ABSTRACT The agricultural sector is considered an important source of greenhouse gases, in which the coffee crop has an important contribution, participating in the dynamics of soil C and CO2 emissions. In this way, the aim of this study was to analyze and quantify CO2 emissions in different phenological phases in soil cultivated with conilon coffee (Coffea canephora Pierre ex A. Froehner), and compare it with CO2 flow in forest and pasture area. The experiment was conducted in the Experimental Area of the Federal Institute of Espírito Santo, Southeastern Brazil, in an experimental plot of 0.5 ha cultivated with conilon Vitória coffee clones. The quantification of soil CO2 flux was performed between July 2016 and June 2017, this period comprising all the seasons of the year, using in two rounds of ratings (day and night), with five replicates each. For comparison purposes, another measurement of the CO2 stream was conducted in June 2017 with an evaluation period of 24 h. The highest CO2 emission occurred during the harvest phases of the fruit (206.7 mg CO2 m-2 h-1). The lowest daily CO2 flow records were observed in the rest and flowering phases with values of 82.1 and 83.6 CO2 mg m-2 h-1, respectively. The CO2 emission from the forest was higher than the emissions that occurred in the area planted with conilon coffee and pasture, during dry season

ABSTRACT This study aimed to assess vegetal cover evolution on a river island within the Ecological Station of (EEP), by remote sensing. For this purpose, Normalized Difference Vegetation Indexes were generated for Landsat 1 (1973) and Landsat 5 (1984, 1990, 2000 and 2011) images. Five landscape units were identified in the field: bare soil, Rough savanna, Typical savanna, Forested savanna and Evergreen dry woods. Only Forested savanna and Evergreen dry woods showed poor spectral splitting, being thus considered as a forestry complex. Changes throughout time have occurred in all units, with decreasing in bare soil areas (-2.56 ha year−1), Rough savanna (-0.66 ha year−1) and Typical savanna (-0.94 ha year−1) and with an increase in the Forested savanna (5.97 ha year−1). Thus, the method used in this study was effective for sorting and assessing plant cover of the landscape units studied over time.

Objetivou-se, neste trabalho, realizar a caracterização física das Bacias Hidrográficas do Igarapé Canela Fina e Igarapé Preto, ambas localizadas no município de Cruzeiro do Sul, AC. Para a classificação não supervisionada empregou-se o ISODATA através dos produtos do sensor TM, a bordo do satélite Landsat-5 fornecidos pelo Instituto Nacional de Pesquisas Espaciais. O período avaliado se estende entre os anos de 2005 a 2010. Os resultados mostram um aumento médio de 48,11% em áreas desflorestadas para essas bacias e redução média em áreas florestadas de 50,58%, em consequência de intervenções antrópicas ocorridas ao longo dos últimos anos. As características físicas calculadas demonstraram similaridade entre as bacias hidrográficas, ambas respondem lentamente a eventos hidrológicos e, portanto, não propícias a enchentes. As modificações realizadas ao longo dos últimos anos na superfície das bacias hidrográficas alteraram possivelmente a dinâmica hídrica dos igarapés e os resultados obtidos indicam a eficiência do Sensoriamento Remoto e Sistema de Informação Geográfica na análise da dinâmica espaço-tempo da vegetação e caracterização morfométrica das bacias hidrográficas.

The aim of this study was to report physical characterization of the hydrographical basins of Igarapé Canela Fina and Igarapé Preto, both located in the municipality of Cruzeiro do Sul, Acre, Brazil. The ISODATA algorithm was used for the non-supervised classification, drawing on data provided by the TM Landsat 5 sensor from the National Institute for Space Research. The covered period of analysis was from 2005 to 2010. The analysis shows an average increase of 48.11% of deforested areas and a mean reduction of 50.58% in the forested areas for both basins, due to anthropic interventions over those five years. The analysis of physical characteristics shows similarities between the hydrographical basins, with both responding slowly to hydrological events, and thus providing conditions not favourable to flooding. Modifications conducted on the surface of the hydrographical basins along the last few years have, however, possibly altered the hydro dynamics of the rivers and streams. The preliminary results indicate that the Remote Sensing and Geographic Information Systems are efficient tools for the analysis of the space-time dynamics of vegetation and the morphometric characterization of hydrographical basins.

Foram estudadas, neste trabalho, a homogeneidade e a existência de tendências na distribuição temporal da precipitação para a microrregião do Sertão do São Francisco, no estado de Alagoas. Os totais médios mensais de precipitação utilizados foram divididos em três períodos climatológicos: 1931-1960,1961-1990 e 1991-2011. Para essas séries temporais foram aplicados os testes estatísticos de Pettitt-Mann-Kendal e a regressão linear com o teste de Student para o coeficiente angular da reta, respectivamente, para identificar a homogeneidade e a tendência nas séries de precipitação. A distribuição temporal da precipitação para os períodos de 1931-1960 e 1961-1990 não apresentou mudanças nem tendência, as quais foram detectadas para o período de 1991-2011, com tendência significativa de aumento da precipitação em sete meses.

The objective of this paper was to study the homogeneity and the existence of trends in temporal distribution of rainfall for microregion of San Francisco in the State of Alagoas. The mean precipitation data used was divided in three climatological periods: 1931-1960, 1961-1990 and 1991-2011. For these time series the statistical tests of Pettitt-Mann-Kendal was applied and linear regression with the Student test for the slope of a line, respectively to identify the homogeneity and trend in the precipitation data. The temporal distribution of precipitation for the periods 1931-1960 and 1961-1990 did not show changes or trends, which were detected for the period of 1991-2011, with a significant trend of increased precipitation in seven months.

O objetivo deste trabalho foi testar o classificador "árvore de decisão", em dados provenientes de sensores orbitais, para identificar área plantada com cana-de-açúcar, em diferentes épocas de plantio na Fazenda Boa Fé, localizada no Triângulo Mineiro, mais especificamente no município de Conquista, Minas Gerais. Acoplaram-se técnicas de Sensoriamento Remoto (SR) em um módulo de Sistema de Informação Geográfica (SIG), permitindo uma análise temporal do uso e ocupação do solo, especialmente com vistas a identificar e a monitorar as áreas agrícolas. Com base no cálculo do viés médio (VM), o presente estudo mostrou que, em áreas de cana-de-açúcar, onde a irrigação é frequente e ocorrem chuvas significativas que antecedem a passagem do satélite Landsat-5, os valores foram ligeiramente subestimados, com valor deste indicador de -0,13 ha. Foi verificado, também, que os valores de NDVI mais altos proporcionaram uma leve superestimativa dos resultados, com valores de viés médio variando de 0,04 a 0,23 ha. Conforme os resultados, o classificador árvore de decisão apresentou um grande potencial para o mapeamento das áreas cultivadas com cana-de-açúcar.

This study was carried out to test the "decision tree" classifier via remote sensing (RS), to identify planted areas with sugarcane, at different planting dates in Boa Fé, located in the Triângulo Mineiro, more specifically in the town of Conquista, Minas Gerais, Brazil. RS techniques, integrated into a Geographic Information System (GIS), allow a temporal analysis of land use and occupation, especially in order to identify and monitor agricultural areas. Based on the calculation of mean bias (VM), this study showed that in areas of sugarcane, where irrigation is frequent and significant rainfall occurring prior to the passage of Landsat-5, the estimated values were slightly underestimated, with the value of this indicator equal to -0.13 ha. It was also verified that the highest values of NDVI provided a slight overestimation of the results, with values of mean bias of 0.04 to 0.23 ha. According to the results, the decision tree classifier had a great potential for mapping the areas cultivated with sugar cane.