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The study of the interaction between light and biological tissue is of great help in the identification of diseases as well as structural alterations in tissues. In the present study, we have developed a tissue diagnostic technique by using multispectral imaging in the visible spectrum combined with principal component analysis (PCA). We used information from the propagation of light through paraffin-embedded tissues to assess differences in the eye tissues of control mouse embryos compared to mouse embryos whose mothers were deprived of folic acid (FA), a crucial vitamin necessary for the growth and development of the fetus. After acquiring the endmembers from the multispectral images, spectral unmixing was used to identify the abundances of those endmembers in each pixel. For each acquired image, the final analysis was performed by performing a pixel-by-pixel and wavelength-by-wavelength absorbance calculation. Non-negative least squares (NNLS) were used in this research. The abundance maps obtained for the first endmember revealed vascular alterations (vitreous and choroid) in the embryos with maternal FA deficiency. However, the abundance maps obtained for the third endmember showed alterations in the texture of some tissues such as the lens and retina. Results indicated that multispectral imaging applied to paraffin-embedded tissues enhanced tissue visualization. Using this method, first, it can be seen tissue damage location and then decide what kind of biological techniques to apply. [ABSTRACT FROM AUTHOR]

Pinus pinaster Ait. is an important timber species in NW Spain and is affected by forest fires every year. The persistence of this species after fire mainly depends on natural regeneration, which is very variable. In this study, we evaluated the combined use of the NDVI and LiDAR data for assessing P. pinaster regeneration success after fire in terms of density, cover and height. For this purpose, we selected a P. pinaster stand affected by a high-severity wildfire in October 2017. Field surveys and remotely piloted aircraft flights (with a high-density LiDAR sensor and multispectral camera) were conducted four years after the fire (October 2021). The study area is characterized as being particularly complex terrain, with a combination of pine trees and a high density of scrub and low vegetation. Field measurements were made in 16 study plots distributed over the burned area. Two different types of software and data processing methods were used to calculate the LiDAR-derived metrics. For pine variables, the LiDAR-based estimates of structural characteristics calculated with both data processing methods proved inadequate and were very poorly correlated with the field-measured data, while for shrubland the estimates proved to be more comparable to the field measurements. The inability of the laser pulses to reach the ground due to the complexity of the area/vegetation could lead to loss of information, calling into question the accuracy of LiDAR data in this type of scenario. LiDAR technology continues to expand in different areas and applications, and in forestry, future studies should focus on application in more complex terrain. [ABSTRACT FROM AUTHOR]

It has been scientifically proven that climate change is a reality. In subarid Mediterranean limates, this fact is observed in the irregular distribution of rainfall, resulting in alternating periods of more or less prolonged drought with episodes of torrential rains concentrated in short periods of time. We have selected 11 natural areas in southern Spain, where we will observe these circumstances and where a series of ecosystems composed of vegetation covers of a high ecological value are found. We start from the question of whether these climatic circumstances are really deteriorating them. For this study, we propose a method that combines three analysis techniques: the design of the time series, the application of vegetation indices, and the use of techniques analysis of changes in land use. From the combination of these techniques in the period from 1997 to 2021, we have observed that there have been a dynamic of changes in land use that has maintained its original characteristics by more than 70%, so it is possible to affirm that the adaptation of ecosystems to climatic conditions has occurred satisfactorily. However, this general statement shows some particularities which are those that we will show in this work. [ABSTRACT FROM AUTHOR]

The development of unmanned aerial vehicles (UAVs) and light sensors has required new approaches for high-resolution remote sensing applications. High spatial and temporal resolution spectral data acquired by multispectral and conventional cameras (or red, green, blue (RGB) sensors) onboard UAVs can be useful for plant water status determination and, as a consequence, for irrigation management. A study in a vineyard located in south-eastern Spain was carried out during the 2018, 2019, and 2020 seasons to assess the potential uses of these techniques. Different water qualities and irrigation application start throughout the growth cycle were imposed. Flights with RGB and multispectral cameras mounted on a UAV were performed throughout the growth cycle, and orthoimages were generated. These orthoimages were segmented to include only vegetation and calculate the green canopy cover (GCC). The stem water potential was measured, and the water stress integral (Sψ) was obtained during each irrigation season. Multiple linear regression techniques and artificial neural networks (ANNs) models with multispectral and RGB bands, as well as GCC, as inputs, were trained and tested to simulate the Sψ. The results showed that the information in the visible domain was highly related to the Sψ in the 2018 season. For all the other years and combinations of years, multispectral ANNs performed slightly better. Differences in the spatial resolution and radiometric quality of the RGB and multispectral geomatic products explain the good model performances with each type of data. Additionally, RGB cameras cost less and are easier to use than multispectral cameras, and RGB images are simpler to process than multispectral images. Therefore, RGB sensors are a good option for use in predicting entire vineyard water status. In any case, field punctual measurements are still required to generate a general model to estimate the water status in any season and vineyard. [ABSTRACT FROM AUTHOR]

