Search Results (733)

Investigating the sonosphere can serve as a valuable proxy for understanding various ecosystem processes. Consequently, an ecoacoustic perspective broadens our capacity to understand how airborne sounds interact along an ecotone at the soil surface with the subterranean sounds generated within a pedon. We explored techniques that could detect, quantify, and analyze the sonic dimensions of a sonosphere in the form of sounds within a unit of soil (sonopedon), sounds from a landscape unit (sonotope), and the sonic ecotone (sonotone) where these phenomena converge. We recorded sounds for 24 h over 20 days in September 2024 at 40 sites distributed evenly across a small rural parcel of agricultural land in Northern Italy. We utilized a sound recording device fabricated with a sonic probe that simultaneously operated inside the soil and the grounds’ surface, which successfully captured sounds attributable both to the soilscape and to the landscape. We calculated the Sonic Heterogeneity Indices, SHI_tf and SHI_ft, and analyzed the Spectral and Temporal Sonic Signatures along with Spectral Sonic Variability, Effective Number of Frequency Bins, and Sonic Dissimilarity. Each calculation contributed to a detailed description of how the sonosphere is characterized across the frequency spectrum, temporal dynamics, and sound sources. The sonosphere in our study area, primarily characterized by the low-frequency spectra, possessed a mix of biological, geophysical, and anthropogenic sounds displaying distinct temporal patterns (sonophases) that coincided with astronomic divisions of the day (daytime, twilights, and nighttime). Full article

Lemon production in Piura, Peru faces climatic challenges such as droughts and El Niño. This study uses satellite indices (NDVI and NDWI) and convolutional neural networks (CNNs, specifically AlexNet) to analyze crop dynamics and health during extreme events. The results highlight the impact of extreme precipitation and temperatures, with reflectance levels as early indicators of deterioration. In 1998, El Niño reduced yield to 9.2 tons/ha, compared to 14 tons/ha in 2014. These variables provide key information for pruning, irrigation, and fertilization decisions, improving crop productivity. Full article

Water stress is a critical factor affecting the health and productivity of ornamental plants, yet early detection remains challenging. This study aims to investigate the spectral responses of four ornamental plant taxa—Rosa hybrid (rose), Itea virginica (itea), Spiraea nipponica (spirea), and Weigela florida (weigela)—under varying levels of water stress using hyperspectral imaging and principal component analysis (PCA). Hyperspectral data were collected across multiple wavelengths and PCA was applied to identify key spectral bands associated with different stress levels. The analyses revealed that the first two principal components captured a majority of variance in the data, with specific wavelengths around 680 nm, 760 nm, and 810 nm playing a significant role in distinguishing between the stress levels. Score plots demonstrated clear separation between different stress treatments, indicating that spectral signatures evolve distinctly over time as water stress progresses. Influence plots identified observations with disproportionate impacts on the PCA model, ensuring the robustness of the analysis. Findings suggest that hyperspectral imaging, combined with PCA, is a powerful tool for early detection and monitoring of water stress in ornamental plants, providing a basis for improved water management practices in horticulture. Full article

Acoustic emission (AE) is a powerful tool for investigating the intermittency of plastic flow by capturing elastic waves generated by dislocation rearrangements under load. This study explores the correlation between AE and plastic instabilities, such as Lüders bands, the Portevin–Le Chatelier (PLC) effect, and necking, each showing distinct AE signatures. Lüders and PLC bands generate significant AE during discontinuous yielding, with a sharp rise in AE levels and a shift in the spectrum to lower frequencies—characteristic of localized deformation. In contrast, necking exhibits limited AE activity, due to reduced strain hardening and dislocation mobility during late-stage deformation. A phenomenological model, based on dislocation dynamics and initially devised for uniform deformation, is discussed to explain the observed AE spectral features during localized plastic flow. This study underscores AE’s potential for non-destructive evaluation and failure prediction in structural metals, emphasizing its sensitivity to microstructural changes and instabilities. Understanding AE behavior across deformation stages offers valuable insights into improving material reliability and predicting failure. Full article

Bulk 2D electronic–vibrational (2D-EV) and 2D vibrational–electronic spectroscopies (2D-VE) were previously developed to correlate the electronic and vibrational degrees of freedom simultaneously, which allow for the study of couplings between electronic and vibrational transitions in photo-chemical systems. Such bulk-dominated methods have been used to extensively study molecular systems, providing unique information such as coherence sensitivity, molecular configurations, enhanced resolution, and correlated states and their dynamics. However, the analogy of interfacial 2D spectroscopy has fallen behind. Our recent work presented interface-specific 2D-EV spectroscopy (i2D-EV). In this work, we develop interface-specific two-dimensional vibrational–electronic spectroscopy (i2D-VE). The fourth-order spectroscopy is based on a Mach–Zehnder IR interferometer that accurately controls the time delay of an IR pump pulse pair for vibrational transitions, followed by broadband interface second-harmonic generation to probe electronic transitions. We demonstrate step-by-step how a fourth-order i2D-VE spectrum of AP3 molecules at the air/water interface was collected and analyzed. The line shape and signatures of i2D-VE peaks reveal solvent correlations and the spectral nature of vibronic couplings. Together, i2D-VE and i2D-EV spectroscopy provide coupling of different behaviors of the vibrational ground state or excited states with electronic states of molecules at interfaces and surfaces. The methodology presented here could also probe dynamic couplings of electronic and vibrational motions at interfaces and surfaces, extending the usefulness of the rich data that are obtained. Full article

