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Airborne remote sensing systems enable higher spatial and temporal resolution imaging than spaceborne systems and provide tremendous flexibility in satisfying space-time sampling requirements for studying dynamic land surface phenomena. The emphasis of this colloquium presentation is on novel means for planning, capturing, processing and analyzing multi-temporal aerial imagery in support of scientific analyses of wildfire behavior and commercially viable monitoring of infrastructure conditions. Repetitive thermal infrared imaging of the progression of active fire fronts enables rate of spread and fire intensity, the two primary variables associated with wildfire behavior, to be estimated at appropriate landscape scales. Examples of quantitative and geovisualization analyses of wildfires burning in Mediterranean shrublands of California will be presented. For the second presentation component, drone systems integrated with an image capture and processing approach called Repeat Station Imaging (RS) © will be explored in the context of monitoring defects in electric utility infrastructure and infrastructure construction. The key to these two applications of multi-temporal airborne imaging is the design of an end-to-end system that maximizes the space-time sampling of the forms and processes under evaluation.