Accurate greenhouse mapping can support environment monitoring and resource management. In an object-based image analysis (OBIA) approach focused on plastic covered greenhouses (PCG) classification, the segmentation is a crucial step for the goodness of the final results. Multiresolution segmentation (MRS) is one of the most used algorithms in OBIA approaches, being greatly enabled by the advent of the commercial software eCognition. Therefore, in addition to the segmentation algorithm used, it is very important to count on tools to assess the quality of segmentation results from digital images in order to obtain the most similar segments to the real PCG objects. In this work, several factors affecting MRS such as the type of input image and the best MRS parameters (i.e., scale, compactness and shape), have been analysed. In this regard, more than 2800 segmentations focused on PCG land cover were conducted from four pre-processed Deimos-2 very high-resolution (VHR) satellite orthoimages taken in the Southeast of Spain (Almería). Specifically, one multispectral and one pansharpened Deimos-2 orthoimages, both with and without atmospheric correction were tested in this work. The free access AssesSeg command line tool, based on a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2), was used to determine the best MRS parameters for all the VHR satellite images. According to both the supervised discrepancy measure ED2 and visual perception, the best segmentation on PCG was obtained over the atmospherically corrected pansharpened Deimos-2 orthoimage, achieving very good results. [ABSTRACT FROM AUTHOR]

Featured Application: This work shows a geomatics workflow that integrates hyperspectral remote sensing with photogrammetry and other traditional topographic techniques that offer an accurate way for the management and study of rock art. Hyperspectral imaging can become an efficient tool for the recognition of figures, coloring matter and state of conservation of such valuable art. Rock art is one of the most fragile and relevant cultural phenomena in world history, carried out in shelters or the walls and ceilings of caves with mineral and organic substances. The fact it has been preserved until now can be considered as fortunate since both anthropogenic and natural factors can cause its disappearance or deterioration. This is the reason why rock art needs special conservation and protection measures. The emergence of digital technologies has made a wide range of tools and programs available to the community for a more comprehensive documentation of rock art in both 2D and 3D. This paper shows a workflow that makes use of visible and near-infrared hyperspectral technology to manage, monitor and preserve this appreciated cultural heritage. Hyperspectral imaging is proven to be an efficient tool for the recognition of figures, coloring matter, and state of conservation of such valuable art. [ABSTRACT FROM AUTHOR]

This study conducted a comparison between biocide treatments based on nanoparticles of silver, copper, ZnO, TiO2 and silver/ TiO2 nanocomposites to analyse their capability to inhibit microalgal fouling on stone buildings. Biofouling is one of the main alterations on stone façades, causes degradation of their constituent materials and interferes with their aesthetic values. The proposed treatments were tested on a limestone from the historic quarry of Estepa (Spain), widely used as construction material in the South of Spain. The applicability of the treatments was evaluated by colorimetry. The biocidal effectiveness of the nanoparticles was studied on stone surfaces by multispectral imaging, digital image analysis and optical coherence tomography. This is a low-cost and efficient protocol to validate biocidal treatments for limestone monuments, and our results demonstrate the potential of silver and ZnO nanoparticles as a protective treatment for stone façades. The results have implications for practitioners working on historic buildings. [ABSTRACT FROM AUTHOR]