Vegetation indices, especially the normalized difference vegetation index (NDVI), are widely used in urban vegetation assessments. However, estimating the vegetation abundance in urban scenes using the NDVI has constraints due to the complex spectral signature related to the urban structure, materials and other factors compared to natural ground surfaces. This paper employs the 3D discrete anisotropic radiative transfer (DART) model to simulate the spectro-directional reflectance of synthetic urban scenes with various urban geometries and building materials using a flux-tracking method under shaded and sunlit conditions. The NDVI is calculated using the spectral radiance in the red (0.6545 μm) and near-infrared bands (0.865 μm). The effects of the urban material heterogeneity and 3D structure on the NDVI, and the performance of three NDVI-based fractional vegetation cover (FVC) inversion algorithms, are evaluated. The results show that the effects of the building material heterogeneity on the NDVI are negligible under sunlit conditions but not negligible under shaded conditions. The NDVI value of building components within synthetic scenes is approximately zero. The shaded road exhibits a higher NDVI value in comparison to the illuminated road because of scattering from adjacent pixels. In order to correct the effects of scattering caused by building geometry, the reflectance of the Landsat 8/OLI image is corrected using the sky view factor (SVF) and then used to calculate the FVC. Jilin-1 satellite images with high spatial resolution (0.5 m) are used to extract the vegetation cover and then aggregated to 30 m spatial resolution to calculate the FVC for validation. The results show that the RMSE is up to 0.050 after correction, while the RMSE is 0.169 before correction. This study makes a contribution to the understanding of the effects of the urban 3D structure and material reflectance on the NDVI and provides insights into the retrieval of the FVC in different urban scenes. Full article

Reflectance anisotropy in remote sensing images can complicate the interpretation of spectral signature, and extracting precise structural information under these pixels is a promising approach. Low-altitude unmanned aerial vehicle (UAV) systems can capture high-resolution imagery even to centimeter-level detail, potentially simplifying the characterization of leaf anisotropic reflectance. We proposed a novel maize point cloud generation method that combines an advanced UAV cross-circling oblique (CCO) photography route with the Structure from the Motion-Multi-View Stereo (SfM-MVS) algorithm. A multi-spectral point cloud was then generated by fusing multi-spectral imagery with the point cloud using a DSM-based approach. The Rahman–Pinty–Verstraete (RPV) model was finally applied to establish maize leaf-level anisotropic reflectance models. Our results indicated a high degree of similarity between measured and estimated maize structural parameters (R² = 0.89 for leaf length and 0.96 for plant height) based on accurate point cloud data obtained from the CCO route. Most data points clustered around the principal plane due to a constant angle between the sun and view vectors, resulting in a limited range of view azimuths. Leaf reflectance anisotropy was characterized by the RPV model with R² ranging from 0.38 to 0.75 for five wavelength bands. These findings hold significant promise for promoting the decoupling of plant structural information and leaf optical characteristics within remote sensing data. Full article

Spectroscopic data allows for the obtaining of relevant information about the composition of samples and has been used for research in scientific disciplines such as chemistry, geology, archaeology, Mars research, pharmacy, and medicine, as well as important industrial use. In archaeology, it allows the characterization and classification of artifacts and ecofacts, the analysis of patterns, the characterization and study of the exchange of materials, etc. Spectrometers provide a large amount of data, the so-called “big data” type, which requires the use of multivariate statistical techniques, mainly principal component analysis, cluster analysis, and discriminant analysis. This work is focused on reducing the dimensionality of the data by selecting a small subset of variables to characterize the samples and presents a mathematical methodology for the selection of the most efficient variables. The objective is to identify a subset of variables based on spectral features that allow characterization of the samples under study with the least possible errors when performing quantitative analyses or discriminations between different samples. The subset is not predetermined and, in each case, is obtained for each set of samples based on the most important features of the samples under study, which allows for a good fit to the data. The reduction of the number of variables to an important performance based on the previously chosen difference between features, with a great fit to the raw data. Thus, instead of 2151 variables, a minimum optimal subset of 32 valleys and 31 peaks is obtained for a minimum difference between peaks or between valleys of 20 nm. This methodology has been applied to a sample of minerals and rocks extracted from the ECOSTRESS 1.0 spectral library. Full article

