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

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

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

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

Automated maintenance and motion planning for unpaved roads are research areas of great interest in the field robotics. Constructing such systems necessitates the development of surface maps for unpaved roads. However, the lack of distinctive features on unpaved roads degrades the performance of light detection and ranging (LiDAR)‐based mapping. To address this problem, this paper proposes three‐dimensionalized feature‐based LiDAR‐visual odometry (TFB odometry) for the online mapping of unpaved road surfaces. TFB odometry introduces a novel interpolation concept to directly estimate the three‐dimensional coordinates of the image features using LiDAR. Furthermore, LiDAR intensity‐weighted motion estimation is proposed to effectively mitigate the effects of dust, which significantly impact the performance of LiDAR. Finally, TFB odometry includes pose graph optimization to efficiently fuse global navigation satellite system data and poses estimated from motion estimation. Through field experiments on unpaved roads, TFB odometry demonstrated successful online full mapping and outperformed other simultaneous localization and mapping methods. Additionally, it demonstrated remarkable performance in accurately mapping road surface anomalies, even in dusty regions. [ABSTRACT FROM AUTHOR]

A lightweight, high‐definition vector map (HDVM) enables fully autonomous vehicles. However, the generation of HDVM remains a challenging problem, especially in complex urban scenarios. Moreover, numerous factors in the urban environment can degrade the accuracy of HDVM, necessitating a reliable error quantification. To address these challenges, this paper presents an open‐source and generic HDVM generation pipeline that integrates the global navigation satellite system (GNSS), inertial navigation system (INS), light detection and ranging (LiDAR), and camera. The pipeline begins by extracting semantic information from raw images using the Swin Transformer. The absolute 3D information of semantic objects is then retrieved using depth from the 3D LiDAR, and pose estimation from GNSS/INS integrated navigation system. Vector information (VI), such as lane lines, is extracted from the semantic information to construct the HDVM. To assess the potential error of the HDVM, this paper systematically quantifies the impacts of two key error sources, segmentation and LiDAR‐camera extrinsic parameter error. An error propagation scheme is first formed to illustrate how these errors fundamentally influence the accuracy of the HDVM. The effectiveness of the proposed pipeline is demonstrated through our codeavailable at https://github.com/ebhrz/HDMap. The performance is verified using typical datasets, including indoor garages and complex urban scenarios. [ABSTRACT FROM AUTHOR]

The spectacular visual displays from the aurora come from curtains of excited atoms and molecules, impacted by energetic charged particles. These particles are accelerated from great distances in Earth's magnetotail, causing them to precipitate into the ionosphere. Energetic particle precipitation is associated with currents that generate electric fields, and the end result is a dissipation of the hundreds of gigawatts to terrawatts of energy injected into Earth's atmosphere during geomagnetic storms. While much is known about how the aurora dissipates energy through Joule heating, little is known about how it does so via small‐scale plasma turbulence. Here we show the first set of combined radar and optical images that track the position of this turbulence, relative to particle precipitation, with high spatial precision. During two geomagnetic storms occurring in 2021, we unambiguously show that small‐scale turbulence (several meters) is preferentially created on the edges of auroral forms. We find that turbulence appears both poleward and equatorward of auroral forms, as well as being nestled between auroral forms in the north‐south direction. These measurements make it clear that small scale auroral plasma turbulence is an integral part of the electrical current system created by the aurora, in the sense that turbulent transport around auroral forms enhances ionospheric energy deposition through Joule heating while at the same time reducing the average strength of the electric field. Plain Language Summary: The aurora continuous to amaze the inhabitants and travelers of Earth's polar regions. Bright shifting folds of light extend down from the nightsky, appearing as green, red, or faint‐blue curtains. During geomagnetic storms the are particularly bright and dynamic, often visible in large parts of the inhabited globe. However, far from being simple displays of light, the aurora can wreak havoc on the thin gas of Earth's upper atmosphere. There, gigantic swirls of electric turbulence are excited in response to the energy that is being pumped into the atmosphere by the aurora. This plasma turbulence is detrimental to satellite communication, such as the principle operation of the GPS network, and future efforts are sorely needed to understand the when and how this turbulence appears. We present a series of photographical and radar‐based images of the aurora and its plasma turbulence, shedding light on the complex relationship between the two phenomena. The images and videos we present are accessible and interesting to a public readership. Key Points: Small‐scale auroral plasma turbulence is created preferably outside of but not far from optical auroral formsTurbulence appears both poleward and equatorward of auroral arcsStrong electric fields that trigger meter‐size E region turbulence are sometimes seen before the onset of optical aurora [ABSTRACT FROM AUTHOR]

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

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

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

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

Fire‐herbivory feedbacks strongly influence the formation of grazing lawns in savanna ecosystems. Preliminary findings suggest that small‐scale (<25 ha) fires can engineer grazing lawns by concentrating herbivores on the post‐burn green flush; however, the persistence of such grazing lawns over the longer term and without repeated fire is unknown.We used high‐resolution Light Detection and Ranging (LiDAR) to investigate the long‐term effects of fire manipulation on short grass structure (height, cover, volume and spatial continuity) and grazing lawn establishment in Kruger National Park, South Africa. We analysed the effects of fire exclusion and experimental burns applied over a 7‐year period (2013–2019) followed by a 1‐year cessation of burning at varying spatial scales during the early and late dry seasons.Fires contributed a fourfold increase in short grass cover, regardless of fire season or size. The distribution of grass height differed significantly between fire‐induced grazing lawns and recently unburnt parts of the landscape where controlled fires were excluded over the experimental period. The volume (corresponding to bulk density) of short grass on the landscape responded strongly to fires, with grass volume <20 cm in height increasing with both early and late dry season fires.Early dry season fires caused larger and more homogeneous short grass patches. Furthermore, early dry season fires were more influential in increasing the cover of the shortest grass height class (1–5 cm).Synthesis and applications. Our results demonstrate that fire‐induced grazing lawns can persist over the longer term, even when fires are no longer applied, leading to the creation of vertical and horizontal heterogeneity in the grass layer. Small‐scale fires, therefore, represent a feasible management approach to expanding grazing lawn extent, potentially benefiting grazer coexistence and diversity. [ABSTRACT FROM AUTHOR]

