vegetation indices derived from the multispectral bands of PlanetScope (Planet Lab, Inc., San Francisco, CA, USA). To the best of our knowledge, this is the first study to utilise PlanetScope imagery data for estimating canopy cover in young boreal forests. Based on the analysis of four bands (green, blue, red, and near-infrared) from PlanetScope imagery, 43 vegetation indices, including four spectral bands and 13 salinity indices, were computed to select predictors in canopy cover modelling. Six regression models were employed to model canopy cover: linear, elastic net, support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine. All the models demonstrated good performance for both the training dataset (R2 = 0.58–0.69) and the testing dataset (R2 = 0.59–0.64, RMSE = 0.16–0.18, rRMSE = 22%–23%, and MAE = 0.12–0.14). Based on the fit statistics in the training and testing datasets and the paired t -test, our study identified the light gradient boosting machine as the most suitable model for predicting canopy cover in young boreal forests. For the light gradient boosting machine, the R2 value was 0.69 (training), and for testing data, the R2 = 0.64, RMSE = 0.16, rRMSE = 22%, and MAE = 0.12. Therefore, we recommend that future researchers utilise Planet multispectral data and the light gradient boosting machine regression to estimate forest canopy cover at a higher spatial resolution. However, exploring additional machine learning algorithms and explicitly boosting methods when computing forest canopy cover using satellite remote sensing is strongly advised. • We modelled the forest canopy cover using PlanetScope satellite-based 43 vegetation indices. • We used six regression models: linear, Enet, SVM, RF, XGboost, and LightGBM. • The reference forest canopy cover was calculated using Light detection and ranging 3D point clouds data and FUSION software. • The performance of the light gradient boosting machine model was the strongest across the six models. [ABSTRACT FROM AUTHOR]

Understanding the sequestration of organic carbon (C) in agroecosystems is of primary importance for greenhouse gas (GHG) accounting in managed lands, to reduce the environmental footprint of land use, and inform crediting programs. However, a broader application of precise C accounting is currently constrained by a limited number of direct flux measurements. Aside well-studied ecosystems via the eddy covariance technique (EC), many still bear significant uncertainty. In this study, we propose and evaluate a method for estimating accumulated C stocks in agricultural sites, by assessing the plant aboveground carbon (AGC) throughout two growing seasons using unstaffed aerial vehicles (UAV) and machine learning (ML) regression methods. Then, we used these estimates to assess total plant C, and benchmarked it with CO 2 fluxes derived from the eddy covariance method from the ICOS DK-Vng site in Denmark. We utilized a light detection and ranging (LiDAR) sensor onboard a UAV to derive the structural characteristics of crops, and we conducted in parallel destructive field-based measurements of AGC. Then, we designed a ML pipeline to provide estimates of AGC as a supervised regression problem, using the LiDAR-derived point cloud data to extract predictive features and the AGC labels as ground-truth target values. The best performing ML model attained predictions of R 2 = 0.71 and R 2 = 0.93 at spatial resolutions of 1 m2 and 2 m2, respectively. The C content in the aboveground plant components was assessed via laboratory analysis (46.6 ± 0.3% of C-to-biomass in barley and 47.7 ± 0.3% in wheat), while the belowground components (root allocation and rhizodeposition) were estimated based on a phenology-dependent allometric ratio. The cumulative value of C uptake along the growing season (i.e. net primary productivity) was compared with the difference of C predictions between UAV-LiDAR survey dates, finding an optimal disagreement between methods below ± 9% in two different cereal crops. The plant carbon budget in croplands, determined through UAV-LiDAR and machine learning regression, aligns with the carbon ecosystem uptake estimated through the eddy covariance technique, showcasing comparable results. Thereby, the proposed method also demonstrates the potential to estimate cumulative CO 2 fluxes in areas lacking direct eddy covariance measurements. Various experimental setups are evaluated as well as the sources of uncertainty resulting from the sampling design. [Display omitted] • Net plant carbon budget in croplands estimated via UAV-LiDAR and ML aligns with NPP. • UAV-LiDAR and flux-based carbon estimates match in two cereal crops within ± 9%. • UAV-LiDAR and ML can estimate cumulative CO 2 fluxes in croplands not monitored by EC. [ABSTRACT FROM AUTHOR]

