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Optical remote sensing allows to efficiently monitor forest ecosystems at regional and global scales. However, most of the widely used optical forward models and backward estimation methods are only suitable for forest canopies in flat areas. To evaluate the recent progress in forest remote sensing over complex terrain, a satellite-airborne-ground synchronous Fine scale Optical Remote sensing Experiment of mixed Stand over complex Terrain (FOREST) was conducted over a 1 km×1 km key experiment area (KEA) located in the Genhe Reserve Areain 2016. Twenty 30 m×30 m elementary sampling units (ESUs) were established to represent the spatiotemporal variations of the KEA. Structural and spectral parameters were simultaneously measured for each ESU. As a case study, we first built two 3D scenes of the KEA with individual-tree and voxel-based approaches, and then simulated the canopy reflectance using the LargE-Scale remote sensing data and image Simulation framework over heterogeneous 3D scenes (LESS). The correlation coefficient between the LESS-simulated reflectance and the airborne-measured reflectance reaches 0.68–0.73 in the red band and 0.56–0.59 in the near-infrared band, indicating a good quality of the experiment dataset. More validation studies of the related forward models and retrieval methods will be done. [ABSTRACT FROM AUTHOR]

Accurately monitoring forest insects and diseases disturbances is important for managing forests and implementing effective forest quality improvement measures. However, monitoring disturbances in the lower and middle parts of the forest canopy with traditional remote sensing technologies is difficult. A new sensor called hyperspectral LiDAR (HSL) makes the monitoring of these forest disturbances possible, however, its applicability to canopy scale has not been fully studied due to the current hardware limitation. This paper assessed the potential of airborne hyperspectral LiDAR (AHSL) for monitoring forest insects and diseases stress using 3D radiative transfer modeling and in-situ measurements. A virtual 3D forest scene with explicitly described structures was first reconstructed from terrestrial laser scanning and field measurement data, upon which a number of different insects and diseases disturbance scenarios with different damage locations and stress levels were defined. AHSL point cloud and the corresponding hyperspectral image (HI) were then simulated with large-scale and remote sensing image simulation (LESS) model for each combination of the different damage locations and stress levels. LiDAR point cloud from different layers of the simulated AHSL point cloud were then extracted and rasterized into images with 3-m spatial resolution, which, along with the hyperspectral images, were used to test the insect and disease monitoring ability by using a random forest model. Results show that AHSL has significant higher overall accuracies (OA: 65.95% ∼ 89.45%) than HI (OA: 33.99% ∼ 57.02%) for predicting insects and diseases stress levels. Compared to AHSL, HI is affected by a variety of factors such as soil and shadows, resulting in poorer monitoring capability for different damage locations, with the highest classification accuracy in the case of entire canopy damage (OA: 57.02%). For AHSL, it has good classification accuracy for all damage locations, with the lowest accuracy in the case of lower canopy damage (OA: 65.95%). This study demonstrates that AHSL is a promising and reliable tool to monitor forest disturbances, especially for structural and spectral changes in the lower and middle parts of canopy, which may have great potential for early detecting forest insects and diseases. • Explicitly described 3D damaged virtual forests were constructed from TLS. • The potential of AHSL to detect forest pests and diseases was assessed using 3D RTM. • AHSL shows higher accuracy to detect pests and diseases than hyperspectral image. • AHSL is capable of detecting pest and disease in lower and middle forest canopy. [ABSTRACT FROM AUTHOR]

Abstract Three-dimensional (3D) radiative transfer modeling of the transport and interaction of radiation through earth surfaces is challenging due to the complexity of the landscapes as well as the intensive computational cost of 3D radiative transfer simulations. To reduce computation time, current models work with schematic landscapes or with small-scale realistic scenes. The computer graphics community provides the most accurate and efficient models (known as renderers) but they were not designed specifically for performing scientific radiative transfer simulations. In this study, we propose LESS, a new 3D radiative transfer modeling framework. LESS employs a weighted forward photon tracing method to simulate multispectral bidirectional reflectance factor (BRF) or flux-related data (e.g., downwelling radiation) and a backward path tracing method to generate sensor images (e.g., fisheye images) or large-scale (e.g. 1 km2) spectral images. The backward path tracing also has been extended to simulate thermal infrared radiation by using an on-the-fly computation of the sunlit and shaded scene components. This framework is achieved through the development of a user-friendly graphic user interface (GUI) and a set of tools to help construct the landscape and set parameters. The accuracy of LESS is evaluated with other models as well as field measurements in terms of directional BRFs and pixel-wise simulated image comparisons, which shows very good agreement. LESS has the potential in simulating datasets of realistically reconstructed landscapes. Such simulated datasets can be used as benchmarks for various applications in remote sensing, forestry investigation and photogrammetry. Highlights • Forward photon tracing and backward path tracing are implemented in LESS. • LESS has been extended to simulate thermal infrared images. • LESS supports simulating large-scale spectral images based on 3D landscapes. • LESS provides an easy-to-use GUI and related tools to construct the 3D scene [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) is a widely used technology for the acquisition of three-dimensional (3D) information about a wide variety of physical objects and environments. However, before conducting a campaign, a test is typically conducted to assess the potential of the utilized algorithm for information retrieval. It might not be a real campaign but rather a simulation to save time and costs. Here, a multi-platform LiDAR simulation model considering the location, direction, and wavelength of each emitted laser pulse was developed based on the large-scale remote sensing (RS) data and image simulation framework (LESS) model, which is a 3D radiative transfer model for simulating passive optical remote sensing signals using the ray tracing algorithm. The LESS LiDAR simulator took footprint size, returned energy, multiple scattering, and multispectrum LiDAR into account. The waveform and point similarity were assessed with the LiDAR module of the discrete anisotropic radiative transfer (DART) model. Abstract and realistic scenes were designed to assess the simulated LiDAR waveforms and point clouds. A waveform comparison in the abstract scene with the DART LiDAR module showed that the relative error was lower than 1%. In the realistic scene, airborne and terrestrial laser scanning were simulated by LESS and DART LiDAR modules. Their coefficients of determination ranged from 0.9108 to 0.9984 . Their mean was 0.9698 . The number of discrete returns fitted well and the coefficient of determination was 0.9986 . A terrestrial point cloud comparison in the realistic scene showed that the coefficient of determination between the two sets of data could reach 0.9849 . The performance of the LESS LiDAR simulator was also compared with the DART LiDAR module and HELIOS++. The results showed that the LESS LiDAR simulator is over three times faster than the DART LiDAR module and HELIOS++ when simulating terrestrial point clouds in a realistic scene. The proposed LiDAR simulator offers two modes for simulating point clouds: single-ray and multi-ray modes. The findings demonstrate that utilizing a single-ray simulation approach can significantly reduce the simulation time, by over 28 times, without substantially affecting the overall point number or ground pointswhen compared to employing multiple rays for simulations. This new LESS model integrating a LiDAR simulator has great potential in terms of simultaneously simulating LiDAR data and optical images based on the same 3D scene and parameters. As a proof of concept, the normalized difference vegetation index (NDVI) results from multispectral images and the vertical profiles from multispectral LiDAR waveforms were simulated and analyzed. The results showed that the proposed LESS LiDAR simulator can fulfill its design goals. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) radiative transfer simulations are critical for studying the radiometric properties of canopies. Efficient and easy-to-use 3D radiative transfer models are required by remote sensing inversion and many validation applications. Extensive efforts have been made to improve the computational efficiency, accuracy, and useability of 3D radiative transfer models. This study focuses on the abstraction of canopies for 3D radiative transfer simulations by proposing a lightweight boundary-based description of leaf clusters (B-cluster) to ease the creation of 3D scenes while keeping the simulation as accurate as possible. B-cluster partitions a tree crown into sub-crown leaf clusters and abstracts each of them into a turbid medium enclosed by a complex and tight boundary, while terrain and branches are described with precise mesh surfaces. The radiative transfer simulation within B-cluster has been developed based on an efficient Monte Carlo path-tracing algorithm and implemented in the LargE-Scale remote sensing data and image Simulation framework (LESS) model by considering the presence of both turbid medium and surface scattering. The performance of the model was assessed by comparing with original LESS version, which describes all landscape elements with mesh surfaces (here called M-surface approach), and with a uniform voxel-based approach (U-voxel) in terms of the multiangle bidirectional reflectance factor (BRF) as well as with pixel-wise images. Results show that B-cluster is highly consistent with M-surface in abstract canopies (mean normalized absolute BRF differences δ ¯ < 2%) and in realistic forest stand (δ ¯ < 5% at 5-m resolution) with considerably reduced requirements for computational memory. Compared with U-voxel, B-cluster is also more robust and better at describing canopy structures with different levels of detail. B-cluster enables to quickly construct accurate 3D scenes with reduced requirements of computational resources. It is also a unified and scale-adaptive approach that can describe crowns as simple as geometric primitives and as complex as explicitly described meshes. The newly proposed approach has been released in new LESS versions at http://lessrt.org/. • A boundary-based description of leaf clusters for 3D RT simulations is proposed. • It is lightweight to represent canopy with reduced need for 3D information. • Surface and volume scattering are considered in a unified RT modeling framework. • It uses less computational resources for giving comparable or higher accuracy. • It is a unified representation scheme and a superset of other approaches. [ABSTRACT FROM AUTHOR]

