Publicações

O conhecimento do balanço de radiação ondas longas (BOL) é importante para determinação das trocas energéticas que ocorrem no sistema superfície-atmosfera. Assim, neste trabalho avaliou-se a variabilidade estacional do BOL numa área de Mata Atlântica, através da análise da influência de diferentes variáveis meteorológicas, discutindo suas possíveis causas. A pesquisa foi realizada numa Reserva Particular do Patrimônio Natural (RPPN) no município de Coruripe-AL. As medições foram feitas por um saldo radiômetro (CNR1, Kipp e Zonen) instalado acima do dossel florestal numa torre micrometeorológica de 26 metros de altura durante os períodos de 03.11.2016 a 31.12.2016 (estação seca) e 01.05.2017 a 30.06.2017 (estação chuvosa). O BOL variou em 101,63% da estação chuvosa para a seca (-11,6 W m-2 a -23,39 W.m-2) em dias nublados e 30,33% (-49,19 W.m-2 a -64,11 W.m-2) em dias claros. A radiação de onda longa atmosférica (Ra) média no período chuvoso foi 430,96 W.m-2 e 418,90 W.m-2 no seco, enquanto a radiação terrestre (Rs) foi 456,46 W.m-2 (chuvoso) e 470,60 W.m-2 (seco). Rs foi sempre maior que Ra e, por consequência, o BOL foi sempre negativo em ambas estações, com uma diferença média de 103,19% de uma estação para outra.

Known as a degenerative and progressive dementia, Alzheimers disease (AD) affects about 25 million elderly people around the world. This illness results in a decrease in the productivity of people and places limits on their daily lives. Electroencephalography (EEG), in which the electrical brain activity is recorded in the form of time series and analyzed using signal processing techniques, is a well-known neurophysiological AD biomarker. EEG is noninvasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Here, we present an original approach based on the use of quantile graphs (QGs) for classifying EEG data. QGs map frequency, amplitude, and correlation characteristics of a time series (such as the EEG data of an AD patient) into the topological features of a network. The five topological network metrics used hereclustering coefficient, mean jump length, betweenness centrality, modularity, and Laplacian Estrada indexshowed that the QG model can distinguish healthy subjects from AD patients, with open or closed eyes. The QG method also indicates which channels (corresponding to 19 different locations on the patients scalp) provide the best discriminating power. Furthermore, the joint analysis of delta, theta, alpha, and beta wave results indicate that all AD patients under study display clear symptoms of the disease and may have it in its late stage, a diagnosis known a priori and supported by our study. Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs.

Estimates of nitrogen deposition play a key role in better understanding the human impacts on the nitrogen biogeochemical cycle. This paper examines the concentration of reactive nitrogen (Nr) in the atmosphere and the average dry deposition in the state of São Paulo, Brazil. The Nr species that were measured were in the gaseous (NO2, NH3 and HNO3) and particulate phases (NH4 +, NO3 −). Denuder-based samplers were spatially distributed in six sites to account for different types of land cover and urbanization. Samples were collected over 2 years (20152017). The dominant form of inorganic Nr in the atmosphere was the gaseous NO2 at all sampling sites, which represented 79% of the total. The average concentrations of NH3, HNO3, NH4 + and NO3 − were 10%, 3%, 6% and 2% of the total Nr, respectively. The average Nr dry deposition in São Paulo State was 10.6 kg N ha−1 yr−1. The highest average deposition of Nr was found in the SP sampling site and is related to intense vehicular traffic, waste management and industrial activities. This work suggests that dry deposition of Nr into the Atlantic Forest next to São Paulo municipality can exceed the critical nitrogen load suggested for most forests. This implies that human activities in the surrounding areas may be affecting the integrity of one of the most important biodiversity hotspots in South America. These results will contribute to improve Nr budget estimates and to advance Nr deposition modelling approaches.

Classifying remote sensing time-series in land cover trajectories can provide essential information about ecosystems functioning and about the impacts of natural phenomena or human activities over the environment. The existing approaches commonly used for this task can present serious drawbacks, such as the possibility to derive invalid trajectories, use of complex data inputs, and several classification steps. To provide a simple method for land cover trajectory classification, we present a novel algorithm named Compound Maximum a Posteriori (CMAP) classifier. CMAP incorporates the knowledge of land cover dynamics and the information of multi-temporal data sets to produce only valid land cover trajectories using a global generative classification approach and simple inputs. CMAP was tested in two case studies, in which we compared land cover trajectories obtained by CMAP to those obtained using the traditional Maximum Likelihood (ML) classifier in a post-classification comparison approach. In the first case study, we classified 6 images from the same sensor, using the same land cover legend. In the second case study, we classified 3 images from different types of sensors, using different land cover legend levels. Because of its formulation, CMAP does not return invalid transitions/trajectories as classification results. The use of ML and post-classification comparison, on the other hand, resulted in invalid land cover trajectories in more than 50% of the used images in both case studies. Furthermore, the use of CMAP leads to better accuracy indexes for land cover classification of each date and reduces the classification noise.

To construct this database, we integrate the nutritional content of 62 crops and 5 livestock categories to estimate the amount of 21 macro and micro-nutrients (including energy) that were produced from agriculture in each Brazilian municipality during the last three decades. Additionally, we allocate these nutrients according to their share in the food system (for example, human food, animal feed, export etc.). It is a unique data source on macro and micro-nutrients availability for human consumption and animal feed, but also regarding another aspects of the food system, such as international agricultural trade, energy production (for example, in the form of ethanol) or post-harvest and post-processing losses, from local to national levels, in a wide time frame of 30 years. This database can be used in scientific research regarding food and nutrition security and in the construction of indicators for monitoring food and agricultural programs and policies that aim at the promotion of food and nutritional security. Also, it has the potential to enable broader analysis of the food system as whole in terms of food stability and resilience.

