Authors: David Szpakowski*, Texas State University - San Marcos, Jennifer LR Jensen, Texas State University - San Marcos, Edwin Chow, Texas State University - San Marcos
Topics: Geographic Information Science and Systems
Keywords: Expert Systems, Classification Tree, Fire Hazard Mapping
Session Type: Poster
Start / End Time: 10:00 AM / 11:40 AM
Room: Napoleon Foyer/Common St. Corridor, Sheraton, 3rd Floor
Presentation File: No File Uploaded
Fire hazard maps allow for the detection of areas which possess the greatest risk of fire ignition or propagation events. These maps can be generated in a number of ways including through fuel maps or remote sensing and topographic data. This study attempts to combine fuel mapping with a more ‘traditional’ remote sensing fire hazard mapping technique in order to improve the accuracy of fire hazard mapping. Expert systems and classification trees were applied to model available fuel for Grand Teton National Park and the surrounding area. Normalized Difference Moisture Index was used to determine the vegetation moisture content and then combined with the fuel map, topographic datasets, and distance from roads to map fire hazard potentials for the study area. The results were compared to a delta Normalized Burn Ratio index of a 2016 fire event to determine the effectiveness of the model. The model underestimated the hazard potentials when compared to the burn severities resulting from the 2016 fire, but did outperform the ‘traditional’ fire hazard model. Further research is required to determine if the model can improve the detection of fire hazard potentials.