Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Mapeamento geoestatístico da vulnerabilidade do Aquífero Guarani no município de Ribeirão Bonito - SP

The Guaraní aquifer has relevant importance both as a source of water for several urban centres and the development of agriculture and livestock. In recharge areas the aquifer is free and, therefore, subject to contamination of effluents and tailings deposited on soils that cover it. Thus, it becomes crucial not only its protection at all levels, as the knowledge of its degree of natural vulnerability. The present work used geostatistics modeling techniques to study the natural vulnerability of the Guaraní aquifer in the city of Rio Bonito, State of São Paulo, southeastern Brazil, where the Guarani aquifer is exposed. These techniques, extensively used in evaluation studies of mineral deposits and oil tanks, can be adapted to produce a spatial classification or a regionalisation of probabilistic indices of vulnerability. By ordinary kriging method maps of vulnerability classification were obtained. To determine the vulnerability of the aquifer was employed the Aquifer Vulnerability Index (AVI), which requires knowledge of unsaturated zone thickness and permeability. The final product was a map with probabilistic index of vulnerability of the Guaraní aquifer, which presented values between 0 to 0.33 years, framing the area studied in AVI class extremely high vulnerability

Self-Organizing Maps as a New Tool for Classification of Plants at Lower Hierarchical Levels

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Classification and characterization of places for mapping of environment using hierarchical neural network and omnivision

Mobile robots need autonomy to fulfill their tasks. Such autonomy is related whith their capacity to explorer and to recognize their navigation environments. In this context, the present work considers techniques for the classification and extraction of features from images, using artificial neural networks. This images are used in the mapping and localization system of LACE (Automation and Evolutive Computing Laboratory) mobile robot. In this direction, the robot uses a sensorial system composed by ultrasound sensors and a catadioptric vision system equipped with a camera and a conical mirror. The mapping system is composed of three modules; two of them will be presented in this paper: the classifier and the characterizer modules. Results of these modules simulations are presented in this paper.

Mapping soil pollution by spatial analysis and fuzzy classification

Spatial analysis and fuzzy classification techniques were used to estimate the spatial distributions of heavy metals in soil. The work was applied to soils in a coastal region that is characterized by intense urban occupation and large numbers of different industries. Concentrations of heavy metals were determined using geostatistical techniques and classes of risk were defined using fuzzy classification. The resulting prediction mappings identify the locations of high concentrations of Pb, Zn, Ni, and Cu in topsoils of the study area. The maps show that areas of high pollution of Ni and Cu are located at the northeast, where there is a predominance of industrial and agricultural activities; Pb and Zn also occur in high concentrations in the northeast, but the maps also show significant concentrations of Pb and Zn in other areas, mainly in the central and southeastern parts, where there are urban leisure activities and trade centers. Maps were also prepared showing levels of pollution risk. These maps show that (1) Cu presents a large pollution risk in the north-northwest, midwest, and southeast sectors, (2) Pb represents a moderate risk in most areas, (3) Zn generally exhibits low risk, and (4) Ni represents either low risk or no risk in the studied area. This study shows that combining geostatistics with fuzzy theory can provide results that offer insight into risk assessment for environmental pollution.

A new approach to soil classification mapping based on the spatial distribution of soil properties

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Mineralogy of the clay fraction of alfisols in two slope curvatures: IV - spatial correlation with physical properties

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mapping the knowledge covered by library classification systems

This study explores, in 3 steps, how the 3 main library classification systems, the Library of Congress Classification, the Dewey Decimal Classification, and the Universal Decimal Classification, cover human knowledge. First, we mapped the knowledge covered by the 3 systems. We used the 10 Pillars of Knowledge: Map of Human Knowledge, which comprises 10 pillars, as an evaluative model. We mapped all the subject-based classes and subclasses that are part of the first 2 levels of the 3 hierarchical structures. Then, we zoomed into each of the 10 pillars and analyzed how the three systems cover the 10 knowledge domains. Finally, we focused on the 3 library systems. Based on the way each one of them covers the 10 knowledge domains, it is evident that they failed to adequately and systematically present contemporary human knowledge. They are unsystematic and biased, and, at the top 2 levels of the hierarchical structures, they are incomplete.

