Search Results (280)

This study developed a copper mineral prospectivity map for the Koldar massif, Kazakhstan, using an integrated approach combining geophysical and satellite methods. A strong spatialgenetic link was identified between faults and hydrothermal mineralization, with faults acting as key conduits for ore-bearing fluids. Lineament analysis and density mapping confirmed the high permeability of the Koldar massif, indicating its structural prospectivity. Hyperspectral and multispectral data (ASTER, PRISMA, WorldView-3) were applied for detailed mapping of hydrothermal alteration (phyllic, propylitic, argillic zones), which are critical for discovering porphyry copper deposits. In particular, WorldView-3 imagery facilitated the identification of new prospective zones. The transformation of magnetic and gravity data successfully delineated geological features and structural boundaries, confirming the fractured nature of the massif, a key structural factor for mineralization. The resulting map of prospective zones, created by normalizing and integrating four evidential layers (lineament density, PRISMA-derived hydrothermal alteration, magnetic, and gravity anomalies), is thoroughly validated, successfully outlining the known Aktogay, Aidarly, and Kyzylkiya deposits. Furthermore, new, previously underestimated prospective areas were identified. This work fills a significant knowledge gap concerning the Koldar massif, which had not been extensively studied using satellite methods previously. The key advantage of this research lies in its comprehensive approach and the successful application of high-quality hyperspectral imagery for mapping new prospective zones, offering a cost-effective and efficient alternative to traditional ground-based investigations. Full article

The precise characterization of surface rupture zones and associated co-seismic displacement fields from large earthquakes provides critical insights into seismic rupture mechanisms, earthquake dynamics, and hazard assessments. Stereo-photogrammetric digital elevation models (DEMs), produced from high-resolution satellite stereo imagery, offer reliable global datasets that are suitable for the detailed extraction and quantification of vertical co-seismic displacements. In this study, we utilized pre- and post-event WorldView-2 stereo images of the 2024 Ms7.1 Wushi earthquake in Xinjiang to generate DEMs with a spatial resolution of 0.5 m and corresponding terrain point clouds with an average density of approximately 4 points/m². Subsequently, we applied the Iterative Closest Point (ICP) algorithm to perform differencing analysis on these datasets. Special care was taken to reduce influences from terrain changes such as vegetation growth and anthropogenic structures. Ultimately, by maintaining sufficient spatial detail, we obtained a three-dimensional co-seismic displacement field with a resolution of 15 m within grid cells measuring 30 m near the fault trace. The results indicate a clear vertical displacement distribution pattern along the causative sinistral–thrust fault, exhibiting alternating uplift and subsidence zones that follow a characteristic “high-in-center and low-at-ends” profile, along with localized peak displacement clusters. Vertical displacements range from approximately 0.2 to 1.4 m, with a maximum displacement of ~1.46 m located in the piedmont region north of the Qialemati River, near the transition between alluvial fan deposits and bedrock. Horizontal displacement components in the east-west and north-south directions are negligible, consistent with focal mechanism solutions and surface rupture observations from field investigations. The successful extraction of this high-resolution vertical displacement field validates the efficacy of satellite-based high-resolution stereo-imaging methods for overcoming the limitations of GNSS and InSAR techniques in characterizing near-field surface displacements associated with earthquake ruptures. Moreover, this dataset provides robust constraints for investigating fault-slip mechanisms within near-surface geological contexts. Full article

Integrating diverse image datasets acquired from different satellites is challenging. Converting images from one sensor to another, like from WorldView-3 (WV) to SuperDove (SD), involves both changing image channel wavelengths and per-band intensity scales because different sensors can acquire imagery of the same scene at different wavelengths and intensities. A parametrized convolutional network approach has shown promise converting across sensor domains, but it introduces distortion artefacts when objects are in motion. The cause of spectral distortion is due to temporal delays between sequential multispectral band acquisitions. This can result in spuriously blurred images of moving objects in the converted imagery, and consequently misaligned moving object locations across image bands. To resolve this, we propose an enhanced model, the Physics-Informed Gaussian-Enforced Separated-Band Convolutional Conversion Network (PIGESBCCN), which better accounts for known spatial, spectral, and temporal correlations between bands via band reordering and branched model architecture. Full article

