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The 'Tomb of the Philosophers', owing its name to the iconographic theme depicted on the interior of its walls, offers a rare example of high-quality early Hellenistic painting, which enhances our knowledge on the use of painting materials and techniques in Greece during the late 4th–early 3rd century BC. The tomb was excavated in 2001 by the 17th Ephorate of Prehistoric and Classical Antiquities, in the area of the east cemetery of the ancient city of Pella and is among the largest built cist graves to have been found in the region to date. This article presents the results of a scientific investigation of the painting materials and techniques used in the tomb's decoration by means of high-resolution visible- and raking-light imaging, broadband imaging (IRR, UIL, UVR, VIL), X-ray fluorescence spectroscopy and SEM-EDAX microanalysis on a representative number of samples. The examination of the paintings testifies to the application of elaborate painting techniques and to the use of a varied and rich chromatic 'palette' (iron oxides, Egyptian blue, malachite, conichalcite, cinnabar, lead white, carbon-based black, a purple organic colorant, yellow-orange arsenic and vanadium-based pigments). Furthermore, iconographic elements of the paintings were better visualized, allowing for a more accurate description and interpretation of the decorative program of the tomb. [ABSTRACT FROM AUTHOR]

The Archaic Acropolis of Europos (region of Central Macedonia, Greece) is an important archaeological site, which was inhabited since the 7th century BC up to the 4th century AD. In the lowland area, south of the acropolis and a short distance away, archaeologists speculate that the extensive ancient cemetery of the acropolis, which is a result of its long-term habitation, was located there. In an effort to locate marks that will support this view, WingtraOne GEN II, one of today's leading Uncrewed Aircraft Systems, was used to collect RGB and multispectral images. After the production of the necessary digital surface models and orthophotomosaics, index maps related to the crops were created. The total of the products allowed the visual identification of 123 marks, which are probably attributed to hitherto unknown covered ancient tombs. In addition, marks of unknown covered ditches of long length were found in the plain area, and on the outskirts of the acropolis marks of its possible wall and the base of one of its towers. [ABSTRACT FROM AUTHOR]

Coastal managers, policymakers, and scientists use shoreline accretion/erosion trends to determine the coastline's historical evolution and generate models capable of predicting future changes. Different solutions have been developed to obtain shoreline positions from Earth observation data in recent years, the so-called Satellite-Derived Shorelines (SDS). Most of the methodologies available in the literature use multispectral optical satellite imagery. This paper proposes two new methods for shoreline mapping at the subpixel level based on PRISMA hyperspectral imagery. The first one analyses the spectral signatures along defined beach profiles. The second method uses techniques more commonly applied to multispectral image analysis, such as Spectral Unmixing algorithms and Spatial Attraction Models. The results obtained with both methodologies are validated on three Mediterranean microtidal beaches located in two different countries, Italy and Greece, using image-based ground truth shorelines manually photointerpreted and digitised. The obtained errors are around 6 and 7 m for the first and second methods, respectively. These results are comparable to the errors obtained from multispectral data. The paper also discusses the capability of the two methods to identify two different shoreline proxies. [ABSTRACT FROM AUTHOR]

Detecting and distinguishing apicultural plants are important elements of the evaluation and quantification of potential honey production worldwide. Today, remote sensing can provide accurate plant distribution maps using rapid and efficient techniques. In the present study, a five-band multispectral unmanned aerial vehicle (UAV) was used in an established beekeeping area on Lemnos Island, Greece, for the collection of high-resolution images from three areas where Thymus capitatus and Sarcopoterium spinosum are present. Orthophotos of UAV bands for each area were used in combination with vegetation indices in the Google Earth Engine (GEE) platform, to classify the area occupied by the two plant species. From the five classifiers (Random Forest, RF; Gradient Tree Boost, GTB; Classification and Regression Trees, CART; Mahalanobis Minimum Distance, MMD; Support Vector Machine, SVM) in GEE, the RF gave the highest overall accuracy with a Kappa coefficient reaching 93.6%, 98.3%, 94.7%, and coefficient of 0.90, 0.97, 0.92 respectively for each case study. The training method used in the present study detected and distinguish the two plants with great accuracy and results were confirmed using 70% of the total score to train the GEE and 30% to assess the method's accuracy. Based on this study, identification and mapping of Thymus capitatus areas is possible and could help in the promotion and protection of this valuable species which, on many Greek Islands, is the sole foraging plant of honeybees. [ABSTRACT FROM AUTHOR]

