Search Results (506)

Due to the high sensitivity of grid-based micro-scale wind–sand flow models to deformation and distortion, this study employs the Smooth Particle Hydrodynamics (SPH) method for numerical simulations. The advantage of the SPH method is that it can dynamically analyze the entire trajectory of the particles, thus allowing the initial positional distribution of sand-buried particles to be traced. This study utilizes the advantages of the SPH method. It develops particle backtracking algorithms based on the SPH method using the C language. It analyses the initial location distribution, concentration, velocity, and particle size distribution of sand-buried particles to formulate targeted measures to cope with wind–sand disasters. Meanwhile, this paper improves a particle modeling algorithm to realize arbitrary mixing particle size and mixing ratio by programming in C language and combining it with pixel recognition technology. In addition, this paper will use the particle backtracking algorithm to analyze the classical embankment wind and sand flow field and then propose adequate measures for embankment wind and sand disaster management by investigating sand particle movement characteristics. Full article

Land use and cover change (LUCC) is a key factor influencing global environmental and socioeconomic systems. Many long-term geospatial LUCC datasets have been developed at various scales during the recent decades owing to the availability of long-term satellite data, statistical data and computational techniques. However, most existing LUCC products cannot accurately reflect the spatiotemporal change patterns of LUCC at the regional scale in China. Based on these geospatial LUCC products, normalized difference vegetation index (NDVI), socioeconomic data and statistical data, we developed multiple procedures to represent both the spatial and temporal changes of the major LUC types by applying machine-learning, regular decision-tree and hierarchical assignment methods using northeastern China (NEC) as a case study. In this approach, each individual LUC type was developed in sequence under different schemes and methods. The accuracy evaluation using sampling plots indicated that our approach can accurately reflect the actual spatiotemporal patterns of LUC shares in NEC, with an overall accuracy of 82%, Kappa coefficient of 0.77 and regression coefficient of 0.82. Further comparisons with existing LUCC datasets and statistical data also indicated the accuracy of our approach and datasets. Our approach unfolded the mixed-pixel issue of LUC types and integrated the strengths of existing LUCC products through multiple fusion processes. The analysis based on our developed dataset indicated that forest, cropland and built-up land area increased by 17.11 × 10⁴ km², 15.19 × 10⁴ km² and 2.85 × 10⁴ km², respectively, during 1980–2020, while grassland, wetland, shrubland and bare land decreased by 26.06 × 10⁴ km², 4.24 × 10⁴ km², 3.97 × 10⁴ km² and 0.92 × 10⁴ km², respectively, in NEC. Our developed approach accurately reconstructed the shares and spatiotemporal patterns of all LUC types during 1980–2020 in NEC. This approach can be further applied to the entirety of China, and worldwide, and our products can provide accurate data supports for studying LUCC consequences and making effective land use policies. Full article

Surface water fraction mapping is an essential preprocessing step for the subpixel mapping (SPM) of surface water, providing valuable prior knowledge about surface water distribution at the subpixel level. In recent years, spectral mixture analysis (SMA) has been extensively applied to estimate surface water fractions in multispectral images by decomposing each mixed pixel into endmembers and their corresponding fractions using linear or nonlinear spectral mixture models. However, challenges emerge when introducing existing surface water fraction mapping methods to hyperspectral images (HSIs) due to insufficient exploration of spectral information. Additionally, inaccurate extraction of endmembers can result in unsatisfactory water fraction estimations. To address these issues, this paper proposes an adaptive unmixing method based on iterative multi-objective optimization for surface water fraction mapping (IMOSWFM) using Zhuhai-1 HSIs. In IMOSWFM, a modified normalized difference water fraction index (MNDWFI) was developed to fully exploit the spectral information. Furthermore, an iterative unmixing framework was adopted to dynamically extract high-quality endmembers and estimate their corresponding water fractions. Experimental results on the Zhuhai-1 HSIs from three test sites around Nanyi Lake indicate that water fraction maps obtained by IMOSWFM are closest to the reference maps compared with the other three SMA-based surface water fraction estimation methods, with the highest overall accuracy (OA) of 91.74%, 93.12%, and 89.73% in terms of pure water extraction and the lowest root-mean-square errors (RMSE) of 0.2506, 0.2403, and 0.2265 in terms of water fraction estimation. This research provides a reference for adapting existing surface water fraction mapping methods to HSIs. Full article