The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. This study explores the innovative application of the so-called Mask Region-based convolutional neural network (Mask R-CNN) algorithm in a GIS environment, utilizing high-resolution satellite imagery from the WorldView-3 system. By combining these state-of-the-art technologies, this study demonstrates the algorithm’s effectiveness at recognizing and segmenting the ancient structures within the archaeological site of Delos, Greece. Despite the computational constraints, the outcomes are promising, with around 25.91% of the initial vector data (434 out of 1675 polygons) successfully identified. The algorithm achieved an impressive F1 Score of 0.93% at a threshold of 0.9, indicating its high precision in differentiating specific features from their environments. This research highlights AI’s crucial role in archaeology, enabling the remote analysis of vast areas through automated or semi-automated techniques. Although these technologies cannot supplant essential on-site investigations, they can significantly enhance traditional methodologies by minimizing costs and fieldwork duration. This study also points out obstacles, such as the complexity of and variability in archaeological remains, which complicate the creation of standardized data libraries. Nevertheless, as AI technologies progress, their applications in archaeology are anticipated to broaden, fostering further innovation within the discipline. Full article

Remote sensing approaches can be cost-effective for estimating forest structural attributes. This study aims to use airborne LiDAR data to assess the robustness of multispectral satellite imagery and topographic attributes derived from DEMs to predict the density of three vegetation layers in a wet eucalypt forest in Tasmania, Australia. We compared the predictive capacity of medium-resolution Landsat-8 Operational Land Imager (OLI) surface reflectance and three pixel sizes from high-resolution WorldView-3 satellite imagery. These datasets were combined with topographic attributes extracted from resampled LiDAR-derived DEMs and a geology layer and validated with vegetation density layers extracted from high-density LiDAR. Using spectral bands, indices, texture features, a geology layer, and topographic attributes as predictor variables, we evaluated the predictive power of 13 data schemes at three different pixel sizes (1.6 m, 7.5 m, and 30 m). The schemes of the 30 m Landsat-8 (OLI) dataset provided better model accuracy than the WorldView-3 dataset across all three pixel sizes (R² values from 0.15 to 0.65) and all three vegetation layers. The model accuracies increased with an increase in the number of predictor variables. For predicting the density of the overstorey vegetation, spectral indices (R² = 0.48) and texture features (R² = 0.47) were useful, and when both were combined, they produced higher model accuracy (R² = 0.56) than either dataset alone. Model prediction improved further when all five data sources were included (R² = 0.65). The best models for mid-storey (R² = 0.46) and understorey (R² = 0.44) vegetation had lower predictive capacity than for the overstorey. The models validated using an independent dataset confirmed the robustness. The spectral indices and texture features derived from the Landsat data products integrated with the low-density LiDAR data can provide valuable information on the forest structure of larger geographical areas for sustainable management and monitoring of the forest landscape. Full article

Accurate and robust information on land cover changes in urban and coastal areas is essential for effective urban land management, ecosystem monitoring, and urban planning. This paper details the methodology and results of a pixel-level classification and change detection analysis, leveraging 1945 Royal Air Force (RAF) aerial imagery and 2011 Very High-Resolution (VHR) multispectral WorldView-2 satellite imagery from the broader area of Patras, Greece. Our attention is mainly focused on the changes in the coastline from the city of Patras to the northeast direction and the two major rivers, Charadros and Selemnos. The methodology involves preprocessing steps such as registration, denoising, and resolution adjustments to ensure computational feasibility for both coastal and riverbed change detection procedures while maintaining critical spatial features. For change detection at coastal areas over time, the Normalized Difference Water Index (NDWI) was applied to the new imagery to mask out the sea from the coastline and manually archive imagery from 1945. To determine the differences in the coastline between 1945 and 2011, we perform image differencing by subtracting the 1945 image from the 2011 image. This highlights the areas where changes have occurred over time. To conduct riverbed change detection, feature extraction using the Gray-Level Co-occurrence Matrix (GLCM) was applied to capture spatial characteristics. A Support Vector Machine (SVM) classification model was trained to distinguish river pixels from non-river pixels, enabling the identification of changes in riverbeds and achieving 92.6% and 92.5% accuracy for new and old imagery, respectively. Post-classification processing included classification maps to enhance the visualization of the detected changes. This approach highlights the potential of combining historical and modern imagery with supervised machine learning methods to effectively assess coastal erosion and riverbed alterations. Full article

