Forest soil short term response to prescribed fire: an approach by Principal Components Analysis

In the current context of serious climate changes, where the increase of the frequency of some extreme events occurrence can enhance the rate of periods prone to high intensity forest fires, the National Forest Authority often implements, in several Portuguese forest areas, a regular set of measures in order to control the amount of fuel mass availability (PNDFCI, 2008). In the present work we’ll present a preliminary analysis concerning the assessment of the consequences given by the implementation of prescribed fire measures to control the amount of fuel mass in soil recovery, in particular in terms of its water retention capacity, its organic matter content, pH and content of iron. This work is included in a larger study (Meira-Castro, 2009(a); Meira-Castro, 2009(b)). According to the established praxis on the data collection, embodied in multidimensional matrices of n columns (variables in analysis) by p lines (sampled areas at different depths), and also considering the quantitative data nature present in this study, we’ve chosen a methodological approach that considers the multivariate statistical analysis, in particular, the Principal Component Analysis (PCA ) (Góis, 2004). The experiments were carried out in a soil cover over a natural site of Andaluzitic schist, in Gramelas, Caminha, NW Portugal, who was able to maintain itself intact from prescribed burnings from four years and was submit to prescribed fire in March 2008. The soils samples were collected from five different plots at six different time periods. The methodological option that was adopted have allowed us to identify the most relevant relational structures inside the n variables, the p samples and in two sets at the same time (Garcia-Pereira, 1990). Consequently, and in addition to the traditional outputs produced from the PCA, we have analyzed the influence of both sampling depths and geomorphological environments in the behavior of all variables involved.

Dynamic behaviour of soft core sandwich beam structures using kriging-based layerwise models

Sandwich structures with soft cores are widely used in applications where a high bending stiffness is required without compromising the global weight of the structure, as well as in situations where good thermal and damping properties are important parameters to observe. As equivalent single layer approaches are not the more adequate to describe realistically the kinematics and the stresses distributions as well as the dynamic behaviour of this type of sandwiches, where shear deformations and the extensibility of the core can be very significant, layerwise models may provide better solutions. Additionally and in connection with this multilayer approach, the selection of different shear deformation theories according to the nature of the material that constitutes the core and the outer skins can predict more accurately the sandwich behaviour. In the present work the authors consider the use of different shear deformation theories to formulate different layerwise models, implemented through kriging-based finite elements. The viscoelastic material behaviour, associated to the sandwich core, is modelled using the complex approach and the dynamic problem is solved in the frequency domain. The outer elastic layers considered in this work may also be made from different nanocomposites. The performance of the models developed is illustrated through a set of test cases. (C) 2015 Elsevier Ltd. All rights reserved.

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.

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

A method to correct the flow distortion of offshore wind data using CFD simulation and experimental wind tunnel tests

The assessment of wind energy resource for the development of deep offshore wind plants requires the use of every possible source of data and, in many cases, includes data gathered at meteorological stations installed at islands, islets or even oil platforms—all structures that interfere with, and change, the flow characteristics. This work aims to contribute to the evaluation of such changes in the flow by developing a correction methodology and applying it to the case of Berlenga island, Portugal. The study is performed using computational fluid dynamic simulations (CFD) validated by wind tunnel tests. In order to simulate the incoming offshore flow with CFD models a wind profile, unknown a priori, was established using observations from two coastal wind stations and a power law wind profile was fitted to the existing data (a=0.165). The results show that the resulting horizontal wind speed at 80 m above sea level is 16% lower than the wind speed at 80 m above the island for the dominant wind direction sector.

High strain rate constitutive modeling forhistorical structures subjected to blast loading

Doctoral Thesis Civil Engineering

Modelling Amazonian forest eddy covariance data: a comparison of big leaf versus sun/shade models for the C-14 tower at Manaus I. Canopy photosynthesis

In this study, we concentrate on modelling gross primary productivity using two simple approaches to simulate canopy photosynthesis: "big leaf" and "sun/shade" models. Two approaches for calibration are used: scaling up of canopy photosynthetic parameters from the leaf to the canopy level and fitting canopy biochemistry to eddy covariance fluxes. Validation of the models is achieved by using eddy covariance data from the LBA site C14. Comparing the performance of both models we conclude that numerically (in terms of goodness of fit) and qualitatively, (in terms of residual response to different environmental variables) sun/shade does a better job. Compared to the sun/shade model, the big leaf model shows a lower goodness of fit and fails to respond to variations in the diffuse fraction, also having skewed responses to temperature and VPD. The separate treatment of sun and shade leaves in combination with the separation of the incoming light into direct beam and diffuse make sun/shade a strong modelling tool that catches more of the observed variability in canopy fluxes as measured by eddy covariance. In conclusion, the sun/shade approach is a relatively simple and effective tool for modelling photosynthetic carbon uptake that could be easily included in many terrestrial carbon models.