[Display omitted] • Inclusion of temporal data can improve detection F 1 -Score by 8% • Infrared bands provide about 4% more F 1 -Score than visible bands despite lower resolution • Using over 80 features provides an increase of about 12% F 1 -Score over using less than 10. • The proposed method successfully detects all test plots with nearly 90% F 1 -Score • A dataset of recent manure application, verified through on-site validation, is made public. The use of manure in agricultural fields during the wet season can lead to environmental pollution by releasing nitrates into nearby water sources. To address this issue, authorities may impose closed periods during which manure application is prohibited. However, ensuring compliance with these regulations can be challenging, as it is difficult to monitor all fields in a country. To tackle this problem, a solution has been proposed that involves employing machine learning techniques in conjunction with satellite imagery to automatically identify freshly manured fields. This paper investigates the relationship and effectiveness of the Sentinel-2 satellite bands and 51 frequently utilized multispectral indices in the context of precision agriculture, by exploring different feature selection methods. The proposed method achieves nearly 90% F 1 -Score and detects all test plots of the northern Spanish region, showing its potential for large-scale use in precision agriculture and environmental monitoring. This method incorporates temporal data, resulting in an 8% improvement in the detection F 1 -Score. Despite their lower spatial resolution, infrared bands have proven to be more effective than visible bands, enhancing the F 1 -Score by 4%. Furthermore, the use of over 80 features contributes to a 12% increase in the F 1 -Score compared to using fewer than 10 features. For further research and future studies, a dataset of recently manured plots, verified on-site, has been developed and made publicly available. [ABSTRACT FROM AUTHOR]

• Inexact footprints from image segmentation are the main source of uncertainty. • The PUK kernel generally outperforms other kernels when used in a SVM. • SVM performance is matched by simpler classifiers, such as logistic regression. • Height and location are the most powerful attributes for MBT classification. • With correct building footprints, the classification accuracy may reach 80%. We present a procedure for assessing the urban exposure and seismic vulnerability that integrates LiDAR data with aerial images from the Spanish National Plan of Aerial Orthophotography (PNOA). It comprises three phases: first, we segment the satellite image to divide the study area into different urban patterns. Second, we extract building footprints and attributes that represent the type of building of each urban pattern. Finally, we assign the seismic vulnerability to each building using different machine-learning techniques: Decision trees, SVM, logistic regression and Bayesian networks. We apply the procedure to 826 buildings in the city of Lorca (SE Spain), where we count on a vulnerability database that we use as ground truth for the validation of results. The outcomes show that the machine learning techniques have similar performance, yielding vulnerability classification results with an accuracy of 77%–80% (F1-Score). The procedure is scalable and can be replicated in different areas. This is particularly relevant in Spain, where more than seven hundred towns have to develop seismic risk studies in the years to come, according to the General Direction of Civil Protection and Emergencies. It is especially interesting as a complement to conventional data gathering approaches for disaster risk applications in cities where field surveys need to be restricted to certain areas, dates or budget. [ABSTRACT FROM AUTHOR]

Efficient irrigation in viticulture requires objective and representative monitoring of the vineyard water status variability. In this work the combination of multispectral, environmental and thermal data (using an infrared radiometer) acquired simultaneously on-the-go (at midday), from a ground moving vehicle (moving at 3 km/h) was tested to assess the vineyard stem water potential (Ψ s) and its spatial variability (three different irrigation treatments were imposed) over two seasons in north Spain. Partial least squares (PLS) cross-validation regression models involving the canopy temperature (T c), environmental and spectral variables yielded determination coefficients (R2 cv) of ~ 0.63 and root mean square error of cross-validation (RMSECV) between 0.124 MPa and 0.206 MPa in the two seasons. Linear discriminant analysis (LDA) involving only the variables used to build the regression models was run to distinguish among low, medium and high water stressed vines, yielding an average percentage of correct classification samples of 74%. The satisfactory performance of the multivariate models involving thermal, environmental and spectral data to either estimate or classify the plant water status within a vineyard supports the approach towards the combination of different data source to improve the capabilities of thermography itself. The inclusion of vegetative spectral indices in the regression and classification models of grapevine water status may provide real-time feedback on grapevine water use as influenced by actual vegetative growth, abiotic and/or biotic stress patterns. This combined approach can be seen as an advancement from existing solutions to assess plant water status variability given the simplicity and potential to automation of the thermal sensor employed and the integration of environmental and canopy vigour data into the model. • On-the-go monitoring of the grapevine water status in commercial vineyards. • Non-destructive technologies on a ground mobile platform to assess water status. • Thermography, multispectral imaging and environmental information were combined. [ABSTRACT FROM AUTHOR]

