Search Results (24,016)

The leaf area index (LAI) serves as a key metric for tracking crop growth and can be integrated into crop models for yield estimation. Although the remote sensing LAI data provide a critical foundation for monitoring crop growth and estimating yields, the existing datasets often exhibit notable errors due to the pixel-level heterogeneity. To improve the applicability and inversion accuracy of MODIS LAI products in the Northeast China (NEC) region, this study upscaled the 500-m resolution MODIS LAI product to a 5-km resolution by initially calculating the mean value. Then, the scale factors were estimated based on the observed LAI data of spring maize. To further refine the accuracy of the remotely sensed LAI, 1-km resolution land use data were resampled to 500-m resolution, and the pixel purity of spring maize was calculated for each 5-km grid cell. The scale factor time series was fitted with and without consideration of pixel purity, and the accuracy of the adjusted LAI using these two methods was compared. Our findings demonstrate that the optimal method for fitting scale factors for spring maize LAI data is piecewise function method which combines Gaussian and quadratic polynomial functions. The time series of scale factors derived from high- and low-purity pixels, differentiated by a 50% purity threshold, resulted in improved performance in adjusting the spring maize LAI compared to traditional remote sensing LAI data. The adjusted LAI performed better in reflecting the growth characteristics of spring maize in the NEC region, with the relative mean square errors between observed and adjusted LAI of spring maize during 2016 and 2020 below 1 m²/m². This study provides crucial support for monitoring the growth process and estimating the yield of spring maize in the NEC region and also offers valuable scientific references for the optimization and application of remote sensing data. Full article

The extensive peatlands of the Tibetan Plateau (TP) play a vital role in sustaining the global ecological balance. However, the distribution of peatlands across this region and the related environmental factors remain poorly understood. To address this issue, we created a high-resolution (10 m) map for peatland distribution in the TP region using 6146 Sentinel-1 and 23,730 Sentinel-2 images obtained through the Google Earth Engine platform in 2023. We employed a random forest algorithm that integrated spatiotemporal features with field training samples. The overall accuracy of the peatland distribution map produced is high, at 86.33%. According to the classification results, the total area of peatlands on the TP is 57,671.55 km², and they are predominantly located in the northeast and southwest, particularly in the Zoige Protected Area. The classification primarily relied on the NDVI, NDWI, and RVI, while the DVI and MNDWI were also used in peatland mapping. B11, B12, NDWI, RVI, NDVI, and slope are the most significant features for peatland mapping, while roughness, correlation, entropy, and ASM have relatively slight significance. The methodology and peatland map developed in this work will enhance the conservation and management of peatlands on the TP while informing policy decisions and supporting sustainable development assessments. Full article

The effect of COVID-19 lockdown (LD) on many ambient air pollutants (NO, NO₂, PM_2.5, PM₁₀, O₃ and SO₂) was assessed for the first time in the Western Balkans with an innovative approach that evaluates a variety of factors including the stringency of the LD measures, the type of location, the pollution sources, the correlation with traffic fluxes and the meteorology. To that end, observations from 10 urban sites were compared with historical time series. The time window 1 February–30 May 2020 was classified in sub-periods on the basis of the stringency of the circulation restrictions. NO₂ and O₃ are the pollutants most affected by restrictions to population circulation due to lockdown during the first phase of the COVID-19 pandemic, and are well correlated with traffic fluxes. A reduction in fine particulate matter (PM_2.5 and PM₁₀) concentrations is observed in all sites only during the full LD periods, while the relation between SO₂ average and maximum hourly concentrations and LD periods in industrial and traffic sites vary from site to site. The reduction in NO₂ concentrations during the LD resulted in a reduction in mortality associated with air pollution in the largest cities, while the interpretation of the changes in O₃ and particulate matter is less clear. Full article

A benchmarking study is reported on a series of modified carbocatalysts to efficiently promote the acetalization of 1,2-diols under heterogeneous conditions. Among the catalysts surveyed, a hydrothermal carbon generated from saccharose, a cheap, abundant, and biobased material, showed excellent performance when tested on two representative diols. All catalysts have been thoroughly characterized, focusing on surface acidity and composition. Optimal working parameters such as temperature and catalyst loading could be established. Remarkably, sonication improved the diol protection, which proceeded faster at 25 °C. The catalyst could be easily recycled and reused several times. In addition, the protocol was successfully translated from batch to continuous flow operation using a packed-bed reactor. Full article

