Multi-scale assessment of post-fire tree mortality models

Furniss, Tucker J.; Larson, Andrew J.; Kane, Van R.; Lutz, James A.

doi:10.1071/wf18031

Cited by 36 publications

(41 citation statements)

References 67 publications

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“…For example, stress disturbance had the lowest user's accuracy (high commission error), mainly due to forest loss caused by fire being incorrectly attributed to stress disturbance. It is common for fire disturbance in forests to result in highly variable within-patch severity (i.e., proportion of forest overstory mortality) [80,81], which could account for this error. However, because the MODIS Active Fire product was used in the post-classification phase, it is likely that some small fires were not detected in the coarser resolution MODIS product (1 km spatial resolution) and, therefore, were classified erroneously as stress disturbance rather than fire.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Object-Based Classification of Forest Disturbance Types in the Conterminous United States

Huo¹,

Boschetti²,

Sparks³

2019

Remote Sensing

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Forest ecosystems provide critical ecosystem goods and services, and any disturbance-induced changes can have cascading impacts on natural processes and human socioeconomic systems. Forest disturbance frequency, intensity, and spatial and temporal scale can be altered by changes in climate and human activity, but without baseline forest disturbance data, it is impossible to quantify the magnitude and extent of these changes. Methodologies for quantifying forest cover change have been developed at the regional-to-global scale via several approaches that utilize data from high (e.g., IKONOS, Quickbird), moderate (e.g., Landsat) and coarse (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) spatial resolution satellite imagery. While detection and quantification of forest cover change is an important first step, attribution of disturbance type is critical missing information for establishing baseline data and effective land management policy. The objective here was to prototype and test a semi-automated methodology for characterizing high-magnitude (>50% forest cover loss) forest disturbance agents (stress, fire, stem removal) across the conterminous United States (CONUS) from 2003–2011 using the existing University of Maryland Landsat-based Global Forest Change Product and Web-Enabled Landsat Data (WELD). The Forest Cover Change maps were segmented into objects based on temporal and spatial adjacency, and object-level spectral metrics were calculated based on WELD reflectance time series. A training set of objects with known disturbance type was developed via high-resolution imagery and expert interpretation, ingested into a Random Forest classifier, which was then used to attribute disturbance type to all 15,179,430 forest loss objects across CONUS. Accuracy assessments of the resulting classification was conducted with an independent dataset consisting of 4156 forest loss objects. Overall accuracy was 88.1%, with the highest omission and commission errors observed for fire (32.8%) and stress (31.9%) disturbances, respectively. Of the total 172,686 km2 of forest loss, 83.75% was attributed to stem removal, 10.92% to fire and 5.33% to stress. The semi-automated approach described in this paper provides a promising framework for the systematic characterization and monitoring of forest disturbance regimes.

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Section: Limitations and Future Workmentioning

confidence: 99%

Object-Based Classification of Forest Disturbance Types in the Conterminous United States

Huo¹,

Boschetti²,

Sparks³

2019

Remote Sensing

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“…Average observed mortality for the more fire-resistant California black oak was no less than for the thinner barked canyon live oak; other studies report comparable mortality rates of about 60% [13,60]. The best RMSE scenario slightly under-predicted mortality, suggesting that resprouting of heavily-scorched trees was not as strong a factor as in canyon live oak.…”

Section: The Role Of Ra Equation and Tree Species Effectsmentioning

confidence: 80%

“…The best RMSE scenario slightly under-predicted mortality, suggesting that resprouting of heavily-scorched trees was not as strong a factor as in canyon live oak. Nevertheless, basal sprouting can greatly affect post-fire stand dynamics, with up to 70% of the top-killed black oak resprouting in one study [13]. California black oak has been experiencing a decline in basal area in California [59], in part due to conifer encroachment into black oak canopies resulting in greater crown-fire caused mortality [61].…”

Section: The Role Of Ra Equation and Tree Species Effectsmentioning

confidence: 95%

“…Crown scorch reflects direct heat damage to foliage while bark thickness serves as a proxy for resistance to fire effects (e.g., cambial heating). While most work on validating the RA mortality model has focused on individual trees, there remains a need to examine mortality at the stand level [13]. Most RA validation studies rely on post-fire crown scorch assessments to evaluate mortality prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

Barker

Fried

Gray

2019

Forests

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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.