• The selection of satellite sensor and radar system parameters greatly affected the model output. • The model was improved when more polarizations, orbital directions and frequencies were involved. • Models built using multiband radar datasets achieved a comparable accuracy to models based on optical data. • The model built by fusing SAR-optical data achieved better results than the model without SAR data. • The development of this continental-scale DSM work largely benefits from GEE. The advent of cloud computing platforms (e.g., Google Earth Engine (GEE)) and the massive amounts of optical and radar Earth Observation (EO) data hosted by these platforms present new opportunities for mapping soil pH at large scales. However, existing studies generally lack consensus on the effects of satellite and radar sensor parameters on GEE-based soil pH prediction models. In this study, we assessed the suitability of long-term radar (C-band Sentinel-1 and L-band PALSAR-1/2) and optical (Sentinel-2) EO data on GEE for the digital mapping of soil pH on a continental (Europe) scale and determined the most appropriate radar sensor parameters. Thirteen scenarios with different data configurations were simulated and combined with the 2018 LUCAS soil database and two machine learners (boosted regression trees and extreme gradient boosting) to develop soil prediction models. Results showed that the selection of modeling techniques, satellite sensors and radar system parameters largely affected the model output. Models involving a single polarization mode of PALSAR-1/2 data performed the worst (RPD = 1.24). Models based on Sentinel-1 data performed better than those built using PALSAR-1/2 data. The model performance was improved when a model involved more polarization bands, orbital directions, and band frequencies. The multiband model built using the two radar datasets achieved a comparable accuracy to the model based on optical data. Moreover, the model that fused radar-optical data achieved better results, with RPD values of 1.56 and 1.46 for the models with and without radar data, respectively; its performance was comparable to that of models built with commonly used variables (topography and climate). The analysis of importance indicated that long-term optical and radar EO data on GEE were important in our model. The modelling of soil pH at the continental scale largely benefits from GEE. The predicted maps exhibited strong spatial heterogeneity among different biogeographic regions, with similar spatial patterns under different modelling scenarios. [ABSTRACT FROM AUTHOR]

• The proposed multi-sensor fusion method can extract navigation lines from complex seedling maize fields in real time. • The Itti algorithm was improved for extracting green plants from different exposed images. • A non-maize point cloud filtering method that fused attitude angles and the image was proposed. • A green feature map overlaid with a maize point cloud was used to accurately extract the navigation line. The navigation line is crucial for the autonomous navigation of spraying and weeding robots in unstructured and complex agricultural fields. The characteristics of the environment, such as unstable light, uneven terrain, and chaotic weeds, lead to the shortcomings of existing navigation line extraction methods, such as low accuracy or insufficient real-time responsiveness. Therefore, this study presents a method (named CLI-Fusion) based on the fusion of a camera, light detection and ranging (LiDAR), and an inertial measurement unit (IMU) to accurately and in real-time extract the navigation line between maize crop rows during the seedling stage. CLI-Fusion also consists of three steps based on achieving multi-sensor spatial synchronization. First, the Itti algorithm is improved to detect the green salient region in the image. The green salient region is then combined with the Otsu and excess green index (ExG) to generate a green feature map, which can separate the green plants from the ground in the image. Second, weeds and ground in the point cloud are filtered out by fusion with the IMU's attitude angles and green feature map. The retained maize point cloud is sectorized, and centerlines of the crop rows are detected with rough accuracy. Third, the sectorized maize point cloud is projected onto the green feature map, and the edge pixels of the green feature map in the projected area are detected. The least squares method is then used to fit more accurate centerlines of the crop rows to extract the accurate navigation line. The experimental results show that the accuracy rate, angle error, and processing time averages are 90.0 %, 1.84°, and 55.4 ms, respectively, suggesting that CLI-Fusion has better accuracy than the single-sensor methods based on ensuring real-time performance. The multi-sensor fusion method for navigation line extraction provides technical support for the navigation of agricultural robots in farmlands. [ABSTRACT FROM AUTHOR]

