Search Results (4,158)

Hyperspectral remote sensing enables precise identification of alteration mineral through spectral–image integration and high-resolution capabilities. However, vegetation interference significantly hinders the extraction of alteration information in vegetated areas, thereby posing challenges to the reliable identification of alteration minerals. This study employs GF-5B satellite AHSI imagery acquired in the Huzhou region of Zhejiang Province, China, to address this challenge via a novel Zonal Adaptive Vegetation Suppression Technique (ZAVST). By constructing segmented statistical models that links reflectance characteristics across multiple spectral bands to NDVI values, ZAVST demonstrates an enhanced capability to mitigate vegetation obscuration effects on subsurface lithological features while substantially improving the identification of subtle spectral signatures characteristic of mineralization. Results reveal distinct spatial patterns: Fe-bearing alteration minerals (hematite, pyrite) align along NE-trending faults and volcanic basin margins; Al-OH alterations (montmorillonite, kaolinite) cluster near intrusive contacts; Mg-OH alterations (chlorite, epidote) occur at interfaces between carbonate sequences and concealed intrusions. Composite alteration anomalies exhibiting stacked mineral signatures (up to four distinct types) were identified across the region, demonstrating a strong spatial correlation with known mineralization centers. By integrating alteration zonation, structural lineaments, stratigraphy, geochemical anomalies, and orebody records, this study delineated four priority targets: Lijiaxiang Town, eastern Meixi Town, Miaoxi Town, and the central Moganshan Volcanic Basin. Full article

As extreme weather events increasingly threaten global food systems, accurately assessing climate risks and predicting regional crop yields remains a critical challenge. Conventional prediction models often rely on direct weather-to-yield relationships, bypassing continuous crop physiological responses and limiting their capacity to capture fine-grained temporal impacts of meteorological anomalies. To address this, we propose a novel two-stage spatiotemporal functional framework that integrates high-resolution daily weather trajectories with satellite-derived indicators, utilizing the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) to represent canopy structural vigor and hydraulic status, respectively. In the first stage, a Historical Functional Linear Model (HFLM) dynamically maps daily meteorological trajectories (temperature, precipitation, and solar radiation) onto continuous physiological curves under strict temporal causality constraints. This generates bivariate coefficient surfaces that reveal dynamic windows of vulnerability and capture divergent, lagged physiological responses to climate stress. In the second stage, a spatially heterogeneous functional additive model integrates these weather-shaped physiological trajectories alongside raw meteorological dynamics as joint predictors for county-level yields. By extracting functional principal components and modeling flexible non-linear biological responses while accounting for continuous spatial heterogeneity, this dual-channel frameworkcaptures key aspects of both chronic physiological stress and acute meteorological shocks. Validated across a 25-year (2000–2024) U.S. Corn Belt panel, the proposed DC-FAM achieves a mean weighted mean squared prediction error (WMSPE) of 242.33 (bu/acre)² and a median out-of-sample $R_{\mathrm{cv}}^2$ of 0.422, outperforming all benchmarks including a random forest. Attribution of the 2012 flash drought further demonstrates the framework’s capacity to mechanistically trace the complete disaster propagation chain from anomalous spring warming to mid-summer hydraulic failure. The proposed framework provides a transparent, biophysically grounded tool for decoding dynamic climate stress trajectories and disaster propagation chains, offering potential implications for adaptive farm management and precision agricultural insurance. Full article

