Seasonal effect on spatial and temporal consistency of the new GPM-based IMERG-v5 and GSMaP-v7 satellite precipitation estimates in Brazil’s Central Plateau Region

This study assesses the performance of the new Global Precipitation Measurement (GPM)-based satellite precipitation estimates (SPEs) datasets in the Brazilian Central Plateau and compares it with the previous Tropical Rainfall Measurement Mission (TRMM)-era datasets. To do so, the Integrated Multi-satellitE Retrievals for GPM (IMERG)-v5 and the Global Satellite Mapping of Precipitation (GSMaP)-v7 were evaluated at their original 0.1 spatial resolution and for a 0.25 grid for comparison with TRMM Multi-satellite Precipitation Analysis (TMPA). The assessment was made on an annual, monthly, and daily basis for both wet and dry seasons. Overall, IMERG presents the best annual and monthly results. In both time steps, IMERG’s precipitation estimations present bias with lower magnitudes and smaller root-mean-square error. However, GSMaP performs slightly better for the daily time step based on categorical and quantitative statistical analysis. Both IMERG and GSMaP estimates are seasonally influenced, with the highest difficulty in estimating precipitation occurring during the dry season. Additionally, the study indicates that GPM-based SPEs products are capable of continuing TRMM-based precipitation monitoring with similar or even better accuracy than obtained previously with the widely used TMPA product

BILATERAL TROCHLEAR PALSY DUE TO CEREBRAL VASCULITIS RELATED TO COVID-19 INFECTION

N/

ANALYSIS OF THE EFFECTS OF ATMOSPHERIC CORRECTION ON ORBITAL IMAGES FOR STUDIES IN INTERIOR WATER BODIES

The water reservoirs, in addition to their significance in electricity generation, serve as vital resources for various other requirements of the population. Images from orbital sensors have been applied to complement the monitoring of these environments and thus overcome the deficiency of spatial and temporal coverage of traditional techniques. However, studies involving water quality are still a great challenge due to the low signal coming from the water body and the interference of external factors (or environmental factors). Image correction/improvement procedures are often proposed, mainly to reduce atmospheric interference. In this study the best available atmospheric correction techniques were evaluated in order to indicate the technique that most closely matches the spectral response of remotely sensed images obtained in the field. During the study six atmospheric correction algorithms were applied (FLAASH, Second simulation of a Satellite Signal in the Solar Spectrum (6S), L8SR, Aquatic Reflectance (NASA/USGS), ACOLITE and Sen2Cor) that, based on the statistical analysis of discriminant analysis and covariance, indicated the 6S for Landsat and Sentinel images and ACOLITE for Landsat images as the most accurate. Although 6S showed a response close to the reference data, low variability in spectral response was observed. For time series, ACOLITE showed better capacity to correct the data. The type of application is also a preponderant factor, since it was evident that the use of time series indicated a different atmospheric correction technique when compared to the analysis of the scenes individually

EFEITOS DA CORREÇÃO ATMOSFÉRICA EM IMAGENS MULTIESPECTRAIS ORBITAIS PARA ESTUDOS EM CORPOS D’ÁGUA INTERIORES

Os reservatórios hídricos além de serem importantes para a produção de energia elétrica, são recursos para outras necessidades da população. Imagens de sensores orbitais são aplicadas para complementar o monitoramento desses ambientes e assim suprir a deficiência  de cobertura espacial e temporal das técnicas tradicionais. No entanto estudos envolvendo análises volumétricas de corpos d’água ainda são um grande desafio devido  ao baixo sinal proveniente do corpo d’água e a interferência de fatores externos (ou fatores ambientais). Procedimentos de correção/melhoramento das imagens são propostos com frequencia, principalmente  para a redução da interferencia atmosférica. Nesse estudo foram avaliadas as melhores técnicas de correção atmosférica disponíveis comercialmente no intuito de indicar aquela técnica que mais se aproxima da resposta espectral de sensoriamento remoto obtida em campo (referência). No decorrer do estudo foram aplicados seis algoritmos de correção atmosférica (FLAASH, Second simulation of a Satellite Signal in the Solar Spectrum (6S), L8SR, Aquatic Reflectance (USGS), ACOLITE e Sen2Cor) que, a partir das análises estatísticas de análise discriminante e covariância apontaram os aplicativos 6S para imagens Landsat e Sentinel e o Acolite para imagens Landsat como os mais acurados. Embora o 6S tenha apresentado resposta próxima dos dados de referencia, observou-se baixa variabilidade na resposta espectral. Para séries temporais, o Acolite apresentou maior capacidade de correção dos dados. O tipo de aplicação também é um fator preponderante, pois ficou evidente que o uso de series temporais indicou uma técnica de correção atmosférica diferente quando comparado com a análise das cenas de forma individual

