Mapping Surface Fuel Loadings of Forests Using Stratified Random Sampling and Geostatistical Analysis Derived Data

Fuel loading is a critical parameter for wildfire management. A spatially explicit map of fuel loadings can help highlight wildfire risk and guide people to ignite fires more carefully and help to minimize the possibility of heat conduction over forest lands. In contrast to the airborne lidar based methods, a geospatial integrated data based algorithm was proposed to derive surface fuel loading map of a mountainous forest. Results showed that our method is able to produce a map of fuel loadings using biological parameter (normalized value of forest-type code) and topographic parameters (classified slope code, classified aspect code, and reciprocal of degree slope) at a reasonable level of accuracy. The prediction bias for the test dataset was at a level of RMSE=3.42 ton/ha and RMSE%=41 %. The estimation bias at each of the plot was averaged 2.3524±2.3528 ton/ha and the minimum and maximum bias was −13.25 and 6.47 ton/ha respectively. A natural logarithm transformation can help to improve the significance of each coefficient at the probability level of 0.05 while keeping the performance of the SFL model.