With the development of remote sensing technology, the estimation of the chlorophyll content (CHLC) of vegetation via satellite data has become an important means of monitoring vegetation health, and high-precision estimation has been the focus of research in this field. In this study, we used larch affected by Yarl’s larch looper (Erannis jacobsoni Djak) in the boundary region of Mongolia as the research object, simulated the multispectral reflectance, downscaled Sentinel-2A satellite data, performed mixed-pixel decomposition, analyzed the potential of Sentinel-2A satellite data for estimating the chlorophyll content by calculating the spectral indices (SIs) and spectral derivatives (SDFs) of images, and then extracted sensitive spectral features as the model training set. Spectral features sensitive to the chlorophyll content were extracted to establish the training set, and, finally, the chlorophyll content estimation model for larch was constructed on the basis of the partial least squares algorithm (PLSR). The results revealed that SI and SDF based on simulated remote sensing data were highly sensitive to the chlorophyll content under the influence of pests, with the SAVI and EVI2 spectral indices as well as the D_B2 and D_B5 spectral derivatives being the most sensitive to the chlorophyll content. The estimation models based on simulated data performed significantly better than models without simulated data in terms of accuracy, especially those based on SDF-PLSR. The simulated spectral reflectance well reflected the spectral characteristics of the larch canopy and was sensitive to damaged larch, especially in the green light, red edge, and near-infrared bands. The proposed approach improves the accuracy of chlorophyll content estimation via Sentinel-2A data and enhances the ability to monitor changes in the chlorophyll content under complex forest conditions through simulations, providing new technical means and a theoretical basis for forestry pest monitoring and vegetation health management. Full article

The monitoring of grape quality parameters within viticulture using airborne remote sensing is an increasingly important aspect of precision viticulture. Airborne remote sensing allows high volumes of spatial consistent data to be collected with improved efficiency over ground-based surveys. Spectral data can be used to understand the characteristics of vineyards, including the characteristics and health of the vines. Within viticultural remote sensing, the use of cover-crop spectra for monitoring is often overlooked due to the perceived noise it generates within imagery. However, within viticulture, the cover crop is a widely used and important management tool. This study uses multispectral data acquired by a high-resolution uncrewed aerial vehicle (UAV) and Sentinel-2 MSI to explore the benefit that cover-crop pixels could have for grape yield and quality monitoring. This study was undertaken across three growing seasons in the southeast of England, at a large commercial wine producer. The site was split into a number of vineyards, with sub-blocks for different vine varieties and rootstocks. Pre-harvest multispectral UAV imagery was collected across three vineyard parcels. UAV imagery was radiometrically corrected and stitched to create orthomosaics (red, green, and near-infrared) for each vineyard and survey date. Orthomosaics were segmented into pure cover-crop_uav and pure vine_uav pixels, removing the impact that mixed pixels could have upon analysis, with three vegetation indices (VIs) constructed from the segmented imagery. Sentinel-2 Level 2a bottom of atmosphere scenes were also acquired as close to UAV surveys as possible. In parallel, the yield and quality surveys were undertaken one to two weeks prior to harvest. Laboratory refractometry was performed to determine the grape total acid, total soluble solids, alpha amino acids, and berry weight. Extreme gradient boosting (XGBoost v2.1.1) was used to determine the ability of remote sensing data to predict the grape yield and quality parameters. Results suggested that pure cover-crop_uav was a successful predictor of grape yield and quality parameters (range of R² = 0.37–0.45), with model evaluation results comparable to pure vine_uav and Sentinel-2 models. The analysis also showed that, whilst the structural similarity between the both UAV and Sentinel-2 data was high, the cover crop is the most influential spectral component within the Sentinel-2 data. This research presents novel evidence for the ability of cover-crop_uav to predict grape yield and quality. Moreover, this finding then provides a mechanism which explains the success of the Sentinel-2 modelling of grape yield and quality. For growers and wine producers, creating grape yield and quality prediction models through moderate-resolution satellite imagery would be a significant innovation. Proving more cost-effective than UAV monitoring for large vineyards, such methodologies could also act to bring substantial cost savings to vineyard management. Full article