Suggesting a curious comparison between the Victorian séance and the contemporary world of immersive virtual environments, Mike Phillips, Professor of Interdisciplinary Arts at the University of Plymouth, and Director of Research at i‐DAT.org, describes the new capabilities of the fulldome, which uses notions of the automatic movements of the planchette, the wooden token that traverses the alphanumeric surface of the notorious Ouija board, as a model for the development of a quasi‐participatory audience interface he dubs the 'phage'. Clusters of phage can be manipulated by the viewer‐occupiers of the dome to instigate all manner of formal, scalar and conceptual transpositionings. [ABSTRACT FROM AUTHOR]

Laid bare by contemporary scanning techniques and technologies, historic buildings reveal a series of nested, diaphanous presences – the membranes of memory and fragments of history. Architectural writer Eva Menuhin investigates a recent project by the University of Greenwich's Captivate: Spatial Modelling Research Group, whose gossamer‐threaded architectural representations decode time, movements, changes, additions and destructions of a London Garrison church – a poetic, ghostly ballet between time, building and nature. [ABSTRACT FROM AUTHOR]

A unique charge‐injection‐narrowing organic photodetector (CIN OPD) is demonstrated for near‐infrared (NIR) range operation. In conventional CIN OPDs, photomultiplication (PM) is achieved within a bulk heterojunction (BHJ) active layer with a donor:acceptor ratio of 100:1. Unfortunately, this approach encounters an issue in which the narrowband detection wavelength is determined solely by an excessive donor quantity. Consequently, the conventional devices face a critical limitation in that their detection range cannot extend to longer wavelengths. However, this limitation has been overcome through a novel mechanism and have fabricated a device that responds at λ = 940 nm. When a perovskite quantum dot interlayer is inserted as the PM‐inducing layer, PM occurs outside the active layer. Therefore, the BHJ active layer is structured with a donor:acceptor ratio of 50:50, resulting in an extension of the detection wavelength to the absorption tail of the narrow‐bandgap acceptor. The device demonstrates a remarkable external quantum efficiency (EQE) of 39 000% with a narrow full‐width at half‐maximum (FWHM) of 98 nm at 60 V. It also has a high specific detectivity and a short response time. The device represents a significant advancement in filter‐free, high‐efficiency photodetectors suitable for optical sensor applications such as light detection and ranging (LiDAR). [ABSTRACT FROM AUTHOR]

Near‐infrared (NIR) photonic materials find extensive applications across important fields such as telecommunications, laser radar, and atmospheric remote sensing. In particular, photonic materials activated by main group elements, which exhibit unique spectral features including ultra‐broadband tunable luminescence and long lifetime, have become rising stars in NIR emitting dopants. In this review, the energy level characteristics of the p‐electrons to gain insight into the fundamentals of the NIR optical response from the main group elements are first introduced. Next, the NIR luminescence properties of the main group elements are discussed. Then, the strategy for the design of main group elements activated materials with the desired properties based on local chemical environment engineering is proposed. In addition, recent advances in the applications of main group element (excluding bismuth) activated materials are highlighted. Furthermore, the key scientific issues that urgently need to be solved for such materials, such as the detailed luminescence mechanism are highlighted. Anticipation also extends to the future research trends in this exciting field, including the ways for enhancing luminescence efficiency and extending spectral region, and the efforts for implementing these advancements in novel cutting‐edge technologies like photovoltaics and biomedicine. [ABSTRACT FROM AUTHOR]

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

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

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

Geolocation error in spaceborne sampling light detection and ranging (LiDAR) measurements of forest structure can compromise forest attribute estimates and degrade integration with georeferenced field measurements or other remotely sensed data. Data integration is especially problematic when geolocation error is not well quantified. We propose a general model that uses airborne laser scanning data to quantify and correct geolocation error in spaceborne sampling LiDAR. To illustrate the model, LiDAR data from NASA Goddard's LiDAR Hyperspectral and Thermal Imager (G‐LiHT) was used with a subset of LiDAR data from NASA's Global Ecosystem Dynamics Investigation (GEDI). The model accommodates multiple canopy height metrics derived from a simulated GEDI footprint kernel using spatially coincident G‐LiHT, and incorporates both additive and multiplicative mapping between the canopy height metrics generated from both datasets. A Bayesian implementation provides probabilistic uncertainty quantification in both parameter and geolocation error estimates. Results show a systematic geolocation error of 9.62 m in the southwest direction. In addition, estimated geolocation errors within GEDI footprints were highly variable, with results showing a ∼$$ \sim $$0.45 probability the true footprint center is within 20 m. Estimating and correcting geolocation error via the model outlined here can help inform subsequent efforts to integrate spaceborne LiDAR data, like GEDI, with other georeferenced data. [ABSTRACT FROM AUTHOR]

