Diagnóstico da degradação ambiental no município de Areia Branca-RN por geotecnologias

The municipality of Areia Branca is within the mesoregion of West Potiguar and within the microregion of Mossoró, covering an area of 357,58 km2. Covering an area of weakness in terms of environmental, housing, together with the municipality of Grossos-RN, the estuary of River Apodi-Mossoró. The municipality of Areia Branca has historically suffered from a lack of planning regarding the use and occupation of land as some economic activities, attracted by the extremely favorable natural conditions, have exploited their natural resources improperly. The aim of this study is to quantify and analyze the environmental degradation in the municipality. Thus initially was performed a characterization of land use using remote sensing, geoprocessing and geographic information system GIS in order to generate data and information on the municipal scale, which may serve as input to the environmental planning and land use planning in the region. From this perspective, were used a Landsat 5 image TM sensor for the year 2010. In the processing of this image was used SPRING 5.2 and applied a supervised classification using the classifier regions, which was employed Bhattacharya Distance method with a threshold at 30%. Thus was obtained the land use map that was analyzed the spatial distribution of different types of the use that is occurring in the city, identifying areas that are being used incorrectly and the main types of environmental degradation. And further, were applied the methodology proposed by Beltrame (1994), Physical Diagnosis Conservationist under some adaptations for quantifying the level of degradation or conservation study area. As results, the indexes were obtained for the parameters in the proposed methodology, allowing quantitatively analyze the degradation potential of each sector. From this perspective, considering a scale of 0 to 100, sector A and sector B had value 31.20 units of risk of physical deterioration. And the C sector, has shown its value - 34.64 units degradation risk and should be considered a priority in relation to the achievement of conservation actions

Avaliação do uso da terra em micro̧bacia utilizando uma matriz de partição fuzzy

Informatics evolution presently offers the possibility of new technique and methodology development for studies in all human knowledge areas. In addition, the present personal computer capacity of handling a large volume of data makes the creation and application of new analysis tools easy. This paper aimed the application of a fuzzy partition matrix to analyze data obtained from the Landsat 5 TMN sensor, in order to elaborate the supervised classification of land use in Arroio das Pombas microbasin in Botucatu, SP, Brazil. It was possible that one single training area present input in more than one covering class due to weight attribution at the signature creation moment. A change in the classification result was also observed when compared to maximum likelihood classification, mainly when related to bigger uniformity and better class edges classification.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Parkinson's disease identification through Optimum-Path Forest

Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification. © 2010 IEEE.

Optimum-Path Forest pruning parameter estimation through Harmony Search

Pattern recognition in large amount of data has been paramount in the last decade, since that is not straightforward to design interactive and real time classification systems. Very recently, the Optimum-Path Forest classifier was proposed to overcome such limitations, together with its training set pruning algorithm, which requires a parameter that has been empirically set up to date. In this paper, we propose a Harmony Search-based algorithm that can find near optimal values for that. The experimental results have showed that our algorithm is able to find proper values for the OPF pruning algorithm parameter. © 2011 IEEE.

Análise temporal do uso do solo e comparação entre os índices de vegetação ndvi e savi no município de Santa Cruz do Rio Pardo - SP usando imagens landsat-5

This work aims to analyze the land use evolution in the city of Santa Cruz do Rio Pardo - SP through supervised classification of Landsat-5 TM satellite images according to the maximum likelihood (Maxlike), as well as verifying the mapping accuracy through Kappa index, comparing NDVI and SAVI vegetation indexes in different adjustment factors for the canopy substrate and determining the vegetal coverage percentage in all methods used on 2007, May 26 th; 2009, January 7 th and 2009, April 29 th. The Maxlike classification showed several spatial changes in land use over the study period. The most appropriated vegetation indexes were NDVI and SAVI - 0,25 factor, which showed similar values of vegetal coverage percentage, but discrepant from the inferred value for Maxlike classification.

Automatic landslide recognition through Optimum-Path Forest

In this paper we shed light over the problem of landslide automatic recognition using supervised classification, and we also introduced the OPF classifier in this context. We employed two images acquired from Geoeye-MS satellite at March-2010 in the northwest (high steep areas) and north sides (pipeline area) covering the area of Duque de Caxias city, Rio de Janeiro State, Brazil. The landslide recognition rate has been assessed through a cross-validation with 10 runnings. In regard to the classifiers, we have used OPF against SVM with Radial Basis Function for kernel mapping and a Bayesian classifier. We can conclude that OPF, Bayes and SVM achieved high recognition rates, being OPF the fastest approach. © 2012 IEEE.

Evolução espaço-temporal do uso e cobertura do solo nas estâncias turísticas de Avaré e Paranapanema, no reservatório de Jurumirim (SP)

Pós-graduação em Geografia - IGCE

Uma análise da infestação por plantas aquáticas utilizando imagens multiescala e redes neurais artificiais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Geoestística e sensoriamento remoto na classificação de imagens em áreas cultivadas com citros

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Diagnóstico da expansão da cultura canavieira e dos conflitos ambientais de uso do solo no Município de Barra Bonita/SP

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Interações entre o ambiente físico, uso e cobertura da terra e as características físicas e químicas no canal fluvial: a bacia hidrográfica do rio Santo Anastácio, Oeste Paulista (Dez. 2009 – Dez. 2010)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Informação polarimétrica PALSAR/ALOS aplicada à discriminação de espécies e estimação de parâmetros morfológicos de macrófitas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Mapa de vulnerabilidade à erosão da região do Alto Rio Pardo - Pardinho (SP)

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Análise multitemporal de indicadores geoambientais como subsídio ao plano de gestão ambiental - Floresta Nacional de Ipanema - SP

The objective of this research was to conduct an analysis of multitemporal landscape Ipanema National Forest, located in the municipalities of Iperó, Capela do Alto and Araçoiaba da Serra – São Paulo estate, Brazil, considering the scenarios of 1965, 2007 and 2011. The multitemporal analysis, using aerial photographs and satellite images, contributed to the contextualization and spatialization of the evolution of the landscape area. Through analysis interpretation of the images, performed by means of supervised classification were obtained thematic maps of the area, equivalent to approximately 53 km2. Through geoprocessing techniques, especially Geographic Information Systems, it was possible the integration and manipulation of data, both spatial and statistical, allowing integrated analysis of data from the entire area of the National Forest of Ipanema. As the main result, we found that the Ipanema National Forest is in landscape evolution positive, with those 46 years examined the increase of native heavy foliage areas. Increasing from 7.1 km2 of the total area of dense vegetation in 1965 to 35.9 km2 in 2011. Overall, it was possible to realize a scenario landscape quite optimistic about the evolution of forest conservation area