Evaluating balance, stability and gait symmetry of stroke patients using kinematic and dynamic gait lab data

Dissertação de mestrado integrado em Engenharia Biomédica

DINAMICA MICROBIANA Y CALIDAD DE LA MATERIA ORGANICA DEL SUELO COMO INDICADORES DE SUSTENTABILIDAD EN EL CHACO ARIDO DE LA PROVINCIA DE CORDOBA Microbial dynamic and organic matter quality as indicators of soil sustainability in Dry Chaco of Córdoba Province

Se propone analizar el efecto del uso productivo en el Chaco Árido de la provincia de Córdoba, mediante la aplicación de indicadores de sustentabilidad relacionados con la calidad de la materia orgánica y la liberación de nutrientes en el suelo, con la finalidad de aportar a un tema de suma interes para la provincia de Córdoba como es la formulación de criterios y pautas de manejo para la implementación de la Ley de Bosques (N° 26331). Se trabajará en la localidad de San Miguel en el departamento Pocho, en un sitio de bosque no disturbado y en tres sistemas productivos: desmonte selectivo con implantación de pasturas; desmonte total con agricultura bajo riego y desmonte total sobrepastoreado. En cada sitio se medirá “in situ” la emisión de CO2 y se tomaran muestras de suelo a las que se les determinará: a) contenido de materia orgánica total (MO), b) contenido de sustancias húmicas (SH), diferenciando ácidos húmicos (AH) y fúlvicos (AF), c) abundancia y actividad de microorganismos nitrificadores y d) propiedades químicas de los AH y AF. Se calcularán los siguientes índices de sustentabilidad a) materia orgánica biodisponible (MOB=MO–SH); b) índice de humificación (IH=SH/MO); c) tipo de humus (TH=AF/AH; d) índice de mineralización de C (IMC=CO2/MO); e) índice de nitrificación (IN=actividad/abundancia); y f) índice de estabilidad de las fracciones humificadas: compuestos aromáticos/ alifáticos. Los datos serán analizados estadísticamente mediante ANOVA y comparación de medias por LSD (P<0.05) y tests multivariados. We proposed analyze the effect of land use in Arid Chaco of Cordoba province, using sustainability indicators related to organic matter quality and nutrient release in soil, with the aim to formulate management criteria for the implementation of the Ley de Bosques (N° 26331) in Córdoba province. The study will be conducted in San Miguel village in Pocho department, in one undisturbed forest site and three productive systems: selective clearing with grass sowing; total clearing with irrigation agriculture and total clearing with overgrazed. In each site "in situ" CO2 emission will be measured and soil samples will be taken, in which the following parameters will be determined: a) total organic matter content (MO), b) humic substances content (SH), in humic acids (AH) and fulvic acids (AF), c) abundance and activity of nitrifier microorganisms and d) chemical properties of AH and AF. The sustainability indexes will be calculated: biodisponible organic matter (MOB=MO–SH); b) humification index (IH=SH/MO); c) humus type (TH=AF/AH; d) C mineralization index (IMC=CO2/MO); e) nitrifying index (IN=activity/abundance); and f) humic fractions stability index: aromatic/aliphatic compounds. The data will be statistically analyzed by ANOVA and the means will be compared by LSD (P<0.05) and multivariate tests.

Biogeography and evolution of Amazonian triatomines (Heteroptera: Reduviidae): implications for Chagas disease surveillance in humid forest ecoregions

An ecological-evolutionary classification of Amazonian triatomines is proposed based on a revision of their main contemporary biogeographical patterns. Truly Amazonian triatomines include the Rhodniini, the Cavernicolini, and perhaps Eratyrus and some Bolboderini. The tribe Rhodniini comprises two major lineages (pictipes and robustus). The former gave rise to trans-Andean (pallescens) and Amazonian (pictipes) species groups, while the latter diversified within Amazonia (robustus group) and radiated to neighbouring ecoregions (Orinoco, Cerrado-Caatinga-Chaco, and Atlantic Forest). Three widely distributed Panstrongylus species probably occupied Amazonia secondarily, while a few Triatoma species include Amazonian populations that occur only in the fringes of the region. T. maculata probably represents a vicariant subset isolated from its parental lineage in the Caatinga-Cerrado system when moist forests closed a dry trans-Amazonian corridor. These diverse Amazonian triatomines display different degrees of synanthropism, defining a behavioural gradient from household invasion by adult triatomines to the stable colonisation of artificial structures. Anthropogenic ecological disturbance (driven by deforestation) is probably crucial in the onset of the process, but the fact that only a small fraction of species effectively colonises artificial environments suggests a role for evolution at the end of the gradient. Domestic infestation foci are restricted to drier subregions within Amazonia; thus, populations adapted to extremely humid rainforest microclimates may have limited chances of successfully colonising the slightly drier artificial microenvironments. These observations suggest several research avenues, from the use of climate data to map risk areas to the assessment of the synanthropic potential of individual vector species.

