Search Results (4,348)

Phytophthora root and crown rot causes irreversible canopy decline in citrus before ground-level symptoms appear, yet UAV-based detection still relies almost exclusively on RGB imagery and, in citrus, on leaf-level classification rather than field-scale localisation. This work addresses that gap with a crown-level object-detection pipeline for Phytophthora in orange orchards, in three contributions. First, a mean-initialised patch embedding expansion that adapts a pretrained detection transformer to N-channel input while preserving its DINOv2 representations and activation magnitude, applicable to any ViT-based detector. Second, a two-stage protocol that screens seven vegetation indices (GNDVI, SAVI, EVI, GRVI, ExG, CARI, MCARI) as fourth channels over three seeds; the screening does not resolve them, and GNDVI is retained because both bands of its ratio respond to root dysfunction-induced chlorophyll degradation and both come from a single sensor. Third, a matched-modality comparison isolating the contribution of the architecture from that of the spectral channel. On 1147 georeferenced RGB–multispectral pairs with 5560 expert-annotated instances, RF-DETR + GNDVI attains a test mAP_50:95 of $0.590\pm 0.008$ and mAP₅₀ of $0.873\pm 0.005$ over three seeds, exceeding a YOLO26n baseline on identical four-channel input by 9.6 and 8.4 percentage points at half the resolution, the architecture proving the decisive component and supporting georeferenced crown-level alerts. Full article

Interpreting multispectral remote sensing data for hydrothermal alteration mapping remains challenging in complex mountainous metallogenic belts because dense pixel-level field labels are difficult to obtain and weak spectral responses are affected by lithological background, vegetation, snow/ice cover, and topographic shadow. This study proposes a principal component analysis (PCA)-guided weakly supervised workflow for mapping hydroxyl- and iron-oxide-related spectral anomalies in the Bulong–Maidan–Tuoyun gold–copper metallogenic belt, southwestern Tianshan, China, using Landsat 8 Operational Land Imager (OLI) imagery. PCA was used as a spectral prior to generate PCA-derived positive spectral anomaly samples for model training. A Residual-ECA Alteration Information Extraction (REA-AIE) model was developed to refine PCA-derived anomalies by learning local spectral–spatial features from multispectral image patches. Under the PCA-constrained random sample-level evaluation, REA-AIE achieved F1 scores of 95.90% and 97.09% for hydroxyl- and iron-oxide-related spectral anomalies, respectively; these values indicate agreement with PCA-derived pseudo-labels rather than spatially independent estimates of mapping performance. Petrography-constrained site-level assessment showed that REA-AIE-predicted spectral anomalies occurred within 90 m of 43 of the 53 altered sites, corresponding to a site-level recall of 81.13% and supporting their consistency with field-based geological evidence. Full article

