Publicações

The importance in monitoring and alert decision-makers as to the quality of water is a necessity of environmental agencies, both to inhibit the clandestine effluent dump to provide the community as a quality product. This work describes the applicability of SISMADEN as early warning system for water quality in a stretch of the River Paraíba do Sul, where three PCDs were used to demonstrate the efficiency and speed in analyzing the parameters of pH, turbidity and electric conductivity. Only a small part of the potential of the system was used in this first phase, being considered the parameters alone and own PCD as alert. PH thresholds used were those that were more sensitive to identify clandestine waste dump that were made during the dawn by a company.

Recursos energéticos renováveis são cada vez mais necessários devido à necessidade da substituição da geração de energia através de combustíveis fósseis, visto que emitem grandes quantidades de gases poluentes na atmosfera. Atualmente no Brasil, o Projeto SONDA (Sistema de Organização Nacional de Dados Ambientais) tem o objetivo de gerar uma base de dados solarimétricos e meteorológicos que atenda a demanda de informações necessárias para a atração de investimentos e a realização de ações efetivas ao desenvolvimento tecnológico e científico para uso de recursos de energia solar e eólica. Portanto, os dados de irradiação solar na superfície coletados pelo Projeto SONDA são utilizados na validação e parametrização das estimativas e previsões fornecidas pelos modelos numéricos. As principais incertezas das estimativas são resultados da variabilidade de aerossóis na atmosfera, juntamente com incertezas sobre a variabilidade da cobertura de nuvens (Pinker and Laszlo, 1989). Fato que leva Projeto ter por objetivo avaliar o potencial energético solar no Sul do Brasil, estudando a variação temporal, espacial e a influência de variáveis climatológicas sobre a irradiação solar incidida sobre a superfície. Os dados analisados são coletados na plataforma SONDA-SMS, em operação desde Agosto de 2004, no Observatório Espacial do Sul OES/CRS/CCR/INPE-MCT, em São Martinho da Serra, RS. O Projeto utiliza Redes Neurais Artificiais (RNAs), para criação de modelos computacionais que possam ser úteis na previsão de irradiação global para curtos períodos de tempo (máximo de 72h), sendo que o modelo de RNA utilizado é o Perceptron de Múltiplas Camadas (MLP), permitindo que diversas combinações de variáveis e topologias de MLPs possam ser realizadas, visando à identificação do melhor conjunto MLP, que represente adequadamente as previsões, realizada através da análise de parâmetros estatísticos e que avaliam o erro entre as previsões fornecidas pelas MLPs e os valores efetivamente medidos. Os resultados obtidos para os tempos de previsão 24h e 48h tiveram erros RMSE% próximos a 36%. Para 72h, observou-se má representatividade dos modelos de RNAs em suas respostas frente às entradas fornecidas e assim sendo considerado inviável.

Image segmentation is one of the most challenging steps in image processing. Its results are used by many other tasks regarding information extraction from images. In remote sensing, segmentation generates regions according to found targets in a satellite image, like roofs, streets, trees, vegetation, agricultural crops, or deforested areas. Such regions differentiate land uses by classification algorithms. In this paper we investigate a way to perform segmentation using a strategy to classify and merge spectrally and spatially similar pixels. For this purpose we use a geographical extension of the Self-Organizing Maps (SOM) algorithm, which exploits the spatial correlation among near pixels. The neurons in the SOM will cluster the objects found in the image, and such objects will define the image segments.

The spatial extent, duration, and depth of inundation are key factors in the functioning of floodplain ecosystems. Satellite-based methods have been developed for mapping flooding extent and water surface height across large floodplain reaches, but on the Amazon floodplain, ground measurements for rigorous validation of these products currently exist for only a few instrumented sites. Here we report results of a field test of networks of Thermochron iButtons® low-cost temperature logging devices for documenting dates of the onset and recession of floodwaters during the 2008-2009 flood cycle at two sites on the Amazon floodplain. Of 237 iButtons deployed on tree trunk gauges at the Mamirauá and Piagaçu Sustainable Development Reserves on the Amazon floodplain during the period 26 November to 11 December 2008, 193 iButtons were retrieved in October-November 2009 and 81 (42%) of those were readable. Flooded and non-flooded phases were clearly identifiable for each iButton by the reduced amplitude of the daily temperature wave when flooded. Based on 6 calibration iButtons attached to staff gauges in Mamirauá Lake, the date of flooding could be estimated within ± 3 days. We conclude that Thermochron iButtons provide a relatively low-cost means of acquiring information on inundation period, which can be used for validating satellite-based mapping or hydrologic models. However, because of the harsh conditions occurring on deeply inundated floodplains, improved waterproofing containers are necessary to ensure iButton performance. Further calibration experiments are recommended in order to refine metrics for estimating the flooding date with greater precision.

