Evaluating Model Predictions of Fire Induced Tree Mortality Using Wildfire-Affected Forest Inventory Measurements

Abstract

Forest land managers rely on predictions of tree mortality generated from fire behavior models to identify stands for post-fire salvage and to design fuel reduction treatments that reduce mortality. A key challenge in improving the accuracy of these predictions is selecting appropriate wind and fuel moisture inputs. Our objective was to evaluate postfire mortality predictions using the Forest Vegetation Simulator Fire and Fuels Extension (FVS-FFE) to determine if using representative fire-weather data would improve prediction accuracy over two default weather scenarios. We used pre- and post-fire measurements from 342 stands on forest inventory plots, representing a wide range of vegetation types affected by wildfire in California, Oregon, and Washington. Our representative weather scenarios were created by using data from local weather stations for the time each stand was believed to have burned. The accuracy of predicted mortality (percent basal area) with different weather scenarios was evaluated for all stands, by forest type group, and by major tree species using mean error, mean absolute error (MAE), and root mean square error (RMSE). One of the representative weather scenarios, Mean Wind, had the lowest mean error (4%) in predicted mortality, but performed poorly in some forest types, which contributed to a relatively high RMSE of 48% across all stands. Driven in large part by over-prediction of modelled flame length on steeper slopes, the greatest over-prediction mortality errors arose in the scenarios with higher winds and lower fuel moisture. Our results also indicated that fuel moisture was a stronger influence on post-fire mortality than wind speed. Our results suggest that using representative weather can improve accuracy of mortality predictions when attempting to model over a wide range of forest types. Focusing simulations exclusively on extreme conditions, especially with regard to wind speed, may lead to over-prediction of tree mortality from fire.

Strategic monitoring informs wilderness management and socioecological benefits

“…Opportunities exist for better use of current data and for collecting additional data to improve knowledge of fire behavior. For example, targeted post‐fire measurements of NFI permanent plots can improve modeling of fire‐severity predictions (Barker, Fried, & Gray, 2019), and expanded sampling of nonforest locations would extend inference to other habitats. Characterizing fuels and predicting fire behavior are therefore central not only to protecting life and property, but also to preserving cultural and economic resources.…”

A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species

“…We should expect more error in modeled variables; nevertheless, this equation is also in need of external validation, and may need to be modified for species with different canopy architecture than the species for it was developed. Barker et al (2019) evaluated Table 11 Statistical comparison of AUCs (area under the receiver operating characteristic [ROC] curve) between samples for which percentage crown volume scorched (CVS) was sampled in the field and calculated based on other measurements of canopy injury (e.g., crown length scorch, change in crown ratio, change in canopy base height). We tested for statistical differences in AUCs between first and second fires using the method of DeLong et al (1988) as modified for the pROC package in the statistical program R to test unpaired ROC curves (Robin et al 2011).…”

“…Data used to evaluate post-fire tree mortality models are from the USA, from fires occurring from 1981 to 2016 mortality based on multiple simulated weather scenarios, and assessed errors in predicted mortality at stand, forest type, and species scales. They found high model errors, with mortality being over-predicted for more extreme fire-weather scenarios due to overpredictions of flame lengths (Barker et al 2019). Thus, validating other steps in the simulation modeling process with independent datasets is very much needed.…”

Modelling Forest Ecosystems: a crossroad between scales, techniques and applications

“…Forestland managers rely on predictions of tree mortality generated from fire behavior models to identify stands for post-fire salvage and to design fuel reduction treatments that reduce mortality (Barker et al, 2019).…”

“…Chen et al (2017) developed a predictive model for estimating forest surface fuel load in Australian eucalypt forests with LiDAR data. Barker et al (2019) evaluated post-fire mortality predictions using the Forest Vegetation Simulator Fire and Fuels extension. Finally, simulated spatiotemporal forest change from field inventory, remote sensing, growth modeling, and management actions.…”