Climate change and soil degradation pose a challenge to grape quality, motivating the development of integrated monitoring approaches combining soil analysis with remote sensing techniques. However, harmonized information addressing this multidisciplinary challenge remains scarce. Therefore, this systematic review synthesizes the scientific literature published since 2020 with the aim of (i) identifying key soil properties and techniques applied, (ii) evaluating remote sensing approaches and their integration with soil data, and (iii) highlighting knowledge gaps and challenges for sustainable precision viticulture. A search in Scopus yielded 197 full-text articles classified into three thematic groups and analyzed using a standardized extraction protocol. Our synthesis reveals that pH, electrical conductivity, soil organic matter, and cation exchange capacity are the most consistently reported physicochemical parameters across the reviewed studies, while next-generation sequencing and multi-omics approaches are increasingly adopted in microbiological research to characterize rhizosphere communities and their links to terroir expression. In remote sensing, multispectral UAV platforms and satellite missions (Sentinel-2, Landsat) combined with vegetation indices, principally NDVI, dominate the toolset for monitoring vine vigor and water status. Nevertheless, genuine integration of remote-sensing outputs with root-zone soil measurements remains uncommon, with most studies treating both data streams independently. The principal knowledge gaps identified concern the absence of standardized sustainability assessment frameworks, limited cross-terroir transferability of predictive models, and insufficient long-term multi-site datasets to underpin climate change adaptation in vineyard management. Full article

Accurately mapping surface freshwater bodies (e.g., ponds, reservoirs, and small lakes) is vital for managing insular ecosystems and communities. However, satellite-based extraction in coastal settings is challenged by seawater intrusion, complex topography, and cloud cover. Focusing on bedrock islands outside China’s Pearl River Estuary, this study developed a robust method to address these issues. We used both Gaofen-1 (GF-1) optical and Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) imagery, supported by field-collected water quality samples from surface freshwater body shorelines for model training and validation. The performance of two index-based methods (the Normalized Difference Water Index, NDWI, and the Normalized Difference Vegetation Index, NDVI), two machine learning algorithms (Random Forest, RF, and Support Vector Machine, SVM), and a U-Net convolutional neural network (U-Net) deep learning model was compared. The U-Net model achieved the highest accuracy, with Area Under the Curve (AUC) values of 0.881 (GF-1) and 0.840 (GF-3). It effectively discriminated freshwater from seawater and mitigated cloud interference, demonstrating superior precision and robustness over traditional methods. This work establishes a high-precision framework for monitoring island freshwater resources, supporting sustainable water management. The proposed framework provides a practical tool for tracking freshwater availability under climate variability and anthropogenic pressures, contributing to the monitoring of Sustainable Development Goal (SDG) indicator 6.3.2 on ambient water quality. Full article

Relict yew plants (Taxus L.) are not only ornamental plants with valuable wood but also have the ability to synthesize the unique compound taxol, which is successfully used in the treatment of cancer due to its powerful cytotoxic effect. Due to the presence of taxol, all parts of yew plants are extremely poisonous, but there have been cases where animals have eaten yew cones without fatal consequences. The biosynthesis of taxol is carried out due to the interaction of the isoprenoid and phenolic pathways of the secondary metabolism of plants. Despite the close attention of researchers to the peculiarities of taxol metabolism, there is very little data on the tissue and intracellular localization of both taxols and phenolic compounds in yew plants. Polyphenols are known to be physiologically active mediators involved in respiration, photosynthesis, plant growth and development, as well as in the process of in vitro dedifferentiation. Since Taxus is a relict species and has a limited and hard-to-reach range in nature, technologies that allow yew plants to be restored without removing plant material from the natural environment are of great practical importance: overcoming deep physiological dormancy of seeds, microclonal reproduction and initiation of plant growth. In vitro cultures are possible sources of biologically active and medicinal products. The aims and objectives of this study are to determine the characteristics of the formation and localization of phenolic compounds with high biological activity in various organs of plants of the genus Taxus and to determine the biological activity of ethanolic extracts from this plant. The objects of this study were the generative organs of Taxus canadensis, collected during the entire growing season (April–October) from plants growing in the Moscow region. The localization of various classes of polyphenols was determined by histochemical methods using light microscopy. Histochemical studies have shown the abundant presence of polyphenols in yew megastrobiles, microstrobiles, cones, seeds and aril. Ethanolic plant extracts were used to determine the biological activity. Flavans were dominant in the aril at various stages of vegetation, which was confirmed by our biochemical and histochemical studies. Extractive substances of T. canadensis show high antibacterial activity, especially in its shoot extracts. Ethanolic extracts from plant shoots showed greater biological activity than seed extracts. Aril extracts had the lowest cytotoxicity. Full article