Modular integrated construction (MiC) offers high construction quality and efficiency. Currently, geometric quality inspections for modular boxes are conducted manually. Therefore, based on BIM and LiDAR, this study proposes an automated inspection approach for outer wall surfaces and rebars of modular boxes. To extract the targeted modular boxes from complex construction sites, a novel method using scanning stations and ray casting algorithm is introduced. The point clouds of different modular boxes are then recognized and segmented into different component categories based on the pairwise registration with as-designed data. Ultimately, a reference plane-based method is proposed to automatically measure the flatness of outer wall surfaces (FOW) and protruding length of rebars (PLR). Experiments on four modular boxes show an accuracy with errors of 2.6 mm for FOW and 2.8 mm for PLR, showing the potential for enhanced quality control and efficiency of modular construction industry. • A method for inspection of outer wall surfaces and rebars of modular boxes. • Recognition of three types of wall surface and an algorithm for grouping rebars. • Scanning station points are used to extract the point cloud of targeted modular boxes. • Algorithms of component segmentation for modular boxes. • Proposed method can be applied to the quality inspection of modular boxes. [ABSTRACT FROM AUTHOR]

Coastal zones are highly exposed to storm waves that can affect natural values and damage coastal facilities and engineering structures to varying degrees. The definition of a regional management strategy along the Basque Coast (150 km), composed mainly of embayed beaches, is challenging due to the high variability in the coastal flooding exposure, nearshore wave conditions and geomorphological characteristics. Here, 3 years of data (2019–2022) from a coastal video-monitoring network (KOSTASystem) deployed at 13 beaches were used to: i) assess the regional variability of storm impact associated to coastal flooding (hereinafter referred as storm impact), ii) analyse the relation between storm impact, nearshore hydrodynamics and pre-storm geomorphology and iii) find common beach behaviours, using an extensive video monitoring network operating along the Basque Coast on a series of embayed beaches. Different types of images were analysed during storms to characterise storm impact through the storm impact regime indicator (SIR, i.e. swash/collision/overtopping). The storm impact was compared against nearshore hydrodynamics (water level and energy flux) and geomorphological parameters, (1) the dry beach width (DBW) obtained from orthorectified Timex images in a predefined profile and (2) the dune/seawall toe (TH) and (3) crest (CH) height obtained from airborne LiDAR (Light Detection and Ranging) surveys. No clear relation was found between nearshore wave conditions and storm impacts. This might be related to the reduced range in the wave and tide values used to perform the analysis during high energy conditions. However, beaches with a wider dry beach and/or higher dune/seawall toe and crest lead to lower storm impact intensity (swash) and inversely (collision/overtopping). The beaches of the study site were described in function of the storm impact regime and the pre-storm geomorphological parameters (heights referred to the 0 of the Spanish topographic institute (NMMA IGN 2008)) as: Group 1) Beaches dominated by the collision regime that present low values of DBW and TH, specifically, DBW < 30 m and TH < 3.5 m. Group 2) Beaches dominated by the swash with few collision events that present moderate DBW and relatively high values of TH, specifically, 40 < DBW < 75 m and 4 < TH < 6.5 m. Group 3) Beaches fully dominated by the swash regime that present relatively high values of DBW and TH, specifically, DBW > 50 m and 4 < TH < 7.5 m. This coastal exposure assessment at regional scale benefits stakeholders and coastal managers by characterising the most exposed areas and identifying the drivers. The results presented here are expected to be valid in sites with similar wave climate and water level regimes. The application of the methodology to other coastal areas with a regional videometry network would help to generalize and give robustness of the results. • The coastal flooding exposure is identified through a regional videometry network. • The regional coastal flooding variability depends mainly on pre-storm geomorphology. • The methodology will help to support coastal management strategies at regional scale. [ABSTRACT FROM AUTHOR]