Nighttime images taken with DSLR cameras from the International Space Station (ISS) can provide valuable information on the spatial and temporal variation of artificial nighttime lighting on Earth. In particular, this is the only source of historical and current visible multispectral data across the world (DMSP/OLS and SNPP/VIIRS-DNB data are panchromatic and multispectral in the infrared but not at visible wavelengths). The ISS images require substantial processing and proper calibration to exploit intensities and ratios from the RGB channels. Here we describe the different calibration steps, addressing in turn Decodification, Linearity correction (ISO dependent), Flat field/Vignetting, Spectral characterization of the channels, Astrometric calibration/georeferencing, Photometric calibration (stars)/Radiometric correction (settings correction - by exposure time, ISO, lens transmittance, etc) and Transmittance correction (window transmittance, atmospheric correction). We provide an example of the application of this processing method to an image of Spain. • DLSR cameras can provide accurate radiometric measurements. • ISS nighttime images using DSLR cameras can provide colour data. • Spatial resolution increased by 150–1000 times compared to popular platforms. • This opens up new possibilities for environmental studies. [ABSTRACT FROM AUTHOR]

The province of León preserves a unique hydraulic infrastructure 1200 km-long, used for the exploitation of auriferous deposits in Roman times. It represents the most extensive waterworks in Europe and is one of the best-preserved examples of mining heritage in Antiquity. In this work, three mining exploitation sectors (upper, middle, and lower) characterized by channels and leats developed in different geological materials were examined, using Unmanned Aerial Vehicles (UAVs). A multi-approach based on a comparison of photogrammetric and multispectral data improved the identification and description of the hydraulic network. Comparison with traditional orthoimages and LiDAR data suggests that UAV-derived multispectral images are of great interest in areas where these sets of data have low resolution or areas that are densely covered by vegetation. The results showed that the size of the channel box and its width were factors that do not depend exclusively on the available water resources, as previously suggested, but also on the geological and hydraulic conditioning factors that intervene in each sector. Additionally, the detailed study allowed the establishment of a water sheet maximum height that was much lower than previously thought. All in all, these inferences might help researchers develop new strategies for mapping the Roman mining infrastructure and establishing the importance of geological inheritance on the construction of the hydraulic system that led the Romans to the accomplishment of the largest mining infrastructure ever known in Europe. [ABSTRACT FROM AUTHOR]

The combination of freely accessible satellite imagery from multiple programs improves the spatio-temporal coverage of remote sensing data, but it exhibits barriers regarding the variety of web services, file formats, and data standards. Ris an open-source software environment with state-of-the-art statistical packages for the analysis of optical imagery. However, it lacks the tools for providing unified access to multi-program archives to customize and process the time series of images. This manuscript introduces RGISTools, a new software that solves these issues, and provides a working example on water mapping, which is a socially and environmentally relevant research field. The case study uses a digital elevation model and a rarely assessed combination of Landsat-8 and Sentinel-2 imagery to determine the water level of a reservoir in Northern Spain. The case study demonstrates how to acquire and process time series of surface reflectance data in an efficient manner. Our method achieves reasonably accurate results, with a root mean squared error of 0.90 m. Future improvements of the package involve the expansion of the workflow to cover the processing of radar images. This should counteract the limitation of the cloud coverage with multi-spectral images. [ABSTRACT FROM AUTHOR]

In olive groves, vegetation ground cover (VGC) plays an important ecological role. The EU Common Agricultural Policy, through cross-compliance, acknowledges the importance of this factor, but, to determine the real impact of VGC, it must first be quantified. Accordingly, in the present study, eleven vegetation indices (VIs) were applied to quantify the density of VGC in olive groves (Olea europaea L.), according to high spatial resolution (10–12 cm) multispectral images obtained by an unmanned aerial vehicle (UAV). The fieldwork was conducted in early spring, in a Mediterranean mountain olive grove in southern Spain presenting various VGC densities. A five-step method was applied: (1) generate image mosaics using UAV technology; (2) apply the VIs; (3) quantify VGC density by means of sampling plots (ground-truth); (4) calculate the mean reflectance of the spectral bands and of the VIs in each sampling plot; and (5) quantify VGC density according to the VIs. The most sensitive index was IRVI, which accounted for 82% (p < 0.001) of the variability of VGC density. The capability of the VIs to differentiate VGC densities increased in line with the cover interval range. RVI most accurately distinguished VGC densities > 80% in a cover interval range of 10% (p < 0.001), while IRVI was most accurate for VGC densities < 30% in a cover interval range of 15% (p < 0.01). IRVI, NRVI, NDVI, GNDVI and SAVI differentiated the complete series of VGC densities when the cover interval range was 30% (p < 0.001 and p < 0.05). [ABSTRACT FROM AUTHOR]