UC Davis

The increasing trend of active wildfire events in California poses risks to both wildland ecosystems and human communities, causing substantial ecological damages and economic losses. However, the mechanism of wildfire damage within vegetated landscapes and human settlements has been understudied, especially for the effects of changing climates and the expanding wildland urban interfaces. Recent advances in remote sensing and machine learning enabled fine-scale observation of land changes from wildfire attacks and an improved analysis of environmental and anthropogenic factors controlling the wildfire damage. This dissertation has sought to bridge the knowledge gaps of wildfire risks across the land through remote sensing and machine learning approaches. Specifically, this research has aimed to 1) disentangle effects of environmental factors on burn severity under various extreme climatic conditions in California’s northern coastal mountains and predict near-real-time burn severity from remote sensing images and geospatial data; 2) develop an accurate and effective framework of biannual analysis of building footprints and wildland urban interface mapping from high resolution aerial photos; 3) perform the first comprehensive analysis of factors determining building damage from wildfire events at the daily scale across California. The results of this dissertation highlighted the importance of targeting areas with high burn severity or building damage risk for fire adaptation and mitigation strategies in a changing climate and expanding human settlement. These findings, when combined with building footprints and wildland urban interface products developed through a modeling framework, contribute to the guidance of land use planning to create fire- adaptive and resilient communities in California.