Spectral Classification Research Articles

Interval-based sparse ensemble multi-class classification algorithm for terahertz data

Terahertz time-domain spectroscopy (THz-TDS) has been widely used for food and drug identification. The classification information of a THz spectrum usually does not exist in the whole spectral band but exists only in one or several small intervals. Therefore, feature selection is indispensable in THz-based substance identification. However, most THz-based identification methods empirically intercept the low-frequency band of the THz absorption coefficients for analysis. In order to adaptively find out important intervals of the THz spectra, an interval-based sparse ensemble multi-class classifier (ISEMCC) for THz spectral data classification is proposed. In ISEMCC, the THz spectra are first divided into several small intervals through window sliding. Then the data of training samples in each interval are extracted to train some base classifiers. Finally, a final robust classifier is obtained through a nonnegative sparse combination of these trained base classifiers. With l1 -norm, two objective functions that based on Mean Square Error (MSE) and Cross Entropy (CE) are established. For these two objective functions, two iterative algorithms based on the Alternating Direction Method of Multipliers (ADMM) and Gradient Descent (GD) are built respectively. ISEMCC transforms the problem of interval feature selection and decision-level fusion into a nonnegative sparse optimization problem. The sparse constraint ensures only a few important spectral segments are selected. In order to verify the performance of the proposed algorithm, comparative experiments on identifying the origin of Bupleurum and the harvesting year of Tangerine peel are carried out. The base classifiers used by ISEMCC are Support Vector Machine (SVM) and Decision Tree (DT). The experimental results demonstrate that the proposed algorithm outperforms six typical classifiers, including Random Forest (RF), AdaBoost, RUSBoost, ExtraTree, and the two base classifiers, in terms of classification accuracy.

Spectral density classification for environment spectroscopy

Spectral densities encode the relevant information characterizing the system–environment interaction in an open-quantum system problem. Such information is key to determining the system’s dynamics. In this work, we leverage the potential of machine learning techniques to reconstruct the features of the environment. Specifically, we show that the time evolution of a system observable can be used by an artificial neural network to infer the main features of the spectral density. In particular, for relevant examples of spin-boson models, we can classify with high accuracy the Ohmicity parameter of the environment as either Ohmic, sub-Ohmic or super-Ohmic, thereby distinguishing between different forms of dissipation.

Long-period Ap stars discovered with TESS data: Cycles 3 and 4

One of the most challenging aspects of the Ap stars is the extreme differentiation of their rotation periods, which span more than five orders of magnitude. The physical origin of this differentiation remains poorly understood. The consideration of the most slowly rotating Ap stars represents a promising approach to gain insight into the processes responsible for the rotational braking to which the Ap stars are subject. However, historically, the study of these stars focused primarily on the most strongly magnetic among them. This bias introduced an ambiguity in the conclusions that could be drawn, as it did not allow the distinction between the rotational and magnetic effects, nor the investigation of possible correlations between rotational and magnetic properties. We previously showed that the identification of super-slowly rotating Ap (ssrAp) star candidates (defined as Ap stars that have rotation periods Prot > 50 d) through systematic exploitation of the available TESS photometric observations of Ap stars is an effective approach to build a sample devoid of magnetic bias. This approach rests on the presence of brightness spots on the surface of Ap stars that are not distributed symmetrically about their rotation axes and show long-term stability, hence are responsible for photometric variations with the stellar rotation period. In our previous analyses of TESS Cycle 1 and Cycle 2 data, we interpreted the Ap stars showing no such variability over the 27-d duration of a TESS sector as being ssrAp star candidates. Here, we applied the same approach to TESS Cycle 3 and Cycle 4 observations of Ap stars. We show, however, that two issues that had not been fully appreciated until now may lead to spurious identification of ssrAp star candidates. On the one hand, a considerable fraction of the Ap stars in the existing lists turn out to have erroneous or dubious spectral classifications. On the other hand, the TESS data processing may remove part of the variability signal, especially for stars with moderately long periods (20 d ≲ Prot ≲ 50 d). After critical evaluation of these effects, we report the identification of 25 new ssrAp star candidates and of eight stars with moderately long periods. Combining this list with the lists of ssrAp stars from Cycles 1 and 2 and with the list of ssrAp stars that were previously known but whose lack of variability was not detected in our study, we confirmed at a higher significance level the conclusions drawn in our earlier work. These include the lower rate of occurrence of super-slow rotation among weakly magnetic Ap stars than among strongly magnetic ones, the probable existence of a gap between ∼2 and ∼3 kG in the distribution of the magnetic field strengths of the ssrAp stars, and the much higher rate of occurrence of rapid oscillations in ssrAp stars than in the whole population of Ap stars. The next step to gain further understanding of the ssrAp stars will be to obtain high-resolution spectra of those for which such observations have not been made yet, to constrain their rotation velocities and their magnetic fields.

