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Remote sensing via unmanned aerial vehicles (UAVs) is becoming a very important tool for augmenting traditional spaceborne and airborne remote sensing techniques. Commercial RGB cameras are often the payload on UAVs, because they are inexpensive, easy to operate and require little data processing. RGB images are increasingly being used for mapping of fractional vegetation cover (FVC). However, the presence of significantly mixed pixels in close-range RGB images prevents the accurate estimation of FVC. Even where pixel unmixing is applied, limited quantitative spectral information and colour variability within these images could lead to profound errors and uncertainties. This paper proposes a colour mixture analysis (CMA) method based on the Hue-Saturation-Value (HSV) colour space to alleviate the above-mentioned concerns, thereby improving the accuracy and efficiency of FVC estimation from UAV-captured RGB images. First, the a priori colour information of the pure vegetation and background endmembers are extracted from the Hue channel of the UAV proximal sensing images, obviating ground-based image capture and the attendant cost and inconvenience. Second, the relationship between the probability distribution of mixed pixels and that of the two endmembers is estimated. Finally, we estimate FVC from UAV remote sensing images with a maximum a posteriori parameter (MAP) estimator. Two UAV-captured RGB image datasets and a synthetic RGB image dataset were used to test the new method. CMA was compared with three other FVC estimation algorithms, namely, FCLS, HAGFVC and LAB2. The FVC estimates by CMA were found to be highly accurate, with root mean squared errors (RMSE) of less than 0.007 and mean absolute error (MAE) of less than 0.01 for both field datasets. The accuracy was shown to be superior to that of all three algorithms. A comprehensive analysis of the estimation accuracy under various spatial resolutions and vegetation cover levels was conducted using both field and synthetic datasets. Results show that the CMA method can robustly and accurately estimate FVC across the full range of vegetation coverage and various resolutions. Uncertainty and sensitivity analysis of colour variability due to heterogeneity and shadow were also tested. Overall, CMA was shown to be robust to variation in colour and illumination.

The test vector leakage assessment (TVLA) is a widely used side-channel power leakage detection technology which requires evaluators to collect as many power traces as possible to ensure accuracy. However, as the total sample size of the power traces increases, the amount of redundant information will also increase, thus limiting the detection efficiency. To address this issue, we propose a principal component analysis (PCA)-TVLA-based leakage detection framework which realizes a more advanced balance of accuracy and efficiency. Before implementing TVLA to detect leakage, we project the original power data onto their most significant feature dimensions extracted by the PCA procedure and screen power traces according to the magnitude of their corresponding components in the variance of the projection vector. We verified the overall performance of the proposed framework by measuring the detection capability and efficiency with t-values and the required time, respectively. The results show that compared with similar existing schemes, under the best circumstances, the proposed framework decreases the t-value by 4.3% while saving time by 25.2% on the MCU platform and decreases the t-value by 2.4% while saving time by 38.0% on the FPGA platform.

Wavelet-based tests for lack-of-fit in semi-strong autoregressive moving average models with conditional heteroscedastic martingale difference innovations are investigated. The chi-square distributions of the Box-Pierce-Ljung methods are not necessarily adequate in this context and adjustments appear necessary. Seasonal irregularities in the spectral density of the innovations can affect the power of the classical tests, providing motivations for studying wavelets. Using the Franklin wavelet, the asymptotic distributions of the empirical wavelet coefficients are derived, and the asymptotic chi-square distributions of the wavelet-based tests are established. Monte Carlo simulations are conducted to study the performance of the methodology under the null and alternative hypotheses, including seasonal alternatives.

In this paper, we consider block thresholding wavelet estimators of spatial regression functions on stationary Gaussian random fields observed over a rectangular domain indexed with $${{\mathbb {Z}}}^2$$, whose covariance function is assumed to satisfy some weak condition. We investigate their asymptotic rates of convergence under the mean integrated squared error when spatial regression functions belong to a large range of Besov function classes $$B^{s}_{p,q}({{\mathbb {R}}}^2)$$. To do this, we derived a result showing the discrepancy between empirical wavelet coefficients and true wavelet coefficients is within certain small rate across above Besov function classes. Based on that, we are able to determine the rates of convergence of our estimators and the supremum norm error over above function classes. The obtained rates of convergence correspond to those established in the standard univariate nonparametric regression with short-range dependence. Therefore, those rates could be considered as sharp as possible. A mild simulation study is carried out to examine the finite sample performance of the proposed estimates.

