An Analysis of Remote Sensing Techniques for Generating Wildfire Models of Wildland Urban Interface Sites in Texas
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The objective of this research was to evaluate selected techniques for wildfire behavior modeling in the wildland urban interface for two sites in Texas. The first approach compared two alternate forms of vegetation data for a study area in Kerr County, Texas: pre-compiled 30-meter LANDFIRE raster datasets created from Landsat 5 TM imagery; and 5-meter raster datasets generated from lidar point cloud data. The two distinct sets of input data were run separately in the wildfire model FlamMap to produce two sets of four output wildfire behavior maps. The lidar-based inputs involved substantially more time and effort for model preparation relative to their LANDFIRE-based counterparts but did not result in any substantial difference in the model output maps. The second approach investigated the effect of accounting for foliar (vegetation) moisture content in the same four output FlamMap wildfire behavior maps. One simulation was run with spatially homogeneous moisture for the same Kerr County study area and another was run with spatially heterogeneous moisture after a conditioning period where the vegetation absorbs moisture from input weather and wind parameters. Change detection was performed to compare the two sets of output maps and the results show that the moisture conditioning (to account for foliar moisture content) produced moderately lower wildfire risk outputs. Next, the effect of foliar moisture content on wildfire spread during a drought was tested for the Possum Kingdom Complex Wildfire study area in Palo Pinto County, Texas. Foliar moisture content can be measured from Landsat 5 TM satellite imagery using the normalized difference infrared index (NDII). NDII values were measured from Landsat 5 TM imagery for the study area captured before the wildfire. Then the distribution of NDII values for the area that burned was compared to the distribution of values for the remnant area. The result was significant at the .001 level allowing acceptance of the research hypothesis that the distributions were different.