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1.
Proc Natl Acad Sci U S A ; 104(12): 4820-3, 2007 Mar 20.
Article in English | MEDLINE | ID: mdl-17360360

ABSTRACT

Despite early speculation to the contrary, all tropical forests studied to date display seasonal variations in the presence of new leaves, flowers, and fruits. Past studies were focused on the timing of phenological events and their cues but not on the accompanying changes in leaf area that regulate vegetation-atmosphere exchanges of energy, momentum, and mass. Here we report, from analysis of 5 years of recent satellite data, seasonal swings in green leaf area of approximately 25% in a majority of the Amazon rainforests. This seasonal cycle is timed to the seasonality of solar radiation in a manner that is suggestive of anticipatory and opportunistic patterns of net leaf flushing during the early to mid part of the light-rich dry season and net leaf abscission during the cloudy wet season. These seasonal swings in leaf area may be critical to initiation of the transition from dry to wet season, seasonal carbon balance between photosynthetic gains and respiratory losses, and litterfall nutrient cycling in moist tropical forests.


Subject(s)
Plant Leaves/anatomy & histology , Plant Leaves/growth & development , Seasons , Trees/anatomy & histology , Trees/growth & development , Brazil , Geography , Organ Size , Plant Leaves/radiation effects , Rain , Satellite Communications/instrumentation , Sunlight , Time Factors , Trees/radiation effects
2.
Acta amaz ; 35(2): 259-272, abr.-jun. 2005. ilus, mapas, tab
Article in Portuguese | LILACS | ID: lil-413341

ABSTRACT

Este artigo se propõe a apresentar exemplos de questões científicas que puderam ser respondidas no contexto do Projeto LBA (Large Sale Biosphere-Atmosphere Experiment in Amazonia) graças à contribuição de informações derivadas de sensoriamento remoto. Os métodos de sensoriamento remoto permitem integrar informações sobre os vários processos físicos e biológicos em diferentes escalas de tempo e espaço. Nesse artigo, são enfatizados aqueles avanços de conhecimento que jamais seriam alcançados sem a concorrência da informação derivada de sensoriamento.


Subject(s)
Pattern Recognition, Automated , Stochastic Processes , Remote Sensing Technology
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