Automatic Classification of Three-Phase Flow Patterns of Heavy Oil in a Horizontal Pipe Using Support Vector Machines

The pipe flow of a viscous-oil-gas-water mixture such as that involved in heavy oil production is a rather complex thereto-fluid dynamical problem. Considering the complexity of three-phase flow, it is of fundamental importance the introduction of a flow pattern classification tool to obtain useful information about the flow structure. Flow patterns are important because they indicate the degree of mixing during flow and the spatial distribution of phases. In particular, the pressure drop and temperature evolution along the pipe is highly dependent on the spatial configuration of the phases. In this work we investigate the three-phase water-assisted flow patterns, i.e. those configurations where water is injected in order to reduce friction caused by the viscous oil. Phase flow rates and pressure drop data from previous laboratory experiments in a horizontal pipe are used for flow pattern identification by means of the 'support vector machine' technique (SVM).

Neural networks in chemosystematic studies of asteraceae: A classification based on a dichotomic approach

This paper describes the application of artificial neural nets as an alternative and efficient method for the classification of botanical taxa based on chemical data (chemosystematics). A total of 28,000 botanical occurrences of chemical compounds isolated from the Asteraceae family were chosen from the literature, and grouped by chemical class for each species. Four tests were carried out to differentiate and classify different botanical taxa. The qualifying capacity of the artificial neural nets was dichotomically tested at different hierarchical levels of the family, such as subfamilies and groups of Heliantheae subtribes. Furthermore, two specific subtribes of the Heliantheae and two genera of one of these subtribes were also tested. In general, the artificial neural net gave rise to good results, with multiple-correlation values R > 0.90. Hence, it was possible to differentiate the dichotomic character of the botanical taxa studied.

HIERARCHICAL VERSUS NONHIERARCHICAL PATTERNS OF GENETIC DISTANCES AMONG POPULATIONS - A SIMULATION STUDY

A simulation study was made of the effects of mixing two evolutionary forces (natural selection and random genetic drift), combined in a single data matrix of gene frequencies, on the resulting genetic distances among populations. Twenty-one, kinds of simulated gene frequencies surfaces, for 15 populations linearly distributed over geographic space, were used to construct 21 data matrices, combining different proportions of two types of surfaces (gradients and random surfaces). These matrices were analysed by Unweighted Pair-Group Method - Arithmetic Averages (UPGMA), clustering and Principal Coordinate Analysis. The results obtained show that ordination is more accurate than UPGMA in revealing the spatial patterns in the genetic distances, in comparison with results obtained using the Mantel test comparing directly genetic and geographic distances.

Oviposition of a leaf-miner on Erythroxylum tortuosum (Erythroxylaceae) leaves: hierarchical variation of physical leaf traits

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Shape-based features for cat ganglion retinal cells classification

This article presents a quantitative and objective approach to cat ganglion cell characterization and classification. The combination of several biologically relevant features such as diameter, eccentricity, fractal dimension, influence histogram, influence area, convex hull area, and convex hull diameter are derived from geometrical transforms and then processed by three different clustering methods (Ward's hierarchical scheme, K-means and genetic algorithm), whose results are then combined by a voting strategy. These experiments indicate the superiority of some features and also suggest some possible biological implications.

Temporal-spatial distribution of the hermit crab Loxopagurus loxochelis (Decapoda: Diogenidae) from Ubatuba Bay, São Paulo State, Brazil

The present work was conducted to determine the distribution of Loxopagurus loxochelis collected monthly, over a period of one year, in Ubatuba Bay (from September/95 to August/96). A total of 179 specimens were collected (138 males, 30 females and 11 ovigerous females). The greatest depth, predominance of very fine sand and highest mean value of organic matter contents of sediment, in combination with the low temperatures registered in winter (July and August), determined the presence of L. loxochelis in the subarea located at the Ubatuba Bay mouth, exposed to the open sea with high water current energy, important because this position insures that spawned larvae will enter into the oceanic circulation.