Combined Use of Satellite Observations and the RIM for Assessing Recovery from Natural Disasters

In this study, we explore using satellite observations to assess community recovery from natural disasters such as fires and hurricanes, supplementing the Resilience Inference Model (RIM). The RIM model has been successfully used to quantify recoveries from hurricanes along the Gulf Coast, but it relies on long-term population changes over years or decades. Our approach integrates satellite observations to enhance recovery assessment with a shorter latency of weeks or months. Using fire, vegetation, and night light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) with daily global observations, Sentinel-2, Landsat-8, and Geostationary Operational Environmental Satellite/Advanced Baseline Imager, we quantitatively evaluate fire intensity, light outage, and urban greenness changes, along with subsequent recovery, focusing on the 2023 Maui fire and selected hurricane cases along the Gulf Coast. This approach complements the RIM model by introducing quantifiable physical parameters with shorter latency, particularly beneficial in areas where census data are either unavailable or unreliable.