This paper delivers an exhaustive analysis of the fusion of multi-sensor technologies, including traditional sensors such as cameras, Light Detection and Ranging(LiDAR), Radio Detection and Ranging(RADAR), and ultrasonic sensors, with Artificial Intelligence(AI) powered methodologies in obstacle detection for Autonomous Vehicles(AVs). With the growing momentum in AVs adoption, a heightened need exists for versatile and resilient obstacle detection systems. Our research delves into study of literatures, where proposed approaches assimilate data from this diverse sensor suite, integrated through Deep Learning(DL) techniques, to refine AV performance. Recent advancements and prevailing challenges within the domain are thoroughly examined, with particular focus on the integration of sensor fusion techniques, the facilitation of real-time processing via edge and fog computing, and the implementation of advanced artificial intelligence architectures, including Convolutional Neural Networks(CNNs), Recurrent Neural Networks(RNNs), and Generative Adversarial Networks(GANs), to enhance data interpretation efficacy. In conclusion, the paper underscores the critical contribution of multi-sensor arrays and deep learning in enhancing the safety and reliability of autonomous vehicles, offering significant perspectives for future research and technological progress. [ABSTRACT FROM AUTHOR]

Law enforcement plays an important role in road safety across the world. Speed enforcement merits special attention as the correlation between exceeding speed limits and the risk of fatalities and serious injuries is well-known. Methods based on radar and light detection and ranging techniques have established themselves as effective methods for measuring vehicle speeds in real time on roads. However, the price of instruments used for these measurements limits their widespread applicability. This work explored the potential of coupling artificial intelligence and audio analysis for vehicle speed assessment. The potential of deep neural networks and extreme gradient boosting is explored on a recently proposed real-world vehicle speed dataset. Frequency analysis techniques are also applied to help reduce the computational demands of experimentation. Additionally, a modified optimization algorithm is introduced to help select optimal control parameters and improve the performance of the suggested method. Five experiments were executed to demonstrate the potential for detecting the exact vehicle speed using regression techniques as well as utilizing classifications to detect vehicles breaking the speed limit. The proposed methods demonstrated promising potential when applied to this pressing challenge. • Introducing technique that uses audio analysis to detect exceeding speed limits. • Investigating capability of audio analysis in accurately estimating vehicle. • Vehicles exceed speed limit classification by optimized XGBoost. • Vehicle speed estimation by tuned deep neural network (DNN). • Introducing adapted metaheuristics to address XGboost and DNN tuning. [ABSTRACT FROM AUTHOR]

Recent classifications in Earth Observation (EO) commonly involve a combination of Hyperspectral Image (HSI) and Light Detection and Ranging (LiDAR) signals. However, many current methods fail to consider the HSI-LiDAR information concurrently, especially in terms of both its intra- and inter-modality aspects. Additionally, current methods are generally limited in their ability to fuse the features extracted from different modalities. Hence, this paper proposes a center-bridged framework, called Interaction Fusion (IF), that can leverage diverse information concerning the intra- and inter-modality relationships at the same time. More specifically, intra- and inter-modality information can be enriched by introducing the center patch of HSI (cp-HSI) as an extra input, This introduces additional contextual information within and across modalities that can be leverage for deeper insights. Further, we propose a fusion matrix as a structural feature map designed to integrate nine views generated by a view generator, enabling the adaptive combination of intra- and inter-modality information. Overall, our approach allows potential patterns to be captured, while mitigating any bias resulting from incomplete information. Extensive experiments conducted on three widely recognized datasets – Trento, MUUFL, and Houston – demonstrate that the IF framework achieves state-of-the-art results, surpassing existing methods. [ABSTRACT FROM AUTHOR]