Brazil is experiencing continuous growth in the solar energy market over the last years, and it is expected that solar power plants will have a significant contribution to the Brazilian energy matrix soon. The solar irradiance variability is an important issue due to its impact on electricity distribution systems. However, solar energy variability has not been comprehensively evaluated in Brazil. The study aimed to assess the capacity of two numerical models of mesoscale in simulating the solar radiation conditions in the Northeastern region of Brazil. For this, a case study was conducted comparing the surface solar irradiation data acquired in 41 automated weather stations operating in the study area and managed by the Brazilian Institute of Meteorology with estimates provided by two weather mesoscale models BRAMS and WRF. The study indicated that the difference between the model’s uncertainties is small (typically less than 5%) when evaluating the entire region of the Brazilian Northeast. However, local comparisons may show more notable discrepancies reflecting uncertainties in the estimates produced by the models.

One of the most crucial parts of solid waste management is determining landfill site location, since multiple factors must be considered and there is no universal formula. The main purpose of this study is to make a worldwide systematic review of restriction criteria used for landfill siting using geographic information systems (GIS). Literature from the last years was thoroughly assessed, and 45 restrictions found were classified as environmental, economic, or social criteria. Our findings show that although the number of articles published has increased recently, they use on average seven restrictions, focusing mainly on environmental over economic and social criteria. In our boxplot statistical analysis, the most frequently used environmental restrictions are the distance from surface water resources (used in 77% of articles), slope (52%), and distance from groundwater founts (40%), with a median of 300 m, 20%, and 250 m, respectively. The most frequently used economic restrictions are distances from roads (60%), airports (40%), and power lines (18%), with medians of 275 m, 3000 m, and 75 m, respectively. The most frequently used social restrictions are distances from urban areas (45%), settlements and residential areas (40%), and cultural heritage or archaeological areas (23%), with medians of 1000 m. This information might help, on the one hand, governments to develop new legislation about landfill siting and on the other hand, decision-makers and scientists to produce new studies with different restrictive scenarios.

Tropical forests regulate the global water and carbon cycles and also host most of the worlds biodiversity. Despite their importance, they are hard to survey due to their location, extent, and particularly, their cloud coverage. Clouds hinder the spatial and radiometric correction of satellite imagery and also diminishing the useful area on each image, making it difficult to monitor land change. For this reason, our purpose is to identify the cloud detection algorithm best suited for the Amazon rainforest on Sentinel2 images. To achieve this, we tested four cloud detection algorithms on Sentinel2 images spread in five areas of the Amazonia. Using more than eight thousand validation points, we compared four cloud detection methods: Fmask 4, MAJA, Sen2Cor, and s2cloudless. Our results point out that FMask 4 has the best overall accuracy on images of the Amazon region (90%), followed by Sen2Cors (79%), MAJA (69%), and S2cloudless (52%). We note the choice of method depends on the intended use. Since MAJA reduces the number of false positives by design, users that aim to improve the producers accuracy should consider its use.

No dia 19 de agosto de 2019, a cidade de São Paulo (SP) presenciou um evento incomum que deixou grande parte da população assustada com o ocorrido. Próximo às 15 h, o céu na cidade de São Paulo escureceu tornando o dia em noite. Diante desse contexto, o objetivo do estudo é descrever o ambiente atmosférico associado ao evento registrado em São Paulo. Para tanto, foram utilizadas diferentes fontes de dados: cartas sinóticas e análises de modelos de tempo do Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), imagens de satélite, dados de diferentes modelos atmosféricos, monitoramento da qualidade do ar Companhia Ambiental do Estado de São Paulo (CETESB) e notícias veiculadas pela mídia. Nos dias prévios ao 19 de agosto foram documentadas queimadas na Amazônia e a presença de ventos em baixos níveis da atmosfera (~700 hPa) que auxiliavam o transporte do material particulado das regiões de queimadas para o sul do país. No dia 19, com a chegada de um sistema frontal em São Paulo, os ventos em baixos níveis passaram a escoar da Amazônia em direção ao sudeste, transportando o material particulado para tal região. O material particulado (pluma de fumaça) não foi registrado em superfície, já que as estações da CETESB não documentaram padrão anômalo nas observações. Sugere-se que o material particulado serviu como núcleo de condensação de nuvem, gerando muitas gotículas de nuvem que refletiram radiação para fora da atmosfera, deixando escura a tarde de São Paulo.

Optical data is broadly used for change detection studies, despite being hindered by atmospheric conditions. Synthetic Aperture Radar (SAR) data can be useful for change detection in areas with frequent cloud coverage as SAR systems are capable of obtaining images almost independently from atmospheric conditions. This study aims to verify the difference in results of using SAR data instead of optical data for change detection purposes. Different levels of one hierarchical legend and both pixel and region-based classifiers were used. Change results were evaluated considering the use of rectangular matrices to incorporate the occurrence of impossible changes and relative comparison between change maps. Although the change maps obtained using only optical data were more accurate than those using either one or two land cover classifications based on L-band SAR data, the difference in the accuracy of change maps decreases with the use of less detailed legends. Additionally, results indicate that L-band SAR and multi-sensor approaches are adequate for deforestation identification even if postclassification results did not achieve global accuracy values superior to 0.86. The most accurate change detection results obtained in this work were not associated with the overall accuracy of land cover classifications, but with the distribution and accuracy of specific land cover classes.