Seagrass meadows are important blue carbon habitats, but their patchy distribution in intertidal zones makes accurate UAV mapping difficult under shallow water cover and complex sediment backgrounds. This study developed a fine-grained semantic segmentation framework with explainability analysis to improve intertidal seagrass extraction from high-resolution UAV visible and multispectral imagery. Exposed seagrass (ESG) and shallow-submerged seagrass (SSG) were mapped separately to represent two observable intertidal states. Visible bands, multispectral bands, and vegetation indices were used as model inputs. U-Net and DeepLabV3+ served as baseline models, while UPerNet-ConvNeXtV2-Tiny was tested under the same settings. Kernel SHAP and permutation importance were used to assess feature contributions. UPerNet-ConvNeXtV2-Tiny achieved the best performance, with an overall accuracy (ACC), mean Intersection over Union (mIoU), and F1 score of 97.45%, 94.63%, and 97.23%, respectively. It outperformed the baseline models in suppressing background interference, preserving patch morphology, and reducing omission errors in weak response and boundary areas, while demonstrating better cross-scenario applicability in independent test areas. Explainability analysis showed that model discrimination was mainly associated with red and green-related features, especially RGB-R, MS-R, MS-G, RGB-G, and NGRDI. ESG and SSG showed different feature dependence patterns, indicating that high-resolution UAV imagery can support accurate seagrass mapping and reveal spectral differences between intertidal seagrass states. These findings provide a practical framework for UAV-based intertidal seagrass mapping and monitoring and offer guidance for feature selection and model explainability analysis. Full article

Shoot density is a key viticultural factor modulating canopy microclimate, berry composition, and wine quality, although yield–quality relationships are strongly influenced by environmental conditions. Saignée, a winemaking technique involving partial juice removal prior to fermentation, increases the skin-to-juice ratio and may enhance phenolic extraction. This study assessed the combined effects of shoot density (33 [T1], 20 [T2], and 15 [T3] shoots/m) and saignée (20% vs. control) on yield, grape composition, and wine chemical and sensory properties in Malbec across two vintages (2021–2022). At harvest, the pruning weight, yield components, general maturity parameters, and phenolic composition were measured. The wines were analysed for their phenolic and elemental composition, polysaccharides and volatile compounds, colour, and sensory attributes. T1 exhibited the highest yields and vegetative imbalance, whereas T2 and T3 achieved optimal Ravaz indices. The general grape maturity parameters were unaffected; however, T3 had increased berry phenolic content in 2022. T2 and T3 had enhanced wine tannins, total phenols, and polymeric pigments, particularly in 2022. Saignée increased the pH, potassium, total phenols, tannins, and acylated anthocyanins. Targeting yields near 4 kg/vine (≈10,500 kg/ha) improved vine balance and phenolic composition, although the responses were strongly modulated by interannual variability. Full article

Point-cloud analysis of sedimentary outcrops using Unmanned Aerial Vehicle (UAV) oblique photogrammetry is a crucial approach to sedimentary system characterization, stratigraphic correlation, and petroleum exploration analog studies. In large-scale field settings, however, outcrops are often scattered and fragmented, vegetation and soil cover is extensive, and class imbalance is pronounced. Manual interpretation is labor-intensive, while existing clustering algorithms, conventional machine learning methods, and general-purpose point-cloud segmentation networks struggle to simultaneously ensure geometric fidelity, rare-class recognition, and multi-scale feature integration. To address these challenges, we propose a method for extracting sedimentary outcrop point clouds from field surface point clouds using a UAV oblique photogrammetry acquisition strategy. The core segmentation module of the method, sedimentary cross-scale self-attention network (SedCSA-Net), is an enhanced version of PointNet++ that integrates collaborative improvements across four dimensions: data augmentation, sampling strategy, feature encoding, and loss optimization. Taking the Cretaceous Qingshuihe Formation in the Louzhuangzi area of the southern Junggar Basin as a case study, our experimental results indicate that SedCSA-Net overcomes the natural variability of UAV oblique photogrammetry point clouds—such as shadows, voids, and uneven density—achieving a mean Intersection over Union(mIoU) of 89.51% and an Overall Accuracy(OA) of 96.08%, with an outcrop-class Intersection over Union(IoU) of 86.90%. Attitude measurements derived from segmentation results deviate by less than 3° from manually annotated references, demonstrating that the proposed framework provides an end-to-end, generalizable approach for intelligent segmentation, geometric reconstruction, and attitude extraction of large-scale sedimentary outcrop point clouds. Full article

