Multi-temporal radar remote sensing for rapid assessment of earthquake-induced infrastructure damage: A comparative study of the 2024 Noto and 2019 Mianeh earthquakes
Abstract
Major earthquakes pose critical risks to urban infrastructure and human safety, necessitating rapid, precise, and quantitative post-event damage assessments. This study introduces a novel comparative framework for evaluating infrastructure damage from the 2024 Noto earthquake in Japan and the 2019 Mianeh earthquake in Iran using multi-temporal radar datasets: Sentinel-1 (C-band) and ALOS PALSAR-2 (L-band). Data preprocessing—including geocoding, radiometric calibration, and speckle filtering—was conducted to produce high-resolution coherence and backscatter intensity maps. Sequential coherence analysis identified reductions of 0.25–0.45 in severely impacted urban sectors, with the most pronounced declines in dense residential areas. Complementary backscatter intensity changes confirmed building collapses and surface deformations, particularly in industrial and critical infrastructure zones. Our results reveal that Sentinel-1 excels in capturing superficial, short-wavelength surface disruptions, while ALOS PALSAR-2 effectively detects deeper structural deformations. Importantly, integrating both datasets enhanced spatial accuracy of damage detection by approximately 18%, demonstrating a robust, quantitative methodology for rapid post-earthquake damage assessment. This integrated SAR approach offers a powerful tool for informed urban resilience planning, prioritizing reconstruction efforts, and advancing disaster response strategies.