Background: Crude oil refinery waste sludge (COWS) ranks among the most compositionally complex and ecotoxicologically hazardous industrial residues. Although bulk total petroleum hydrocarbon (TPH) and summed polycyclic aromatic hydrocarbon (PAH) removal are routinely reported, the metabolite-level biochemical fate of individual petrogenic compounds, spanning ring dihydroxylation, catechol cleavage, and entry into central carbon metabolism, remains largely unmapped under co-composting with diverse animal manures. Objectives: This study aimed to construct a metabolite-resolved, microbially anchored biochemical fate map of crude oil sludge during co-composting. Methods: Aerobic microcosms combining crude oil sludge, garden soil, and a wood-chip bulking agent were amended separately with poultry, horse, cow, or swine/pig manure alongside an unamended control and then incubated at 22 °C for 300 days. Analyses integrated untargeted gas chromatography-mass spectrometry (GC-MS) metabolomics, targeted PAH quantification (EPA Methods 3541/8270), 16S rRNA gene amplicon sequencing (Illumina MiSeq, V1–V3, paired-end 300 bp), physicochemical monitoring, and culture-dependent isolation, with National Institute of Standards and Technology (NIST) Mass Spectral library annotation. Results: GC-MS resolved 1169 metabolite features across 17 samples, comprising 538 annotated compounds within 11 chemical classes and 631 unknowns, of which 151 recurred in at least 10 samples. Petrogenic markers (n-alkanes C₁₄–C₃₆, hopanoids, steranes, and alkylated dibenzothiophenes) and ring-cleavage intermediates (2-hydroxyfluorene, 1,4-naphthoquinone, phenanthrene-methanol, benzenediols, butanedioic acid, fatty alcohols C₁₆–C₂₀) elucidated a four-stage degradation cascade consistent with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways map01220 and map00624. PAH mean-removal ranked swine/pig (88.0%) > horse (87.0%) > poultry (80.5%) > cow (79.1%) > control (68.2%). Sequencing recovered 2969 operational taxonomic units (OTUs) enriched in Pseudomonas, Achromobacter, Stutzerimonas, Dietzia, Gordonia, and Mycobacterium, with Pseudomonas dominating high-removal systems; respiration peaked at 18.7 mg CO₂-C g⁻¹ in poultry treatments. Conclusions: This work establishes a metabolite-resolved map linking hydrocarbonoclastic taxa to separate degradation steps. The co-occurrence of oxygenated PAH intermediates with decreasing parent PAH concentrations serves as an indicator of transformation processes and may assist in identifying potential residual-risk signals, thereby supporting remediation evaluation and process optimization. Full article

Accurate and up-to-date information is essential for environmental monitoring, particularly in regions characterized by complex topography and heterogeneous landscapes. This study presents a multisource remote sensing–based approach for forest vegetation classification on La Palma Island (Canary Islands, Spain), which was further used to illustrate its potential for monitoring the temporal dynamics of different forest habitat types. Very high-resolution multispectral data from the WorldView-2/3 satellites were used, complemented by multispectral and LiDAR data acquired by an unmanned aerial vehicle (UAV). Four target forest vegetation types were mapped within a six-class classification scheme that also included “Other vegetation” and “Soil/Others” as non-target/background classes. The performance of ten supervised classification algorithms was evaluated, including Minimum Distance, Mahalanobis Distance, Parallelepiped, Spectral Angle Mapper, Maximum Likelihood, Naïve Bayes, K-Nearest Neighbors, Random Forest, Support Vector Machine, and the transformer-based deep learning model SegFormer. The results indicate that Random Forest achieved the highest overall accuracy, while Support Vector Machine and SegFormer also showed competitive performance, particularly when spectral information was integrated with vegetation indices and topographic variables. The study provides practical evidence on the selection of input data and classifiers for detailed forest vegetation mapping in a large and topographically complex island. Full article

Accurate cloud segmentation from ground-based fisheye camera imagery is essential for solar irradiance forecasting and photovoltaic system optimization. Traditional computer vision approaches, such as HSV thresholding and K-means clustering, face significant limitations when applied globally to sky images due to the spectral similarity between cloud regions and sky areas under varying atmospheric conditions. This study presents a hybrid methodology that leverages YOLOv8 instance segmentation to provide contextual cloud regions followed by refined HSV thresholding within these detected areas. The approach incorporates solar trajectory modeling using pvlib for accurate sun disk detection and exclusion, preventing false cloud classification. The methodology was developed and validated at the CIESOL using Mobotix Q71 fisheye cameras, and later tested in Antofagasta (Chile) and Huelva (Spain). The YOLOv8l-seg model achieved a mask precision of 0.821 and box mAP@0.5 of 0.680 on validation data. The results show a promising correlation with radiometric measurements such as clearness index $k_t$ and diffuse fraction $k_d$ in preliminary validation cases. While YOLOv8 demonstrates good cross-site generalization, HSV thresholding requires camera-specific calibration for optimal performance. The method addresses the context-dependency limitations of traditional algorithms, though computational performance and broader validation remain areas for future work. Full article