Accurate and efficient measurement of leaf angle distribution (LAD) is important for characterizing canopy structures and understanding solar radiation regimes within the plant canopy. The main challenge for obtaining LAD is measuring the orientations of individual leaves rapidly and accurately in complex field conditions. In this letter, we propose an efficient and low-cost approach to estimate both leaf zenith and azimuth angles from smartphone photographs by using a structure from motion (SfM) point cloud and pyramid convolutional neural network (PCNN)-based leaf detection. This SfM-PCNN method first detects individual leaves from 2-D photographs by delineating leaf boundaries, while minimizing the influences of interior leaf textures. The segmented image with leaf annotations is then used to partition the 3-D SfM point cloud into leaf clusters, each of which is fit by a plane to calculate the leaf orientation. The method was validated with manual measurements for five plant species with different leaf sizes, leaf shapes, and leaf textures. The accuracy is satisfactory for a leaf-to-leaf comparison over a Euonymus japonicus Thunb. with R-squared values of 0.84 (RMSE = 6.27°) and 0.97 (RMSE = 12.61°) for zenith and azimuth angle estimations, respectively. The method allows researchers to efficiently acquire LADs of different plants with low cost yet high accuracy.

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.

Accurate estimates of fractional vegetation cover (FVC) using remotely sensed images collected using unmanned aerial vehicles (UAVs) offer considerable potential for field measurement. However, most existing methods, which were originally designed to extract FVC from ground-based remotely sensed images (acquired at a few meters above the ground level), cannot be directly used to process aerial images because of the presence of large quantities of mixed pixels. To alleviate the negative effects of mixed pixels, we proposed a new method for decomposing the Gaussian mixture model and estimating FVC, namely, the half-Gaussian fitting method for FVC estimation (HAGFVC). In this method, the histograms of pure vegetation pixels and pure background pixels are firstly fit using two half-Gaussian distributions in the Commission Internationale d’Eclairage (CIE) L*a*b* color space. Then, a threshold is determined based on the parameters of Gaussian distribution to generate a more accurate FVC estimate. We acquired low-altitude remote-sensing (LARS) images in three vegetative growth stages at different flight altitudes over a cornfield. The HAGFVC method successfully fitted the half-Gaussian distributions and obtained stable thresholds for FVC estimation. The results indicate that the HAGFVC method can be used to effectively and accurately derive FVC images, with a small mean bias error (MBE) and with root mean square error (RMSE) of less than 0.04 in all cases. Comparatively, other methods we tested performed poorly (RMSE of up to 0.36) because of the abundance of mixed pixels in LARS images, especially at high altitudes above ground level (AGL) or in the case of moderate vegetation coverage. The results demonstrate the importance of developing image-processing methods that specially account for mixed pixels for LARS images. Simulations indicated that the theoretical accuracy (no errors in fitting the half-Gaussian distributions) of the HAGFVC method reflected an RMSE of less than 0.07. Additionally, this method provides a useful approach to efficiently estimating FVC by using LARS images over large areas.

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.

Ethnopharmacological relevance The dried roots of Sophora flavescens Ait. (Leguminosae) is traditionally used as antipyretic medicine to reduce inflammation. It is well known that alkaloids and flavonoids are the main constituents of S. flavescens. However, the clinical researches and applications of S. flavescens is mainly based on its water-extracted alkaloids, its flavonoids may still remain in residues and have been underused. With development and manufacturing of S. flavescens in recent years, more herb residues are being produced. Since they are typically treated as waste and dumped openly in landfill sites, which can cause pollution, there is a great need to explore these wastes as recyclable resources and increase their added value. To date, whether other bioactive components would be found in the residues of S. flavescens is still unknown. If the extraction method of these active ingredients was established, the residues of S. flavescens could be turned from the harm to a benefit and make great sense of the comprehensive utilization of S. flavescens resources. This study aimed to establish an extraction method of the residues of S. flavescens and investigate the anti-inflammatory effect of it both in vivo and in vitro. Materials and methods Dried S. flavescens were decocted with distilled water firstly, then the residues were powdered and extracted with ethyl acetate by using ultrasonic wave. HPLC was utilized to analyze the chemical constituents of the water extracts of S. flavescens (WSF) and the ethyl acetate extracts of residues of S. flavescens (RSF). In vivo, the anti-inflammatory effect of WSF and RSF were evaluated using the xylene-induced auricle edema, acetic acid-induced peritoneal permeability and carrageenan-induced hind paw edema methods. In vitro, the inhibitory activities of WSF and RSF on NO, TNF-α, IL-6 and MCP-1 production of LPS-treated RAW264.7 cells were measured. Results The major ingredients of RSF were flavonoids, while WSF almost had no flavonoids. In vivo, WSF and RSF (200 mg/kg) could significantly inhibit the edema in the xylene-induced mice auricle edema and carrageenan-induced hind paw edema as well as the peritoneal permeability increased by acetic acid. They can also lower production levels of PGE2 in inflamed paw tissues. In vitro experimental results showed that RSF (25-100 μg/mL) could significantly inhibit the release of pro-inflammatory cytokines NO, TNF-α, IL-6 and MCP-1 on LPS-induced RAW264.7 cells. The in vitro suppress effect of WSF had no dose-response relationship. Conclusions The residues of S. flavescens had obvious flavonoids with anti-inflammatory activity. This study provided evidence for the reuse of residues from S. flavescens in the food additive, medicine and cosmetic industries.