Pinellia ternata tuber (PTT), the dried tuber of Pinellia ternata (Thunb.) Breit., has a long history of use in traditional Chinese medicine for drying dampness, resolving phlegm, down-bearing counterflow to check vomiting and dissipating masses. Modern pharmacology studies have revealed that PTT has diverse pharmacological effects such as antitussive and expectorant, anti-emetic, anti-tumor, and anti-inflammatory effect, etc. This review aims to provide a critical and comprehensive evaluation on ethnopharmacological uses, chemical constituents, pharmacological and toxicological effects, analytical methods and quality control of PTT, which would provide scientific evidence for exploring future therapeutic, and formulating quality and safety criteria of PTT. Pertinent information was systematically collected from several electronic scientific databases including Web of Science, Science Direct, PubMed, Elsevier, Wiley Online Library and China national knowledge infrastructure (CNKI), as well as the classic Chinese medical books. PTT is reported to be widely used traditionally for the treatment of cough, vomiting, infection, and inflammatory diseases in many southeast Asian countries. Phytochemical studies have revealed the presence of a total of 233 compounds belonging to alkaloids, nucleosides, organic acids, polysaccharides, volatile oils, amino acids, proteins, starches, etc. The extracts and components of PTT have possessed diverse pharmacological activities, such as antitussive, antiemetic, antitumor, antibacterial, and sedative-hypnotic activities. Raw P. ternata tuber (RPTT) with a pungent taste causes acrid irritation of the oral and laryngopharynx mucosa when taken by mistake, while its toxicity and side effects of RPTT can be dramatically reduced with proper processing. Three kinds of processed P. ternata tuber with different processing methods are available and traded in market, as well as applied in clinical treatments. Additionally, although raw or processed PTT have been recorded in several mainstream pharmacopoeias such as Chinese Pharmacopoeia, Japanese Pharmacopoeia, and Korean Pharmacopoeia, the quality items and requirements varies a lot. Therefore, a unified international standard of raw and processed PTT is urgent need to be done. The ethnopharmacological, phytochemical, pharmacological and toxicological and quality evaluation of PTT were highlighted in this review, which provides potential reference information to future investigate and commercially explore for pharmaceutical applications. Nevertheless, an efficient method for chemical profiling is still unavailable to find potent bioactive markers for quality control, and then comprehensive pharmacological effects and mechanisms and toxicological evaluation of PTT require further detailed research to ensure their quality and safety. [Display omitted] • This is the first review to comprehensively summarize the traditional uses, phytochemistry, pharmacological effects, toxicity and quality evaluation of Pinellia ternata (Thunb.) Breit. • A total of 233 compounds belonging to different chemical classes were isolated from PTT. • Analytical methods and quality control of PTT were discussed in detail. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Synergic effects between metal hydrides and borohydrides are proposed. • Dehydrogenation temperature of KBH 4 is greatly decreased for the mixture. • The apparent activation energy significantly reduced due to the mixture. Abstract Synergic effects of rare earth-magnesium based hydrides (LaH 3 -MgH 2) on thermal dehydrogenation of potassium borohydride (KBH 4) are discussed in the paper. Comparing to above 675 K for the LaH 3 -MgH 2 or KBH 4 , the dehydrogenation temperature can be decreased to 346 K for the composites (LaH 3 -MgH 2 -KBH 4). The apparent activation energy of desorption process for the mixture is also greatly reduced. On the other hand, the products in the temperature programmed desorption process are also illustrated. [ABSTRACT FROM AUTHOR]

The influence of leaf temperature on transpiration, photosynthesis, respiration, and other metabolic activities is critical to plant growth, development, production and distribution. However, traditional measurement of canopy temperature by thermocouples or thermal infrared thermometers is laborious and difficult, especially for tall trees. The recent development of a handheld thermal infrared imager has made it possible to perform high temporal and spatial canopy temperature measurements efficiently. However, the signal recorded by the sensor is often a mixture of radiation from the target and the atmosphere, which must be corrected to get the true temperature. In this study, we propose a ground-based indirect measurement method of canopy temperature by a handheld thermal infrared imager through upward observation. Visible and thermal images are combined to distinguish the canopy pixels and sky pixels. To remove the atmospheric radiation from the sky, an empirical atmospheric model is established, which can perform atmospheric correction accurately and efficiently. To validate the proposed method, we collected canopy temperatures of 36 species of trees with a FLIR T420 thermal infrared imager and compared the estimated temperatures with those directly measured by thermocouples. The accuracy of the corrected canopy temperature has been significantly improved with mean absolute error reduced from 3.73 °C to 0.64 °C. This proposed canopy temperature measurement method can be used to various applications in remote sensing product validation, and ecosystem and forestry studies. [ABSTRACT FROM AUTHOR]

Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability ( P gap ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various lidar instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible lidar point intensity, including radiometric quantities derived from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact P gap as footprint size increases. By contrast, we verified that physical methods using lidar point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate P gap and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate P gap and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from lidar instrument vendors. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) radiative transfer models are the most accurate remote sensing models. However, presently the application of 3D models to heterogeneous Earth scenes is a computationally intensive task. A common approach to reduce computation time is abstracting the landscape elements into simpler geometries (e.g., ellipsoid), which, however, may introduce biases. Here, a hybrid scene structuring approach is proposed to accelerate the radiative transfer simulations while keeping the scene as realistic as possible. In a first step, a 3D description of the Earth landscape with equal-sized voxels is optimized to keep only non-empty voxels (i.e., voxels that contain triangles) and managed using a bounding volume hierarchy (BVH). For any voxel that contains triangles, within-voxel BVHs are created to accelerate the ray–triangle intersection tests. The hybrid scheme is implemented in the Discrete Anisotropic Radiative Transfer (DART) model by integrating the Embree ray-tracing kernels developed at Intel. In this paper, the performance of the hybrid algorithm is compared with the original uniform grid approach implemented in DART for a 3D city scene and a forest scene. Results show that the removal of empty voxels can accelerate urban simulation by 1.4×~3.7×, and that the within-voxel BVH can accelerate forest simulations by up to 258.5×. [ABSTRACT FROM AUTHOR]

