Search Results (475)

Digital mining has become a tangible reality in recent years and the digital revolution enables and requires data exchange for autonomous machines and operational flow management. LoRa technology and its underground propagation behavior can make an important contribution to this digitalization. This paper presents a Data Mule approach that enabled progress in digitalization at refueling stations in active underground mining areas of a mine near Werra, Germany, operated by the K+S Group. This demonstration aimed to automate manual data collection at fuel gauges by using a dynamic LoRa network. We used specially developed LoRa Data Mule modules for operations over many square kilometers. LoRa was chosen for its industrial functionality and long-range capabilities, particularly in underground environments. The Data Mule modules used were in-house-designed units with underground mining-rated casing and connectors, as well as commercial LoRa boards and custom communication protocols. Connectivity between all systems was realized at travel speeds of 20 to 40 km/h, with connection data successfully relayed for 180 to 770 m, despite 90° turns and no line of sight. It was shown that the LoRa Data Mule approach can be used in a network of remote but active data generation points. Full article

The agricultural sector is undergoing a profound digital transformation driven by artificial intelligence, the Internet of Things, remote sensing, robotics, blockchain, and edge computing, which are being integrated into crop monitoring, irrigation management, disease detection, and supply chain transparency systems. This study employs systematic evidence mapping to characterize the applications of emerging digital technologies in sustainable agriculture; it delineates technological trajectories, areas of application, implementation gaps, and opportunities for improvement. Adhering to the PRISMA 2020 reporting protocol, 101 peer-reviewed articles indexed in Scopus and Web of Science (2020–2025) were identified, screened, and subjected to integrated thematic and bibliometric synthesis, using RStudio Version: 2026.01.1+403 and VOSviewer 1.6.20 for data mining on keywords and technological evolution patterns. Results show that deep learning and computer vision models achieved diagnostic accuracies of 90–99%, smart irrigation systems reduced water consumption by 10–30%, predictive yield models frequently reported R² values above 0.80, and greenhouse automation reduced energy consumption by approximately 20–30%. Blockchain-based architectures improved traceability and secure data transmission by 15–20%, while remote sensing integration enhanced spatial estimation accuracy up to R² = 0.92. The findings demonstrate a measurable transition toward data-driven, resource-efficient agricultural ecosystems supported by validated digital architectures. However, interoperability limitations, lack of standardized performance metrics, scalability challenges, and uneven geographical implementation—identified in nearly 40% of studies—highlight the need for harmonized evaluation frameworks, cross-platform integration standards, and long-term field validation to ensure sustainable and scalable digital transformation. Full article

High spatio-temporal resolution satellite imagery has become increasingly accessible thanks to advancements in the aerospace industry which, combined with a growing computational power, has enabled the spring of novel techniques regarding recognition in remote sensing (RS) images. However, there is still a lack of studies around 3D convolutions for spatio-temporal data applied to classification problems in RS. Hence, this study investigates the feasibility of 3D convolutional neural networks (3DCNNs) within a spatio-temporal perspective for scene classification and semantic segmentation in RS images, focusing on the identification of mining sites. We firstly developed a dataset covering several parts of Brazil based on MapBiomas products and Planet imagery, then we evaluated the effectiveness of 3DCNNs in capturing temporal information from a sequence of monthly captured images. Moreover, not only for scene classification but also for semantic segmentation, we compared 3D and 2D approaches. As for scene classification, a 3DCNN was better than the corresponding 2D model, while a 2D U-Net was better than a U-Net3D for semantic segmentation. The main explanation for this lies in the fact that a less costly annotation and training time strategy was adopted, but this may have harmed spatio-temporal approaches for semantic segmentation but not for scene classification. However, U-Net3D presented the highest Precision of all models, meaning that it is highly accurate when it predicts a positive. Moreover, 3DCNN (U-Net3D) presented significantly better performance with respect to semantic segmentation compared to other spatio-temporal approaches like ConvLSTM+U-Net and TempCNN. Sensitivity analysis revealed that the near-infrared (NIR) band played a decisive role in distinguishing mining areas, emphasizing its importance in highlighting subtle spectral variations associated with land-cover disturbances. Full article