The design of a sampling unit, whether a simple plot or a subplot within a clustered structure, including shape and size, has received little attention in inferential forestry research. The use of auxiliary variables from remote sensing impacts the precision of estimators from both model-assisted and model-based inference perspectives. In both cases, model parameters are estimated from a sample of field plots and information from pixels corresponding to these units. In studies assisted by remote sensing, the shape of the plot used to fit regression models (typically circular) often differs from the shape of the population elements for prediction, where the area of interest is divided into equal tessellated parts. This raises interest in understanding the effect of the sampling unit shape on the mean of variables in forest stands of interest. Therefore, the objective of this study was to evaluate the effect of circular and square subplots, concentrically overlapped and arranged in an inverted Y cluster structure, over tree density, basal area, and aboveground biomass in a managed temperate forest in central Mexico. We used a Multivariate Generalised Linear Mixed Model, which considers the Gamma distribution of the variables and accounts for spatial correlation between Secondary Sampling Units nested within the Primary Sampling Unit. The main findings of this study indicate that the type of secondary sampling unit of the same area and centroid, whether circular or square, does not significantly affect the mean tree density (trees), basal area (m²), and aerial biomass. Full article

As one of the major global food crops, the monitoring and management of the winter wheat planting area is of great significance for agricultural production and food security worldwide. Today, the development of high-resolution remote sensing imaging technology has provided rich sources of data for extracting the visual planting information of winter wheat. However, the existing research mostly focuses on extracting the planting plots that have a simple terrain structure. In the face of diverse terrain features combining mountainous areas, plains, and saline alkali land, as well as small-scale but complex planting structures, the extraction of planting plots through remote sensing imaging is subjected to great challenges in terms of recognition accuracy and model complexity. In this paper, we propose a modified Segformer model for extracting winter wheat planting plots with complex structures in rural areas based on the 0.8 m high-resolution multispectral data obtained from the Gaofen-2 satellite, which significantly improves the extraction accuracy and efficiency under complex conditions. In the encoder and decoder of this method, new modules were developed for the purpose of optimizing the feature extraction and fusion process. Specifically, the improvement measures of the proposed method include: (1) The MixFFN module in the original Segformer model is replaced with the Multi-Scale Feature Fusion Fully-connected Network (MSF-FFN) module, which enhances the model’s representation ability in handling complex terrain features through multi-scale feature extraction and position embedding convolution; furthermore, the DropPath mechanism is introduced to reduce the possibility of overfitting while improving the model’s generalization ability. (2) In the decoder part, after fusing features at four different scales, a CoordAttention module is added, which can precisely locate important regions with enhanced features in the images by utilizing the coordinate attention mechanism, therefore further improving the model’s extraction accuracy. (3) The model’s input data are strengthened by incorporating multispectral indices, which are also conducive to the improvement of the overall extraction accuracy. The experimental results show that the accuracy rate of the modified Segformer model in extracting winter wheat planting plots is significantly increased compared to traditional segmentation models, with the mean Intersection over Union (mIOU) and mean Pixel Accuracy (mPA) reaching 89.88% and 94.67%, respectively (an increase of 1.93 and 1.23 percentage points, respectively, compared to the baseline model). Meanwhile, the parameter count and computational complexity are significantly reduced compared to other similar models. Furthermore, when multispectral indices are input into the model, the mIOU and mPA reach 90.97% and 95.16%, respectively (an increase of 3.02 and 1.72 percentage points, respectively, compared to the baseline model). Full article