Early-season winter wheat mapping is crucial for agricultural management and food security, but reliable identification remains challenging under weak spectral conditions during early growth stages. To address this challenge, this study developed a CNN–LSTM with a lag-aware gated fusion model (CL-LGFM) for winter wheat mapping in the Chaohu Basin, China, using a reconstructed 5-day Sentinel-2 NDVI time series and precipitation data from the Global Precipitation Measurement (GPM) mission. The model employs a dual-branch architecture to jointly learn vegetation and precipitation features and introduces a lag-aware dynamic gated fusion module to capture the delayed response of vegetation to precipitation and enhance multi-source feature fusion. The results show that the proposed method achieved reliable early-season winter wheat mapping (OA ≥ 0.90, Kappa ≥ 0.80) on 26 January, at least 10 days earlier than traditional methods, including SVM, RF, DTW, and TCN, using the same reconstructed 5-day NDVI time series. Optimal performance uses a 7 × 7 patch size and 30-day precipitation window. Full article

The increasing tourism pressure on the UNESCO World Heritage Altai Mountains calls for efficient environmental monitoring tools. This study presents a Google Earth Engine framework that couples the Remote Sensing Ecological Index (RSEI) with land use/land cover (LULC) mapping to assess ecological changes in the Lake Manzherok area between 2020 and 2025. RSEI was derived from Sentinel-2 and Landsat imagery by combining four indicators (NDVI, MNDWI, NDBSI, LST) through principal component analysis. LULC classification was carried out using Random Forest trained exclusively on Sentinel-2 spectral bands. The results confirm that RSEI effectively captures ecological gradients in complex mountainous terrain, with the first principal component explaining 57–62% of the total variance. While 92% of the study area remained stable, 5.9% showed a decline in ecological status, spatially coinciding with a near doubling of built-up and bare surfaces from 9.89 km² to 18.17 km². The largest negative RSEI changes were associated with transitions from forestland (ΔRSEI = −0.29) and grassland (ΔRSEI = −0.20) to built-up/bare land, whereas reverse transitions displayed positive ΔRSEI values. These spatial patterns are consistent with the visible development related to tourism. However, because the built-up/bare land class also includes naturally bare surfaces, and because interannual climate variability may affect the RSEI components, it is important to interpret the ΔRSEI values as relative changes rather than absolute measurements of tourism impact. The proposed framework provides a reproducible and transferable tool for monitoring ecological quality in data-scarce mountain regions, delivering spatially explicit evidence that can support conservation and land-use planning. Full article

Sustainable urban disaster response requires Earth observation (EO) resources to be selected according to event demands, environmental conditions, sensing capabilities, and urban targets. This study proposes an Observation Linked Open Data (O-LOD)-based Urban Disaster Observation Task (UDOT) enhancement framework for semantic EO resource configuration. O-LOD organizes heterogeneous data through four dimensions, Event, Context, Subject, and Object, which an instantiation algorithm populates as task graphs. Layered GeoSPARQL queries then match thematic and analytical capabilities, qualify orbit-derived observation opportunities against context and object constraints, and rank feasible alternatives using the Observation Capability Evaluation Model (OCEM). Evaluation on the 2020 Khartoum flood and Bobcat wildfire narrowed 202 satellite–sensor pairs to three flood-capable and four wildfire-capable pairs and returned two Khartoum and eight Bobcat ranked opportunities, with complete queries executing in 3.0 s and 0.07 s. Constraint ablation and resolution sensitivity analyses identified the conditions governing the feasible set. Publicly accessible Sentinel, Landsat, and MODIS products corroborated both retained and excluded results, supporting water-extent and burn-severity mapping where coverage, spectral bands, and image quality met the task requirements. O-LOD therefore provides a traceable semantic link from disaster observation demand to qualified and ranked EO opportunities and subsequent image assessment, supplying task-oriented inputs for downstream scheduling and supporting context-aware sensing for sustainable urban disaster response. Full article

