Search Results (645)

Volcanic eruptions, as a natural phenomenon, can generate electric field disturbances that may interfere with the ionosphere, potentially impacting communication systems and electronic devices. This paper conducts identification and classification analyses of the electromagnetic pulse (EMP) disturbances in the very low frequency (VLF) electric field data observed by the China Seismo-Electromagnetic Satellite (CSES) and finds that volcanic eruption events can trigger EMP sequences. This paper first applies Fourier transform to the VLF electric field waveform data to convert it into 4 s spectrograms. Then, a series of operations such as grayscale conversion, edge feature enhancement, and binarization are performed on the spectrograms. Subsequently, a K-means clustering algorithm is applied to the binarized results to identify EMP events on the spectrograms. Finally, a classification analysis is performed on the identified results, revealing that multiple volcanic eruption events generate EMP sequences. The results of this paper not only provide new insights into the impact of volcanic eruptions on the electromagnetic environment but also have significant implications for enhancing the anti-interference capability of communication systems and optimizing electromagnetic environment monitoring technologies. Full article

The increasing frequency and severity of forest fires globally highlight the critical need to understand their environmental impacts. This study applies spatial autocorrelation techniques to analyze the dispersion patterns of carbon monoxide (CO) and nitrogen dioxide (NO₂) emissions during the 2021 Manavgat forest fires in Türkiye, using Sentinel-5P satellite data. Univariate (UV) Global Moran’s I values indicated strong spatial autocorrelation for CO (0.84–0.93) and NO₂ (0.90–0.94), while Bivariate (BV) Global Moran’s I (0.69–0.84) demonstrated significant spatial correlations between the two gases. UV Local Moran’s I analysis identified distinct UV High-High (UV-HH) and UV Low-Low (UV-LL) clusters, with CO concentrations exceeding 0.10000 mol/m² and exhibiting wide dispersion, while NO₂ concentrations, above 0.00020 mol/m², remained localized near intense fire zones due to its shorter atmospheric lifetime. BV Local Moran’s I analysis revealed overlapping BV-HH (high CO, high NO₂) and BV-LL (low CO, low NO₂) clusters, influenced by topography and meteorological factors. These findings enhance the understanding of gas emission dynamics during forest fires and provide critical insights into the influence of environmental and combustion processes on pollutant dispersion. Full article

Sulfur dioxide (SO₂) is sourced by degassing magma in the shallow crust; hence its monitoring provides information on the rates of magma ascent in the feeding conduit and the style and intensity of eruption, ultimately contributing to volcano monitoring and hazard assessment. Here, we present a new algorithm to extract SO₂ data from the TROPOMI imaging spectrometer aboard the Sentinel-5 Precursor satellite, which delivers atmospheric column measurements of sulfur dioxide and other gases with an unprecedented spatial resolution and daily revisit time. Specifically, we automatically extract the volcanic clouds by introducing a two-step approach. Firstly, we used the Simple Non-Iterative Clustering segmentation method, which is an object-based image analysis approach; secondly, the K-means unsupervised machine learning technique is applied to the segmented images, allowing a further and better clustering to distinguish the SO₂. We implemented this algorithm in the open-source Google Earth Engine computing platform, which provides TROPOMI imagery collection adjusted in terms of quality parameters. As case studies, we chose three volcanoes: Mount Etna (Italy), Taal (Philippines) and Sangay (Ecuador); we calculated sulfur dioxide mass values from 2018 to date, focusing on a few paroxysmal events. Our results are compared with data available in the literature and with Level 2 TROPOMI imagery, where a mask is provided to identify SO₂, finding an optimal agreement. This work paves the way to the release of SO₂ flux time series with reduced delay and improved calculation time, hence contributing to a rapid response to volcanic unrest/eruption at volcanoes worldwide. Full article