Sea level rise (SLR) is causing vegetation regime shifts on both the seaward and landward sides of many coastal ecosystems, with the eastern coast of North America experiencing accelerated impacts due to land subsidence and the weakening of the Gulf Stream. Tidal wetland ecosystems, known for their significant carbon storage capacity, are crucial but vulnerable blue carbon habitats. Recent observations suggest that SLR rates may exceed the threshold for elevation gain primarily through vertical accretion in many coastal regions. Therefore, research has focused on mapping the upslope migration of marshes into suitable adjacent lands, as this landward gain may be the most salient process for estimating future wetland resiliency to accelerated rates of SLR. However, our understanding of coastal vegetation characteristics and dynamics in response to SLR is limited due to a lack of in situ data and effective mapping strategies for delineating the boundaries, or ecotones, of these complex coastal ecosystems. In order to effectively study these transitioning ecosystems, it is necessary to employ reliable and scalable landscape metrics that can differentiate between marsh and coastal forests. As such, integrating vegetation structure metrics from light detection and ranging (lidar) could enhance traditional mapping strategies compared to using optical data alone. Here, we used terrestrial laser scanning (TLS) to measure changes in forest structure along elevation gradients that may be indicative of degradation associated with increased inundation in the Delaware Bay estuary. We analyzed a set of TLS‐derived forest structure metrics to investigate their relationships with elevation, specifically seeking those that showed consistent change from the forest edge to the interior. Our findings revealed a consistent pattern between elevation and the plant area index (PAI), a metric that holds potential for enhancing the delineation of complex coastal ecosystem boundaries, particularly in relation to landward marsh migration. This work provides support for utilizing lidar‐derived forest structural metrics to enable a more accurate assessment of future marsh landscapes and the overall coastal carbon sink under accelerated SLR conditions. [ABSTRACT FROM AUTHOR]

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

Ecologists, foresters and conservation practitioners need 'biodiversity scanners' to effectively inventory biodiversity, audit conservation progress and track changes in ecosystem function. Quantifying biological diversity using remote sensing methods remains challenging, especially for small invertebrates. However, insect aggregations can drastically alter landscapes and vegetation, and these 'extended phenotypes' could serve as environmental landmarks of insect presence in remotely sensed data.To test the feasibility of this approach, we studied symbiotic ants that alter the canopy shape of whistling thorn acacias (Acacia [syn. Vachellia] drepanolobium), a keystone tree species of the black cotton soils of east African savannas. We demonstrate a protocol for using light detection and ranging (LiDAR) data to collect, prepare (including a customizable tree‐segmentation algorithm) and apply a convolutional neural network‐based classification for the detection of ant‐inhabited acacia tree phenotypic variations. Applying this protocol enabled us to effectively detect intra‐specific tree phenotypic variation induced by insects.Surveying ant occupancy across 16 ha and 9680 acacia trees took 1000 work hours, whereas surveyed patterns of ant distribution were replicated by our trained classifier using only an hour‐long airborne LiDAR collection time.We suggest that large‐scale surveys of insect occupancy (including insect‐vectored disease) can be automated through a combination of airborne LiDAR and machine learning. [ABSTRACT FROM AUTHOR]

Autonomous surface vehicles face the challenge of accurately detecting nearby ships in the complex and ever‐changing maritime environment, which is vastly different from land areas. To address this issue, we propose an image‐based multisensor fusion object detection method that combines Light Detection and Rangings and cameras. Since point clouds have poor semantics, our method primarily relies on images, with point clouds used to support image detection. Our image detection scheme employs a tracking‐assisted detection method that leverages historical information to compensate for possible detection failures. Additionally, we designed a confidence‐association‐based fusion strategy to determine the final targets among the candidates. We conducted field experiments in an open‐sea area to demonstrate the accuracy and robustness of our method. The results of these experiments showed that our method is highly accurate and robust in challenging maritime scenarios. Our code and data set will be released on https://github.com/flakeice/mssd. [ABSTRACT FROM AUTHOR]

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

Unmanned aerial vehicles (UAVs) offer many advantages over ground vehicles, including quadruped robots, based on high maneuverability when performing exploration in complex and unknown environments. However, due to their limited computational capability, UAVs require lightweight but accurate state estimation algorithms for reliable exploration. In this paper, we propose a segmented map‐based exploration system based on light detection and ranging (LiDAR)‐based state estimation for UAVs. The proposed system includes capabilities such as exploration, obstacle avoidance, and object detection with localization using three‐dimensional (3D) dense maps generated by tightly coupled LiDAR Inertial Odometry (LIO). Our proposed system is a hybrid system that can switch between guided and exploration modes, making it practical for search and rescue missions in disaster scenarios. The proposed LIO algorithm adapts to its surroundings, allowing for fast and accurate state estimation in complex environments. The proposed exploration algorithm is designed to cover specific regions in the 3D dense map generated by the proposed LIO, with the UAV determining if map points are included within the coverage area. We tested the proposed system in both simulation and real‐world environments and validated that the proposed system outperforms state‐of‐the‐art algorithms in various aspects such as localization accuracy and exploration efficiency in complex environments. [ABSTRACT FROM AUTHOR]

The technological developments behind autonomous vehicles cover several areas and engineers training in this field represents a challenge. The main layers include perception, decision making, and acting. In the first one, different technologies can be used. The processing of the information provided by the sensors must allow successive modules to understand the environment and Laser imaging Detection and Ranging (LiDAR) technology is one of the most promising ones nowadays for this task. It offers great robustness in detection, but the extraction of information from the point cloud involves the development of complex algorithms that could be very time‐consuming if an experimental teaching is intended. This article presents two educational solutions for deepening in perception algorithms using LiDAR for autonomous driving: a closed ad‐hoc computer application for two‐dimensional (2D) LiDAR point cloud processing and an oriented set of commands for three‐dimensional (3D) LiDARs in Matlab. Their use allows main concept exploration in practical sessions with little time consumption and provides students a general overview of the tasks that must be performed by the perception layer in the autonomous vehicles. Furthermore, these tools provide the possibility of organizing different activities in the classroom related to theoretical and experimental issues, and understanding of results because the most tedious tasks are eased. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

