Search Results (1,164)

Alpine wetlands in the Qinghai Lake Basin, located on the northeastern Qinghai–Tibetan Plateau, are ecologically important but highly vulnerable to climate change and anthropogenic disturbance. Traditional field-based surveys are labor-intensive and spatially constrained, underscoring the need for automated remote sensing approaches for large-scale wetland mapping. In this study, an object-based image analysis (OBIA) framework was developed by integrating Sentinel-2 optical imagery with Sentinel-1 synthetic aperture radar (SAR) data to classify two representative plateau wetland types: marsh meadows and inland tidal flats. Seven categories of features were evaluated, including spectral features, vegetation indices, water indices, red-edge features, topographic variables, radar backscatter, and geometric-textural metrics. The Separability and Thresholds (SEaTH) algorithm was employed for feature selection and optimization prior to classification using a Random Forest model. The results indicate that the incorporating geometric and textural features significantly improved classification performance, achieving an overall accuracy (OA) of 82.53% and a Kappa coefficient of 0.74. Moreover, the SEaTH-based feature optimization scheme yielded the best performance, with an OA of 86.24% and a Kappa coefficient of 0.79. Compared with the full feature set, this approach improved producer’s accuracy by 3.96–6.11% and increased overall accuracy by 1.48%. The proposed framework provides an effective and computationally efficient approach for mapping ecologically fragile alpine wetlands and offers valuable support for wetland conservation in the Qinghai Lake Basin. Full article

The design of novel aggregation-induced emission (AIE)-active molecules represents a cutting-edge strategy for integrated phototheranostics in the second near-infrared (NIR-II) window. This review systematically outlines rational molecular engineering approaches based on D-A, D-A-D, and A-D-A systems to achieve red-shifted NIR-II absorption/emission, enhanced AIE characteristics, and balanced radiative and non-radiative decay pathways. These AIEgens enable high-contrast NIR-II fluorescence imaging (FLI) and photoacoustic imaging (PAI) for precise tumor localization, while concurrently facilitating efficient photothermal therapy (PTT) and robust photodynamic therapy (PDT) through both type-I and type-II mechanisms. Nanoformulations of these molecules exhibit excellent stability, biocompatibility, and passive targeting via the enhanced permeability and retention (EPR) effect. We further highlight representative “all-in-one” AIE platforms that demonstrate synergistic PTT/PDT under multimodal imaging guidance, offering a promising paradigm for precision cancer theranostics. Challenges and future directions in clinical translation and combination therapy are also discussed. Full article

Madagascar’s expanding blue economy is largely underpinned by seaweed aquaculture, particularly Kappaphycus alvarezii (Cottonii), which offers an alternative to declining small-scale fisheries and strengthens the resilience of coastal socio-ecosystems. Ensuring the sustainability of this economic activity requires effective ecological monitoring of aquaculture sites and surrounding habitats. This study examines and compares the performance of two imaging configurations—an RGB composite derived from a subset of multispectral images capturing red (650 nm), green (560 nm), and blue (450 nm) bands; and a five-band multispectral (MS) image encompassing blue, green, red, red-edge (730 nm), and near-infrared (840 nm) bands—combined with a Random Forest (RF) classification model, for benthic habitat mapping in a seaweed cultivation context. High-resolution orthomosaics (2 cm/pixel) enabled the discrimination of Kappaphycus cultivation plots from three shallow-water habitats: (i) ‘benthic macrophytes’, which comprise: seagrass meadows and benthic macroalgal; (ii) ‘sandy bottom’ and (iii) ‘green algae’. The RF classification achieved an overall accuracy of 87% (Kappa = 0.82) across ~10 hectares. Producer’s accuracy exceeded 80% for Kappaphycus cultivation, green algae, and sandy bottom for both the RGB and MS datasets, indicating strong classification performance. However, early-stage seaweed was occasionally misclassified as benthic macrophytes, likely due to its low biomass and weak spectral signature. This UAS-based approach provided a robust and cost-effective framework for monitoring off-bottom seaweed farms and associated natural habitats. This approach supports sustainable aquaculture development and integrated coastal management in Madagascar and comparable tropical reef socio-ecosystems. Full article

