931 resultados para geo-reference
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Beyond the classical statistical approaches (determination of basic statistics, regression analysis, ANOVA, etc.) a new set of applications of different statistical techniques has increasingly gained relevance in the analysis, processing and interpretation of data concerning the characteristics of forest soils. This is possible to be seen in some of the recent publications in the context of Multivariate Statistics. These new methods require additional care that is not always included or refered in some approaches. In the particular case of geostatistical data applications it is necessary, besides to geo-reference all the data acquisition, to collect the samples in regular grids and in sufficient quantity so that the variograms can reflect the spatial distribution of soil properties in a representative manner. In the case of the great majority of Multivariate Statistics techniques (Principal Component Analysis, Correspondence Analysis, Cluster Analysis, etc.) despite the fact they do not require in most cases the assumption of normal distribution, they however need a proper and rigorous strategy for its utilization. In this work, some reflections about these methodologies and, in particular, about the main constraints that often occur during the information collecting process and about the various linking possibilities of these different techniques will be presented. At the end, illustrations of some particular cases of the applications of these statistical methods will also be presented.
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Trabalho de Projecto para obtenção do grau de Mestre em Engenharia Informática e de Computadores
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This paper proposes the use of an autonomous assistant mobile robot in order to monitor the environmental conditions of a large indoor area and develop an ambient intelligence application. The mobile robot uses single high performance embedded sensors in order to collect and geo-reference environmental information such as ambient temperature, air velocity and orientation and gas concentration. The data collected with the assistant mobile robot is analyzed in order to detect unusual measurements or discrepancies and develop focused corrective ambient actions. This paper shows an example of the measurements performed in a research facility which have enabled the detection and location of an uncomfortable temperature profile inside an office of the research facility. The ambient intelligent application has been developed by performing some localized ambient measurements that have been analyzed in order to propose some ambient actuations to correct the uncomfortable temperature profile.
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This paper proposes the use of an autonomous assistant mobile robot in order to monitor the environmental conditions of a large indoor area and develop an ambient intelligence application. The mobile robot uses single high performance embedded sensors in order to collect and geo-reference environmental information such as ambient temperature, air velocity and orientation and gas concentration. The data collected with the assistant mobile robot is analyzed in order to detect unusual measurements or discrepancies and develop focused corrective ambient actions. This paper shows an example of the measurements performed in a research facility which have enabled the detection and location of an uncomfortable temperature profile inside an office of the research facility. The ambient intelligent application has been developed by performing some localized ambient measurements that have been analyzed in order to propose some ambient actuations to correct the uncomfortable temperature profile.
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The project investigated whether it would be possible to remove the main technical hindrance to precision application of herbicides to arable crops in the UK, namely creating geo-referenced weed maps for each field. The ultimate goal is an information system so that agronomists and farmers can plan precision weed control and create spraying maps. The project focussed on black-grass in wheat, but research was also carried out on barley and beans and on wild-oats, barren brome, rye-grass, cleavers and thistles which form stable patches in arable fields. Farmers may also make special efforts to control them. Using cameras mounted on farm machinery, the project explored the feasibility of automating the process of mapping black-grass in fields. Geo-referenced images were captured from June to December 2009, using sprayers, a tractor, combine harvesters and on foot. Cameras were mounted on the sprayer boom, on windows or on top of tractor and combine cabs and images were captured with a range of vibration levels and at speeds up to 20 km h-1. For acceptability to farmers, it was important that every image containing black-grass was classified as containing black-grass; false negatives are highly undesirable. The software algorithms recorded no false negatives in sample images analysed to date, although some black-grass heads were unclassified and there were also false positives. The density of black-grass heads per unit area estimated by machine vision increased as a linear function of the actual density with a mean detection rate of 47% of black-grass heads in sample images at T3 within a density range of 13 to 1230 heads m-2. A final part of the project was to create geo-referenced weed maps using software written in previous HGCA-funded projects and two examples show that geo-location by machine vision compares well with manually-mapped weed patches. The consortium therefore demonstrated for the first time the feasibility of using a GPS-linked computer-controlled camera system mounted on farm machinery (tractor, sprayer or combine) to geo-reference black-grass in winter wheat between black-grass head emergence and seed shedding.
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Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).