• The H 2 ab/desorption properties of LaAl 2-x Ni x alloys were studied. • Ni substitutions in LaAl 2 decrease the alloys thermodynamic stabilities. • The Ea during the dehydrogenation process was discussed. The hydrogenation properties of the Laves phase compounds LaAl 2- x Ni x have been studied at the various temperatures in the pressure of 5 MPa. Trace of Ni substitutions for Al in the alloys does not change their basic structures, and the hydrogen absorption rate is improved efficiently. The La 3 Al 11 and LaH 2.3 phases can be formed in their hydrogenated states when the x value is greater than 0.04. The hydrogen storage capacity can reach above 0.4 wt% for the LaAl 1.92 Ni 0.08 alloy. The dehydrogenation activity energies of the various hydrides increase with the Ni contents in the alloys. [ABSTRACT FROM AUTHOR]

The canopy bidirectional reflectance distribution function (BRDF) plays a pivotal role in estimating the biophysical parameters of plants, whereas soil background anisotropy creates challenges for their retrieval. Soil optical properties affect canopy anisotropic characteristics, especially in open-canopy areas. However, the remote sensing of background anisotropy is challenging due to the difficulties of information extraction in complex forest ecosystems and varying illumination conditions. This study develops an efficient photogrammetric technique to extract the background soil bidirectional reflectance factor (BRF) from unmanned aerial vehicle (UAV)-based multiangular images and to verify the need for accurate soil anisotropy information in canopy radiative transfer modeling. Soil BRF profiles were measured over three open-canopy sample plots from multiangular remotely sensed multispectral images collected with a hexacopter. As validation, reference soil BRF profiles were synchronously acquired by a ground-based multiangular imaging system. A high level of consistency between the ground- and UAV-measured soil BRF was observed with an RMSE of less than 0.012. Uncertainty analysis of the measured soil BRF showed that multiple scattering between sunlit soil in large sunflecks and foliage elements contributed less than 5%. Both results demonstrated that soil anisotropy can be accurately extracted from UAV multiangular measurements. To explicitly demonstrate that the use of soil anisotropy can reduce uncertainties in canopy radiative transfer simulations, we simulated the canopy BRF with Lambertian soil and with anisotropic soil using a three-dimensional (3D) radiative transfer model under different soil moisture content (SMC) levels, canopy cover (CC) levels and solar zenith angles (SZAs) with simulated realistic forest scenes. We found that less CC, lower SZAs and less SMC lead to a more significant influence of soil anisotropy on canopy reflectance; e.g., the reflectance bias reaches up to 0.3 in the hotspot direction. This illustrates that neglecting soil anisotropy can cause considerable errors in the modeling of the canopy BRF of open forests (i.e., CC levels of less than 0.5). The proposed technique facilitates the characterization of anisotropic forest background soil, which is important for advancing canopy radiative transfer modeling and validation and for the retrieval of vegetation parameters. [ABSTRACT FROM AUTHOR]

A relatively simple, but very effective method via magnetron sputtering process was used in the surface doping of the electrode plates. Molybdenum nano-particles coated electrodes were studied in the paper. The flower-like deposits appeared on the electrode plate surface, and Ni 4 Mo could be in-situ formed with the coating process. The hydrogen ab/desorption rates were accelerated at the beginning and dropped subsequently. The max discharge capacity and retention rate after twenty cycles of the St 5min sample reached about 370mAh/g and 92%, respectively. Comparing to the typical hydrogen storage alloys, they were much closed to the A 2 B 7 -type and have surpassed the most AB 3 -type Co-containing alloys. The activation energies and enthalpies of the various electrodes during the ab/dehydrogenation process were also discussed in details. Image 1 • Nano-Mo based coatings were used as surface modification for H 2 storage alloy. • Electrode surface transitions during the coating process were discussed. • C max and S 20 surpassed the most AB 5 -type alloys, and closed to the A 2 B 7 -type. • Properties improvements were related to the newer formed Ni 4 Mo compounds. [ABSTRACT FROM AUTHOR]

Abstract A simple method of the magnetron sputtering is applied on the surface modification process of the hydrogen storage alloys. Surface doping of La 2 Mg 17 alloy with yttrium-based coatings is illustrated in the research. Some new intermetallics can be formed on the alloys surface via three steps of magnetron sputtering, surface absorption and heat treatment molding. For the hydrogen storage properties, the reversible capacity and cycle stability of the doped sample are all improved, and the hydrogen desorption activation energy is decreased. On the other hand, the surface conditions during the hydrogen absorption and desorption process are also discussed through the SEM, XRD and XPS analyses. Highlights • Y-based coatings are applied to the La 2 Mg 17 alloy modification. • The reversible hydrogen storage properties are improved effectively. • The hydrogen desorption activation energy are decreased. • Surface conditions are analyzed during the hydrogen ab/desorption process. [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]

• Canopy-scale chlorophyll content (CCC) estimation conducted on open-canopy orchards. • A simplified 3D radiative transfer model is coupled with PROSPECT-D model. • Vegetation indices and model inversion methods are assessed with UAV data. • Look-up table including vegetation indices can estimate CCC of ITCs from image spectroscopy. • The inversion method based on the prior knowledge provides accurate estimation of CCC. Chlorophyll content is a key trait for understanding the functioning of agroforestry ecosystems and has important implications for leaf and canopy photosynthesis. However, fine-scale monitoring of canopy chlorophyll content (CCC) of individual fruit trees is rather challenging. This study aims to use a 3D radiative transfer model (RTM) and proposes a joint inversion model based on prior knowledge to estimate the CCC of individual tree crowns (ITCs) in apple orchards. The widely recognized 3D RTM LESS (large-scale remote sensing data and image simulation framework over heterogeneous 3D scenes) was adopted for large-scale apple orchard 3D scenes radiative transfer computing and image simulation. LESS was first evaluated with unmanned aerial vehicle (UAV) multispectral imagery and the results showed that it reasonably characterized the reflectance of apple tree canopies (RMSE = 0.02). An original look-up table (LUT) with reflectance was then produced using LESS, and the final vegetation indices LUT (VI LUT) including Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Index (CIgreen), Red edge Chlorophyll Index (CIred edge) and Green NDVI (GNDVI) was generated from the original LUT form VI interpolation. A physically-based joint inversion model coupling prior knowledge of leaf pigments and leaf area index (LAI) was developed to estimate the CCC of ITCs from high-resolution UAV images. The solution first used linear interpolation to produce a weighted VI LUT corresponding to the sample based on estimated LAI. Linear interpolation was then adopted to screen multiple combinations of leaf chlorophyll a+b (C ab) and leaf carotenoids (C xc) contents from the VI LUT. A prior relationship between C ab and C xc was finally used to regularize the constraints on multiple VI combinations and determine the estimation of C ab and CCC. The joint inversion model demonstrated an accurate estimation of CCC of ITCs. The model driven by GNDVI yielded the highest result for CCC estimation (R2 = 0.84, RMSE = 24.12 μg/cm2). In addition, CIgreen (R2 = 0.82, RMSE = 32.22 μg/cm2) and CIred edge (R2 = 0.81, RMSE = 34.05 μg/cm2) also achieved satisfactory results. The proposed model facilitates CCC estimation of ITCs from high-resolution imagery in heterogeneous orchard canopies, which is important for advancing the precise nutrition management of fruit trees. [ABSTRACT FROM AUTHOR]