Hyperspectral imagery and advanced classification techniques can significantly enhance remote sensing’s role in forest monitoring. Thanks to recent missions, such as the Italian Space Agency’s PRISMA (PRecursore IperSpettrale della Missione Applicativa—Hyperspectral PRecursor of the Application Mission), hyperspectral data in narrow bands spanning visible/near infrared to shortwave infrared are now available. In this study, hyperspectral data from PRISMA were used with the aim of testing the applicability of PRISMA with different band sizes to classify tree species in highly biodiverse forest environments. The Serre Regional Park in southern Italy was used as a case study. The classification focused on forest category classes based on the predominant tree species in sample plots. Ground truth data were collected using a global positioning system together with a smartphone application to test its contribution to facilitating field data collection. The final result, measured on a test dataset, showed an F1 greater than 0.75 for four classes: fir (0.81), pine (0.77), beech (0.90), and holm oak (0.82). Beech forests showed the highest accuracy (0.92), while chestnut forests (0.68) and a mixed class of hygrophilous species (0.69) showed lower accuracy. These results demonstrate the potential of hyperspectral spaceborne data for identifying trends in spectral signatures for forest tree classification. Full article

Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human observers consider multiple symptoms, but these are difficult to measure automatically in the field. Therefore, the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms was investigated to detect the symptoms solely based on the spectral signature of collected leaf samples. In the growing seasons 2019 and 2020, a total of 1160 leaf samples were collected. Hyperspectral imaging with a dual camera setup in spectral bands from 400 nm to 2500 nm was accompanied with subsequent PCR analysis of the samples to provide reference data for the machine learning approaches. Data processing consists of preprocessing for segmentation of the leaf area, feature extraction, classification and subsequent analysis of relevance of spectral bands. The results show that imaging multiple leaves of a tree enhances detection results, that spectral indices are a robust means to detect the diseased trees, and that the potentials of the full spectral range can be exploited using machine learning approaches. Classification models like rRBF achieved an accuracy of 0.971 in a controlled environment with stratified data for a single variety. Combined models for multiple varieties from field test samples achieved classification accuracies of 0.731. Including spatial distribution of spectral data further improves the results to 0.751. Prediction of qPCR results by regression based on spectral data achieved RMSE of 14.491 phytoplasma per plant cell. Full article

Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) time series (backscattering coefficients and polarimetric parameters), alongside phenological features derived from both S1 and S2 time series (harmonic coefficients and median features), for classifying sunflower, soybean, and maize. Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost classifiers were applied across various dataset configurations and train-test splits over two study sites and years in France. Additionally, the InceptionTime classifier, specifically designed for time series data, was tested exclusively with time series datasets to compare its performance against the three general machine learning algorithms (RF, XGBoost, and MLP). The results showed that XGBoost outperformed RF and MLP in classifying the three crops. The optimal dataset for mapping all three crops combined S1 backscattering coefficients with S2 vegetation indices, with comparable results between phenological features and time series data (mean F1 scores of 89.9% for sunflower, 76.6% for soybean, and 91.1% for maize). However, when using individual satellite sensors, S1 phenological features and time series outperformed S2 for sunflower, while S2 was superior for soybean and maize. Both phenological features and time series data produced close mean F1 scores across spatial, temporal, and spatiotemporal transfer scenarios, though median features dataset was the best choice for spatiotemporal transfer. Polarimetric S1 data did not yield effective results. The InceptionTime classifier further improved classification accuracy over XGBoost for all crops, with the degree of improvement varying by crop and dataset (the highest mean F1 scores of 90.6% for sunflower, 86.0% for soybean, and 93.5% for maize). Full article

Grasslands are one of the most abundant and biodiverse ecosystems in the world. However, in southern European countries, the abandonment of traditional management activities, such as extensive grazing, has caused many semi-natural grasslands to be invaded by shrubs. Therefore, there is a need to characterize semi-natural grasslands to determine their aboveground primary production and livestock-carrying capacity. Nevertheless, current methods lack a realistic identification of vegetation assemblages where grassland biophysical parameters can be accurately retrieved by the inversion of turbid-medium radiative transfer models (RTMs) in fine-grained landscapes. To this end, in this study we proposed a novel framework in which multiple endmember spectral mixture analysis (MESMA) was implemented to realistically identify grassland-dominated pixels from Sentinel-2 imagery in heterogeneous mountain landscapes. Then, the inversion of PROSAIL RTM (coupled PROSPECT and SAIL leaf and canopy models) was implemented separately for retrieving grassland biophysical parameters, including the leaf area index (LAI), fractional vegetation cover (FCOVER), and aboveground biomass (AGB), from grassland-dominated Sentinel-2 pixels while accounting for non-vegetated areas at the subpixel level. The study region was the southern slope of the Cantabrian Mountains (Spain), with a high spatial variability of fine-grained land covers. The MESMA grassland fraction image had a high accuracy based on validation results using centimetric resolution aerial orthophotographs (R² = 0.74, and RMSE = 0.18). The validation with field reference data from several mountain passes of the southern slope of the Cantabrian Mountains featured a high accuracy for LAI (R² = 0.74, and RMSE = 0.56 m²·m⁻²), FCOVER (R² = 0.78 and RMSE = 0.07), and AGB (R² = 0.67, and RMSE = 43.44 g·m⁻²). This study provides a reliable method to accurately identify and estimate grassland biophysical variables in highly diverse landscapes at a regional scale, with important implications for the management and conservation of threatened semi-natural grasslands. Future studies should investigate the PROSAIL inversion over the endmember signatures and subpixel fractions depicted by MESMA to adequately address the parametrization of the underlying background reflectance by using prior information and should also explore the scalability of this approach to other heterogeneous landscapes. Full article

