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Prediction of Fuel Loading Following Mastication Treatments in Forest Stands in North Idaho, USA

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  • Ryer M. Becker

    (Forest Operations Research Lab, College of Natural Resources, University of Idaho, 875 Perimeter Drive, Moscow, ID 83844-4264, USA)

  • Robert F. Keefe

    (Forest Operations Research Lab, College of Natural Resources, University of Idaho, 875 Perimeter Drive, Moscow, ID 83844-4264, USA)

Abstract

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 ( p = 0.0477), though not between TPH and fuel loading ( p = 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. Relatively large root-mean-square errors associated with the random forest models for SDI (36%) and TPH (46%) suggest that increased sampling intensity and modified methods that better account for fine spatial variability in fuels resulting from within-stand conditions, treatment prescriptions and machine operators may be needed. 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.

Suggested Citation

  • Ryer M. Becker & Robert F. Keefe, 2020. "Prediction of Fuel Loading Following Mastication Treatments in Forest Stands in North Idaho, USA," Sustainability, MDPI, vol. 12(17), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7025-:d:405502
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    References listed on IDEAS

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    1. Huggett Jr., Robert J. & Abt, Karen L. & Shepperd, Wayne, 2008. "Efficacy of mechanical fuel treatments for reducing wildfire hazard," Forest Policy and Economics, Elsevier, vol. 10(6), pages 408-414, August.
    2. Hartsough, Bruce R. & Abrams, Scott & Barbour, R. James & Drews, Erik S. & McIver, James D. & Moghaddas, Jason J. & Schwilk, Dylan W. & Stephens, Scott L., 2008. "The economics of alternative fuel reduction treatments in western United States dry forests: Financial and policy implications from the National Fire and Fire Surrogate Study," Forest Policy and Economics, Elsevier, vol. 10(6), pages 344-354, August.
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