PRECIPITAÇÃO ESTIMADA POR SENSORIAMENTO REMOTO NO ESTADO DE SERGIPE

Atualmente, dados de sensoriamento remoto, como os do Tropical Rainfall Measuring Mission (TRMM), vem sendo utilizados para monitorar a distribuição da chuva no tempo e no espaço. O objetivo deste trabalho foi avaliar a qualidade dos dados da precipitação pluvial estimada pelo produto 3B43-TRMM no estado de Sergipe, nas escalas mensal e anual, entre 1998 e 2013. Os valores pontuais estimados pelo TRMM foram comparados com os dados de precipitação obtidos em 13 postos pluviométricos da Agência Nacional de Águas (ANA). Os indicativos estatísticos considerados foram o coeficiente de determinação (R²), erro médio absoluto (EMA), raiz do erro quadrado médio (REQM) e índice de concordância de Willmott (d). Os valores de R² foram de 0,49 e 0,16 nas escalas mensal e anual, respectivamente. Para a escala de tempo mensal as melhores estimativas do produto TRMM foram encontradas na região Semiárida do estado de Sergipe, com valores de R², EMA, REQM e d iguais a 0,54, 27,18 mm e 38,71 mm e 0,83, respectivamente.Palavras-chave: 3B43-TRMM; climatologia; hidrologia; chuva. ANALYSIS OF ESTIMATED PRECIPITATION DATA BY REMOTE SENSING IN THE SERGIPE STATE ABSTRACT: Currently, remote sensing data, such as that of the Tropical Rainfall Measuring Mission (TRMM), has been used to monitor the distribution of rain over time and space. The objective of this work was to evaluate the quality of the rainfall data estimated by the product 3B43-TRMM in the state of Sergipe, on the monthly and annual scales, between 1998 and 2013. The point values estimated by the TRMM were compared with the precipitation data obtained in 13 pluviometric stations of the National Water Agency (ANA). The statistical indications considered were the coefficient of determination (R²), mean absolute error (EMA), root of the mean square error (REQM) and Willmott's agreement index (d). The R² values were 0.49 and 0.16 on the monthly and annual scales, respectively. For the monthly time scale, the best estimates of the TRMM product were found in the semi-arid region of the state of Sergipe, with values of R², EMA, REQM and d equal to 0.54, 27.18 mm and 38.71 mm and 0.83, respectively.Keywords: 3B43-TRMM, climatology; hydrology; rain

Modelling the spatial dependence of the rainfall erosivity index in the Brazilian semiarid region

O objetivo deste trabalho foi modelar a dependência espacial e mapear o índice de erosividade das chuvas (EI30) na região semiárida do Brasil. Foram utilizados registros de erosividade mensal de 210 postos pluviométricos, com série temporal diária igual ou superior a 15 anos. Com base nos valores do EI30, a modelagem da dependência espacial foi realizada pelo ajuste do semivariograma. A partir dos modelos de semivariograma, foram gerados mapas de isolinhas de erosividade com interpolador da krigagem. De acordo com a série histórica de dados, o valor máximo mensal médio do EI30 foi observado em março, e o valor anual variou de 1.439 a 5.864 MJ mm ha-1 por ano, classificado como baixo e moderado, respectivamente. Os maiores valores do EI30 foram obtidos nos extremos norte e sul da região semiárida. Foi observada dependência espacial média para erosividade da chuva, para a maioria dos meses, principalmente com o modelo de semivariograma esférico. O alcance da erosividade variou entre 62 e 1.508 km, para o EI30 mensal, e foi de, aproximadamente, 1.046 km para o anual. A modelagem aplicada, com a validação dos semivariogramas pelo teste de jackknife, permite a espacialização do EI30 para a região semiárida do Brasil.The objective of this work was to model the spatial dependence and to map the rainfall erosivity index (EI30) in the semiarid region of Brazil. Registers of monthly erosivity from 210 rainfall stations were used, with daily time series equal to or greater than 15 years. Based on the values of the EI30, a spatial dependence model was made by adjusting the semivariogram. From the semivariogram models, erosivity isoline maps were generated with a kriging interpolator. According to the historical data series, the maximum monthly average value of the EI30 was observed in March, and the annual value ranged from 1,439 to 5,864 MJ mm ha-1 per year, classified as low and moderate, respectively. The highest EI30 values were obtained in the northern and southern extremes of the semiarid region. Average spatial dependence was observed for rainfall erosivity, in most months, especially with the spherical semivariogram model. The range of erosivity varied from 62 to 1,508 km for the monthly EI30 and was of approximately 1,046 km for the annual one. The applied model, with the validation of the semivariograms using the jackknife test, allows the spatialization of the EI30 for the semiarid region of Brazil