In situ rapid classification of rock lithology is crucial in various fields, including geological exploration and petroleum logging. Laser-induced breakdown spectroscopy (LIBS) is particularly well-suited for in situ online analysis due to its rapid response time and minimal sample preparation requirements. To facilitate in situ raw rock discrimination analysis, a portable LIBS device was developed specifically for outdoor use. This device built upon a previous multi-directional optimization scheme and integrated machine learning to classify seven types of original rock samples: mudstone, basalt, dolomite, sandstone, conglomerate, gypsolyte, and shale from oil logging sites. Initially, spectral data were collected from random areas of each rock sample, and a series of pre-processing steps and data dimensionality reduction were performed to enhance the accuracy and efficiency of the LIBS device. Subsequently, four classification algorithms—linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost)—were employed for classification discrimination. The results were evaluated using a confusion matrix. The final average classification accuracies achieved were 95.71%, 93.57%, 92.14%, and 98.57%, respectively. This work not only demonstrates the effectiveness of the portable LIBS device in classifying various original rock types, but it also highlights the potential of the XGBoost algorithm in improving LIBS analytical performance in field scenarios and geological applications, such as oil logging sites. Full article

Groundwater plays a crucial role in the formation of the Badain Jaran Desert-Sand Dune Lake System, which has been designated a UNESCO World Heritage Site in 2024. However, the region’s wetland ecosystem is significantly impacted by climate change and human activities. This study utilizes GRACE satellite data and in situ observation data to establish a groundwater storage anomaly (GWSA) time series for the Badain Jaran Desert and its surrounding areas (BJDCA) from 2003 to 2022. The analysis reveals the spatiotemporal patterns of groundwater drought and sustainability, as well as the underlying factors affecting regional groundwater sustainability. The results indicate that 99.2% of the study area exhibited a significant decline in GWSA (α ≤ 0.01), with the annual mean GRACE Groundwater Drought Index (GGDI) dropping from 1.44 to −1.54, reflecting a worsening groundwater drought. In 2022, the GGDI in the southeastern part of the BJDCA reached as low as −3.04, highlighting severe groundwater stress. Furthermore, the Sustainability Index (SI) of the study area declined markedly from 1.00 to 0.01, underscoring the critical impact of human activities on groundwater resources in the BJDCA. These findings provide valuable insights for formulating more effective groundwater resource management policies and promoting sustainable development in arid regions. Full article

With the advancement of large-scale coal development and utilization, low-rank coal (LRC) is increasingly gaining prominence in the energy sector. Upgrading and ash reduction are key to the clean utilization of LRC. Flotation technology based on gas/liquid/solid interfacial interactions remains an effective way to recover combustible materials and realize the clean utilization of coal. The traditional collector, kerosene, has demonstrated its inefficiency and environmental toxicity in the flotation of LRC. In this study, four eco-friendly tetrahydrofuran ester compounds (THF-series) were investigated as novel collectors to improve the flotation performance of LRC. The flotation results showed that THF-series collectors were more effective than kerosene in enhancing the LRC flotation. Among these, tetrahydrofurfuryl butyrate (THFB) exhibited the best performance, with combustible material recovery and flotation perfection factors 79.79% and 15.05% higher than those of kerosene, respectively, at a dosage of 1.2 kg/t. Characterization results indicated that THF-series collectors rapidly adsorbed onto the LRC surface via hydrogen bonding, resulting in stronger hydrophobicity and higher electronegativity. High-speed camera and particle image velocimeter (PIV) observation further demonstrated that THFB dispersed more evenly in the flotation system, reducing the lateral movement of bubbles during their ascent, lowering the impact of bubble wakes on coal particles, and promoting the stable adhesion of bubbles to the LRC surface within a shorter time (16.65 ms), thereby preventing entrainment effects. This study provides new insights and options for the green and efficient flotation of LRC. Full article

