Remotely sensed infrared images are often used to assess forest fire conditions.Meanwhile

fire propagation mod-els are in use to forecast future conditions.The traditional method of fire monitoring is mapping or extraction of burn scar and hotspot generally depending on abnormity character of spectral radiation and thermal infrared（TIR） channels.In the Dynamic Data-Driven Application System（DDDAS） concept

the fire propagation model will react to the image data

which should pro-duce more accurate predictions of fire propagation.So the parameters of fire line and propagation direction extracted from the fire edge map are more important than burnt area estimation in real-time fire forecast application.The method capable of auto-matically determining the fire perimeter

active fire line

and fire direction is developed in this paper based on some image proc-essing techniques.And MODIS（Moderate resolution Imaging Spectrometer） is used as experimental data because of its widely spectral range bands and dynamic monitoring capacity for the earth.First

the optimal bands choice construction for spectral in-dices of NDBR（Normalized Difference Burn Ratio） capable of describing active fire intensity is discussed.Then the multi-spectral image gradient together with TIR channels and solar reflectance channels is used to map the fire edge which can be put on NDBR image for restriction against active fire line.And B-spline model is applied to fit fire line segments to a spline curve for fire propagation front estimation by normal of the spline.At last

the kriging interpolation method based on hotspots with high gradient magnitude of fire line is used to extrapolate fire edge region to validate these results.The image of forest fires located in Great Xing’an Mountains is processed and another image after 30mins is used as the referenced image.Two aspects prove that propagation of fire line are essentially consistent with the referenced image.And expectation

entropy value of image context and kappa for spatial fire pixel identification can respectively reach 86%

81%

80.2% compared with the referenced one.So it is suggested by the results that this automatic process for extracting fire propagation parameters can be proved with well performance and efficient in dynamic fire mapping and monitoring.