Objective: To address small bolt defect targets, complex background interference, and limited edge deployment in hilly and mountainous rail transport environments, a detection method balancing accuracy, lightweight design, and real-time performance was proposed. Methods: A track image dataset containing missing bolts, loose bolts, and missing nuts was constructed. Based on YOLOv11m, HFERBC3K2 was developed by replacing the standard bottleneck in C3K2 with a High-Frequency Enhancement Residual Block to strengthen edge, texture, and local structural feature extraction. A Spectral Enhanced Feed-Forward module was introduced into C2PSA to form SEFFNC2PSA, enhancing defect-related frequency components and suppressing background interference through adaptive frequency-domain modulation. The integrated model was named HFSE-YOLOv11. Channel-level structured pruning was then applied, and the model with a pruning ratio of 0.5 was named LHFSE-YOLOv11. Results: On the validation set, HFSE-YOLOv11 achieved 91.7% precision, 93.4% recall, 91.3% mAP@0.5, and 80.2% mAP@0.5:0.95, improving upon YOLOv11m by 3.6, 2.2, 1.2, and 3.1 percentage points, respectively. After pruning, LHFSE-YOLOv11 had 15.9 M parameters, 53.8 GFLOPs, and a 32.5 MB model size, representing reductions of 16.3%, 14.3%, and 11.7%, while mAP@0.5 and mAP@0.5:0.95 decreased by only 0.3 and 0.9 percentage points. On the independent test set, it achieved 91.7% precision, 93.4% recall, 91.0% mAP@0.5, 79.3% mAP@0.5:0.95, and 81.5 FPS, outperforming all compared models in the four detection metrics. Conclusion: LHFSE-YOLOv11 balances accuracy, efficiency, and model size, supporting deployment on vehicle-mounted inspection terminals and resource-constrained edge devices. Full article

Cardiac amyloidosis (CA) is an infiltrative cardiomyopathy caused by extracellular deposition of misfolded proteins, most commonly immunoglobulin light chains (AL) or transthyretin (ATTR). Once considered a rare disease, CA is increasingly recognised due to improved diagnostic strategies and the availability of disease-modifying therapies. Early diagnosis is crucial, as treatment efficacy and clinical outcomes are strongly influenced by the stage of cardiac involvement. Multimodality cardiac imaging plays a central role in the diagnostic pathway, risk stratification, and evaluation of therapeutic response in CA. Echocardiography represents the first-line imaging modality and is essential for raising clinical suspicion through the identification of characteristic structural and functional abnormalities, including ventricular wall thickening, diastolic dysfunction, and distinctive strain patterns. Bone scintigraphy has revolutionised the non-invasive diagnosis of ATTR-CA, allowing accurate identification of transthyretin-related disease in the absence of monoclonal gammopathy, which needs to be excluded via serum and urinary immunofixation. Cardiovascular magnetic resonance provides advanced tissue characterisation through late gadolinium enhancement and quantitative mapping techniques, enabling detection of early myocardial involvement and robust prognostic stratification. Emerging imaging modalities, including dual-energy (spectral) computed tomography and positron emission tomography tracers, show promise in myocardial amyloid quantification and subtype differentiation, although their role is still evolving. Integration of imaging findings with clinical and laboratory parameters allows comprehensive disease assessment, facilitating early diagnosis, guiding therapeutic decisions, and improving risk stratification. This review summarises the current role of multimodality imaging in CA, highlighting its contribution from early detection to prognostic evaluation and monitoring of treatment efficacy, with particular emphasis on the emerging role of quantitative imaging in monitoring treatment response. Full article

In space target surveillance missions, effective detection of dim space targets remains a challenge due to dense star interference and the random nature of target motion. To quickly and accurately extract dim space targets from complex star backgrounds, this paper proposes a dim space target detection and track association method for dense star backgrounds. This paper analyzes various features of the target and background from a combined spatiotemporal perspective, including three main stages. First, inter-frame registration is used to filter out bright stars, followed by connected domain post-processing, which simplifies the star map background while enhancing the signal-to-noise ratio of dim targets. Secondly, an image difference fusion coarse processing module is proposed. The reconstructed multi-impulse function is derived from the spectral phase difference to estimate the displacement parameters of different components, after which the image difference fusion is designed to obtain candidate targets. Third, a directional track association algorithm is designed, with the candidate targets as the center and the motion parameters as thresholds, narrowing the association range to a fan-shaped region. This enables fast detection of target tracks while removing excess false alarms. The experimental results on four datasets demonstrate that this method outperforms traditional baseline methods in terms of target detection and localization accuracy. Full article

