Weather Impact Analysis for UAV-based Deforestation Monitoring Systems

Uncrewed Aerial Vehicles (UAVs) are increasingly employed for deforestation monitoring due to their flexibility, cost-effectiveness, and high-resolution sensing capabilities. However, their performance is highly susceptible to adverse weather conditions such as rainfall and wind, which significantly reduce flight endurance and detection accuracy. Among various adverse weather factors, rainfall intensity and wind speed emerge as the most influential contributors to heavy rainfall conditions, which represent one of the most critical external risks to UAV operations. This study presents an integrated methodology combining Failure Mode and Effects Analysis (FMEA) and Causal Loop Diagrams (CLDs) to identify weather-induced vulnerabilities and model their cascading impact on UAV-based monitoring systems. We extend an existing CLD model by incorporating rainfall intensity rate and wind speed. Based on the causal relationships identified, we develop a functional model to quantitatively evaluate UAV performance degradation. A numerical experiment is conducted to evaluate the impact of four representative weather scenarios: Clear, Light, Moderate, and Severe, on battery life, detection accuracy, and ultimately, the UAV's ability to reduce CO2 emissions through timely deforestation detection. Results show that battery life declines from 120 minutes to 27.3 minutes, and accuracy drops from 95% to 78.8% as weather severity increases. Consequently, annual CO2 emission reduction falls from 179.6 tons under clear conditions to just 32.9 tons under severe conditions. These findings illustrate how environmental risks propagate through system performance degradation, reducing environmental effectiveness. The proposed approach offers a valuable basis for resilience-oriented design and weather-aware mission planning in UAV-based environmental monitoring systems.