Path planning is a critical aspect of autonomous driving, necessitating the calculation of the most efficient route considering real‐time traffic conditions, road status, and obstacles. The primary goal of path planning algorithms is to identify the optimal route from a starting point to a destination within a given environment. In autonomous driving, these algorithms are broadly categorizedinto global planning and local planning. Noteworthy global path planning algorithms include Dijkstra's algorithm, A*, and others. Local path planning algorithms encompass local perception and obstacle avoidance, utilizing real‐time information from devices like laser radar and cameras. This approach, suitable for complex and dynamic environments, involves collision avoidance and path adjustments. Analysis reveals that both commonly used global planning algorithms and local path planning algorithms possess distinct advantages and drawbacks. Whether in simulation debugging or practical operations, the collaborative functioning of both types of algorithms is imperative for ensuring the safe and efficient operation of autonomous vehicles in complex and dynamic environments. [ABSTRACT FROM AUTHOR]

Nacelle‐mounted, forward‐facing light detection and ranging (LIDAR) technology can deliver benefits to rotor speed regulation and loading reductions of floating offshore wind turbines (FOWTs) when assisting with blade pitch control in above‐rated wind speed conditions. Large‐scale wind turbines may be subject to significant variations in structural loads due to differences in the wind profile across the rotor‐swept area. These loading fluctuations can be mitigated by individual pitch control (IPC). This paper presents a novel LIDAR‐assisted feedforward IPC approach that uses each blade's rotor azimuth position to allocate an individual pitch command from a multi‐beam LIDAR. In this study, the source code of OpenFAST wind turbine modelling software was modified to enable LIDAR simulation and LIDAR‐assisted control. The LIDAR simulation modifications were accepted by the National Renewable Energy Laboratory (NREL) and are now present within OpenFAST releases from v3.5 onwards. Simulations of a 15 MW FOWT were performed across the above‐rated wind spectrum. Under a turbulent wind field with an average wind speed of 17 ms−1, the LIDAR‐assisted feedforward IPC delivered up to 54% reductions in the root mean squared errors and standard deviations of key FOWT parameters. Feedforward IPC delivered enhancements of up to 12% over feedforward collective pitch control, relative to the baseline feedback controller. The reductions to the standard deviation and range of the rotor speed may enable structural optimization of the tower, while the reductions in the variations of the loadings present an opportunity for reduced fatigue damage on turbine components and, consequently, a reduction in maintenance expenditure. [ABSTRACT FROM AUTHOR]

Atoll nations such as Tuvalu are considered to be amongst those most vulnerable to the effects of climate change. Here we present a national‐scale coastal flood hazard assessment for Tuvalu based on high‐resolution Light Detection and Ranging (LiDAR) topography and bathymetry. We follow a fully probabilistic approach, considering sea level anomalies, tides, and extreme wave conditions from a mixed climate (i.e., from distant extra‐tropical storms and local tropical cyclones). Nearshore processes such as wave setup and runup are also accounted for. Hazard maps were calculated for the present sea level, as well as for sea level rise projections corresponding to different shared socioeconomic pathways (SSP2 4.5 and SSP5 8.5) and time horizons (2060 and 2100). With a mean elevation of 1.55 m above mean sea level (1.37 m above mean high water spring) >25% of land area is inundated once every 5 years and >50% of land area floods once every 100 years nationally. Results indicate that present day 1‐in‐50 years floods (>45% of land area flooded) will occur more than once every 5 years by 2060 (annual exceedance probability >20%), even under the moderate SSP2 4.5 sea level rise projections. Results of this study highlight the pressing need for ambitious and large‐scale adaptation solutions which are commensurate with projected sea level rise and marine hazard impacts. The methodologies presented in this paper can easily be applied to other low‐lying islands in the tropical Pacific, where mixed climates (i.e., regular and TC conditions) and non‐linear nearshore processes dominate extreme water levels and flooding. Plain Language Summary: Low‐lying atoll nations such as Tuvalu are widely recognized to be amongst those most impacted by the effects of climate change. To make informed adaptation decisions, accurate baseline data (i.e., topography and bathymetry) and marine hazard information are fundamental. In this paper we present a national‐scale coastal flood hazard assessment for Tuvalu based on state‐of‐the art high‐resolution baseline data and statistical and numerical models. We considered the present‐day sea levels and sea level rise projections corresponding to different climate change scenarios. Under present‐day sea levels >25% of Tuvalu's land area floods once every 5 years and >50% of land area floods once every 100 years. Our results indicate a significant increase in severity and frequency of extreme coastal flooding due to climate change with present‐day 1‐in‐50‐year floods occurring more than once every 5 years by 2060. This study highlights the pressing need for ambitious and large‐scale adaptation solutions. The methodology presented here is suitable to be used in other Pacific Island locations. Key Points: We present a probabilistic flood hazard assessment of Tuvalu considering tides, sea level anomalies, storm surges, and waves from a mixed climate (i.e., generated by tropical and extratropical storms)A mean elevation of 1.55 m above MSL makes Tuvalu highly vulnerable to wave driven flooding with >25% of land area inundated once every 5 yearsPresent day 1‐in‐50 years floods (>45% of land area flooded) will occur more than once every 5 years by 2060 due to sea level rise [ABSTRACT FROM AUTHOR]