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.

Canopy radiative transfer (RT) modeling is critical for the quantitative retrieval of vegetation biophysical parameters and has been under intensive research over the decades. RT models of discontinuous canopies, such as three-dimensional (3D) RT models, posed challenges for the early one-dimensional (1D) hypothesis. Although 3D RT models have higher accuracy, theoretically, they suffer from two problems: detailed scene parameters and complex computational steps. To overcome these problems, the stochastic radiative transfer (SRT) theory, which is known to have the accuracy of 3D RT while being as simple as 1D RT, has been adapted from atmospheric research to the study of vegetation canopies. While the SRT model has been adopted into the operational production of vegetation parameters, its accuracy needs further improvement because of the insufficient consideration of hotspot effects. Additionally, the evaluation and validation of SRT process are still preliminary, which hinders its further development and application. To provide the community with missing information and a scientific basis for subsequent model improvement, we modified, evaluated, and validated the SRT model in this study. First, we proposed the new version of SRT model to better achieve the coupling of SRT process and hotspot effect by dividing the previous SRT into four subproblems. Then, we evaluated the performance of the modified SRT by comparing multiple intermediate variables in the SRT process with 3D computer simulations, and analyzed the model sensitivity to key input parameters as well as the spatial distribution and conservation of radiation energy. Our findings reconfirmed that the SRT theory can well describe the radiation regime of discontinuous canopies with balanced efficiency and accuracy. Moreover, the newly proposed coupling scheme of hotspot effect further improves the model performance in the hotspot regions. Finally, the unmanned aerial vehicle (UAV) observations served as a reference to validate the modeled canopy reflectance, which shows a high concordance. These results provide a detailed theoretical basis for applications and further improvements of the SRT model.

In the present work, a Moving Morphable Components (MMC)-based approach is proposed for topological design of stiffened plate structures, where the stiffeners are used as the building blocks of optimization. Unlike traditional approaches where three-dimensional solid model is often adopted for numerical analysis, in the proposed approach, beam model and plate model are adopted for the modeling of the stiffeners and base plate, respectively, in the context of Element-Free Galerkin (EFG) method. This treatment can not only reduce the computational cost associated with structural analysis substantially but also capture the concerned structural responses more accurately. Several numerical examples are provided to demonstrate the effectiveness and efficiency of the proposed approach.

General circulation models predict that precipitation will become more extreme, i.e. rainfall events of larger size but reduced frequency. Studies in North American grasslands have shown that such repackaging of precipitation into fewer, larger events enhanced above ground net primary productivity (ANPP), likely due to deeper soil moisture infiltration favoring plant water use over evaporation. However, ANPP responses in other regions remain poorly understood, and responses of carbon and water exchanges with the atmosphere remain unknown. Here we manipulated rainfall in a steppe ecosystem of northern China over 4 years to investigate how temporal packaging of precipitation impacts ANPP, evapotranspiration (ET), net ecosystem CO2 exchange (NEE) and the component fluxes gross primary productivity (GPP) and ecosystem respiration (RE). Experimental plots received precipitation equivalent to the 60-year growing-season average of 240 mm, variously packaged into 6, 10, 16, or 24 events representing extreme (P6) to historical average (P24) rainfall frequency. Extraordinarily extreme frequency (6 large events) reduced NEE, GPP, RE, ET and water use efficiency (WUE = |NEE|/ET). The average NEE, GPP and RE declined 35%, 45% and 48% respectively in the P6 treatment as compared to P16, which showed maximum ET and CO2 exchange. After peaking in the 16-event treatment, GPP and WUE in P24 were not distinguishable from P6. These peaks suggest that P16 was optimal for photosynthesis, with sufficiently frequent rain to maintain unregulated plants and adequately deep soil moisture infiltration to favour transpiration, with associated carbon uptake, over evaporation. Path analysis indicated the lower CO2 fluxes were influenced by reduced soil water content and leaf area index and higher soil temperature, with ET regulating the effects of these microclimatic drivers. ANPP showed a monotonic but non-significant decline with decreasing precipitation frequency, consistent with reduced CO2 fluxes. We found an increase in ANPP of xerophyte plants partially compensated for the ANPP decline in the dominant eurytopic xerophyte plants. Our results suggest that extreme temporal repackaging of precipitation into few events with correspondingly long dry intervals may reduce the capacity of steppe ecosystems to assimilate atmospheric CO2, although community diversity may moderate impacts.