The frequency and intensity of heavy rainfall on the Chinese Loess Plateau (CLP) are increasing due to global climate change, which has caused severe soil erosion. However, precise evaluation of heavy rainfall-induced soil erosion, deposition, and related topographical changes at the catchment scale is lacking, and there is uncertainty as to how effectively trees can inhibit erosion, especially during heavy rainfall. In this study, we selected a pair of catchments with afforestation and natural grassland restoration on the CLP. Precise evaluation of erosion and topographic changes induced by heavy rainfall was conducted based on airborne light detection and ranging (LiDAR) data. In the grassland catchment, the gully bed area increased by 17.6 %, the gully slope and inter-gully land area decreased by 1.4 %, and the length of the gully shoulder line decreased by 4.4 %. Conversely, the forestland catchment experienced a decrease in gully bed area of 8.8 %, an increase in gully slope area of 5.3 %, a decrease in inter-gully land area of 9.9 %, and a decrease in the length of the gully shoulder line of 11.4 %. Following heavy rainfall, both catchments experienced a substantial increase in terrain relief. Soil erosion in the gully slope accounted for >90 % of the total erosion in both catchments, while deposition in the same area contributed to >75 % of the total deposition. The grassland catchment experienced a unit area erosion of 233,908 m3 km−2, which was 4.7 times greater than that of the forestland catchment (49,768 m3 km−2). In both catchments, erosion predominantly transpired in the steep slope region adjacent to the gully shoulder line, accounting for >15 % of the gully slope erosion area. The grassland catchment exhibited a continuous distribution pattern of erosion and deposition, whereas the forestland catchment exhibited a scattered and intertwined distribution pattern. This study is essential for further research on erosion and topographic changes induced by extreme weather events and to determine appropriate measures. • Rainstorm erosion was observed on the Loess Plateau; • Compared to the grassland catchment, the forestland catchment substantially reduced unit area erosion; • The terrain relief increased after rainstorm erosion in both catchments; • Grassland and forestland catchments showed different patterns of erosion and deposition; • Both catchments experienced different changes in gully landform evolution. [ABSTRACT FROM AUTHOR]

In the wake of severe drought coupled with altered disturbance regimes across the Sierra Nevada, understanding current forest conditions and pace of forest change is critical for managing for human and resource protection goals. Widespread tree mortality and heavy accumulations of dead and downed fuels pose immediate risks to human safety, wildlife habitat, cultural resources, and functioning forest ecosystems. We combine field-based forest monitoring, terrestrial and airborne light detection and ranging, and satellite data in statistical modeling frameworks that improve our understanding of conditions leading to tree mortality and the subsequent effects of forest disturbance across multiple spatial scales within Yosemite National Park to inform management planning, prioritization, and implementation. Despite drought mortality and landscape scale fires of increased severity, we observed relatively high basal area across the Park, particularly in low and mid montane forests. Parkwide, 32.8% of basal area was dead, with mortality varying by species and forest type and strongly associated with higher total stand basal area and lower elevations. Change in satellite-derived Normalized Difference Vegetation Index over time also closely tracked mortality, demonstrating its utility as an indicator of mortality across larger spatial scales and longer timescales. While mortality may shift live basal area towards the historic range of variability, the resulting forest conditions are unlikely to reflect resilient structure and composition. The continuity of standing and downed dead fuels, loss of large-diameter trees, and mismatched species composition of mortality and regeneration suggests shifting fire risk and forest composition. Fire history also strongly influenced components of forest structure and succession that drive subsequent fire risk and fire behavior and are therefore characteristics that can be manipulated through fire management. For example, duff depth and ladder fuels decreased with total times burned and increased with time since fire. Characterizing current conditions of an iconic landscape after the compounding effects of mega-drought and fire offers insights into which biophysical settings might be resilient to future episodic events and how proactive management of forest structure through fire and fuels reduction might be moderated to enhance the potential for survival and regeneration. Incorporating a suite of technologies and data sources also adds value to standard forest monitoring across a large spatial extent and improves managers' ability to focus actions and interventions according to current mortality trends. • High basal area remains across the Park, particularly in low and mid montane forests. • Parkwide mortality was 32.8% of basal area, strongly associated with higher total stand basal area and lower elevations. • Mortality tracked with NDVI change, an indicator of forest condition across larger spatial scales and longer timescales. • Fire history influenced elements of forest structure driving fire risk and behavior that fire management can manipulate. • Dead fuel continuity, large-diameter tree loss, and changes in recruitment hint shifting fire risk and forest composition. [ABSTRACT FROM AUTHOR]

