| Summary: | Fuel reduction in forests is a high management priority in the western United States and mechanical mastication treatments are implemented common to achieve that goal. However, quantifying post-treatment fuel loading for use in fire behavior modeling to forecast treatment effectiveness is difficult due to the high cost and labor requirements of field sampling methods and high variability in resultant fuel loading within stands after treatment. We evaluated whether pre-treatment LiDAR-derived stand forest characteristics at 20 m × 20 m resolution could be used to predict post-treatment surface fuel loading following mastication. Plot-based destructive sampling was performed immediately following mastication at three stands in the Nez Perce Clearwater National Forest, Idaho, USA, to correlate post-treatment surface fuel loads and characteristics with pre-treatment LiDAR-derived forest metrics, specifically trees per hectare (TPH) and stand density index (SDI). Surface fuel loads measured in the stand post-treatment were consistent with those reported in previous studies. A significant relationship was found between the pre-treatment SDI and total resultant fuel loading (<i>p</i> = 0.0477), though not between TPH and fuel loading (<i>p</i> = 0.0527). SDI may more accurately predict post-treatment fuel loads by accounting for both tree number per unit area and stem size, while trees per hectare alone does not account for variations of tree size and subsequent volume within a stand. Conditions within treated stands and fuels produced during mastication are highly variable and may explain the lack of relationship between fuel classes and loading. Root-mean-square errors of 36 and 46 percent of the random forest LiDAR models for SDI and TPH may limit the ability to predict the highly variable fuel loads produced from mastication. Use of LiDAR to predict fuel loading after mastication is a useful approach for managers to understand the efficacy of fuel reduction treatments by providing information that may be helpful for determining areas where treatments can be most beneficial.
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