Phosphorus (P) deficiency severely limits maize growth and yield, yet early detection remains challenging, as visible symptoms appear only after prolonged starvation. This study evaluated the capability of hyperspectral imaging (HSI) combined with machine learning to detect P deficiency in maize seedlings at both symptomatic and pre-symptomatic stages. Two greenhouse experiments were conducted: a long-term pot system under high and low P conditions and a short-term hydroponic experiment with three P concentrations of 500, 100, and 0 μmol/L phosphate (Pi). After long-term P deficiency, significant reductions in shoot biomass and Pi content were observed, while root biomass increased and nutrient profiles were altered. Hyperspectral signatures revealed distinct wavelength-specific differences across visible, red-edge, and near-infrared (NIR) regions, with P-deficient leaves showing lower reflectance in green and NIR regions but higher reflectance in the red band. A multilayer perceptron machine learning model achieved 99.65% accuracy in discriminating between P treatments. In the short-term experiment, P deficiency significantly reduced tissue Pi content within one week without affecting pigment composition or photosynthetic parameters. Despite the absence of visible symptoms, hyperspectral measurements detected subtle spectral changes, particularly in older leaves, enabling classification accuracies of 80.71–84.56% in the first week and 85.88–90.98% in the second week of P treatment. Conventional vegetation indices showed weak correlations with Pi content and failed to detect early P deficiency. These findings demonstrate that HSI combined with machine learning can effectively detect P deficiency before visible symptoms emerge, offering a non-destructive, rapid diagnostic tool for precision nutrient management in maize production systems. Full article

Maize cultivation in tropical Oxisols during the second growing season faces significant climatic risks, where spatial heterogeneity in soil water retention often dictates economic viability. This study integrated a trimodal sensing approach, combining multispectral, thermal, and LiDAR data, with proximal physiological measurements to map isohydric responses and yield variability. Conducted in the Brazilian Cerrado, the research monitored a one-hectare maize field using UAV-based sensors alongside ground truth evaluations of gas exchange, leaf water potential, and soil moisture. Results revealed high yield variability (6.6 to 13.4 Mg ha⁻¹) primarily governed by clay content-mediated water availability. Maize exhibited strict isohydric behavior, maintaining homeostatic leaf water potential through preventive stomatal closure, which limited CO₂ assimilation in zones with lower water retention. A significant statistical decoupling was observed between plant height and final grain yield, as water stress impacted reproductive stages more severely than vegetative growth. Furthermore, the Temperature Vegetation Dryness Index (TVDI) served as a robust proxy for biomass vigor rather than mere water deficit. These results confirm that yield variability in tropical Oxisols was not a product of hydraulic failure, but rather a consequence of carbon limitation necessitated by the crop’s conservative hydraulic management to maintain leaf water potential within safe thresholds. Full article

Although blackberries are associated with health benefits, their impact on adipocyte biology remains poorly understood. Here, we investigated the effect of a blackberry extract (Rubus fruticosus fruit extract, RFE) on adipogenesis and lipolysis in the 3T3-L1 cell model and characterized its transcriptomic response. Adipogenesis and lipolysis were assessed by Oil Red O and AdipoRed™ staining and glycerol release, respectively. RNA-Seq analysis was processed with the PIGx pipeline, and differential gene expression was evaluated with edgeR. RFE strongly promoted adipogenesis, increasing Oil Red O staining by 29% (n = 3, p < 0.01), and showed anti-lipolytic activity, reducing glycerol release by 51% (n = 3, p < 0.05). Whole-transcriptome analysis revealed that RFE significantly regulated 4904 genes, enhancing the adipogenic program. Functional profiling identified metabolic pathways influenced by RFE, including those related to lipid biosynthesis. Notably, RFE also modulated extracellular matrix (ECM) pathways, suggesting a shift toward a less fibrotic microenvironment. These findings indicate that RFE promotes subcutaneous adipose tissue expansion while supporting ECM remodeling, favoring healthy adipose growth and reduced fibrosis. To our knowledge, this is the first evidence that RFE simultaneously stimulates adipocyte differentiation and ECM remodeling. Overall, RFE emerges as a promising active ingredient for lipofilling cosmetic applications aimed at improving adipose tissue volume and quality. Full article