Educomunicação e diversidade: múltiplas abordagens

Esta coletânea de capítulos intitulada “Educomunicação e Diversidade: múltiplas abordagens” reúne estudos apresentados no VI Encontro Brasileiro de Educomunicação e III EducomSul, realizado em Porto Alegre em 2015. Nessa obra, percebe-se que as dimensões interculturais, transversais e cidadãs suscitadas pela educomunicação vêm contribuindo para o aumento de intervenções comunicacionais diversas, em termos de linguagens e de conteúdos, em práticas educativas formais e não formais. Denotando a diversidade como uma área em expansão na educomunicação

Avaliação da cobertura vegetal por meio dos Índices de Vegetação SR, NDVI, SAVI e EVI na bacia do rio Japaratuba-Mirim em Sergipe

One of the most important applications of the remote sensing is the use of spectral data to estimate parameters of vegetation. The environmental assessment of river basins has used remote sensing data to obtain information on vegetation cover. The vegetation indices are applied to quantify the areas, types and densities of vegetation cover. This study sought to evaluate the behavior of vegetation in the river basin Japaratuba-Mirim in the state of Sergipe, Brazil, calculating the following vegetation indices: SR, NDVI, SAVI and EVI. We used a Landsat 5 TM (Thematic Mapper), orbit 215, point 67, with low cloud cover, dated 03/04/2009. It was performed atmospheric correction, the conversion of digital number values for reflectance and map algebra to calculate the indices using the software ArcGIS. The results showed that the indices SAVI and EVI were more suitable for the quantification of vegetation, considering the classes of vegetation existing in basin and the limits of literature. SR index showed large coefficient of variation - calculated from the values of image pixels - in comparison to others obtained indices. NDVI index had a poor performance to identification of bare soil and soil with sparse vegetation. However, it was observed that all the indices used allowed a clear distinction between the present vegetation cover.Pages: 1357-136

Applicability of TRMM Precipitation for Hydrologic Modeling in a Basin in the Northeast Brazilian Agreste

<div><p>Abstract Determining precipitation using remote sensing is gaining space in hydrologic studies, helping make up for the lack of data in many regions of Brazil. The products from satellite TRMM (Tropical Rainfall Measuring Mission) are widely applied in studies in Brazil, but there are still few results about their applicability for hydrologic modeling in the Northeast Region, which is characterized by an irregular precipitation regime. The objective of this study is to evaluate the feasibility of using the TRMM 3B42 V7 data for hydrologic modeling in the Japaratuba river basin in Sergipe at three timescales: daily, every ten days, and monthly. The comparative analysis between the rainfall data from rain gauges and TRMM did not indicate satisfactory adequacy at these studied scales, since the TRMM data underestimated the total rainfall for all stations used in the study. However, for the hydrologic modeling, acceptable values were obtained for the efficiency coefficients evaluated only for the ten-day and monthly scales.</p></div