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Background: Cancer is the second leading cause of death in Argentina, and there is little knowledge about its incidence. The first study based on population-based cancer registry described spatial incidence and indicated that there existed at least county-level aggregation. The aim of the present work is to model the incidence patterns for the most incidence cancer in Córdoba Province, Argentina, using information from the Córdoba Cancer Registry by performing multilevel mixed model approach to deal with dependence and unobserved heterogeneity coming from the geo-reference cancer occurrence. Methods: Standardized incidence rates (world standard population) (SIR) by sex based on 5-year age groups were calculated for 109 districts nested on 26 counties for the most incidence cancers in Cordoba using 2004 database. A Poisson twolevel random effect model representing unobserved heterogeneity between first level-districts and second level-counties was fitted to assess the spatial distribution of the overall and site specific cancer incidence rates. Results: SIR cancer at Córdoba province shown an average of 263.53±138.34 and 200.45±98.30 for men and women, respectively. Considering the ratio site specific mean SIR to the total mean, breast cancer ratio was 0.25±0.19, prostate cancer ratio was 0.12±0.10 and lower values for lung and colon cancer for both sexes. The Poisson two-level random intercepts model fitted for SIR data distributed with overdispersion shown significant hierarchical structure for the cancer incidence distribution. Conclusions: a strong spatial-nested effect for the cancer incidence in Córdoba was observed and will help to begin the study of the factors associated with it.
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Groundwater with underground residence times between days and a few years have been investigated over more than 20 years from 487 remote sites located in different aquifer types in the Alpine belt. Analysis of the data reveals that groundwaters evolved in crystalline, evaporite, carbonate, molasse, and flysch aquifers can be clearly distinguished based on their major and trace element composition and degree of mineralisation. A further subdivision can be made even within one aquifer type based on the trace element compositions, which are characteristic for the lithologic environment. Major and trace element concentrations can be quantitatively described by interaction of the groundwater with the aquifer- specific mineralogy along the flow path. Because all investigated sites show minimal anthropogenic influences, the observed concentration ranges represent the natural background concentrations and can thus serve as a “geo-reference” for recent groundwaters from these five aquifer types. This “geo-reference” is particularly useful for the identification of groundwater contamination. It further shows that drinking water standards can be grossly exceeded for critical elements by purely natural processes
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En general las metodologías utilizadas para la evaluación de impacto ambiental han empleado ponderaciones subjetivas, o mediciones de ciertos componentes en forma parcializada, en donde no se tiene en cuenta el territorio sobre el cual se desarrollan las acciones y los efectos. Esto hace que las EIA no sean ajustadas, tornándose más dificultoso aún en las evaluaciones relacionadas con temas de Ordenamiento Territorial, donde la complejidad está dada no solo por la cantidad de variables a incluir sino por el nivel de interacciones a considerar. A pesar de que no siempre es tenida en cuenta, la componente espacial es fundamental en toda EIA, no solo para describir el entorno en el que las acciones se realizan sino para el análisis y correlación de fenómenos que impactan en el territorio y el medio ambiente. En este trabajo se persigue: • Identificar indicadores geográficos posibles de incorporar en las EIA. • Medir en forma cuali y cuantitativa los indicadores seleccionados . • Utilizar geotecnologías como son los SIG y la Teledetección en dicha medición. La incorporación de indicadores geográficos en la EIA de temas tan complejos y comprometidos como los relacionados con el Ordenamiento Territorial se torna necesario debido a la propia naturaleza de ciertos fenómenos que requieren de un enfoque integral georeferenciado, con indicadores medidos en forma cualitativa o cuantitativa.
