Find the full publication in the Frontiers in Forests and Global Change Journal, here.

Map of our study locations (Pepperwood Preserve and Saddle Mountain Open Space Preserve) and plots (indicated by gray squares and circles) in Sonoma County, California. The perimeter of each wildfire that occurred during our research study is shown in red.

INTRODUCTION: There is an urgent need in the western United States to reduce wildfire hazard and restore wildfire’s historic role as a beneficial ecological process (Stephens et al., 2012; Duff et al., 2013, 2019). Surface fuels, often < 1 m in height and mostly horizontal in orientation, carry fire across the ground when there is continuity of litter, slash, herbaceous vegetation, shrubs, small conifers, and downed woody material (Brown, 1982; Schmidt et al., 2008). Ladder fuels, which are live and dead vegetation that bridge the gap between the surface and the canopy, can provide a conduit for a low-severity surface fire to become a high-severity canopy fire (Menning and Stephens, 2007; Ottmar et al., 2007). Management targeted at reducing surface and ladder fuels can effectively mitigate wildfire intensity and burn severity (Agee and Skinner, 2005; Ritchie et al., 2007; Safford et al., 2012; Prichard et al., 2013). Due to the scale of the area that requires treatment, targeted management must be informed by mapping the spatial distribution of ladder fuels to help prevent high-severity fires.

It is generally time consuming and ineffective to quantify ladder fuel loads in the field using traditional forestry methods (e.g., Keane et al., 2005). Sometimes, fuels are measured indirectly via canopy base height (CBH), the average distance between the bottom of the canopy and the ground. An alternative approach is to estimate fuel structure via remote sensing technology. Relative to field-based techniques, remote sensing allows for measurements across large and inaccessible areas at a potentially lower cost, depending on scale of measurements (Gale et al., 2021). Sensing can be from a top-down or downward (typically airborne or spaceborne) or a bottom-up or upward (typically ground-based) view of forest structure (Skowronski et al., 2011). Depending on photon flux at a particular wavelength, biochemistry, and three-dimensional (3D) structure, downward sensing will generally detect more upper-canopy components due to progressive attenuation of photons from the top of the canopy to the surface, whereas, for the same reasons, upward sensing will be relatively sensitive to lower-canopy components.

From the downward sensing perspective, the use of airborne laser scanners (ALS), or LiDAR, has been used to estimate spatially explicit fuel parameters over landscape to regional scales (Andersen et al., 2005; Jakubowski et al., 2013; Kelly and Di Tommaso, 2015; González-Ferreiro et al., 2017), and can contribute to reliable and robust estimates of modeled forest fire behavior (Kelly et al., 2017). For example, in oak woodlands of northern California, ALS data were used to estimate canopy cover, canopy height, and ladder fuels at 1-m resolution at a county scale (∼458,000 ha; Green et al., 2020).

At plot to stand scales (i.e., 1 to 50 ha), unoccupied aerial systems (UAS; Joyce et al., 2021) can be outfitted to acquire LiDAR (i.e., active sensing) or digital aerial images (i.e., passive sensing) at lower costs relative to airplane-mounted sensors, which is useful for repeated forest monitoring (Campbell et al., 2020; Hillman et al., 2021a,b). When UAS are flown to capture images with sufficient overlap (e.g., 75–85%), Structure from Motion (SfM) data processing can generate 3D point clouds of vegetation structure, which has the potential to quantify fuel loads. Although UAS-SfM generally provides highly variable or unresolved data of below-canopy vegetation structure (Wallace et al., 2016; Graham et al., 2019; Hillman et al., 2021a,b; Reilly et al., 2021), the technology has been used to successfully estimate canopy height and cover, DBH, and stem count (Wallace et al., 2016; Shin et al., 2018; Puliti et al., 2020; Reilly et al., 2021).