The total mass flux due to meteoric input is not well constrained and estimates vary greatly depending on the measurement technique used. The source of this discrepancy remains an open question in the field. Previous studies investigating the discrepancy by directly comparing mass estimates made using two techniques have been limited by extremely small sample sizes. This work presents a set of 166 meteors observed simultaneously by the MAARSY radar (53.5 MHz) and two nearby optical cameras. Independent masses are estimated using observations from both systems and compared against each other. The resulting mass estimates using both methods agree to within a factor of three on average. The results show two dominant trends: better agreement as meteoroid velocity increases and underestimation of the radar mass for the largest meteoroids observed (>10 mg). These trends had not been quantified by previous studies limited by very small sample sizes, and could help to explain the historic discrepancy between mass estimates by different systems. The general agreement between the radar and photometric masses indicates that both methods perform well independently, and can reliably be applied to radar or optical observations without restriction of simultaneous observations by two systems. [ABSTRACT FROM AUTHOR]

Wildlife depends on specific landscape features to persist. Thus, characterizing the vegetation available in an area can be essential for management. The ocelot (Leopardus pardalis) is a federally endangered, medium‐sized felid adapted to woody vegetation. Quantifying the characteristics of vegetation most suitable for ocelots is essential for their conservation. Furthermore, understanding differences in the selection of sympatric bobcats (Lynx rufus) and coyotes (Canis latrans) can provide insight into the mechanisms of coexistence between species. Because of differences in hunting strategy (cursorial vs. ambush) and differences in use of land cover types between species, these three carnivores may be partitioning their landscape as a function of vegetation structure. Light detection and ranging (LiDAR) is a remote sensing platform capable of quantifying the sub‐canopy structure of vegetation. Using LiDAR data, we quantified the horizontal and vertical structure of vegetation cover to assess habitat selection by ocelots, bobcats, and coyotes. We captured and collared 8 ocelots, 13 bobcats, and 5 coyotes in southern Texas from 2017 to 2021. We used step selection functions to determine the selection of vegetation cover at the population and individual level for each species. Ocelots selected for vertical canopy cover and dense vegetation 0–2 m in height. Bobcats selected cover to a lesser extent and had a broader selection, while coyotes avoided under‐story vegetation and selected areas with dense high canopies and relatively open understories. We observed a high degree of variation among individuals that may aid in facilitating intraspecific and interspecific coexistence. Management for ocelots should prioritize vegetation below 2 m and vertical canopy cover. We provide evidence that fine‐scale habitat partitioning may facilitate coexistence between sympatric carnivores. Differences among individuals may enhance coexistence among species, as increased behavioral plasticity of individuals can reduce competition for resources. By combining accurate, fine‐scale measurements derived from LiDAR data with high‐frequency global positioning system locations, we provide a more thorough understanding of the habitat use of ocelots and two sympatric carnivores. [ABSTRACT FROM AUTHOR]

Cover image: Fire fighters use a cool evening to apply prescribed burns in a South African savanna. New research uses high‐resolution Light Detection and Ranging (LiDAR) to investigate the long‐term effects of fire manipulation on grass structure and grazing lawn establishment in Kruger National Park, South Africa. (pp.1531–1545).Photo: © Jason E. Donaldson.. [Extracted from the article]

A massive landslide occurred in May 2015 in the Mt. Hakusan Sennindani area, leading to the discharge of a substantial amount of sediment into the Tedori River. We conducted a cross‐sectional analysis of secondary data to assess the potential effects of discharged sediments on the downstream environment, including long‐term turbidity in rivers, alterations in fish habitats, and groundwater depletion. To analyse the spatio‐temporal changes in the river floodplain elevation and paddy fields, aerial photographs and airborne light detection and ranging data were assessed using the ArcGIS software. After the landslide, the turbidity of the Tedori River increased and continued flowing turbid for approximately 6 months. Turbid water spread in the alluvial fan through the irrigation canal network and sediment was deposited in the paddy fields, leading to a reduction in infiltration rates. The groundwater level in the alluvial fan area decreased by more than 2.0 m following the 2015 Sennindani landslide. The sediment deposition in the river floodplain increased by 0.58 m from 2013 to 2015 (pre‐ and post‐landslide). The sediment from upstream destroyed the spawning sites of the Ayu fish along the Tedori River, leading to a decrease in the number of eggs laid in 2015 and 2016 to the lowest levels. The Tomiyo fish disappeared in 2016 and 2017, downstream of the alluvial fan, which received water recharge from the Tedori River. Moreover, Chum salmon showed an exceptionally high anadromous movement towards the Tedori River from coastal areas during 2015 and 2016. In conclusion, discharged sediment from deep upstream landslides can have various adverse impacts on downstream ecosystems, and recovery to their original state may require a considerable amount of time. [ABSTRACT FROM AUTHOR]