Masson Pine (Pinus massoniana), an important afforestation species in southern China, is severely threatened by pine wilt disease caused by pine wood nematode (Bursaphelenchus xylophilus, PWN). To differentiate mortality induced by B. xylophilus from that caused by abiotic environmental factors, hyperspectral imaging and needle chlorophyll content were measured and analyzed for the early detection physiological changes in Masson pine seedlings under various environmental stressors. Four-year-old Masson pine seedlings were subjected to PWN inoculation, mechanical injury, drought, and waterlogging treatments. Hyperspectral reflectance and needle chlorophyll content of Masson pine were measured concurrently at 7-day intervals. The results showed that hyperspectral imaging responses varied among the stressors. Both PWN and waterlogging stress induced rapid mortality, with spectral changes observed as early as the 3rd week and reaching statistical significance by the 5th week. Under PWN infection, hyperspectral reflectance increased markedly in the 405–580 nm range, accompanied by a pronounced blue-shift of the red edge position (680–750 nm), while needle chlorophyll content declined sharply from approximately 0.8 mg g⁻¹ to 0.48 mg g⁻¹. Waterlogging stress produced a uniform increase in reflectance within the 500–580 nm range, with the hyperspectral curve gradually flattening, and needle chlorophyll content decreasing from 0.75 mg g⁻¹ to 0.6 mg g⁻¹. Conversely, drought-stressed seedlings exhibited only minor hyperspectral changes and maintained relatively stable chlorophyll levels, demonstrating the inherent drought tolerance of Masson pine. The RF and XGBoost models performed best in fitting the entire process of pine wood nematode infection and waterlogging stress, with all R² values greater than 0.69. The distinct hyperspectral imaging patterns under nematode infection and water-related stresses provide a reliable basis for early diagnosis and monitoring pine wilt disease in Masson pine stands. Full article

Forest biomass reflects the carbon storage capacity of forest ecosystems. Although remote sensing-based biomass estimation techniques have become increasingly mature, the issue of signal saturation in optical remote sensing still requires further investigation. This study was conducted in the Picea schrenkiana forest of the Ili River Valley in the western Tianshan Mountains. By integrating multimodal data from ICESat-2 LiDAR and GF-6 optical imagery, we developed machine learning and deep learning models to achieve high-precision biomass estimation. Based on forest management inventory data, we extracted spectral and textural features from GF-6, along with canopy structure attributes derived from the four acquisition modes (day/night, strong/weak beams) of ICESat-2. After correlation-based feature selection, LightGBM, CatBoost, and TabNet models were trained and compared. The results showed that models integrating multi-source data significantly outperformed those based on a single data source. The TabNet model not only achieved high estimation accuracy but also provided clear feature importance rankings, with ICESat-2-derived canopy height percentiles and GF-6 red-edge vegetation indices contributing most significantly to the biomass estimation of Picea schrenkiana. These findings demonstrate the feasibility of synergistically utilizing domestic high-resolution satellites and multi-mode spaceborne LiDAR for forest biomass estimation in arid regions, providing an effective technical reference for accurate carbon sink monitoring of specific tree species in forest areas. Full article

Accurate and timely cotton-field mapping is essential for irrigation management, water resource allocation, and regional yield assessment in arid irrigated agroecosystems. However, existing deep-learning-based crop mapping approaches generally lack interpretability and often exhibit performance variability across phenological stages, thereby limiting their reliability for operational deployment. To address these limitations, we developed an interpretable semantic segmentation framework for cotton mapping in the Wei-Ku Oasis, Xinjiang, China, under multi-source remote sensing conditions. The proposed model integrates Sentinel-2 surface reflectance, Sentinel-1 VV/VH backscatter, DEM, vegetation indices, and GLCM texture features. By incorporating a receptive-field enhancement mechanism together with an embedded feature-attribution module, the framework enables importance estimation of multi-source predictors within the network architecture, thereby providing intrinsic model interpretability. Under a unified training and evaluation protocol, the proposed model achieved an mIoU of 85.62% and an F1-score of 92.96% on the test set, outperforming U-Net, DeepLabV3+, and SegFormer baselines. Monthly classification results indicated that August provided the most discriminative acquisition window (mIoU = 85.54%, F1 = 92.83%), while June–July also maintained high recognition accuracy. Feature attribution results indicate that the importance of different predictors varies across phenological stages: Sentinel-2 red-edge bands remained highly influential throughout the growing season, NDVI/EVI exhibited increased contributions during June–August, SAR VH showed relatively higher importance during peak canopy development, and DEM maintained stable information contribution across all stages. Cross-year and cross-region experiments further demonstrated the model’s generalization capability, achieving an mIoU of 82.81% in same-region cross-year evaluation and 74.56% under cross-region transfer. Overall, the proposed segmentation framework improves classification accuracy while explicitly modeling and quantifying feature importance, providing a methodological reference for cotton-field mapping and acquisition timing selection in arid irrigated regions. Full article