During the Hellenistic period and under the growing influence of the art of painting, the polychromy of Greek terracotta figurines focused not only on an elaborate rendering of color, but also on the interplay of light and shadow. Some of the best-preserved examples clearly show the subtlety of such pictorial effects. Among them is a statuette of a standing Maenad, held in the collections of the National Archaeological Museum in Athens (inv. 5000). Dating back to 150–100 BCE, it is a high-quality testament to the sculptural, as well as pictorial, coroplastic production in the workshops of Myrina (Eolide, Turkey). Combining multi-scale examination, multi-spectral imaging and non-invasive spectroscopic investigations (XRF, FTIR, FORS), a scientific study of the artefact was carried out within the framework of the Pilina project, a collaborative research program between the Louvre, the C2RMF, the National Archaeological Museum and the French School in Athens. This article presents the main results of the study by discussing the color scheme, identification of some pigments and colorants (clays of the kaolinite group, ochres, cinnabar, Egyptian blue, an anthraquinone of plant origin, likely madder, gold leaf), and painting techniques aiming at achieving chiaroscuro effects. [ABSTRACT FROM AUTHOR]

In line with precision viticulture, in recent years new methods of vineyard management have been introduced, so as to optimize vine cultivation and production of wine of the highest quality. Following on the methodologies developed for mapping other crop parameters, there is currently a growing research effort for the discrimination and mapping of vine varieties, as this information is useful for vineyard-scale management, local and regional inventory and planning purposes, application of EU Directives, and support of certification and production of high quality wines. This research focuses on developing a methodology, based on UAV-borne multispectral data, for discriminating and mapping three vine varieties in Attica, Greece, employing three non-parametric classifiers, namely Random Forest (RF), Support Vector Machines (SVM) and Spectral Angle Mapper (SAM), and selected vegetation indices (VIs). The suggested methodology uses easy to obtain and process, cost-effective images and relies mostly on free open-source software. Study conclusions suggest that although the multispectral images used did not result in the accurate discrimination of the vine varieties at pixel level, expressed by highest overall accuracy (OA) 61.6%, they nevertheless proved useful in mapping varieties at the plot level. Therefore, it is considered effective for applications that require such level mapping. [ABSTRACT FROM AUTHOR]

In this study, a preliminary test was implemented with the purpose of evaluating Sentinel-2 MultiSpectral Instrument (MSI) imagery for forest growing stock volume (GSV) estimation, using spectral bands and vegetation indices, in a heterogeneous Mediterranean forest in northeastern Greece. This evaluation scheme was complemented with the comparative use of a Landsat-8 Operational Land Imager (OLI) imagery acquired also on June 2016. All Sentinel-2 MSI bands but the blue and red parts of the spectrum recorded by the sensor, provided negative but significant correlation results with growing stock volume. The use of traditional vegetation indices did not improve the strength of the correlation. The Difference Vegetation Index (DVI), Enhanced Vegetation Index (EVI) and Perpendicular Vegetation Index (PVI) were found to have the strongest correlation for the retrieval of GSV. The replacement of the Near InfraRed (NIR) band in the indices formula with the first rededge band improved the magnitude of the relationship significantly. The information content of Sentinel-2 MSI imagery was also found to be slightly higher compared to the Landsat-8 OLI imagery based on the results of the Random Forest regression algorithm that was used to model the relationship between the spectral information inherent in both sensors and the GSV. [ABSTRACT FROM PUBLISHER]

Abstract: This paper refers to the study of an ancient papyrus, dated ca. 420/430 BC, found in 1981 during an excavation at Daphne, Athens, Greece, using multispectral imaging combined with image processing analysis. It was assumed that the papyrus contained ancient Greek musical notation but the condition of the object hindered the drawing of any conclusion based only on visual examination. This fact, combined with the significance of the object itself, as it is the oldest papyrus which carries Greek text, pointed to the application of non-invasive techniques to enhance its readability. The multispectral imaging carried out in the range of 420–1000 nm enabled the detection of more letters on surface layers, at various places and orientations. False colour imaging proved to yield better results in distinguishing the letters compared to single wavelength recording. In some cases, letters from several layers underneath are revealed in the infrared. The letters present different greylevels according to the layer they belong. An interesting result coming from a simple subtraction of the infrared image at 1000 nm from the visible one at 660 nm is that different layers of the papyrus can be distinguished. The Application of Principal Component Analysis (PCA) showed that the best results, as far as the extraction of the letters from the background is concerned, are obtained when the second and the third images are merged. The automatic extraction of the letters is feasible to some extent but there is a certain amount of noise with similar characteristics that cannot be removed. [Copyright &y& Elsevier]

