Detection of Volcanic Ash Using Goes-16 Satellite Imagery: Case Study of Popocatépetl Volcano, Mexico

Abstract

Objective: The objective of this study is to investigate the accuracy of volcanic ash detection using satellite imagery, to improve existing monitoring tools to adapt them to the Popocatépetl volcano in Mexico. Theoretical Framework: Volcanic ash detection via remote sensing employs Pavolonis' algorithms that compare radiances in the thermal and mid-infrared spectrum. This approach differentiates ash from clouds and water vapor by leveraging its differential absorption. Satellites such as GOES-16, which capture multispectral data, optimize the continuous detection and monitoring of ash dispersion, even in areas of low concentration. Method: Three volcanic ash detection algorithms were compared using images from the Popocatépetl eruption event on May 21, 2023: two developed by Pavolonis, which combine thermal and mid-infrared bands, and a third, a spectral adaptation designed to enhance precision at the edges of the ash cloud, tailored to the study area. Results were validated with an RGB ash image provided by NOAA. Results and Discussion: Pavolonis' algorithms provided a solid foundation for general detection, but the third algorithm, specifically designed for this study, significantly improved edge detection where classification conditions are more complex. This improvement was reflected in a higher agreement with the RGB ash image used as a reference. However, limitations related to atmospheric interference were identified, which require further adjustments in low ash concentration scenarios. Research Implications: The findings of this research have significant practical and theoretical implications for volcanic risk management and environmental protection. Enhanced accuracy in volcanic ash detection can optimize monitoring and early warning strategies, reducing risks to public health and infrastructure. Originality/Value: This study contributes to the literature by introducing a specific spectral adaptation to improve edge detection of volcanic ash, a novel approach not previously addressed. The research's relevance lies in its ability to refine monitoring methodology, providing more effective tools for volcanic risk management and offering a model applicable to other areas facing similar remote sensing challenges.