Patterns of resource selection are driven by the decision-making processes of animals occurring at multiple scales from where to establish a home range (i.e., second order selection) to which resource patches to use within the home range (i.e., third order selection). Elk (Cervus canadensis) were reintroduced to southwestern Virginia, USA, from 2012 to 2014 following successful translocations onto reclaimed surface coal mines in the region. We sought to understand how elk have acclimated following their translocation using location data from GPS-collared adult female elk (n = 33) collected from 2019 to 2022 along with remotely sensed terrain and land cover data. We utilized continuous-time movement models paired with generalized linear mixed-effects modeling to describe seasonal resource selection at second and third orders. At both scales of selection and throughout the year, female elk selected reclaimed surface mines, conifer forests, ridgetops, and areas with lower terrain roughness, while avoiding mixed hardwood and oak (Quercus spp.) forests. Unmined open land was only selected at the third order during periods of forage scarcity (i.e., winter) and increased metabolic requirements (i.e., late gestation). Although surface coal mining leaves legacy environmental impacts on the landscape, management of these sites provides benefits to elk and maintains open habitat that is otherwise limited. Full article

With a typical floodplain in Poyang Lake selected as the study area, this paper employed the remote sensing Waterline Extraction Method (WEM) to invert its topographic changes based on 264 Landsat images from 1987 to 2024. The research systematically revealed the spatiotemporal variations in sediment accretion rates over the past 40 years and their influencing factors. By comparing different WEMs, the object-based method was identified as the most suitable for this study area. Accuracy validation of the topographic inversion showed that when using no fewer than 13 images, the average elevation error rate remained below 7.0%, indicating good reliability. The period from 1987 to 2024 was divided into 15 sub-periods, and digital elevation models of the floodplain were reconstructed for each. Results indicated that: (1) natural floodplain unaffected by sand mining experienced continuous accretion, with an average rate of approximately 3.1 ± 0.7 cm yr⁻¹ (surface elevation change) between 1987 and 2024; (2) in areas impacted by sand mining, the sediment accretion rate after mining (about 1.7 ± 0.8 cm yr⁻¹) was lower than that before mining (about 2.6 ± 2.7 cm yr⁻¹), likely due to the loss of vegetation cover reducing sediment retention capacity; (3) different vegetation types notably influenced accretion rates, with mixed Carex–T. lutarioriparia communities showing a consistently higher rate (about 3.5 ± 0.9 cm yr⁻¹) than pure Carex communities (about 1.7 ± 0.7 cm yr⁻¹), primarily attributable to differences in plant morphology, root architecture, and inundation tolerance. Further analysis revealed that riverine sediment supply was the fundamental material source for floodplain accretion. The phased decline in sediment discharge from the Ganjiang and Xiushui rivers since 1996 generally corresponds to the decreasing trend in sediment accretion rates observed after 2004. Full article

Publicly accessible spaceborne remote sensing datasets often lack the spatial resolution required to reliably distinguish archeological features from their surrounding geomorphological contexts. In this study, we assess the potential of super-resolution (SR) products derived from multiple public-domain remote sensing datasets for a systematic archeological survey in the Caral–Supe region. We focus on Synthetic Aperture Radar (SAR) and topographic datasets—including Sentinel-1, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR), and Digital Elevation Models (DEMs)—because of their capacity to detect subtle surface expressions and shallow subsurface structures obscured by vegetation or sediment cover. Using state-of-the-art deep learning algorithms, primarily employing the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) architecture, we integrated multi-source SAR imagery and DEM data to generate SR products that reveal distinct signatures in areas containing dense archeological remains and clearly delineate shallow, buried anthropogenic features. We further developed deep learning classification models that combine SR SAR and DEM inputs and trained them on known archeological site locations. This approach enabled the detection of previously undocumented structural features distributed along the coastal margin and throughout the Supe Valley. Our findings indicate that enhancing publicly available remote sensing datasets with advanced SR techniques can provide cost-effective and practical high-resolution archeological data, compared to data mining using aerial photography and high-resolution commercial satellite imagery, in terms of both cost and obstacle penetration. Full article

