A comparison of five sampling techniques to estimate surface fuel loading in montane forests

Abstract

Designing a fuel-sampling program that accurately and efficiently assesses fuel load at relevant spatial scales requires knowledge of each sample method’s strengths and weaknesses. We obtained loading values for six fuel components using five fuel load sampling techniques at five locations in western Montana, USA. The techniques included fixed-area plots, planar intersect, photoloads, a photoload macroplot, and a photo series. For each of the six fuels, we compared (1) the relative differences in load values among techniques and (2) the differences in load between each method and a reference sample. Totals from each method were rated for how much they deviated from totals for the reference in each fuel category. The planar-intersect method, which used 2.50 km of transects, was rated best overall for assessing the six fuels. Bootstrapping showed that at least 1.50 km of transect were needed to obtain estimates that approximate the reference sample. A newly developed photoload method, which compared fuel conditions on the forest floor with sets of pictures calibrated for load by fuel type, compared well with the reference and planar intersect. The commonly used photo series consistently produced higher mean load estimates than any other method for total fine woody debris (0.05–0.20 kg m–2) and logs (0.50–1.25 kg m–2).

Spatiotemporal Variability of Wildland Fuels in US Northern Rocky Mountain Forests

“…This variability is different for each fuel component and it is so high that it often overwhelms most statistical analyses and classification schemes [48]. Traditional approaches of over-simplifying fuel descriptions for fire simulations may rarely be appropriate, if they ever worked at all [9,46]. Fire behavior fuel models, for example, may provide acceptable results in a one-dimensional application, such as the BEHAVE model [53], but three-dimensional fire behavior models may need more realistic inputs of spatial fuel distributions [18].…”

“…Conventional fuel inventory techniques, such as photo series [45], may be inappropriate because fuels vary at scales different from the scales represented by the picture in the series; the limited area of evaluation, for example, may be too small to accurately assess loadings for CWD [44]. Inappropriate sampling methods may result in higher uncertainty in fuel loadings that may mask subtle treatment effects [46]. Remotely sensed products used to map fuels, such as Landsat Thematic Mapper, may have resolutions that are inappropriate for capturing the spatial distribution of fuels, especially FWD [47].…”

Bayesian Techniques for Surface Fuel Loading Estimation

“…These values were established based on results of a study done by Sikkink and Keane (2008) where technicians used the photo series and photoload method to estimate fuel loads. Since the planar intercept method performed better than the fixed-area method we chose narrower priors for the planar intercept method.…”

“…A method not examined in the Keane and Gray (2013) study is the photo series method. This method, initially developed by Maxwell and Ward (1976), is a technique that uses photos with known fuel loadings to estimate FWF (Sikkink & Keane, 2008). Although not examined in the Keane and Gray (2013) study, the photo series method could be used for the methods discussed in the work being presented here.…”

An Exploratory Assessment of a Smartphone Application for Public Participation in Forest Fuels Measurement in the Wildland-Urban Interface

“…The first activity was an introductory slideshow with definitions for terms and concepts related to forest fuels ( Figure 1A). The second activity was a visual rapid classification of fuel conditions, aided by reference images and illustrations (inspired by the Photoseries and Photoload rapid assessment techniques evaluated by Sikkink et al [22]; Figure 1B). In the third activity, participants took six pictures of the fuel components at the site (in four directions at right angles relative to the direction of ground slope, straight up at the forest canopy, and straight down at the forest floor) and measured the location using the global positioning system ( Figure 1C).…”