Objectives To investigate the 5-year prevalence of patients admitted to public inpatient care units due to a mental disorder, stratifying them by age group and diagnosis, and to assess trends of admissions over this time period in Porto Alegre. Methods All admissions to the public mental health care system regulated by the city-owned electronic system Administração Geral dos Hospitais (AGHOS) were included in the analysis. The total population size was obtained by estimations of Fundação de Economia e Estatística (FEE). General information about 5-year prevalence of inpatient admissions, time-series trends e prevalence by age groups and diagnosis were presented. Results There were 32,608 admissions over the 5-year period analyzed. The overall prevalence of patients was 1.62% among the total population, 0.01% among children, 1.12% among adolescents, 2.28% among adults and 0.93% among the elderly. The most common diagnosis was drug-related, followed by mood, alcohol-related and psychotic disorders. There was a linear trend showing an increase in the number of admissions from 2013 to the midst of 2014, which dropped in 2015. Conclusions Admissions due to mental disorders are relatively common, mainly among adults and related to drug use and mood disorders. Time trends varied slightly over the 5 years. Prevalence rates in real-world settings might be useful for policymakers interested in planning the public mental health system in large Brazilian cities.

R E S U M ONeste trabalho objetivou-se estimar e mapear áreas plantadas com soja [Glycine max (L.) Merr.] por meio de imagens multitemporais EVI/MODIS e classificação de imagens baseada em geo-objeto. A área de estudo compreendeu o Sul do estado do Maranhão. Para o mapeamento das lavouras de soja foram utilizados o índice de vegetação realçado (EVI) e o índice de valorização das culturas (CEI) para a classificação das imagens do sistema-sensor Terra/MODIS. Para tal cálculo foram utilizadas doze imagens compreendendo entressafra e safra da cultura, conforme calendário agrícola do Estado. Além disto, foi empregada a segmentação utilizando-se parâmetros de escala 250, os algoritmos "classification" e "merge region" e extração de atributos para classificação baseada em geo-objeto. Foram empregados, para avaliar a precisão da classificação, os parâmetros Kappa e Exatidão Global e nas suas resultantes foi aplicado o teste Z; logo, foram estabelecidos, como hipótese nula (H0) a igualdade dos índices e o inverso para suas diferenças (H1), a um nível de 0,05 de significância. Os resultados obtidos indicam que a metodologia proposta se mostrou eficiente para mapeamento da soja, com 0,89 para o parâmetro Kappa. Discrimination of soybean areas through images EVI/MODIS and analysis based on geo-object A B S T R A C TThis study aimed to estimate and map areas planted with soybean [Glycine max (L.) Merr.] through multitemporal images EVI/MODIS and classification of images based on geo-object. The study area comprised the southern part of the State of Maranhão. For the mapping of the soybean crop the Enhanced Vegetation Index (EVI) and the Crop Enhancement Index (CEI) for image classification sensor-system Terra/MODIS was used. For this calculation twelve images were used, including offseason and harvest of the crop, as per the state agricultural calendar. In addition, the segmentation was employed using scaling parameters 250, the algorithms "classification" and "merge region", and extracting attributes for classification GEOgraphic-Object-Based Image Analysis (GEOBIA). To assess the accuracy of the classification, parameters Kappa and Accuracy Global and its resulting Z test were applied. A null hypothesis (H0) of equal and opposite rates for their differences (H1) at 0.05 level of significance was established. The results indicate that the proposed methodology is efficient for mapping the soybean crop, with 0.89 for the parameter Kappa.Discriminação de áreas de soja por meio de imagens EVI/MODIS e análise baseada em geo-objeto

Differentiation of grassland/forage types and accurate estimates of their location and extent are important for understanding their ecological processes and for applying appropriate management practices. We are aiming to reveal the different spectral characteristics of six grassland/forage land covers in three ecoregions located in the Canadian Prairies, based on field data and satellite images. Three spectral indices representing productivity (Normalized Difference Vegetation Index (NDVI)), moisture content (Normalized Difference Moisture Index (NDMI)), and plant photosynthetic activity (Plant Senescence Reflectance Index (PSRI)) were used for comparison of means, comparison of coefficient of variation (CV), and analysis of variance (ANOVA). The results indicated that different grassland types show distinguishable spectral characteristics in the Moist-Mixed and Mixed Ecoregions, while it was not possible to differentiate the classes in the Fescue Ecoregion. To further investigate the within-sites and between-sites heterogeneity, we calculated the CV in a 3 × 3 window and placed them in comparative triangles to demonstrate their potential separability. Results indicated that the triangles based on the CV offered greater class separability in the Fescue Ecoregion and in the Mixed Ecoregion.

Precipitation is crucial for the hydrological cycle and is directly related to many ecological processes. Historically, measurements of precipitation totals were made at weather stations, but spatial and temporal coverage suffered due to the lack of a robust network of weather stations and temporal gaps in observations. Several products have been proposed to identify the location of the occurrence of precipitation and measure its intensity from different types of estimates, based on alternative data sources, that have global (or quasi-global) coverage with long historical time series. However, there are concerns about the accuracy of these estimates. The objective of this study is to evaluate the accuracy of the ERA5 product for two ecoregions of the Canadian Prairies through comparison with monthly means measured from 1981–2019 at ten weather stations (in-situ), as well as to assess the intraseasonal variability of precipitation and identify dry and wet periods based on the annual Standardized Precipitation Index (SPI) derived from ERA5. A significant relationship between in-situ data and ERA5 data (with the R2 varying between 0.42 and 0.76) (p < 0.01)) was observed in nine of the ten weather stations analyzed, with lower RMSE in the Mixed Ecoregion. The Mean Absolute Percentage Error (MAPE) results showed greater agreement between the datasets in May (average R value of 0.84 and an average MAPE value of 32.33%), while greater divergences were observed in February (average R value of 0.57 and an average MAPE value of 50.40%). The analysis of wet and dry periods, based on the SPI derived from ERA5, and the comparison with events associated with the El Niño-Southern Oscillation (ENSO), showed that from the ERA5 data and the derivation of the SPI it is possible to identify anomalies in temporal series with consistent patterns that can be associated with historical events that have been highlighted in the literature. Therefore, our results show that ERA5 data has potential to be an alternative for estimating precipitation in regions with few in-situ stations or with gaps in the time series in the Canadian Prairies, especially at the beginning of the growing season.

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