Red–Green–Blue–Thermal (RGBT) object detection is critical for robust all-weather perception. However, deploying dual-stream networks on resource-constrained edge devices is severely hindered by insufficiently adaptive multimodal fusion, the loss of small-object features during downsampling, and substantial computational overhead. To address these challenges, we propose GEM-YOLO, a real-time and lightweight RGBT detector. Specifically, an Adaptive Multimodal Gated Fusion Mechanism (GFM) is designed to dynamically calibrate modality weights and suppress noise. Furthermore, Space-to-Depth (SPD) convolutions are integrated into the backbone to achieve lossless downsampling, preventing the feature collapse of small targets. Finally, a lightweight Ghost-Neck is constructed using Ghost modules and GSConv to eliminate computational redundancy. Extensive experiments on the Forward-Looking Infrared (FLIR) and Multi-Modal Multispectral Fusion Dataset (M3FD) datasets demonstrate the effectiveness of the proposed method. With only 7.58 Giga Floating-Point Operations (GFLOPs) and 3.44 million parameters (M), GEM-YOLO reduces the computational cost by 18.6% relative to the dual-stream YOLOv11n baseline. Concurrently, it achieves competitive mean Average Precision at IoU = 0.5 (mAP@50) scores of 82.8% and 69.0% on FLIR and M3FD, respectively, with more evident gains on small-target localization. In practice, GEM-YOLO maintains competitive detection performance while keeping computational overhead low, making it promising for real-time multispectral perception on resource-constrained edge platforms. Full article

Accurate and timely diagnosis of citrus diseases is essential for reducing economic losses in global agriculture. Although deep learning models provide high diagnostic accuracy, their computational demands often hinder deployment on resource-limited edge devices. To overcome this challenge, this study proposes an optimized hybrid framework for phytopathological classification. The methodology combines handcrafted descriptors (Blue-Green-Red “BGR” color statistical moments) with hierarchical spatial abstractions derived from a pre-trained Visual Geometry Group 16-layer (VGG16) deep architecture. An initial high-dimensional feature space was created by concatenating 360 handcrafted statistical descriptors and 12,800 deep textural features. By implementing a Wrapper-Greedy Stepwise selection strategy, this original space was reduced by over 96%. The resulting Elite Model identifies 12 and 18 critical attributes across two independent, transcontinental datasets (Mexico and Pakistan, respectively), effectively capturing both subtle chromatic anomalies and complex structural lesions. Experimental benchmarking confirms that this parsimonious hybrid approach delivers robust classification accuracy ranging from 87.30% to 95.23%, significantly outperforming unimodal architectures. Ultimately, this framework provides a highly efficient, interpretable, and scalable solution for real-time disease monitoring in precision agriculture. Full article

Benggang (Benggang), a typical landform characterized by severe erosion and a geohazard in the red-soil hilly regions of southern China, is characterized by a fragmented texture, irregular boundaries, and high similarity to background objects such as bare soil and roads, which poses a dual challenge of “multiscale variability + strong noise” for automated identification at regional scales. To address insufficient information from a single modality and the limited representation of cross-scale features, this study proposes a dual-stream feature-fusion network (DF-Net) for multisource data consisting of a digital orthophoto map (DOM) and a digital elevation model (DEM). The method adopts ResNeSt50d as the backbone of the two branches: on the DOM side, a Canny-edge channel is stacked to enhance high-frequency boundary information; on the DEM side, derived terrain factors, including slope, aspect, curvature, and hillshade, are introduced to provide morphological constraints. In the cross-modal fusion stage, a multiscale sparse attention fusion module is designed, which acquires contextual information via multiwindow average pooling and suppresses noise interference through top-K sparsification. In the decision stage, a multibranch ensemble is employed to improve classification stability. Taking Anxi County, Fujian Province, as the study area, a coregistered dataset of GF-2 (1 m) DOM and ALOS (12.5 m) DEMs is constructed, and a zonal partitioning strategy is adopted to evaluate the model’s generalization ability. The experimental results show that DF-Net achieves 97.44% accuracy, 85.71% recall, and an 82.98% F1 score in the independent test zone, outperforming multiple mainstream CNN/transformer classification models. This study indicates that the strategy of “multimodal feature enhancement + sparse attention fusion” tailored to Benggang erosional landforms can significantly improve recognition performance under complex backgrounds, providing technical support for rapid Benggang surveys and governance-effectiveness assessments. Full article