Accurate characterization of tree stems is critical for assessing commercial forest health, estimating merchantable timber volume, and informing sustainable value management strategies. Conventional ground-based manual measurements, although precise, are labor-intensive and impractical at large scales, while remote sensing approaches using satellite or UAV imagery often lack the spatial resolution needed to capture individual tree attributes in complex forest environments. To address these challenges, this study provides a significant contribution by introducing a large-scale dataset encompassing 40 plots in Western Australia (WA) with varying tree densities, derived from Hovermap LiDAR acquisitions and destructive sampling. The dataset includes parameters such as plot and tree identifiers, DBH, tree height, stem length, section lengths, and detailed diameter measurements (e.g., DiaMin, DiaMax, DiaMean) across various heights, enabling precise ground-truth calibration and validation. Based on this dataset, we present the Forest Stem Extraction and Modeling (FoSEM) framework, a LiDAR-driven methodology that efficiently and reliably models individual tree stems from dense 3D point clouds. FoSEM integrates ground segmentation, height normalization, and K-means clustering at a predefined elevation to isolate stem cores. It then applies circle fitting to capture cross-sectional geometry and employs MLESAC-based cylinder fitting for robust stem delineation. Experimental evaluations conducted across various radiata pine plots of varying complexity demonstrate that FoSEM consistently achieves high accuracy, with a DBH RMSE of 1.19 cm (rRMSE = 4.67%) and a height RMSE of 1.00 m (rRMSE = 4.24%). These results surpass those of existing methods and highlight FoSEM’s adaptability to heterogeneous stand conditions. By providing both a robust method and an extensive dataset, this work advances the state of the art in LiDAR-based forest inventory, enabling more efficient and accurate tree-level assessments in support of sustainable forest management. Full article

Ozone pollution in Hangzhou Bay, one of the seven petrochemical clusters in China, is severe. Early ozone pollution has been detected recently, such as the maximum daily 8 h average (MDA8) ozone concentration in Jiaxing achieving 171.0 μg/m³ on 7 March 2023. Satellites have observed tropospheric column concentrations of ozone precursors formaldehyde (HCHO) and nitrogen dioxide (NO_x), and quantitative models are proposed to reveal the causes of the early onset of ozone pollution. VOC-limited and transitional regimes dominate most areas in Hangzhou Bay, and NO_x-limited regimes dominate the region around Hangzhou Bay, such as northeastern Jiangsu Province. Results show that HCHO column concentrations are increasing in VOC-limited regions, and NO_x column concentrations are increasing more rapidly than HCHO in NO_x-limited regions. According to multivariate linear regression (MLR), early spring ozone pollution in Hangzhou Bay is mainly caused by meteorological drivers. Hangzhou Bay has formed an atmospheric meteorological environment with high temperature and low humidity. The richer solar radiation intensifies the photochemical reactions associated with tropospheric ozone formation, producing more tropospheric ozone. Based on the Shapley Additive Explanation (SHAP) algorithm, ozone pollution increases when solar radiation exceeds 12 million J/m² and is accompanied by high temperatures. Overall, reducing VOC emissions helps to mitigate ozone growth in Shanghai and northern Hangzhou Bay, while reducing NO_x emissions is more effective in northeastern Jiangsu Province. Full article

Road hazardous material transportation plays a critical role in road traffic management. Due to the dangerous nature of the cargo, hazardous material transportation trucks (HMTTs) have different route selection and driving characteristics compared to traditional freight trucks. These differences lead to unique travel and emission patterns, which in turn affect traffic management strategies and emission control measures. However, existing research predominantly focuses on safety aspects related to individual vehicle behavior, with limited exploration of the broader travel and emission characteristics of HMTTs. To bridge this gap, this study develops a comprehensive framework for analyzing the travel patterns and emissions of HMTTs. The methodology begins by applying a Gaussian mixture distribution model to identify vehicle stop points, eliminating biases associated with subjective settings. Origin–destination (OD) pairs are then determined through stop time clustering, followed by the extraction of travel characteristics using non-negative matrix factorization. Emissions are subsequently calculated based on the identified trip data. The relationship between emissions and land use characteristics is further analyzed using geographically weighted regression (GWR). Crucially, this study leverages data from the BeiDou Satellite Navigation System, focusing on HMTTs operating within Shanghai. The processed data reveal three distinct travel modes of HMTTs, categorized by spatiotemporal patterns: Daytime—Surrounding cities, Early morning—In-city, and Midnight—Scattered. Moreover, unlike other road vehicles, HMTT emissions are heavily influenced by industrial and company-related points of interest (POIs). These findings highlight the significant role of BeiDou Satellite Navigation System data in optimizing HMTT management strategies to reduce emissions and improve overall safety. Full article

