Remotely sensed infrared images are often used to assess wildland ¯re conditions. Separately, ¯re propagation models are in use to forecast future conditions. In the Dynamic Data Driven Application System (DDDAS) concept, the ¯re propagation model will react to the image data, which should produce more accurate predictions of ¯re propagation. In this study we describe a series of image processing tools that can be used to extract ¯re propagation parameters from multispectral infrared images so that the parameters can be used to drive a ¯re propagation model built upon the DDDAS concept. The method is capable of automatically determining the ¯re perimeter, active ¯re line, and ¯re propagation direction. A multi-band image gradient calculation, the Normalized Di®erence Vegetation Index, and the Normalized Di®erence Burn Ratio along with several standard image processing techniques are used to identify and constrain the ¯re propagation parameters. These ¯re propagation parameters can potentially be used within the DDDAS modeling framework for model update and adjustment