The accurate modeling of urban microclimate is a challenging task given the high surface heterogeneity of urban land cover and the vertical structure of street morphology. Recent years have witnessed significant efforts in numerical modeling and data collection of the urban environment. Nonetheless, it is difficult for the physical‐based models to fully utilize the high‐resolution data under the constraints of computing resources. The advancement in machine learning (ML) techniques offers the computational strength to handle the massive volume of data. In this study, we proposed a modeling framework that uses ML approach to estimate point‐scale street‐level air temperature from the urban‐resolving meso‐scale climate model and a suite of hyper‐resolution urban geospatial data sets, including three‐dimensional urban morphology, parcel‐level land use inventory, and weather observations from a sensor network. We implemented this approach in the City of Chicago as a case study to demonstrate the capability of the framework. The proposed approach vastly improves the resolution of temperature predictions in cities, which will help the city with walkability, drivability, and heat‐related behavioral studies. Moreover, we tested the model's reliability on out‐of‐sample locations to investigate the modeling uncertainties and the application potentials to the other areas. This study aims to gain insights into next‐gen urban climate modeling and guide the observation efforts in cities to build the strength for the holistic understanding of urban microclimate dynamics. Plain Language Summary: Estimating air temperature at street‐level is a difficult task because of the complex environment in cities and the limitations of the current urban numerical models. In recent years, with the rapid development of data collection and analysis techniques, it is possible to fully utilize the hyper‐local data harvested from urban areas by advanced machine learning algorithms. In this study, we present a modeling pipeline to estimate point‐scale street‐level air temperature from conventional urban weather model and a suite of hyper‐resolution urban data sets. These data sets were collected using state‐of‐art techniques, such as sub‐meter level Light Detection and Ranging technology and wireless weather observation network. We demonstrated the use of this modeling method over the City of Chicago and estimated the street‐level temperature. The modeling results have multiple real‐world applications, such as to provide navigation suggestions to reduce thermal discomfort of the pedestrians as an example. Moreover, it is possible to expand the use of our model to other areas given the current data availability. The results of this study can also help the development of the next‐generation urban climate and weather models and guide observation efforts in cities. These together can build the strength for the holistic understanding of urban microclimate dynamics. Key Points: The study presents a modeling framework to estimate street‐level air temperature using a suite of detailed urban climate informaticsModel results showed hyper‐local urban features have significant impacts on street‐level temperature but with a limited influence radiusThe investigations on model sensitivity implies the existence of the optimum scale in urban modeling and critical locations in observation [ABSTRACT FROM AUTHOR]

Simulating realistic three‐dimensional (3D) forest scenes is useful in understanding the links between forest structure and ecosystem functions (e.g. radiative transfer). Light detection and ranging (LiDAR) technology provides useful 3D data for forest reconstructions since it can characterise 3D structures of individual trees and canopies. High‐density terrestrial LiDAR (terrestrial laser scanning, TLS) is suitable for fine‐scale reconstructions but is limited to smaller forest plots; low‐density airborne LiDAR (airborne laser scanning, ALS) can cover larger areas but is only suitable for coarse‐scale reconstructions. How to take advantage of TLS and ALS to enable fine‐scale forest simulations in large areas needs to be studied.We propose a new voxel‐based method for forest simulations using the integration of TLS and ALS data. TLS data of representative reference trees are used to approximate the detailed architectures of the whole forest scene, with structural information on each individual tree extracted from ALS data. The high‐density point cloud data derived from TLS and ALS data are voxelised using high resolution solid voxels for scene representation. We tested the proposed method using two virtual forests (108 m × 108 m) and a real forest (300 m × 300 m) with conifer and broadleaf species. The physically based ray tracer (PBRT) was used to visualise the true virtual forest scenes, whereas voxel‐based radiative transfer (VBRT) was used to visualise the modelled forest scenes from LiDAR data. For the real forest scene, simulated and real ALS data were compared.Our results demonstrate that the images simulated by VBRT and PBRT are similar in the virtual forest scenes, with average radiance values of 1.02 and 1.72, respectively. In the real forest scene, the distributions of points and individual tree attributes (tree height, crown radius, and tree volume) derived from real and simulated ALS also match well, with Kullback–Leibler divergence ranging from 0.006 to 0.06.We conclude that the new method is capable of modelling fine‐scale 3D forests in large areas (over 1 ha) when TLS and ALS data are available, and it has good potential in studying the process of radiative transfer in conifer and broadleaf forests. [ABSTRACT FROM AUTHOR]

In recent decades, fluvial geomorphology and ecohydraulic research have extensively used field observations, remote sensing or hydrodynamic modelling to understand river systems. This study presents an innovative approach that combines field surveys, Light Detection and Ranging (LiDAR)‐based topographical and biomass analyses and model‐derived hydro‐morphodynamic geostatistics to examine short‐term biogeomorphological changes in the wandering gravel‐bed Orco River in Italy. Our primary hypothesis is that hydro‐morphological variables can be robust descriptors for riparian vegetation distribution. From a geomorphological perspective, our study confirms the prevalent wandering behaviour of the Orco River. Moreover, we identified a widening trend in braiding and anabranching sections, particularly downstream. This is evident because of hotspots of flood‐induced morphological reactivation and the redistribution of sediments from the riverbed to lateral bars, resulting in a multi‐thread pattern. Our analysis reveals a net increase in biomass during the observation period despite frequent flood disturbances. We attributed it to two opposing biogeomorphological dynamics: the reduced flow disturbance in some regions due to flood‐induced geomorphological changes and the self‐healing of lateral connectivity through river wandering. Such a net increase indicates that transitional rivers store carbon in the form of vegetation biomass due to their short‐term morphological instability and the different timescales between vegetation and morphological adjustments. Finally, we supported our initial hypothesis with three key findings: (i) a signature of vegetation not just on topography but also on hydro‐morphological conditions, summarised by inundation probability; (ii) the lower variance in vertical topographical changes in vegetated areas compared with bare ones; and (iii) the introduction of a new parameter, named inundation viscosity, derived from the product of mean bed shear stress and average inundation duration, as a discriminating factor for colonisation conditions. These results underscore the value of our comprehensive approach. [ABSTRACT FROM AUTHOR]