Globally, the agricultural landscape is the most exposed due to urbanisation. Therefore, finding the spatial and temporal patterns of changes in agricultural landscapes is essential for sustainable development. This study developed a workflow to address this information gap and determine the spatial patterns and characteristics of agricultural landscapes along an urban–rural gradient. The workflow comprised three steps. First, remote sensing data were classified to map crop types. Second, landscape metrics were used to examine the spatial patterns of agricultural land cover concerning urbanisation levels. Finally, unsupervised clustering was applied to categorise agricultural landscape types along the urban–rural interface. The workflow was tested using WorldView-3 satellite data in Bengaluru, India. It identified four major herbaceous crop types (millet, maize, pulses, and cash crops) and woody plantations as agricultural land cover. An analysis revealed that agricultural land cover increased from urban to rural areas, with diverse patterns in transition zones. The cluster analysis characterised four agricultural landscapes. The findings imply that changes in an agricultural landscape along an urban–rural gradient are not linear. The newly developed integrated workflow empowers stakeholders to make informed and well-reasoned decisions, and it can be periodically implemented to maintain the ongoing monitoring of urbanisation’s effect on food systems. Full article

Monitoring river ice is crucial for planning safe navigation routes, with ice–water classification being one of the most important tasks in ice mapping. While high-resolutions satellite imagery, such as synthetic aperture radar (SAR), is well-suited to this task, manual interpretation of these data is challenging due to the large data volume. Machine learning approaches are suitable methods to overcome this; however, training the models might not be time-effective when the desired result is a narrow structure, such as a river, within a large image. To address this issue, we proposed a model incorporating a graph neural network (GNN), called learnable weights graph convolution network (LWGCN). Focusing on the winters of 2017–2021 with emphasis on the Beauharnois Canal and Lake St Lawrence regions of the Saint Lawrence River. The model first converts the SAR image into graph-structured data using simple linear iterative clustering (SLIC) to segment the SAR image, then connecting the centers of each superpixel to form graph-structured data. For the training model, the LWGCN learns the weights on each edge to determine the relationship between ice and water. By using the graph-structured data as input, the proposed model training time is eight times faster, compared to a convolution neural network (CNN) model. Our findings also indicate that the LWGCN model can significantly enhance the accuracy of ice and water classification in SAR imagery. Full article

Precision agriculture has recently experienced significant advancements through the use of technologies such as unmanned aerial vehicles (UAVs) and satellite imagery, enabling more efficient and precise agricultural management. Yield estimation from these technologies is essential for optimizing resource allocation, improving harvest logistics, and supporting decision-making for sustainable vineyard management. This study aimed to evaluate grape cluster numbers estimated by using YOLOv7x in combination with images obtained by UAVs from a vineyard. Additionally, the capability of several vegetation indices calculated from Sentinel-2 and PlanetScope satellites to estimate grape clusters was evaluated. The results showed that the application of the YOLOv7x model to RGB images acquired from UAVs was able to accurately predict grape cluster numbers (R² value and RMSE value of 0.64 and 0.78 clusters vine⁻¹). On the contrary, vegetation indexes derived from Sentinel-2 and PlanetScope satellites were found not able to predict grape cluster numbers (R² lower than 0.23), probably due to the fact that these indexes are more related to vegetation vigor, which is not always related to yield parameters (e.g., cluster number). This study suggests that the combination of high-resolution UAV images, multispectral satellite images, and advanced detection models like YOLOv7x can significantly improve the accuracy of vineyard management, resulting in more efficient and sustainable agriculture. Full article

Building type information is widely used in various fields, such as disaster management, urbanization studies, and population modelling. Few studies have been conducted on fine-grained building classification in rural areas using China’s Gaofen-7 (GF-7) high-resolution stereo mapping satellite data. In this study, we employed a two-stage method combining supervised classification and unsupervised clustering to classify buildings in the rural area of Pingquan, northern China, based on building footprints, building heights, and multispectral information extracted from GF-7 data. In the supervised classification stage, we compared different classification models, including Extreme Gradient Boosting (XGBoost) and Random Forest classifiers. The best-performing XGBoost model achieved an overall roof type classification accuracy of 88.89%. Additionally, we proposed a template-based building height correction method for pitched roof buildings, which combined geometric features of the building footprint, street view photos, and height information extracted from the GF-7 stereo image. This method reduced the RMSE of the pitched roof building heights from 2.28 m to 1.20 m. In the cluster analysis stage, buildings with different roof types were further classified in the color and shape feature spaces and combined with the building height information to produce fine-grained building type codes. The results of the roof type classification and fine-grained building classification reveal the physical and geometric characteristics of buildings and the spatial distribution of different building types in the study area. The building classification method proposed in this study has broad application prospects for disaster management in rural areas. Full article

