ABSTRACT Matter and energy flux dynamics of wetlands are important to understand environmental processes that govern biosphere-atmosphere interactions across ecosystems. This study presents analyses about hourly interaction between wind speed and energy fluxes in Brazilian Wetlands - Mato Grosso - Brazil. This study was conducted in Private Reserve of Natural Heritage (PRNH SESC, 16º39'50''S; 56º47'50''W) in Brazilian Wetland. According to Curado et al. (2012), the wet season occurs between the months of January and April, while the June to September time period is the dry season. Results presented same patterns in energies fluxes in all period studied. Wind speed and air temperature presented same patterns, while LE was relative humidity presented inverse patterns of the air temperature. LE was predominant in all seasons and the sum of LE and H was above 90% of net radiation. Analyses of linear regression presented positive interactions between wind speed and LE, and wind speed and H in all seasons, except in dry season of 2010. Confidence coefficient regression analyses present statistical significance in all wet and dry seasons, except dry season of 2010, suggest that LE and H had interaction with other micrometeorological variables.

ABSTRACT The study of radiation entrance and exit dynamics and energy consumption in a system is important for understanding the environmental processes that rule the biosphere-atmosphere interactions of all ecosystems. This study provides an analysis of the interaction of energy in the form of photosynthetically active radiation (PAR) in the Pantanal, a Brazilian wetland forest, by studying the variation of PAR reflectance and its interaction with local rainfall. The study site is located in Private Reserve of Natural Heritage, Mato Grosso State, Brazil, where the vegetation is a monodominant forest of Vochysia divergens Phol. The results showed a high correlation between the reflection of visible radiation and rainfall; however, the behavior was not the same at the three heights studied. An analysis of the hourly variation of the reflected waves also showed the seasonality of these phenomena in relation to the dry and rainy seasons. A predictive model for PAR was developed with a neural network that has a hidden layer, and it showed a determination coefficient of 0.938. This model showed that the Julian day and time of measurements had an inverse association with the wind profile and a direct association with the relative humidity profile.

A aceleração da atividade antropogênica aumentou a concentração de carbono na atmosfera, o que causa mudanças no clima regional. A produção primária bruta (GPP) é uma variável importante nos estudos do ciclo global de carbono, uma vez que define a taxa de extração de carbono da atmosfera pelos ecossistemas terrestres. O objetivo deste estudo foi estimar a GPP da Floresta de Transição Amazônia-Cerrado pelo modelo de fotossíntese da vegetação (VPM) utilizando dados meteorológicos locais e dados de sensoriamento remoto das refletâncias do MODIS e Landsat 5 TM entre 2005 e 2008. A GPP foi estimada pelo índice de Vegetação da Diferença Normalizada (NDVI) e pelo Índice de Vegetação Aprimorada (EVI) calculados com as imagens do MODIS e Landsat 5 TM. As GPP estimadas foram comparadas com medições em uma torre de fluxo de covariância de vórtices turbulentos. A GPP medida na torre foi consistente com os valores mais elevados durante a estação chuvosa e houve uma tendência de aumentar entre 2005 e 2008. A GPP estimada pelo VPM mostrou a mesma tendência de aumento observada nas GPP medidas e apresentou alta correlação e coeficiente de Willmott e baixas métricas de erro em comparação à GPP medida. Estes resultados indicaram alto potencial das imagens Landsat 5 TM para estimar a GPP da Floresta de Transição Amazônia-Cerrado pelo VPM.

The acceleration of the anthropogenic activity has increased the atmospheric carbon concentration, which causes changes in regional climate. The Gross Primary Production (GPP) is an important variable in the global carbon cycle studies, since it defines the atmospheric carbon extraction rate from terrestrial ecosystems. The objective of this study was to estimate the GPP of the Amazon-Cerrado Transitional Forest by the Vegetation Photosynthesis Model (VPM) using local meteorological data and remote sensing data from MODIS and Landsat 5 TM reflectance from 2005 to 2008. The GPP was estimated using Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) calculated by MODIS and Landsat 5 TM images. The GPP estimates were compared with measurements in a flux tower by eddy covariance. The GPP measured in the tower was consistent with higher values during the wet season and there was a trend to increase from 2005 to 2008. The GPP estimated by VPM showed the same increasing trend observed in measured GPP and had high correlation and Willmott's coefficient and low error metrics in comparison to measured GPP. These results indicated high potential of the Landsat 5 TM images to estimate the GPP of Amazon-Cerrado Transitional Forest by VPM.