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.

Publication Date



Copyright © 2006 Elsevier Inc. All rights reserved.

This is the pre-print of an article published by Elsevier. The final, published version is located here: https://doi.org/10.1016/j.rse.2006.09.029

This work was sponsored by NASA Grant NAG5-10051 and NSF Grant CNS-0324989. The AVIRIS images used were provided by the AVIRIS Data facility. MAS data sets were provided by NASA Langley Research Center while WASP data were provided by LIAS group at the Rochester Institute of Technology.

Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type


Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


RIT – Main Campus