Soil heavy metal pollution caused by mineral resource extraction activities poses a serious threat to the ecological environment within and surrounding mining areas. As a highly concealed toxic heavy metal, arsenic (As) urgently requires the establishment of efficient pollution monitoring methods to achieve pollution prevention and control, as well as environmental remediation in mining areas. This study investigated the feasibility of hyperspectral remote sensing inversion for soil heavy metal arsenic based on ZY-1 02E hyperspectral satellite imagery, focusing on a mining area and its surrounding soils in Sanlisha’an, Wuxuan County, Guangxi. Full Constrained Least Squares (FCLS) was employed to separate mixed pixels and enhance soil spectral contributions in ZY-1 02E imagery, thereby mitigating vegetation interference. Six mathematical transformations, including RT, AT, FD, RTFD, ATFD, and SD, were applied to both the original and enhanced spectra to enhance spectral features. The correlations between the transformed spectra, as well as the original image spectra (S), and soil As concentration were analyzed; then the spectra strongly correlated with soil As concentration were selected to construct Ratio Spectral Index (RSI) and Normalized Difference Spectral Index (NDSI). Correlation matrices were calculated between RSI/NDSI indices and As concentration. Sensitive features were screened using an improved Successive Projection Algorithm (SPA). As concentration inversion was also performed with four models: traditional regression models, PLSR and MLR, and ensemble learning models (RF and XGBoost). In the soil contribution-enhanced spectral modeling results, the optimal transformation–index combination is ATFD-NDSI. The performance indicators of each model are as follows: MLR test set R² = 0.65, PLSR test set R² = 0.62, RF test set R² = 0.7, and XGBoost test set R² = 0.64. The results indicate that the ATFD-NDSI-RF ensemble model provides the best performance. By integrating multiple decision trees, RF effectively handles complex nonlinear relationships, thus enhancing the accuracy and generalization ability of predication. The analysis of NDSI–ATFD–RF inversion results based on sampling points indicates that model error correlates with the pollution intensity gradient, showing greater errors, especially in high-concentration areas, but still maintaining strong correlations (tailings reservoir: r = 0.92, forested areas: r = 0.96, and cropland: r = 0.83). The spatial distribution reveals that the inversion results are closely similar to the spatial distribution of IDW interpolation. Areas with high As concentrations are concentrated in the tailings reservoir and in the southeastern part of the study area. The correlation coefficient between the inversion results and IDW interpolation is 0.6, which further verifies that the inversion results effectively reproduce the spatial distribution trend of highly polluted areas. Full article

Remote sensing image change detection serves as a core technology in environmental monitoring. While the widespread availability of high-resolution remote sensing data provides essential support for detailed detection, it also presents technical challenges such as complex terrain interference, subtle change recognition, and large-scale scene processing. Current mainstream deep learning methods, despite their global modeling advantages, demonstrate limitations in cross-temporal fine-grained correlation mining and are prone to ambiguous edge localization in changing areas due to spatial detail loss. This paper proposes a high-resolution change detection network (TiBT-Net) that integrates bi-temporal space enhancement with token interaction. The model achieves precise change detection through dynamic token interaction and adaptive enhancement (TDIAE), utilizing deformable attention to capture semantic correlations. It constructs a Bi-Temporal Information Interaction Module (BTII) that enhances spatial details via multi-scale convolutions and channel attention, while introducing a delayed fusion mechanism (DLF) to dynamically balance dual-branch feature contributions. Experimental validations on LEVIR-CD, WHU-CD, and DSIFN-CD datasets achieved F1 scores of 90.38%, 86.74% and 96.28%, respectively, with Intersection-Union Ratios (IoU) of 82.46%, 76.59% and 92.82%. The overall accuracy (OA) reached up to 99.04%. This model effectively resolves the integration conflict between semantic information and spatial details, providing a reliable technical solution for high-precision change detection in complex scenarios. Full article

Soil heavy metal contamination in mining areas poses a serious environmental challenge, requiring monitoring approaches with both wide coverage and high accuracy. Hyperspectral remote sensing provides an effective solution, yet its performance in complex mining environments is often limited by mixed-pixel effects and nonlinear spectral responses. To address these issues, this study proposes a Physically-Constrained Collaborative Endmember Extraction (PCCEE) framework that integrates spectral unmixing with machine learning for multi-element inversion. Using Gaofen-5 hyperspectral imagery, a collaborative workflow combining Pixel Purity Index (PPI), Vertex Component Analysis (VCA), and prior-spectral-constrained Spectral Angle Mapper (SAM) was developed to improve endmember purity and physical interpretability. Among three unmixing models (LMM, NMF, and SVR), the Linear Mixing Model achieved the best balance between accuracy and efficiency. Random Forest regression using retrieved abundances enabled high-accuracy inversion of eight heavy metals (mean R² = 0.85). Spatial analysis revealed significant co-enrichment of Pb, Cd, and Zn related to sulfide weathering, while PCA distinguished compound and independent pollution sources. The proposed PCCEE framework effectively mitigates mixed-pixel interference and provides a transferable approach for heavy metal monitoring and risk assessment in complex mining environments. Full article