Testing procedures for assessing whether two stationary and independent linear processes with unequal lengths have the same spectral densities or same auto-covariance functions are investigated. New test statistics are proposed based on the difference of the two wavelet-based estimates of the two spectral densities. The asymptotic normal distributions of the empirical wavelet coefficients are derived based on Bartlett type approximation of a quadratic form with dependent variables by the corresponding quadratic form with independent and identically distributed (i.i.d.) random variables. The limit distributions of the proposed test statistics are derived from those asymptotic results, and they asymptotically follow known chi-square distributions. The advantage of those new procedures is that those test statistics are constructed very simply and can be used for two time series with arbitrary lengths. The performance of those new tests is compared with some recent test statistics, with respect to their exact levels and powers. Simulation studies show that our proposed tests are very comparable to the current tests.

A meshless formulation for fracture analysis of stiffened plates is introduced in this paper. Based on an improved meshless model of stiffened plates proposed by the authors, in which flat plate and ribs are combined by implementing the displacement compatibility conditions between them, a crack is introduced by the diffraction method. The expanded basis function and the weight function based on t−1 distribution are employed. Inheriting the meshless advantages from the model, the ribs in our formulation can be set at any location on the flat plate, and the remeshing of the flat plate is naturally avoided when rib location changes. Some numerical examples are investigated by the proposed formulation and the commercial FEM software ANSYS, and the accuracy of the proposed formulation is verified. The effects of ribs on the displacement, stress and SIFs of the stiffened plates that have cracks are discussed.

Text detection in complex scenes is very hard realize by the diversification of text distribution, direction, and typesetting. This paper proposes one scene text detection method with end-to-end structure with parallel backbone network and region segmentation. With multiple deformable convolutions and extracting features of multi-dimensional text regions, multiple candidate regions of different sizes are generated and corresponding states are further given. Experiments show that compared with baseline, this method can further adapt to the problem that the different shapes and angles of the target in the image lead to the decrease of accuracy.

Global climate change is projected to increase both the intensity and frequency of extreme precipitation events (EPEs), which are considered to have stronger impacts on ecosystem functions than gradual changes in mean precipitation conditions. In this study, a consecutive 20-day extreme precipitation event (282 mm) was applied during the mid- and late-growing season periods in a semiarid steppe for three years to investigate the effects of extreme large precipitation events on aboveground net primary productivity (ANPP) and ecosystem carbon dioxide (CO 2 ) fluxes, including net ecosystem carbon absorption (NEE), gross primary productivity (GPP) and ecosystem respiration (Re). Although soil moisture was significantly increased by extreme precipitation, and even exceeded field capacity during the treatment periods, ANPP remained stable across all the treatments. There was also little change in mean growing season ecosystem CO 2 fluxes under the two precipitation treatments, despite GPP rates decreased by 34.4 and 26.3%, and NEE rates were suppressed by 77 and 68% during the mid- and late-season treatment periods, respectively. The stable CO 2 fluxes could be attributed to the recovery of GPP and NEE in 7 and 12 days after the end of EPEs. Our study demonstrated that both ANPP and CO 2 fluxes in this semiarid steppe were very stable in the face of extreme large precipitation events, regardless of the timing of events occur. Nevertheless, future, long-term studies need to investigate the potential tipping points or thresholds for ecosystem function shifts, as an increasing occurrence of EPEs has been forecasted in future climate change scenarios.

