Publicações

Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.

This paper introduces the gtfs2emis model, a bottom-up method available as an R package to estimate emissions of public transport systems. The method uses General Transit Feed Specification (GTFS) data, a standard format for public transport data widely adopted worldwide, which makes the method easily applicable to cities with limited data. The model requires a GTFS feed of a given transport system and a table with general characteristics of the vehicle fleet profile. The package can estimate over 16 pollutants and energy consumption based on emission factor models from Europe, the United States, and Brazil. It also includes functions to help users examine how emissions are distributed across space, at different times of the day, and by types of vehicles. This paper presents a reproducible example of the city of S ao Paulo (Brazil) to demonstrate the gtfs2emis package and to discuss the potential applications and limitations of the proposed model.

over the past few decades, a significant amount of agricultural land has been lost due to soil degradation/desertification. in addition, the increasing frequency of extreme events, such as intense droughts and forest fires, has negatively impacted various ecosystem services. two of the main brazilian biomes-the cerrado and the caatinga-have been affected by increased rainfall variability, leading to desertification, increased fire frequency, and, consequently, rising concerns regarding the water and food security of the local population. in this study, we develop a methodology to assess these impacts using a socio-environmental vulnerability index (sevi) that combines physical, environmental, and socio-economic indicators related to exposure, sensitivity, and adaptation, as well as including socio-environmental feedback. the developed sevi is then applied to the sao francisco and parnaiba river basins. the proposed index is based on the medalus methodology and is adapted to include multiple biological, physical, and socio-economic indicators, allowing for the discrimination of areas characterized by different levels of vulnerability. we also analyze the effectiveness of governmental policies, such as the creation of conservation areas and the rural registration of properties, in reducing vulnerability. the sevi analysis highlights that adaptive capacity is the main constraint for reducing socio-environmental vulnerability in the parnaiba basin, while exposure and sensitivity are the greater challenges in the sao francisco basin. the results of this study are crucial for the prioritization of recovery actions in degraded areas.

The joint effects of climate change and landscape fragmentation to the genetic viability of isolated populations has barely been addressed for the Atlantic Forest fauna. Therefore, this work explored the potential habitat suitability for the southern muriqui (Brachyteles arachnoides), by modeling climate change, landscape fragmentation, and genetic diversity loss of the species. Maxent was used to model its potential distribution in 2050, with two climate change scenarios. A land use and land cover change model was applied to describe current and future forest fragmentation patterns, and a Population and Habitat Viability Analysis (PHVA) was used to describe the retention of genetic diversity of the southern muriqui. Although PHVA modeling provided a low risk of extinction of the southern muriqui, climate change and fragmentation could result in the loss of >65% of the suitable forest patches, and reduce the habitat suitability to only 11% of the potential distribution area, which could lead to future genetic diversity loss and decreased capacity of self-sustained populations. In both climate change scenarios, the suitable areas for the southern muriqui in Paraná and Rio de Janeiro states will decrease more drastically. Areas where the primate occurs in the interior of São Paulo and Rio de Janeiro states will disappear or be climatically disconnected from the core potential habitat. Alike preventing further deforestation, Atlantic Forest restoration actions are needed to connect the viable populations for compensating the projected land use and climate change impacts to the long term persistence of the southern muriqui.

Nitrogen pollution is one of the most widespread and challenging problems in Brazil, being the cause of several environmental and health issues. The focus of this thesis is to investigate the underlying drivers of reactive nitrogen emissions, the sustainability of nitrogen use, and the policy responses to address the pollution problem, with the aim of contributing to improving nitrogen management in the country. To accomplish this main objective, we carried out the following activities, in which specific methods were applied for each one of them, and unique but also complementary results were obtained: (i) development of a Conceptual Framework Model to understand the complexity of N dynamics in the country, the diversity of drivers, and potential damage to environment and human health; (ii) calculation of the level of nitrogen sustainability using the Entropy Weight Method (EWM) on a set of N-related indicators, within the environmental, economic, social, and institutional subsystems, and (iii) application of the Policy Coherence Analytical Framework to show how coherent interactions are among N-related policies in Brazil and how these policies can promote the development of a potential integrated nitrogen policy approach. As an overall result, we conclude that the driving forces behind the increase in N emissions are the productive activities aimed at meeting the demand for food, energy, and housing by a rapidly growing population, leading to a weak-to-basic level of sustainability of nitrogen management in the country. Our results also indicate that political and institutional factors (e.g., good governance, regulatory quality, and political stability) are essential to prepare and implement successful responses to address the problem of nitrogen pollution in Brazil. Finally, the policy coherence analysis shows a favorable outcome, since most of the analyzed policies present positive interactions, which enables and reinforces the achievement of their objectives. An exception is the 2022 National Fertilizer Plan, considering that the absence of environmental care actions could make several other policies unfeasible. In this case, we suggest reformulating the plan with the involvement of multiple actors and stakeholders, to include environmental issues related to fertilizer production and use, as well as to avoid counteraction with other N-related policies.

Os marcos geodésicos (MGs) são essenciais para desenvolver projetos e obras, porém, estão sujeitos à erosão. Neste estudo, foram estimadas a perda de solo nos MGs na região sul do estado de Santa Catarina considerando dados históricos e cenários futuros de mudanças climáticas. Os resultados mostraram que a perda de solo diminuiu de 1985 a 2020, porém, houve um aumento de MGs na classe de perda de solo nula, causado pelo crescimento urbano. Para a maioria dos cenários futuros houve aumento na perda de solo de 2020 a 2100, embora com valores inferiores ao passado. Estes cenários consideraram apenas alterações de precipitação, sendo que a modelagem da mudança da cobertura do solo para o futuro poderia trazer melhorias na análise. Portanto, estudos como este podem ser usados para entender os efeitos das mudanças climáticas nos processos erosivos e apoiar a tomada de decisão para manter e aumentar a rede geodésica.

