Methodology for automated greenhouse gas data collection and transmission to cloud repository using low-cost drones

Greenhouse gas (GHG) emissions are crucial for monitoring and mitigating climate change and the degradation of sensitive biomes. Such demand motivates the search for automated processes to collect and manage GHG emissions data, with open access to researchers and institutions working with sustainability. This work proposes an automated data collection process using low-cost drones, with direct data transfer to a cloud-based data space. Low-cost drones were equipped with onboard sensors to measure ${\mathrm{CO}}_2$ and methane emissions. The focus was not on data accuracy but on automating data collection and transmission, drone design specifications, and testing, exploring the balance between data accuracy and low-cost sensors. The first practical proof-of-concept experiments demonstrating the system's capabilities used a drone prototype with simple sensors in an outdoor campus environment, sending data to a cloud-based data space called Digital Amazon (intended to store GHG emissions from the Amazon Forest), via 4G internet communication network. The system's design addressed aspects such as avoiding interference during data collection and trajectory adjustment, data transfer, and finalizing dataset composition in the cloud. The results provide initial evidence supporting the feasibility of the proposed system in an outdoor environment. However, its application to more complex scenarios, such as forests, other biomes, or urban areas, will be explored in subsequent research based on the reference model presented and will require further validation under diverse environmental and operational conditions. Enhancements to accommodate future communication based on Low Earth Orbit (LEO) and Very Low Earth Orbit (VLEO) satellite systems would help reduce transmission latency, but this issue was not assessed in the present study.