Normalized difference vegetation index (NDVI) of highly dense vegetation (NDVIv) and bare soil (NDVIs), identified as the key parameters for Fractional Vegetation Cover (FVC) estimation, are usually obtained with empirical statistical methods However, it is often difficult to obtain reasonable values of NDVIv and NDVIs at a coarse resolution (e.g., 1 km), or in arid, semiarid, and evergreen areas. The uncertainty of estimated NDVIs and NDVIv can cause substantial errors in FVC estimations when a simple linear mixture model is used. To address this problem, this paper proposes a physically based method. The leaf area index (LAI) and directional NDVI are introduced in a gap fraction model and a linear mixture model for FVC estimation to calculate NDVIv and NDVIs. The model incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters product (MCD43B1) and LAI product, which are convenient to acquire. Two types of evaluation experiments are designed 1) with data simulated by a canopy radiative transfer model and 2) with satellite observations. The root-mean-square deviation (RMSD) for simulated data is less than 0.117, depending on the type of noise added on the data. In the real data experiment, the RMSD for cropland is 0.127, for grassland is 0.075, and for forest is 0.107. The experimental areas respectively lack fully vegetated and non-vegetated pixels at 1 km resolution. Consequently, a relatively large uncertainty is found while using the statistical methods and the RMSD ranges from 0.110 to 0.363 based on the real data. The proposed method is convenient to produce NDVIv and NDVIs maps for FVC estimation on regional and global scales.

Terrain affects surface solar radiation (SSR) of mountainous area greatly. However, reliable observational data and methods are absent in mountainous areas. The stations located in these areas, typically built on flat places equipped with horizontal radiometers, are hard to capture the topographic effects. We proposed a tilted SSR observation group for mountainous areas based on ground stations. In 2015 and 2016, the method was tested in Chengde, China. Five ground stations were built on hilltop, valley, and three slopes to measure SSR. The radiometers on slopes and hilltop were set up parallel to the ground surfaces, while the radiometer in the valley was set horizontally to compare with the tilted observation method. A topographic radiation model along with a 12.5m digital elevation model (DEM) data was used to simulate the downward SSR and compare with observations. The result showed good consistency with the observations on slopes with R2 values as high as 0.99, but relatively big deviations were found at the hilltop and valley stations, caused by the slope calculation errors and unsuitable observational method. The results demonstrate the fact of that the topographic radiation model should be validated using proposed method with high accuracy DEM.

We consider wavelet-based nonlinear estimators, which are constructed by using the thresholding of the empirical wavelet coefficients, for the mean regression functions with strong mixing errors and investigate their asymptotic rates of convergence. We show that these estimators achieve nearly optimal convergence rates within a logarithmic term over a large range of Besov function classes Bsp, q. The theory is illustrated with some numerical examples. A new ingredient in our development is a Bernstein-type exponential inequality, which is established based on Merlevede et al. (2011), for a sequence of random variables with certain mixing structure and are not necessarily bounded or sub-Gaussian. This moderate deviation inequality may be of independent interest.

International audience; Vegetation cover estimation for overstory and understory layers provides valuable information for modeling forest carbon and water cycles and refining forest ecosystem function assessment. Although previous studies demonstrated the capability of light detection and ranging (LiDAR) in the three-dimensional (3D) characterization of forest overstory and understory communities, the high cost inhibits its application in frequent and successive survey tasks. Low-cost commercial red-green-blue (RGB) cameras mounted on unmanned aerial vehicles (UAVs), as LiDAR alternatives, provide operational systems for simultaneously quantifying overstory crown cover (OCC) and understory vegetation cover (UVC). We developed an effective method named back-projection of 3D point cloud onto superpixel-segmented image (BAPS) to extract overstory and forest floor pixels using 3D structure-from-motion (SfM) point clouds and two-dimensional (2D) superpixel segmentation. The OCC was estimated from the extracted overstory crown pixels. A reported method, called half-Gaussian fitting (HAGFVC), was used to segement green vegetation and non-vegetation pixels from the extracted forest floor pixels and derive UVC. The UAV-based RGB imagery and field validation data were collected from eight forest plots in Saihanba National Forest Park (SNFP) plantation in northern China. The consistency of the OCC estimates between BAPS and canopy height model (CHM)-based methods (coefficient of determination: 0.7171) demonstrated the capability of the BAPS method in the estimation of OCC. The segmentation of understory vegetation was verified by the supervised classification (SC) method. The validation results showed that the OCC and UVC estimates were in good agreement with reference values, where the root-mean-square error (RMSE) of OCC (unitless) and UVC (unitless) reached 0.0704 and 0.1144, respectively. The low-cost UAV-based observation system and the newly developed method are expected to improve the understanding of ecosystem functioning and facilitate ecological process modeling.