Cosmogenic nuclide exposure dating is an important technique for reconstructing glacial histories. Many of the most commonly applied cosmogenic nuclides are extracted from the mineral quartz, meaning sampling of felsic (silica-rich) rock is often preferred to sampling of mafic (silica-poor) rock for exposure dating studies. Fieldwork in remote regions such as Antarctica is subject to time constraints and considerable logistical challenges, making efficient sample recovery critical to successful research efforts. Remote sensing offers an effective way to map the geology of large areas prior to fieldwork and expedite the sampling process. In this study, we assess the viability of multispectral remote sensing to distinguish felsic from mafic rock outcrops at visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelengths using both the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and very high-resolution Worldview-3 (WV-3) imagery. We applied a combination of spectral mapping and ground truth from spectral measurements of 17 rock samples from Mount Murphy in the Amundsen Sea sector of West Antarctica. Using this approach, we identified four dominant rock types which we used as a basis for felsic–mafic differentiation: felsic granites and gneisses, and mafic basalts and fragmental hydrovolcanic rocks. Supervised classification results indicate WV-3 performs well at differentiating felsic and mafic rock types and that ASTER, while coarser, could also achieve satisfactory results and be used in concert with more targeted WV-3 image acquisitions. Finally, we present a revised felsic–mafic geological map for Mt Murphy. Overall, our results highlight the potential of spectral mapping for preliminary reconnaissance when planning future cosmogenic nuclide sampling campaigns in remote, unvisited areas of the polar regions. Full article

Detecting targets in remote sensing imagery, particularly when identifying sparsely distributed materials, is crucial for applications such as defense, mineral exploration, agriculture, and environmental monitoring. The effectiveness of detection and the precision of the results are influenced by several factors, including sensor configurations, platform properties, interactions between targets and their background, and the spectral contrast of the targets. Environmental factors, such as atmospheric conditions, also play a significant role. Conventionally, target detection in remote sensing has relied on statistical methods that typically assume a linear process for image formation. However, to enhance detection performance, it is critical to account for the geometric and spectral variabilities across multiple imaging platforms. In this research, we conducted a comprehensive target detection experiment using a unique benchmark multi-platform hyperspectral dataset, where man-made targets were deployed on various surface backgrounds. Data were collected using a hand-held spectroradiometer, UAV-mounted hyperspectral sensors, and airborne platforms, all within a half-hour time window. Multi-spectral space-based sensors (i.e., Worldview and Landsat) also flew over the scene and collected data. The experiment took place on 23 July 2021, at the Rochester Institute of Technology’s Tait Preserve in Penfield, NY, USA. We validated the detection outcomes through receiver operating characteristic (ROC) curves and spectral similarity metrics across various detection algorithms and imaging platforms. This multi-platform analysis provides critical insights into the challenges of hyperspectral target detection in complex, real-world landscapes, demonstrating the influence of platform variability on detection performance and the necessity for robust algorithmic approaches in multi-source data integration. Full article

Acid Mine Drainage (AMD) presents significant environmental challenges, particularly in regions with extensive mining activities. Effective monitoring and mapping of AMD are crucial for mitigating its detrimental impacts on ecosystems and water quality. This study investigates the application of Machine Learning (ML) algorithms to map AMD by fusing multispectral imagery from Sentinel-2 with high-resolution imagery from WorldView-3. We applied three widely used ML models—Random Forest (RF), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP)—to address both classification and regression tasks. The classification models aimed to distinguish between AMD and non-AMD samples, while the regression models provided quantitative pH mapping. Our experiments were conducted on three lakes in the Outokumpu mining area in Finland, which are affected by mine waste and acidic drainage. Our results indicate that combining Sentinel-2 and WorldView-3 data significantly enhances the accuracy of AMD detection. This combined approach leverages the strengths of both datasets, providing a more robust and precise assessment of AMD impacts. Full article