This study aimed to identify areas at risk of deforestation in the Cerrado biome of the Brazilian Midwest (states of Mato Grosso, Mato Grosso do Sul, and Goiás) and to estimate potential soil losses due to water erosion under land-use change scenarios. The methodology integrated the Universal Soil Loss Equation (USLE), spatializing rainfall erosivity (R), soil erodibility (K), topographic factor (LS), and cover-management factor (CP), with the ACEU (Accessibility, Cultivability, Extractability and Unprotected/protection status) model to assess deforestation risk based on accessibility, agricultural suitability, extractive activities, and legal protection status. Results indicated an average soil loss of 0.11 t ha⁻¹ year⁻¹ under natural vegetation cover, with 90% of the area presenting losses below 0.25 t ha⁻¹ year⁻¹. However, 27.5% of the remaining natural cover is located in areas classified as high or very high deforestation risk, indicating significant environmental vulnerability. Simulated scenarios of land-use conversion to pasture and annual crops revealed substantial increases in soil loss, particularly under annual cropping systems, potentially exceeding soil loss tolerance thresholds across millions of hectares. The findings demonstrate that integrating deforestation risk assessment with erosion modeling is a strategic tool for environmental planning, reinforcing the importance of preserving native vegetation to maintain ecosystem services and ensure long-term environmental sustainability. Full article

Changes in vegetation phenology affect ecosystem carbon uptake and water use, thereby regulating water use efficiency (WUE). However, in alpine ecosystems of the Qinghai-Tibetan Plateau (QTP), uncertainty remains regarding the phenological information characterized by different remote-sensing data sources and its associations with WUE. Using solar-induced chlorophyll fluorescence (SIF) and MODIS normalized difference vegetation index (NDVI) data from 2001 to 2018, we derived the start of growth (SOG) and end of growth (EOG) using multiple phenology extraction methods. WUE was calculated using gross primary productivity (GPP) and evapotranspiration (ET) data. We then employed trend analysis, statistical modeling, and a machine learning interpretive framework to systematically evaluate spatiotemporal differences in phenology derived from SIF and NDVI and their associations with WUE. The results showed that: (1) WUE generally increased across the QTP at approximately 0.15 g C m⁻² mm⁻¹ decade⁻¹, with significant increases mainly in the central-eastern and southeastern regions. Both NDVI- and SIF-derived SOG advanced at rates of −1.08 and −1.14 doy decade⁻¹, respectively. In contrast, EOG showed clear data source divergence: EOG_NDVI was delayed by 0.62 doy decade⁻¹, whereas EOG_SIF advanced by −0.48 doy decade⁻¹. SOG_SIF occurred on average 6.6 days later than SOG_NDVI, EOG differences were larger, with EOG_SIF occurring 17.2 days earlier than EOG_NDVI on average. Trend consistency was also higher for SOG than for EOG, whereas opposite EOG trends accounted for 25.3%. (2) After accounting for climatic covariates, SIF- and NDVI-derived phenological indicators showed distinct model-based associations with WUE, but their explanatory contributions were generally weaker than those of key climatic variables. (3) GAM results further showed that SOG was generally negatively associated with standardized WUE in both phenological datasets, whereas the EOG–WUE partial association differed between SIF and NDVI, with positive associations for EOG_SIF and negative associations for EOG_NDVI. This study highlights the differences between SIF- and NDVI-derived phenological indicators and their model-based associations with WUE, providing complementary remote-sensing information for interpreting vegetation phenology and WUE dynamics on the QTP. Full article