In recent years, a rise in interest in using Unmanned Aerial Vehicles (UAV) with LiDAR (Light Detection and Ranging) to capture the 3D structure of forests for forestry and ecosystem monitoring applications has been witnessed. Since the terrain is an essential basis for the vertical structure modeling of a forest, the point cloud filtering delivering a highly accurate Digital Terrain Model (DTM) contributes significantly to forest studies. Conventional point cloud filtering algorithms require users to select suitable parameters according to the knowledge of the algorithm and the characteristics of scanned scenes, which are normally empirical and time-consuming. Deep learning offers a novel method in classifying and segmenting LiDAR point cloud, while there are only few studies reported on utilizing deep learning to filter non-ground LiDAR points of forested environments. In this study, we proposed an end-to-end and highly-efficient network named Terrain-net which combines the 3D point convolution operator and self-attention mechanism to capture local and global features for UAV point cloud ground filtering. The network was trained with over 15 million labeled points of 70 forest sites and was evaluated at 17 sites covering various forested environments. Terrain-net was compared with four classical filtering algorithms and one of the most well-recognized point convolution-based deep learning methods (KP-FCNN). Results indicated that Terrain-net achieved the best performance in respect of the Kappa coefficient (0.93), MIoU (0.933) and overall accuracy (98.0%). Terrain-net also performed well in transferring to an additional third-party open dataset for ground filtering in large-scale scenes and other vegetated environments. No parameters need to be tuned in transferring predictions. Terrain-net will hopefully be widely applied as a new highly-efficient, parameter-free, and easy-to-use tool for LiDAR data ground filtering in varying forest environments. [ABSTRACT FROM AUTHOR]

Burn severity assessment is critical for understanding the pattern of post-fire vegetation recovery and ecosystem resilience. Previous studies proposed various field criteria (e.g., Composite Burn Index (CBI)) to quantify burn severity from strata level to total site level, yet suffering from surveyors' subjective interpretation across site conditions. High-resolution passive remote sensing allows for more objective assessment based on the strong relation between fire damages and spectral features. Importantly, burn severity generally characterizes differences between forest overstory and understory layers due to their discrepancies in vegetation structures and environmental conditions. Spatially explicit mapping of strata-level burn severity is vital for post-fire forest management and ecological function evaluation. Until now, almost no available spaceborne remote sensing method can concurrently assess overstory and understory burn severity over heterogeneous forests. In this study, we proposed a Hybrid Composite Burn Index (HCBI) that comprehensively indicates the fire-induced spectral and structural changes along the vertical profile by integrating spectral and waveform attributes from both active and passive remote sensing data. Firstly, we introduced two remote sensing-based rating factors namely relative spectral change (RSC) and relative waveform change (RWC), and established HCBI through weighting and scoring the rating factors. Subsequently, we evaluated the effectiveness and generality of overstory, understory, and total site HCBI based on various pairs of simulated remote sensing data of pre-fire and post-fire forest scenes. Thirdly, we derived the spatially discontinuous maps of overstory, understory, and total site HCBI of the Xiushan fire site in Great Xing'an Mountain using WorldView-2 (WV-2) multispectral imagery and Global Ecosystem Dynamics Investigation (GEDI) full-waveform LiDAR data. Finally, we produced wall-to-wall HCBI maps using multiple predictive variables from pre-fire and post-fire Sentinel-2 MultiSpectral Instrument (MSI) images with a Random Forest (RF) algorithm. The predicted overstory, understory, and total site HCBI were validated by field-surveyed CBI. The assessment results based on simulation data showed HCBI was sensitive to the fire damage regardless of burn severity levels and vegetation cover levels (R2 of larger than 0.97 and RMSE of <0.15). The prediction results based on RF models achieved reliable HCBI of the total site, overstory, and understory levels (R2 of around 0.85 and RMSE of around 0.30). We found the wall-to-wall maps of HCBI captured the subtle horizontal and vertical variation even in the case of understory burn alone. We concluded that the newly proposed HCBI can advance the remote sensing-based assessment of stratified burn severity, offering opportunities for making fine-scale forest management decisions. • HCBI was proposed to quantify overstory, understory and total site burn severity. • Remote sensing-based assessment form relating rating factors to HCBI was established. • HCBI was consistent with reference unbiased CBI using synthetic data from 3D model. • HCBI maps were produced to explore the stratified burn severity heterogeneity. [ABSTRACT FROM AUTHOR]

• 3D RT simulations are used to assess the VI performance for estimating forest chlorophyll concentration. • Influences of saturation effect, soil backgrounds, and leaf area index disturbance on the chlorophyll concentration retrieval are evaluated. • Concern about decoupling vegetation canopy structure and spectral information. Remote-sensed vegetation indices (VIs) have emerged as essential tools for retrieving forest chlorophyll concentration. Although VIs are widely used, some concerns regarding VIs for estimating chlorophyll remain to be addressed, such as saturation effect, leaf area index (LAI) disturbance, and soil brightness influence. Currently, a systematic study on such performance evaluation of chlorophyll-related VIs considering these issues is still insufficient. This study coupled two radiative transfer models, the PROSPECT model and the LESS model, to simulate Eucalyptus monocultures with different chlorophyll content and systematically evaluated the 18 broad-band VIs' ability in chlorophyll estimation at different scales. Our results indicate that most VIs designed for chlorophyll estimation were relatively resistant to saturation, except for SIPI and some classical VIs (e.g., NDVI and DVI), which were insensitive to chlorophyll decreases and tended to reach saturation quickly (when leaf chlorophyll content (LCC) exceeded 40 ug/cm2). The relationships between NDVI, SR, DVI, and LCC were easily influenced by soil brightness and LAI. S2REP, MTCI, TGI, TCARI, and EVI were insensitive to soil brightness when estimating LCC. Overall, S2REP was best at quantitatively retrieving chlorophyll and resisting interference from other factors. For practical applications, our study suggests that it is preferable to use S2REP for LCC estimation when the red-edge band is available; otherwise, CVI can be used instead. The judicious utilization of VI can effectively depict chlorophyll levels and improve the understanding of vegetation response to climate change. Our findings provide the necessary information for the selection of specific VIs tailored to specific vegetation parameters. [ABSTRACT FROM AUTHOR]