Small- and medium-sized landslides frequently occur in clusters and exhibit fragmented morphologies, irregular boundaries, and spectral characteristics similar to surrounding roads, bare soil, and sparsely vegetated surfaces, making their automated extraction from remote sensing imagery challenging. Although the Segment Anything Model (SAM) provides strong general-purpose segmentation capabilities, its direct application to landslide mapping is limited by the geoscience domain gap and its dependence on external prompts. This study proposes the Asymmetric Boundary-aware Segment Anything Model (AB-SAM), a parameter-efficient adaptation of SAM for automated landslide semantic segmentation. AB-SAM integrates three task-specific components. First, the offline Multi-Feature Variation-Guided Prompting (MF-VGP) module generates cached auxiliary bounding boxes from registered pre- and post-event images without accessing ground-truth masks. Second, the Asymmetric Feature Augmentation (AFA) strategy combines geometric perturbation, CutMix, and asymmetric dual-branch supervision, in which a Hint-free branch serves as the primary optimization pathway and a lower-weight box-guided branch provides auxiliary spatial supervision. Third, the Boundary-Aware Morphological Prompting (BAMP) module injects trainable boundary-aware morphological information into the largely frozen SAM image encoder. During validation, testing, and application, only the Hint-free branch is retained, enabling inference using post-event imagery without external point, box, or mask prompts. On the fixed, spatially disjoint Zixing test set, AB-SAM achieved an overall accuracy of 96.171%, a precision of 68.149%, a recall of 60.011%, an F1-score of 63.822%, a landslide-class Intersection over Union of 46.867%, and a mean Intersection over Union of 71.452%. Repeated experiments with three random seeds showed low run-to-run variation. Direct evaluation without retraining on the Hokkaido Iburi-Tobu dataset yielded a mean Intersection over Union of 66.136%, providing evidence of cross-region and cross-event transferability. These results demonstrate that AB-SAM provides a practical parameter-efficient framework for automated, hint-free landslide segmentation, although further evaluation across additional regions, sensors, and landslide-size distributions remains necessary. Full article

High-dimensional engineering datasets often combine class imbalance, noisy structures, and limited ground truth, making unsupervised analysis difficult to evaluate reliably. This study quantifies how three properties—partition stability, minority class recovery, and topological interpretability—are traded off across clustering methods, using a capacity-matched 25-seed comparison on a TCGA-derived RNA expression dataset (10,095 samples, 19 cancer types, 13,634 genes). We compare K-means across cluster counts $k\in \{19,\dots ,400\}$, self-organizing maps (SOMs) across lattice sizes from 25 to 625 nodes, consensus K-means, a granularity-matched SOM-Super20 control, and four modern baselines (HDBSCAN, spectral clustering, Gaussian mixtures, and Leiden). At matched prototype budgets, K-means is both more reproducible and substantially better at recovering minority classes than SOMs: at 400 prototypes, K-means achieves pairwise NMI $0.819$ versus $0.621$ for the $20\times 20$ SOM and recovers the smallest cancers 6–$14\times$ more effectively (pancreas effective coverage $0.760$ vs. $0.054$).Crucially, the SOM does not close this gap even when given more prototypes ($0.07$ at 625 nodes), so, under matched capacity, minority recovery is better explained by representational capacity and centroid allocation freedom than by topology preservation. The recovery is not free: increasing k overfragments the partition and lowers the pairwise ARI stability ($0.643\to 0.419$ from $k=20$ to $k=400$), while the NMI remains robust ($\approx 0.82$). The hardest minority, pancreas, is recovered only by high-capacity K-means and by no other method evaluated, including SOMs at any size, consensus K-means, SOM-Super20, HDBSCAN, Gaussian mixtures, spectral clustering, and Leiden. The SOM’s distinct value is therefore not stability or recovery but the interpretable two-dimensional topological visualization that it uniquely provides, including a gradient-organized structure that is reproducible across seeds for kidney (weaker for uterus). No single method optimizes all three properties; the appropriate choice depends on whether a task prioritizes reproducibility, minority recovery, or visual interpretability. Because these conclusions follow from the shape of the data and the allocation behavior of the algorithms rather than from biological semantics, we expect them to transfer to high-dimensional imbalanced engineering data, such as those from fault clustering, condition monitoring, and anomaly detection. Full article