Several species-specific habitat management guidelines have been created over the past decade to help recover declining populations of forest birds in eastern North America. Conserving eastern forest birds through forest management requires understanding the factors driving species occurrence patterns and how the needs of various species complement or contrast with each other. Our study assessed and compared occupancy patterns of two at-risk species associated with riparian forests: the Wood Thrush (Hylocichla mustelina) and the Canada Warbler (Cardellina canadensis). We conducted point-count surveys and vegetation sampling at 304 unique locations along 33 stream sections in central and southwestern Pennsylvania from May – June 2018 and 2019. We used single-season occupancy models to examine the influence of vegetation composition (i.e., microhabitat conditions), forest structure (i.e., from Light Detection and Ranging data, "LiDAR"), and other factors (i.e., stream order and elevation) on focal species occurrence patterns. Wood Thrush occupancy (mean ᴪ ˆ : 0.57) was best explained by the presence of mountain laurel (Kalmia latifolia ; negative effect), elevation (negative effect), canopy height (positive effect), and canopy structural heterogeneity (positive effect). Canada Warbler occupancy (mean ᴪ ˆ : 0.55) was best explained by the presence of mountain laurel (positive effect), stream order (positive effect), elevation (positive effect), density of understory vegetation (positive effect), and canopy structural heterogeneity (negative effect). While both bird species occurred relatively frequently in riparian forests, our results indicate that habitat factors at multiple spatial scales play a role in regulating their occurrence patterns. Moreover, the two species exhibited contrasting habitat associations. Specifically, Canada Warblers preferred sites supporting dense mountain laurel, typically at elevations >600 m, and with a largely intact canopy. These patterns were reversed for Wood Thrushes. These contrasting associations will limit opportunities to manage riparian forests in a manner that conveys co-benefits for our two focal species. As a general rule, forest managers in our study region can target habitat management for Canada Warblers in riparian areas above 600 m and in areas below 600 m for Wood Thrushes. Given our focal species' use of other (non-riparian) forest types as breeding habitat and broad geographic distributions, it is important to recognize that our findings may not apply outside of riparian forests or beyond the Central Appalachian Mountains. • We monitored Wood Thrush and Canada Warbler at 306 locations in riparian forests. • Elements of vegetation structure and composition influenced occupancy for both species. • Vegetation structure and composition associations differed between both species. • The two species were largely separated along an elevational gradient. • Working at respective elevations will reduce concerns about contrasting habitat needs. [ABSTRACT FROM AUTHOR]

This review delves into the integration prospects of Urban Metabolism (UM) with industry 4.0 technologies, particularly digital twin (DT), to bolster sustainable urban development. Embracing a DT framework can enable the visualization of interdependencies within civil systems, facilitating the prediction of interventions to mitigate future climate change impacts. Therefore, the scope of this review was to determine how UM results are represented to end users, such as architects, and engineers and evaluate if these methods align with industry practices. Initially, we analyzed 70 UM studies to investigate the methods and visualization techniques employed. Trends revealed minimal integration of UM with Industry 4.0 technologies or DTs. The findings underscored a lack of dynamic and geographically oriented visualization in UM, with static representation methods like bar charts and Sankey diagrams prevailing, and mostly disconnected from the geographical location of the flows represented. Consequently, the research was further expanded to explicitly review the use of emerging technologies from Industry 4.0, including DTs, using targeted words. An extended review showed a gap in the integration of the UM framework with DT technologies, although an increased use in mapping was depicted. There was inconsistency in the adoption of specific technologies or visualization methods, highlighting ongoing research in this area. The increased use of geographic information system (GIS) and light detection and ranging (LiDAR) marked a pivotal change for more elaborate mapping of UM outcomes. Limited application of dynamic visualizations in UM persists, with MFA diagrams, Sankey diagrams, and bar charts being commonly used methods, despite the need to develop such type of visualizations in the sustainability field. There is an obvious lack of interoperability of these methods with Computer-Aided Design (CAD) software as well as Building Information Model (BIM) software, which are standard tools in urban planning and design. This limitation is particularly significant in environments where the ability to modify planning scenarios and work with precise spatial representations is crucial. We argue that UM can be more effective if integrated with technologies such as DT, which can resolve the spatial issue and describe UM contextualizing it and potentially depicting it in real-time. Additionally, DTs can offer promising solution by integrating UM analysis results and facilitating modifications to the 3-dimensional environment based on this information. This capability empowers planners and designers to conduct simulations, update UM analyses, and iterate the design process, aligning with contemporary practices in planning and design. Given the interdisciplinary nature of UM, which includes various concepts such as urban flows, city flows, system of systems, and urban networks, our study deliberately focuses solely on UM and its associated frameworks. This targeted approach may limit the breadth of concepts explored, but it ensures depth and coherence in our analysis of UM for this study. • Inconsistent methodologies hinder urban metabolism research advancements. • Results highlight the need for improved visualization in urban metabolism. • Results shows need for Industry 4.0 integration in urban metabolism analysis. • Standardization in urban metabolism to foster collaboration and enhance research. • Digital twins' interface may facilitate analysis regardless of technical proficiency. [ABSTRACT FROM AUTHOR]