Brazil is the largest coffee producer in the world. However, it has been a challenge to manage the main pest affecting the plant’s foliar part, the Coffee Leaf Miner (CLM) Leucoptera coffeella (Lepidoptera: Lyonetiidae). To mitigate this, remote sensing has been employed to spectrally characterize various stresses on coffee trees. This study establishes the groundwork for efficient pest detection by investigating the spectral characteristics of CLM infestation at different levels. This research aims to characterize the spectral signature of leaves at different CLM levels of infestation and identify the optimal spectral regions for discriminating these levels. To achieve this, hyperspectral reflectance measurements were made of healthy and infested leaves, and the classes of infested leaves were grouped into minimally, moderately, and severely infested. As the infestation level rises, the 700 nm region becomes increasingly suitable for distinguishing between infestation levels, with the visible region also proving significant, particularly during severe infestations. Reflectance thresholds established in this study provide a foundation for agronomic references related to CLM. These findings lay the essential groundwork for enhancing monitoring and early detection systems and underscore the value of terrestrial hyperspectral data for developing sustainable pest management strategies in coffee crops. Full article

Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can be vital for obtaining information over large, limited access areas with global coverage. This is important since conventional techniques for collecting vegetation water content are expensive, time-consuming, and spatially limited. This work aims to evaluate the vegetation live fuel moisture content (LFMC) seasonal variability using a multiscale remote sensing approach, particularly on rockroses, the Cistus ladanifer species, a Western Mediterranean basin native species with wide spatial distribution, over the Herdade da Mitra at the University of Évora, Portugal. This work used four dataset sources, collected monthly between June 2022 and July 2023: (i) Vegetation samples used to calculate the LFMC; (ii) Vegetation reflectance spectral signature using the portable spectroradiometer FieldSpec HandHeld-2 (HH2); (iii) Multispectral optical imagery obtained from the Multispectral Instrument (MSI) sensor onboard the Sentinel-2 satellite; and (iv) Multispectral optical imagery derived from a camera onboard an Unmanned Aerial Vehicle Phantom 4 Multispectral (P4M). Several temporal analyses were performed based on datasets from different sensors and on their intercomparison. Furthermore, the Random Forest (RF) classifier, a machine learning model, was used to estimate the LFMC considering each sensor approach. MSI sensor presented the best results (R² = 0.94) due to the presence of bands on the Short-Wave Infrared Imagery region. However, despite having information only in the Visible and Near Infrared spectral regions, the HH2 presents promising results (R² = 0.86). This suggests that by combining these spectral regions with a RF classifier, it is possible to effectively estimate the LFMC. This work shows how different spatial scales, from remote sensing observations, affect the LFMC estimation through machine learning techniques. Full article

An innovative solution for real-time monitoring of reactions within confined spaces, optimized for Raman spectroscopy applications, is presented. This approach involves the utilization of a hollow-core waveguide configured as a compact flow cell, serving both as a conduit for Raman excitation and scattering and seamlessly integrating into the effluent stream of a cracking catalytic reactor. The analytical technique, encompassing device and optical design, ensures robustness, compactness, and cost-effectiveness for implementation into process facilities. Notably, the modularity of the approach empowers customization for diverse gas monitoring needs, as it readily adapts to the specific requirements of various sensing scenarios. As a proof of concept, the efficacy of a spectroscopic approach is shown by monitoring two catalytic processes: CO₂ methanation (CO₂ + 4H₂ → CH₄ + 2H₂O) and ammonia cracking (2NH₃ → N₂ + 3H₂). Leveraging chemometric data processing techniques, spectral signatures of the individual components involved in these reactions are effectively disentangled and the results are compared to mass spectrometry data. This robust methodology underscores the versatility and reliability of this monitoring system in complex chemical environments. Full article