The present study examines the relationship between thermal and configurational entropy in two resistors in parallel and in series. The objective is to introduce entropy in electric circuit analysis by considering the impact of system geometry on energy conversion in the circuit. Thermal entropy is derived from thermodynamics, whereas configurational entropy is derived from network modelling. It is observed that the relationship between thermal entropy and configurational entropy varies depending on the configuration of the resistors. In parallel resistors, thermal entropy decreases with configurational entropy, while in series resistors, the opposite is true. The implications of the maximum power transfer theorem and constructal law are discussed. The entropy generation for resistors at different temperatures was evaluated, and it was found that the consideration of resistor configurational entropy change was necessary for consistency. Furthermore, for the sake of generalization, a similar behaviour was observed in time-dependent circuits, either for resistor–capacitor circuits or circuits involving degradation. Full article

This paper considers the features of a time series of plankton concentrations, which are further compared with such phenomena as the alteration of day and night and tidal processes. The analysis of experimental data recorded as a result of long-term monitoring measurements under field conditions showed that the diurnal variability in plankton concentrations can be described using a model harmonic function. At the same time, based on the parameters of the diurnal variability model, it is possible to build a bioindication system to detect the influence of estimated abnormal environmental factors, such as pollution. This study discusses the ideology of building such a system based on regular observations of the behavior of autochthonous plankton using a submersible digital holographic camera. Full article

Invasive species are often central to conservation efforts, particularly when concerns involve potential impacts on rare, endemic native species. The lower New River drainage of the eastern United States is a watershed that warrants conservation assessment, as the system is naturally depauperate of native fish species and it is nearly saturated with non-native fish species: there are 31 natives, including at least nine endemic taxa, and 63 non-natives. For endemic taxa, we examined temporal distribution shifts (range expansions or contractions) based on percent change in the occupied watershed area. We contrasted these findings with time series analyses on distribution trends of non-native minnows (Leuciscidae) and darters (Percidae) based on growth curve models of the cumulative sum of the total area of occupied 12-digit hydrologic unit codes. We documented range reductions for six of nine endemic taxa. We determined that 11 of 18 non-native minnows and 6 of 8 non-native darters were invasive based on range expansions and associated invasion curve models. The endemic taxa are of conservation concern given the limited distribution ranges and documented population declines. Although among-species comparisons of range shifts do not support causal inference, documentation of changes in distribution ranges of endemic and invasive species is critical to inform conservation efforts. Full article

This paper introduces a kinetic model of crowd evacuation from a bounded domain, integrating social behavior and contagion dynamics. The model describes the spatial movement of individuals in a crowd, taking into account interactions with other people and the geometry of the environment. Interactions between healthy and infectious individuals can lead to disease transmission and are considered. The approach is grounded in the kinetic theory of active particles, where the activity variable represents both the infectious disease status of individuals (e.g., susceptible, infected) and the psychological state of pedestrians, including contagion awareness. Varying awareness levels influence individual behavior, leading to more cautious movement patterns, potentially reducing the overall infection rate. The performance of the model is evaluated through a series of numerical simulations. Different scenarios are examined to investigate the impact of awareness levels on pedestrian behavior, infectious disease spread, and evacuation times. Additionally, the effects of population immunization and individual contagion awareness are assessed to determine the most effective strategy for reducing infections. The results provide valuable insights into targeted strategies to mitigate contagion. Full article

Slope displacement monitoring is essential for assessing slope stability and preventing catastrophic failures, particularly in geotechnically sensitive areas. However, continuous data collection is often disrupted by environmental factors, sensor malfunctions, and communication issues, leading to missing data that can compromise analysis accuracy and reliability. This study addresses these challenges by evaluating advanced machine learning models—SAITS, ImputeFormer, and BRITS (Bidirectional Recurrent Imputation for Time Series)—for missing data imputation in slope displacement datasets. Sensors installed at two field locations, Yangyang and Omi, provided high-resolution displacement data, with approximately 34,000 data points per sensor. We simulated missing data scenarios at rates of 1%, 3%, 5%, and 10%, reflecting both random and block missing patterns to mimic realistic conditions. The imputation performance of each model was evaluated using Mean Absolute Error, Mean Squared Error, and Root Mean Square Error to assess accuracy and robustness across varying levels of data loss. Results demonstrate that each model has distinct advantages under specific missingness patterns, with the ImputeFormer model showing strong performance in capturing long-term dependencies. These findings underscore the potential of machine learning-based imputation methods to maintain data integrity in slope displacement monitoring, supporting reliable slope stability assessments even in the presence of significant data gaps. This research offers insights into the optimal selection and application of imputation models for enhancing the quality and continuity of geotechnical monitoring data. Full article

The increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is challenging due to phenological differences in crop growth stages between training and target areas. This study proposes the Adaptive Month Matching (AMM) method to align the phenological stages of crops between training and target areas for enhanced transfer learning in cropland segmentation. In the AMM method, an optimal Sentinel-2 monthly time series is identified in the training area based on deep learning model performance for major crops common to both areas. A month-matching process then selects the optimal Sentinel-2 time series for the target area by aligning the phenological stages between the training and target areas. In this study, the training area covered part of the Mississippi River Delta, while the target areas included diverse regions across the US and Canada. The evaluation focused on major crops, including corn, soybeans, rice, and double-cropped winter wheat/soybeans. The trained deep learning model was transferred to the target areas, and accuracy metrics were compared across different time series chosen by various phenological alignment methods. The AMM method consistently demonstrated strong performance, particularly in transferring to rice-growing regions, achieving an overall accuracy of 98%. It often matched or exceeded other phenological matching techniques in corn segmentation, with an average overall accuracy across all target areas exceeding 79% for cropland segmentation. Full article

Dynamic harmonic estimation is important for the monitoring and control of power-electronic grids. But the high-precision dynamic harmonic estimation algorithms usually have a heavy computational burden and occupy a large memory space, making them difficult to implement in the embedded platform. Thus, the motivation of this paper lies in providing an estimator with low computational complexity and less storage space consumption. A purely real-valued fast dynamic harmonics estimator is proposed. Firstly, a purely real-valued estimation model is established based on the Taylor series expansion on the time-varying amplitude and phase angle. Secondly, the estimation filter bank is computed in the least-squares sense, and the corresponding estimation error is theoretically analyzed. Finally, the purely real-valued fast dynamic harmonics estimator is designed. The advantage includes significantly reducing the computational complexity and memory space consumption while maintaining high-precision estimation. The testing results show that the proposed estimator can achieve the highest harmonics estimation precision under dynamic conditions. The frequency error, magnitude error, and phase angle error are less than 5 × 10⁻² Hz, 7 × 10⁻¹%, and 8 × 10⁻² degrees, respectively, which verifies the advantage of high-precision estimation. The proposed estimator achieves a computational speed-up of approximately 430, 396, and 330 times compared to the Prony method, ESPRIT method, and iterative Taylor Fourier transform method, respectively. The computational load rate for executing the proposed estimator on the embedded prototype using C6748 DSP for estimating 50 harmonics is approximately only 2.05%, which verifies the advantage of a low computational load rate. Full article

Video instance segmentation, a key technology for intelligent sensing in visual perception, plays a key role in automated surveillance, robotics, and smart cities. These scenarios rely on real-time and efficient target-tracking capabilities for accurate perception and intelligent analysis of dynamic environments. However, traditional video instance segmentation methods face complex models, high computational overheads, and slow segmentation speeds in time-series feature extraction, especially in resource-constrained environments. To address these challenges, a Dual-Channel and Frequency-Aware Approach for Lightweight Video Instance Segmentation (DCFA-LVIS) is proposed in this paper. In feature extraction, a DCEResNet backbone network structure based on a dual-channel feature enhancement mechanism is designed to improve the model’s accuracy by enhancing the feature extraction and representation capabilities. In instance tracking, a dual-frequency perceptual enhancement network structure is constructed, which uses an independent instance query mechanism to capture temporal information and combines with a frequency-aware attention mechanism to capture instance features on different attention layers of high and low frequencies, respectively, to effectively reduce the complexity of the model, decrease the number of parameters, and improve the segmentation efficiency. Experiments show that the model proposed in this paper achieves state-of-the-art segmentation performance with few parameters on the YouTube-VIS dataset, demonstrating its efficiency and practicality. This method significantly enhances the application efficiency and adaptability of visual perception intelligent sensing technology in video data acquisition and processing, providing strong support for its widespread deployment. Full article