Monitoring crops with high spatio-temporal resolution satellites provides valuable observations to ensure food security in the global change context. This study focuses on estimating the Green Area Index (GAI) to monitor wheat crops with a spatial resolution of 3 m and daily satellite observations from the SuperDove constellation. With an easier access to large training datasets of ground GAI measurements, and the improvement of the realism of radiative transfer model simulations, the choice of the optimal approach (data-driven or model-driven) constitutes a key question when retrieving GAI from satellite observations. This study compares a data-driven and a model-driven approach to estimate GAI from the SuperDove satellites. Both approaches are based on Gaussian Process Regression (GPR) machine learning techniques. The data-driven approach uses over 300 ground GAI measurements collected from 12 sites in China and France, each with 20 to 51 contrasting plots. The model-driven approach uses 10,000 simulations of top of canopy reflectance and the corresponding GAI values generated by the LESS radiative transfer model applied to 3D scenes built with the ADEL-Wheat (Architectural model of Development based on L-systems) model. Results confirm that the SuperDove reflectance are reliable and consistent with Sentinel-2 values. When estimating GAI using GPR with SuperDove top of canopy reflectance, the model-driven approach (R2 = 0.83, RMSE = 0.80, Accuracy = 0.01 and Precision = 0.80) generally outperforms the data-driven approach (R2 = 0.80, RMSE = 0.88, Accuracy = −0.13 and Precision = 0.87), except for small GAI values. In-silico experiments show that the uncertainties in the ground-measured GAI and the size and diversity of the training datasets limit the data-driven approach. In contrast, the model-driven approach is mostly constrained by the realism of the reflectance simulations, particularly for low GAI values. Two ensemble solutions based on the weighted average of the two previous approaches are then proposed: the global ensemble solution (R2 = 0.86, RMSE = 0.75, A = −0.06 and P = 0.74) where the weight is assumed independent from the GAI values, and the adaptive ensemble solution (R2 = 0.85, RMSE = 0.76, A = −0.08 and P = 0.76) where the weight depends on the GAI values. Both solutions perform similarly, improving both data-driven and model-driven approaches. Finally, applying both solutions to monitor wheat plots along the growth cycle allows clear differentiation of nitrogen modalities and cultivar effects. However, a minimum plot size of 12 m × 12 m (4 × 4 pixels) is recommended to minimize the co-registration errors and increase estimate precision. • Data-driven and model-driven approaches for GAI retrieval of wheat from PlanetScope. • The model-driven approach outperforms the data-driven one except for low GAI values. • Both approaches were combined in an ensemble solution which improves GAI estimates. • The proposed methodology allows accurate and precise monitoring of wheat crops. [ABSTRACT FROM AUTHOR]

Understory vegetation plays an important role in the structure and function of forest ecosystems. Light detection and ranging (LiDAR) can provide understory information in the form of either point cloud or full-waveform data. Point cloud data have a remarkable ability to represent the three-dimensional structures of vegetation, while full-waveform data contain more detailed information on the interactions between laser pulses and vegetation; both types have been widely used to estimate various forest canopy structural parameters, including leaf area index (LAI). Here, we present a new method for quantifying understory LAI in a temperate forest by combining the advantages of both types of LiDAR data. To achieve this, we first estimated the vertical distribution of the gap probability using point cloud data to automatically determine the height boundary between overstory and understory vegetation at the plot level. We then deconvolved the full-waveform data to remove the blurring effect caused by the system pulse to restore the vertical resolution of the LiDAR system. Subsequently, we decomposed the deconvolved data and integrated the plot-level boundary height to differentiate the waveform components returned from the overstory, understory, and soil layers. Finally, we modified the basic LiDAR equations introducing understory leaf spectral information to quantify the understory LAI. Our results, which were validated against ground-based measurements, show that the new method produced a good estimation of the understory LAI with an R2 of 0.54 and a root-mean-square error (RMSE) of 0.21. Our study demonstrates that the understory LAI can be successfully quantified through the combined use of point cloud and full-waveform LiDAR data. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is an essential input parameter for quantitatively studying the energy and mass balance in soil-vegetation-atmosphere transfer systems. As an active remote sensing technology, light detection and ranging (LiDAR) provides a new method to describe forest canopy LAI. This paper reviewed the primary LAI retrieval methods using point cloud data (PCD) obtained by discrete airborne LiDAR scanner (DALS), its validation scheme, and its limitations. There are two types of LAI retrieval methods based on DALS PCD, i.e., the empirical regression and the gap fraction (GF) model. In the empirical model, tree height-related variables, LiDAR penetration indexes (LPIs), and canopy cover are the most widely used proxy variables. The height-related proxies are used most frequently; however, the LPIs proved the most efficient proxy. The GF model based on the Beer-Lambert law has been proven useful to estimate LAI; however, the suitability of LPIs is site-, tree species-, and LiDAR system-dependent. In the local validation in previous studies, poor scalability of both empirical and GF models in time, space, and across different DALS systems was observed, which means that field measurements are still needed to calibrate both types of models. The method to correct the impact from the clumping effect and woody material using DALS PCD and the saturation effect for both empirical and GF models still needs further exploration. Of most importance, further work is desired to emphasize assessing the transferability of published methods to new geographic contexts, different DALS sensors, and survey characteristics, based on figuring out the influence of each factor on the LAI retrieval process using DALS PCD. In addition, from a methodological perspective, taking advantage of DALS PCD in characterizing the 3D structure of the canopy, making full use of the ability of machine learning methods in the fusion of multisource data, developing a spatiotemporal scalable model of canopy structure parameters including LAI, and using multisource and heterogeneous data are promising areas of research. [ABSTRACT FROM AUTHOR]

Occlusion effect, an inherent problem of terrestrial laser scanning (TLS) measurements, limits the potential of TLS data in tree attribute estimation. Multiple scans seek to mitigate this effect to provide enhanced scan completeness. However, the numbers and locations of the scans (i.e., the scan design) are usually determined via a subjective assessment of the tree density, spatial patterns of trees, and attributes to be derived. These could cause suboptimal scan completeness and limit tree attribute estimation. This study proposed an iterative-mode scan design to minimize the occlusion effect. First, we introduced a PoTo index based on visibility analysis to evaluate how many trees can be scanned from a location and to select effective candidates for the optimal TLS location. Second, we introduced a cumulative degree of ring closure (CDRC) to quantify the scan completeness for each candidate and determine the optimal TLS location. The TLS data sets of virtual forests with field-measured and synthetic plot parameter settings were simulated according to iterative- and regular-mode designs by using a Heidelberg light detection and ranging (LiDAR) Operations Simulator (HELIOS). The results demonstrated that an iterative-mode design can improve the scan completeness of trees compared to the regular-mode design. The tree attribute (diameter at breast height (DBH), tree height, stem curve, and crown volume) estimates of the iterative-mode design were less erroneous than those of the regular-mode design (e.g., the root-mean-square error (RMSE) could decrease the stem curve estimation by 38% and the crown volume estimation by 15%). This study suggests that the iterative-mode design can obtain an improved quality of the TLS data, especially for dense stands. [ABSTRACT FROM AUTHOR]

Estimation of downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. Although various algorithms have been proposed, effective validation methods are absent for rugged terrains due to the lack of rigorous methodology and reliable field measurements. We propose a two-step validation method for rugged terrains based on computer simulations. The first step is to perform point-to-point validation at local scale. Time-series measurements were applied to evaluate a three-dimensional (3-D) radiative transfer model. The second step is to validate the DSR at pixel-scale. A semiempirical model was built up to interpolate and upscale the DSR. Key terrain parameters were weighted by empirical coefficients retrieved from ground-based observations. The optimum number and locations of ground stations were designed by the 3-D radiative transfer model and Monte Carlo method. Four ground stations were selected to upscale the ground-based observations. Additional three ground stations were set up to validate the interpolated results. The upscaled DSR was finally applied to validate the satellite products provided by MODIS and Himawari-8. The results showed that the modeled and observed DSR exhibited good consistency at point scale with correlation coefficients exceeding 0.995. The average error was around 20 W/m2 for the interpolated DSR and 10 W/m2 for the upscaled DSR in theory. The accuracies of the satellite products were acceptable at most times, with correlation coefficients exceeding 0.94. From an operational point of view, our method has an advantage of using small amount of ground stations to upscale DSR with relatively high accuracy over rugged terrains. [ABSTRACT FROM AUTHOR]