Accurate spatiotemporal information about crop progress during the growing season is critical for crop yield estimation. Crop progress monitoring at field scale requires high resolution remote sensing data in both time and space. Remote sensing data from a single sensor cannot satisfy the requirement at present. Data fusion approach has been developed to fuse remote sensing imagery from Landsat and MODIS instruments. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is one of the most popular spatial and temporal data fusion algorithms and has been applied in many applications. The data fusion accuracy was evaluated for many sites. Previous studies found that the accuracy of data fusion results depended on the pair images used. In this study, several Landsat-8 reflectance images (path28/row31) in 2015 were selected as pair images to evaluate the data fusion accuracy. Results were assessed based on the observed Landsat data that have not been used as pair images due to partial cloud coverage or image gaps. Several statistic metrics, including average absolute difference, root mean square error, correlation coefficient, and the spectral angle mapper, were calculated to assess the data fusion results. The initial results show that the predictability of each images pair at different dates is different. Closer dates have better prediction accuracy as expected. Interestingly, the different crop type (corn and soybeans) shows different data fusion accuracies even using same image pair. This study suggests that data fusion results could be further improved if an appropriate image pair is selected. Accurate dense time-series data at Landsat resolution will enhance our ability in crop condition monitoring and crop yield estimation at field scale.

We consider non-linear wavelet-based estimators of spatial regression functions with (known) random design on strictly stationary random fields, which are indexed by the integer lattice points in the \(N\)-dimensional Euclidean space and are assumed to satisfy some mixing conditions. We investigate their asymptotic rates of convergence based on thresholding of empirical wavelet coefficients and show that these estimators achieve nearly optimal convergence rates within a logarithmic term over a large range of Besov function classes \(B^{s}_{p,q}\). Therefore, wavelet estimators still achieve nearly optimal convergence rates for random fields and provide explicitly the extraordinary local adaptability.

We consider non-linear wavelet-based estimators of density functions with stationary random fields, which are indexed by the integer lattice points in the N -dimensional Euclidean space and are assumed to satisfy some mixing conditions. We investigate their asymptotic rates of convergence based on thresholding of empirical wavelet coefficients and show that these estimators achieve nearly optimal convergence rates within a logarithmic term over a large range of Besov function classes B p , q s . Therefore, wavelet estimators still achieve nearly optimal convergence rates for random fields and provide explicitly the extraordinary local adaptability.

Test procedures for assessing whether two stationary and independent time series with unequal lengths have the same spectral density (or same auto-covariance function) are investigated. A new test statistic is proposed based on the wavelet transform. It relies on empirical wavelet coefficients of the logarithm of two spectral densities' ratio. Under the null hypothesis that two spectral densities are the same, the asymptotic normal distribution of the empirical wavelet coeffcients is derived. Furthermore, these empirical wavelet coefficients are asymptotically uncorrelated. A test statistic is proposed based on these results. The performance of the new test statistic is compared to several recent test statistics, with respect to their exact levels and powers. Simulation studies show that our proposed test is very comparable to the current test statistics in most cases. The main advantage of our proposed test statistic is that it is constructed very simply and is easy to implement.

A method based on cosegmentation is applied to change detection to segment image patches belonging to each image. The image patches have the characteristics of spatial correspondence in multi-temporal images and precise boundary in its own image. By construction and optimization of energy function that consists of change feature item and image feature item, both of spectrum and shape change can successfully be given the description between different temporal images. The optimal segment results of multi-temporal images are achieved as the energy function reaches its minimum, in this process, the method of min-cut/max-flow will be used.

This study presents the mathematical model for predicting chloride penetration into saturated concrete under non-isothermal condition. The model considers not only diffusion mechanism but also migration process of chloride ions and other chemical species in concrete pore solution such as sodium, potassium, and hydroxyl ions. The coupled multi-ionic transport in concrete is described by the Nernst-Planck equation associated with electro-neutrality condition. The coupling parameter taken into account the effect of temperature on ion diffusion obtained from available test data is proposed and explicitly incorporated in the governing equations. The coupled transport equations are solved using the finite element method. The numerical results are validated with available experimental data and the comparison shows a good agreement.