Stem biomass is a fundamental component of the global carbon cycle that is essential for forest productivity estimation. Over the last few decades, Light Detection and Ranging (LiDAR) has proven to be a useful tool for accurate carbon stock and biomass estimation in various biomes. The aim of this study was to investigate the potential of multispectral LiDAR data for the reliable estimation of single-tree total and barkless stem biomass (TSB and BSB) in an uneven-aged structured forest with complex topography. Destructive and non-destructive field measurements were collected for a total of 67 dominant and co-dominant Abies borisii-regis trees located in a mountainous area in Greece. Subsequently, two allometric equations were constructed to enrich the reference data with non-destructively sampled trees. Five different regression algorithms were tested for single-tree BSB and TSB estimation using height (height percentiles and bicentiles, max and average height) and intensity (skewness, standard deviation and average intensity) LiDAR-derived metrics: Generalized Linear Models (GLMs), Gaussian Process (GP), Random Forest (RF), Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost). The results showcased that the RF algorithm provided the best overall predictive performance in both BSB (i.e., RMSE = 175.76 kg and R2 = 0.78) and TSB (i.e., RMSE = 211.16 kg and R2 = 0.65) cases. Our work demonstrates that BSB can be estimated with moderate to high accuracy using all the tested algorithms, contrary to the TSB, where only three algorithms (RF, SVR and GP) can adequately provide accurate TSB predictions due to bark irregularities along the stems. Overall, the multispectral LiDAR data provide accurate stem biomass estimates, the general applicability of which should be further tested in different biomes and ecosystems. [ABSTRACT FROM AUTHOR]

In this study, a spatial analysis of agronomic and remote sensing data is carried out to derive accurate rice crop yield estimation. The variability of a series of vegetation indices (VIs) was calculated from remote sensing data obtained via a commercial UAS platform (e-Bee) at four dates (per stage of development), and the development of estimation models was conducted. The study area is located in the region of Chalastra (municipality of Thessaloniki, North Greece) and the primary data were obtained during the 2016 growing season. These data include ultra-high resolution remote sensing multispectral images of 18 plots totaling 58 hectares of Ronaldo and Gladio rice crop varieties, 97 sample point data related to yield, and many other pieces of information recorded in the producer's field log. Ten simple and compound VIs were calculated, and the evolution of their values during the growing season as well as their comparative correlation were studied. A study of the usability of each VI was conducted for the different phenological stages of the cultivation and the variance of VIs and yield; the more correlated VIs were identified. Furthermore, three types of multitemporal VI were calculated from combinations of VIs from different dates, and their contribution to improving yield prediction was studied. As Ronaldo is a Japonica type of rice variety and Gladio is Indica type, they behave differently in terms of maturation time (Gladio is approximately 20 days earlier) and the value of every VI is affected by changes in plant physiology and phenology. These differences between the two varieties are reflected in the multitemporal study of the single-date VIs but also in the study of the values of the multitemporal VIs. In conclusion, Ronaldo's yield is strongly dependent on multitemporal NDVI (VI6th July + VI30 Aug, R2 = 0.76), while Gladio's yield is strongly dependent on single-date NDVI (6 July, R2 = 0.88). The compound VIs RERDVI and MCARI1 have the highest yield prediction (R2 = 0.77) for Ronaldo (VI6th July + VI30 Aug) and Gladio (R2 = 0.95) when calculated in the booting stage, respectively. For the Ronaldo variety, the examination of the multitemporal VIs increases yield prediction accuracy, while in the case of the Gladio variety the opposite is observed. The capabilities of multitemporal VIs in yield estimation by combining UAVs with more flights during the different growth stages can improve management and the cultivation practices. [ABSTRACT FROM AUTHOR]

Plantations of fast-growing forest species such as black locust (Robinia Pseudoacacia) can contribute to energy transformation, mitigate industrial pollution, and restore degraded, marginal land. In this study, the synergistic use of Sentinel-2 and Sentinel-1 time series data is explored for modeling aboveground biomass (AGB) in black locust short-rotation plantations in northeastern Greece. Optimal modeling dates and EO sensor data are also identified through the analysis. Random forest (RF) models were originally developed using monthly Sentinel-2 spectral indices, while, progressively, monthly Sentinel-1 bands were incorporated in the statistical analysis. The highest accuracy was observed for the models generated using Sentinel-2 August composites (R2 = 0.52). The inclusion of Sentinel-1 bands in the spectral indices' models had a negligible effect on modeling accuracy during the leaf-on period. The correlation and comparative performance of the spectral indices in terms of pairwise correlation with AGB varied among the phenophases of the forest plantations. Overall, the field-measured AGB in the forest plantations plots presented a higher correlation with the optical Sentinel-2 images. The synergy of Sentinel-1 and Sentinel-2 data proved to be a non-efficient approach for improving forest biomass RF models throughout the year within the geographical and environmental context of our study. [ABSTRACT FROM AUTHOR]

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites. [ABSTRACT FROM AUTHOR]