Cropland is a fundamental basis for agricultural development and a prerequisite for ensuring food security. The segmentation and extraction of croplands using remote sensing images are important measures and prerequisites for detecting and protecting farmland. This study addresses the challenges of diverse image sources, multi-scale representations of cropland, and the confusion of features between croplands and other land types in large-area remote sensing image information extraction. To this end, a multi-source self-annotated dataset was developed using satellite images from GaoFen-2, GaoFen-7, and WorldView, which was integrated with public datasets GID and LoveDA to create the CRMS dataset. A novel semantic segmentation network, the Global–Local Context Aggregation Network (GLCANet), was proposed. This method integrates the Bilateral Feature Encoder (BFE) of CNNs and Transformers with a global–local information mining module (GLM) to enhance global context extraction and improve cropland separability. It also employs a multi-scale progressive upsampling structure (MPUS) to refine the accuracy of diverse arable land representations from multi-source imagery. To tackle the issue of inconsistent features within the cropland class, a loss function based on hard sample mining and multi-scale features was constructed. The experimental results demonstrate that GLCANet improves OA and mIoU by 3.2% and 2.6%, respectively, compared to the existing advanced networks on the CRMS dataset. Additionally, the proposed method also demonstrated high precision and practicality in segmenting large-area croplands in Chongzhou City, Sichuan Province, China. Full article

Matched line segments are crucial geometric elements for reconstructing the desired 3D structure in stereo satellite imagery, owing to their advantages in spatial representation, complex shape description, and geometric computation. However, existing line segment matching (LSM) methods face significant challenges in effectively addressing co-linear interference and the misdirection of parallel line segments. To address these issues, this study proposes a “continuous–discrete–continuous” cyclic LSM method, based on the Voronoi diagram, for stereo satellite images. Initially, to compute the discrete line-point matching rate, line segments are discretized using the Bresenham algorithm, and the pyramid histogram of visual words (PHOW) feature is assigned to the line segment points which are detected using the line segment detector (LSD). Next, to obtain continuous matched line segments, the method combines the line segment crossing angle rate with the line-point matching rate, utilizing a soft voting classifier. Finally, local point-line homography models are constructed based on the Voronoi diagram, filtering out misdirected parallel line segments and yielding the final matched line segments. Extensive experiments on the challenging benchmark, WorldView-2 and WorldView-3 satellite image datasets, demonstrate that the proposed method outperforms several state-of-the-art LSM methods. Specifically, the proposed method achieves F1-scores that are 6.22%, 12.60%, and 18.35% higher than those of the best-performing existing LSM method on the three datasets, respectively. Full article

This article identifies and analyzes four passages in the ancient Chinese Christian manuscript Yishen Lun (YSL) that exhibit distinct Jewish characteristics. The phrase “yizhong zuo shenghua” (lines 356–358) mirrors the Book of Acts’ theme of “sanctification of the Gentiles,” rooted in the Jewish dichotomous worldview, placing Jews, or Shihu Ren, at the center. The author’s use of this phrase distinguishes him from yizhong ren (Gentiles) and aligns him with Shihu Ren. In lines 256–263, YSL directly attributes messianic declarations to Jesus, a central issue in Jewish accusations of false Messiahship. In contrast, the Gospel accounts avoid making such direct accusations, as Jesus did not openly declare himself the Messiah. This distinction highlights YSL’s closer alignment with Jewish polemical traditions and legal concerns. Additionally, the use of “City of Judah” in lines 345–347 as an archaic designation for Jerusalem, predominantly found in Jewish traditions, contrasts with the more common “City of David” in other biblical texts. A philological analysis of lines 279–281 reveals imagery analogous to the synagogue parochet covering the Ark of the Scrolls. These four Jewish elements complement the one analyzed in the author’s earlier 2024 article, “The Parable of Wise and Foolish Builders in Yishen Lun and Rabbinic Literature.” That study concludes that the parable of wise and foolish builders in lines 146–156 of YSL aligns more closely with Jewish rabbinic traditions than the Gospel version. These new hermeneutical insights should provide interesting and fresh data for ongoing research into YSL. Full article

The detection of people in urban environments from satellite imagery can be employed in a variety of applications, such as urban planning, business management, crisis management, military operations, and security. A WorldView-3 satellite image of Prague was processed. Several variants of feature-extracting networks, referred to as backbone networks, were tested alongside the Faster R–CNN model. This model combines region proposal networks with object detection, offering a balance between speed and accuracy that is well suited for dense and varied urban environments. Data augmentation was used to increase the robustness of the models, which contributed to the improvement of classification results. Achieving a high level of accuracy is an ongoing challenge due to the low spatial resolution of available imagery. An F1 score of 54% was achieved using data augmentation, a 15 cm buffer, and a maximum distance limit of 60 cm. Full article