The lightning behavior of all known cases of extremely severe storms, most of them supercells, which occurred from 1999 onwards, is discussed and summarized in this paper. In addition, a new aspect, viz. Intra-Cloud (IC) strokes, has also been included since the installation of 2 special sensors in 2008. However, the location of these sensors in the Paraiba Valley is not favorable for storms observed by IPMet´s radars due to the great distance, resulting in a possible offset of IC locations. The feasibility of a correction mechanism of this location error will be investigated until more sensors become available.

Nowadays, agricultural commodity market requires more efficient and better control of information on crop production. Due to characteristics of Brazil, in this market, the attainment of reliable and timely agricultural estimates is very important. The objective of this study was to evaluate the coupled models performance of crop area and crop productivity estimates, for an adequate temporal and spatial resolution. The productivity model proposed is complementarily added to the previously developed model of crop area estimate. The coupled model results were compared to Brazilian Institute of Geography and Statistics (IBGE) data, in a State and municipality level. Both, MODIS Crop Detection Model (MCDM) and MODIS Productivity Detection Model (MPDM) were developed at the National Institute for Space Research (INPE) and use analysis of the temporal-spectral profile of agricultural crop development, achieved with EVI/MODIS images. Results indicate that application of the coupled model is able to generate timely crop area and productivity estimates for soybean in the Rio Grande do Sul in a municipality level. In a State level, estimates of MPDM from 2001 to 2008, obtained R 2 =0.89, with a higher deviation for 2002. R 2 =0.96 were obtained by subtracting harvest of 2002 from the analysis. Kriging productivity maps, of MPDM and IBGE, were compared and shown a visual similarity in their spatial distribution and development trend.

Cada vez mais, as pesquisas realizadas na Amazônia têm demonstrado a importância dessa região para o equilíbrio do meio ambiente na América do Sul. Nessa linha, este trabalho tem por objetivo o estudo dos efeitos das mudanças globais na bacia do Rio Madeira. Para isso, foi utilizado o downscaling dinâmico de cenários de mudanças climáticas desenvolvidos no CPTEC-INPE, juntamente com cenários futuros de uso do solo, para obter cenários hidrológicos na bacia com o uso do Modelo Hidrológico de Grandes Bacias do INPE. Realizou-se a correção do bias nos campos de precipitação simulados, utilizando informações pluviométricas existentes no CPTEC-INPE, previamente consistidas. Efetuaram-se simulações do tempo presente e projeções do futuro, com cada um dos 4 cenários regionais do CPTEC-INPE. Com a utilização do Modelo Hidrológico de Grandes Bacias do Instituto Nacional de Pesquisas Espaciais (MGB-INPE), foram realizadas as projeções hidrológicas nos períodos de 1961 a 1990 e 2011 a 2099, comparando-as a partir de seus valores de Precipitação, Evapotranspiração e Vazão. Assim, pode-se identificar as alterações nas sazonalidades e ocorrências de valores extremos. Fez-se uso das curvas de permanência, para relacionar a Precipitação e Vazão com a probabilidade de ocorrerem ao longo do tempo. Ainda com esses dados, trabalhou-se com a climatologia de cada sub-bacia (11 no total) do Rio Madeira. Nas análises elaboradas, pode-se perceber o aumento dos valores de Evapotranspiração e, em geral, a diminuição da Precipitação, enquanto a Vazão decresce nos cenários futuros.

Meteorological and environmental aspects are the number one cause of electric utility power distribution interruptions all over the planet. In Brazil, about 40-50% of the interruptions in the distribution systems are caused only by lightning. In this work is described a new computational system called CLIMA 1.0 that integrates meteorological and environmental parameters, including surface temperature, humidity, wind intensity and direction, and precipitation, all provided by a mesoscale WRF model, vegetation information, provided by satellite high resolution images, and total lightning information, provided by the Brazilian lightning detection network (BrasilDat), into a geographical information system (GIS) platform, which contains the information about the EDP distribution system located in the southeastern region of Brazil. The system should contribute to the EDP effort to implement smart grids.