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

Ryer Becker,Robert Keefe

doi:10.3390/su12177025

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.

Highlights

Due to the variability of species, management objectives, spatial configuration of management areas, regulatory restrictions, landowner funding availability, fuels characteristics, and other geographic and vegetative factors, developing a one-size-fits-all approach for wide-scale fire management is challenging [1,2,3,4]
Based on results reported by previous mastication studies, the average total fuel depth of 16.7 cm recorded in the mixed conifer stands we studied was significantly higher than those in Battaglia et al [10], but only slightly higher than those shown by Stephens and Moghaddas [16]: 14.6 and 14.7 cm
The ability to quickly, efficiently, and effectively predict surface fuel loads resulting from mastication treatments is a valuable tool, as increased implementation of this management technique occurs

Summary

Introduction

Due to the variability of species, management objectives, spatial configuration of management areas, regulatory restrictions, landowner funding availability, fuels characteristics, and other geographic and vegetative factors, developing a one-size-fits-all approach for wide-scale fire management is challenging [1,2,3,4]. Forest management practices such as fire exclusion have resulted in historically uncharacteristic stand attributes in many forests in the western United States, including dense, small-diameter stands with increased surface fuel loads [5,6]. Fuel reduction in stands that have lacked prior density management is a high priority in many areas of the western United States, especially on federal lands. Understanding the unique challenges and selecting strategies to best suit the needs of each management area, typically applied through one or more treatments applied at the stand level, is vital to long-term management success [2]. Designed and implemented fuel treatments have been found to increase fire resilience and resistance while simultaneously changing the behavior of wildfires that impact treated areas [5,7,8,9]

Methods

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Discussion

Conclusion