The consumption of vegetable oils is steadily increasing, especially in Asian countries. Once used, the utilized cooking oils are either thrown into landfills or dumped there, endangering both the environment and people. One common method is to convert waste cooking oil (WCO) into biofuel; however, since WCO contains many free radicals, burning it releases large quantities of pollutants, meaning that disposal of WCO poses significant environmental risks. To stabilize the WCO (Cocos nucifera) before converting it into biofuel, this study analyzed the extraction, optimization, and use of antioxidant-rich bio-oil from Anisomeles malabarica leaves as a natural additive. Solvent screening revealed that a hexane–ethanol ratio of 4:2 was optimal for generating 76.7% bio-oil at room temperature. A maximum yield of 77% was attained by temperature and time optimization, which determined that 50 °C and 20 min were ideal. The extraction exhibits zero-order kinetics during the increasing phase, according to kinetic studies, with rate constants ranging from 0.54 to 1.44% min⁻¹ (R² = 0.950–0.997). The Peleg equilibrium model (average R² = 0.806) was used to describe the extraction profile. The regression equation ln(k) = 1799.3 × (1/T) − 10.828 (R² = 0.9748, p = 0.0002) was obtained using Arrhenius analysis. It was found that the compounds responsible for the antioxidant scavenging activity were found to be phytol, hexadecenoic acid, and tocopherol (vitamin E). The DPPH (2,2-diphenyl-1-picrylhydrazyl) test confirmed that 3% (v/v) bio-oil scavenged about 95% of free radicals, whereas the conjugated diene experiment demonstrated that over 90% of lipid oxidation in WCO was prevented. The combustion and emission properties of biofuel (WCB), which was created by transesterifying bio-oil-treated WCO, were compared to those of neat diesel and untreated WCO-derived biofuel (WC). In comparison to both WC50 and neat diesel, WCB50 demonstrated an equivalent in-cylinder pressure and heat release rate, but significantly reduced emissions of NO_x, CO, hydrocarbons, and smoke. These results show that Anisomeles malabarica bio-oil works well as a natural antioxidant addition for clean combustion and biodiesel stabilization. Full article

Most remote sensing extraction studies utilizing vegetation indices typically classify and extract land cover features based on the phenological characteristics of the study area or rely on a single vegetation index. When attempting to extract multiple land cover types simultaneously, classification accuracy often declines significantly because a single vegetation index is unsuitable for all features. While some recent studies employ deep learning and neural networks for classification and extraction, their complex mechanisms and “black-box effect” hinder clear explanations for accuracy outcomes. In response to the issues outlined above, this paper proposes a simpler and more intuitive method for the hierarchical extraction of typical land cover features. This approach analyzes the difficulty of separating these features based on spectral reflectance data to determine the following extraction order: first water bodies, followed by reed, then Suaeda salsa, and finally tidal flat. Furthermore, by selecting appropriate parameters and substituting vegetation indices for bands that perform better, high extraction accuracy is achieved. The classification and interpretation results were validated using a combination of field survey data and Google imagery, together with a validation sample. Accuracy assessments using overall accuracy and Kappa coefficient demonstrate the following optimal results for the hierarchical approach: NDWI for water, S2REP for reeds, and MSAVI for Suaeda salsa. Overall accuracy reached 98.5% with a Kappa coefficient of 0.9796, validating the effectiveness of this spectral-feature-based hierarchical extraction method using diverse vegetation indices. Using a hierarchical extraction approach to classify typical land cover features in the study area from 2020 to 2025, accuracy rates exceeded 98% in all cases. Based on these classification results, the INVEST model was employed to simulate carbon stock trends in the Liaohe Estuary region over the past five years. The study found that, although factors such as tides and the date of image acquisition had a certain impact on the study area compared with the problems caused by historical development, the ecological environment in the study area is gradually stabilizing at the present stage. Full article