Retrogressive thaw slumps (RTS) are a form of abrupt permafrost thaw that can rapidly mobilize ancient frozen soil carbon, magnifying the permafrost carbon feedback. However, the magnitude of this effect is uncertain, largely due to limited information about the distribution and extent of RTS across the circumpolar region. Although deep learning methods such as Convolutional Neural Networks (CNN) have shown the ability to map RTS from high-resolution satellite imagery (≤10 m), challenges remain in deploying these models across large areas. Imagery selection and procurement remain one of the largest challenges to upscaling RTS mapping projects, as the user must balance cost with resolution and sensor quality. In this study, we compared the performance of three satellite imagery sources that differed in terms of sensor quality and cost in predicting RTS using a Unet3+ CNN model and identified RTS characteristics that impact detectability. Maxar WorldView imagery was the most expensive option, with a ground sample distance of 1.85 m in the multispectral bands (downloaded at 4 m resolution). Planet Labs PlanetScope imagery was a less expensive option with a ground sample distance of approximately 3.0–4.2 m (downloaded at 3 m resolution). Although PlanetScope imagery was downloaded at a higher resolution than WorldView, the radiometric footprint is around 10–12 m, resulting in less crisp imagery. Finally, Sentinel-2 imagery is freely available and has a 10 m resolution. We used 756 RTS polygons from seven sites across Arctic Canada and Siberia in model training and 63 RTS polygons in model testing. The mean IoU of the validation and testing data sets were 0.69 and 0.75 for the WorldView model, 0.70 and 0.71 for the PlanetScope model, and 0.66 and 0.68 for the Sentinel-2 model, respectively. The IoU of the RTS class was nonlinearly related to the RTS Area, showing a strong positive correlation that attenuated as the RTS Area increased. The models were better able to predict RTS that appeared bright on a dark background and were less able to predict RTS that had higher plant cover, indicating that bare ground was a primary way the models detected RTS. Additionally, the models performed less well in wet areas or areas with patchy ground cover. These results indicate that all imagery sources tested here were able to predict larger RTS, but higher-quality imagery allows more accurate detection of smaller RTS. Full article

The invasion of Spartina alterniflora (S. alterniflora) has posed serious threats to the sustainability, quality and biodiversity of coastal wetlands. To safeguard coastal ecosystems, China has enacted large-scale S. alterniflora removal projects, which set the goal of effectively controlling S. alterniflora throughout China by 2025. The accurate monitoring of S. alterniflora with remote sensing is urgent and requisite for the scientific eradication, control and management of this invasive plant. In this study, we combined multi-temporal WorldView-2/3 (WV-2/3) and Sentinel-1/2 imagery to monitor the S. alterniflora dynamics before and after the S. alterniflora removal projects in Zhangjiang Estuary. We put forward a new method for S. alterniflora detection with eight-band WV-2/3 imagery. The proposed method first used NDVI to discriminate S. alterniflora from water, mud flats and mangroves based on Ostu thresholding and then used the red-edge, NIR1 and NIR2 bands and support vector machine (SVM) classifier to distinguish S. alterniflora from algae. Due to the contamination of frequent cloud cover and tidal inundation, the long revisit time of high-resolution satellite sensors and the short-term S. alterniflora removal projects, we combined Sentinel-1 SAR time series and Sentinel-2 optical imagery to monitor the S. alterniflora removal project status in 2023. The overall accuracies of the S. alterniflora detection results here are above 90%. Compared with the traditional SVM method, the proposed method achieved significantly higher identification accuracy. The S. alterniflora area was 115.19 hm² in 2015, 152.40 hm² in 2017 and 15.29 hm² in 2023, respectively. The generated S. alterniflora maps clearly show the clonal growth of S. alterniflora in Zhangjiang Estuary from 2015 to 2017, and the large-scale S. alterniflora eradication project has achieved remarkable results with a removal rate of about 90% in the study area. With the continuous implementation of the “Special Action Plan for the Prevention and Control of Spartina alterniflora (2022–2025)” which aims to eliminate more than 90% of S. alterniflora in all provinces in China by 2025, the continual high-spatial resolution monitoring of S. alterniflora is crucial to control secondary invasion and restore coastal wetlands. Full article