Os dados meteorológicos são amplamente utilizados em vários tipos de estudos em diversas regiões geográficas. Entretanto, sua obtenção por medições diretas através de estações meteorológicas possui distribuição espacial limitada. Para transpor essa limitação, dados de sensoriamento remoto podem ser utilizados como uma alternativa. Com o objetivo de avaliar o uso desses dados para a região do Matopiba, o presente trabalho comparou os dados de precipitação provenientes dos produtos CHIRPS e PERSIANN com os dados de estações automáticas do INMET. Para comparação, calculamos coeficiente de correlação de Spearman e o índice de concordância de Willmott modificado. Os resultados mostraram que os dados CHIRPS e PERSIANN são coerentes e podem ser usados como alternativa aos dados de estação meteorológicas, com melhor desempenho para os dados de precipitação acumulada.

Forest degradation is a major issue and a key component of tropical forests and carbon emissions. In this study, we use repeated Airborne Laser Scanning (ALS) data to quantify carbon losses in degraded forests due to logging at the Mato Grosso state rainforests. We identified logged areas using Planet Norway’s International Climate and Forests Initiative (NICFI) satellite imagery and estimated aboveground carbon density (ACD) and changes (ΔACD) using canopy structure derived from ALS data acquired before and after the logging. Logging caused carbon losses between 16-35% of the original ACD, but also as high as 89% in heavily disturbed areas. Our findings bring estimates to limited sites, so we recommend caution on using them for estimates of carbon loss elsewhere. Spatialized and continuous estimates should be explored in future studies connecting ALS estimates with other optical and SAR remote sensing datasets.

Here, we evaluated the combination of five satellites/sensors for improving burned area (BA) detection over the Amazon basin. Using a data set of 2,400 burn/no burn points by visual inspection in 2016, we investigated several spectral indices and ingested them into data mining algorithms to evaluate their burning area detection performance. Better results were provided with attribute selection combining Sentinel-2 (S2) and MODIS indexes (96 %), which were not significantly better than S2 indexes alone (95 %). The worst was the Sentinel-1 SAR data with 85 % accuracy. This is the first large-scale data research to evaluate the potentiality of combined temporal, spectral, and spatial resolutions for BA detection across the Amazon.

A compreensão da taxa de crescimento da biomassa acima do solo (BAS) em florestas sob regeneração natural é essencial para mensurar o sequestro de carbono florestal e auxiliar na mitigação das mudanças climáticas. Neste estudo, estimamos a BAS em 57 amostras de florestas secundárias amazônicas, por meio da combinação de dados aerotransportados LiDAR e hiperespectrais e modelos de aprendizado de máquinas. A idade das florestas secundárias foi determinada pela análise da série temporal de imagens Landsat de 1984 a 2017. As florestas secundárias exibiram uma taxa de recuperação de BAS de 5,7 Mg.ha-1.ano-1, calculadas por meio de regressão linear (R2 = 0,91). A integração de dados multisensores mostrou grande potencial para apoiar iniciativas de mitigação do clima.

The southeastern Amazon has recently been shown to be a net carbon source, which is partly caused by drying conditions. Drying depends on a number of factors, one of which is the land cover at the locations where the moisture has originated as evaporation. Here we assess for the first time the origins of the moisture that precipitates in the Amazon carbon source region, using output from a Lagrangian atmospheric moisture tracking model. We relate vegetation productivity in the Amazon carbon source region to precipitation patterns and derive land-cover data at the moisture origins of these areas, allowing us to estimate how the carbon cycle and hydrological cycle are linked in this critical part of the Amazon. We find that, annually, 13% of the precipitation in the Amazon carbon source region has evaporated from that same area, which is half of its land-derived moisture. We further find a moisture-recycling-mediated increase in gross primary productivity of roughly 41 Mg carbon km(-2) yr(-1) within the Amazon carbon source region if it is fully forested compared to any other land cover. Our results indicate that the parts of the Amazon forest that are already a net carbon source, still help sustain their own biomass production. Although the most degraded parts of the Amazon depend mostly on oceanic input of moisture, further degradation of this region would amplify carbon losses to the atmosphere.

The achievement of several sustainable development goals and the Paris Climate Agreement depends on rapid progress towards sustainable food and land systems in all countries. We have built a flexible, collaborative modeling framework to foster the development of national pathways by local research teams and their integration up to global scale. Local researchers independently customize national models to explore mid-century pathways of the food and land use system transformation in collaboration with stakeholders. An online platform connects the national models, iteratively balances global exports and imports, and aggregates results to the global level. Our results show that actions toward greater sustainability in countries could sum up to 1 Mha net forest gain per year, 950 Mha net gain in the land where natural processes predominate, and an increased CO2 sink of 3.7 GtCO2e yr−1 over the period 2020-2050 compared to current trends, while average food consumption per capita remains above the adequate food requirements in all countries. We show examples of how the global linkage impacts national results and how different assumptions in national pathways impact global results. This modeling setup acknowledges the broad heterogeneity of socio-ecological contexts and the fact that people who live in these different contexts should be empowered to design the future they want. But it also demonstrates to local decision-makers the interconnectedness of our food and land use system and the urgent need for more collaboration to converge local and global priorities.