Search Results (3)

Choirbooks are historical heritage manuscripts used for the performance of vocal music in religious ceremonies in colonial times. This study aimed to understand the characteristics of choirbook manuscripts produced in the Real Audiencia de Quito during the 17th century. The methodology combined non-invasive techniques, such as infrared false-color imaging (IRFC) and X-ray fluorescence (XRF), together with spot analysis by scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) and Fourier transform infrared spectroscopy with attenuated total reflection (FTIR-ATR). The analytical results revealed the use of pumice, chalk and lime carbonate as support materials in the manufacturing process and surface treatment of the parchment. In the illuminations, three pictorial techniques based on protein, polysaccharide and lipid binders were recognized, establishing that the pigments used with greater regularity in the illuminations were vermilion, minium, verdigris, orpiment, azurite, and indigo, preferably in a pure state. Materials used less regularly were also identified, such as yellow ochre, saffron, smalt, red ochre, and bone black, among others. Regarding the vulnerability of the pictorial materials, it was determined that, although most of the pigments exhibit chemical stability, they present some vulnerabilities associated with their intrinsic composition and the medium that contains them. Full article

Drones can be very helpful in precision agriculture. Currently, most drone-based solutions for plant disease detection incorporate multispectral, hyperspectral, or thermal cameras, which are expensive. In addition, there is a trend nowadays to apply machine learning techniques to precision agriculture, which are computationally complex and intensive. In this work, we explore the feasibility of detecting ochre spot disease in almond plantations based on conventional techniques of computer vision and images from a very low-cost RGB camera that is placed on board a drone. Such an approach will allow the detection system to be simple and inexpensive. First, we made a study of color on the ochre spot disease. Second, we developed a specific algorithm that was capable of processing and analyzing limited-quality images from a very low-cost camera. In addition, it can estimate the percentage of healthy and unhealthy parts of the plant. Thanks to the GPS on board the drone, the system can provide the location of every sick almond tree. Third, we checked the operation of the algorithm with a variety of photographs of ochre spot disease in almonds. The study demonstrates that the efficiency of the algorithm depends to a great extent on environmental conditions, but, despite the limitations, the results obtained with the analyzed photographs show a maximum discrepancy of 10% between the estimated percentage and the ground truth percentage of the unhealthy area. This approach shows great potential for extension to other crops by making previous studies of color and adaptations. Full article

Information on habitat preferences is critical for the successful conservation of endangered species. For many species, especially those living in remote areas, we currently lack this information. Time and financial resources to analyze habitat use are limited. We aimed to develop a method to describe habitat preferences based on a combination of bird surveys with remotely sensed fine-scale land cover maps. We created a blended multiband remote sensing product from SPOT 6 and Landsat 8 data with a high spatial resolution. We surveyed populations of three bird species (Yellow-breasted Bunting Emberiza aureola, Ochre-rumped Bunting Emberiza yessoensis, and Black-faced Bunting Emberiza spodocephala) at a study site in the Russian Far East using hierarchical distance sampling, a survey method that allows to correct for varying detection probability. Combining the bird survey data and land cover variables from the remote sensing product allowed us to model population density as a function of environmental variables. We found that even small-scale land cover characteristics were predictable using remote sensing data with sufficient accuracy. The overall classification accuracy with pansharpened SPOT 6 data alone amounted to 71.3%. Higher accuracies were reached via the additional integration of SWIR bands (overall accuracy = 73.21%), especially for complex small-scale land cover types such as shrubby areas. This helped to reach a high accuracy in the habitat models. Abundances of the three studied bird species were closely linked to the proportion of wetland, willow shrubs, and habitat heterogeneity. Habitat requirements and population sizes of species of interest are valuable information for stakeholders and decision-makers to maximize the potential success of habitat management measures. Full article