Optical and passive remote sensing-based estimation of aboveground biomass (AGB) using forest structural stratification has shown improvements over global models. This study investigated whether stratification by human-mediated disturbances improves prediction accuracy. Disturbance variables included proximity to mines, roads, and settlements, evaluated across three regimes: the full Atewa landscape (“FSR”), the Atewa Range Forest Reserve (“FR”), and the surrounding disturbed area (“SR”). Predictor selection for regimes was performed using recursive feature elimination with cross-validation, applied to random forest (RF) and support vector machine (SVM) algorithms. AGB was then estimated using local, global, and retuned global models, and the results were compared using the coefficient of determination (r²) and root mean square error (RMSE). The global RF model achieved the best performance (r² = 0.54; RMSE = 57.71 Mg/ha), likely due to structured heterogeneity captured across combined regimes. The “SR” models, however, performed poorly, indicating that excessive unstructured heterogeneity introduces noise and redundancy that weaken predictions. The low performance of the “FR” regime was attributed to spectral saturation and limited variance in observed AGB. Although disturbance factors added minimal bias, heteroscedasticity was evident in the “SR” and “FSR” regimes. Overall, this study indicates that disturbance-based stratification may not necessarily improve AGB estimation accurately compared to global models. However, it highlights the value of disturbance information for AGB modeling in heterogeneous forest landscapes. Full article

This study examines the application of mining effluents as feed solutions in a bench scale pressure retarded osmosis (PRO) system for energy generation and the prospect of water recycling or safe discharge to the environment. Effluents were characterized and pretreated by ultrafiltration (UF) and nanofiltration (NF) prior to PRO. The PRO process was then conducted over 6 h in a cross flow flat plate cell with an effective membrane area of 34 cm², a hydraulic pressure of 12.4 bar and a 3M ammonium carbonate (NH₄)₂CO₃ as draw solution. Effluent 1 contained ions such as Cl⁻ (539 mg/L), NO₃⁻ (585 mg/L), SO₄²⁻ (3000 mg/L), Na⁺ (560 mg/L), and Mg²⁺ (656 mg/L), with a total dissolved solids (TDS) concentration of 5400 mg/L, chemical oxygen demand (COD) of 136 mg/L, total organic carbon (TOC) concentration of 3.5 mg/L, and acidic pH of 3.8, while effluent 2 was highly dominated by Cl⁻ (32,100 mg/L), NO₃⁻ (9720 mg/L), SO₄²⁻ (6512 mg/L), Na⁺ (14,306 mg/L), and Mg²⁺ (5336 mg/L), had a TDS concentration of 73,315 mg/L, COD of 8100 mg/L, TOC concentration of 10.2 mg/L, and pH of 7.4. These physiochemical properties indicated a significant potential of fouling and scaling which necessitated the appropriate pretreatments. It was shown that integrating UF and NF pretreatments was highly effective in refining the quality of effluents with a significant removal efficiency of above 90% for ions and heavy metals by NF, led to fouling mitigation, higher and more stable power density as well as potential water reuse or safe environmental discharge. The achieved water fluxes and power densities were 54 L/m²h and 18.6 W/m², for effluent 1, and 38 L/m²h and 13 W/m², for effluent 2, respectively. The outcome of this study is applicable for the mining sector especially in remote areas with the potential for water and energy recoveries to contribute to more sustainable mining operations. Full article

As an emerging learning paradigm, self-supervised learning (SSL) has attracted extensive attention due to its ability to mine features with effective representation from massive unlabeled data. In particular, SSL, driven by contrastive learning and masked modeling, shows great potential in general visual tasks. However, because of the diversity of ground target types, the complexity of spectral radiation characteristics, and changes in environmental conditions, existing SSL frameworks exhibit limited feature extraction accuracy and generalization ability when applied to complex remote sensing scenarios. To address this issue, we propose a hybrid SSL framework that integrates the advantages of contrastive learning and masked modeling to extract more robust and reliable features from remote sensing images. The proposed framework includes two parallel branches: one branch uses a contrastive learning strategy to strengthen global feature representation and capture image structural information by constructing positive and negative sample pairs; the other branch adopts a masked modeling strategy, focusing on the fine analysis of local details and predicting the features of masked areas, thereby establishing connections between global and local features. Additionally, to better integrate local and global features, we adopt a hybrid CNN+Transformer architecture, which is particularly suitable for intensive downstream tasks such as semantic segmentation. Extensive experimental results demonstrate that the proposed framework not only exhibits superior feature extraction ability and higher accuracy in small-sample scenarios but also outperforms state-of-the-art mainstream SSL frameworks on large-scale datasets. Full article

