The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-1
07 Nov 2014
 | 07 Nov 2014

Rapid Response Tools and Datasets for Post-fire Erosion Modeling: Linking Remote Sensing and Process-based Hydrological Models to support Post-fire Remediation

M. E. Miller, W. J. Elliot, K. A. Endsley, P. R. Robichaud, and M. Billmire

Keywords: Forestry, Hydrology, Hazards, Forest fire, Databases, Soil, Land Cover

Abstract. Post-fire flooding and erosion can pose a serious threat to life, property, and municipal water supplies. Increased peak flows and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern to both resource managers and the public. To respond to this threat, interdisciplinary Burned Area Emergency Response (BAER) Teams are formed to assess potential erosion and flood risks. These teams are under tight deadlines as remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making these decisions is a burn severity map derived from remote sensing data (typically Landsat) that reflects fire induced changes in vegetative cover and soil properties. Slope, soils, land cover, and climate are also important parameters that need to be considered when accessing risk. Many modeling tools and datasets have been developed to assist BAER teams, but process-based and spatially explicit empirical models are currently under-utilized compared to simpler, lumped models because they are both more difficult to set up and require spatially explicit inputs such as digital elevation models, soils, and land cover. We are working to facilitate the use of models by preparing spatial datasets within a web-based tool that rapidly modifies model inputs using burn severity maps derived from earth observation data. Automating the creation of model inputs facilitates the wider use of more accurate, process-based models for spatially explicit predictions of post-fire erosion and runoff.