Entendendo os mecanismos da floresta amazônica para manutenção da sua produtividade diante de mudanças antrópicas e climáticas

Abstract

Amazon rainforests are among the most important and most biodiverse areas on earth. They contribute to ecosystem functions, including regulating water flow and maintaining one of the planet's most important carbon sinks, and provide resources for important economic activities such as timber and non-timber products, fish and other foods. However, tropical deforestation caused by the expansion of agricultural activities and unsustainable logging has caused losses and degradation of this forest However, tropical-equatorial deforestation caused by the expansion of agricultural activities and unsustainable logging has caused losses and degradation of this forest. The lack of inspection and conservation-oriented policies only strengthens this devastating scenario. A study on evapotranspiration, extreme weather events, fires, carbon, and light absorption by the plant has helped in understanding these factors, as well as in the development of impact mitigation strategies. This study investigates the various mechanisms used by forests to remain standing and productive, in the face of human interventions and severe climate change, combining, analyzing and making future projections of different types of agents operating in the Amazon region. The thesis was divided into three chapters, where the first one assessed the health status of the vegetation for the state of Amazonas, there were also positive and negative correlations of this index with other variables such as air temperature, rainfall, soil moisture, foci of fire and surface temperature. Also in this chapter, the Autoregressive Integrated Moving Average (ARIMA) was applied in the Vegetation Health Index (VHI) series for future simulation of vegetation health, which allowed us to verify how the forest will behave in the future, year 2030 In the second chapter, remote sensing data were used, the six forest typologies found for the state and meteorological elements of fourteen conventional weather stations distributed throughout the state of Amazonas, to investigate the processes of vegetation degradation in the year of events of the El Niño Oscilação-Sul phenomenon. The third chapter was focused on improving the data coming from the flow tower, which are used to make inferences about the vegetation. The ARIMA methodology was applied in filling the failures of raw primary productivity data from a flow tower (K34) in the state of Amazonas.