Triple solids and scrolls

Let $Y$ be a smooth projective variety of dimension $n \geq 2$ endowed with a finite morphism $\phi:Y \to \mathbb P^n$ of degree $3$, and suppose that $Y$, polarized by some ample line bundle, is a scroll over a smooth variety $X$ of dimension $m$. Then $n \leq 3$ and either $m=1$ or $2$. When $m=1$, a complete description of the few varieties $Y$ satisfying these conditions is provided. When $m=2$, various restrictions are discussed showing that in several instances the possibilities for such a $Y$ reduce to the single case of the Segre product $\mathbb P^2 \times \mathbb P^1$. This happens, in particular, if $Y$ is a Fano threefold as well as if the base surface $X$ is $\mathbb P^2$

Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series

Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, WorldView-2 was mainly used to segment the study area focusing on individual greenhouses. Basic spectral information, spectral and vegetation indices, textural features, seasonal statistics and a spectral metric (Moment Distance Index, MDI) derived from Landsat 8 time series and/or WorldView-2 imagery were computed on previously segmented image objects. In order to test its temporal stability, the same approach was applied for two different years, 2014 and 2015. In both years, MDI was pointed out as the most important feature to detect greenhouses. Moreover, the threshold value of this spectral metric turned to be extremely stable for both Landsat 8 and WorldView-2 imagery. A simple decision tree always using the same threshold values for features from Landsat 8 time series and WorldView-2 was finally proposed. Overall accuracies of 93.0% and 93.3% and kappa coefficients of 0.856 and 0.861 were attained for 2014 and 2015 datasets, respectively

A WEBGIS FRAMEWORK FOR DISSEMINATING PROCESSED REMOTELY SENSED ON LAND COVER TRANSFORMATIONS

Mediterranean regions have experienced significant soil degradation over the past decades. In this context, careful land observation using satellite data is crucial for understanding the long-term usage patterns of natural resources and facilitating their sustainable management to monitor and evaluate the potential degradation. Given the environmental and political interest on this problem, there is urgent need for a centralized repository and mechanism to share geospatial data, information and maps of land change. Geospatial data collecting is one of the most important task for many users because there are significant barriers in accessing and using data. This limit could be overcome by implementing a WebGIS through a combination of existing free and open source software for geographic information systems (FOSS4G). In this paper we preliminary discuss methods for collecting raster data in a geodatabase by processing open multi-temporal and multi-scale satellite data aimed at retrieving indicators for land degradation phenomenon (i.e. land cover/land use analysis, vegetation indices, trend analysis, etc.). Then we describe a methodology for designing a WebGIS framework in order to disseminate information through maps for territory monitoring. Basic WebGIS functions were extended with the help of POSTGIS database and OpenLayers libraries. Geoserver was customized to set up and enhance the website functions developing various advanced queries using PostgreSQL and innovative tools to carry out efficiently multi-layer overlay analysis. The end-product is a simple system that provides the opportunity not only to consult interactively but also download processed remote sensing data

Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

tThis paper shows the first comparison between data from Sentinel-2 (S2) Multi Spectral Instrument (MSI)and Landsat 8 (L8) Operational Land Imager (OLI) headed up to greenhouse detection. Two closely relatedin time scenes, one for each sensor, were classified by using Object Based Image Analysis and RandomForest (RF). The RF input consisted of several object-based features computed from spectral bands andincluding mean values, spectral indices and textural features. S2 and L8 data comparisons were alsoextended using a common segmentation dataset extracted form VHR World-View 2 (WV2) imagery totest differences only due to their specific spectral contribution. The best band combinations to performsegmentation were found through a modified version of the Euclidian Distance 2 index. Four differentRF classifications schemes were considered achieving 89.1%, 91.3%, 90.9% and 93.4% as the best overallaccuracies respectively, evaluated over the whole study area

AssesSeg—A Command Line Tool to Quantify Image Segmentation Quality: A Test Carried Out in Southern Spain from Satellite Imagery