The Greater Cape Floristic Region (GCFR) in South Africa is a fire-prone biodiversity hotspot where high species richness, structural complexity, and small plant sizes (0.0001–4 m²) pose substantial challenges for remote sensing-based biodiversity assessment. Spectral similarity among species and the mismatch between plant size and sensor pixel dimensions limit the capacity of current and forthcoming spaceborne systems to resolve individual species and accurately detect plot-level diversity changes. We therefore developed a physics-based simulation framework that couples fynbos trait measurements with radiative transfer modeling in the DIRSIG (Digital Imaging and Remote Sensing Image Generation) environment towards quantifying information loss across spectral and spatial scales and to define theoretical limits for biodiversity monitoring. We constructed a three-dimensional virtual scene of post-fire fynbos communities in Grootbos Private Nature Reserve, integrating high-resolution imagery, terrestrial laser scanning (TLS), and structure-from-motion (SfM)-derived point clouds. Field measurements of mean diameter and percent cover were used to scale vegetation models and constrain species abundance. We distributed plant instances using a blue noise sampling algorithm, guided by density maps derived from unmanned aerial system (UAS) imagery. Species-specific optical properties were parameterized using field-measured reflectance data and the PROSPECT radiative transfer model, while terrain structure was derived from SfM-based digital terrain models. The integrated scene was used to simulate multispectral (DJI Mavic 3 MSI), hyperspectral (AVIRIS-NG), and light detection and ranging (LiDAR) observations. Agreement between simulated outputs were evaluated against corresponding field-acquired datasets using spectral signatures and vegetation indices. This framework enables systematic assessment of sensor specification effects on spectral biodiversity metrics and provides a pathway for evaluating theoretical limits of species discrimination across airborne and satellite platforms. Full article

In karst regions, sugarcane mapping faces challenges from fragmented fields, undulating terrain, spectral confusion, and persistent cloud cover, which limit traditional optical remote sensing. To address these issues, we propose a fine-scale extraction framework that integrates Sentinel-2 optical and Sentinel-1 synthetic aperture radar (SAR) imagery through image-level fusion, and introduces a UNet-DFH network with a Multi-Scale Edge Fusion (MSEF) module and an Attention-Deformable Fusion Module (ADFM). This study makes three core contributions: (1) we construct a dedicated optical–SAR collaborative sugarcane extraction dataset for typical karst regions, alleviating the scarcity of multimodal labeled samples; (2) we propose the UNet-DFH network, where MSEF enhances boundary preservation and topological detail in shallow decoding stages, while ADFM improves robustness to geometric deformation and local misalignment in deep semantic stages; (3) we demonstrate that the joint mechanism of edge-preserving filtering and deformable adaptation yields a synergistic effect in addressing the precision–recall trade-off. Experiments in a typical karst area of Guangxi, China, demonstrate that optical–SAR fusion achieves an IoU of 80.08% and an OA of 92.09% during the sugar accumulation and maturity stage. During the more challenging tillering stage, UNet-DFH maintains relatively stable performance under optical-only conditions, with an IoU of 72.98%, Recall of 82.78%, and OA of 92.12%. Moreover, optical–SAR fusion improves Recall by 5.5 percentage points over optical-only inputs (from 83.54% to 89.04%), while Precision exhibits a moderate decrease from 91.89% to 88.84%, reflecting the expected trade-off associated with speckle noise. These results confirm the complementary value of multimodal data and the effectiveness of the proposed modules in preserving fragmented plot boundaries and improving segmentation performance in complex karst terrain. The framework offers a promising approach for high-precision crop mapping in the studied karst agricultural landscape. Full article