Context: Accurate citrus crop mapping is essential for agricultural monitoring, production planning, and supply-chain management, particularly in Brazil, one of the world’s leading orange producers and the leading orange-juice exporter. Satellite image time series from Sentinel-2 provide rich spectral and temporal information for crop identification. However, citrus mapping remains challenging due to fragmented agricultural landscapes, cloud contamination, class imbalance, and spectral overlap with other vegetation classes. Problem: Conventional machine learning models often depend on handcrafted vegetation indices, while attention-based deep learning models may require larger datasets and can become unstable under geographically constrained conditions. Therefore, there is a need for a compact and robust deep learning architecture capable of extracting citrus phenological signatures directly from multispectral time-series data. Methods: This study evaluates a Spatio-Temporal Pixel-Set Encoder Convolutional Neural Network (PSE-CNN) for citrus crop classification in the immediate geographic regions of São João da Boa Vista and Mogi Guaçu, São Paulo, Brazil. MapBiomas Collection 10.1 data from 2019 to 2024 were used to derive reference polygons, and Sentinel-2 imagery was processed into cloud-masked, 15-day temporal composites using ten spectral bands. The proposed PSE-CNN was benchmarked against PSE-TAE, PSE-Transformer, Random Forest, and XGBoost using spatially grouped data partitioning and temporal test years. Results: The proposed PSE-CNN achieved the highest Unified F1-Score of $0.704$ and the lowest coefficient of variation of $3.03\%$, indicating stronger inter-annual stability across test years and random seeds among the evaluated models. It also outperformed classical models that relied on handcrafted vegetation indices and demonstrated greater overall stability than attention-based deep learning alternatives. Conclusions: The results indicate that combining pixel-set encoding with temporal convolution provides a resource-aware and stable framework for retrospective citrus crop mapping from Sentinel-2 satellite image time series. These findings suggest that PSE-CNN can support scalable agricultural monitoring, contributing to sustainable crop inventory systems in regions where labeled data and computational infrastructure are limited. Full article

Agri-food byproducts are increasingly recognized as sustainable feedstocks for high-value bioactive compounds; but their practical valorization requires integrated evidence on recovery conditions; chemical composition; bioactivity; and application readiness. This review critically examines green recovery strategies and chemical profiling platforms for bioactive compounds recovered from peels; pomace; seed residues; hulls; vegetation waters; and pruning waste. Emphasis is placed on how extraction variables shape chemical profiles; extract quality; and reported biological activities. Ultrasound- and microwave-assisted extraction; enzyme- and fermentation-assisted recovery; supercritical fluid extraction; pressurized liquid extraction; pulsed electric field-assisted pretreatment; and green solvent-based extraction are discussed in terms of target-compound selectivity; solvent and energy demand; process safety; scalability; and sustainability-related evidence. Chromatographic; mass-spectrometric; spectroscopic; and metabolomics-based profiling approaches are evaluated for identification; annotation; quantification; fingerprinting; quality-marker selection; and standardization; with confidence levels distinguished according to authentic-standard matching; tandem mass spectrometry evidence; spectral libraries; or fingerprint-level evidence. Circular valorization pathways in food; nutraceutical; cosmetic; pharmaceutical, and biopesticide-related applications are further considered with attention to feedstock heterogeneity; process standardization; stability; safety; regulatory feasibility; scalability; and techno-economic feasibility. Overall; this review provides a linkage-oriented framework for developing standardized; application-readiness-oriented bioactive candidates from agri-food byproducts. Full article

The increasing availability of airborne LiDAR data supports advanced three-dimensional analysis of urban vegetation. However, the development of deep learning methods for tree species classification remains limited by the lack of annotated datasets at the individual-tree level. This study presents UrbanTree3D, a field-validated dataset comprising segmented individual trees extracted from airborne LiDAR point clouds and enriched with species information from field inventory data. The dataset was generated through a structured workflow, including noise removal, vegetation extraction, height normalization based on a digital elevation model (DEM), and temporal consistency verification. Individual trees were segmented using a hybrid approach integrating DBSCAN and Watershed algorithms, and subsequently matched to field inventory data using a nearest neighbor method. A field validation campaign was conducted to ensure data reliability. The final dataset contains 152 individual urban trees and includes six tree species. It provides high-quality annotations, consistent point clouds, and field validation data, supporting its use for training and evaluating deep learning models. UrbanTree3D addresses the current shortage of annotated LiDAR datasets and supports applications in urban forestry, smart cities and urban digital twins. Full article