This letter presents the capabilities of a command line tool created to assess the quality of segmented digital images. The executable source code, called AssesSeg, was written in Python 2.7 using open source libraries. AssesSeg (University of Almeria, Almeria, Spain; Politecnico di Bari, Bari, Italy) implements a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2) and was tested on different satellite images (Sentinel-2, Landsat 8, and WorldView-2). The segmentation was applied to plastic covered greenhouse detection in the south of Spain (Almería). AssesSeg outputs were utilized to find the best band combinations for the performed segmentations of the images and showed a clear positive correlation between segmentation accuracy and the quantity of available reference data. This demonstrates the importance of a high number of reference data in supervised segmentation accuracy assessment problems

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

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

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%

Perfil da população atendida em uma unidade de emergência referenciada

Acquiring knowledge concerning the characteristics of the population that seeks an emergency department can support the planning of health actions. This study identifies the socio-demographic profile and the main complaints of the adult population cared for in a Referral Emergency Unit (RECU). This descriptive and retrospective study was conducted in the RECU of a university hospital in the State of Sao Paulo, Brazil. The sample was composed of the services care forms generated for the period between January and December 2008. The instrument was based on data contained in the care forms. Young women (14 to 54 years old), residents of neighborhoods near the RECU, spontaneously sought the service during the week from 7am to 7pm. The most frequent complaints were headache, back pain, abdominal and chest pain. The conclusion is that most of the sample was young adults, of productive age, female, who spontaneously sought the service on weekdays during the day. Most complaints were of low complexity.El objetivo del estudio fue identificar el perfil sociodemográfico y las principales quejas de la población adulta atendida en la Unidad de Emergencia de Referencia (UER). El conocimiento de las características de la población que frecuenta un servicio de emergencia constituye una herramienta de planificación de las acciones en salud. Se trata de un estudio descriptivo/retrospectivo desarrollado en la UER de un hospital de enseñanza en el interior del estado de Sao Paulo. La muestra fue constituida por los boletines de atención(BAU) del período de enero a diciembre de 2.008. El instrumento utilizado fue estructurado con base en los datos del BAU. La búsqueda espontánea fue hecha por mujeres jóvenes (14 a 54 años) de los barrios próximos a la UER durante la semana y en el horario de las siete a las diecinueve horas. Las quejas más frecuentes fueron: cefalea, lumbago, dolores (abdominal, torácica). Se concluye que el perfil de la muestra fue en su mayoría adulto y joven, en edad productiva, de sexo femenino, con demanda espontánea y en el período diurno en días útiles. Las atenciones apuntaron para quejas de baja complejidad.O conhecimento das características da população que frequenta um serviço de emergência constitui ferramenta de planejamento de ações em saúde. O objetivo deste estudo foi identificar o perfil sociodemográfico e as principais queixas da população adulta, atendida na unidade de emergência referenciada (UER). O método usado foi o descritivo/retrospectivo, desenvolvido na UER de um hospital de ensino no interior do Estado de São Paulo. A amostra ficou constituída pelos boletins de atendimento (BAU), referentes ao período de janeiro a dezembro/2008. O instrumento utilizado foi estruturado com base nos dados do BAU.. Os resultados mostraram que a procura espontânea foi feita por mulheres jovens (14 aos 54 anos) dos bairros próximos à UER, durante a semana e no horário das sete às dezenove horas. As queixas mais frequentes foram: cefaleia, lombalgia, dores (abdominal, torácica). Conclui-se que o perfil da amostra foi, na maioria, composta por adulto jovem, idade produtiva, sexo feminino, demanda espontânea e no período diurno em dias úteis. Os atendimentos apontaram para queixas de baixa complexidade

A New Threshold Relative Radiometric Correction Algorithm (TRRCA) of Multiband Satellite Data

It is well known that remote sensed scenes could be affected by many factors and, for optimum change detection, these unwanted effects must be removed. In this study a new algorithm is proposed for PIF (Pseudo Invariant Features) extraction and relative radiometric normalization. The new algorithm can be labeled as a supervised one and combines three methods for the detection of PIFs: Moment distance index (MDI), Normalized Difference Vegetation Index (NDVI) masks morphological erosion and dilate operators. In order to prove its effectiveness, the algorithm was tested by using Landsat 8 scenes of the “Mar de Plstico” landscape of the Andalusian Almer´ıa. Many tests were performed in order to provide a set of valid input parameters for the chosen environments. Lastly, the results were statistically assessed with parametric and non-parametric tests showing very good and stable results in the four different study area