Monitoring multi-temporal forest vertical structure in anthropogenically disturbed and topographically complex landscapes remains a major challenge, particularly when low-cost remote sensing technologies are used. This study aims to quantify forest vertical structure change and to determine whether these changes are systematically regulated by geomorphometric controls rather than occurring randomly. A multi-temporal unmanned aerial vehicle (UAV) photogrammetry workflow based on Structure from Motion (SfM) was applied to generate annual Canopy Height Models (CHMs) for 2023, 2024, and 2025. To ensure temporal robustness, the 95th percentile of canopy height (P₉₅) was adopted as the primary structural metric, and vertical change was quantified using a difference-based indicator (ΔP₉₅). Random Forest (RF) regression was used to model the relationship between canopy height change and terrain-derived predictors, including slope, aspect, and Topographic Wetness Index (TWI). The results reveal a consistent vertical growth signal across the study area, with a mean ΔP₉₅ increase of 0.65 m over the monitoring period, clearly exceeding the photogrammetric vertical error (RMSE = 0.082 m). Positive canopy height changes are concentrated on moisture-favored, moderately sloping and north-facing terrain, whereas negative changes (down to −1.20 m) are mainly associated with mining-disturbed and steep surfaces. The RF model achieved high explanatory performance (training R² = 0.919) and identified aspect (20%), slope (18%), and TWI (18%) as the dominant controls on forest vertical dynamics. These findings demonstrate that forest vertical structure evolution in disturbed landscapes is not stochastic but is systematically governed by terrain-driven hydro-morphological and microclimatic conditions. The main contribution of this study is the development of an interpretable, change-focused UAV–machine learning framework that moves beyond single-epoch canopy height estimation and enables process-oriented analysis of terrain–vegetation interactions. The proposed approach provides a cost-effective and transferable tool for forest monitoring and post-mining restoration planning in complex terrain settings. Full article

Coal mining is a major source of methane emissions globally, and monitoring these emissions has become a sustained area of interest in both scientific research and policy-making. Coal mine methane emissions typically manifest as discrete point sources, such as individual mines or ventilation shafts, and spatially concentrated area sources, such as mining clusters. In recent years, satellite remote sensing technology has become a key tool for monitoring and assessing methane emissions from coal mines. Notable progress has been made in quantifying emissions through point-source inversion using high-resolution satellite data, such as GF-5B/AHSI, and in estimating regional-scale area-source emissions using wide-swath instruments, such as S5P/TROPOMI. However, there remains a lack of systematic comparison between inversion results derived from these two types of satellite data with differing spatial resolutions. This study comprehensively analyzes the strengths and limitations of the GF-5B/AHSI and S5P/TROPOMI sensors for quantifying methane emissions. It conducts a spatiotemporal comparative analysis of point-source and area-source methane emission datasets from the coal-mining regions of Shanxi Province. The research aims to clarify the intrinsic relationship between remote-sensing data at different observational scales and to systematically evaluate how prior information on emission-source locations influences emission quantification results. The comparative analysis between TROPOMI grid-level emissions and GF-5B/AHSI point-source emissions indicates that TROPOMI-gridded emission data, owing to its longer time series, can more effectively characterize the annual-average methane emission levels in mining areas. Meanwhile, high-resolution observations from GF-5B/AHSI show distinct advantages in detecting small-scale plumes and attributing emissions to specific facilities. Although the regional-average emissions derived from TROPOMI are significantly higher than point-source emission rate estimates, their data ranges overlap within their uncertainty intervals, demonstrating substantial consistency between the monitoring results of the two methods. Furthermore, the study reveals that when key emission facilities, such as ventilation shafts, are located far from the core operational areas of mines, relying solely on point-source observations may not fully capture the spatial distribution pattern of methane emissions at the mine scale. Full article

The rapid development of Convolutional Neural Networks (CNNs) has markedly boosted the performance of object detection in remote sensing. Nevertheless, tiny objects typically account for an extremely small fraction of the total area in remote sensing images, rendering existing IoU-based or area-based evaluation metrics highly sensitive to minor pixel deviations. Meanwhile, classic detection models face inherent bottlenecks in efficiently mining discriminative features for tiny objects, leaving the task of tiny object detection in remote sensing images as an ongoing challenge in this field. To alleviate these issues, this paper proposes a tiny object detection method based on Normalized Gaussian Label Assignment and Multi-scale Hybrid Attention. Firstly, 2D Gaussian modeling is performed on the feature receptive field and the actual bounding box, using Normalized Bhattacharyya Distance for precise similarity measurement. Furthermore, a candidate sample quality ranking mechanism is constructed to select high-quality positive samples. Finally, a Multi-scale Hybrid Attention module is designed to enhance the discriminative feature extraction of tiny objects. The proposed method achieves 25.7% and 27.9% AP on the AI-TOD-v2 and VisDrone2019 datasets, respectively, significantly improving the detection capability of tiny objects in complex remote sensing scenarios. Full article