Water erosion of dystrophic Red Latosols (Oxisols)

In their natural state, Latosols (Oxisols) present great stability and resistance to erosion, being the most abundant and used soils for farming and cattle raising activities in southern Minas Gerais State, Brazil. However, along the last one hundred years, they have been submitted to intensive cultivation and managements which favor water erosion. This study aimed to estimate the water erosion rates of dystrophic Red Latosols from the Revised Universal Soil Loss Equation, compared with the soil loss tolerance limits, and assess the impact on water erosion of the managements more common in the region, by alternative conservation management simulation. Soil loss tolerance limits ranged from 8.94 Mg ha-1 year-1 to 9.99 Mg ha-1 year-1, with the study area presenting a susceptibility of soil loss of 23.86 Mg year-1, with an average rate of 8.40 Mg ha-1 year-1, corresponding to 34.80 % of the area with values above the soil loss tolerance limit. The biggest annual losses occur in areas with use and management of eucalyptus grown downhill (30.67 Mg ha-1 year-1) and pasture under continuous occupancy (11.10 Mg ha-1 year-1). However, when the average loss per type of use is considered, the areas more susceptible to water erosion are those with potato and eucalyptus crops, grown downhill, and those in bare soil. Nevertheless, in the simulated conservation management scenario, the average losses would be drastically reduced (8.40 Mg ha-1 year-1 to 2.84 Mg ha-1 year-1) and only 4.00 % of the area with soil loss would remain above the tolerance limits

SPATIAL AND TEMPORAL MODELING OF WATER EROSION IN DYSTROPHIC RED LATOSOL (OXISOL) USED FOR FARMING AND CATTLE RAISING ACTIVITIES IN A SUB-BASIN IN THE SOUTH OF MINAS GERAIS

Water erosion is one of the most important soil degradation processes and it can be intensified by land use and vegetal covering changes. Thus, water erosion modeling studies associated to multi temporal analyses of land use are effective in assessing how changes in land cover affects sediment yield. Therefore, considering the modifications in the land use from 1986 to 2011, the aim of this study ranged to estimate water erosion rates and compare them to the soil loss tolerance (SLT) limit in the Latosols (Oxisols) at Ribeirão Caçús sub-basin, in the South of Minas Gerais State, Southeast Brazil, by means of the Revised Universal Soil Loss Equation (RUSLE) in association with the geographic information system (GIS), and geostatistical techniques. So, for each year mapped, soil loss averages were compared by t test at 5% significance to assess the soil degradation stage. The results indicated that, in the period, the soil loss average rate was from 2.4 to 2.6 Mg ha-1 year-1 and the areas with soil loss above the limit of SLT were around 8.0%. The t test demonstrated there was no considerable difference among the soil loss averages (p = 0.18). In consequence, the area of degraded soils did not increase. Thus, the RUSLE model in GIS is a simple and useful tool to estimate the soil loss and help define soil conservation and recovery measures

A Polarization Approach for Understanding Online Conflicts in Times of Pandemic: A Brazilian Case Study

As society becomes digitalized, online social networks tend to be primary places for debate but can turn into a battlefield for imposing conflicting narratives. Automating the identification of online conflicts is a challenge due to difficulties in defining antagonist communities and controversial discussions. Here, we propose a polarization approach for understanding Twitter conflicts in Brazil during the COVID-19 pandemic, where a small group of polarizers influences a larger group of polarizees according to their ideological leaning. Polarizers are automatically identified by centrality metrics in following, retweet, and reply networks, and manually labeled as leftists, rightists, or undefined. We collected and analyzed the polarization of 21 potentially conflicted political events in Brazil. Our results show that polarizers adequately represent the polarization of events, the traditional media is giving way to a new breed of tweeters, and retweet and reply play different roles within a conflict that reflects their polarization level