New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions

Dissertação para obtenção do Grau de Doutor em Estatística e Gestão do Risco, especialidade em Estatística

Georeferencing Road Accidents with Google Earth: Transforming Information into Knowledge for Decision Support

Over the last fifty years mobility practices have changed dramatically, improving the way travel takes place, the time it takes but also on matters like road safety and prevention. High mortality caused by high accident levels has reached untenable levels. But the research into road mortality stayed limited to comparative statistical exercises which go no further than defining accident types. In terms of sharing information and mapping accidents, little progress has been mad, aside from the normal publication of figures, either through simplistic tables or web pages. With considerable technological advances on geographical information technologies, research and development stayed rather static with only a few good examples on dynamic mapping. The use of Global Positioning System (GPS) devices as normal equipments on automobile industry resulted in a more dynamic mobility patterns but also with higher degrees of uncertainty on road traffic. This paper describes a road accident georeferencing project for the Lisbon District involving fatalities and serious injuries during 2007. In the initial phase, individual information summaries were compiled giving information on accidents and its majour characteristics, collected by the security forces: the Public Safety Police Force (Polícia de Segurança Pública - PSP) and the National Guard (Guarda Nacional Republicana - GNR). The Google Earth platform was used to georeference the information in order to inform the public and the authorities of the accident locations, the nature of the location, and the causes and consequences of the accidents. This paper also gives future insights about augmented reality technologies, considered crucial to advances to road safety and prevention studies. At the end, this exercise could be considered a success because of numerous consequences, as for stakeholders who decide what to do but also for the public awareness to the problem of road mortality.

A Web based geospatial application for disaster preparedness in Uganda

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Image processing for displacement measurements

Since the invention of photography humans have been using images to capture, store and analyse the act that they are interested in. With the developments in this field, assisted by better computers, it is possible to use image processing technology as an accurate method of analysis and measurement. Image processing's principal qualities are flexibility, adaptability and the ability to easily and quickly process a large amount of information. Successful examples of applications can be seen in several areas of human life, such as biomedical, industry, surveillance, military and mapping. This is so true that there are several Nobel prizes related to imaging. The accurate measurement of deformations, displacements, strain fields and surface defects are challenging in many material tests in Civil Engineering because traditionally these measurements require complex and expensive equipment, plus time consuming calibration. Image processing can be an inexpensive and effective tool for load displacement measurements. Using an adequate image acquisition system and taking advantage of the computation power of modern computers it is possible to accurately measure very small displacements with high precision. On the market there are already several commercial software packages. However they are commercialized at high cost. In this work block-matching algorithms will be used in order to compare the results from image processing with the data obtained with physical transducers during laboratory load tests. In order to test the proposed solutions several load tests were carried out in partnership with researchers from the Civil Engineering Department at Universidade Nova de Lisboa (UNL).

Estudo sócio-ecológico sobre tráfico de seres humanos para fins de exploração laboral na região do Alentejo : análise espacial e estatística

O presente relatório, inserido no Mestrado em Gestão do Território, Área de Especialização em Deteção Remota e Sistemas de Informação Geográfica, lecionado pelo Departamento de Geografia e Planeamento Regional da Faculdade de Ciências Sociais e Humanas da Universidade Nova de Lisboa, pretende descrever o trabalho desenvolvido pelo mestrando enquanto estagiário no Observatório do Tráfico de Seres Humanos (OTSH). O relatório está estruturado em três capítulos distintos. No primeiro capítulo é realizada uma abordagem teórica sobre o Tráfico de Seres Humanos e a distinção entre o mesmo com o Auxílio à Imigração Ilegal. Neste, é também feita uma pequena referência à problemática dos novos fluxos de refugiados/migrantes que, no momento da realização do mesmo, constituem uma questão bastante complexa sobretudo ao nível europeu. No segundo capítulo é realizada uma caracterização da área de estudo, assim como a descrição dos dados utilizados e a metodologia aplicada no mesmo. No terceiro capítulo são apresentados os resultados finais do estudo e a cartografia de síntese que sustenta os mesmos. Para a realização deste estudo recorreu-se a uma análise multicritério em SIG para prever a localização de áreas de maior suscetibilidade de ocorrência de novos casos relativos ao crime do tráfico de seres humanos para exploração laboral na agricultura, na região do Alentejo (distritos de Beja, Évora e Portalegre), através do recurso a dados estatísticos disponibilizados tanto pelo OTSH, como por outras entidades. A metodologia apresentada integra um SIG baseado num modelo raster com o Analytical Hierarchy Process (AHP). Através da realização deste estudo, a importância dos SIG como ferramenta no auxílio ao processo de tomada de decisão, pôde ser testada, conjuntamente com o processo metodológico AHP, através dos resultados apresentados. Com um possível desenvolvimento deste modelo analítico, pretende-se que o mesmo seja adaptável a outras regiões e em última instância, outros tipos de exploração e/ou tráfico.