A comprehensive semigray climate model

A climate model is developed for Earth climate history simulations or snapshots of possible conditions on Earthlike exoplanets. It includes estimates for shortwave and longwave optical thickness based on data from Venus, Earth, and Mars; expressions for atmospheric shortwave absorption and surface convective heat loss; climate feedbacks due to water vapor, ice-albedo, clouds, and lapse rate; and a new model for planetary and surface albedo which takes account of surface cover, Rayleigh scattering, and differing wavelength fractions due to primary spectral class. While somewhat complex, it is still orders of magnitude faster than full-spectrum methods or radiative-convective convergence. The model can be modified for use with tidally locked planets, and is here applied to Proxima Centauri b as an example.

Hyperspectral Image Classification Based on Mutually Guided Image Filtering

Hyperspectral remote sensing images (HSIs) have both spectral and spatial characteristics. The adept exploitation of these attributes is central to enhancing the classification accuracy of HSIs. In order to effectively utilize spatial and spectral features to classify HSIs, this paper proposes a method for the spatial feature extraction of HSIs based on a mutually guided image filter (muGIF) and combined with the band-distance-grouped principal component. Firstly, aiming at the problem that previously guided image filtering cannot effectively deal with the inconsistent information structure between the guided and target information, a method for extracting spatial features using muGIF is proposed. Then, aiming at the problem of the information loss caused by a single principal component as a guided image in the traditional GIF-based spatial–spectral classification, a spatial feature-extraction framework based on the band-distance-grouped principal component is proposed. The method groups the bands according to the band distance and extracts the principal components of each set of band subsets as the guide map of the current band subset to filter the HSIs. A deep convolutional neural network model and a generative adversarial network model for the filtered HSIs are constructed and then trained using samples for HSIs’ spatial–spectral classification. Experiments show that compared with the traditional methods and several popular spatial–spectral HSI classification methods based on a filter, the proposed methods based on muGIF can effectively extract the spatial–spectral features and improve the classification accuracy of HSIs.

"Integrated Geospatial Solutions for Enhanced Kukadi River Basin Delineation using Remote Sensing and GIS Techniques"

This study offers an innovative approach to enhance the precision and accuracy of the delineation of the Kukadi River Basin by integrating cutting-edge geospatial technologies, particularly Remote Sensing (RS) and Geographic Information System (GIS) methods. Due to its varied topography and land cover, the Kukadi River Basin play a crucial hydrological entity in the area which presents difficulties for standard delineation techniques. High-resolution satellite imagery is used in the study to provide exact information on land cover in the Kukadi River Basin. The work aims to extract comprehensive land cover data by utilizing spectral analysis and image classification techniques, which are part of RS. In order to improve the delineation of the Basin borders, this data is seamlessly integrated with GIS spatial analytic tools, which also incorporate digital elevation models, slope, and other terrain metrics. The research findings demonstrate the possibility of combining RS and GIS technology to overcome the challenges associated with varying terrains, hence furthering the improvement of Basin delineation approaches. The results provide a useful and dependable paradigm for defining river Basin in a variety of geographical contexts, which is important for water resource management, Environmental Conservation, Land use planning, decision-makers, researchers, and practitioners working on hydrological studies.