Three dimensional (3D) object detection using the camera and light detection and ranging (LiDAR) fusion model has received much attention for meeting the environmental sensing requirement in autonomous vehicles. Due to the intrinsic data difference and the uneven distribution of LiDAR data, integrating the camera and LiDAR to solve the problem of the uneven spatial distribution of LiDAR data is still challenging. This paper proposes a novel 3D detector, which decorates the point cloud voxels with the semantic features at multi-scale and fuses the multi-source features by cross-attention to improve the detection performance. Specifically, the multi-modal fusion aims to maintain the cross-modal consistency by using the frustum model to construct the correspondence between point voxels and image pixels at multi-scale. To fully exploit the geometric constraints of voxels, we developed a adaptive sampling radius module to dynamically select the sampling radius of the voxel set abstraction module based on softmax. Moreover, we propose a multi-source fusion module which utilizes the cross-attention and takes the raw point cloud's spatial distribution as the clue to fuse the features of point clouds, bird's eye view information and the aggregated voxels to obtain the multi-scale and multi-source fusion features. Finally, region proposal network is adopted to generate and refine the 3D bbox and class prediction based on the fusion feature and bird's eye view feature. Extensive experiments are conducted on the two publicly available 3D object datasets proposed by Karlsruhe Institute of technology and Toyota Technological Institute (KITTI) and the nuScenes. The proposed model reaches 86.27% mean Average Precision (mAP) for car on KITTI and obtains 6.56% gains than PointPainting. Moreover, our model reaches 70.70% mAP on nuScens and improves 2.61% than Transfusion. Furthermore, comprehensive ablation experiments are conducted to validate the effects and contribution of the different components of the proposed model. [ABSTRACT FROM AUTHOR]

This study examines the impact of bias correction on the prediction of offshore wind power ramp events (WPREs) using NOAA's High-Resolution Rapid Refresh (HRRR) model. The analysis focuses on New England's offshore region, using Light Detection and Ranging (LiDAR) wind observations and the power curve derived from the nearby Block Island wind farm as a reference. The study characterizes bias in terms of amplitude and phase, finding a 30-min systematic lag error and a marginal underestimation of high wind speeds. Perfect bias correction treatments including both the amplitude and the phase are applied to HRRR wind speed, and their impact on WPREs is assessed using categorical performance measures. A large discrepancy in the number of cases with HRRR having much fewer cases than the reference is found, which is attributed to the model's temporal and spatial resolution. The results show that the amplitude bias correction has a marginal impact on reducing frequency bias, while the dynamic time warping (DTW) used in the phase bias correction shows promising results but limited performance. The study concludes that phase bias correction can be a feasible option to reduce bias in WPREs in offshore power systems. [ABSTRACT FROM AUTHOR]