Mapping and characterization of a green biofilm inside of Vilar de Frades Church (Portugal)

Vilar de Frades church is integrated in the Vilar de Frades Monastery, located in the North part of Portugal (Barcelos). The monastery, founded in 566, suffered several architectural modifications and restoration works, the most relevant was in the XVI century. The church, in granite, has one nave and six bays,holding ten chapels with vaults of crossed ribbings. Nowadays, the chapels present a severe biological colonization characterised by an intense green biofilm, which becoming apparent in other locations inside the church. In the course of a general survey concerning the conservation state of the church, an accurate campaign was planned in order to assess the main biodeterioration agents, map biological colonization and determine the environmental conditions. Laboratory analyses were accomplished with optical microscopy and spectrofluorometry. This study presents the results of this campaign. Details on conservation or preservation works that need to be implemented are also presented.

LADAR based mapping and obstacle detection system for service robots

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Gender Differences in Spatial Cognition: Wayfinding Performance and Sketch Mapping Activity of German Speakers

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies

Mapping Mediterranean Portugal: Pastoral and Counter-pastoral

This article focuses on the different images of Mediterranean Portugal developed by three important Portuguese social scientists of the 20th century: the geographer Orlando Ribeiro, the ethnologist Jorge Dias and the social anthropologist José Cutileiro. The article argues that these different images stem from different ideological attitudes towards the countryside, ranging from pastoral to counter-pastoral, and are also related to different ways of addressing the links between the countryside and national identity.

Land cover mapping analysis and urban growth modeling using remote sensing techniques

The rapid growth of big cities has been noticed since 1950s when the majority of world population turned to live in urban areas rather than villages, seeking better job opportunities and higher quality of services and lifestyle circumstances. This demographic transition from rural to urban is expected to have a continuous increase. Governments, especially in less developed countries, are going to face more challenges in different sectors, raising the essence of understanding the spatial pattern of the growth for an effective urban planning. The study aimed to detect, analyse and model the urban growth in Greater Cairo Region (GCR) as one of the fast growing mega cities in the world using remote sensing data. Knowing the current and estimated urbanization situation in GCR will help decision makers in Egypt to adjust their plans and develop new ones. These plans should focus on resources reallocation to overcome the problems arising in the future and to achieve a sustainable development of urban areas, especially after the high percentage of illegal settlements which took place in the last decades. The study focused on a period of 30 years; from 1984 to 2014, and the major transitions to urban were modelled to predict the future scenarios in 2025. Three satellite images of different time stamps (1984, 2003 and 2014) were classified using Support Vector Machines (SVM) classifier, then the land cover changes were detected by applying a high level mapping technique. Later the results were analyzed for higher accurate estimations of the urban growth in the future in 2025 using Land Change Modeler (LCM) embedded in IDRISI software. Moreover, the spatial and temporal urban growth patterns were analyzed using statistical metrics developed in FRAGSTATS software. The study resulted in an overall classification accuracy of 96%, 97.3% and 96.3% for 1984, 2003 and 2014’s map, respectively. Between 1984 and 2003, 19 179 hectares of vegetation and 21 417 hectares of desert changed to urban, while from 2003 to 2014, the transitions to urban from both land cover classes were found to be 16 486 and 31 045 hectares, respectively. The model results indicated that 14% of the vegetation and 4% of the desert in 2014 will turn into urban in 2025, representing 16 512 and 24 687 hectares, respectively.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.