Estimating Ladder Fuels: A New Approach Combining Field Photography with LiDAR

Abstract

Forests historically associated with frequent fire have changed dramatically due to fire suppression and past harvesting over the last century. The buildup of ladder fuels, which carry fire from the surface of the forest floor to tree crowns, is one of the critical changes, and it has contributed to uncharacteristically large and severe fires. The abundance of ladder fuels makes it difficult to return these forests to their natural fire regime or to meet management objectives. Despite the importance of ladder fuels, methods for quantifying them are limited and imprecise. LiDAR (Light Detection and Ranging), a form of active remote sensing, is able to estimate many aspects of forest structure across a landscape. This study investigates a new method for quantifying ladder fuel in the field (using photographs with a calibration banner) and remotely (using LiDAR data). We apply these new techniques in the Klamath Mountains of Northern California to predict ladder fuel levels across the study area. Our results demonstrate a new utility of LiDAR data to identify fire hazard and areas in need of fuels reduction.Remote Sens. 2016, 8, 766 2 of 23 90% of trees are killed), which have been increasing [10,11]. These altered contemporary fire patterns are due to a number of factors, but considerable increases in surface and ladder fuels play an important role [12]. Ladder fuels allow fire to transition into overstory tree crowns by providing greater vertical fuel continuity, and fire burning ladder fuels also preheats canopy fuels that have not yet ignited [13]. Additionally, dense ladder fuels can make suppression more difficult and increase wildland firefighter exposure to hazardous conditions, especially when fire behavior shifts unexpectedly, by inhibiting escape to safety zones [14]. In addition to the biophysical influences of ladder fuels on fire behavior and fire management, they can also reduce habitat quality through decreasing accessibility and foraging efficiency for wildlife and tribal subsistence gathering [15].Despite the importance of ladder fuels to fire behavior, effects, and firefighter safety, they have not been directly quantified except in a few cases [13,16,17], all of which are sampled on the ground, with no explicit connection to remote sensing. Because ladder fuels are below the canopy, passive remote sensing platforms are not able to capture their composition, except in very open forests. In most cases, fire models utilize a surrogate for ladder fuels, which is comprised of a combination of canopy base height (CBH) and fuel model (sometimes with an adjustment of fire behavior) [18]. CBH is the height above which there is enough fuel per unit volume to carry the fire upward. The fuel density necessary to carry fire is most commonly 0.012 kg·m −3 [19], but other thresholds have been suggested and used [20][21][22][23].In addition to a variable fuel density threshold, estimations for CBH are commonly based on allometric equations, moving them further from a consistent direct measurement. Although activ...

Spotted owl foraging patterns following fuels treatments, Sierra Nevada, California

“…Following Kramer et al () and North et al (), we estimated the proportion of large trees by first calculating the maximum canopy height across the landscape in 1 × 1‐m pixels and distributing the maximum heights into 6 classes: 0 to 2, >2 to 8, >8 to 16, >16 to 32, >32 to 48, and >48 m. We predicted that owls would preferentially select for higher proportions of 32–48‐m trees (class 5), considered nesting habitat, and 16–32‐m trees (class 4), which are often considered suitable foraging habitat (Verner et al ). We were unable to include the tallest size class, >48 m, because of its rarity within the study area (0.3% of all LiDAR‐derived pixels).…”

“…To estimate vertical canopy complexity, we generated a Shannon diversity index (Equation ) for canopy cover. Following North et al () and Kramer et al (), we divided the vertical canopy into 5 horizontal bands at >2 to 8‐m, >8 to 16‐m, >16 to 32‐m, >32 to 48‐m, and >48‐m height strata, and estimated LiDAR‐derived canopy cover within each band. We then calculated the Shannon diversity index (H’) for cover across the telemetry error ellipse as where S is the number of horizontal canopy strata (5) and P i is the mean canopy cover for each strata within the ellipse.…”

Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA

“…It is important to note that while Lidar data is commonly evaluated in comparison to field-gathered data, there are differences in forest structure data measurements between Lidar and field-based protocols. The Lidar-derived forest structure products are created using regression models between point cloud metrics and direct measures (e.g., canopy height) or modeled estimates using allometry (e.g., canopy base height) [38]. This represents a real difference in method (i.e., regressions with direct measures vs. modeled estimates) and complicates the discussion about error in Lidar data.…”

Accessible light detection and ranging: estimating large tree density for habitat identification

“…, Kelly and Di Tommaso , Kramer et al. ). This is particularly true in areas with tall (and likely large diameter) trees (Bergen et al.…”

“…In the past, imagery from passive remote sensors, such as LANDSAT, was widely used to estimate the structure of forests (Forsman 1995, Hunter et al 1995, Moen and Guti errez 1997, McDermid et al 2005. Light detection and ranging (LiDAR) is a form of active remote sensing that is better able to detect the height of vegetation than passive remote sensing, and therefore may be a very useful tool for identifying wildlife habitat (Lefsky et al 2002, Vierling et al 2008, Martinuzzi et al 2009, Selvarajan et al 2009, Kelly and Di Tommaso 2015, Kramer et al 2016. This is particularly true in areas with tall (and likely large diameter) trees (Bergen et al 2009, Wing et al 2010, Garc ıa-Feced et al 2011, Ackers et al 2015.…”