The effects of surface modification of the La–Mg–Ni based alloys through doping a trace of Y 2 O 3 on the electrochemical properties are researched in this study. The electrochemical hydrogen storage capacities and cycle stabilities of the Y 2 O 3 @La 1.7 Mg 1.3 Ni 9 samples are all significantly improved and the performance reaches the best for the 10 h milled sample. Comparing to the previous data, the comprehensive performances have already surpassed the most alloys, especially in the cyclic stability properties. The Y 2 O 3 coating layer with the amorphous structures will be formed on the alloys surface during the milling process, which has a certain capability of electricity catalysis to further improve the electrochemical hydrogen storage properties. Meanwhile, the results also indicate that much longer milled duration is harmful to the electrons transfer process, and the point is further confirmed through XPS analyses. • Trace Y 2 O 3 is applied to the hydrogen storage alloy surface modification. • Structure transition is investigated during the milled process. • The C max and S 30 can reach above 390mAh/g and 90%. • The main hydrogen storage properties have surpassed the most alloys. [ABSTRACT FROM AUTHOR]

Accurate simulations of the bidirectional reflectance distribution function (BRDF) of snow-covered forests are crucial for retrieving snow and canopy properties, which affects the analysis of net radiation, radiative forcing, and climate change. The anisotropic reflectance of snow in conifer forests is greatly influenced by the high reflectance of snow and the complex travel pathways of radiation in 3D canopy structures, which complicates BRDF simulations. Although bidirectional reflection models have been widely established for snow-free forests, few studies have attempted to simulate the BRDF in snow-covered forests. Therefore, we propose a new snow-covered forest bidirectional reflectance (SFBR) model by coupling geometric optical and radiative transfer models, which includes a snow anisotropic reflectance model (ART), needle leaf optical properties model (LIBERTY), canopy radiative transfer model (4SAIL), and geometric optical model (GOST2). The SFBR model mainly focuses on conifer forests with snow backgrounds; it can simulate anisotropic reflectance spectra from 400 nm to 2500 nm and is the only model to fully consider discontinuous canopy distribution, topographic effects, and snow anisotropy. The SFBR model was validated by simulating the reflectance over snow-covered forests, the results of which showed good consistency with simulations of 3D scene model and remote sensing observations. The proposed SFBR model has the potential to quantify radiation interactions between the snow background and forest overstory, evaluate the sensitivity of canopy reflectance to snow and forest properties, and retrieve snow-covered forest parameters from satellite-observed reflectance data. • A new bidirectional reflectance model for snow-covered forests. • It couples GO and RT models for snow, leaf, and canopy. • Canopy heterogeneity, topographic effects, and snow anisotropy are considered. • Simulated reflectance has good consistency with 3D scene model and MODIS data. • Model flexibility allows application for spectra simulation and mechanism analysis. [ABSTRACT FROM AUTHOR]

Vegetation indices (VIs) have been used extensively for qualitative and quantitative remote sensing monitoring of vegetation vigor and growth dynamics. However, the saturation phenomenon of VIs (i.e., insignificant change at moderate to high vegetation densities) poses a known limitation to their ability to characterize surface vegetation over the dense canopy. Although the mechanisms underlying saturation are relatively straightforward and several VIs have been proposed to mitigate the saturation effect, the assessment of the saturation effect of VIs remains insufficient. Notably, no unified metric has been proposed to quantify the VI saturation phenomenon, limiting VI selection in practical applications. In this study, we proposed two indicators to describe the saturation phenomenon and utilized a well-validated three-dimensional (3D) canopy radiative transfer (RT) model large-scale remote sensing data and image simulation framework (LESS) to simulate the bidirectional reflectance factor (BRF) of six forests scenes and assessed the variations in VIs in relation to leaf area index (LAI) values over different backgrounds, sun-sensor geometries, and spatial distribution types. The saturation characteristics of 36 VIs were evaluated in combination with simulation results and satellite observations from multiple sensors. The ranking of VI saturation from simulated and satellite results revealed a good agreement. Our results indicated that the simple ratio vegetation index (SR) performed best with the highest saturation point and can well characterize the surface vegetation condition until LAI reaches 4. Besides, we found that the saturation effect of VIs was influenced by soil brightness, sun-sensor geometry, and canopy structure. SR, modified simple ratio (MSR) and normalized green red difference index (NGRDI) were the most susceptible to these disturbing factors, although they had higher resistance to saturation. Modified triangular vegetation index 1 (MTVI1), modified non-linear vegetation index (MNLI), triangular greenness index (TGI), and triangular vegetation index (TriVI) performed well overall, combining the ability to resist saturation and disturbance factors. Appropriate application of VIs can help better understand vegetation responses to climate change and accurately assess ecosystem status. Our results contribute to the understanding of the VI saturation effect and provide a combined model and satellite data experimental workflow in appropriate VI selection to accurately characterize vegetation. • Two metrics are proposed to quantify the VI saturation from application viewpoint. • 3D RT simulation and satellite data are combined to assess VI saturation in forests. • Effects of soil, sun-sensor geometry and distribution type on VI are assessed. [ABSTRACT FROM AUTHOR]

As a result of the high albedo of snow or ice and the rugged surface, high altitude mountainsget more reflection between the slopes, which increases the incoming radiation at theground surface. Because of the absence of measurements in snow-covered highmountains, the accurate surface solar radiation (SSR) in this area is not easy toderive. A large-scale remote sensing data and image simulation framework (LESS),which is a ray-tracing based three-dimensional (3-D) radiative transfer model, wasused in this study to have an accurate simulation of SSR in a mountainous area inthe Tibetan Plateau. The direct and diffuse fluxes without topographic effect weresimulated with MODTRAN5 for clear sky, used as the input for the LESS simulation.The simulation result indicates that the radiation increases in mountainous area,even if there is no snow cover. The increased energy origins from two parts: directand diffuse radiation and terrain-reflected radiation. The anomalies of the directand diffuse flux with reference to a horizontal surface is related to the slope andaspect distribution, and varies in a day. The terrain-reflected radiation is positivelycorrelated with the surface albedo, and it can be as large as 360 W/m2 in the areacovered with snow. The domain daily energy increase because of the terrain reflectionis generally less than 40W/m2, which is around 8% of downward SSR on a flatsurface. Three terrain configuration factors using in simplified approaches wereevaluated by the ray tracing simulation. Only one terrain configuration factor has verysimilar result with the LESS in domain-averaged (12km*12km) terrain-reflected flux,the other two factors both underestimate the radiation. However, the simulationwith terrain configuration factor cannot derive a reliable result on local position. [ABSTRACT FROM AUTHOR]

Highlights • Path length distribution model is implemented on TLS data for LAI estimation. • Alpha hull is used for 3D crown modeling and calculation of path length. • The proposed TLS-extracted LAI compared well with the hemispherical photography. • Two simulated plots were built to provide more objective validation. Abstract Terrestrial Laser Scanning (TLS) is an active technology that can acquire the finest characteristics of canopy structure and plays an increasing role in estimating Leaf Area Index (LAI) in forest canopies. However, 3D information is not directly used in conventional TLS-based methods using the gap fraction theory. In addition, quantifying clumping effect within canopies is still a difficult task. In this paper, we presented a method to reduce clumping effect and estimate LAI using TLS data. Our recently proposed path length distribution model was applied to TLS data. Instead of converting 3D points to 2D image, the path length distribution can be extracted using the TLS-recorded 3D data and the crown models built with the alpha shapes algorithm. Two simulated scenes and one actual forest plot were utilized for validation. The results of the proposed method agree well with both the true LAI (in the simulated scenes) and the extracted PAI by the digital hemispherical photography (in the actual plot). This LAI estimation method using TLS and the path length distribution model provides a novel way for ground-based LAI measurements and shows its great potential. [ABSTRACT FROM AUTHOR]