An Aero-Engine Classification Method Based on Fourier Transform Infrared Spectrometer Spectral Feature Vectors

Aiming at the classification identification problem of aero-engines, this paper adopts a telemetry Fourier transform infrared spectrometer to collect aero-engine hot jet infrared spectrum data and proposes an aero-engine classification identification method based on spectral feature vectors. First, aero-engine hot jet infrared spectrum data are acquired and measured; meanwhile, the spectral feature vectors based on CO2 are constructed. Subsequently, the feature vectors are combined with the seven mainstream classification algorithms to complete the training and prediction of the classification model. In the experiment, two Fourier transform infrared spectrometers, EM27 developed by Bruker and a self-developed telemetry FT-IR spectrometer, were used to telemeter the hot jet of three aero-engines to obtain infrared spectral data. The training data set and test data set were randomly divided in a ratio of 3:1. The model training of the training data set and the label prediction of the test data set were carried out by combining spectral feature vectors and classification algorithms. The classification evaluation indicators were accuracy, precision, recall, confusion matrix, and F1-score. The classification recognition accuracy of the algorithm was 98%. This paper has considerable significance for the fault diagnosis of aero-engines and classification recognition of aircrafts.

TOI-6883.01: A Single-transit Planet Candidate Detected from TESS

A new candidate exoplanet, proposed in ExoFOP by authors, it was promoted from TIC 393818343 to TOI-6883.01 at coordinates R.A.(J2000)20:41:10.01 decl.(J2000) + 3:38:17.87 in the Delphinus constellation and its distance is (93.73 ± 0.35) pc from Earth. The target star is a Sun-type having G0 class according to Skiff spectral classification and it has photometric magnitude V(Johnson) = 9.5 mag. We analyzed the transit light curve using Transit Exoplanet Survey Satellite’s ExoMAST tool, and evaluated the Lightcurve Analysis Tool for Transiting Exoplanets-report by excluding false positives, studying the background, satellite and centroid motion, and analyzing the pixels in sector 55 to define the source goodness. The candidate exoplanet transit has a duration of TD = 3.957 hr, Depth = 0.1196% and mid-transit time occurs at TBJD = 2811.24. Only one event was observed, so the orbital period cannot be evaluated.

Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery

The paradigm shift brought by deep learning in land cover object classification in hyperspectral images (HSIs) is undeniable, particularly in addressing the intricate 3D cube structure inherent in HSI data. Leveraging convolutional neural networks (CNNs), despite their architectural constraints, offers a promising solution for precise spectral data classification. However, challenges persist in object classification in hyperspectral imagery or hyperspectral image classification, including the curse of dimensionality, data redundancy, overfitting, and computational costs. To tackle these hurdles, we introduce the spectrally segmented-enhanced neural network (SENN), a novel model integrating segmentation-based, multi-layer CNNs, SVM classification, and spectrally segmented dimensionality reduction. SENN adeptly integrates spectral–spatial data and is particularly crucial for agricultural land classification. By strategically fusing CNNs and support vector machines (SVMs), SENN enhances class differentiation while mitigating overfitting through dropout and early stopping techniques. Our contributions extend to effective dimensionality reduction, precise CNN-based classification, and enhanced performance via CNN-SVM fusion. SENN harnesses spectral information to surmount challenges in “hyperspectral image classification in hyperspectral imagery”, marking a significant advancement in accuracy and efficiency within this domain.

Research on bark-frequency spectral coefficients heart sound classification algorithm based on multiple window time-frequency reassignment

The multi-window time-frequency reassignment helps to improve the time-frequency resolution of bark-frequency spectral coefficient (BFSC) analysis of heart sounds. For this purpose, a new heart sound classification algorithm combining feature extraction based on multi-window time-frequency reassignment BFSC with deep learning was proposed in this paper. Firstly, the randomly intercepted heart sound segments are preprocessed with amplitude normalization, the heart sounds were framed and time-frequency rearrangement based on short-time Fourier transforms were computed using multiple orthogonal windows. A smooth spectrum estimate is calculated by arithmetic averaging each of the obtained independent spectra. Finally, the BFSC of reassignment spectrum is extracted as a feature by the Bark filter bank. In this paper, convolutional network and recurrent neural network are used as classifiers for model comparison and performance evaluation of the extracted features. Eventually, the multi-window time-frequency rearrangement improved BFSC method extracts more discriminative features, with a binary classification accuracy of 0.936, a sensitivity of 0.946, and a specificity of 0.922. These results present that the algorithm proposed in this paper does not need to segment the heart sounds and randomly intercepts the heart sound segments, which greatly simplifies the computational process and is expected to be used for screening of congenital heart disease.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information.

Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.

Dynamic Spatial–Spectral Feature Optimization-Based Point Cloud Classification

With the development and popularization of LiDAR technology, point clouds are becoming widely used in multiple fields. Point cloud classification plays an important role in segmentation, geometric analysis, and vegetation description. However, existing point cloud classification algorithms have problems such as high computational complexity, a lack of feature optimization, and low classification accuracy. This paper proposes an efficient point cloud classification algorithm based on dynamic spatial–spectral feature optimization. It can eliminate redundant features, optimize features, reduce computational costs, and improve classification accuracy. It achieves feature optimization through three key steps. First, the proposed method extracts spatial, geometric, spectral, and other features from point cloud data. Then, the Gini index and Fisher score are used to calculate the importance and relevance of features, and redundant features are filtered. Finally, feature importance factors are used to dynamically enhance the discriminative power of highly distinguishable features to strengthen their contribution to point cloud classification. Four real-scene datasets from STPLS3D are utilized for experimentation. Compared to the other five algorithms, the proposed algorithm achieves at least a 37.97% improvement in mean intersection over union (mIoU). Meanwhile, the results indicate that the proposed algorithm can achieve high-precision point cloud classification with low computational complexity.

Homogeneous fine classification of Markarian galaxies based on SDSS medium-resolution spectroscopy

Context. Markarian (Mrk) galaxies, revealed due to their ultraviolet excess are very famous; they play a significant role in many astro-physical problems and come in a variety of types: Seyferts (Sys), low-ionization narrow emission-line regions (LINERs), starbursts (SBs), blue compact dwarf galaxies (BCDGs), as well as some quasars (QSOs) and blazars. They also appear as sources of non-optical radiation, such as gamma-ray, X-ray, ultraviolet (UV), infrared (IR), and radio, including some extremely high-energy gamma-ray sources (Mrk 421, Mrk 501) and very high-luminosity IR galaxies (Mrk 231). Aims. The classifications of Mrk galaxies for activity types have been carried out based on old mostly poor-quality and inhomoge-neous spectra (relatively low-resolution photographic spectra and low S/N spectra). The Sloan Digital Sky Surveys (SDSS) provides ~90% of the existing medium-resolution spectra, and is a relevant homogeneous database for classification of galaxies. Our aim was to obtain homogeneous classifications of Mrk galaxies by means of the SDSS spectra. Methods. Based on our work on the classification of newly revealed objects from various recent catalogs, we developed a fine classification scheme for activity types based on the homogeneous database of the SDSS spectroscopy. This scheme was applied to Mrk galaxies having spectra in the SDSS (779 out of 1544, 50.45%). Results. As a result, 779 Mrk galaxies now have homogeneous optical spectral classification that can be used for further studies and statistics of their physical and spatial properties. We revealed 2 QSOs, 49 classical Seyferts (broad-line Seyferts 1, BLS1s), 17 narrow-line Seyferts 1 (NLS1s), 4 Sy2s, 12 LINERs, 11 active galactic nuclei (AGN) without a definite type, 31 Composite spectrum objects, and 533 HII/Starbursts. Due to low-quality spectra, some objects were left without definite activity types and were classified as emission or absorption galaxies.

Generative data augmentation and automated optimization of convolutional neural networks for process monitoring.