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“…Fuel properties vary with forest overstory species (van Wagtendonk et al 1998), forest age (Agee and Huff 1987;van Wagtendonk and Moore 2010), disturbance history (Jenkins et al 2012), and ecosystem productivity (Keane et al 2000). Understanding the variability of pre-fire fuel loadings at spatial scales similar to that of prescribed fires (i.e., 25 ha to 250 ha) can be important both to managers seeking to reintroduce fire (Collins et al 2010) and also for calculating the likely effects of burning on tree survival (Lutes et al 2009;Furniss et al 2019;Hood et al 2018). Similarly, measurements of surface fuel combustion and residual fuel loadings immediately after fire are required to evaluate the effectiveness of fire as a fuel reduction and ecosystem restoration treatment (Knapp et al 2005;Varner et al 2005); to understand fire impacts to understory plant and fungal species (Moore et al 2006;Larson et al 2016); and to estimate fire-caused ecosystem changes, such as direct fire effects on aboveground carbon storage, pyrogenic carbon emissions (Campbell et al 2007), soil heating (Swezy and Agee 1991), and related changes to soil chemistry and structure (Certini 2005;Hille and Den Ouden 2005).…”

Section: Introductionmentioning

confidence: 99%

Fuel dynamics after reintroduced fire in an old-growth Sierra Nevada mixed-conifer forest

et al. 2019

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Background: Surface fuel loadings are some of the most important factors contributing to fire intensity and fire spread. In old-growth forests where fire has been long excluded, surface fuel loadings can be high and can include woody debris ≥100 cm in diameter. We assessed surface fuel loadings in a long-unburned old-growth mixedconifer forest in Yosemite National Park, California, USA, and assessed fuel consumption from a managementignited fire set to control the progression of the 2013 Rim Fire. Specifically, we characterized the distribution and heterogeneity of pre-fire fuel loadings, both along transects and contained in duff mounds around large trees. We compared surface fuel consumption to that predicted by the standard First Order Fire Effects Model (FOFEM) based on pre-fire fuel loadings and fuel moistures. We also assessed the relationship between tree basal area-calculated for two different spatial neighborhood scales-and pre-fire fuel loadings. Results: Pre-fire total surface fuel loading averaged 192 Mg ha −1 and was reduced by 79% by the fire to 41 Mg ha −1 immediately after fire. Most fuel components were reduced by 87% to 90% by the fire, with the exception of coarse woody debris (CWD), which was reduced by 60%. Litter depth in duff mounds were within 1 SD of plot means, but duff biomass for the largest trees (>150 cm diameter at breast height [DBH]) exceeded plot background levels. Overstory basal area generally had significant positive relationships with pre-fire fuel loadings of litter, duff, 1-hour, and 10-hour fuels, but the strength of the relationships differed between overstory components (live, dead, all [live and dead], species), and negative relationships were observed between live Pinus lambertiana Douglas basal area and CWD. FOFEM over-predicted rotten CWD consumption and under-predicted duff consumption. Conclusions: Surface fuel loadings were characterized by heterogeneity and the presence of large pieces. This heterogeneity likely contributed to differential fire behavior at small scales and heterogeneity in the post-fire environment. The reductions in fuel loadings at our research site were in line with ecological restoration objectives; thus, ecologically restorative burning during fire suppression is possible.

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Multi-scale assessment of post-fire tree mortality models

Cited by 36 publications

References 67 publications

Object-Based Classification of Forest Disturbance Types in the Conterminous United States

Object-Based Classification of Forest Disturbance Types in the Conterminous United States

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

Fuel dynamics after reintroduced fire in an old-growth Sierra Nevada mixed-conifer forest

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