PRECIPITAÇÃO ESTIMADA POR SENSORIAMENTO REMOTO NO ESTADO DE SERGIPE

Atualmente, dados de sensoriamento remoto, como os do Tropical Rainfall Measuring Mission (TRMM), vem sendo utilizados para monitorar a distribuição da chuva no tempo e no espaço. O objetivo deste trabalho foi avaliar a qualidade dos dados da precipitação pluvial estimada pelo produto 3B43-TRMM no estado de Sergipe, nas escalas mensal e anual, entre 1998 e 2013. Os valores pontuais estimados pelo TRMM foram comparados com os dados de precipitação obtidos em 13 postos pluviométricos da Agência Nacional de Águas (ANA). Os indicativos estatísticos considerados foram o coeficiente de determinação (R²), erro médio absoluto (EMA), raiz do erro quadrado médio (REQM) e índice de concordância de Willmott (d). Os valores de R² foram de 0,49 e 0,16 nas escalas mensal e anual, respectivamente. Para a escala de tempo mensal as melhores estimativas do produto TRMM foram encontradas na região Semiárida do estado de Sergipe, com valores de R², EMA, REQM e d iguais a 0,54, 27,18 mm e 38,71 mm e 0,83, respectivamente.Palavras-chave: 3B43-TRMM; climatologia; hidrologia; chuva. ANALYSIS OF ESTIMATED PRECIPITATION DATA BY REMOTE SENSING IN THE SERGIPE STATE ABSTRACT: Currently, remote sensing data, such as that of the Tropical Rainfall Measuring Mission (TRMM), has been used to monitor the distribution of rain over time and space. The objective of this work was to evaluate the quality of the rainfall data estimated by the product 3B43-TRMM in the state of Sergipe, on the monthly and annual scales, between 1998 and 2013. The point values estimated by the TRMM were compared with the precipitation data obtained in 13 pluviometric stations of the National Water Agency (ANA). The statistical indications considered were the coefficient of determination (R²), mean absolute error (EMA), root of the mean square error (REQM) and Willmott's agreement index (d). The R² values were 0.49 and 0.16 on the monthly and annual scales, respectively. For the monthly time scale, the best estimates of the TRMM product were found in the semi-arid region of the state of Sergipe, with values of R², EMA, REQM and d equal to 0.54, 27.18 mm and 38.71 mm and 0.83, respectively.Keywords: 3B43-TRMM, climatology; hydrology; rain

Modelling the spatial dependence of the rainfall erosivity index in the Brazilian semiarid region

The objective of this work was to model the spatial dependence and to map the rainfall erosivity index (EI30) in the semiarid region of Brazil. Registers of monthly erosivity from 210 rainfall stations were used, with daily time series equal to or greater than 15 years. Based on the values of the EI30, a spatial dependence model was made by adjusting the semivariogram. From the semivariogram models, erosivity isoline maps were generated with a kriging interpolator. According to the historical data series, the maximum monthly average value of the EI30 was observed in March, and the annual value ranged from 1,439 to 5,864 MJ mm ha-1 per year, classified as low and moderate, respectively. The highest EI30 values were obtained in the northern and southern extremes of the semiarid region. Average spatial dependence was observed for rainfall erosivity, in most months, especially with the spherical semivariogram model. The range of erosivity varied from 62 to 1,508 km for the monthly EI30 and was of approximately 1,046 km for the annual one. The applied model, with the validation of the semivariograms using the jackknife test, allows the spatialization of the EI30 for the semiarid region of Brazil.O objetivo deste trabalho foi modelar a dependência espacial e mapear o índice de erosividade das chuvas (EI 30 ) na região semiárida do Brasil. Foram utilizados registros de erosividade mensal de 210 postos pluviométricos, com série temporal diária igual ou superior a 15 anos. Com base nos valores do EI 30, a modelagem da dependência espacial foi realizada pelo ajuste do semivariograma. A partir dos modelos de semivariograma, foram gerados mapas de isolinhas de erosividade com interpolador da krigagem. De acordo com a série histórica de dados, o valor máximo mensal médio do EI 30 foi observado em março, e o valor anual variou de 1.439 a 5.864 MJ mm ha -1 por ano, classificado como baixo e moderado, respectivamente. Os maiores valores do EI 30 foram obtidos nos extremos norte e sul da região semiárida. Foi observada dependência espacial média para erosividade da chuva, para a maioria dos meses, principalmente com o modelo de semivariograma esférico. O alcance da erosividade variou entre 62 e 1.508 km, para o EI 30 mensal, e foi de, aproximadamente, 1.046 km para o anual. A modelagem aplicada, com a validação dos semivariogramas pelo teste de jackknife, permite a espacialização do EI 30 para a região semiárida do Brasil

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data