The cost of hyperspectral image (HSI) classification primarily stems from the annotation of image pixels. In real-world classification scenarios, the measurement and annotation process is both time-consuming and labor-intensive. Therefore, reducing the number of labeled pixels while maintaining classification accuracy is a key research focus in HSI classification. This paper introduces a multi-strategy triple network classifier (MSTNC) to address the issue of limited labeled data in HSI classification by improving learning strategies. First, we use the contrast learning strategy to design a lightweight triple network classifier (TNC) with low sample dependence. Due to the construction of triple sample pairs, the number of labeled samples can be increased, which is beneficial for extracting intra-class and inter-class features of pixels. Second, an active learning strategy is used to label the most valuable pixels, improving the quality of the labeled data. To address the difficulty of sampling effectively under extremely limited labeling budgets, we propose a new feature-mixed active learning (FMAL) method to query valuable samples. Fine-tuning is then used to help the MSTNC learn a more comprehensive feature distribution, reducing the model’s dependence on accuracy when querying samples. Therefore, the sample quality is improved. Finally, we propose an innovative dual-threshold pseudo-active learning (DSPAL) strategy, filtering out pseudo-label samples with both high confidence and uncertainty. Extending the training set without increasing the labeling cost further improves the classification accuracy of the model. Extensive experiments are conducted on three benchmark HSI datasets. Across various labeling ratios, the MSTNC outperforms several state-of-the-art methods. In particular, under extreme small-sample conditions (five samples per class), the overall accuracy reaches 82.97% (IP), 87.94% (PU), and 86.57% (WHU). Full article

The standardized spectral mixture model combines the specificity of a physically based representation of a spectrally mixed pixel with the generality and portability of a spectral index. Earlier studies have used spectrally and geographically diverse collections of broadband and spectroscopic imagery to show that the reflectance of the majority of ice-free landscapes on Earth can be represented as linear mixtures of rock and soil substrates (S), photosynthetic vegetation (V) and dark targets (D) composed of shadow and spectrally absorptive/transmissive materials. However, both broadband and spectroscopic studies of the topology of spectral mixing spaces raise questions about the completeness and generality of the Substrate, Vegetation, Dark (SVD) model for imaging spectrometer data. This study uses a spectrally diverse collection of 40 granules from the EMIT imaging spectrometer to verify the generality and stability of the spectroscopic SVD model and characterize the SVD topology and plane of substrates to assess linearity of spectral mixing. New endmembers for soil and non-photosynthetic vegetation (NPV; N) allow the planar SVD model to be extended to a tetrahedral SVDN model to better accommodate the 3D topology of the mixing space. The SVDN model achieves smaller misfit than the SVD, but does so at the expense of implausible fractions beyond [0, 1]. However, a refined spectroscopic SVD model still achieves small (<0.03) RMS misfit, negligible sensitivity to endmember variability and strongly linear scaling over more than an order of magnitude range of spatial resolution. Full article

Due to the security weaknesses of chaos-based pseudorandom number generators, in this paper, a new pseudorandom number generator (PRNG) based on mixing three-dimensional variables of a cat chaotic map is proposed. A uniformly distributed chaotic sequence by a logistic map is used in the mixing step. Both statistical tests and a security analysis indicate that our PRNG has good randomness and is more complex than any one-dimensional variable of a cat map. Furthermore, a new image encryption algorithm based on the chaotic PRNG is provided to protect the content of artwork images. The core of the algorithm is to use the sequence generated by the pseudorandom number generator to achieve the process of disruption and diffusion of the image pixels, so as to achieve the effect of obfuscation and encryption of the image content. Several security tests demonstrate that this image encryption algorithm has a high security level. Full article

Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product. Full article

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R² of 0.72, RMSE 0.41) and FCOVER (R² 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management. Full article

With the continuous development of science and technology, laser ranging technology will become more efficient, convenient, and widespread, and it has been widely used in the fields of medicine, engineering, video games, and three-dimensional imaging. A time-of-flight (ToF) camera is a three-dimensional stereo imaging device with the advantages of small size, small measurement error, and strong anti-interference ability. However, compared to traditional sensors, ToF cameras typically exhibit lower resolution and signal-to-noise ratio due to inevitable noise from multipath interference and mixed pixels during usage. Additionally, in environments with scattering media, the information about objects gets scattered multiple times, making it challenging for ToF cameras to obtain effective object information. To address these issues, we propose a solution that combines ToF cameras with single-pixel imaging theory. Leveraging intensity information acquired by ToF cameras, we apply various reconstruction algorithms to reconstruct the object’s image. Under undersampling conditions, our reconstruction approach yields higher peak signal-to-noise ratio compared to the raw camera image, significantly improving the quality of the target object’s image. Furthermore, when ToF cameras fail in environments with scattering media, our proposed approach successfully reconstructs the object’s image when the camera is imaging through the scattering medium. This experimental demonstration effectively reduces the noise and direct ambient light generated by the ToF camera itself, while opening up the potential application of ToF cameras in challenging environments, such as scattering media or underwater. Full article