Permafrost study in the Spanish Central System (~41°N) has remained elusive in past decades. Although numerous periglacial features have been described, none has yielded conclusive information about the existence/distribution of permafrost across these mountains. This work focuses on integrating light detection and ranging and unmanned aerial vehicle data with geophysical methods and sedimentary records, along with preliminary thermal information, for the comprehensive study of a frost mound. The singularity of this feature resides in its location, currently dominated by a seasonal frost regime, and its size reaching 59 m long, 22.5 m wide and 4 m high, representing the largest landform of this type in Iberia. From top‐to‐bottom, the internal structure comprises three resistivity units (G1, G2 and G3). The most superficial unit (G1) consists of a sequence of burial soils alternating with sand and silts, with pebbles and gravel at the bottom, followed by a high porous layer that belongs to a landslide deposit (G2), likely composed of gravel and pebbles laying over unit G3 (bedrock). Calibrated 14C ages suggest this landform formed at least ~4300 years ago during the mid‐Holocene. Finally, the preliminary surface thermal map indicates the presence of sectors affected by strong thermal gradients likely driven by water table variations. Our results conclude that the dynamics are primarily controlled by the seasonal frost regime influencing local subsurface drainage, which allows us to classify this landform as a hydrodynamic pingo‐like structure. We also demonstrate that our approach is suitable for assessing the evolution of this type of periglacial landform. Implementing long‐lasting annual monitoring programmes would help to understand local periglacial dynamics better. [ABSTRACT FROM AUTHOR]

Light detection and ranging (lidar) has emerged as a valuable tool for examining the fine‐scale characteristics of vegetation. However, lidar is rarely used to examine coastal wetland vegetation or the habitat selection of small mammals. Extensive anthropogenic modification has threatened the endemic species in the estuarine wetlands of the California coast, such as the endangered salt marsh harvest mouse (Reithrodontomys raviventris; SMHM). A better understanding of SMHM habitat selection could help managers better protect this species. We assessed the ability of airborne topographic lidar imagery in measuring the vegetation structure of SMHM habitats in a coastal wetland with a narrow range of vegetation heights. We also aimed to better understand the role of vegetation structure in habitat selection at different spatial scales. Habitat selection was modeled from data compiled from 15 small mammal trapping grids collected in the highly urbanized San Francisco Estuary in California, USA. Analyses were conducted at three spatial scales: microhabitat (25 m2), mesohabitat (2025 m2), and macrohabitat (~10,000 m2). A suite of structural covariates was derived from raw lidar data to examine vegetation complexity. We found that adding structural covariates to conventional habitat selection variables significantly improved our models. At the microhabitat scale in managed wetlands, SMHM preferred areas with denser and shorter vegetation and selected for proximity to levees and taller vegetation in tidal wetlands. At the mesohabitat scale, SMHM were associated with a lower percentage of bare ground and with pickleweed (Salicornia pacifica) presence. All covariates were insignificant at the macrohabitat scale. Our results suggest that SMHM preferentially selected microhabitats with access to tidal refugia and mesohabitats with consistent food sources. Our findings showed that lidar can contribute to improving our understanding of habitat selection of wildlife in coastal wetlands and help to guide future conservation of an endangered species. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) sensor is widely recognized as a critical component for accurate perception. However, there are a host of challenges that impede their performance, including low spatial resolution, high costs, large size, low reliability, and susceptibility to interference. It is challenging to overcome these issues using a single LiDAR module, necessitating the need for a review of current LiDAR technologies. The paper commences by introducing the fundamental principles of various laser rangefinders and discussing the optical modulation technologies used to prevent interference and ghost images. Next, the paper delves into the latest developments in laser technology, with a focus on enhancing the switching rate, compliance with eye safety regulations, miniaturization, and improving stability. One highly promising innovation is the photonic crystal surface emitting laser (PCSEL), a novel light source that boasts high‐speed, small divergence angles, and high‐power output. Finally, the paper discusses the advancements made in non‐solid‐state scanning and solid‐state scanning, such as improving stability, increasing scanning angles, and optimizing the manufacturing of mechanical and micro‐electromechanical systems (MEMS). Additionally, the paper highlights the recent advancements in nanotechnology, specifically metasurface technology, which offers superior capabilities such as beam deflection, enhanced field‐of‐view (FOV), and dynamic modulation. [ABSTRACT FROM AUTHOR]

Large‐scale disturbances such as wildfire can have profound impacts on the composition, structure, and functioning of ecosystems. Bees are critical pollinators in natural settings and often respond positively to wildfires, particularly in forests where wildfire leads to more open conditions and increased floral resources. The use of Light Detection and Ranging (LiDAR) provides opportunities for quantifying habitat features across large spatial scales and is increasingly available to scientists and land managers for post‐fire habitat assessment. We evaluated the extent to which LiDAR‐derived forest structure measurements can predict forest bee communities after a large, mixed‐severity fire. We hypothesized that LiDAR measurements linked to post‐fire forest structure would improve our ability to predict bee abundance and species richness when compared to satellite‐based maps of burn severity. To test this hypothesis, we sampled wild bee communities within the Douglas Fire Complex in southwestern Oregon, USA. We then used LiDAR and Landsat data to quantify forest structure and burn severity, respectively, across bee sampling locations. We found that the LiDAR forest structure model was the best predictor of abundance, whereas the Landsat burn severity model had better predictive ability for species richness. Furthermore, the Landsat burn severity model was better at predicting the presence and species richness of bumble bees (Bombus spp.), an ecologically distinct and economically important group within the Pacific Northwest. We posit that the divergent responses of the two modeling approaches are due to distinct responses by bee taxa to variation in forest structure as mediated by wildfire, with bumble bees in particular depending on closed‐canopy forest for some portions of their life cycle. Our study demonstrates that LiDAR data can provide information regarding the drivers of bee abundance in post‐wildfire conifer forest, and that both remote sensing approaches are useful for predicting components of wild bee diversity after large‐scale wildfire. [ABSTRACT FROM AUTHOR]