Test procedures for serial correlation of unknown form with wavelet methods are investigated. A new test statistic is motivated using a canonical multivariate normal hypothesis testing model. It relies on empirical wavelet coefficients of a wavelet-based spectral density estimator. The choice of the Haar wavelet function is advocated, since evidence demonstrates that the choice of the wavelet function is not critical. Under the null hypothesis of no serial correlation, the asymptotic distribution of a vector of empirical wavelet coefficients is derived, which is asymptotically a multivariate normal distribution. A test statistic is proposed based on that asymptotic result, which presents the serious advantage to be completely data-driven or adaptive, avoiding the selection of any smoothing parameters. Furthermore, under a suitable class of fixed alternatives, the wavelet-based method is consistent against serial correlation of unknown form. The test statistic is expected to exhibit good power properties when the true spectral density displays significant spatial inhomogeneity, such as seasonal or business cycle periodicities. However, the convergence of the test statistic towards its asymptotic distribution is relatively slow. Thus, Monte Carlo methods based on random samples are suggested to determine the corresponding critical values. In a simulation study, the new methodology is compared with several test statistics, with respect to their exact levels and powers. The robustness properties of the spectral methods based on Monte Carlo critical values are also investigated empirically, when the error terms are weak white noises.

In order to minimize the central deflection of stiffened square plates under different loads, stiffener/rib layout optimization of stiffened plate is studied in the present work based on meshfree method and a hybrid genetic algorithm. The stiffened plates are composed of flat plates and straight beams with constant cross section. In order to combine the flat plates and beams, are introduced. The calculate method of the stiffened plate is the meshfree method, and the ribs can be placed beyond mesh lines. During the entire process of rib layout optimization, no remeshing of the plate is required, which is convenient in programming and cost-efficient in computation. The optimization is carried out through the hybrid genetic algorithm which combines the genetic algorithm (GA) and the controlled random search algorithm. The controlled random search algorithm can effectively shorten the computation time of the genetic algorithm and increase the probability to find the efficient points. The validity and adaptability of the proposed method are tested by calculating several numerical examples.

In this paper, we consider tests for assessing whether two stationary and independent time series have the same spectral densities (or same autocovariance functions). Both frequency domain and time domain test statistics for this purpose are reviewed. The adaptive Neyman tests are then introduced and their performances are investigated. Our tests are adaptive, that is, they are constructed completely by the data and do not involve any unknown smoothing parameters. Simulation studies show that our proposed tests are at least comparable to the current tests in most cases. Furthermore, our tests are much more powerful in some cases, such as against the long orders of autoregressive moving average (ARMA) models such as seasonal ARMA series.

We consider the wavelet-based estimators of mean regression function with long memory moving average errors and investigate their asymptotic rates of convergence based on thresholding of empirical wavelet coefficients. We show that these estimators achieve nearly optimal minimax convergence rates within a logarithmic term over a large range of Besov function classes \(B^{s}_{p,q}\). Therefore, in the presence of long memory non-Gaussian moving average noise, wavelet estimators still achieve nearly optimal convergence rates and provide explicitly the extraordinary local adaptability. The theory is illustrated with some numerical examples.

A comprehensive simulation model for the transport process of fully coupled moisture and multi-species in non-saturated concrete structures is proposed. The governing equations of moisture and ion diffusion are formulated based on Fick`s law and the Nernst-Planck equation, respectively. The governing equations are modified by explicitly including the coupling terms corresponding to the coupled mechanisms. The ionic interaction-induced electrostatic potential is described by electroneutrality condition. The model takes into account the two-way coupled effect of moisture diffusion and ion transport in concrete. The coupling parameters are evaluated based on the available experimental data and incorporated in the governing equations. Differing from previous researches, the material parameters related to moisture diffusion and ion transport in concrete are considered not to be constant numbers and characterized by the material models that account for the concrete mix design parameters and age of concrete. Then, the material models are included in the numerical analysis and the governing equations are solved by using finite element method. The numerical results obtained from the present model agree very well with available test data. Thus, the model can predict satisfactorily the ingress of deicing salts into non-saturated concrete.

This paper proposes a procedure of synthetic detection for the location of a change point and outliers in bilinear time series models with a change after an unknown time point. Based on Bayesian framework, we first derive the conditional posterior distribution of the change point and from that distribution estimate the position of the change point. Then we use these results to detect the outliers in the time series before and after that change point via Gibbs sampler algorithm. Our simulation studies show that the proposed procedure is effective.