Porcine latissimus dorsi muscle (LDM) is a crucial source of pork products. Meat quality indicators, such as the proportion of muscle fibers and intramuscular fat (IMF) deposition, vary during the growth and development of pigs. Numerous studies have highlighted the heterogeneous nature of skeletal muscle, with phenotypic differences reflecting variations in cellular composition and transcriptional profiles. This study investigates the cellular-level transcriptional characteristics of LDM in large white pigs at two growth stages (170 days vs. 245 days) using single-nucleus RNA sequencing (snRNA-seq). We identified 56,072 cells across 12 clusters, including myofibers, fibro/adipogenic progenitor (FAP) cells, muscle satellite cells (MUSCs), and other resident cell types. The same cell types were present in the LDM at both growth stages, but their proportions and states differed. A higher proportion of FAPs was observed in the skeletal muscle of 245-day-old pigs. Additionally, these cells exhibited more active communication with other cell types compared to 170-day-old pigs. For instance, more interactions were found between FAPs and pericytes or endothelial cells in 245-day-old pigs, including collagen and integrin family signaling. Three subclasses of FAPs was identified, comprising FAPs_COL3A1⁺, FAPs_PDE4D⁺, and FAPs_EBF1⁺, while adipocytes were categorized into Ad_PDE4D⁺ and Ad_DGAT2⁺ subclasses. The proportions of these subclasses differed between the two age groups. We also constructed differentiation trajectories for FAPs and adipocytes, revealing that FAPs in 245-day-old pigs differentiated more toward fibrosis, a characteristic reminiscent of the high prevalence of skeletal muscle fibrosis in aging humans. Furthermore, the Ad_PDE4D⁺ adipocyte subclass, predominant in 245-day-old pigs, originated from FAPs_PDE4D⁺ expressing the same gene, while the Ad_DGAT2⁺ subclass stemmed from FAPs_EBF1⁺. In conclusion, our study elucidates transcriptional differences in skeletal muscle between two growth stages of pigs and provides insights into mechanisms relevant to pork meat quality and skeletal muscle diseases. Full article

Atmospheric deposition is an important source of heavy metal in soil and the use of dust collection cylinders is a traditional monitoring method. This method has limitations in agricultural areas because polluted soil particles may become resuspended and eventually deposited into these cylinders, leading to overestimates in the amount of atmospheric deposition in soil. To address this concern, we propose that frequent snowfall can help suppress local soil dust resuspension and that fresh snow can serve as an efficient surrogate surface when collecting atmospheric deposition samples. To investigate the rationality of this method, 52 snow samples were collected from sites surrounding smelting plants in Anyang, an industrial region of North China. The results revealed that the concentration of cadmium in the melted snow ranged between 0.03 and 41.09 μg/L, with mean values three times higher around the industrial sites (5.31 μg/L) than background farmlands (1.54 μg/L). In addition, the cadmium concentration in the snow from sites surrounding the factories was higher in the north than in the south because of prevailing winds blowing from the southwest. Moreover, snow samples from sites with high concentrations of cadmium and sulfate can be categorized into different groups via the clustering method, conforming to the spatial distribution of particulate matter emissions and sulfur dioxide satellite column concentrations. Finally, a positive correlation was found between the cadmium content in the snow and the production capacity (R² = 0.90, p < 0.05) and total permitted emissions (R² = 0.69, p > 0.05) of the nearby factories. These findings demonstrate that snow is a reliable medium for documenting atmospheric dry deposition associated with specific industrial emissions. Full article