From the upward sensing perspective, terrestrial laser scanning (TLS) is a ground-based form of LiDAR mounted on a tripod. This technology has been used successfully to estimate plot-scale variables related to the spread of canopy fires, subtle fire-induced change, and forest fuels structural metrics (García et al., 2011; Gupta et al., 2015; Chen et al., 2016; Hillman et al., 2021a,b), with millimeter accuracy and precision (Disney, 2019). The use of TLS allows for fine-scale and high-resolution measurement of forest structure, enabling studies to measure quantitative changes in fuels over time (Wallace et al., 2016; Singh et al., 2018).

Handheld-mobile laser scanners (HMLS), also used from the upward sensing perspective, are a lightweight LiDAR about 30% the cost of a TLS. HMLS have been used to accurately estimate tree height under 25 m (Hyyppä et al., 2020) and diameter at breast height (DBH; Chudá et al., 2020; Hyyppä et al., 2020) with less variation than field measurements. In addition, HMLS technology typically requires less processing time compared to TLS and reduces the issues of occlusion that occur with TLS sampling on a fixed grid due to HMLS being one single walking scan. This allows for many different scan angles and locations, albeit with the compromise of lower range and precision compared to TLS, particularly in scanning the upper canopy (Ryding et al., 2015; Almeida et al., 2019; Soma et al., 2021). Currently, few studies have investigated the use of HMLS to examine forest structure parameters (Marselis et al., 2016; Donager et al., 2021), and we are unaware of any studies that have focused on HMLS and ladder fuels.

While remote sensing approaches are valuable, calibration and validation of remote sensing data with ground-based data is crucial. There are very few studies to date that validate fuels measured by remote sensing with ground-based direct measurements, such as destructive sampling or intercept methods (Hillman et al., 2019). Kramer et al. (2016) presented a low-cost ground-based photographic technique (referred to hereafter as “photo banner”) to visually measure ladder fuels from 1 to 4 m. Importantly, photo banner measurements were found to correlate with ladder fuel density developed using ALS data from 1 to 8 m (Kramer et al., 2016). Currently, land managers and conservation groups in Sonoma County, California have explored machine learning models to predict wildfire severity that include ladder fuel point densities from 1 to 4 m via ALS measurements (Green et al., 2020). The higher the density of shrubs and forest ladder fuels, the higher the canopy damage observed following wildfires.

Given the important predictive role of ladder fuels estimated by ALS data, but the often prohibitive cost and effort required for repeat monitoring with ALS data, the purpose of this study was to compare a suite of remote sensing approaches (TLS, HMLS, UAS-SfM, and ALS) and field measurements (photo banner) to measure plot-scale ladder fuels in oak woodlands in the same region. In addition, we compared the use of various measurements of ladder fuel densities to predict wildfire burn severity in an effort to provide alternative options to ALS data. Specifically, we aimed to answer the following questions: (1) What is the linear relationship between ladder fuel densities estimated using TLS, HMLS, UAS-SfM, ALS, and photo banner methods and do the strength of these correlations change in plots with different forest structure (i.e., mean CBH)?; (2) For each method, can ladder fuels be used to predict wildfire burn severity (i.e., Landsat-based Relativized delta Normalized Burn Ratio; RdNBR) at a plot scale? If so, which ladder fuel density strata from 1 to 8 m is the most important predictor variable?; and, (3) When predicting burn severity, do different methods of estimating ladder fuel densities or including CBH lead to different predictive capabilities?

We hypothesized that ladder fuel densities from different approaches would be correlated to each other if their measurement approach was similar (i.e., terrestrial or airborne perspectives, laser or image based). In addition, we hypothesized that TLS and HMLS collected data would most accurately predict burn severity (RdNBR) due to high point density, closely followed by ALS. We predicted UAS-SfM and the photo banner would not be able to significantly predict burn severity (estimated using RdNBR; Miller and Thode, 2007) due to the lack of below canopy detection of UAS-SfM and 4-m height limit of the banner. We hypothesized that the most important predictor of burn severity would be ladder fuel density strata from 1 to 4 m, as it was highly significant in the Green et al. (2020) model.