5,200 research outputs found

    Tectosedimentary and stratigraphic contex in Meirama's coal bassin (A Coruña, Spain).

    Get PDF
    [Resumen] La sucesión de Unidades Litoestratigráficas dentro de la Cuenca Noeógena de Meirama, demuestra una compleja evolución Tectosedimentaria. Se constata la presencia de médios deposicionales Lacustres, Fluviales, así como grandes periodos de desarrollo forestal. La influencia tectónica dentro de la cuenca, aparece marcada tanto en etapas resedimentárias, como en etapas Sin- y Postsedimentárias.[Abstract] Lithoestratigraphic units succesion in Meirama's Neogene-Bassin, prove a complex Tectosedimentary evolution. It is defined moving of diverse Lacustrine and Fluvial sedimentary environments, also great periods of woodland development. Tectonic influence into bassin, is trade in diverse stages: Pre-, Syn and Postsedimentary ones

    Correlación entre dos secuencias lacustres pliocenas en los sectores marginales de Orce y Gorafe (Depresión de Guadix-Baza. Granada)

    Get PDF
    Las características sedimentológicas (secuencias deposicionales y megasecuencias), estratigráficas (discontinuidades estatigráficas de la misma edad) y estructurales (sistemas de fracturas de salto en di- rección N70E y N135E) permiten deducir un ambiente lacustre-palustre y unas condiciones ambientales-estructurales correlacionables en los dos extremos de la cuenca de Guadix-Baz

    Evolución de fascies abanico aluvial-fluvial-lacustre en el Plioceno de la Depresión de Guadix-Baza

    Full text link
    El corte del Río de Gor muestra la evolución lateral de los materiales de la Fm. Guadix (fluvial) a la Fm. Gorafe-Huelago (lacustre). Desde el borde de la cuenca hacia el interior (en 6 km) se reconocen, en los materiales pliocenos, los siguientes medios sedimentarios yuxtapuestos: A. Abanico aluvial medio. B. Abanico aluvial medio. C. Ríos trenzados (braided). D. Meandriforme proximal. E. Meandriforme distal. F. Llanura lutítica. G. Medio lacustr

    Contrasting human perceptions of and attitudes towards two threatened small carnivores, Lycalopex fulvipes and Leopardus guigna, in rural communities adjacent to protected areas in Chile

    Get PDF
    Indexación: Scopus.The interaction between humans and small carnivores is a phenomenon especially frequent in rural fringes, as is the case of communities surrounding natural areas. In Chile, two species of threatened carnivores, the Darwin's Fox and the Guigna, have increased their contact with humans due to human-induced changes in their habitat. The objective of this study was to characterize the interactions of these species with humans by assessing human perceptions and attitudes toward them, and to assess livestock and poultry ownership and management practices in local communities to evaluate their possible roles in the phenomenon. We conducted semi-structured interviews in rural communities adjacent to natural protected areas of two different regions in southern Chile. We found that people have a more positive perception of Darwin's Foxes than Guignas, but both species are considered damaging due to poultry attacks. Livestock and poultry management was generally deficient. Improvements in animal management and education programs could lead to a significant decrease in negative interactions. © Sacristan et al. 2018.https://www.threatenedtaxa.org/index.php/JoTT/article/view/4030/442

    Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)

    Get PDF
    A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary preclassification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8

    Improving georeferencing accuracy of Very High Resolution satellite imagery using freely available ancillary data at global coverage

    Get PDF
    While impressive direct geolocation accuracies better than 5.0 m CE90 (90% of circular error) can be achieved from the last DigitalGlobe’s Very High Resolution (VHR) satellites (i.e. GeoEye-1 and WorldView-1/2/3/4), it is insufficient for many precise geodetic applications. For these sensors, the best horizontal geopositioning accuracies (around 0.55 m CE90) can be attained by using third-order 3D rational functions with vendor’s rational polynomial coefficients data refined by a zero-order polynomial adjustment obtained from a small number of very accurate ground control points (GCPs). However, these high-quality GCPs are not always available. In this work, two different approaches for improving the initial direct geolocation accuracy of VHR satellite imagery are proposed. Both of them are based on the extraction of three-dimensional GCPs from freely available ancillary data at global coverage such as multi-temporal information of Google Earth and the Shuttle Radar Topography Mission 30 m digital elevation model. The application of these approaches on WorldView-2 and GeoEye-1 stereo pairs over two different study sites proved to improve the horizontal direct geolocation accuracy values around of 75%
    corecore