Utilização das imagens LISS III para a classificação do uso do solo aplicado ao cadastro geométrico da propriedade rústica

Relatório de Estágio de Mestrado em Gestão do Território - Especialização em Detecção Remota e Sistemas de Informação Geográfica

Spatial analysis and investigation of fire events occurrences in the Valencian Community, Spain

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

Recent droughts and the impact of North Atlantic Oscillation in Iberia; a spatiotemporal analysis based on vegetation temperature condition Index (VTCI)

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

Análise multicritério em SIG para determinação de um índice espacializado de pressão antrópica litoral. Casos de Espinho, Caparica e Faro

Dissertação apresentada para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Gestão do Território especialização em Detecção Remota e Sistemas de Informação Geográfica

Gas emissions on rice culture and its importance on air quality

Dissertação para obtenção do Grau de Mestre em Engenharia do Ambiente Perfil Gestão de Sistemas Ambientais

Development of a satellite based PM10 concentration product for urban areas

Dissertação para obtenção do Grau de Doutor em Engenharia do Ambiente

Modelação do risco de incêndio florestal à escala municipal com recurso a dados de detecção remota

Neste projecto é testada uma metodologia para actualização de mapas de ocupação do solo já existentes, derivados de fotografia aérea, usando uma imagem satélite para posterior modelação com vista à obtenção da cartografia do risco de incêndio actualizada. Os diferentes passos metodológicos na fase da actualização dos mapas de ocupação de solo são: Classificação digital das novas ocupações, Produção do mapa de alterações, Integração de informação auxiliar, Actualização da Cartografia Temática. Para a produção do mapa de alterações a detecção de alterações foi efectuada através de expressões de Álgebra de Mapas. A classificação digital foi realizada com um classificador assistido - Classificador da Máxima Verosimilhança. A integração de informação auxiliar serviu para melhorar os resultados da classificação digital, nomeadamente em termos das áreas ardidas permitindo uma resolução temática mais detalhada. A actualização resultou da sobreposição do mapa das áreas alteradas classificadas com o mapa desactualizado. Como produto obteve-se a Carta de Alterações da Ocupação do Solo com Potencial Influência no Risco de Incêndio actualizada para 2008, base para a fase da Modelação do Risco. A metodologia foi testada no concelho de Viseu, Centro de Portugal. A Carta de Uso e Ocupação do Solo de Portugal Continental para 2007 (COS2007) foi utilizada como carta de referência. A nova carta actualizada para 2008, no concelho de Viseu, apresenta 103 classes temáticas, 1ha de unidade mínima e 90% de precisão global. A modelação do risco de incêndio geralmente é feita através de índices que variam, de forma geral, numa escala qualitativa, tendo como fim possibilitar a definição de acções de planeamento e ordenamento florestal no âmbito da defesa da floresta contra incêndios. Desta forma, as cartas de risco são indicadas para acções de prevenção, devendo ser utilizadas em conjunto com a carta da perigosidade que juntas podem ser utilizadas em acções de planeamento, em acções de combate e supressão. A metodologia testada, neste projecto, para elaboração de cartografia de risco foi, a proposta por Verde (2008) e adoptada pela AFN (2012). Os resultados apresentados vão precisamente ao encontro do que diz respeito no Guia Técnico para Plano Municipal de Defesa da Floresta Contra Incêndios, "O mapa de Risco combina as componentes do mapa de perigosidade com as componentes do dano potencial (vulnerabilidade e valor) para indicar qual o potencial de perda face ao incêndio florestal".

Urban land use change analysis and modelling: a case study of Setubal-Sesimbra, Portugal

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

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Rediscovery of Leptaxis vetusta (Morelet & Drouet, 1857), a subfossil land snail from the Quaternary of Santa Maria (Azores)

During a recent field work on the southern coast of the island of Santa Maria (Azores) a bulk sample of 37 shells and 25 fragments of Leptaxis vetusta was assembled from Late Pleistocene and Holocene slope deposits outcropping in the area. These specimens are the first of this rare subfossil species to be mentioned since the original descriptions of Arthur Morelet and Henri Drouet (1857). The purposes of our paper are a systematic and biometric description of L. vestuta. For the first time, the original type: locality was localized with accuracy over the southern downslopes of Pico do Facho, between Figueiral and Prainha. The subfossil specimens were collected in slope deposits and detritic fans, overlying a fossiliferous marine deposit situated over the 2-3 m abrasion platform of Praia and Prainha bay. The age and factors associated to the extinction of this species are discussed, including the destruction of the original laurel cover and the colonization by Otala lactea (Muller, 1774), a continental helicid introduced and widespread in Santa Maria.

Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.

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

Effects of Spatial Pattern of GreenSpace on Land Surface Temperature: A case study on Oasis City Aksu, northwest China

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

The use of analytical hierarchy process in spatial decision support system for land use management

Geographic information systems give us the possibility to analyze, produce, and edit geographic information. Furthermore, these systems fall short on the analysis and support of complex spatial problems. Therefore, when a spatial problem, like land use management, requires a multi-criteria perspective, multi-criteria decision analysis is placed into spatial decision support systems. The analytic hierarchy process is one of many multi-criteria decision analysis methods that can be used to support these complex problems. Using its capabilities we try to develop a spatial decision support system, to help land use management. Land use management can undertake a broad spectrum of spatial decision problems. The developed decision support system had to accept as input, various formats and types of data, raster or vector format, and the vector could be polygon line or point type. The support system was designed to perform its analysis for the Zambezi river Valley in Mozambique, the study area. The possible solutions for the emerging problems had to cover the entire region. This required the system to process large sets of data, and constantly adjust to new problems’ needs. The developed decision support system, is able to process thousands of alternatives using the analytical hierarchy process, and produce an output suitability map for the problems faced.