Soil salinization severely threatens agricultural sustainability in saline–alkali regions, and high-throughput, efficient screening of salt-tolerant rice varieties is critical to mitigating this threat. Traditional evaluation methods are constrained by low throughput, limited spatiotemporal resolution, and the lack of standardized indicators. To address these gaps, this study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice. Field experiments were conducted with 12 rice lines at five key growth stages in Ningxia, China, with synchronous ground spectral measurements and UAV image acquisition on the same day for each stage. Five feature selection methods were employed to screen salt stress-sensitive hyperspectral bands, with classification accuracy validated via a Support Vector Machine (SVM) model. The results showed that: (1) rice spectral characteristics varied dynamically across growth stages, and first-order differential transformation effectively amplified subtle spectral variations in stress-sensitive regions; (2) the Minimum Redundancy–Maximum Relevance (mRMR) method outperformed other methods, achieving 100% classification accuracy at key growth stages, with sensitive bands dominated by red edge bands (58.33%); (3) the constructed Salt Stress Index (SIR) showed strong correlations with classical vegetation indices and rice yield, and could clearly distinguish salt-tolerant and salt-sensitive rice varieties, with stable performance against field environmental noise; and (4) band matching between UAV and Sentinel-2 data enabled multi-scale data fusion and regional-scale salt stress monitoring. This framework realizes the transformation from qualitative spectral description to quantitative salt tolerance evaluation, providing standardized technical support for salt-tolerant rice breeding and precision management of saline–alkali lands. Full article

Coffee, as a perennial commodity crop, plays a crucial role in global agricultural markets, regional livelihoods, and poverty alleviation. Yunnan Province of China (21°8′–29°15′N) represents the northernmost coffee-growing region worldwide, and its production has gained increasing attention in international markets. However, the absence of a spatially explicit and high-resolution coffee distribution dataset has constrained environmental assessment, land-use analysis, and policy-making in this subtropical and marginal growing region. In this study, we developed the first 10 m resolution Arabica coffee distribution dataset for Yunnan Province for the year 2023 using Sentinel-2 optical imagery and Shuttle Radar Topographic Mission (SRTM) terrain data within the Google Earth Engine (GEE) platform. An object-based workflow was implemented to generate spatially coherent mapping units, followed by supervised classification to identify coffee plantations. The resulting map achieved an overall accuracy (OA) of 0.87, with user accuracy (UA), producer accuracy (PA), and F1 score of 0.90, 0.96, and 0.93 for the coffee class, demonstrating its reliability for regional-scale applications. Feature contribution analysis indicates that shortwave infrared (SWIR) and red-edge information, particularly during the dry season, plays an important role in coffee discrimination. These results enhance confidence in the ecological relevance and stability of the mapping framework. The proposed workflow provides a practical and transferable approach for perennial crop mapping in complex mountainous environments. More importantly, the generated high-resolution coffee distribution dataset establishes a spatial baseline for monitoring land-use dynamics, assessing ecological impacts, and supporting sustainable coffee development in southwestern China. Full article

Accurate mapping of grass species in biodiverse ecosystems, such as wetlands, is critical for ecological protection. Rapid advancements in remote sensing have established satellite data as a critical tool for wetland grass species mapping; however, its relatively coarse spatial resolution and susceptibility to cloud contamination limit the distinction of co-occurring species at fine scales. While Unmanned Aerial Vehicle (UAV) remote sensing offers high resolution and operational flexibility, relying on single-source features is often insufficient for fine-scale wetland species mapping due to the spectral similarity of co-occurring species. On the other hand, the fusion of multi-source remote sensing features (i.e., spectral, textural, and geometric features) likely provides a promising solution for achieving accurate, fine-scale grass species mapping in biodiverse ecosystems. In this study, we developed a wetland grass species mapping framework integrating spectral, textural, and geometric features derived from UAV RGB and multispectral imagery. Using a dataset of 95,880 image objects representing 24 wetland grass species classes collected in two years in Dajiu Lake National Wetland Park of China, we evaluated three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—across various feature combinations. We found that while spectral features (i.e., red edge, normalized green–red difference index [NGRDI], and normalized difference vegetation index [NDVI]) (related to leaf pigment concentrations and cellular structures) exhibited the highest importance in wetland grass species mapping, textural (i.e., contrast) and geometric features (i.e., aspect ratio) significantly enhanced classification performance as complementary information, yielding improvements of up to 10.5% in overall accuracy (OA) and 0.103 in Macro-F1 scores. Specifically, the fusion of spectral, textural, and geometric features achieved optimal performance with an OA of 81.9% and a Macro-F1 of 0.807. Furthermore, the XGBoost model outperformed SVM and RF, improving OA by 9.4% and 2.8%, and Macro-F1 by 0.08 and 0.035, respectively. By identifying the optimal feature combination and machine learning algorithm, this study establishes an accurate method for wetland grass species mapping, offering new opportunities for ecological assessment and precision conservation in biodiverse landscapes. Full article