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One of the more aspects that have shaped the landscape is the human impact. The human impact has the clearest indicator of the density of settlements in a particular geographic region. In this paper we study all settlements shown on the map of the Kingdom of Valencia, Spain Geographic Atlas (AGE) of Tomas Lopez (1788), and their correspondence with the current ones. To meet this goal we have developed a specific methodology, the systematic study of all existing settlements in historical cartography. This will determine which have disappeared and which have been renamed. The material used has been the historical cartography of Tomas Lopez, part of the AGE (1789), the Kingdom of Valencia (1789), sheets numbers (78, 79, 80 and 81); Current mapping of the provinces of Alicante, Valencia, Castellon, Teruel, Tattagona and Cuenca; As main software ArcGis V.9.3. The steps followed in the methodology are as follows: 1. Check the scale of the maps. Analyze the possible use of a spherical earth model. 2. Geo-reference of maps with latitude and longitude framework. Move the historical longitude origin to the origin longitude of modern cartography. 3 Digitize of all population settlements or cities. 4 Identify historic settlements or cities corresponding with current ones. 5. If the maps have the same orientation and scale, replace the coordinate transformation of historical settlements with a new one, by a translation in latitude and longitude equal to the calculated mean value of all ancient map points corresponding to the new. 6. Calculation of absolute accuracy of the two maps, i.e. the linear distance between the points of both maps. 7 draw in the GIS, the settlements without correspondence, in the current coordinates, and with a circle of mean error of the sheet, in order to locate their current location. If there are actual settlements exist within this circle, they are candidates to be the searched settlements. We analyzed more than 2000 settlements represented in the Atlas of Tomas Lopez of the Kingdom of Valencia (1789), of which almost 14.5% have no correspondence with the existing settlements. The rural landscape evolution of the Valencia, oldest kingdom of Valencia, one can say that can be severely affected by the anthropization suffered in the period from 1789 to the present, since 70% of existing settlements actually have appeared after Tomas Lopez¿s cartography, dated on 1789
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Purpose: In this paper we study all settlements shown on the map of the Province of Madrid, sheet number 1 of AGE (Atlas Geográfico de España of Tomas Lopez 1804) and their correspondence with the current ones. This map is divided in to zones: Madrid and Almonacid de Zorita. Method: The steps followed in the methodology are as follow: 1. Geo-reference of maps with latitude and longitude framework. Move the historical longitude origin to the origin longitude of modern cartography. 2 Digitize of all population settlements or cities (97 on Madrid and 42 on Almonacid de Zorita), 3 Identify historic settlements or cities corresponding with current ones. 4. If the maps have the same orientation and scale, replace the coordinate transformation of historical settlements with a new one, by a translation in latitude and longitude equal to the calculated mean value of all ancient map points corresponding to the new. 5. Calculation of absolute accuracy of the two maps. 6 draw in the GIS, the settlements accuracy. Result: It was found that all AGE settlements have good correspondence with current, ie only 27 settlements lost in Madrid and 2 in Almonacid. The average accuracy is 2.3 and 5.7 km to Madrid and Almonacid de Zorita respectively. Discussion & Conclusion: The final accuracy map obtained shows that there is less error in the middle of the map. This study highlights the great work done by Tomas Lopez in performing this mapping without fieldwork. This demonstrates the great value that has been the work of Tomas Lopez in the history of cartography.
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Recolher dados para actualizar os conhecimento da diversidade parasitária dos mesocarnívoros mais abundantes nas regiões do sul de Portugal e relacionar a dispersão parasitária, a conservação e a saúde humana constituíram os principais objetivos deste estudo. Raposas (Vulpes vulpes), sacarrabos (Herpestes ichneumon), fuinhas (Martes foina), ginetes (Genetta genetta) e texugos (Meles meles) atropelados foram os carnívoros-hospedeiros em estudo. Um grupo de raposas caçadas foi também considerado parte da amostra. A informação geo-referenciada de todos os animais serviu para executar a análise espacial. Realizaram-se necrópsias meticulosas e procedeu se à recolha, identificação e preservação dos parasitas encontrados. Pela primeira vez em Portugal é registada a presença do parasita da gineta Ancytostoma martinezi. A correlação entre os factores humanos e ambientais e, a riqueza de espécies foi determinada estatisticamente. Densidade populacional, disponibilidade de égua, tipo de uso de solo e distância mínima às sedes de concelho não apresentaram uma relação estatisticamente significativa com a infecção parasitária das raposas. Os resultados obtidos relativamente à presença de parasitas zoonóticos nos animais silvestres em estudo fomentam o trabalho multidisciplinar entre a Biologia da Conservação e as ciências médicas. ABSTRACT; The aim of this study was to collect data in order to update the information related to the parasitic diversity of the predominant mesocarnívores in the southern regions of Portugal and to establish relationships between the parasite dispersal, conservation and human health. Road killed foxes (Vulpes vulpes), mongoose (Herpestes ichneumon), stone marten (Martes foina), common genets (Genetta genetta) and euroasian badgers (Meles meles) were the considered carnivore-hosts. A sample of hunted foxes was also regarded.AD the animals had geo-reference information, ultimately used for spatial analysis. Thorough necropsies were performed and macroparasites collected, identified and preserved. For the first time in Portugal Ancylostoma martinezi, a common genets parasite.is recorded. Statistical species richness and correlation between human and environmental factors were determined. Human population density, water drainage, soil use, minimum distance to head council cities and the infection status of foxes proved no significant statistical relation. Results obtained on zoonotic parasites present in wild animals enhance the necessity of multidisciplinary work between Biology conservation and medical sciences.