The composition of clay minerals in soils, and more particularly the presence of montmorillonite (as part of the smectite family), is a key factor in soil swell–shrinking as well as off–road vehicle mobility. Detecting these topsoil clay minerals and quantifying the montmorillonite abundance are a challenge since they are usually intimately mixed with other minerals, soil organic carbon and soil moisture content. Imaging spectroscopy coupled with unmixing methods can address these issues, but the quality of the estimation degrades the coarser the spatial resolution is due to pixel heterogeneity. With the advent of UAV-borne and proximal hyperspectral acquisitions, it is now possible to acquire images at a centimeter scale. Thus, the objective of this paper is to evaluate the accuracy and limitations of unmixing methods to retrieve montmorillonite abundance from very-high-resolution hyperspectral images (1.5 cm) acquired from a camera installed on top of a bucket truck over three different agricultural fields, in Loiret department, France. Two automatic endmember detection methods based on the assumption that materials are linearly mixed, namely the Simplex Identification via Split Augmented Lagrangian (SISAL) and the Minimum Volume Constrained Non-negative Matrix Factorization (MVC-NMF), were tested prior to unmixing. Then, two linear unmixing methods, the fully constrained least square method (FCLS) and the multiple endmember spectral mixture analysis (MESMA), and two nonlinear unmixing ones, the generalized bilinear method (GBM) and the multi-linear model (MLM), were performed on the images. In addition, several spectral preprocessings coupled with these unmixing methods were applied in order to improve the performances. Results showed that our selected automatic endmember detection methods were not suitable in this context. However, unmixing methods with endmembers taken from available spectral libraries performed successfully. The nonlinear method, MLM, without prior spectral preprocessing or with the application of the first Savitzky–Golay derivative, gave the best accuracies for montmorillonite abundance estimation using the USGS library (RMSE between 2.2–13.3% and 1.4–19.7%). Furthermore, a significant impact on the abundance estimations at this scale was in majority due to (i) the high variability of the soil composition, (ii) the soil roughness inducing large variations of the illumination conditions and multiple surface scatterings and (iii) multiple volume scatterings coming from the intimate mixture. Finally, these results offer a new opportunity for mapping expansive soils from imaging spectroscopy at very high spatial resolution. Full article

The detection of change in remote-sensing images is broadly applicable to many fields. In recent years, both supervised and unsupervised methods have demonstrated excellent capacity to detect changes in high-resolution images. However, most of these methods are sensitive to noise, and their performance significantly deteriorates when dealing with remote-sensing images that have been contaminated by mixed random noises. Moreover, supervised methods require that samples are manually labeled for training, which is time-consuming and labor-intensive. This study proposes a new unsupervised change-detection (CD) framework that is resilient to mixed random noise called self-supervised denoising network-based unsupervised change-detection coupling FCM_SICM and EMD (SSDNet-FSE). It consists of two components, namely a denoising module and a CD module. The proposed method first utilizes a self-supervised denoising network with real 3D weight attention mechanisms to reconstruct noisy images. Then, a noise-resistant fuzzy C-means clustering algorithm (FCM_SICM) is used to decompose the mixed pixels of reconstructed images into multiple signal classes by exploiting local spatial information, spectral information, and membership linkage. Next, the noise-resistant Earth mover’s distance (EMD) is used to calculate the distance between signal-class centers and the corresponding fuzzy memberships of bitemporal pixels and generate a map of the magnitude of change. Finally, automatic thresholding is undertaken to binarize the change-magnitude map into the final CD map. The results of experiments conducted on five public datasets prove the superior noise-resistant performance of the proposed method over six state-of-the-art CD competitors and confirm its effectiveness and potential for practical application. Full article