Satellite image generation has a wide range of applications. For example, parts of images must be restored in areas obscured by clouds or cloud shadows or areas that must be anonymized. The need to cover a large area with the generated images faces the challenge that separately generated images must maintain the structural and color continuity between the adjacent generated images as well as the actual ones. This study presents a modified architecture of the generative adversarial network (GAN) pix2pix that ensures the integrity of the generated remote sensing images. The pix2pix model comprises a U-Net generator and a PatchGAN discriminator. The generator was modified by expanding the input set with images representing the known parts of ground truth and the respective mask. Data used for the generative model consist of Sentinel-2 (S2) RGB satellite imagery as the target data and OpenStreetMap mapping data as the input. Since forested areas and fields dominate in images, a Kneedle clusterization method was applied to create datasets that better represent the other classes, such as buildings and roads. The original and updated models were trained on different datasets and their results were evaluated using gradient magnitude (GM), Fréchet inception distance (FID), structural similarity index measure (SSIM), and multiscale structural similarity index measure (MS-SSIM) metrics. The models with the updated architecture show improvement in gradient magnitude, SSIM, and MS-SSIM values for all datasets. The average GMs of the junction region and the full image are similar (do not exceed 7%) for the images generated using the modified architecture whereas it is more than 13% higher in the junction area for the images generated using the original architecture. The importance of class balancing is demonstrated by the fact that, for both architectures, models trained on the dataset with a higher ratio of classes representing buildings and roads compared to the models trained on the dataset without clusterization have more than 10% lower FID (162.673 to 190.036 for pix2pix and 173.408 to 195.621 for the modified architecture) and more than 5% higher SSIM (0.3532 to 0.3284 for pix2pix and 0.3575 to 0.3345 for the modified architecture) and MS-SSIM (0.3532 to 0.3284 for pix2pix and 0.3575 to 0.3345 for the modified architecture) values. Full article

(1) This paper selects the modern delta formed by the Ganjiang tributary in Poyang Lake. By performing high density statistical analysis of distribution channel parameters in the area using satellite images and geographic information processing software (LucaSpaceViewer 4.5.2, ArcGIS Pro 3.0.2, Global Mapper v23.1), including length, width, bifurcation angle, bifurcation frequency, and channel sinuosity, the distribution characteristics of delta distribution channels are derived and quantitatively characterized. (2) Classification and evaluation of these characteristics are carried out using factor and cluster analysis, ultimately identifying controlling factors affecting the morphology and distribution of the distribution channels. By statistically analyzing the geometric and bifurcation data of the channels, factor and cluster analysis for data reduction and classification, the channel is finally divided into three categories: Type I channels have relatively high channel length, width, sinuosity, bending amplitude, and a lower bifurcation (or confluence) growth rate; Type II channels are characterized by low channel length, moderate channel width, low sinuosity, low bending amplitude, and a high bifurcation (or confluence) growth rate; Type III channels are defined by moderate channel length, low width, high sinuosity, high bending amplitude, and low bifurcation (or confluence) frequency. (3) After excluding the influence of other factors, it was found that the main controlling factor for the morphology of the Ganjiang Delta channel is flow velocity, which is influenced by changes in the terrain slope. Flow velocity directly affects channel sinuosity, bending amplitude, and bifurcation (or confluence) frequency, and indirectly affects channel length and width. Full article

Coastal zones, particularly sandy beaches, are highly dynamic environments subject to a variety of natural and anthropogenic forcings. Instantaneous shoreline is a widely used indicator of beach changes in image-based applications, and it can display undulations at different spatial and temporal scales. Megacusps, periodic seaward and landward shoreline perturbations, are an example of such undulations that can significantly modify beach width and impact its usability. Traditionally, the study of these phenomena relied on video monitoring systems, which provide high-frequency imagery but limited spatial coverage. Instead, this study explored the potential of employing multispectral satellite-derived shorelines, specifically from Sentinel-2 (S2) and PlanetScope (PLN) platforms, for characterizing and monitoring megacusps’ formation and their dynamics over time. First, a tool was developed and validated to guarantee accurate shoreline detection, based on a combination of spectral indices, along with both thresholding and unsupervised clustering techniques. Validation of this shoreline detection phase was performed on three micro-tidal Mediterranean beaches, comparing with high-resolution orthomosaics and in-situ GNSS data, obtaining a good subpixel accuracy (with a mean absolute deviation of 1.5–5.5 m depending on the satellite type). Second, a tool for megacusp characterization was implemented and subsequent validation with reference data proved that satellite-derived shorelines could be used to robustly and accurately describe megacusps. The methodology could not only capture their amplitude and wavelength (of the order of 10 and 100 m, respectively) but also monitor their weekly–daily evolution using different potential metrics, thanks to combining S2 and PLN imagery. Our findings demonstrate that multispectral satellite imagery provides a viable and scalable solution for monitoring shoreline megacusp undulations, enhancing our understanding and offering an interesting option for coastal management. Full article