Padrões espaciais e sazonais dos balanços de energia e carbono na caatinga no Estado do Rio Grande do Norte

Abstract

The state of Rio Grande do Norte is located in the Northeast of Brazil (NEB) and has most of its territory under cover of the caatinga vegetation. The caatinga biome is the only one exclusively Brazilian with significant ecological and social importance, but it is still the least studied among Brazilian natural regions and the least protected. This work was conceived with the central objective of analyzing the spatial and temporal variability of energy and mass changes (water vapor and CO2) between the caatinga vegetation and the atmosphere in the state of Rio Grande do Norte, using the MODIS sensor, onboard of Earth satellite. For this purpose, remote sensing techniques were used with their methods for large-scale temporal and spatial monitoring and compared with surface (in situ) data. The method allowed quantifying the radiation and energy balances and estimating CO2 fluxes between the surface and the atmosphere. The results showed a strong influence of water availability on the patterns of the studied variables, with a good relationship between the values of the instantaneous radiation balance and the albedo. The study pointed out that the suppression and substitution of native vegetation increase areas with exposed soils and compromise the radiation and energy balance in these regions. It was also verified that the available energy stock does not vary much throughout the year in the State. The remote sensing technique showed that in the first half of the year most of the energy used is spent in the process of latent heat transfer, whereas in the second half of the year most of the energy used occurs in the heat transfer process to the atmosphere. The caatinga behaved like CO2 sink, even in the driest periods with the values increasing in the period where the soil moisture is higher. Thus, the rain is a differentiating factor in the CO2 capture capacity. The technique used proved to be a useful tool in the quantification of most of the environmental parameters studied, necessitating adjustments to reduce percentage errors in comparison with surface data.