Chemometric modeling for spectral data is considered a key technology in biopharmaceutical processing to realize real-time process control and release testing. Machine learning (ML) models have been shown to increase the accuracy of various spectral regression and classification tasks, remove challenging preprocessing steps for spectral data, and promise to improve the transferability of models when compared to commonly applied, linear methods. The training and optimization of ML models require large data sets which are not available in the context of biopharmaceutical processing. Generative methods to extend data sets with realistic in silico samples, so-called data augmentation, may provide the means to alleviate this challenge. In this study, we develop and implement a novel data augmentation method for generating in silico spectral data based on local estimation of pure component profiles for training convolutional neural network (CNN) models using four data sets. We simultaneously tune hyperparameters associated with data augmentation and the neural network architecture using Bayesian optimization. Finally, we compare the optimized CNN models with partial least-squares regression models (PLS) in terms of accuracy, robustness, and interpretability. The proposed data augmentation method is shown to produce highly realistic spectral data by adapting the estimates of the pure component profiles to the sampled concentration regimes. Augmenting CNNs with the in silico spectral data is shown to improve the prediction accuracy for the quantification of monoclonal antibody (mAb) size variants by up to 50% in comparison to single-response PLS models. Bayesian structure optimization suggests that multiple convolutional blocks are beneficial for model accuracy and enable transfer across different data sets. Model-agnostic feature importance methods and synthetic noise perturbation are used to directly compare the optimized CNNs with PLS models. This enables the identification of wavelength regions critical for model performance and suggests increased robustness against Gaussian white noise and wavelength shifts of the CNNs compared to the PLS models.

A novel model for hyper spectral image enhancement and classification: PCA, MBAO and CNN integration

A novel model for hyper spectral image enhancement and classification: PCA, MBAO and CNN integration

Classification and parameterization of a large Gaia sample of white dwarfs using XP spectra

Context. The latest Gaia data release in July 2022, DR3, in addition to the refinement of the astrometric and photometric parameters from DR2, added a number of important data products to those available in earlier releases, including radial velocity data, information on stellar multiplicity, and XP spectra of a selected sample of stars. Gaia has proved to be an important search tool for white dwarf stars, which are readily identifiable from their absolute G magnitudes as low luminosity objects in the Hertzsprung–Russell (H–R) diagram. Each data release has yielded large catalogs of white dwarfs, containing several hundred thousand objects, far in excess of the numbers known from all previous surveys (∼40 000). While the normal Gaia photometry (G, GBP, and GRP bands) and astrometry can be used to identify white dwarfs with high confidence, it is much more difficult to parameterize the stars and determine the white dwarf spectral type from this information alone. Observing all stars in these catalogs with follow-up spectroscopy and photometry is also a huge logistical challenge with current facilities. Aims. The availability of the XP spectra and synthetic photometry presents an opportunity for a more detailed spectral classification and measurement of the effective temperature and surface gravity of Gaia white dwarfs. Methods. A magnitude limit of G < 17.6 was applied to the routine production of XP spectra for Gaia sources, which would have excluded most white dwarfs. Therefore, we created a catalog of 100 000 high-quality white dwarf identifications for which XP spectra were processed, with a magnitude limit of G < 20.5. Synthetic photometry was computed for all these stars, from the XP spectra, in Johnson, SDSS, and J-PAS, published as the Gaia Synthetic Photometry Catalog – White Dwarfs (GSPC-WD). We took this catalog and applied machine learning techniques to provide a classification of all the stars from the XP spectra. We have then applied an automated spectral fitting program, with χ-squared minimization, to measure their physical parameters (effective temperature and log g) from which we could estimate the white dwarf masses and radii. Results. We present the results of this work, demonstrating the power of being able to classify and parameterize such a large sample of ≈100 000 stars. We describe what we can learn about the white dwarf population from this dataset. We also explored the uncertainties in the process and the limitations of the dataset.