The training model for Southwest University's no-fee teacher education program has taken shape over several years. Based on a review of the documentation and interviews with administrators and no-fee preservice students from different specialties, this article analyzes Southwest University's no-fee teacher-training model in terms of three main areas—curriculum, practice, and activities. The analysis includes the three dimensions of student knowledge, skills, and emotions as specific expressions of the educational goals of Southwest University's no-fee teacher-training model, and also addresses the characteristics of the model.

Monitoring crop areas is a key issue in remote sensing studies. A Crop Proportion Phenology Index (CPPI) model has previously been developed for estimation of winter wheat areas. Here we test the CPPI model in different areas using remote sensing data for varied kernel functions, including linear regression (LR), Artificial Neural Network (ANN), and Support Vector Regression (SVR). The differences of the model performances among different kernel functions were found to be small for areas with simple planting structure. For areas where multiple crop types have similar phenology cycles, the non-linear model of ANN was found to perform the best. This study indicates that the CPPI model can be applied to map winter wheat distribution in areas with complex planting structures, thus it holds promises for estimating fractional areas of winter wheat areas over large geographic areas.

Corrosion of reinforcing steel due to chloride ions is one of the severe deterioration problems in longterm performance of reinforced concrete structures. The deterioration process is frequently found in marine concrete structures, highway pavements, and bridges exposed to deicing salts. The diffusion of chloride ions is associated and strongly affected by other ions in the pore solution in concrete. In this paper, chloride penetration into concrete structures was mathematically characterized by the Nernst-Planck equation which considered not only diffusion mechanism of the chloride ions but also ionic interaction among other ions coming from externally applied deicers and within the Portland cement paste. Electroneutrality was used to determine the electrostatic potential induced by the ionic interaction. The material models of chloride binding capacity and chloride diffusion coefficient were incorporated in the governing equations. The governing equations were solved by using finite element method. A numerical example was used to illustrate the coupling effect of multi-ionic interactions and the effect of influential parameters. The numerical results obtained from the present model agreed very well with available test data.

Omnibus procedures for testing serial correlation are developed, using spectral density estimation and wavelet shrinkage. The asymptotic distributions of the wavelet coefficients under the null hypothesis of no serial correlation are derived. Under some general conditions on the wavelet basis, the wavelet coefficients asymptotically follow a normal distribution. Furthermore, they are asymptotically uncorrelated. Adopting a spectral approach and using results on wavelet shrinkage, new one-sided test statistics are proposed. As a spatially adaptive estimation method, wavelets can effectively detect fine features in the spectral density, such as sharp peaks and high frequency alternations. Using an appropriate thresholding parameter, shrinkage rules are applied to the empirical wavelet coefficients, resulting in a non-linear wavelet-based spectral density estimator. Consequently, the advocated approach avoids the need to select the finest scale J, since the noise in the wavelet coefficients is naturally suppressed. Simple data-dependent threshold parameters are also considered. In general, the convergence of the spectral test statistics toward their respective asymptotic distributions appears to be relatively slow. In view of that, Monte Carlo methods are investigated. In a small simulation study, several spectral test statistics are compared, with respect to level and power, including versions of these test statistics using Monte Carlo simulations.

This letter presents a new nonparametric approach, based on spline (SP) regression, for estimating the satellite altimeter sea-state bias (SSB) correction. Model evaluation is performed with models derived from a local linear kernel (LK) smoothing, the method which is currently used to build operational altimeter SSB models. The key reasons for introducing this alternative approach for the SSB application are simplicity in accurate model generation, ease in model replication among altimeter research teams, reduced computational requirements, and its suitability for higher dimensional SSB estimation. It is shown that the SP- and LK-based SSB solutions are effectively equivalent within the data-dense portion, with an offset below 0.1 mm and a rms difference of 1.9 mm for the 2-D (wave height and wind speed) model. Small differences at the 1-5-mm level do exist in the case of low data density, particularly at low wind speed and high sea state. Overall, the SP model appears to more closely follow the bin-averaged SSB estimates.

Hall et al. (1999) proposed block-thresholding methods to estimate mean regression functions with independent random errors. They showed that block-thresholded wavelet estimators attain minimax-optimal convergence rates when the mean functions belong to a large class of functions that involve a wide variety of irregularities, including chirp and Doppler functions, and functions with jump discontinuities. In this article, we show that block-thresholded wavelet estimators still attain minimax convergence rates when the mean functions belong to a wide range of Besov classes (where s > 1/p, p ≥ 1 and q ≥ 1) with long-memory Gaussian errors. Therefore, in the presence of long-memory Gaussian errors, wavelet estimators still provide extensive adaptivity.