In this study, three commercially available low‐density polyethylene (LDPE) polymers produced via a tubular reactor process, with varying melt flow rates at 190°C/2.16 kg (4.0, 1.9, and 0.75 g/10 min), have been selected and subjected to high temperature‐size exclusion chromatography (SEC) analysis coupled with an infrared‐5 (IR‐5), viscometer (VISCO), and multiangle laser light‐scattering detectors. The molecular weight (MW), MW distribution, short‐chain branching (SCB), and long‐chain branching parameters were investigated. It was found that MW obtained by the universal technique (∼1.57–1.7 times) and multiangle laser light‐scattering detection technique is (∼1.43–1.55 times) higher than that of the conventional calibration technique, which could be attributed to structural complexity associated with LDPEs which is not clearly understood by conventional SEC mode alone. The bulk SCB per 1000 total carbon atoms estimated by IR‐5 detection was found to range from 16.50 to 17.80. On the other hand, long chain branching frequency per 1000 total carbon atoms obtained by online VISCO and multiangle laser light‐scattering detection ranged from 0.46 to 0.54 and 0.65 to 0.94, respectively. Further, the significance of long chain branching parameters on the polymer processing behavior was studied in correlation with rheological property (Die swell ratio). [ABSTRACT FROM AUTHOR]

The Bahamian Archipelago has been the subject of extensive studies of stratigraphy, carbonate island morphology and diagenetic overprinting for many decades. A recently‐acquired dataset comprised of high‐resolution light detection and ranging, tightly spaced cored boreholes with image logs, thin sections, porosity and permeability measurements, and isotopic geochemical analyses provides a unique opportunity to perform detailed sequence stratigraphic analysis. This multi‐faceted study first establishes a new high‐resolution sequence stratigraphic framework for the Sandy Point area of San Salvador Island, encompassing six unconformity bound sequences (exposure surfaces) representing varying lengths of missing time. The stratigraphically lowest sequence is Late Pliocene in age and is dominated by reef facies capped by an extensive laminar caliche that represents 1.5 million years of exposure. The overlying Early Pleistocene is split into two sequences, EP1 and EP2, which are both dominated by low‐energy subtidal facies. The upper three sequences are tied to the Marine Isotope Stage 11, 9 and 5e interglacials, and are distinctly different in facies composition and architecture, being dominated by higher energy facies distributed in a more complex mosaic. The new stratigraphic framework highlights significant changes in depositional style and facies architecture that can be linked to increasing sea‐level amplitude oscillations and greater climate gradient at the Mid Pleistocene Transition. The well‐constrained framework is then used to provide key context for observed complex diagenetic evolution through repeated sea‐level inundation and subaerial exposure. These results suggest that well‐developed subaerial exposure surfaces drive increases in non‐matrix features in the subsequent stratigraphic package following exposure as the well‐cemented exposure surface acts to concentrate fluid flow and dissolution. Integration of non‐matrix features within this framework highlights the importance of dissolution/cementation patterns on formation of these modified unconformity surfaces for focusing later meteoric diagenesis and creating enhanced fluid flow pathways for continued multicyclic diagenetic events. [ABSTRACT FROM AUTHOR]

We are currently experiencing an unprecedented moment in forest restoration, where monitoring activities in recovery areas play a vital role in problem identification and method adaptation. However, it is crucial to shift away from expensive traditional methodologies and develop technologies that enable large‐scale monitoring using more accessible and cost‐effective tools. In an effort to provide an evidence‐based perspective, we conducted a systematic review of how remotely piloted aircraft systems equipped with various sensors have been employed for forest monitoring. We consulted three databases and included 53 articles in our review. The results revealed a trend toward research in tropical ecosystems, with forest structure being the most frequently assessed attribute, and canopy height being the most widely measured structural indicator. Red‐green‐blue sensors were commonly used, both individually and in combination with others, and there was a noticeable shift toward the use of light detection and ranging. Data validation primarily relied on forest inventory methods, often involving comparisons of outputs from different sensors and the use of artificial intelligence algorithms. Despite the wide range of studies utilizing sensor‐equipped drones to analyze forest attributes, there is a notable scarcity of research specifically addressing the application of these technologies in forest restoration monitoring. Filling this research gap is essential, as employing techniques that enable large‐scale monitoring, such as aerial photogrammetry and remote sensing, aligns with current trends and contributes to global commitments to environmental restoration and conservation. [ABSTRACT FROM AUTHOR]

Chromatic confocal technology has enabled multispectral information detection without any axial mechanical scanning. Especially, the chromatic confocal sensor is well applied in industry, such as device defect detection and ranging. However, for 3D imaging, chromatic confocal microscopy (CCM) still suffers from insufficient speed due to the slow refresh rate of a traditional spectrometer. To address this problem, a time‐stretch chromatic confocal microscopy (TSCCM) is presented for multi‐depth imaging. A supercontinuum laser is stretched and then focused on the sample using a home‐built chromatic lens, which disperses the laser to a different depth. The time‐of‐flight signal is collected by a high‐speed photodiode and is recorded and analyzed by a 3 GHz digitizer. A novel approach is achieved to significantly improve the speed of multi‐depth imaging with an up to 1 MHz A‐scan rate and 5 Hz volumetric imaging speed, which is an order faster compared with previous work of spectrometer‐based chromatic confocal microscopy. Multi‐depth volumetric imaging of nude‐mouse skin is performed to show the potential for biomedical applications. In this method, with its high A‐scan rate and multi‐depth imaging capability, a virtual tissue "light detection and ranging (LIDAR)" may be achieved. [ABSTRACT FROM AUTHOR]