Demography with drones: detecting growth and survival of shrubs with unoccupied aerial systems

Large‐scale disturbances, such as megafires, motivate restoration at equally large extents. Measuring the survival and growth of individual plants plays a key role in current efforts to monitor restoration success. However, the scale of modern restoration (e.g., >10,000 ha) challenges measurements of demographic rates with field data. In this study, we demonstrate how unoccupied aerial system (UAS) flights can provide an efficient solution to the tradeoff of precision and spatial extent in detecting demographic rates from the air. We flew two, sequential UAS flights at two sagebrush (Artemisia tridentata) common gardens to measure the survival and growth of individual plants. The accuracy of Bayesian‐optimized segmentation of individual shrub canopies was high (73–95%, depending on the year and site), and remotely sensed survival estimates were within 10% of ground‐truthed survival censuses. Stand age structure affected remotely sensed estimates of growth; growth was overestimated relative to field‐based estimates by 57% in the first garden with older stands, but agreement was high in the second garden with younger stands. Further, younger stands (similar to those just after disturbance) with shorter, smaller plants were sometimes confused with other shrub species and bunchgrasses, demonstrating a need for integrating spectral classification approaches that are increasingly available on affordable UAS platforms. The older stand had several merged canopies, which led to an underestimation of abundance but did not bias remotely sensed survival estimates. Advances in segmentation and UAS structure from motion photogrammetry will enable demographic rate measurements at management‐relevant extents.

Using Remote Sensing to Assess the Vegetation Cover of a Protected Salt Marsh Subjected to Artificial Recharge and Groundwater Abstractions during the Period 1925–2022 (Alicante, SE Spain)

The Agua Amarga salt marsh has been subjected to artificial seawater recharge on its surface during the period 1925–1969 for industrial purposes (saltwork activity) and from 2008 to present to compensate for coastal groundwater abstraction to supply Alicante desalination plants. This groundwater abstraction has caused piezometric depletion in the coastal aquifer connected to the protected salt marsh. The seawater recharge program also involved vegetation monitoring to control the impact on the salt marsh ecosystem, allowing data to be collected about the halophyte vegetation species growing in the salt marsh (Arthrocnemum macrostachyum, Sarcocornia fruticosa, and Ruppia maritima, among others) from spring and autumn field surveys. In this work, vegetation development is assessed with remote sensing for the period 1929–2022 using images with visible and near-infrared spectral resolution. Different spectral indices (NDVI, BI, and NDWI) and classification algorithms (random forest) are used to calculate the vegetation cover. Field data are employed to evaluate the protocols and compare the results, showing a 46% decrease caused by the salt works and a 50% increase as a result of natural evolution and artificial recharge. The spread of Phragmites australis is also addressed by comparing LiDAR data with field monitoring, showing an increase of 12% during the period 2005–2023. The advantages and complementarity of field monitoring and remote sensing information are explained.

Multi-epoch sampling of the radio star population with the Australian SKA Pathfinder

ABSTRACT The population of radio-loud stars has to date been studied primarily through either targeted observations of a small number of highly active stars or wide-field, single-epoch surveys that cannot easily distinguish stellar emission from background extragalactic sources. As a result it has been difficult to constrain population statistics such as the surface density and fraction of the population producing radio emission in a particular variable or spectral class. In this paper, we present a sample of 36 radio stars detected in a circular polarization search of the multi-epoch Variables and Slow Transients (VAST) pilot survey with ASKAP at 887.5 MHz. Through repeat sampling of the VAST pilot survey footprint we find an upper limit to the duty cycle of M-dwarf radio bursts of $8.5 \,\rm {per\,cent}$, and that at least 10 ± 3 $\rm {per\,cent}$ of the population should produce radio bursts more luminous than $10^{15} \,\rm {erg}\mathrm{s}^{-1} \,\mathrm{Hz}^{-1}$. We infer a lower limit on the long-term surface density of such bursts in a shallow $1.25 \,\mathrm{m}\rm {Jy}\rm\ {PSF}^{-1}$ sensitivity survey of ${9}^{\, +{11}}_{-{7}}\times 10^{-3}$ $\,\deg ^{-2}$ and an instantaneous radio star surface density of 1.7 ± 0.2 × 10−3 $\,\deg ^{-2}$ on 12 min time-scales. Based on these rates we anticipate ∼200 ± 50 new radio star detections per year over the full VAST survey and ${41\, 000}^{\, +{10\, 000}}_{-{9\, 000}}$ in next-generation all-sky surveys with the Square Kilometre Array.