Amazon Forest Digital Twin: design based on Petri Nets and Cloud Computing

Abstract

Climate change risks stimulate a transdisciplinary approach to systems to monitor and analyze its impacts, especially in forests. On the other hand, this same demand raises the problem of agility and productivity in collecting, providing, and sharing data. Greenhouse Gas (GHG) emissions have a particular demand, managing a large amount of data combining physics, meteorology, and environmental data to provide data analysis based on cloud services. Methodological approaches for data repository design address the agility of a transdisciplinary approach, leading to service systems directed to a heterogeneous segmentation of users. This article presents the modeling and design of an IT-enable service system, conceived an asynchronous cloud digital twin applied to monitor GHG emissions in the Amazon forest in Brazil.