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We report large photoluminescence (PL) enhancement in Eu(3+)-doped GeO(2)-Bi(2)O(3) glasses containing gold nanoparticles (NPs). Growth of approximate to 1000% in the PL intensity corresponding to the Eu(3+) transition (5)D(0)->(7)F(2), at 614 nm, was observed in comparison with a reference sample that does not contain gold NPs. Other PL bands from 580 to 700 nm are also enhanced. The enhancement of the PL intensity is attributed to the increased local field in the Eu(3+) locations due to the presence of the NPs and the energy transfer from the excited NPs to the Eu(3+) ions.
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Tick-borne zoonoses (TBZ) are emerging diseases worldwide. A large amount of information (e.g. case reports, results of epidemiological surveillance, etc.) is dispersed through various reference sources (ISI and non-ISI journals, conference proceedings, technical reports, etc.). An integrated database-derived from the ICTTD-3 project (http://www.icttd.nl)-was developed in order to gather TBZ records in the (sub-)tropics, collected both by the authors and collaborators worldwide. A dedicated website (http://www.tickbornezoonoses.org) was created to promote collaboration and circulate information. Data collected are made freely available to researchers for analysis by spatial methods, integrating mapped ecological factors for predicting TBZ risk. The authors present the assembly process of the TBZ database: the compilation of an updated list of TBZ relevant for (sub-)tropics, the database design and its structure, the method of bibliographic search, the assessment of spatial precision of geo-referenced records. At the time of writing, 725 records extracted from 337 publications related to 59 countries in the (sub-)tropics, have been entered in the database. TBZ distribution maps were also produced. Imported cases have been also accounted for. The most important datasets with geo-referenced records were those on Spotted Fever Group rickettsiosis in Latin-America and Crimean-Congo Haemorrhagic Fever in Africa. The authors stress the need for international collaboration in data collection to update and improve the database. Supervision of data entered remains always necessary. Means to foster collaboration are discussed. The paper is also intended to describe the challenges encountered to assemble spatial data from various sources and to help develop similar data collections.
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Tick-borne zoonoses (TBZ) are emerging diseases worldwide. A large amount of information (e.g. case reports, results of epidemiological surveillance, etc.) is dispersed through various reference sources (ISI and non-ISI journals, conference proceedings, technical reports, etc.). An integrated database-derived from the ICTTD-3 project (http://www.icttd.nl)-was developed in order to gather TBZ records in the (sub-)tropics, collected both by the authors and collaborators worldwide. A dedicated website (http://www.tickbornezoonoses.org) was created to promote collaboration and circulate information. Data collected are made freely available to researchers for analysis by spatial methods, integrating mapped ecological factors for predicting TBZ risk. The authors present the assembly process of the TBZ database: the compilation of an updated list of TBZ relevant for (sub-)tropics, the database design and its structure, the method of bibliographic search, the assessment of spatial precision of geo-referenced records. At the time of writing, 725 records extracted from 337 publications related to 59 countries in the (sub-)tropics, have been entered in the database. TBZ distribution maps were also produced. Imported cases have been also accounted for. The most important datasets with geo-referenced records were those on Spotted Fever Group rickettsiosis in Latin-America and Crimean-Congo Haemorrhagic Fever in Africa. The authors stress the need for international collaboration in data collection to update and improve the database. Supervision of data entered remains always necessary. Means to foster collaboration are discussed. The paper is also intended to describe the challenges encountered to assemble spatial data from various sources and to help develop similar data collections.