Modeling and Analysis of Deforestation Prevention by Uncrewed Aerial Vehicles-based monitoring systems. (December 2022) - Machida, Fumio; Andrade, Ermeson
Abstract: The Brazilian Amazon is suffering from serious deforestation that may lead to catastrophic consequences for the world's climate and biodiversity. Preventing illegal deforestation activities is an emergent and crucial mission for the world. Recently, automated monitoring systems using Uncrewed Aerial Vehicles (UAVs) appear as a promising solution to combat deforestation. However, the quality design of such systems considering the deforestation prevention impacts remains under-explored. In this paper, we propose a socio-ICT (Information Communication Technology) model that enables to analyze the deforestation prevention impacts of a UAV-based monitoring system quantitatively. The socio-ICT model uses a causal loop diagram to formulate the relations among system quality measures and key performance indicators (KPIs) associated with deforestation prevention. Through the numerical study, we estimate the effectiveness of the UAV monitoring system by annual deforestation rate and CO2 gas emissions. Our analysis shows important design trade-offs need to be considered when planning UAV-based monitoring systems. Highlights: Impacts of UAV-based deforestation monitoring are quantitatively analyzed. Socio-ICT model combines a system-level quality model with a social impact model. A large monitoring area by many drone flights causes a decreased visiting frequency. The maintenance period and the activity exposure time are significant factors. A design tradeoff between drone autonomy and detection accuracy is identified.