Estimation of daily downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. The combination of satellite-based instantaneous measurements and temporal extrapolation models is the most feasible way to capture daily radiation variations at large scales. However, previous studies did not pay enough attention to topographic effects and simple temporal extrapolation methods were applied directly to rugged terrains which cover a large amount of the land surface. This paper, divided into two parts, aims at analyzing the topographic uncertainties of existing models and proposing a better method based on a mountain radiative transfer (MRT) model to calculate daily DSR. As the first part, this paper analyze the spatiotemporal variations of DSR influenced by topographic effects and checks the applicability of three temporal extrapolation methods on cloud-free days. Considering that clouds also have a strong influence on solar radiation, cloud-free days are chosen for targeted analysis of topographic effects on DSR. Three indices, the coefficient of variation, entropy-based dispersion coefficient (CH), and sill of semivariogram, are put forward to give a quantitative description of spatial heterogeneity. Our results show that the topography can dramatically strengthen the spatial heterogeneity of DSR. The index, CH, has an advantage for quantifying spatial heterogeneity as it offers a tradeoff between accuracy and efficiency. Spatial heterogeneity distorts the daily variation of DSR. Application of extrapolation methods in rugged terrains leads to overestimation of daily average DSR up to 60 W/m2 and a maximum 200 W/m2 error of instantaneous DSR on cloud-free days. This paper makes a quantitative analysis of topographic effects under different spatiotemporal conditions, which lays the foundation for developing a new extrapolation method. [ABSTRACT FROM AUTHOR]

Ground filtering is an essential procedure in almost all LiDAR applications. However, most existing ground filtering algorithms require different amounts of user input to manually set up initial parameters, such as terrain relief amplitude and average slope, which is subjective, time consuming, and prone to errors. Here, we propose a simple terrain relief index derived from raw airborne LiDAR data to automatically tune the slope-related parameters of ground filtering algorithms. The terrain relief index is a ratio between the height difference of the entire point cloud and the maximum above ground level of non-ground points. The latter variable can be estimated with the maximum local height difference of raw LiDAR data through gridding. We validated our method using the benchmark airborne LiDAR datasets provided by the International Society for Photogrammetry and Remote Sensing. The results showed a high correlation ( r  = 0.876) between the terrain relief index and the referential terrain relief amplitude. The degree of correlation was greater across larger areas ( r  = 0.926) than small areas ( r  = 0.861) regardless of the type of land cover (e.g., city or forest). The terrain relief index was introduced into two existing filtering algorithms: Cloth Simulation Filtering (CSF) and Progressive Morphological (PM) Filter, by relating the terrain relief index to the cloth rigidness of the CSF and the slope threshold of the PM filter. To compare the results, the two algorithms were implemented both with manually tuned parameters and with the parameters derived from the terrain relief index. The results showed that there was only a slight discrepancy in average Total Error (0.1%) between them in the CSF, which means that the terrain relief index can automatically determine the cloth rigidness without noticeable loss of accuracy. The average difference between the slope threshold provided by the terrain relief index and the manually tuned optimal slope threshold was 0.142 rad (8.136°) for the PM filter, which is acceptable relative to manually parameter setting without any prior knowledge. The terrain relief index can estimate the terrain relief amplitude from raw airborne LiDAR data, and the parameter settings suggested in this paper for the filtering algorithm can improve automation. [ABSTRACT FROM AUTHOR]

Background: Interleukin-1 alpha (IL-1α) plays an important role in tumorigenesis and angiogenesis of gastric cancer. The interleukin-1 receptor antagonist (IL-1RA) inhibits IL-1 selectively and specifically through IL-1R type I (IL-1RI). However, the underlying mechanism by which IL-1RA modulates the interactions of tumor cells and their micro-environment is poorly understood. We have evaluated the role of IL-1RA in the metastatic process as well as the mutual or reciprocal actions between gastric cancer cells and stromal cells.Materials and methods: The expressions of IL-1α, vascular endothelial growth factor (VEGF), and IL-1RI mRNA were determined by reverse transcriptase-PCR. The regulatory effect of IL-1RA on the secretion of VEGF in human gastric cancer cells and human umbilical vein endothelial cells (HUVECs) was detected by enzyme-linked immunosorbent assay. The effect of IL-1RA on metastatic potential was evaluated using proliferation, invasion, and angiogenesis assays, respectively, including in vitro co-culture system models consisting of tumor cells and stromal cells that were used to detect invasion and angiogenesis.Results: Interleukin-1α mRNA was detected in the higher liver metastatic gastric cell line MKN45. IL-1α protein was expressed in MKN45 cells and in HUVECs. VEGF mRNA and protein were detected in the three gastric cancer cell lines (MKN4, NUGC-4, and AGS). Levels of VEGF secreted by gastric cancer cells and HUVECs appeared to be reduced through the action of IL-1RA via IL-1RI in a dose-dependent manner (P < 0.01). IL-1RA significantly inhibited the proliferation and migration of HUVECs (P < 0.01) and tube formation by HUVECs (P < 0.01), both in a dose-dependent manner. Compared with HUVECs grown without cancer cells (control) or with NUGC-4 cells, tube formation by HUVECs was significantly enhanced by co-culture with MKN45 cells (P < 0.01). The enhanced tube formation in the presence of MKN45 cells was inhibited by the addition of IL-1RA (P < 0.01).Conclusions: The IL-1RA downregulated the metastatic potential of gastric cancer through blockage of the IL-1α/VEGF signaling pathways. IL-1RA has the potential to play a role in the treatment of gastric cancer. [ABSTRACT FROM AUTHOR]

Many studies have been focusing on reconstructing the branch skeleton of a three-dimensional (3D) tree structure that is based on photos or point clouds scanned by a terrestrial laser scanner (TLS), but leaves, as the important component of a tree, are often ignored or simplified because of their complexity. Therefore, we develop a voxel-based method to add leaves to a reconstructed 3D branches structure based on TLS point clouds. The location and size of each leaf depend on the spatial distribution and density of leaves points in the voxel. We reconstruct a small 3D scene with four realistic 3D trees and a virtual tree (including trunk, branches, and leaves), and validate the structure of each tree through the directional gap fractions calculated based on simulated point clouds of this reconstructed scene by the ray-tracing algorithm. The results show good coherence with those from measured point clouds data. The relative errors of the directional gap fractions are no more than 4.1%, though the method is limited by the effects of point occlusion. Therefore, this method is shown to give satisfactory consistency both visually and in the quantitative evaluation of the 3D structure. [ABSTRACT FROM AUTHOR]

Accurate and efficient in situ measurement methods of leaf area index (LAI) and leaf angle distribution (LAD) are needed to estimate the fluxes of water and energy in agricultural settings. However, available methods: to estimate these two parameters, especially LAD, are limited. In this study, we propose a field measurement method using multi-angular digital images to estimate LAI and LAD simultaneously from the area proportions of: (i) sunlit soil; (ii) sunlit leaves; (iii) shaded soil; and (iv) shaded leaves. A new expression of the fraction of sunlit leaves is developed based on the radiative transfer theory. Coupling the measured and modeled fractions with an optimization scheme, LAI and the LAD parameters are derived from inverting a fractional model of sunlit and shaded leaves and soil. Through four tests using simulated scenes and in situ measurements for row crops, it is determined that our method performs well. The absolute error of LAI estimation is less than 0.1 when LAI is low (i.e., <1.2), and the absolute deviations of LAI estimates are approximately 0.5 when the reference LAI is 3.5. The estimation errors of LAI and the G function (a representative of LAD which quantifies the projection of unit foliage area) for in situ measurements are respectively less than 0.2 and 0.06 in general. In addition, the accuracy of estimation is even higher when leaves are simulated as randomly distributed disks or observations from multiple azimuth planes are used. One of the most interesting features of this method is its ability to estimate reasonable LAD directly from the fractions of sunlit and shaded leaves, even when LAI is high (i.e., >3), so little background soil is seen. The sensitivity and uncertainty analysis is consistent with the estimation errors. Theoretically, the application of this method is not limited to row crops or to field measurement, as the derived formulae of sunlit and shaded components can be used for other types of vegetation by introducing the clumping index and can be used in the modeling of canopy vegetation parameters (e.g., canopy reflectance). [ABSTRACT FROM AUTHOR]