Dynamic graphics of parametrically linked multivariate methods used in compositional data analysis

Many multivariate methods that are apparently distinct can be linked by introducing oneor more parameters in their definition. Methods that can be linked in this way arecorrespondence analysis, unweighted or weighted logratio analysis (the latter alsoknown as "spectral mapping"), nonsymmetric correspondence analysis, principalcomponent analysis (with and without logarithmic transformation of the data) andmultidimensional scaling. In this presentation I will show how several of thesemethods, which are frequently used in compositional data analysis, may be linkedthrough parametrizations such as power transformations, linear transformations andconvex linear combinations. Since the methods of interest here all lead to visual mapsof data, a "movie" can be made where where the linking parameter is allowed to vary insmall steps: the results are recalculated "frame by frame" and one can see the smoothchange from one method to another. Several of these "movies" will be shown, giving adeeper insight into the similarities and differences between these methods

Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants

Data mining can be defined as the extraction of previously unknown and potentially useful information from large datasets. The main principle is to devise computer programs that run through databases and automatically seek deterministic patterns. It is applied in different fields of application, e.g., remote sensing, biometry, speech recognition, but has seldom been applied to forensic case data. The intrinsic difficulty related to the use of such data lies in its heterogeneity, which comes from the many different sources of information. The aim of this study is to highlight potential uses of pattern recognition that would provide relevant results from a criminal intelligence point of view. The role of data mining within a global crime analysis methodology is to detect all types of structures in a dataset. Once filtered and interpreted, those structures can point to previously unseen criminal activities. The interpretation of patterns for intelligence purposes is the final stage of the process. It allows the researcher to validate the whole methodology and to refine each step if necessary. An application to cutting agents found in illicit drug seizures was performed. A combinatorial approach was done, using the presence and the absence of products. Methods coming from the graph theory field were used to extract patterns in data constituted by links between products and place and date of seizure. A data mining process completed using graphing techniques is called ``graph mining''. Patterns were detected that had to be interpreted and compared with preliminary knowledge to establish their relevancy. The illicit drug profiling process is actually an intelligence process that uses preliminary illicit drug classes to classify new samples. Methods proposed in this study could be used \textit{a priori} to compare structures from preliminary and post-detection patterns. This new knowledge of a repeated structure may provide valuable complementary information to profiling and become a source of intelligence.

The illusion of control, the structures of prizes and the demand for football pools in Spain

Using data for all the fixtures for the seasons from 1972-73 to 2002-03, we estimate a dynamic model of demand for football pools in Spain paying attention to whether their main economic explanatory variable is the effective price of a ticket or the jackpot. Additionally, we evaluate the importance of the composition of the list of games in terms of whether First Division matches are included or not. Results show that the jackpot model is preferred to the effective price model, having important implications in terms of how the structure of the game should be changed in order to increase demand.

Dynamic graphics of parametrically linked multivariate methods used in compositional data analysis

Many multivariate methods that are apparently distinct can be linked by introducing oneor more parameters in their definition. Methods that can be linked in this way arecorrespondence analysis, unweighted or weighted logratio analysis (the latter alsoknown as "spectral mapping"), nonsymmetric correspondence analysis, principalcomponent analysis (with and without logarithmic transformation of the data) andmultidimensional scaling. In this presentation I will show how several of thesemethods, which are frequently used in compositional data analysis, may be linkedthrough parametrizations such as power transformations, linear transformations andconvex linear combinations. Since the methods of interest here all lead to visual mapsof data, a "movie" can be made where where the linking parameter is allowed to vary insmall steps: the results are recalculated "frame by frame" and one can see the smoothchange from one method to another. Several of these "movies" will be shown, giving adeeper insight into the similarities and differences between these methods.

Evaluating leaf litter beetle data sampled by Winkler extraction from Atlantic forest sites in southern Brazil

Evaluating leaf litter beetle data sampled by Winkler extraction from Atlantic forest sites in southern Brazil. To evaluate the reliability of data obtained by Winkler extraction in Atlantic forest sites in southern Brazil, we studied litter beetle assemblages in secondary forests (5 to 55 years after abandonment) and old-growth forests at two seasonally different points in time. For all regeneration stages, species density and abundance were lower in April compared to August; but, assemblage composition of the corresponding forest stages was similar in both months. We suggest that sampling of small litter inhabiting beetles at different points in time using the Winkler technique reveals identical ecological patterns, which are more likely to be influenced by sample incompleteness than by differences in their assemblage composition. A strong relationship between litter quantity and beetle occurrences indicates the importance of this variable for the temporal species density pattern. Additionally, the sampled beetle material was compared with beetle data obtained with pitfall traps in one old-growth forest. Over 60% of the focal species captured with pitfall traps were also sampled by Winkler extraction in different forest stages. Few beetles with a body size too large to be sampled by Winkler extraction were only sampled with pitfall traps. This indicates that the local litter beetle fauna is dominated by small species. Hence, being aware of the exclusion of large beetles and beetle species occurring during the wet season, the Winkler method reveals a reliable picture of the local leaf litter beetle community.