Obtaining radiation output through satellite images for the island of Bananal-TO.

O sensoriamento remoto possibilita identificar em tempo real alterações que ocorrem no planeta Terra, resultantes de diversos fenômenos naturais, mas também de correntes de vários processos antrópicos. Muitas dessas alterações podem ser diagnosticadas a partir da determinação e monitoramento das trocas radiativas que se processam a superfície. Neste sentido, o presente estudo tem por objetivo a determinação do saldo da radiação a superfície por meio de imagens geradas pelo sensor Thematic Mapper (TM) do satélite Landsat 5 na Ilha do Bananal – TO. Para o estudo foram utilizadas quatro imagens TM – Landsat 5 nas seguintes datas: 03 de junho, 21 de julho, 06 de agosto e 22 de agosto de 2005. Foram geradas cartas do albedo, índice de vegetação da diferença normalizada (IVDN), temperatura da superfície–Ts(K), saldo de radiação instantâneo–Rn, inst (Wm2) e saldo de radiação diário –Rs, 24h (Wm2), utilizando o algoritmo SEBAL. Os dados obtidos foram validados com medições realizadas na torre micrometeorológica instalada no interior da Ilha do Bananal. Para duas imagens (03 de julho e 22 de agosto de 2005) foram selecionadas duas diferentes áreas (antropizadas e com vegetação natural) para melhor avaliar os impactos resultantes da ocupação humana de áreas daquele ambiente. Pode se perceber grandes alterações nos locais desmatados em relação a locais com vegetações nativas: em áreas de ocupação humana o albedo se mostrou maior,o IVDN menor, a temperatura da superfície maior e os saldos de radiação (instantâneo e diário) se mostraram menores. Constata-se, assim, a influência e importância da vegetação nativa no clima local. Para o saldo de radiação diário a superfície, com relação aos dados medidos, foram obtidos os seguintes erros: EMR, EMA e REQM valores de 1,95%, 2,78Wm2 e 3,36Wm2, respectivamente. De acordo com os resultados obtidos neste trabalho pode se afirmar que a metodologia aqui proposta para estimativa do Rn alcançou boa acurácia.Remote sensing enables real time to identify changes that occur on Earth, resulting from various natural phenomena, but also due to anthropic processes. Many of these changes can be diagnosed from the determination and monitoring of radiative exchanges that take place at the surface. In this sense, this study aims to determine the net radiation at surface only by means of images generated by the Thematic Mapper (TM) of Landsat 5. at Ilha do Bananal - Tocantins State. Four TM - Landsat 5 images obtained on: June 03, July 21, August 06 and August 22, 2005 were used in the study. Thematic maps of albedo. Normalized Difference Vegetation Index - NDVI, land surface temperature - Ts (K), instantaneous net radiation - Rn,inst (WnT2) and daily net radiation - Rn24 (W m"2), were generated using the SEBAL algorithm. The Rn values were validated with measurements made in a micrometeorological tower installed inside the Ilha do Bananal. For two images (July 3 and August 22, 2005) two different areas (disturbed and natural vegetation) were selected to better assess the environmental impacts resulting from human occupation of that area. It can be seen large changes in deforested sites compared to sites with native vegetation: in deforested areas the albedo and land surface temperature increased and the same time NDVI and Rn (instantaneous and daily) were smaller than those observed in the native areas. The results show the influence and importance of native vegetation for the local climate. According with validation of the results the daily net radiation at surface presented EMR, EMA and REQM errors respectively equals to 1.95%, 2.78 W m"2 and 3.36 W m"2. According to the results obtained in this work we conclude that the methodology used for Rn determination presented good accuracy.CNP

Determinação de Índices de Vegetação para a análise da cobertura vegetal em bacia hidrográfica do Agreste pernambucano

This study aims to evaluate the vegetation cover in the Agreste of Pernambuco, using remote sensing techniques in determining vegetation indices, investigate the behavior of vegetation in the watershed. The Normalized Difference Vegetation Index (NDVI) was used and also the Soil Adjusted Vegetation Index (SAVI). The TM/Landsat-5, orbit 215, Section 66, was applied for the month of September, the dry season in the study area, the dates of 09/23/2008 and 09/29/2010. The vegetation indices were calculated and grouped into nine classes. The average NDVI values obtained for 23/09/2008 and 29/09/2010 were 0.401 and 0.402, respectively. The mean values of SAVI were 0.273 for both studied periods. Rainfall data from a station located in Pesqueira were used to verify the influence of this parameter the plant on the behavior during the study period. The rainfall contribution in 2010 was higher than in 2008, with cumulative rainfall from January to September of 811.20 mm and 554.45 mm, reflecting positively on the vegetation response. Differences were observed between vegetable toppings determined by NDVI and SAVI. The cover crops classified as intermediate and dense had higher expression when observed in scenes generated by NDVI. Unlike the SAVI showed better representation of bare soil and sparse vegetation classes.Pages: 7009-701

Modelling Soil Water Dynamics from Soil Hydraulic Parameters Estimated by an Alternative Method in a Tropical Experimental Basin

Knowledge about soil moisture dynamics and their relation with rainfall, evapotranspiration, and soil physical properties is fundamental for understanding the hydrological processes in a region. Given the difficulties of measurement and the scarcity of surface soil moisture data in some places such as Northeast Brazil, modelling has become a robust tool to overcome such limitations. This study investigated the dynamics of soil water content in two plots in the Gameleira Experimental River Basin, Northeast Brazil. For this, Time Domain Reflectometry (TDR) probes and Hydrus-1D for modelling one-dimensional flow were used in two stages: with hydraulic parameters estimated with the Beerkan Estimation of Soil Transfer Parameters (BEST) method and optimized by inverse modelling. The results showed that the soil water content in the plots is strongly influenced by rainfall, with the greatest variability in the dry–wet–dry transition periods. The modelling results were considered satisfactory with the data estimated by the BEST method (Root Mean Square Errors, RMSE = 0.023 and 0.022 and coefficients of determination, R2 = 0.72 and 0.81) and after the optimization (RMSE = 0.012 and 0.020 and R2 = 0.83 and 0.72). The performance analysis of the simulations provided strong indications of the efficiency of parameters estimated by BEST to predict the soil moisture variability in the studied river basin without the need for calibration or complex numerical approaches