Scale effect, which is caused by a combination of model nonlinearity and surface heterogeneity, has been of interest to the remote sensing community for decades. However, there is no current analysis of scale effect in the ground-based indirect measurement of leaf area index (LAI), where model nonlinearity and surface heterogeneity also exist. This paper examines the scale effect on the indirect measurement of LAI. We built multiscale data sets based on realistic scenes and field measurements. We then implemented five representative methods of indirect LAI measurement at scales (segment lengths) that range from meters to hundreds of meters. The results show varying degrees of deviation and fluctuation that exist in all five methods when the segment length is shorter than 20 m. The retrieved LAI from either Beer's law or the gap-size distribution method shows a decreasing trend with increasing segment lengths. The length at which the LAI values begin to stabilize is about a full period of row in row crops and 100 m in broadleaf or coniferous forests. The impacts of segment length on the finite-length averaging method, the combination of gap-size distribution and finite-length methods, and the path-length distribution method are relatively small. These three methods stabilize at the segment scale longer than 20 m in all scenes. We also find that computing the average LAI of all of the short segment lengths, which is commonly done, is not as good as merging these short segments into a longer one and computing the LAI value of the merged one. [ABSTRACT FROM AUTHOR]

Rare earth (RE) inclusions with high melting points as heterogeneous nucleation in liquid steel have stimulated many recent studies. Evaluating the potency of RE inclusions as heterogeneous nucleation sites of the primary phase is still a challenge. In this work, the edge-to-edge matching (E2EM) model was employed to calculate the atomic matching mismatch and predict the orientation relationship between La2O2S and γ-Fe from a crystallographic point of view. A rough orientation relationship (OR) was predicted with the minimum values of f r = 9.43 % and f d = 20.72 % as follows: [ 2 1 ¯ 1 ¯ 0 ] La 2 O 2 S ∥ [ 100 ] γ - Fe and (000 3 ¯) La 2 O 2 S ∥ (00 2 ¯) γ - Fe . The interface energy and bonding characteristics between La2O2S and γ-Fe were calculated on the atomic scale based on a crystallographic study using the first-principles calculation method. The calculations of the interface energy showed that the S-terminated and La(S)-terminated interface structures were more stable. The results of difference charge density, electron localization function (ELF), the Bader charges and the partial density of states (PDOS) study indicated that the La(S)-terminated interface possessed metallic bonds and ionic bonds, and the S-terminated interface exhibited metallic bond and covalent bond characteristics. This work addressed the stability and the characteristics of the La2O2S/γ-Fe interface structure from the standpoint of crystallography and energetics, which provides an effective theoretical support to the study the heterogeneous nucleation mechanism. As a result, La2O2S particles are not an effective heterogeneous nucleation site for the γ-Fe matrix from crystallography and energetics points of view. [ABSTRACT FROM AUTHOR]

Freehand exercises help improve physical fitness without any requirements for devices or places. Existing fitness assistant systems are typically restricted to wearable devices or exercising at specific positions, compromising the ubiquitous availability of freehand exercises. In this paper, we develop MobiFit, a contactless freehand exercise assistant using just one cellular signal receiver placed on the ground. MobiFit passively monitors the ubiquitous cellular signals sent by the base station, which frees users from the space constraints and deployment overheads and provides accurate repetition counting, exercise type recognition and workout quality assessment without any attachments to the human body. The design of MobiFit faces new challenges of the uncertainties not only on cellular signal payloads but also on signal propagations because the sender (base station) is beyond the control of MobiFit and located far away. To tackle these challenges, we conducted experimental studies to observe the received cellular signal sequence during freehand exercises. Based on the observations, we constructed the analytic model of the received signals. Guided by the insights derived from the analytic model, MobiFit segments out every repetition and rest interval from one exercise session through spectrogram analysis and extracts low-frequency features from each repetition for type recognition. Extensive experiments were conducted in both indoor and outdoor environments, which collected 22,960 exercise repetitions performed by ten volunteers over six months. The results confirm that MobiFit achieves high counting accuracy of 98.6%, high recognition accuracy of 94.1% and low repetition duration estimation error within 0.3 s. Besides, the experiments show that MobiFit works both indoors and outdoors and supports multiple users exercising together. [ABSTRACT FROM AUTHOR]

Leaf angle distribution (LAD) is an important attribute of forest canopy architecture and affects the solar radiation regime within the canopy. Terrestrial laser scanning (TLS) has been increasingly used in LAD estimation. The point clouds data suffer from the occlusion effect, which leads to incomplete scanning and depends on measurement strategies such as the number of scans and scanner location. Evaluating these factors is important to understand how to improve LAD, which is still lacking. Here, we introduce an easy way of estimating the LAD using open source software. Importantly, the influence of the occlusion effect on the LAD was evaluated by combining the proposed complete point clouds (CPCs) with the simulated data of 3D tree models of Aspen, Pin Oak and White Oak. We analyzed the effects of the point density, the number of scans and the scanner height on the LAD and G-function. Results show that: (1) the CPC can be used to evaluate the TLS-based normal vector reconstruction accuracy without an occlusion effect; (2) the accuracy is slightly affected by the normal vector reconstruction method and is greatly affected by the point density and the occlusion effect. The higher the point density (with a number of points per unit leaf area of 0.2 cm−2 to 27 cm−2 tested), the better the result is; (3) the performance is more sensitive to the scanner location than the number of scans. Increasing the scanner height improves LAD estimation, which has not been seriously considered in previous studies. It is worth noting that relatively tall trees suffer from a more severe occlusion effect, which deserves further attention in further study. [ABSTRACT FROM AUTHOR]

Hyperspectral light detection and ranging (LiDAR) (HSL) combines the characteristics of hyperspectral imaging and LiDAR techniques into a single instrument without any data registration. It provides more information than hyperspectral imaging or LiDAR alone in the extraction of vegetation physiological and biochemical parameters. However, the laser pulse intensity is affected by the incident angle, and its effect on HSL has not yet been fully explored. It is important for employing HSL to investigate vegetation properties. The aim of this paper is to study the incident angle effect of leaf reflectance with HSL and build a model about this impact. In this paper, we studied the angle effect of leaf reflectance from indoor HSL measurements of individual leaves from four typical tree species in Beijing. We observed that (a) the increasing of incident angle decreases the leaf reflectance; (b) the leaf spectrum observed by HSL from 650 to 1000 nm with 10 nm spectral resolution (36 channels) are consistent with those that measured by Analytica Spectra Devices (ASD) spectrometer (R2 = 0.9472 ~ 0.9897); (c) the specular reflection is significant in the red bands, and clear non-Lambertian characteristics are observed. In the near-infrared, there is little specular reflection, but it follows the Lambert-scattering law. We divided the whole band (650–1000 nm) into six bands and established an empirical model to correct the influence of angle effect on the reflectance of the leaf for HSL applications. In the future, the calibration of HSL measurements applied for other targets will be studied by rigorous experiments and modelling. [ABSTRACT FROM AUTHOR]

Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users. [ABSTRACT FROM AUTHOR]